Research Handbook on Information Systems and the Environment (Research Handbooks in Information Systems) 1802201858, 9781802201857

Table of contents :
Front Matter
Copyright
Contents
Contributors
Abbreviations
1. Introduction to the Research Handbook on Information Systems and the Environment
2. Energy informatics: origins, emergence, and future
3. SDU Center for Energy Informatics: background, and current and future research directions
4. Data collection and exploitation strategies for Green Information Systems
5. How to unlock the potential of information systems for a circular economy
6. From environmental towards sustainability management information systems
7. Designing information systems that support environmental sustainability: a framework-based review
8. Digital technology affordances for sustainable business practices
9. Green IS: an imperative and an opportunity for IT services
10. The persuasive potential of digital nudging for eco-sustainable behaviour
11. Comfort vs money: influencing the energy user for sustainable consumption
12. Understanding the collaborative consumption of sustainable products and services: the impact of psychological ownership
13. Information systems and behavioural change: feedback interventions to curb the consumption of natural resources
14. The role of smart home technology in the sustainable transformation
15. Smart grids and energy markets: towards a real-time energy system
16. Blockchain-enabled markets: a literature review with a focus on decentralised energy markets
17. Engineering markets and information systems for Citizen Energy Communities
Index

Citation preview

RESEARCH HANDBOOK ON INFORMATION SYSTEMS AND THE ENVIRONMENT

RESEARCH HANDBOOKS IN INFORMATION SYSTEMS This new and exciting series brings together authoritative and thought-provoking contributions on the most pressing topics and issues in Information Systems. Handbooks in the series feature specially commissioned chapters from eminent academics, each overseen by an Editor internationally recognized as a leading name within the field. Chapters within the Handbooks feature comprehensive and cutting-edge research, and are written with a global readership in mind. Equally useful as reference tools or high-level introductions to specific topics, issues, methods and debates, these Research Handbooks will be an essential resource for academic researchers and postgraduate students. Titles in the series include: Handbook of Big Data Research Methods Edited by Shahriar Akter and Samuel Fosso Wamba Research Handbook on Information Systems and the Environment Edited by Vanessa A. Cooper, Johann J. Kranz, Saji K. Mathew and Richard T. Watson

Research Handbook on Information Systems and the Environment Edited by

Vanessa A. Cooper Professor of Information Systems, Department of Information Systems and Business Analytics, RMIT University, Australia

Johann J. Kranz Professor of Digital Services and Sustainability, LMU School of Management, University of Munich, Germany

Saji K. Mathew Professor of Information Systems, Department of Management Studies, Indian Institute of Technology Madras, India

Richard T. Watson Research Director, Digital Frontier Partners, Australia and Regents Professor Emeritus, University of Georgia, USA

RESEARCH HANDBOOKS IN INFORMATION SYSTEMS

Cheltenham, UK • Northampton, MA, USA

© Vanessa A. Cooper, Johann J. Kranz, Saji K. Mathew and Richard T. Watson 2023

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA A catalogue record for this book is available from the British Library Library of Congress Control Number: 2023936978 This book is available electronically in the Business subject collection http://dx.doi.org/10.4337/9781802201864

ISBN 978 1 80220 185 7 (cased) ISBN 978 1 80220 186 4 (eBook)

EEP BoX

Contents

List of contributorsvii List of abbreviationsxiv 1

Introduction to the Research Handbook on Information Systems and the Environment1 Vanessa A. Cooper, Johann J. Kranz, Saji K. Mathew and Richard T. Watson

2

Energy informatics: origins, emergence, and future Marie-Claude Boudreau, Richard T. Watson and Natalie Jeszke

3

SDU Center for Energy Informatics: background, and current and future research directions Bo Nørregaard Jørgensen

4

Data collection and exploitation strategies for Green Information Systems  Vijaya Lakshmi, Jacqueline Corbett and Jane Webster

53

5

How to unlock the potential of information systems for a circular economy Anne Ixmeier, Johann J. Kranz, Jan Recker and Roman Zeiss

74

6

From environmental towards sustainability management information systems Tyge-F. Kummer and Kenan Degirmenci

7

Designing information systems that support environmental sustainability: a framework-based review Jan Recker

8

Digital technology affordances for sustainable business practices Stefan Seidel, Jan Recker and Jan vom Brocke

149

9

Green IS: an imperative and an opportunity for IT services Saji K. Mathew and Thillai Rajan

165

10

The persuasive potential of digital nudging for eco-sustainable behaviour Anne Ixmeier, Anna Seidler, Christopher Henkel, Marina Fiedler, Johann J. Kranz and Kim Strunk

182

11

Comfort vs money: influencing the energy user for sustainable consumption Silpa Sangeeth L.R., Saji K. Mathew and Richard T. Watson

207

12

Understanding the collaborative consumption of sustainable products and services: the impact of psychological ownership Laurens Rook, Joshua Paundra, Jan van Dalen and Wolfgang Ketter v

9

27

100

114

231

vi  Research handbook on information systems and the environment 13

Information systems and behavioural change: feedback interventions to curb the consumption of natural resources Thorsten Staake, Verena Tiefenbeck and Thomas Stiefmeier

14

The role of smart home technology in the sustainable transformation Philipp Wunderlich and Daniel Veit

275

15

Smart grids and energy markets: towards a real-time energy system Jason Dedrick, Gilbert Fridgen, Marc-Fabian Körner and Jens Strüker

295

16

Blockchain-enabled markets: a literature review with a focus on decentralised energy markets Anselma Wörner, Verena Tiefenbeck and Wolfgang Ketter

17

Engineering markets and information systems for Citizen Energy Communities 341 Philipp Staudt and Christof Weinhardt

253

315

Index366

Contributors

Marie-Claude Boudreau is an associate professor and Department Head of the Department of MIS at the University of Georgia’s Terry College of Business. Her research has focused on organisational change induced by information technologies and IS, the relationship between IS on organisational culture and identity, and how IS can play a role in support of environmental sustainability. Marie has published in journals such as Organization Science, Information Systems Research, MIS Quarterly, Journal of Management Information Systems, The Academy of Management Executive, Information Technology & People, Journal of the AIS, and many conference proceedings. Jan vom Brocke is the chair of Information Systems and Business Process Management at the University of Münster and Director of the European Research Center for Information Systems (ERCIS). He is an AIS Fellow, a Schoeller Senior Fellow and chairman of the Hilti Labs. His work explores new ways of creating value through IT, particularly with respect to sustainability goals. He has been published in, among others, Management Science, MIS Quarterly, Journal of Management Information Systems, Information Systems Research and MIT Sloan Management Review, as well as in popular books such as Green Business Process Management. He is a member of the AIS SIG Green Advisory Board and a member of the Liechtenstein Academy of Sciences. Vanessa A. Cooper is a professor in Information Systems in the Department of Information Systems and Business Analytics at RMIT University. Vanessa’s research in information systems (IS) examines the impact of technology in complex contexts such as environmental sustainability, emergency management, social justice and the future of work. Her research has been published in journals such as the Information Systems Journal, Journal of Cleaner Production, Information Technology & People and Communications of the AIS. Her research has won multiple awards, including Best Paper awards at international conferences and from the Australian Computer Society. Jacqueline Corbett is a professor of Management Information Systems in the Faculty of Business Administration at Université Laval, in Quebec City, Canada. She received her PhD from Queen’s University, Canada. Her research examines the design and use of IS to support sustainable development, with a particular interest in technology innovations in the smart grid, smart cities and enterprises. Her papers have been published in leading IS and management journals, including the Strategic Entrepreneurship Journal, Journal of Business Ethics, Information Systems Journal, Journal of the Association for Information Systems and International Journal of Information Management. Jan van Dalen is Associate Professor of Statistics at the Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University. He has a background in econometrics and obtained his PhD in Quantitative Modelling of Wholesaling. His main research interests are in quantitative analysis of information, logistics, trade and organisational processes. He has published in journals like Decision Sciences, vii

viii  Research handbook on information systems and the environment Journal of Transport Economics, Industrial and Corporate Change, European Journal of Operations, Journal of Operations Management, Journal of Information Technology, Journal of Environmental Psychology, Journal of Cleaner Production and Omega. He has been involved in various private–public research programs, such as monitoring trade and traffic flows with CBS (Statistics Netherlands), trade lane risk assessment in the Cassandra project and cross-chain collaboration in 4C4More/Dinalog. He is a co-founder of the Erasmus Center for Data Science and Business Analytics, and a co-founder of the Knowledge Lab Urban Big Data, together with the City of Rotterdam. Besides research, he has extensive teaching experience in applied statistics, forecasting and big data in bachelor’s, master’s and executive teaching programs. Jason Dedrick is Professor Emeritus at the School of Information Studies, Director of the Smart Grid Research Center and a Center of Excellence Fellow at Syracuse University. His research interests include adoption and impacts of smart grid technologies, cybersecurity and privacy issues associated with distributed energy markets, global value chains in the electronics industry, and the economic and organisational impacts of information technologies. His work has been supported by grants from the US National Science Foundation and the Alfred P. Sloan Foundation. He holds a PhD in Management from the University of California, Irvine. Kenan Degirmenci is a lecturer in the School of Information Systems at Queensland University of Technology. He received his doctorate from Leibniz University Hannover. His research focuses on business analysis and decision making, IS adoption, sustainability, and information security and privacy. His publications have appeared in outlets such as the International Journal of Information Management, AI & Society, Sustainable Cities & Society, Cities, Transportation Research Part D: Transport & Environment and Urban Climate. Marina Fiedler is the Chair of Management, People and Information at the University of Passau. Her research focuses on the interface of three central topics of digitalisation: (1) the role of AI in organisations and IT platforms, (2) governance and management of sustainable behaviour, and (3) changes in designing work. Her research on ways to successfully promote sustainable employee behaviour has been funded by Deutsche Forschungsgemeinschaft. Marina’s work has been published in the Journal of the Association of Information Systems, Journal of Business Research, Journal of Economic Behaviour and Organization, Organization, Organization Studies, Research Policy and elsewhere. Gilbert Fridgen is Professor and PayPal-FNR PEARL Chair in Digital Financial Services at the Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg. In his research, he analyses the transformative effects of digital technologies on individual organisations and on the relationship between organisations. He addresses potentially disruptive technologies like distributed ledgers, verifiable credentials, artificial intelligence and the Internet of Things. His research involves IS engineering, IT strategy and (risk) management, as well as regulatory compliance. In his projects and partnerships, he collaborates with partners in financial services, energy, mobility, manufacturing and consulting, as well as with public bodies and governments. Christopher Henkel is a solutions architect at AWS. He holds a PhD in Business Information Systems acquired at the Professorship for Internet Business and Internet Services at the LMU Munich’s School of Management. His research focuses on sustainable IS.

Contributors  ix Anne Ixmeier is a postdoctoral researcher at the Professorship of Digital Services and Sustainability at the LMU Munich’s School of Management, where she previously completed her PhD. Her research focuses on digital solutions for sustainable development, with a focus on circular economy and pro-environmental behaviour. Her work has been published in journals including Information Systems Journal and MIS Quarterly Executive. Natalie Jeszke is a graduate student at the University of Georgia’s Terry College of Business. Bo Nørregaard Jørgensen is a professor and Head of the University of Southern Denmark’s (SDU) Center for Energy Informatics, which he founded in 2013. He is Editor-in-Chief for Springer Open Energy Informatics, which he co-founded in 2018. His research focuses on software technologies and innovative applications for supporting the digital transformation of the energy sector. In the past 15 years he has led and co-led research and development projects for more than 400 million DKK. He is the author of 240 publications and 3 US-granted software patents. Wolfgang Ketter is Professor of Information Systems and Director of the Cologne Institute of Information Systems at the University of Cologne, where he leads research focusing on how digital transformation can create a faster and more stable transition to sustainable energy and mobility. He is also a professor and director at Erasmus University and an energy policy advisor to the German government. He has served as Editor for ISR and MISQ and won the Best ISR Paper award for the year 2020. Marc-Fabian Körner is a postdoctoral researcher at the University of Bayreuth. He is also affiliated with the Branch Business & Information Systems Engineering of the Fraunhofer FIT and the Research Center FIM where he leads a research group on Green IS and Digital Decarbonization. His research focusses on Green IS and related topics of energy informatics, data ecosystems and cross-organizational collaboration, as well as digital identity management in private and public organizations. His interdisciplinary work on Green IS has been published in journals including Business & Information Systems Engineering, Applied Energy, and Renewable & Sustainable Energy Reviews. Johann J. Kranz is a professor of Digital Services and Sustainability at the LMU Munich’s School of Management. His research focuses on IS–business alignment and governance in the digital age, and green IS for enabling circular economies, smart grids, sustainable mobility and pro-environmental behaviour. From 2019–2021, he served as President of the Association for Information Systems (AIS) “Green IS” Special Interest Group. His research has been published in the Journal of Strategic Information Systems, Information Systems Journal, Journal of Service Research, Energy Policy and MISQ Executive. Tyge-F. Kummer is an associate professor at the Queensland University of Technology. His research interests include judgement and decision making in relation to IS, accounting IS, business process management and trust. His research has been published in journals including the Journal of the Association of Information Systems, Decision Support Systems, International Journal of Information Management, Information & Management, and Business & Information Systems Engineering. Vijaya Lakshmi (PhD in plant science) is a PhD candidate in Management Information Systems at Université Laval. Her research interests include artificial intelligence (AI), sustain-

x  Research handbook on information systems and the environment able agriculture, green IS and sustainability. Her research has been published in conference proceedings of major IS conferences such as International Conference on Information Systems, Americas Conference on Information Systems, Pacific Asia Conference on Information Systems and Hawaii International Conference on System Sciences. Her research on AI and sustainable agriculture received an honourable mention at the Administrative Sciences Association of Canada Conference 2021 and was also nominated for Best Paper at the Hawaii International Conference on Systems Sciences (HICSS) 2022. Saji K. Mathew is a professor at the Department of Management Studies, Indian Institute of Technology Madras. As a Fulbright scholar, he did his postdoctoral research on offshore IT outsourcing at the Goizueta Business School of Emory University. His current research focuses on behavioural cyber security, information privacy and IS for electricity demand response. He has published articles in leading journals while also making editorial contributions to some of them. He is a founding member of the AIS India Chapter (INAIS) and presently serves as its Vice President. Joshua Paundra received his PhD from Rotterdam School of Management, Erasmus University Rotterdam. His research interest lies at the intersection of psychology, the sharing economy and transportation. Joshua investigates the development of shared transportation services, and its relation to ownership. In recent years, he has published in journals such as the Journal of Environmental Psychology, Journal of Cleaner Production and Case Studies on Transport Policy. Thillai Rajan is a professor in the Department of Management Studies and Centre for Research on Start-ups and Risk Financing (CREST) at the Indian Institute of Technology Madras. He was also an associate at the Mossavar Rahmani Center for Business and Government, Harvard Kennedy School, Harvard University from January 2017 to August 2022. His research interests encompass start-ups, ventures, and small and medium-sized enterprises. During 2009–18, he edited the annual India Venture Capital and Private Equity Report. In 2020, he played a key role in the formation of the Innovation, Venturing, and Entrepreneurship in India Network (iVEIN), a multi-institutional consortium to research innovation and venturing in India. He is also a co-founder of YNOS Venture Engine, a start-up set up to address the pain points of early-stage entrepreneurs. Jan Recker is an AIS Fellow, Alexander-von-Humboldt Fellow, Nucleus Professor of Information Systems and Digital Innovation at the University of Hamburg and Adjunct Professor at Queensland University of Technology. His research focuses on systems analysis and design, digital innovation and entrepreneurship, and digital solutions for sustainable development. Laurens Rook is Assistant Professor at the Technology, Policy, and Management Faculty of Delft University of Technology. He received his PhD from the Rotterdam School of Management, and his bachelor’s and master’s degrees in Communication Studies from the University of Amsterdam. His research focuses on personalisation of intelligent IS aimed at establishing (pro-environmental) behaviour change in targeted user groups. Laurens publishes in journals such as Omega, Journal of Cleaner Production, Journal of Environmental Psychology and the International Journal of Human-Computer Studies, and in conference proceedings of various IEEE and Association for Computing Machinery programs.

Contributors  xi Silpa Sangeeth L.R. is an associate professor at the Department of Information Technology, Government Engineering College Palakkad. Her doctoral research was on information processing in electricity demand response systems. Her broad areas of interest include energy informatics and behavioural modelling for demand response systems. She holds an MS in Computer Science from Illinois Institute of Technology. She has about 13 years of experience teaching courses on machine learning, data analytics, problem solving using Python, user interface and user experience design, data structures and algorithms, database management systems, data modelling and design. Stefan Seidel is a professor of Information Systems and Innovation at the University of Cologne and an honorary professor of Business Information Systems at the University of Galway. His research explores how emerging technologies like artificial intelligence or Internet of Things technologies enable, and are involved in, organisational, societal, and environmental change and innovation. Anna Seidler is a strategy consultant and senior manager at Roland Berger specialising in complex organisational transformations. She consults with global organisations and advises on people strategy, organisational restructuring, workforce transformation, organisational development and change management. Anna holds a PhD in Organisational Behaviour and Change Management from the University of Passau School of Business, Economics and Information Systems. Thorsten Staake holds the Chair of Information Systems and Energy Efficient Systems at the University of Bamberg, and is a co-director of the Bits to Energy Lab, a research initiative of ETH Zurich and the University of St. Gallen. He and his team combine machine learning and behavioural science to gain insights into human behaviour, support everyday decision making, and develop interactive digital products and services. Thorsten is a co-founder of the cleantech companies BEN Energy AG (machine learning for energy services) and Amphiro AG (Internet of Things components for faucets) as well as a member of the supervisory board of Hoval AG and Zumtobel Group AG, two leading companies in the heating and lighting sector. Philipp Staudt is Professor for Environmental and Sustainable Information Systems at the Carl von Ossietzky University of Oldenburg. In his research he focuses on the affordances of digital technology for energy-related decision making and the corresponding impacts on the energy transition and energy markets. He works on the interface of green IS, energy informatics and energy economics. He received his PhD from the Karlsruhe Institute of Technology in 2019 and subsequently worked as a postdoctoral researcher at the Massachusetts Institute of Technology under a Marie Skłodowska-Curie Fellowship. Thomas Stiefmeier is Founder and CEO of Amphiro AG, a Swiss-based cleantech start-up that develops and markets Internet of Things components and analytics services for drinking water systems. Prior to founding the company, he gained experience in various positions at Infineon AG, Siemens and Mannesmann VDO. Thomas received his PhD in Electrical Engineering with a focus on machine learning and wearable computing from the Swiss Federal Institute of Technology Zurich, and a diploma in Electrical Engineering with a focus on data science from the Technical University of Darmstadt. Jens Strüker is Professor for Information Systems and Digital Energy Management at the University of Bayreuth in Germany and acts as a Deputy Academic Director of the brand

xii  Research handbook on information systems and the environment Business and Information Systems Engineering of the Fraunhofer Institute FIT. His research focuses on the potential of information technology for the decarbonisation of the (energy) economy. Jens is especially interested in the transfer and application of IS knowledge and sits on the scientific advisory boards of green tech startups, think tanks, and energy companies. Jens has consulted clients from the private and public sector, including the European Commission, the International Energy Agency (Paris), the German Federal Ministry for Economic Affairs and Climate Action, and the German Energy Agency. Kim Strunk is a postdoctoral researcher in the Chair of Management, People, and Information at the University of Passau School of Business, Economics and Information Systems, where he previously completed his PhD. His research focuses on contemporary changes in organising work, organisational sustainability transformations and the impact of social support in virtual work. His work has been published in journals including Organization, Computers in Human Behavior and MIS Quarterly Executive. Verena Tiefenbeck is Assistant Professor of Digital Transformation at the Friedrich-Alexander-Universität Erlangen-Nürnberg. Since receiving her PhD, she has led the Bits to Energy Lab at ETH Zurich, an interdisciplinary team combining digital technologies and behavioural sciences with the goal of promoting sustainable behaviour. Previously, she had spent more than three years at the Massachusetts Institute of Technology and as a research associate at the Fraunhofer Center for Sustainable Energy Systems. Her work has been published in leading academic journals (including Management Science, Information Systems Research, Nature Energy, Applied Energy, Global Environmental Change, Energy Policy and Transportation Research: Part D) and at leading conferences in the fields of information systems, economics, human–computer interaction and energy informatics. Daniel Veit is a professor and Chair of Information Systems and Management at the Department of Business Administration of the Faculty of Business and Economics of the University of Augsburg. His research focuses on transformational effects of IS and digitalisation in society with a specific focus on sustainability. His publications have appeared in outlets such as MIS Quarterly, the Journal of Management Information Systems and many others. He serves as a senior editor for the Journal of the Association for Information Systems and Information Systems Journal. Richard T. Watson is a regents professor and the J. Rex Fuqua Distinguished Chair for Internet Strategy in the Terry College of Business at the University of Georgia. He is a former president of the AIS and was awarded its highest honour, a LEO, for his achievements in IS. For about a decade, he was Research Director for the Advanced Practices Council of the Society of Information Management and a visiting researcher at the Research Institutes of Sweden (RISE). In 2021, he was awarded an honorary doctorate by the University of Liechtenstein. Jane Webster received her PhD from New York University and is the E. Marie Shantz Chair Emerita in Management Information Systems at Queen’s University, Canada. Jane served as a senior editor for MIS Quarterly, Program Chair for the International Conference on Information Systems and VP Publications for the Association for Information Systems. Over the last 15 years, she has studied ways in which to encourage more environmentally sustainable behaviours in organisations. Her research has also investigated the impacts of technologies

Contributors  xiii in the support of distributed work, organisational communications, employee recruitment and selection, employee monitoring, training and learning, and human–computer interaction. Christof Weinhardt holds a Chair for Information Systems at the Institute of Information Systems and Marketing (IISM) at the Karlsruhe Institute of Technology and is Director at FZI – Research Center Informatics in Karlsruhe. Since 2006 he has been a member of the Energy Interest Group of BDI (the Federation of German Industries). From 2008–2016 he was an elected member of the Review Board at DFG (the German Research Association), and from 2010–2013 he was an expert advisor to the Committee of Enquiry “Internet and Digital Society” of the German Bundestag. Since 2019 he has been Editor-in-Chief of BISE – Business & Information Systems Engineering. After studying industrial engineering (1986) he received his PhD in 1989 (Economics) and his Habilitation in 1994 (Business Administration). With this background his research is mainly on platform economics and market engineering, especially in the sharing economy, new energy markets, and finance and retail markets. Anselma Wörner received her PhD from the Department of Management, Technology and Economics at ETH Zurich in 2020. Her research is concerned with leveraging information technology to integrate distributed energy resources in the energy market and to foster energy conservation among the broader public. She has already published in leading conferences and journals such as Information Systems Research and Nature Energy, and she recently completed a research visit at Stanford University where she joined the Energy Resources Engineering group. Philipp Wunderlich is Professor of Digital Marketing and Business Innovation at Reutlingen University. His research focuses on IT service innovations, green IS, incentive systems and motivation psychology to further understand the influencing factors of digitalisation regarding an economically, societally and especially ecologically sustainable future. His publications have appeared in outlets such as MIS Quarterly and the Journal of Service Research, and in various proceedings of leading IS conferences. Roman Zeiss holds a PhD in Information Systems from the Cologne Institute for Information Systems at the University of Cologne. His research focuses on sustainable IS and the related business models in a circular economy.

Abbreviations

AC AFSC AHU AI AIS AMCIS AMI ATUT AUC BISE BITS BRT BYOT CCCI CDP CE CECs CHP CMM CP CPLC CPS CPUC CRM CSI CV DID DLT DOI DP DR DSM

air conditioner agricultural food supply chain air handling units artificial intelligence Association for Information Systems Americas Conference on Information System automated metering infrastructure attitudes to using technology auction mechanism business and information systems business IT solutions bus rapid transport bring-your-own-thermostat Carbon Centric Computing Initiative Carbon Disclosure Project Circular Economy Citizen Energy Communities combined heat and power Capability Maturity Model Computer Playfulness Carbon Pricing Leadership Coalition cyber-physical systems California Public Utilities Commission customer relationship management consulting & systems integration contingent valuation difference-in-difference distributed ledger technologies diffusion of innovation design principles demand response demand-side management xiv

Abbreviations  xv DSS DTPB EBA ECIS EEB EHS EI EIBOK EPA ERP ESB ESG ESRS EU EURct FED FGD FLISR FSB GDM GDPR GFT GHG GRI HOV HT HTTP HVAC ICIS ICO ICT IM InDEED IoT IPR IS ISEC

decision support system Decomposed Theory of Planned Behaviour environmental beliefs and attitudes European Conference on Information Systems energy efficiency in buildings environmental, health & safety energy informatics Energy Informatics Body of Knowledge Environmental Protection Agency enterprise resource planning eco-sustainable behaviour environmental, social and governance Enterprise Sustainability Reporting Solution European Union eurocents Flexible Energy Denmark focus group discussion Fault Location Isolation and Service Restoration Systems Financial Standards Board global delivery model General Data Protection Regulation Goal Framing Theory greenhouse gas Global Reporting Initiative high-occupancy vehicle high tension Hypertext Transfer Protocol heating, ventilation and air conditioning International Conference in Information Systems initial coin offering information and communications technology Information & Management Infrastructure Data Platform for Energy Generation Documentation Internet of Things intellectual property rights information systems Infosys Sustainability Executive Council

xvi  Research handbook on information systems and the environment IT kW LAMP LAN MATH MICO MRP MS MSBA OIT PACIS PANAS PEB PEN PET PIIT PLOC PMU PN PoA PoS PPS PSPD REST-API RFID RTP SCADA SCT SDG SDU SHT SIG SMART SMIS SMT SOV SSI

information technology kilowatts Landau Microgrid Project local area network Model of Adoption of Technology in Households Motor Industries Co Ltd material resource planning Management Science Master of Science in Business Analytics Organismic Integration Theory Pacific Asia Conference on Information Systems Positive and Negative Affect Schedule pro-environmental behaviours personal ecological norms periodic tariff personal innovativeness regarding information technology perceived locus of causality Phaser Measurement Units personal norms proof of authority proof of stake products, platforms and services profitability-sustainability-predictability-de-risking representational state transfer application programming interface radio frequency identification real-time pricing supervisory control and data acquisition Social Cognitive Theory Sustainable Development Goals University of Southern Denmark smart home technology special interest group sustainability modelling and reporting sustainability management information system smart metering technology single-occupancy vehicles self-sovereign digital identities

Abbreviations  xvii STARS STE SWEBOK TAM TBL TCFD tCO2e TCS TNEB ToD ToU TPB TRA UAV UBAP UC Berkeley UNCED UNFCCC UNICAMP UNITEN UTAUT VRES WAN WBCSD WCED WSSD WTA WTP ZKP

Sustainable Technology Adoption in the Residential Sector eco-socio-technical Software Engineering Body of Knowledge guide Technology Acceptance Model triple bottom line Taskforce on Climate-related Financial Disclosures tonnes of CO2 emission equivalent Tata Consultancy Services Tamil Nadu Electricity Board time of day time-of-use Theory of Planned Behaviour Theory of Reasoned Action unmanned aerial vehicles utility bill automation and processing solution University of California, Berkeley United Nations Conference on Environment and Development United Nations Framework Convention on Climate Change University of Campinas Universiti Tenaga Nasional Unified Theory of Acceptance and Use of Technology variable renewable energy sources wide area network World Business Council for Sustainable Development World Commission on Environment and Development World Summit for Sustainable Development willingness to accept willingness to pay zero-knowledge proofs

1. Introduction to the Research Handbook on Information Systems and the Environment Vanessa A. Cooper, Johann J. Kranz, Saji K. Mathew and Richard T. Watson

The case for solving global change is accepted by nearly everyone who does not have a vested interest in preserving the fossil fuel industry or has not been persuaded by that industry to ignore reality. We feel no need to restate the evidence, as we see and hear about the effects of global warming almost every day in the print and visual media. In general, the world has been slow to acknowledge the problem and take steps to reduce global emissions. Indeed, it might be too late for effective action. Information systems (IS) academics are among the many laggards. The problem has been ignored by most IS scholars, along with their peers in cognate disciplines. If you count the number of articles in leading IS journals over the last decade that address environmental issues, you might conclude it is a problem to which IS could make a minor contribution (Boudreau, Watson & Jeszke, this volume). Solving global climate change requires actionable knowledge. It requires practical solutions that contribute to carbon emission reduction. Theory needs to be put into action, yet many journals are centred on theory development and testing. Producing applicable knowledge is a minor goal. Nevertheless, a group of IS scholars has emerged who have embraced the problem and investigated means of using IS to address environmental concerns. This Handbook reflects their concerns and scholarship. Maybe it should have been published 10 or 20 years ago. Nevertheless, we hope it is not too late for IS scholars to contribute to carbon emissions reduction. We hope this book inspires more of our colleagues to address the most critical problem of our times.

1.

ENERGY INFORMATICS

Scholarship can advance significantly when researchers coalesce around a concept or research centre. Energy informatics emerged as a notion that captured attention because it succinctly recognised that energy efficiency is a crucial lever in reducing carbon emissions. Energy informatics asserts that IS can be used to collect and analyse data to improve energy efficiency (Watson et al., 2010). The development of energy informatics is detailed in Chapter 2 (Boudreau et al.). Turning an idea into practical knowledge requires visionary leadership and execution excellence, as we see with the SDU Center for Energy Informatics in Chapter 3 (Jørgensen). Energy informatics emerged as a subfield of IS following an Issues and Opinions article in MIS Quarterly (Watson et al., 2010). The authors of Chapter 2 link the emergence of energy informatics with a shift in dominant logic from service to sustainability. This transition in societal logic requires new types of IS, such as those that raise energy efficiency and capital productivity. The academic interest in sustainability is charted from the perspective of communities, forums, and journals. The authors lament IS scholars’ general lack of interest in sus1

2  Research handbook on information systems and the environment tainability research and a paucity of actionable findings. The growth and extent of corporate environmental, social, and governance (ESG) reporting are examined, and the need for new types of IS to measure ESG dimensions (e.g., social capital) is identified. An examination of corporate letters to shareholders reveals that most companies do not explore sustainability in depth. Collectively, they seem to recognise that ESG is important, and the authors are hopeful their interest will grow. The authors conclude that recent developments suggest that early optimism for a substantive and influential body of IS research on sustainability was misplaced. They observe that they are unaware of any IS scholars participating in any of the major climate change forums. The general failure of IS researchers at large to embrace sustainability, the most critical problem of our time, is a regrettable lack of academic social responsibility. We hope this book is a step towards redressing this shortcoming. The University of Southern Denmark (SDU), arguably the most prominent of the energy informatics research centres, is described in depth in Chapter 3. Established in 2013, the SDU Center for Energy Informatics exemplifies how to build interdisciplinary research capacity. Its vision is firmly grounded on the work of Ban Ki-moon, former Secretary-General of the United Nations, and Gro Harlem Brundtland, former Norwegian Prime Minister and Chair of the World Commission on Environment and Development. Furthermore, there is an admirable internal consistency in the operation of the centre. For example, the teaching and office buildings are living labs to study building energy efficiency. The centre’s close collaboration with industry recognises that solving global warming requires applicable knowledge. The centre’s greenhouse energy project demonstrates the practical and valuable knowledge SDU is creating. The centre has established the Energy Informatics Journal1 and the Energy Informatics Academy to leverage its impact and grow its international relationships.2 IS scholars recognise that reducing carbon emissions is a data-intensive problem; consequently, they can play a key role in developing systems to facilitate a sustainable society. Because they are data-intensive, green IS create some particular challenges. The contribution of Chapter 4 (Lakshmi, Corbett & Webster) is to point out these challenges broadly and then concretely through six use cases. Notably, the authors use an eco-socio-technical system to frame the data-related issues. This framing could be used in the classroom to assist students in understanding how IS can contribute to sustainability. It could be followed by an idea-generation session when students could identify some additional use cases. As scholars, we can leverage our students’ creativity to identify new Green IS and nudge some of them into Green IS careers.

2.

A CHANGING ECONOMY

To have a significant impact, the environmentalist’s mantra – reduce, reuse, and recycle – needs to extend beyond individual behaviour to enterprise behaviour. We need industrial-scale practices that can institute economies of scale and scope. Chapter 5 (Ixmeier, Kranz, Recker & Zeiss) illustrates how IS are essential for a circular economy. As the world’s population grows and individual wealth increases, the finiteness of many of the world’s resources is more apparent. The winners in the world’s open economies are those with the highest level of capital productivity. Without government subsidies or regulations, many enterprises will not change their practices unless an alternative, such as adopting circular economic principles, raises their capital productivity. Thus, the chapter’s SPREAD case is vital because it documents the

Introduction  3 potential of a circular economy to improve capital productivity or simply the ability to produce more with less. This comprehensive chapter identifies the main IS issues (e.g., data ownership) that need to be addressed and the digital technologies (e.g., digital twins) required for a circular economy. The authors further augment the case made by several chapters for the necessary blending of IS and economics principles to build a sustainable economy. A precept most MBAs learn is that management requires measurement. The ESG movement is an attempt to measure how organisations affect people, profit, and the planet more accurately. We are investors in the earth, and we have a right to know how businesses impact our world. An exclusive focus on the primacy of the shareholder and the maximisation of their wealth is destructive to human, social, and environmental capital (Gelles, 2022). Incidentally, the long-term effect of many key proponents of maximising shareholder wealth was to destroy economic prosperity (Gelles, 2022). As we move away from a single principle for corporate decision-making, we need new measures and supporting IS to guide a more comprehensive and societally responsive approach to enterprise deliberation on issues that affect all stakeholders. In Chapter 6, Kummer and Degirmenci analyse the impact of a triple bottom line orientation by leading Australian public companies. They report that current sustainability management information systems (SMIS) are limited to documenting financial expenditures and their impact on environmental capital but tend to neglect social capital. That is, they neglect to report the interrelations between all three dimensions of the triple bottom line. This is not surprising because measuring social capital creation, for example, is challenging. However, without such measures, management is flying blind in the ESG world. Additionally, ESG is not only about stakeholder reporting; its findings also need to influence strategic planning directly. Executives can use an SMIS to improve organisational performance (Kranz et al., 2021).

3.

MINDSET AND SUSTAINABILITY

Society expects applied sciences to solve problems. Thus, the ultimate contribution of IS to ameliorating the effects of global climate change is to design and implement systems that reduce carbon emissions. Chapter 7 (Recker) is an important proximate stage towards the final goal. The literature is analysed using the Belief–Action–Outcome model (Melville, 2010) and affordance theory, a topic of Chapter 8 (Seidel, Recker & Vom Brocke). Green IS are classified by principles of function and form to expose four major findings. Notably, the author concludes that Green IS design science research is in an emerging phase, which underscores our early-expressed concern for the failure of the IS field to engage in implementing climate change solutions. Admittedly, the literature review covered 2010–2016, but as observers of Green IS research, we have not seen an explosion of activity in recent years. This chapter is an essential read for those planning to follow a design science approach to creating a Green IS application. Humans are wonderfully creative. First, they create IS to solve problems. Second, if a system is sufficiently malleable, as with most digital technologies, people find uses, and misuses, that the original designer never envisioned. Systems typically have latent features, affordances, that are discovered when they are put into practice or when tangential thinkers see different applications. A spreadsheet exemplifies how a system designed with budgeting and other accounting tasks in mind is now used for many tasks far remote from its accounting

4  Research handbook on information systems and the environment heritage. However, as Pasteur said, “In the field of observation, chance favours the prepared mind.”3 In an organisation, managers are prepared to follow one or more institutional logics, which can be conflicting. As mentioned previously, the long-dominant primary goal of maximisation of shareholder wealth is challenged by the ESG movement. In Chapter 8, Seidel et al. examine the relationship between a system’s affordances and the host organisation’s institutional logics. After dissecting affordances from multiple levels, they report that IS are well suited to creating affordance for sense-making and promoting sustainable practices, if a managerial team’s mindset is prepared to seek environmental goals. This mindset will be more ready to change if it observes that environmental practices, such as the circular economy (see Chapter 5), support the prevailing institutional logic. For the Indian global IT services giant Infosys, sustainability was among its four founding principles. It has applied ESG thinking since its establishment in 1981. Chapter 9 (Mathew & Rajan) chronicles Infosys’ sustainability journey, which has gained international recognition. There is a double pay-off for Infosys. First, as a proponent of sustainability, it is a resource-efficient organisation. It does not pay a premium for being green but instead earns a reward. Second, the knowledge gained from practising sustainability can be applied to exploiting the demand for effective procedures and software to implement ESG agendas. A firm marketing green solutions has a considerable advantage when its strategic actions demonstrate a solid commitment to sustainability. Doing more with less raises capital productivity, and Infosys has turned this precept into an internal and external advantage. This case needs to be part of every executive’s ongoing education.

4.

CONSUMER BEHAVIOUR

The notion that people can be nudged to change their behaviour (Thaler & Sunstein, 2008) has gained popularity, and government departments have been established to alter social behaviour through nudging.4 The state of the environment is determined by consumer behaviour – what foods we eat, how much we drive, how we set thermostats, and many other aspects of life. Successful nudging for many different circumstances will be necessary to create a sustainable society. Because games can teach lessons about living (e.g., Monopoly for managing money or chess for anticipating consequences), it is also possible for them to nudge behaviour, as examined in Chapter 10 (Ixmeier, Seidler, Henkel, Fiedler, Kranz & Strunk). Gamified nudges effectively strengthen and align normative behaviour, such as eco-sustainable actions (Seidler et al., 2020). By using games, digital nudging, “the use of user-interface design elements to guide people’s behaviour in digital choice environments” (Weinmann et al., 2016, p. 433), has the potential for a low-cost and effective means of ingraining environmentally desirable behaviours. Just imagine the possible ecological benefits if such nudges could be built into some of the popular computer games enjoyed by millions. Such interventions need to be empirically tested before implementation, and this chapter provides an example of how to design, implement, and analyse an appropriate pre-intervention experiment. Tomorrow’s environment is a result of what we consume today. If we collectively consume more fossil fuels today than yesterday, CO2 levels will rise. The shift to renewables will change some of the results of human consumption but will not eliminate an environmental impact. There is no free lunch, but there is a potentially cheaper lunch by creating a sustainable society. A change in consumption behaviour often requires a trade-off, and Chapter 11

Introduction  5 (Sangeeth, Mathew & Watson) explores the comfort versus money trade-off of electricity consumption. Because renewables are intermittent, there will be periods of undersupply. When it is too costly to build additional generating or storage capacity to handle the exceptional demand created by extreme hot or cold weather, utility companies must consider behavioural approaches to reducing demand. Sangeeth and her colleagues empirically examine how electricity consumers respond to options framed as a willingness to accept (WTA) or a willingness to pay (WTP). As we transition to renewable and electric vehicles, decision-makers will face situations where knowledge of the consequences of how trade-offs are presented to consumers will be essential. For example, there is a trade-off between battery cost and driving range. What range will consumers accept, or how much are they willing to pay for additional range? The chapter provides a theoretical grounding and empirical implementation that could serve many researchers interested in exploring the trade-offs of the new energy supply and transportation sectors. The quest to understand how to motivate humans to reduce their environmental impact is continued in Chapter 12 (Rook, Paundra, Van Dalen & Ketter). The authors apply the concept of psychological ownership to understand the general nature of sharing resources and to interpret two specific cases: mobility sharing in Indonesia and energy cooperatives. Psychological ownership takes a longer-term perspective than a transactional view, as we might see with WTA or WTP. We need both to solve the tragedy of the global commons. Carbon emissions in any part of the world eventually affect every part. The critical question is, of what part of that world do we believe we have some psychological ownership? The two cases cover extreme ownership spaces. Does anyone of the 30 million people of Jakarta and its outskirts have psychological ownership of any components of this vast city? In contrast, an energy cooperative is a micro situation. Additionally, electricity is a commodity, whereas mobility is a service. People might have psychological ownership of the cooperative, and the cost of this ownership is readily apparent when it produces a commodity. The authors report that psychological ownership can moderate behaviour based on an experiment. This chapter offers a foundation and thought-provoking examples for stimulating research on creating a sense of psychological ownership that could contribute to positive environmental behaviour. Water and energy are essential components of everyday consumption. The demand for both rises with population and wealth growth. Water is a finite resource, and climate changes are affecting availability, such as the long-term droughts in the west of the US. The sun provides sufficient energy to meet our needs. Still, it requires a massive investment in renewable generating capacity to harvest it directly, such as solar, or indirectly, such as wind. The focus of Chapter 13 (Staake, Tiefenbeck & Stiefmeier) is on using feedback to reduce water and energy consumption and the investment required to create a sustainable society. The five reported studies demonstrate the power of feedback to direct behaviour in a sustainability desirable direction. These robust findings, peer-reviewed by leading science journals, are based on nearly 400,000 observations in multiple countries. While the results are very important, the main contribution of this chapter is the authors’ recollections of 10 years of research directed at demonstrating the power of salient feedback to alter consumption. Their work is inspiring because it shows that the IS field can significantly contribute to reducing global climate change. Research on how to motivate individual behavioural change continues in Chapter 14 (Wunderlich & Veit). The authors point out that in the EU private households account for

6  Research handbook on information systems and the environment 26 per cent of energy consumption to satisfy their personal needs. The authors present the case for smart home technology (SHT) and how smart meters and appliances can support demand-side management. The Theory of Reasoned Action (Fishbein & Ajzen, 1975) and the IS adoption literature, particularly that related to households, provide a starting point for developing processes to encourage householders to accept smart metering technology and intelligent appliances. The authors provide a comprehensive review of the adoption of relevant household technology and detail the key factors before reporting an empirical study of a Sustainable Technology Adoption in the Residential Sector (STARS) model. There is a considerable contrast in the complexity of the STARS model with its 14 explanatory factors and Sangeeth et al.’s (Chapter 11) examination of willingness to accept or a willingness to pay. Both approaches are worthy of consideration by scholars investigating how to change householders’ behaviour to handle demand response.

5. MARKETS When conditions are appropriate (Roth, 2008), markets are the most effective way to allocate scarce resources, particularly for well-defined commodities, such as electricity. The world is undergoing two essential and complementary transitions. We are moving to renewable energy sources, such as solar and wind, and electrifying transportation. Chapter 15 (Dedrick, Fridgen, Körner & Strüker) examines the many issues related to these dual critical transformations. The authors identify the critical technological components, such as smart meters and edge computing, that are necessary to enable the operation of a real-time electricity market. They reveal the combinatorial complexity of integrating many technologies to allow the data flows necessary to operate simultaneously a highly resilient grid and efficient market. The envisioned real-time market will connect buyers and sellers of electricity, and the grid must deliver on their contracts seamlessly at high speed while maintaining a balanced grid. Solving this problem of concurrently processing millions of bits and electrons is indubitably the digital transformation of the forthcoming decade (Watson et al., 2022). The authors provide a well-defined roadmap for executing this revolution. This critical path to the future requires IS specialists, economists, and electrical engineers to collaborate to build the core component of a resilient and sustainable society. The exploration of electronic markets to allocate scarce resources, such as electricity, continues in Chapter 16 (Wörner, Tiefenbeck & Ketter), with an analysis of the potential of blockchain to disintermediate a market and enable peer-to-peer trading. While blockchain is not necessary for market disintermediation, it might offer benefits that make it a superior option. This chapter’s major contribution is a review of the relevant literature from an economics perspective. Roth’s (2008) four criteria for a functional market guide the review. This chapter’s balanced analysis of the pros and cons of blockchain-facilitated markets is refreshing, as so much of the reporting in the trade and academic channels is rather rosy-eyed. To engage citizens in the transition to renewables, the European Union has introduced Citizen Energy Communities (CECs), and Chapter 17 (Weinhardt & Staudt) documents the implementation of the Landau Microgrid Project (LAMP). Two developments, smart metering and residential and community solar installations, drive the development of CECs. They give consumers a stake in the energy market because they can become informed prosumers. The authors provide considerable detail on the design and hardware of LAMP; thus, this chapter

Introduction  7 gives those considering such a microgrid project a valuable head start. The knowledge gained from a multi-year LAMP study is important because they reveal a significant gap between expected and actual behaviour. It was assumed that participants would demonstrate a social shift by buying locally generated electricity and be utilitarian by shifting consumption to take advantage of lower prices. After an initial period, participants reverted to their prior behaviour. The authors conclude that most residents probably want “set-and-forget” software to execute their preferences automatically.

6. CONCLUSION We thank the contributors for the quality of their work. They provide readers with a comprehensive coverage of the current state of Green IS research and offer many ideas for future research. IS scholars have the necessary systems mindset, expertise, and knowledge to reduce carbon emissions. We fervently hope this book inspires more Green IS research directed at solving the most significant challenge to humanity in recent centuries.

NOTES 1. https://​energyinformatics​.springeropen​.com/​about. 2. https://​www​.energyinformatics​.academy4. 3. Lecture, University of Lille (7 December 1854). 4. https://​obamawhitehouse​.archives​.gov/​the​-press​-office/​2015/​09/​15/​executive​-order​-using​ -behavioral​-science​-insights​-better​-serve​-american.

REFERENCES Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Gelles, D. (2022). The Man Who Broke Capitalism: How Jack Welch Gutted the Heartland and Crushed the Soul of Corporate America – And How to Undo His Legacy. New York, NY: Simon & Schuster. Kranz, J., Fiedler, M., Seidler, A., Strunk, K., & Ixmeier, A. (2021). Unexpected benefits from a shadow environmental management information system. MISQ Executive, 20(3), 6. https://​doi​.org/​10​.17705/​ 2msqe​.00051. Melville, N.P. (2010). Information systems innovation for environmental sustainability. MIS Quarterly, 34(1), 1–21. Roth, A.E. (2008). What have we learned from market design? Economic Journal, 118(527), 285–310. Seidler, A.-R., Henkel, C., Fiedler, M., Kranz, J., Ixmeier, A., & Strunk, K.S. (2020). Promoting Eco-Sustainable Behavior with Gamification: An Experimental Study on the Alignment of Competing Goals. Paper presented at the ICIS. Thaler, R.H., & Sunstein, C.R. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. New Haven, CT: Yale University Press. Watson, R.T., Boudreau, M.-C., & Chen, A.J.W. (2010). Information systems and environmentally sustainable development: Energy Informatics and new directions for the IS community. MIS Quarterly, 34(1), 23–38. Watson, R.T., Ketter, W., Recker, J., & Seidel, S. (2022). Sustainable energy transition: Intermittency policy based on digital mirror actions. Journal of the Association for Information Systems, 23(3), 631–8. https://​doi​.org/​10​.17705/​1jais​.00752.

8  Research handbook on information systems and the environment Weinmann, M., Schneider, C., & Vom Brocke, J. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433–6.

2. Energy informatics: origins, emergence, and future Marie-Claude Boudreau, Richard T. Watson and Natalie Jeszke

1. EMERGENCE 1.1

Energy Informatics as a New Subfield

In 2010, in an “Issues and Opinions” article published in the premier information systems (IS) journal MIS Quarterly, Watson, Boudreau, and Chen (2010) lament that the IS academic community has been slow to acknowledge environmental sustainability as a major problem. They propose several ways for IS scholars to engage in the development of environmentally sustainable business practices, encompassing actions in research, teaching, and services. They also advocate a research agenda to establish a new subfield of energy informatics, which applies information systems thinking and skills to increase the efficiency of energy systems, or other types of systems that are based on scarce resources (such as water and materials). In a nutshell, energy informatics is concerned with the analysis, design, and implementation of information systems to increase the efficiency of the demand and supply systems underlying scarce resources, energy being the prime example. The framework emphasises that the demand and supply sides are equipped with sensitised objects (demand), and sensor and flow networks (supply). Information systems have the ability to tie together the data generated by these various elements to provide information supporting better optimisation, transparency, monitoring, and control, ultimately improving the efficiency of the overall system. Based on this framework, the authors suggest nine related research questions to advance research in energy informatics. Another key message in this opinion piece is that IS researchers should commit to promoting scholarship at the intersection of sustainability and information systems, and journal editors should create the structure to encourage this type of research. In this chapter, we assess the extent to which the IS community has embraced this plea. On the one hand, it appears that this recommendation was heard, as we observed that the original energy informatics article has accumulated over 1,200 Google Scholar citations, and stimulated the launch of a journal (Energy Informatics1), multiple special issues by journals on sustainability, master’s programmes,2 and university departments of energy informatics.3 On the other hand, as we consider the size and significance of the environmental challenge that we are facing, we argue that the impact of this growth still falls short in terms of providing actionable solutions. In the following pages, we take stock of how energy informatics and sustainability-related concerns have been addressed both in research and practice. To set the stage, we first explain how a shift in dominant logic has positioned sustainability as the core logic of our current economy. Considering this dominant logic and assessment of the work done, we propose a path forward, one that is heavily anchored in renewable energy sources. 9

10  Research handbook on information systems and the environment Table 2.1

The shift in dominant logic

Economy

Subsistence

Agricultural

Industrial

Service

Sustainable

Question

How to survive?

How to farm?

How to manage

How to create

How to reduce

resources?

customers?

environmental impact?

Dominant issue

Survival Production Customer service Sustainability

Source: Watson et al. (2012)

1.2

Dominant Logic Shift

Dominant logic is the underlying motivation that an entity subscribes to in order to make business decisions. There is a dominant issue facing each stage of the economy, as shown in Table 2.1 (Watson, Lind & Haraldson, 2012). One dominant issue does not replace another, but rather early ones remain in place, typically with reduced importance. Thus, some societies, even if they are primarily anchored in a higher-level dominant logic (say, “service economy”), may occasionally struggle to produce, import, or distribute enough food during a drought to feed their population (i.e., “agricultural economy”). That is, even in advanced economies, governments devote resources to ensuring food and nutritional security. In most advanced economies, sustainability is rising as the dominant issue, though production and customer service still exist as key elements of their economies. Let us first review what other dominant issues have been over time, so as to better distinguish them from this century’s problem to solve; i.e., the reduction of our environmental impact. A subsistence economy is highly dependent on natural capital and a few tools for killing, slaughtering, cooking, and carrying food. Measurement is based on observation and memory, such as “Near the big bend on the river was a good place for berries last summer.” A system of engagement enabled cooperation to enhance survival, the dominant issue. There are still some indigenous people living in subsistence economies, and refugee camps have many features of a subsistence society. Agrarian society was concerned with productive farming, and farmers needed to keep stock of how many animals they had, and their gender, age, condition, and so forth. They needed to measure the yield of each field by type of crop. For early farmers, many of these measures were likely kept in mind rather than recorded. For today’s farmer, such measurements are part of their system of record, and some are generated automatically. Society’s focus shifted during the industrial era to management of resources (e.g., raw materials, labour, logistics) and creating procedures for efficient large-scale production. Josiah Wedgwood developed cost accounting (organisational capital) in the late 18th century to manage his pottery factory. The industrial era created procedures for running an assembly line or chemical process, with techniques like six sigma, lean manufacturing, just-in-time, and project management. Perhaps the most important development was the emergence of enterprise resource planning (ERP) systems, which give a detailed and integrated view of a company’s operations. These systems were inspired by earlier systems, such as material resource planning (MRP), and were extended to include accounting functions. In the mid-1980s, there was a shift in attention from production of goods to the delivery of services and customer creation and retention. There was an over-supply of many consumer

Energy informatics: origins, emergence, and future  11 products (e.g., cars), and companies had to adjust to learn how to identify services and product features that would attract customers. They became concerned with determining what types of customers to recruit, finding out what they wanted, and building a relationship with them. As a result, we saw the rise of business analytics and customer relationship management (CRM), and such systems were also incorporated into ERP packages. As suggested earlier, we are now in a transition to a new era, sustainability, where attention shifts to assessing environmental impact because, after several centuries of industrialisation, atmospheric CO2 levels have become alarmingly high. We are also reaching the limits of the planet’s resources as its population now exceeds seven billion people. When the earth had a small and less affluent population, natural capital processes operated to meet most needs. Now, we put such pressure on natural resources that we need to attend to their protection. Accordingly, many organisations now recognise that they can no longer ignore environmental concerns because of the risks induced by global warming. The shift in dominant logic requires new types of information systems that directly address sustainability and related issues. The core purpose of these new systems is to raise energy efficiency and capital productivity (Watson, 2020). Irrespective of the now favourable economics of renewables, society needs to still raise its level of energy efficiency to lower carbon emission from burning fossil fuels, because energy transitions can take roughly half a century (Smil, 2014). Also, energy efficiency is a source of national competitiveness as the cost of energy is a component of almost every product and service. Improvements in capital productivity are necessary so that economic growth does not come at the expense of the environment. For example, increasing the utilisation of the container shipping fleet reduces the need to build more vessels. However, as the next section will demonstrate, whereas the call for a shift in IS focus that was made in 2010 has been heard, the extent to which IS research produced actionable solutions to tackle environmental challenges is questionable (Watson et al., 2010).

2.

LOOKING BACK

2.1

Academic Interest in Sustainability

2.1.1 Communities, conferences, new journals A few academic communities have emerged in the specific areas of energy informatics and management IS (MIS) scholarship with a sustainability focus. For one, the Energy Informatics Academy was created as a global community for researchers and practitioners of energy informatics in various scientific, technological, engineering, and social fields. According to its website, members from this group are involved in research and applications of digital technology and information management theory and practice to facilitate the global transition towards sustainable and resilient energy systems. The academy is involved in energy informatics conferences and offers a repository of academic research centres, groups, and departments that have emerged with a focus on energy informatics. The DACH+ conference series on energy informatics, specifically targeting the Germany– Austria–Switzerland region, has been active since 2013. Its goal is to promote the research, development, and implementation of information and communication technologies (ICT) in the energy domain and foster the exchange between academia, industry, and service providers.

12  Research handbook on information systems and the environment Every year, it has had a doctoral workshop co-located with its conference, thus encouraging research in energy informatics among the younger generation of scholars. ACM SIGEnergy is a professional forum for scientists, engineers, educators, and professionals for discussing energy systems and energy informatics.4 From this group stems the ACM eEnergy conference, which recognises energy research as a relevant field within computer science. It is now considered the flagship conference of energy informatics by this research community. As for IS scholars’ main conferences, the International Conference in Information Systems (ICIS), the Americas Conference on Information Systems (AMCIS), the European Conference on Information Systems (ECIS), and the Pacific Asia Conference on Information Systems (PACIS), they all have had sustainability-related tracks or mini-tracks in the past decade. In addition, our professional association, the Association for Information Systems (AIS), supported the creation of a new Special Interest Group (SIG) in 2012, one focused on sustainability (i.e., SIG Green). From its early beginning, SIG Green organised a Pre-ICIS Workshop to provide an annual forum for sustainability research in IS. In addition, new journals have also emerged, such as the Energy Informatics journal, which was launched in 2018, and the Sustainable Computing: Informatics and Systems journal in 2011. These, however, might not have gathered much attention from IS scholars, as they are rather targeting research conducted by computer scientists and engineering scholars. However, IS scholars have contributed a number of sustainability-related papers in special issues in their main journals, discussed next. 2.1.2 IS journal special issues As summarised in Table 2.2, many special issues on IS and sustainability have been published in the past 13 years. In 2008, the Journal of Systems and Information Technology led the way, with three published papers (from 15 submissions). The editors prefaced the special issue with a claim that “research in information systems has been slow to enter into the sustainability debate but recent conferences have increasingly been incorporating sustainability research streams” (Standing & Jackson, 2008). Then, in 2010 the Australasian Journal of Information Systems published a special issue on ICT and climate change; the journal published seven papers selected from the 2008 Carbon Centric Computing Initiative (CCCI) where an international team of experts presented on a range of topics, methods, and approaches for conducting research into the positive application of IT in areas affecting the environment. The editors then reminded readers that, whereas “the current discourse on IT and climate change views IT in a negative light […], what remains unrecognised is the critical role of IT in providing a range of tools to model, manage and optimize the ‘planet earth’ supply chain” (Hasan, Ghose & Spedding, 2009, p. 19). The year 2011 was a prolific year for the publication of articles at the crossroad of IS and sustainability, with four journals planning special issues in that area. One that was expected but did not materialise was that of Information and Organization. Only one paper ended up being accepted and published (Jenkin, Webster & McShane, 2011), not enough to refer to it as a special issue. Earlier that year, the Journal of Strategic Information Systems had a much better outcome, with a total of eight papers published in its special issue on the “greening of IT”, each focusing on one of four domains: regulations, technologies, assessment, and consequences. As a third special issue that year, the ACM Transactions on Intelligent Systems and Technology released seven papers on computational sustainability. This special issue was interdiscipli-

Energy informatics: origins, emergence, and future  13 nary, bringing together computational sciences and a variety of other disciplines as diverse as environmental sciences, engineering, biology, economics, and sociology. We mention it here as it highlights the need for IS researchers to reach other disciplines as they strive to address sustainability issues with solutions that can be implemented and tested. Finally that year, the Australasian Journal of Information Systems came up with another special issue, this one on Green IS and IT, which included five empirical papers. The editorial team then lamented that “there is a lack of rigorous empirical studies which are theory and evidence based to provide a sound basis for understanding IT green best practices and how these can be best adopted in organizations” (Lane, Kolbe & Zarnekow, 2011, p. 3). In 2013, two IS journals had special issues at the crossroad of sustainability and IS. Information Systems Frontiers published nine such papers. In their introductory article, this special issue’s editors take a strong stance on the type of research conducted by IS researchers to resolve our greatest environmental challenge, global climate change. They question whether the traditional IS research paradigm of social science research is adequate given the crisis we face. They contend that “we need research that can take giant steps towards solving global warming,” and that for IS researchers, “it is very difficult to take these giant steps” (Sarkis, Koo & Watson, 2013, p. 700). They argue that solution science, rather than social science, might be what is the most needed at this time. Also in 2013, one of the top journals in the IS field, MIS Quarterly, released a special issue with three articles related to IS and sustainability. Echoing the sentiment found in Information Systems Frontiers, the editors stated that looking back, “very little research is being done in the areas of design science and impact dimensions” (Malhotra, Melville & Watson, 2013, p. 1266). Three years later, in 2016, the Journal of the Association for Information Systems released a special issue on IS solutions for environmental sustainability. The emphasis on solutions reiterated the urgent need for IS scholars to do more than acknowledging the problem. Disappointedly, only one paper was deemed worthy of publication. Again, the editors pleaded that IS researchers need to “step up these efforts,” adding that “the academic discipline is still by far too slow relative to the needs of society. Too few people are working on green IS given its importance, and fewer still are publishing papers about IS solutions that could contribute to dealing with climate change” (Gholami, Watson, Molla, Hasan & Bjørn-Andersen, 2016, p. 521). The following year, Information Systems Journal published its own special issue, referring to environmental sustainability as an imperative for urgent action. Although four papers were published, this special issue’s editors upheld the familiar plea for more impactful research. They wrote, “regrettably, many IS researchers tend to follow technology trends rather than considering how technology may be applied appropriately to address issues of global importance” (Elliot & Webster, 2017, p. 373). They recommended that the IS community “demonstrate its relevance through more systemic collaboration with business, governments and societies to support global efforts addressing climate change” (p. 377). The Institute of Electrical and Electronics Engineers (IEEE), the largest technical professional organisation for the advancement of technology, has also shown keen interest in sustainability and technology. To name a few of its publications, IEEE Systems Journal had a special issue on green communications, computing, and systems in 2017, and boasted an impressive number of submissions (63) and published papers (22). The issue touches on different aspects of sustainability, including energy efficiency and management, resource efficiency, and environmental protection. Another one is IEEE Industrial Informatics, which in 2018 published

3

Articles

7

2010

AJIS

2008

JSIT

Year

1

I&O

2011

8

JSIS

2011

7

on IST

ACM Tsx

2011

Special issues on IS and sustainability

Journal

Table 2.2 2011

5

AJIS

2013

9

ISF

2013

3

MISQ

2016

1

JAIS

2017

4

ISJ

2017

19

Informatics 22

Industrial Journal

3

3

2021 BISE

2020 EM

2018 IEEE Systems

IEEE

14  Research handbook on information systems and the environment

Energy informatics: origins, emergence, and future  15 a special section on energy informatics for green cities with over 100 submissions and 19 published papers. Many more papers at the crossroads of technology and sustainability have appeared in IEEE journals, most of which with a heavy engineering slant given the typical audience of these journals. Going back to a traditional IS audience, in 2020 Electronic Markets published its own special issue with three papers focusing on how to incorporate sustainability into business and e-business models. In 2021 Business & Information Systems Engineering also sought to publish an energy informatics special issue providing an outlet for publication of research offering implementable solutions capable of reducing carbon emissions. The editorial team received ten submissions and published two research papers and one featured interview (Lehnhoff, Staudt & Watson, 2021). In summary, the academic community has put efforts to rise to the challenge set forth by Watson et al. (2010). Indeed, many journals did set up special issues encouraging research in this area (although only one, MISQ Executive, has dedicated a section of its journals to Green IS and energy informatics, as originally recommended by Watson et al., 2010). However, as lamented by many special issues’ editors, IS researchers are less prone to conduct research that ultimately proposes solutions. Given the urgency and significance of the environmental challenges we are facing, we are joining our voice to theirs in that more actionable research is needed. If IS scholars are to address the fundamental issue (i.e., environmental degradation) associated with the sustainability dominant logic, they will need to get involved in topics that generate applicable knowledge that will result in societal productivity gains, such as optimisation, simulation, prototyping, and digital twinning. The current IS focus on theory building by uncovering associations between variables is not directly aimed at identifying and testing interventions that advance sustainability. Journals with an interdisciplinary focus have been more inclined to publish actionable research, and therefore, it may be time for IS scholars to reach out to colleagues from other areas, particularly engineering. 2.2

Corporate Interest in Sustainability

2.2.1 ESG reporting The appearance of environmental, social, and governance (ESG) standards is a change in thinking about measuring organisational success and an indicator of the shift in dominant logic discussed earlier. Intangibles now represent about 80 per cent of the S&P 500 market value (Gleeson-White, 2014) so there is a need to take an integrative approach to measuring the value of an enterprise that assesses all forms of capital (Watson, 2020). Accordingly, corporations have steadily increased their sustainability reporting in the past decade. For example, in the U.S. only 20 per cent of the largest 500 companies published a sustainability report in 2011, which increased to 75 per cent in 2014, and then to 90 per cent in 2019.5 Companies have felt the pressure to report their ESG metrics and to provide greater transparency to not only investors, but also employees, customers, and communities at large. Notably, in January 2020 BlackRock, the world’s largest asset manager, called for companies to change the way they disclose ESG metrics to their investors. Larry Fink, BlackRock’s CEO, requested that companies provide a clearer picture of how they are managing sustainability-related questions and serve their full set of stakeholders. In an impactful statement, Fink also warns:

16  Research handbook on information systems and the environment Given the groundwork we have already laid engaging on disclosure, and the growing investment risks surrounding sustainability, we will be increasingly disposed to vote against management and board directors when companies are not making sufficient progress on sustainability-related disclosures and the business practices and plans underlying them.6

Today, although sustainability is largely a voluntary effort, many companies disclose their sustainability metrics on their websites and via one (or many) of the ESG reporting frameworks. Some prominent frameworks are: 1. The Global Reporting Initiative (GRI)7 2. The CDP (originally known as the Carbon Disclosure Project)8 3. B Corp9 4. The Sustainability Accounting Standards Board (SASB)10 5. The Financial Stability Board’s Taskforce on Climate-related Financial Disclosures (TCFD)11 As an independent international organisation, the GRI has pioneered sustainability reporting in 2000. It has developed standards for reporting corporate performance across the United Nations’ (UN’s) 17 Sustainability Development Goals (UN SDGPR, 2020). The GRI’s sustainability reporting framework is now the most widely used by multinational organisations, governments, small and medium-sized enterprises, non-governmental organisations, and industry groups.12 In a recent survey of sustainability reporting, KPMG reported that almost all (96 per cent) of the world’s largest 250 companies (the G250) disclosed their sustainability performance, and that the GRI standard was used by about three-quarters (73 per cent) of them.13 The GRI claims around 10,000 reports from companies based in 100 countries. Also created in 2000, the CDP is a not-for-profit organisation that provides a global disclosure framework for investors, companies, cities, states, and regions to manage their environmental impacts. It claims that over 13,000 companies reported through their framework (focusing on one or many of three areas: climate change, water security, forests), and that over 1,100 cities, states, and regions disclosed environmental information through them. The B Corps movement, which began in 2006, has certified over 4,000 organisations in over 70 countries. B Corporations, or “B Corps” for short, are businesses that meet the standards of verified social and environmental performance, public transparency, and legal accountability to balance profit and purpose. The criteria that a business needs to satisfy in order to be certified as a B Corporation are set by the B Lab. Being a B Corporation demonstrates a company’s commitment to its stakeholders and the environment, both now and over the longer term. B Lab’s Impact Assessment tool, used to evaluate a business’ impact, contains many of the same metrics found in the GRI, but goes into more depth in consideration of social aspects such as diversity and inclusion, pay equity, and employee benefits. The SASB, founded in 2011, includes 77 industry-specific standards spread across different sustainability dimensions. These are designed to enable businesses around the world to identify, manage, and communicate financially-material sustainability information to their investors. The SASB was designed to complement other frameworks such as the GRI, TCFD, and CDP. Over 1,000 SASB reporters (i.e., companies who have disclosed SASB metrics in public company communications) were accounted for in 2021, about half of those based in the US. Finally, the TCFD aims at improving the reporting of climate-related financial information. This group was established in 2015 at the request of G20 finance ministers and central bank

Energy informatics: origins, emergence, and future  17 governors, who asked the Financial Standards Board (FSB) to review how the financial sector can take account of climate-related issues, which is increasingly mandated by regional governments. As of February 2020, over 1,000 organisations representing a market capitalisation of over $12 trillion support the TCFD disclosures.14 Whereas the TCFD includes voluntary recommendations that organisations have flexibility in terms of how they choose to disclose them, the GRI, SASB, and CDP, and B Corp are more metrics-driven and specifically indicate what to include for each report. These various standards and the data reported by those following one or more of them are sources for IS scholarship on measurement, usage, and impact. Measuring ESG goals, which are often associated with the UN’s SDGs, requires the development of new metrics that often are not readily comparable with financial measures. For example, how do you measure social capital and social impact and then compare these to financial measures such as asset value and return on investment? New types of IS measurement systems, systems of record, and systems of inquiry are required to capture, store, and report these data, respectively. IS scholars have an opportunity to participate in the shaping of these systems. Perhaps the most important IS contribution is to show how these new sources of information can positively inform operational and strategic planning. ESG reporting should be more than information for investors. It should be integrated into organisational planning as there are benefits to be achieved, as IS scholars have reported (e.g., Kranz, Fiedler, Seidler, Strunk & Ixmeier, 2021). 2.2.2 Letters to shareholders Looking at corporate interest from a different angle, we decided to examine the increasing importance of sustainability in corporations across the period 2010 to 2020 by analysing “Letters to the Shareholders” across leaders in eight industries, as shown in Figure 2.1. For each letter, a count of words relating to sustainability (e.g., air pollution, fossil fuels, sustainability) was computed. Typically, the letters are 2–5 pages in length, and a variety of topics are covered.

Figure 2.1

Analysis of sustainability terms in some letters to shareholders by leaders in major industries

18  Research handbook on information systems and the environment As can be seen, for most leading companies, letters to shareholders from 2020 were clearly more inclusive of a sustainability-related discourse than those from 2010. Only one company, Cardinal Health, showed a decline in the count of sustainability terms between 2010 and 2020; this could be explained by how this industry was directly affected by the pandemic, and consequently had additional pressing issues to deal with. Also, three companies, namely Amazon, Bank of America, and Microsoft, had a zero count in 2010. Delving into some of these letters, we observe that Amazon’s 2020 shareholder letter has a high count as a result of elaborating on its “Climate Pledge”, which states “smart action on climate change will not only stop bad things from happening, it will also make our economy more efficient, help drive technological change, and reduce risks”.15 Similarly, Microsoft’s score of 14 reflects its exploration of climate, ecosystem, and compliance in the context of its ambitious climate goals and plans to achieve them.16 These goals include “carbon negative, zero waste, and water positive by 2030” (p. 6) as well as developing a “$1 billion Climate Innovation Fund” to help empower others to do the same. All seven companies that demonstrated a greater sustainability-related discourse in 2020 had several general objectives guided by a larger commitment: a pledge. Some of them (i.e., Amazon, Microsoft, Verizon) signed the Climate Pledge with Global Optimism.17 Also, while several companies share the ambition of achieving net zero carbon emission by 2050, Amazon aims for 2040, and Verizon even sooner: 2035. These companies acknowledge that taking strides towards sustainability adds value both to the planet and their company. It can generate efficiency, client favourability, and an overall better brand. Not only are these companies making changes within their own organisation, they are also allocating funds to develop other companies pursuant of sustainability. Amazon allocated $2 billion towards its Climate Pledge Fund, a programme that invests in visionary companies that aim to facilitate low carbon economy; Bank of America developed the industry-first $2 billion Equality Progress Sustainability Bond, designed to advance many social and environmental issues; and Microsoft created a $1 billion Climate Innovation Fund to accelerate innovation in climate protection. All in all, sustainability and environmental efforts were not deeply and widely explored by most companies. Some refer readers to a separate corporate sustainability report (e.g., Caterpillar), while Cardinal Health seemingly fails to explore sustainability altogether. Johnson & Johnson does not outline its specific sustainability actions, but it does acknowledge its success, including two A-List ratings from the Carbon Disclosure Project.18 While the selected shareholder letters provide a glimpse into the most important issues the companies are focusing on, they are not extensive or exhaustive. Rather they provide a snapshot of the actions and plans of some of the largest players in several industries. To learn whether and how the selected companies have acted on the plans set out in their shareholder letters, we sought academic reports that provide a critical assessment of these companies’ policies.19 Analyses were found for Microsoft, Amazon, ExxonMobil, and Caterpillar. While not every company had a published analysis, there was an interesting commonality among the literature available: the lack of clarity and concrete evidence following the description of intentions. An analysis of Amazon’s sustainability efforts was conducted using textual analysis and discursive practice by examining Amazon’s Climate Pledge website and Climate Pledge YouTube video, and a speech delivered by Jeff Bezos. The analysis argues that as a “customer-centric” company, Amazon seemingly passes along its responsibility “on the cus-

Energy informatics: origins, emergence, and future  19 tomer to make green choices,” positioning itself as a “vessel through which people can make ‘sustainable’ choices” (Campbell, 2020). Another study investigated Amazon’s implementation of its Climate Pledge (Voit, 2020). It calls for improvement in Amazon’s communication, engagement, and delivery. The study points to a lack of employee awareness of its strategy, resulting in limited employee engagement. It can indeed be argued that for sustainability to be a top priority, it must be seen and practiced throughout all levels of an organisation, not just reflected in its managerial discourse. ExxonMobil’s Energy Solutions Campaign was criticised for greenwashing, like others within the industry. Indeed, although it appears to be making grand sustainable innovations, the bottom line is that it is continuing to cause environmental devastation by drilling oil (Palmer, 2020; Plec & Pettenger, 2012). Like Exxon, oil companies tend to emphasise scientific authority and technological innovation to cover what is rather an incrementalistic approach to sustainability. As for Microsoft, it was criticised for having inconsistent definitions of its net zero carbon goals when seeking proposals from companies towards meeting this effort (Joppa et al., 2021). While 189 proposals were submitted, only two met Microsoft’s criteria for high-quality CO2 removal. This is partly a result of Microsoft’s lack of clarity of net zero definition as some proposals were to avoid emissions rather than remove emissions. However, Microsoft was praised for successfully taking action including Microsoft’s FarmBeats, and its collaboration with NCX and partnership with Sol Systems (Joppa et al., 2021). Finally, Caterpillar was recognised for implementing a circular business model by remanufacturing machines and salvaging materials. The value created is passed onto its shareholders as a concrete demonstration of the value of sustainability decisions (Aboulamer, Soufani & Esposito, 2020). While academic research has found concrete demonstrations of action for a few companies like Caterpillar and Microsoft, there is also a concern with vague action steps, vague definitions, and thus vague results. Companies are setting goals for sustainability efforts as a means of satisfying the customer rather than specific details of initiatives (Palmer, 2020). Because companies are not legally bound to forward-looking statements, the use of aspirational statements can become problematic in corporate discourse (Christensen, Morsing & Thyssen, 2011; Ihlen, 2015). While this brief review of published analysis is far from representative, it highlights a frequent disconnect between intention and behaviour. The findings of the shareholder analysis align with the increasing attention given to ESG reporting (Eccles, Lee & Stroehle, 2020) and the United Nations’ Sustainable Development Goals (UN SDGPR, 2020). As Walmart’s sustainability chief noted, “You can’t separate environmental, social and economic success” (Gillian, Edgecliffe-Johnson, Talman & Temple-West, 2020). Industry leaders seem to agree that it is no longer acceptable for corporations to assert that their sole purpose is to maximise shareholder wealth and that other issues are irrelevant. Indeed, paying attention to the environment and marketing sustainable products might be a superior way to simultaneously serve society and shareholders (e.g., Kranz et al., 2021). As integrative reporting (Cohen, Holder-Webb, Nath & Wood, 2012) addressing all forms of capital in financial reporting moves from voluntary to mandatory, we expect the attention paid to ESG and SDGs will grow.

20  Research handbook on information systems and the environment

3.

MOVING FORWARD

3.1

New Opportunities

Enterprises were the original target for the energy informatics concept, and most of the published research is on the organisational domain. Indeed, the first presentation on energy informatics that we are aware of was made by two of this chapter’s authors to a group of about 30 chief information officers (CIOs) attending a meeting of the Advanced Practices Council of the Society of Information Management20 in Chicago in May 2009. They introduced the term by writing on a flip chart “Energy + Information < Energy” and informing CIOs their role was to put information into the energy equation. Now, we assert that energy informatics research needs to shift to investigate regional- or national-level grids because mass-scale introduction of renewables requires dealing with the intermittency problem. Around 2020, we reached a critical juncture where renewables, mainly solar and wind, became a cheaper source of electricity than burning fossil fuels.21 Furthermore, the cost of solar and wind is declining more rapidly than any fossil fuel source. The major renewable sources for electricity, wind and solar, are subject to the vagaries of the weather and diurnal rhythms. Intermittency is not just an electricity production problem. There is also demand-side intermittency, which is influenced by factors such as the weather, the day of the week, holidays, sporting events, or pandemics. While batteries can balance the mismatch between electricity supply and demand to maintain grid equilibrium, the round-trip efficiency for a charge/discharge cycle over 15 years with one cycle per day is 85 per cent (Cole & Frazier, 2019). It is more efficient to use electricity when generated. The transition from burning fossil fuels to renewables opens the door for the application of energy informatics principles at the grid level to manage electricity supply and demand regionally or nationally to minimise the use of batteries while maintaining grid stability. Managing intermittency is an information-intensive problem that will require a massive digital transformation of the current grid management system (Watson, Ketter, Recker & Seidel, 2022). Millions of transactions will need to be processed in real time to dynamically maintain a balance between energy demand and supply to ensure grid stability (Figure 2.2). Intermittency creates situations of over- and under-supply, the mirrors of under- and over-demand. To maintain grid equilibrium, several mirror actions are conceivable. For example, in the case of over-supply: ● Deferrable electricity uses can be turned on, such as charging cars and heating water. ● Production rights can be purchased to reduce supply, which implies paying generators not to produce. ● Prices can be lowered to stimulate demand. ● Batteries can be charged. Implementation at scale across a grid of the listed actions and their mirror versions (the right side of Figure 2.2) requires a massive number of real-time operational decisions based on the current supply and demand of electricity from a multitude of generators and consumers, respectively. Maintaining grid stability while minimising the use of batteries is information-intensive and critically dependent on digital technology. It is the most critical digital transformation of the decade (Watson et al., 2022).

Energy informatics: origins, emergence, and future  21

Source: Based on Watson et al. (2022)

Figure 2.2

Operational choices for maintaining grid equilibrium

Solving the intermittency problem will require both engineering (e.g., battery technology) and information systems research (building a massive real-time decision system that processes millions of inputs and issues millions of control commands every few seconds). This is an opportunity for IS scholars to investigate how suppliers and consumers respond to various options (e.g., price changes), and some initial work in understanding consumers’ reactions to how incentives are proffered is reported in this book (see Chapter 11). IS scholars could also, for example, extend the work on simulating decision making in complex energy environments (Ketter, Peters, Collins & Gupta, 2016). Importantly, we can involve ourselves in understanding how to design and operate such a massive information system. Such research is, we believe, core to the IS academic domain. Based on our early analysis of the IS scholarship on energy informatics, however, we fear that this will likely be another missed opportunity for IS academics to make a major contribution to solving the critical problem of our time. 3.2

Key IS Research Questions

Our review of the emergence and status of energy informatics suggests several IS research questions. ● What does the growth of a new subfield reveal about scholarly relative inertia? There is a high level of consensus that global climate change is a significant problem, yet the response from the IS community has been modest, and a variety of rationales are proffered (Gholami et al., 2016). If the most pressing problem of our times is essentially ignored by IS scholars, then they are implicitly replicating the inaction of the climate deniers. IS scholarship is seemingly bound to a path that favours publication volume over publication relevance. If we had the necessary influence, we would suggest that the case for promotion and tenure should be based on a scholar’s impact on the UN’s SDGs. Such an intervention might overcome scholarly torpor and incent researchers to tackle problems that matter. We need research on what motivates scholars to redirect their efforts towards socially relevant problems and provide actionable solutions.

22  Research handbook on information systems and the environment ● How can IS scholars produce more actionable solutions towards addressing climate change? As discussed earlier, IS scholars have responded moderately to the call of many special issues on energy informatics and sustainability. For the most part, they have published papers that provided incremental insights into the topic. Indeed, most have focused on the uncovering of relationships between variables to the expense of exploring interventions that could be more impactful in terms of addressing the climate crisis. There are, of course, exceptions. For example, a group of European researchers publishing in Management Science leveraged the energy informatics framework to explore how real-time feedback fosters resource conservation (Tiefenbeck et al., 2018). They report on a large-scale field experiment where participants received real-time feedback on the resource consumption of a daily, energy-intensive activity: showering. Real-time feedback provided by a smart shower meter can reduce resource consumption by 22 per cent and generate savings of 1.2 kWh per day and household (see Box 2.1). Another paper that also leveraged energy informatics to provide an actionable outcome is one published by this chapter’s first two authors in collaboration with a faculty member from horticulture (Watson, Boudreau & Van Iersel, 2018). This group researched how electricity used by greenhouses’ lighting can be reduced. They report on three simulations focusing on growing lettuce within a greenhouse equipped with LED lighting, considering five years of historical solar radiation data for the greenhouse location. Their results show that lighting costs (and thus usage) could potentially be reduced by approximately 60 per cent, which would have a significant impact if applied widely. These two papers exemplify what we have in mind when we encourage research that is more actionable. Just as Paul Hawken does with Project Drawdown,22 we believe we should focus on substantive solutions to climate change. Engaging in interdisciplinary teams, as was the case for the two aforementioned papers, will help IS scholars achieve this goal. ● How can IS education reposition to address the shift in dominant logic? The ESG movement and re-evaluation of the purpose of an organisation support the contention that there is a societal shift in dominant logic. IS educators need to prepare their students to meet this change. The demand for data analytics is an appropriate shift of attention to developing skills in prediction and prescriptive. For example, accurately predicting electricity generated by wind and solar is central to solving the intermittency problem discussed previously. In the case of prediction, we worked with some Master of Science in Business Analytics (MSBA) students to explore how neural networks and the maximum incident solar energy at any given time, day, and location could be combined for theory-informed machine learning to predict solar electricity production throughout the given day for the given location.23 For prescription, in our Energy Informatics course, we show how to use linear programming and travelling salesperson software to solve resource allocation problems. Both methods have been used by MSBA students in capstone projects. Complex problems with non-linear relationships exceed humans’ information processing and perception capabilities. Thus, we consider it is necessary for students to learn the basics of systems dynamics (Forrester, 1968, 1971) to understand how climate scientists model the world and how complex business problems can be addressed. We emphasise the connection between systems dynamics and digital twins.

Energy informatics: origins, emergence, and future  23

BOX 2.1 VIGNETTE: ENERGY CONSUMPTION WHILE SHOWERING Many claim to be environmentally friendly and aspire to make decisions aligned with their beliefs. However, they often fail, and this discrepancy can be attributed to the salience bias existing at the moment of decision making. “Saliency” refers to how some features of a decision may be vivid and perceptible, while others are diffused and difficult to quantify. Salience bias can favour a present-biased behaviour if immediate rewards are more visible than the long-term costs of that behaviour. In terms of energy consumption, for example, a benefit of using energy (e.g., comfort) is immediate and perceptible, whereas a negative implication (e.g., environmental impact) is often elusive and difficult to gauge, thus creating a bias towards usage. As a result, individuals who want to use natural resources efficiently often do not follow through with their intentions. Research conducted by Tiefenbeck and colleagues (2018) addresses this problem. Their large-scale field experiment provided individuals with real-time feedback on a specific behaviour – showering – thus reducing the salience gap between benefits and implications, rendering both immediate, perceptible, and quantifiable. Specifically, they equipped the showers of participating households (recruited among the residential customers of a Swiss utility company) with smart shower meters providing real-time feedback on energy and water consumption in a simple and intuitive way. Results from their study showed that real-time feedback reduced resource consumption by 22 per cent, thus leading to larger conservation gains in absolute terms than conventional policy interventions that provide aggregate feedback on resource use. Remarkably, the intervention yielded its full treatment effect from the first instance feedback was being provided (that is, participants did not need repeated exposure for their behaviour to change). In addition, there were no attenuation effects in that the impact of the intervention was stable over time. This research highlights how digitisation in our everyday lives makes information available that can help individuals overcome salience bias and act more in line with their environmental preferences.

4.

OPTIMISM POSTPONED OR MISPLACED?

The first two authors of this chapter embraced the concept of energy informatics full of optimism. We thought our message was so critical that our colleagues would react to our suggestion that they allocate a portion of their research efforts to solving the most critical problem of our time. Very few budged, as is clear from the earlier analysis in this chapter. We argued that sustainability was not a topic for a special issue but should be fostered as an ongoing section of the field’s major journals with a special editorial team devoted to ensuring that high-quality applicable knowledge addressing sustainability was published. The typical response was that sustainably was no more important than any other IS issue and did not deserve special treatment. Finally, we have had some modest success. There was a sustainability summit at ICIS in 2019 (Watson et al., 2021) and the topic was on the AIS Senior Scholars College agenda for its 2020 meeting. The MISQ Executive has now reserved a slot for one sustainability article in each issue (Watson & Kranz, 2021) and the first has appeared (see Kranz et al., 2021). ESG is

24  Research handbook on information systems and the environment now filtering into the business school curriculum, so that is a seed for hope. However, as far as we know, there are no IS scholars participating in any of the major climate deliberations, such as COP27, which we believe is the gold standard for recognition of the value of a field’s scholarship to solving the existential issue of global climate change.

NOTES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

https://​energyinformatics​.springeropen​.com. For example, https://www.fh-ooe.at/en/hagenberg-campus/studiengaenge/master/energy​-informatics/​. For example, https://​www​.mn​.uio​.no/​ifi/​english/​research/​groups/​nd/​energy​-informatics/​. https://​energy​.acm​.org/​. State of Green Business Report, 2021. Larry Finks’ 2020 Letter to CEOs, available at https://​www​.blackrock​.com/​us/​individual/​larry​-fink​ -ceo​-letter. https://​www​.globalreporting​.org/​. www​.cdp​.ne. https://​bcorporation​.net/​. https://​www​.sasb​.org/​. https://​www​.fsb​-tcfd​.org/​. “Global Reporting Initiative: Facts and Figures”. Global Reporting Initiative. https://​www​.globalreporting​.org/​about​-gri/​news​-center/​2020​-12​-01​-sustainability​-reporting​-is​ -growing​-with​-gri​-the​-global​-common​-language/​. https://​assets​.bbhub​.io/​company/​sites/​60/​2020/​02/​PR​-TCFD​-1000​-Supporters​_FINAL​.pdf. https://​www​.aboutamazon​.com/​news/​company​-news/​2020​-letter​-to​-shareholders. https://​view​.officeapps​.live​.com/​op/​view​.aspx​?src​=​https://​c​.s​-microsoft​.com/​en​-us/​CMSFiles/​ 2020​_Shareholder​_Letter​.docx​?version​=​e783c2cc​-537c​-fdb7​-5a0c​-73a848250f05. http://​www​.globaloptimism​.com. https://​www​.cdp​.net/​en. A number of these are student project reports. https://​apc​.simnet​.org. https://​www​.lazard​.com/​perspective/​levelized​-cost​-of​-energy​-and​-levelized​-cost​-of​-storage​-2020/​. https://​drawdown​.org/​. See https://​cran​.r​-project​.org/​web/​packages/​solaR/​solaR​.pdf for details of the method.

REFERENCES Aboulamer, A., Soufani, K., & Esposito, M. (2020). Financing the circular economic model. Thunderbird International Business Review, 62(6), 641–6. Campbell, E. (2020). The Other Amazon: Deconstructing Amazon’s Climate Pledge and Sustainability Discourse. Retrieved from https://​lup​.lub​.lu​.se/​student​-papers/​search/​publication/​9035263. Christensen, L.T., Morsing, M., & Thyssen, O. (2011). The polyphony of corporate social responsibility: Deconstructing accountability and transparency in the context of identity and hypocrisy. In Handbook of Communication Ethics (pp. 457–74). Mahwah, NJ: Lawrence Erlbaum Associates. Cohen, J.R., Holder-Webb, L.L., Nath, L., & Wood, D. (2012). Corporate reporting of nonfinancial leading indicators of economic performance and sustainability. Accounting Horizons, 26(1), 65–90. Cole, W.J., & Frazier, A. (2019). Cost Projections for Utility-Scale Battery Storage. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-73222. https://​www​.nrel​.gov/​docs/​ fy19osti/​73222​.pdf. Eccles, R.G., Lee, L.-E., & Stroehle, J.C. (2020). The social origins of ESG: An analysis of Innovest and KLD. Organization & Environment, 33(4), 575–96. https://​doi​.org/​10​.1177/​1086026619888994.

Energy informatics: origins, emergence, and future  25 Elliot, S., & Webster, J. (2017). Editorial: Special issue on empirical research on information systems addressing the challenges of environmental sustainability – An imperative for urgent action. Information Systems Journal, 27(4), 367–78. Forrester, J.W. (1968). Industrial dynamics-after the first decade. Management Science, 14(7), 398–415. Forrester, J.W. (1971). Counterintuitive behavior of social systems. Technological Forecasting and Social Change, 3, 1–22. Gholami, R., Watson, R.T., Molla, A., Hasan, H., & Bjørn-Andersen, N. (2016). Information systems solutions for environmental sustainability: How can we do more? Journal of the Association for Information Systems, 17(8), 521. Gillian, T., Edgecliffe-Johnson, A., Talman, K., & Temple-West, P. (2020, July 17). Walmart’s sustainability chief: “You can’t separate environmental, social and economic success.” Financial Times. https://​www​.ft​.com/​content/​72365edf​-40a2​-44d4​-8e05​-4c0011053a86​?shareType​=​nongift. Gleeson-White, J. (2014). Six Capitals: The Revolution Capitalism Has to Have – Or Can Accountants Save the Planet? Sydney: Allen & Unwin. Hasan, H., Ghose, A., & Spedding, T. (2009). IS Solution for the Global Environmental Challenge: An Australian Initiative. https://​ro​.uow​.edu​.au/​infopapers/​781. Ihlen, Ø. (2015). “It is five minutes to midnight and all is quiet”: Corporate rhetoric and sustainability. Management Communication Quarterly, 29(1), 145–52. Jenkin, T.A., Webster, J., & McShane, L. (2011). An agenda for “green” information technology and systems research. Information and Organization, 21(1), 17–40. Joppa, L., Luers, A., Willmott, E., Friedmann, S.J., Hamburg, S.P., & Broze, R. (2021). Microsoft’s million-tonne CO2-removal purchase: Lessons for net zero. Nature. https://​www​.nature​.com/​articles/​ d41586​-021​-02606​-3. Ketter, W., Peters, M., Collins, J., & Gupta, A. (2016). A multiagent competitive gaming platform to address societal challenges. MIS Quarterly, 40(2), 447–60. Kranz, J., Fiedler, M., Seidler, A., Strunk, K., & Ixmeier, A. (2021). Unexpected benefits from a shadow environmental management information system. MIS Quarterly Executive, 20(3), 6. https://​doi​.org/​10​ .17705/​2msqe​.00051. Lane, M.S., Kolbe, L., & Zarnekow, R. (2011). Editorial for special issue of AJIS on Green IT/IS (sustainable computing). Australasian Journal of Information Systems, 17(1). Lehnhoff, S., Staudt, P., & Watson, R.T. (2021). Changing the climate in information systems research. Business & Information Systems Engineering, 63(3), 219–22. https://​doi​.org/​10​.1007/​s12599​-021​ -00695​-y. Malhotra, A., Melville, N.P., & Watson, R.T. (2013). Spurring impactful research on information systems for environmental sustainability. MIS Quarterly, 37(4), 1265–74. Palmer, E. (2020). Technocentric Journeys: A Content Analysis of Corporate Sustainability Rhetoric. https://​static1​.squarespace​.com/​static/​5fb2c​2d74906936​058f4faea/​t/​606dd​6e21312ee4​d76cd1959/​ 1617811171379/​EPalmer+​Thesis+​_Technocentric+​Journeys+​copy​.pdf. Plec, E., & Pettenger, M. (2012). Greenwashing consumption: The didactic framing of ExxonMobil’s energy solutions. Environmental Communication: A Journal of Nature and Culture, 6(4), 459–76. https://​doi​.org/​10​.1080/​17524032​.2012​.720270. Sarkis, J., Koo, C., & Watson, R.T. (2013). Green information systems & technologies: This generation and beyond – Introduction to the Special Issue. Information Systems Frontiers, 15, 695–704. https://​ doi​.org/​10​.1007/​s10796​-013​-9454​-5. Smil, V. (2014). The long slow rise of solar and wind. Scientific American, 310(1), 52–57. Standing, C., and Jackson, P. (2008). Special issue on sustainability and information systems. Journal of Systems and Information Technology, 10(3). https://​doi​.org/​10​.1108/​jsit​.2008​.36510caa​.001. Tiefenbeck, V., Goette, L., Degen, K., Tasic, V., Fleisch, E., Lalive, R., & Staake, T. (2018). Overcoming salience bias: How real-time feedback fosters resource conservation. Management Science, 64(3), 1458–76. UN SDGPR (2020). Sustainable Development Goals Report. https://​ www​ .un​ .org/​ sus​ tainablede​ velopment/​progress​-report/​. Voit, J.E. (2020). Corporate commitment on sustainability and employees’ engagement: The example of Amazon’s Climate Pledge. https://​repositorio​.ucp​.pt/​handle/​10400​.14/​31221.

26  Research handbook on information systems and the environment Watson, R.T. (2020). Capital, Systems and Objects: The Foundation and Future of Organizations. Singapore: Springer. Watson, R.T., Boudreau, M.-C., & Chen, A.J.W. (2010). Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Quarterly, 34(1), 23–38. Watson, R.T., Boudreau, M.-C., & Van Iersel, M.W. (2018). Simulation of greenhouse energy use: An application of energy informatics. Energy Informatics, 1(1), 1–14. Watson, R.T., Elliot, S., Corbett, J., Farkas, D., Feizabadi, A., Gupta, A., … Webster, J. (2021). How AIS can improve its contributions to the UN’s Sustainability Development Goals: Towards a framework for scaling collaborations and evaluating impact. Communications of AIS, 48(1), 476–502. https://​doi​ .org/​10​.17705/​1CAIS​.04841. Watson, R.T., Ketter, W., Recker, J., & Seidel, S. (2022). Sustainable energy transition: Intermittency policy based on digital mirror actions. Journal of the Association for Information Systems, 23(3), 631–8. Watson, R.T., & Kranz, J.J. (2021). Guest editorial: Moving from good intentions to measurable sustainability results. MIS Quarterly Executive, 20(2), v–viii. Watson, R.T., Lind, M., & Haraldson, S. (2012). The emergence of sustainability as the new dominant logic: Implications for information systems. Paper presented at the International Conference on Information Systems, Orlando, FL.

3. SDU Center for Energy Informatics: background, and current and future research directions Bo Nørregaard Jørgensen

1. INTRODUCTION During the last few decades the world’s demand for energy has grown at an alarming rate and the negative impact on the world’s climate of CO2 emissions from burning fossil fuels, like crude oil and natural gas, has become an existential threat. Global warming and climate change have made it to the top of the political agenda of the international community and set the direction for governments’ policies for transforming their national energy sector towards large-scale integration of renewable CO2-neutral energy sources and improving energy efficiency across all sectors of society. Even though the large number of extreme weather events which happened around the world in 2021 (IPCC, 2021) has strengthened the focus of the international community on the devastating consequences for mankind of climate change, early warnings date back to as early as 1987, when the World Commission on Environment and Development (WCED), which had been set up in 1983, published a report entitled Our Common Future (Brundtland et al., 1987). It came to be known as the Brundtland Report after the Commission’s chair, Gro Harlem Brundtland. The Brundtland Report stated that critical global environmental problems were primarily the result of the enormous poverty of the South and the non-sustainable patterns of consumption and production in the North. It called for a new global strategy that balances societal development with its impact on the environment, now commonly known as sustainable development. The report describes sustainable development as “Sustainable development is the development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” This guiding principle defines sustainable development as it is generally understood and recognised today. The report discusses the sustainable energy pathway as a crucial element to achieving sustainable development, and points out that the rapidly growing populations of developing nations will need much more energy if they are to reach the same development level as industrialised nations by the year 2025. In 1987, the average person in an industrial nation used more than 80 times as much energy as a person in sub-Saharan Africa. Hence bringing developing countries up to the same level as industrialised nations by the year 2025 was expected to increase the global energy use by a factor of five. The planetary ecosystem would not be able to sustain this, especially if the growth were to be based on non-renewable fossil fuels. Thus, any realistic future global energy scenario must provide for the substantial integration of renewable energy resources. If not, threats of global warming and acidification of the environment were predicted. An era of economic growth in developing nations should 27

28  Research handbook on information systems and the environment therefore be less energy-intensive than growth has been in the past for industrialised nations, implying that energy efficiency policies must be the central focus of national energy strategies for sustainable development. Governments, therefore, had to make it the explicit goal of their energy policies to encourage the adoption of energy-saving measures. It was also clear that the substantial changes required in the presented global energy mix could not be achieved by market mechanics alone; governments had to provide economic incentives for supporting the transition to renewable energy resources. It was imperative that a safe, environmentally sound, and economically viable energy pathway that could sustain human progress into the distant future was needed, and that it would require new dimensions of political will and institutional cooperation to achieve it. In 1989, the report was debated in the United Nations (UN) General Assembly, which decided to organise the first UN Conference on Environment and Development (UNCED). The conference took place in Rio de Janeiro from 3 to 14 June 1992 and is now known by its popular title, the Rio de Janeiro Earth Summit. During the UNCED, 154 state parties signed the United Nations Framework Convention on Climate Change (UNFCCC) (United Nations, 1992). The text of the UNFCCC was produced during the meeting of an Intergovernmental Negotiating Committee in New York from 30 April to 9 May 1992. The UNFCCC aimed to establish an international environmental treaty to combat “dangerous human interference with the climate system”, in part by stabilising greenhouse gas concentrations in the atmosphere. The treaty called for ongoing scientific research and regular meetings, negotiations, and future policy agreements designed to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened, and to enable economic development to proceed sustainably. The UNFCCC was adopted on 9 May 1992 and opened for signature on 4 June 1992 at the Rio de Janeiro Earth Summit. The UNFCCC entered into force on 21 March 1994 and led to the establishment of its Secretariat, headquartered in Bonn. The UNFCCC was later extended by the Kyoto Protocol (United Nations, 1997). The Kyoto Protocol is an international treaty that ran from 2005 to 2020 and commits state parties to reduce greenhouse gas emissions, based on the scientific consensus that global warming is occurring and that human-made CO2 emissions are driving it. The Kyoto Protocol was adopted in Kyoto on 11 December 1997 and entered into force on 16 February 2005. It implemented the objective of the UNFCCC to reduce the onset of global warming by reducing greenhouse gas concentrations in the atmosphere to “a level that would prevent dangerous anthropogenic interference with the climate system”. The Protocol’s first commitment period started in 2008 and ended in 2012. A second commitment period until 2020, known as the Doha Amendment to the Kyoto Protocol, was agreed to in 2012. However, the Doha Amendment never succeeded in obtaining the necessary support from the state parties. After the failing attempt of the Doha Amendment, the Kyoto Protocol was superseded by the Paris Agreement (United Nations, 2015a). This is a legally binding international treaty on climate change that aims to limit global warming to well below 2º, preferably to 1.5º, compared to pre-industrial levels. The Paris Agreement is a separate instrument under the UNFCCC rather than an amendment of the Kyoto Protocol. It was adopted at the 21st Conference of the Parties (COP21) in Paris on 12 December 2015 and entered into force on 4 November 2016. As of 2020, the agreement had 197 signatory parties. At the same time in 2015, the UN General Assembly adopted the 2030 Agenda for Sustainable Development (United Nations, 2015b) and announced its 17 Sustainable Development Goals (SDGs), which include a dedicated and standalone goal on energy, SDG

SDU Center for Energy Informatics  29 7 – Affordable and Clean Energy. SDG 7 aims to ensure access to affordable, reliable, sustainable, and modern energy for everybody. Hence, energy plays a central role in both the Paris Agreement and the 2030 Agenda for Sustainable Development. Since 2015 the UN’s SDGs and the Paris Agreement have strongly influenced the national energy policies of the state parties who have signed the treaties and thereby dictated the directions of the global trends for development and adoption of energy technologies. For example, the European Union’s (EU’s) ambition to become the world’s first climate-neutral continent, as presented in the European Green Deal (European Commission, 2019) by President Ursula von der Leyen of the European Commission, calls for transformative innovative digital energy solutions that can accelerate a social sustainable transition of the energy sector towards renewable and CO2-neutral energy resources. Common to many national policies (Danish Climate Agreement for Energy and Industry 2020) is a focus on decarbonisation of the energy sector by deregulation, decentralisation, and emerging democratisation of energy production, which altogether is made possible through the digitalisation of all aspects of the energy ecosystem. This global transformative trend is also known as the 5Ds of the green energy transition. “Decarbonisation” refers to eliminating carbon-based fuels for electricity generation. While the influx of renewable energy sources means that the grid is more sustainable, renewable generation can be highly intermittent. Due to the intermediated nature of renewable energy resources, the demand side needs to be able to respond to the availability of energy at the supply side, contrary to the past, where the supply side had to ramp up and down to meet the needs of the demand side. As the weather is unpredictable, solar and wind cannot be relied upon, which creates problems with the balance of supply and demand, and in turn affects the frequency stability. Dependency on intermittent renewable energy resources therefore requires software tools that can predict the generation of electricity from wind and solar, based on weather forecasts, and balance the needs of the demand side with the energy available at the supply side. “Deregulation” is the liberalisation of the existing electricity market and seeks to prevent energy monopolies by increasing competition. Deregulation works through reverse auctions, where each company offers to sell its generated electricity at the lowest possible rate. Independent retailers then purchase the electricity needed to suit the demand they predict, and then set the best competitive rate they can offer their customers. This allows electricity consumers to choose from multiple electricity retailers based on rates that suit their needs and specialised product offerings. Electricity is still delivered through the existing utility infrastructure, but the utility companies that own the infrastructure are now only responsible for transmitting electricity to customers. The deregulation of the electricity market requires changes to existing software for electricity trading and new software platforms for exchanging data between distribution system operators and electricity retailers. “Decentralisation” refers to the reduction in reliance on large power plants. This means dispersing generation across many smaller plants. It also refers to the increasing amount of embedded generation coming online; for example, combined heat and power plants on industrial sites or solar panels on residential properties. The large-scale integration of renewable energy resources requires an unprecedented level of interconnection and coordination across energy networks compared to before. This interconnection and coordination can only be realised reliably and efficiently through the adoption and implementation of digital solutions by all stakeholders in the energy ecosystem. This decentralisation trend requires software platforms

30  Research handbook on information systems and the environment for managing the generation from many smaller power plants and aggregating it for sale in the electricity market. Democratisation of energy generation and supply facilitates access to energy as well as the flexibility to choose the source of energy. Distributed energy resources are an efficient approach to democratise energy generation and supply. Initially, the cost-effectiveness, flexibility, and scalability of renewable energy resources were a cause of concern, but solar and wind power are now viable due to the sharp decline in the price of solar panels and wind turbines. Furthermore, rapid innovation in battery technology will drive consumer-level adoption of small-scale storage by decreasing prices. The emergence of citizen-based energy communities is a consequence of the democratisation trend. The establishment of energy communities requires new software systems for peer-to-peer trading and local energy management systems to balance demand with supply. “Digitalisation” refers to the adoption of digital solutions by all stakeholders in the energy ecosystem. Digitalisation is expected to make the energy sector more connected, intelligent, efficient, reliable, and sustainable, by implementing effective monitoring and management systems using state-of-the-art digital technology. One central integrating element in the digital transformation of the energy sector is the smart grid concept (Farhangi, 2010), which provides a cyber layer to the physical layer of the electricity system. The smart grid facilitates optimisation and coordination of the electricity supply chain, from electricity generation over transmission and distribution to consumption. The digitalisation trend is essential to deliver the other four trends. Unleashing the full potential of digital solutions for our common sustainable energy future requires the involvement and active participation of all stakeholders in the energy ecosystem. This includes not only stakeholders related to the energy sector’s primary value chain but also stakeholders in secondary value chains whose activities, including products and services, support the primary value chain. Stakeholders in the energy ecosystem include government agencies, regulatory bodies, infrastructure operators, technology providers, industries, communities, and energy end-users. In response to the many technological and societal challenges that emerge from finding solutions to the successful implementation of the policies that govern the green transition to a sustainable energy future, we saw the beginning of the energy informatics field in 2010 (Watson, Boudreau & Chen, 2010). Today, energy informatics researchers work with all stakeholders in the energy ecosystem to create innovative digital energy solutions that will promote a socially responsible and environmentally sustainable transformation of the energy sector. This chapter aims to describe the role of energy informatics in the energy ecosystem and give recommendations for how a research and innovation centre for energy informatics can be established. The remainder of this chapter is structured as follows: first, an introduction to the field of energy informatics is given. Then the background for starting SDU – Center for Energy Informatics is presented. This is followed by sections discussing how to select research areas, build research capacity, organise research activities, create living labs, and establish industrial collaboration, and examples of R&D projects. The chapter continues with discussions of initiating international networking and collaboration, Ph.D. and master’s programs in Energy Informatics, and the creation of a new journal on energy informatics. Finally, the chapter concludes by discussing future directions in the energy informatics field.

SDU Center for Energy Informatics  31

2.

WHAT IS ENERGY INFORMATICS?

Energy informatics is a relatively young research field that has gained much traction during the last decade. The field was initially proposed by Professor Watson and colleagues (Watson et al., 2010). In this pioneering work the fundamental principle of energy informatics was defined as: Energy + Information < Energy This definition states that combining information with the use of energy will result in less use of energy. This is further described as, “Energy informatics is concerned with analysing, designing, and implementing systems to increase the efficiency of energy demand and supply systems. This requires collection and analysis of energy data sets to support optimization of energy distribution and consumption networks” (Watson et al., 2010). Since its inception, it has been increasingly recognised that energy informatics plays an essential role in the optimisation of the energy supply chain, from energy-generation plants over transmission and distribution grids to end consumption sites. However, the energy informatics field is much broader than this. As described in the introduction, intergovernmental organisations like the UN and EU have a large influence on defining the climate policies that form the background for issuing the national regulations which determine feasible pathways for technology development and adoption. As each nation is responsible for issuing its national regulations, there is no such thing as “one size fits all” for digital energy solutions, but instead there is a need for many individual solutions which comply with the regulations of the national markets scattered around the world. Besides compliance with national regulations, successful market adoption also requires digital energy solutions to consider other important influential factors for technology adoption like social norms and economic incentive structures. Climate policies not only play a role in defining national regulations, but also influence the definition of national policies for creating green economic growth in terms of so-called green jobs. An example is the European Green Deal (European Commission, 2019). The development of new digital energy solutions does therefore not exist in a vacuum but is part of a very complex ecosystem (Ma, 2019). It is not enough to engineer standalone digital energy solutions; it is necessary to engineer holistic digital energy solutions that consider all aspects of the energy ecosystem in a specific market (Ma, Billanes & Jørgensen, 2017b). All stakeholders and important factors must be considered when developing digital energy solutions. Successful adoption of a digital energy solution depends on how well it was engineered to meet the needs of relevant stakeholders by considering factors that influence these stakeholders. The development and adoption of digital energy solutions exist in the nexus of climate change, social norms, technology development, economic incentives, and policymaking. Energy informatics researchers, therefore, must consider many non-technical aspects when developing digital energy solutions, which means energy informatics cuts across the boundaries of many academic fields. Hence, energy informatics is evolving as a transdisciplinary research field that exchanges knowledge from the academic fields of information systems, computer science, software engineering, control engineering, electrical engineering, mechanical engineering, and management engineering. These fields contribute in different ways to creating digital models and constructs presenting the physical world of the energy sector.

32  Research handbook on information systems and the environment Figure 3.1 shows the author’s perspective on the overarching societal goals that energy informatics addresses and the academic fields it involves. The societal goals are connected to the academic fields through the many stakeholders in the energy ecosystem. Based on its transdisciplinary nature, energy informatics research aims to support a socially responsible and economically sustainable transition of the energy sector by addressing research questions on how to develop innovative digital energy solutions that can increase energy end-use efficiency and facilitate the integration of renewable and CO2-neutral energy sources in the energy sector.

3.

STARTING A NEW RESEARCH CENTRE

SDU Center for Energy Informatics was established in 2013 as a strategic initiative for consolidating the ongoing research at the Mærsk Mc-Kinney Møller Institute in digital energy solutions. This research was based on combining information and communication technology (ICT) with mathematical modelling for improving the efficiency of energy supply and consumption systems. When creating the centre, I was inspired by the energy informatics framework proposed by Professors Watson and Boudreau in their book Energy Informatics (Watson & Boudreau, 2011). The ideas and concepts in the energy informatics framework presented were a perfect match for the research activities and the collaboration with the industry that preceded the creation of the centre. The initial centre staff of 10 researchers was carried over from my research group in Smart Energy Solutions and included researchers at different academic levels. In addition to the initial staff, the Dean at the time, Professor Per Michael Johansen, allocated 10 tenure positions to the centre to facilitate the necessary recruitment for facilitating the transdisciplinary research nature of energy informatics. At the moment of writing the centre has a staff of 25 researchers. It is organised with a head of centre, a vice-head of centre, and a group of senior researchers who are responsible for managing research and development projects within the centre’s selected research areas. The centre’s vision is inspired by the vision statement “Sustainable Energy for All” by Ban Ki-moon, Secretary-General of the UN (Ki-moon, 2011), and the guiding principle for sustainable development outlined in the Brundtland Report. SDU Center for Energy Informatics’ vision for a desirable energy future is formulated as: Affordable, reliable, and sustainable energy for present and future generations.

This vision is the guiding beacon for implementing the centre’s mission: To facilitate a socially responsible and environmentally sustainable transformation of the energy sector, by promoting the energy informatics field for developing digital energy solutions that consider the needs of all stakeholders in the energy ecosystem.

To realise its mission, the centre applies a methodology based on ecosystem thinking that considers climate, social, technological, economic, and political factors in the development of new R&D projects. An important aspect of this methodology is that research activities must follow the fundamental principle “scientific rigor meets societal relevance”. Hence, the starting point

Figure 3.1

Relationships and aspects of energy informatics

SDU Center for Energy Informatics  33

34  Research handbook on information systems and the environment for all our research projects is that they must be relevant to society. Practically, this implies that the centre’s R&D projects take a solution-driven approach to solve a societal challenge. An important quality of taking a solution-driven approach is that it eases communication with industrial partners, whose mindsets are typically more focused on the application of technologies than on the underpinning research topics. Hence, its ability to bridge between science and industry is an important quality of solution-driven research. Furthermore, solution-driven research fits well with the funding schemes of the Danish R&D funding bodies, where industrial co-financing of up to 50 per cent is typically required of successful project proposals. Since its start, the centre’s scientific output from R&D projects has surpassed more than 500 contributions to journals, book chapters, and conferences (SDU CEI Publications, 2021). 3.1

Selecting Research Areas

At the time of starting the centre, the smartness concept became a megatrend for addressing societal challenges in many sectors. The term “Smart” was prefixed to the name of a sector to indicate that the sector was going through a digital transformation process. “Smart” indicates that artefacts and processes in the sector are digitalised to reduce environmental impacts of resource consumption and CO2 emissions while improving the quality of products and services. By now the smart trend has spread to most sectors of society. Sectors currently undergoing a smart transformation include Smart Agriculture, Smart Buildings, Smart Cities, Smart Communities, Smart Construction, Smart Energy, Smart Factories, Smart Government, Smart Grid, Smart Healthcare, Smart Horticulture, Smart Industries, Smart Logistics, Smart Mobility, and Smart Tourism. Common to all sectors undergoing a smart transformation process is that they depend on ICT to make key aspects of products and services smarter and more efficient. It is therefore important to understand how the flow of data and information relates to the flow of products and services in a sector’s business ecosystem. This observation led to the definition of our research area, Smart Energy Business Ecosystems. This is accompanied by four other research areas that represent the physical energy infrastructure, the geographical and social context, and the two most significant energy consumers in the energy ecosystem. The centre’s five research areas are defined as follows: ● Smart Energy Business Ecosystems: The research investigates how the flow of products and services, and monetary value, relates to the flow of data and information between stakeholders in the business ecosystem of the energy sector. The research applies a methodology based on ecosystem thinking that systematically considers climate, social, technological, economic, and political factors in the design of ecosystem solutions based on digital technology (Ma, Christensen & Jørgensen, 2021). A holistic approach is essential to ensure a socially responsible and environmentally sustainable transformation of the energy sector through the adoption of digital energy solutions. Research examples include the evaluation of market design (Christensen, Ma & Jørgensen, 2021), and simulation of stakeholders’ adoption of digital energy solutions (Christensen, Ma, Demazeau & Jørgensen, 2020). ● Smart Energy Networks: The research focuses on developing digital solutions for facilitating coupling between the electricity, gas, and heating/cooling sectors. The digital solutions use distributed artificial intelligence (AI) to coordinate and optimise the operation of the electricity grid, gas grid, and district heating and cooling networks, with storage capacities,

SDU Center for Energy Informatics  35 such as batteries and geothermal storage. Sector coupling is essential to achieve a transition to a 100 per cent CO2-neutral energy system. Research examples include optimisation of district heating with storage (Bjørnskov et al., 2021), and grid integration of heat pumps (Johra et al., 2019). ● Smart Cities and Communities: The research focuses on developing digital solutions for investigating synergies between energy demand patterns and the availability of renewable energy supply for providing citizens with affordable, reliable, and sustainable energy. The digital solutions are based on simulation models that evaluate the cost, reliability, and sustainability of energy supply by considering the citizens’ energy demand patterns, the availability of renewable energy sources, and weather conditions. Creating synergy between energy demand and energy supply will increase the adoption of renewable energy sources and improve system resilience in extreme weather situations, like hurricanes and flooding. Research examples include the development of island microgrids (Santos et al., 2020) and the energy flexibility of public hospitals (Billanes, Ma & Jørgensen, 2018). ● Smart Buildings: The research focuses on developing digital solutions for improving the energy efficiency of existing buildings and ensuring the energy performance of new buildings certified according to energy efficiency and sustainability standards, without compromising building usage and occupant comfort. The digital solutions are based on advanced mathematical and AI models of the building energy profile that correlate a building’s energy use with relevant factors including weather conditions, operation of building systems, building typology, and building thermodynamics. Predictable control of buildings’ energy use enables close integration with the smart energy networks sustaining them. Research examples include building energy performance monitoring (Jradi, Veje & Jørgensen, 2018), building aggregation potential (Ma, Billanes & Jørgensen, 2017a), and grid integration of buildings (Ma, Clausen, Lin & Jørgensen, 2021). ● Smart Industries: The research focuses on developing digital solutions for improving the energy efficiency and predictability of energy-intensive industrial processes, without compromising process and product quality. The digital solutions are based on advanced mathematical and AI models of the industrial processes that correlate process parameters, production tasks, product quality, and energy consumption. Predictable control of the processes’ energy consumption enables close integration with the energy markets and energy networks sustaining the energy flow of the processes. Research examples include industrial consumers’ participation in the electricity market (Fatras, Ma & Jørgensen, 2021) and smart grid adoption (Ma, Asmussen & Jørgensen, 2018). 3.2

Building Research Capacity

When creating a new centre for energy informatics it is important to recruit researchers with the right expertise and mindset. However, this was not an easy task at the time of creating SDU Center for Energy Informatics, as energy informatics was a young research field and there was a shortage of researchers in the field. Thus, in the initial research capacity building phase of the centre, we had to recruit people from different academic disciplines and then try to re-school them into the transdisciplinary research culture of energy informatics.

36  Research handbook on information systems and the environment In the initial building phase of SDU Center for Energy Informatics, we recruited researchers from the following academic disciplines: ● Software engineering, with a focus on the subfields: multi-agent systems, machine learning, data analytics, software evolution, distributed systems, middleware, and ubiquitous computing, to increase the decision and planning intelligence of energy demand and distribution systems. ● Applied physics, focusing on mathematical modelling and simulation at the component and system level to analyse and optimise energy consumption and flows in energy demand and distribution systems. ● Control engineering, focusing on optimal control and fault detection and diagnosis at the component and system level to guarantee safe and efficient operation. ● Management engineering, focusing on innovation management, international management, and system analysis to understand the factors influencing the development of digital energy solutions in a global Smart Energy Business Ecosystem context. During the initial building phase, we had to carefully recruit our initial group of researchers from the pool of academic disciplines, so the number of researchers balanced with the number of academic disciplines from the beginning. It is important to avoid having too many researchers within one discipline compared to others, as this can be counterproductive in creating a new shared transdisciplinary culture, as people with the same background often have a natural tendency to group. The shortage of researchers in the field was a challenge when recruiting the first generation of researchers in the centre, especially when recruiting to senior positions, as senior researchers may have a tendency to be stuck in their past academic traditions and can therefore have difficulties adopting a new mindset. It is relatively easier to recruit for junior research positions like postdocs and assistant professors as they are in the early stage of their academic careers. But it is also not easy to recruit for these positions due to the lack of researchers with a Ph.D. degree in Energy Informatics. To address this shortage of researchers in the field, we established a Ph.D. program to educate the first generation of energy informatics researchers. As the educational background of our first generation of Ph.D. students in Energy Informatics did not include a master’s degree in Energy Informatics, we created a flexible Ph.D. program that allows individual Ph.D. students to supplement the topics they are missing in their educational backgrounds. Now we have also developed a master’s program in Energy Informatics to establish an educational supply chain that covers both master’s and Ph.D. programs. These two programs constitute the foundation for our research capacity building in energy informatics. Research capacity-building provides the foundation for the centre’s industrial collaboration projects, as we shall discuss later. 3.3

Organising Research Activities

The centre’s research is organised into short-, medium-, and long-term activities. The time relationship among the activities is shown in Figure 3.2. The long-term activities include the development of technology and methodology platforms and the establishment of experimental facilities. The technology and methodology platforms constitute the centre’s background intellectual property rights (IPR) and thereby its value proposition for creating industrial collaboration projects. The experimental facilities serve

SDU Center for Energy Informatics  37

Figure 3.2

Timespan for research activities

for testing and showcasing digital energy solutions that are built on top of the technology and methodology platforms. The centre’s experimental facilities include both physical living labs and digital software-in-the-loop simulation environments. Medium-term activities focus on the development of application families of digital energy solutions and span multiple R&D projects. These activities provide valuable feedback to the development of the technology and methodology platforms that are part of the long-term research activities. Short-term activities include R&D projects together with industry which typically last for two to four years. These projects address the development of a digital energy solution for a specific challenge in the energy ecosystem. They create new foreground IPR, based on the project partner’s background IPR. The ownership of the foreground IPR is managed by the project’s collaboration agreement. The digital energy solutions developed as part of the short-term activities contribute as input to the application families developed as part of the medium-term R&D activities. 3.4

Creating Living Labs

The purpose of creating living labs is to provide experimental facilities to support the development and testing of digital energy solutions. In addition to being a member of the Danish living lab initiative Uni-Lab.dk, the centre is also creating its own living labs. They play an important role in supporting the centre’s research areas. For example, to support its research area in Smart Buildings, the centre has established two building living labs at the University of Southern Denmark’s main campus in Odense. The two living labs are the teaching and office buildings OU44 (9600 m2) and OU33 (2560 m2); the latter is also officially known as the Mærsk Mc-Kinney Møller building. The two buildings serve as living labs for research in digital energy solutions aiming to improve the energy efficiency of public buildings, with the capability to monitor, manage, and control the building operation (Jørgensen et al., 2015). This section provides an overview of the largest living lab, OU44. Details on the OU33 living lab can be found in Jradi, Veje, and Jørgensen (2017b).

38  Research handbook on information systems and the environment 3.4.1 Building Living Lab OU44 The OU44 building is located at the southeast corner of the campus of the University of Southern Denmark and was opened for staff and students in November 2015 (Figure 3.3). It is mainly devoted to teaching, with classrooms, meeting and seminar rooms, and offices spreading across three floors, with a full basement comprising technical rooms, a storage facility, and technical installations. The total floor area is 8500 m2 and the basement area is 1100 m2.

Figure 3.3

The OU44 building at the SDU campus in Odense, Denmark

The construction of the building started in 2014 and was completed in November 2015. The overall period from design until the building’s first operation was less than 13 months, delivering a highly energy-efficient building from the physical envelope and the technical energy supply systems perspective and with no extra costs compared to other similar buildings. At the initial design phase, the plan was to have an energy-efficient building complying with the Danish Building Low Energy Class 2015. However, due to careful and well-organised planning, design, and construction phases, and the close collaboration between the Danish Building and Property Agency, the building contractors, the university technical department, and the centre’s researchers, it was shown during commissioning that the building complies with the highest building standard in Denmark, being one of the first and few public buildings in Denmark to comply with the Danish Building Energy Class 2020, consuming only 42 kWh/ m2, marking it at the top of energy-efficient public buildings in the world. Using collected energy data, the building performance in its first year of operation is reported in Jradi et al. (2017a), showing that the building agrees with the expected design numbers, consuming about 41.45 kWh/m2. The building is equipped with energy-efficient technologies including ventilation units with heat recovery, LED lights, radiators, and underfloor heating. A photovoltaic system with a capacity of 12 kW power was installed on the building’s roof to deliver renewable-energy-based electricity generation. In terms of energy supply systems, the building is fully connected to the district heating network to cover the heating demands, with additional small electric boilers for providing hot water in kitchenettes and restrooms.

SDU Center for Energy Informatics  39 The building has no cooling systems and relies solely on ventilation, with four identical balanced-ventilation systems with air handling units (AHUs) of 35,000 m3/h nominal capacity of fresh air each. The ventilation air supply temperature is by default set to 21°C. Each AHU serves a section of the building and is equipped with two fans (supply and extract), a rotary wheel heat exchanger, and a heating coil. The AHU fans operate to maintain constant pressure in the ductwork, so the variable air volume (VAV) damper position in each room is correlated with the fan electricity consumption. If more dampers are open, the fans need to increase speed to maintain the pressure. In the default control mode, the VAV dampers are controlled based on the indoor CO2 level (maximum openness for 800 ppm). The building’s four AHU sections are divided into 190 thermal zones, each equipped with VAV dampers. For heating, the building is equipped with a hydronic heating system. All rooms are equipped with radiators with valves controlled by the building management system. The valve opening depends on the indoor temperature and the default temperature setpoint is 21°C. Meters are installed for measuring the main consumption of electricity, district heating, and water. In addition to these main meters, several submeters are installed to measure electricity and heating consumption at floor level. All rooms and hallways are equipped with temperature, humidity, CO2, passive infrared (PIR), and illuminance sensors. Building entrances and a few selected classrooms are equipped with stereo vision occupancy sensors for counting the number of occupants entering or leaving. Outdoor illuminance sensors mounted on the building’s walls are used for automatic blind control. Other measured quantities include blind positions, ventilation damper and radiator valve openings, fan electricity consumption, and temperatures in the AHU. Tables 3.1 and 3.2 list the number of meters and sensors in OU44. Table 3.1

Metering infrastructure of OU44

Metering type

Installed units

Electricity main meters

1

Electricity submeters

70

Heating main meters

1

Heating submeters

11

Water main meters

1

Water submeters

3

Table 3.2

Sensor infrastructure of OU44

Sensor type

Installed units

PIR

81

Illuminance (indoor)

66

Illuminance (outdoor)

4

Irradiation (rooftop sensor)

1

Temperature

86

Humidity

68

CO2

68

Stereo vision occupancy sensors

17

40  Research handbook on information systems and the environment 3.5

Establishing Industrial Collaboration

Industrial collaboration is an essential element in realising the centre’s mission. Without collaboration with stakeholders in the energy ecosystem, it will not be possible for the centre to develop and promote the energy informatics field as an enabling factor for developing smart energy solutions that can facilitate a socially responsible and environmentally sustainable transformation of the energy sector. Industrial collaboration is, however, important not only for realising the centre’s mission, but also for positioning the centre as a research and innovation capacity in the business ecosystem of the energy sector. This positioning is an important factor for attracting more industrial collaboration and thereby funding for the centre’s ongoing and future research activities. The centre’s industrial collaboration takes the form of R&D projects that focus on real challenges, unmet needs, or customer pains of the various stakeholders in the energy ecosystem. When developing project proposals, we apply ecosystem thinking as a methodology to zoom out and get a holistic perspective on these issues. The identified challenges, unmet needs, or customer pains provide the background for exploring different ideas and selecting the most promising ones for the creation of new R&D projects. The most promising ideas are the ideas that best match the political agenda and thereby fulfil the success criteria of the funding agencies. In a European (European Commission, 2019) and Danish (Danish Climate Agreement for Energy and Industry 2020) context, this will include success criteria related to reducing CO2 emission and creating green economic growth. New R&D projects are organised by project consortia that include the stakeholders who are relevant to the parts of the supply chain in the energy ecosystem that the projects are focusing on. The primary supply chain of the energy ecosystem (Ma, Christensen & Jørgensen, 2021) is shown in Figure 3.4.

Figure 3.4

Supply chain of the energy ecosystem

Project consortia will therefore typically include research institutions, technology providers, grid operators, electricity retailers, and different types of consumers – e.g., industrial, commercial, or residential – depending on the focus of the projects. It is an important factor for success that all relevant stakeholders take part in a project. As part of providing a societal impact that fulfils the goals of the political agenda, the R&D projects must create value for all the stakeholders involved in the projects. Together with research capacity building, the industrial collaboration projects provide essential input to identifying the academic disciplines that are needed to create a transdisciplinary educational master’s program in Energy Informatics.

SDU Center for Energy Informatics  41 3.6

Examples of R&D Projects

Since its beginning in 2013, the centre has been involved in many R&D projects. It is not possible to give a full overview here; instead, two projects from the centre’s research areas of Smart Energy Business Ecosystems and Smart Industries will be presented. A full overview of R&D projects can be found on the centre’s website (SDU CEI Research Projects, 2021). 3.6.1 Smart Energy Business Ecosystems: Flexible Energy Denmark (FED) Introduction The transition to a low-carbon society based on intermittent renewable energy sources calls for a change to an energy system where the demand for energy equals the production from low-carbon sources. This requires the development of new methods to enable flexibility at all levels of the energy system of modern society. In the absence of flexibility in the energy system, a low-carbon society will require an abundance of both energy production facilities and transmission and distribution grids. Therefore, this project targets the development of intelligent adaptable energy storage solutions for maximising output from existing green energy production solutions and minimising investment needs in the utility sector at the distribution level and investment needs in new energy production facilities. Furthermore, the project is aimed at both optimisation at the transmission level, and at creating the basis for supporting balancing markets. Project aims and objectives The FED project aims to address the following challenges: ● Solutions have often been developed for a single sector and only focus on one aspect (e.g., 4th-generation district heating, intelligent power systems) instead of focusing on synergies in integrated energy systems. ● The digitalisation of the energy systems is fragmented. ● A data lake that facilitates and enables integrated energy system services is missing. ● Knowledge to manage the operational issues of the next generation of digitalised integrated energy systems is not yet available due to a lack of data sources, tools, and non-integrated energy infrastructures and lab facilities. ● A proper characterisation of the energy flexibility of integrated energy systems is a scientific challenge. ● An operational understanding of the flexibility of an asset calls for developing complex big data analytics and AI tools. ● The current regulatory framework conditions, energy market, and energy taxes often eliminate the incentives and possibilities for utilising energy flexibility. With a total budget of DKK 43.67 million funded by Innovation Fund Denmark (Project no. 8090–00069B) and a project period of 2019 to 2023, FED was designed to: 1. Offer tools and solutions for integrated energy systems. 2. Enable a digitalisation of the energy grids, allowing for data collection and smart control. 3. Offer a representative and common living lab infrastructure to elaborate tools and solutions.

42  Research handbook on information systems and the environment 4. Establish a coherent mathematical framework for describing flexibility for energy systems analysis. 5. Describe how flexibility can be used to provide grid ancillary and balancing services. 6. Provide guidelines for how to design the next generation of energy markets on all spatial and temporal resolutions and cover the entire spectrum of energy carriers. State of the art The FED project is based on big data analytics, AI, and control to identify solutions for data intelligent flexibility. These technologies have the potential to solve related stakeholders’ problems in the future, such as distribution system operators. The technologies can address unmet needs identified in leading European smart grid projects, such as developing new methods for voltage control and congestion management in the electricity distribution networks, establishing a new framework for transmission–distribution grid coordination for grid operations, facilitating balance between responsible parties with new tools for energy balancing, and developing methods to optimise buildings’ functionality for serving special grid needs.

Figure 3.5

Flexible Energy Denmark ecosystem

Methods The project will achieve its aims by establishing the FED ecosystem (Figure 3.5), which includes: ● A data ecosystem (a so-called “data lake”, which contains many different energy-related data, which is collected from the project’s living labs, but also from other sources such as the Danish building register and the Danish Meteorological Institute) ● A digital tools ecosystem (AI tools enabled by big data from the data ecosystem)

SDU Center for Energy Informatics  43 ● An ecosystem for digital solutions (digital solutions that combine some of the developed tools and which are to be used for managing energy flexibility in Denmark) Results The project’s technical deliverables constitute five main parts: ● Living labs: Collect metadata from the living labs, import living labs data from existing monitoring systems into the data lake, install and test additional sensors or other hardware in the living labs. ● Data lake: Data lake with analytic capabilities; techniques and tools to ensure security, privacy, and fulfilment of the GDPR (General Data Protection Regulation); methods to integrate data and enable consistent analysis across data from different living labs. ● Tools: Algorithms, methods, and tools for monitoring, aggregation, cyber-physical modelling, prediction and forecasting, optimal operation, and control. ● Solutions: A platform using the monitoring tools to visualise relevant data, forecasting solutions, dynamic optimisation of distributed energy resources, a cloud service solution for aggregating and controlling loads, a coherent regulatory framework proposal. ● FED foundation: Identify and model the business ecosystems, model the global flexible energy business ecosystem for big-data-driven products and services, explore and quantify business models for the value propositions offered by the flexible energy solutions developed (Christensen, Ma, Demazeau & Jørgensen, 2021). Added value and value chain partners The FED project unlocks, describes, and tests energy flexibility at all levels, by empowering integrated energy systems using digitalisation and data intelligence. The new nationwide data platform of connected living labs facilitates the coupling of existing domain-specific labs and enables new synergies. The FED project addresses all steps of the value chain from sensors to analysis to decision support. FED creates innovative data-driven solutions, state-of-the-art tools, and existing and new products from leading industry partners. The FED project provides data, AI solutions, and IT methodologies enabling the industry to be highly competitive in the digitalisation of energy systems. Hence, the FED project provides the foundation for national leadership on green innovation. The project consortium consists of four Danish universities, 22 industrial partners (of these, nine are small and medium-sized enterprises), one public institution, and one foundation. The project brings together leading research institutions within the fields of integrated energy system analysis and tools development, data management, and data processing, and applied research in the field of integrated energy systems. Furthermore, major energy companies and industrial stakeholders in the field are partners in the consortium. Hence it represents a vast majority of the Danish energy sector. 3.6.2 Smart Industries: Greenhouse Industry 4.0 Introduction Like other energy and labour-intensive industries that compete with low-cost regions, the Danish horticultural greenhouse industry must continuously improve its energy efficiency,

44  Research handbook on information systems and the environment production throughput, and productivity, without compromising product quality or sustainability, to stay competitive. This competitive pressure calls for a new disruptive approach for vertical integration and optimisation of the greenhouse production processes. Therefore, the Greenhouse Industry 4.0 (GHI4.0) project aimed to secure a position for the Danish horticultural greenhouse industry as the world leader in energy-efficient and environmentally sustainable production by creating a Greenhouse Industry 4.0 digital twin software platform for combining the Industry 4.0 enabling technologies – the Internet of Things (IoT), AI, big data, cloud computing, and digital twins – as integrated parts of the greenhouse production systems. The integration provided a new disruptive approach for vertical integration and optimisation of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability (Howard et al., 2021). Project aims and objectives The primary purpose of the GHI4.0 project was to: ● Combine Industry 4.0 and AI technologies to build a digital twin ecosystem that supports the deployment of research results to promote sustainable greenhouse production. ● Investigate the energy and production effects of deploying Industry 4.0 and digital twin technologies in greenhouse production. With a total budget of DKK 16 million funded by the Danish Energy Technology Development and Demonstration Program (EUDP Project no. 64019–0018) and a project period of 2019 to 2022, the following research objectives were designed to reach the project goal: ● Develop three digital twins covering the greenhouse climate compartments, production flow, and energy system. ● Establish a common information model that serves as a single horizontal integration point for new software. ● Test the digital twins at the three participating greenhouse facilities and report the observed effects. State of the art GHI4.0 built on Industry 4.0 concepts that are the key enabling technologies for the project. Industry 4.0 encompasses digitalisation technologies such as the IoT, cyber-physical systems, AI, and ICT. The use of digitalisation technologies enables virtual product and process planning. Digital twin technology is a critical enabler for digital transformation. Digital twins have been implemented increasingly in recent years for aiding companies in optimising parts of their production. Digital twins of greenhouse production flows can provide an opportunity for simulating and optimising the production system, including its logistical aspects (Howard, Ma, Aaslyng & Jørgensen, 2020). The intelligent use of greenhouse technology, such as automatic climate control systems, has been demonstrated for greenhouse climate control with weather forecasts, energy prices, etc. Several planning tools have been developed to provide growers with decision support, such as model predictive controls using digital models of physical systems and optimisation methods,

SDU Center for Energy Informatics  45 and predictions regarding the future greenhouse climate conditions to find the optimal control strategy for participating in the energy market (Clausen et al., 2015; Ma & Jørgensen, 2018). Methods The project adopted the application of the Industry 4.0 digital twin concept to the Danish horticulture greenhouse industry. The overall project (Figure 3.6) consisted of three greenhouse sections: the climate compartment, energy system, and production flow. All the digital twins communicated through a developed common information model that exchanged information with a dashboard. The connection of multiple digital twins within a single facility provided a novel approach for a distributed digital twin system that collectively constituted the greenhouse facility’s behaviour. A greenhouse grower could obtain an overview of the essential greenhouse parameters for decision-making from the dashboard.

Figure 3.6

Greenhouse Industry 4.0 Project concept

The Industry 4.0 digital twin concept comprises the use of digital models to simulate and evaluate the performance of the physical greenhouse facility and the production processes. A digital twin combines modelling, AI, and big data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under different operation conditions. This supports the optimisation of the production schedule, energy consumption, and labour cost, by considering influential factors including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. Results The project’s technical deliverables constituted five main parts: three digital twins, a common information model supported by a communication protocol, and a web-based dashboard that collected the interfaces of all the digital twins into one single point of access for the greenhouse grower. GHI4.0 demonstrated the application of the Industry 4.0 digital twin concept for modelling and simulating the physical greenhouse facility, including the production processes and flow,

46  Research handbook on information systems and the environment which can support greenhouse growers in decision-making that seeks to optimise production throughput and energy use. Added value and value chain partners By implementing GHI4.0 in the horticultural greenhouse, the greenhouse industry will benefit from improved productivity and energy cost efficiency while supporting the UN global goals for affordable and clean energy (SDG 7), together with Industry, Innovation, and Infrastructure (SDG 9). The Industry 4.0 digital twin concept application will introduce and combine mathematical models and simulation, AI, IoT sensors, cloud computing, and big data analytics to the Danish horticulture greenhouse industry. The project covered the whole greenhouse ecosystem, including research institutions, trade organisations, technology suppliers, energy companies, export network organisations, and commercial greenhouse growers. Research partners included SDU Center for Energy Informatics, Aarhus University – Department of Food Science, and the Danish Technological Institute. The participating trade organisation was Dansk Gartneri. The climate computer supplier was Senmatic A/S and the production planning software supplier was NB Data. The energy company was Energi Danmark. Commercial greenhouse growers included Hjortebjerg A/S, By Growers A/S, and Knud Jepsen A/S. Dissemination and potential export of the project’s results was managed by the Danish Cleantech Hub. 3.7

Initiating International Networking and Collaboration

The centre has adopted a proactive strategy for establishing international networking and collaboration with universities, research institutions, businesses, organisations, and government agencies around the world. International networking and collaboration activities play an essential role in promoting the Energy Informatics field. An overview of the centre’s networking and collaboration activities can be found on the centre’s website (SDU CEI Cooperation, 2021). An important element in the centre’s international networking and collaboration strategy is to participate in the International Energy Agency’s (IEA’s) Technology Collaboration Programmes. Out of the IEA’s eight such programmes, the centre is currently participating in the Energy in Buildings and Communities programme (IEA-EBC, 1977), and the Industrial Energy-Related Technologies and Systems programme (IEA-IETS, 2005). The IEA-EBC Programme is an international energy research and innovation programme in the buildings and communities field. It helps its 26 member states to carry out collaborative research and development activities aiming at achieving near-zero energy and carbon emissions in the built environment. These joint research and development activities are directed at energy-saving technologies and the adoption of modern building technology practice. The centre’s research area on Smart Buildings relates to this programme. The IEA-IETS Programme focuses on energy use in a broad range of industry sectors. It supports the work of independent, international groups of experts that enable governments and industries from around the world to advance the research, development, and commercialisation of energy technologies. The centre’s research area on Smart Industries relates to this programme. In addition to participation in international collaboration networks, the centre has also established collaboration with universities and research organisations around the world on

SDU Center for Energy Informatics  47 research topics within the centre’s strategic research areas. In the research area of Smart Buildings, we have, for instance, established research collaboration with the University of California, Berkeley (UC Berkeley), in the US, the University of Campinas (UNICAMP) in Brazil, and Universiti Tenaga Nasional (UNITEN) in Malaysia. These research collaborations focus on the creation of living labs and the development of supporting digital technologies. For example, the research collaboration with UNITEN is part of a joint R&D project where we are creating a 19,000 m2 IoT living lab at its campus in Kuala Lumpur for experimenting with digital solutions for retrofitting buildings with wireless sensors for indoor climate monitoring and optimisation of building energy management (Madsen, Santos & Jørgensen, 2021). Another example of international collaboration is within the research area of Smart Industries, where we have joint research activities with Natural Resources Canada. The research is focusing on the application of digital twin technology for improving the energy efficiency of energy-intensive industrial processes. Many of the centre’s international collaborations have been established together with Innovation Centre Denmark (Innovation Centre Denmark, 2006), which is a cooperation between the Danish Ministry of Higher Education and Science, and the Danish Ministry of Foreign Affairs, with regional offices in Shanghai, Silicon Valley, Boston, Munich, New Delhi/Bangalore, Seoul, and Tel Aviv. Its purpose is to help Danish research institutions and companies gain access to foreign knowledge, networks, technology, capital, and market opportunities. Being part of the Danish Ministry of Foreign Affairs, Innovation Centre Denmark has also provided us with networking opportunities through the Danish embassies. The latest international initiative led by the centre was the creation of the non-profit organisation Energy Informatics.Academy (Energy Informatics.Academy, 2020). This initiative aims to create a global community for researchers in energy informatics, who share the vision of creating “A generation of researchers and practitioners that explore and build intelligent energy solutions for a better planet”. To facilitate this vision, the mission of the Energy Informatics.Academy is “To create an opportunity for researchers and practitioners to connect and collaborate, through community activities, and joint initiatives”. The first community initiative of the Energy Informatics.Academy was to create the Energy Informatics.Academy Asia conference series. The first conference took place in Beijing 2021 (Ma, Jørgensen, Chen & Jørgensen, 2021) and the next in Denmark in August 2022. An Energy Informatics. Academy conference series for South America is also in the planning phase. The first conference is expected to take place at UNICAMP in 2024. 3.8

Creating Ph.D. and Master’s Programs in Energy Informatics

Education of Ph.D. candidates and master’s students in Energy Informatics is another essential element in realising the centre’s mission. Education of Ph.D. researchers is needed to support long-term research capacity building, and the education of master’s students is needed to provide society with the engineers it needs to implement a socially responsible and environmentally sustainable transformation of the energy sector. The Ph.D. program in Energy Informatics is a three-year program that includes a study element of 30 European Credit Transfer and Accumulation System (ECTS) course activities, a research exchange visit to an international research institution, and an individual research project. The 30 ECTS course activities include mandatory courses and individual course

48  Research handbook on information systems and the environment activities that serve to supplement the student’s knowledge on topics that were not sufficiently covered by their master’s program. The master’s program in Energy Informatics is a two-year program that consists of 90 ECTS course activities and a 30 ECTS master’s thesis. The current structure of the program is shown in Figure 3.7.

Figure 3.7

Structure of the master’s program in Energy Informatics

The selection and design of the courses are based on the needs observed as part of research capacity building and R&D projects in collaboration with the industry. The course program teaches students topics relevant to energy informatics from the subfields of computer science, data science, software engineering, applied mathematics, and mechanical and control engineering. A master’s program in Energy Informatics can build upon a relevant bachelor’s degree in electrical, mechanical, control, or computer engineering, but preferable is a degree that takes a broader perspective on energy technology in a societal context. In our case, we extended the existing master’s program in Energy Technology at the University of Southern Denmark, with a dedicated specialisation in Energy Informatics, in 2020. The Energy Technology education, including the master’s specialisation in Energy Informatics, is managed by our Vice-Head of Center, Professor Dr. Christian T. Veje.

4.

SPRINGER OPEN’S ENERGY INFORMATICS

As a spinoff from creating the centre, I co-founded the Springer Open journal Energy Informatics (Springer Open, 2018), together with Associate Professor Dr. Zheng Ma. Energy Informatics is an international, peer-reviewed, and open-access journal. It is devoted solely to publishing original research and technological advances in the Energy Informatics field. The inaugural issue was published in July 2018 (Jørgensen, 2018). Since that issue, around 180 articles have been published in the journal. We decided to create the journal as we found the energy informatics community was missing an appropriate publication outlet that can cover the wide breadth and interdisciplinary nature of Energy Informatics. Lacking a primary publication outlet, we observed that researchers in Energy Informatics had to publish their research across many different journals covering various topics related to energy, the environment, buildings, transport, policy, etc. The absence of a primary publication outlet also made it difficult for researchers in energy informatics to find the work of their peers. It is the hope that Energy Informatics can fill this gap.

SDU Center for Energy Informatics  49 Since Energy Informatics is a relatively young and very dynamic research field, we decided to support various types of article formats to facilitate and speed up the dissemination of discoveries and insights in the field. The selected article formats provide authors with multiple options for presenting their work, whether it is theoretical, empirical, or policy related. By supporting a broad range of article types, we hope to stimulate international dialogue among academics, industry, retailers, investors, and policymakers with mutual interests in the energy sector. It is the ambition that Energy Informatics shall become the primary journal for publication of the most recent theoretical results and technological advances in the Energy Informatics field. To achieve this ambition, the journal is supported by an excellent international editorial board consisting of more than 40 experts who cover the full breadth of Energy Informatics. I hope you will visit the journal’s webpage and join us in the effort to make Energy Informatics a community journal by contributing to future issues.

5.

FUTURE DIRECTIONS

The energy informatics field has come a long way since its inception. Globally, new research groups have been formed and research centres have been established. Individual courses, as well as educational programs, have been created. Initiatives to establish conferences dedicated to the field have been taken. However, there are still challenges that need to be addressed before energy informatics will evolve into a truly transdisciplinary field with its own unique identity and community. Since energy informatics includes knowledge areas from many other academic disciplines, the practising of energy informatics can easily be mistaken with related research areas such as green computing, or the application of AI and data science in the energy sector. These areas do overlap with energy informatics, but they do not share the same holistic ecosystem perspective on developing digital energy solutions. To differentiate energy informatics from related fields it is necessary to establish a clear profile of what energy informatics is. Hence, the further development of energy informatics as a transdisciplinary field may require the energy informatics community to create an Energy Informatics Body of Knowledge guide, an EIBOK, similar to the Software Engineering Body of Knowledge guide (SWEBOK) that was created for describing the software engineering field (Bourque et al., 2004). The SWEBOK guide describes the subfields of other disciplines that constitute parts of software engineering. A similar EIBOK initiative is needed in energy informatics for identifying and defining its constituent subfields from other academic disciplines. An EIBOK guide will be a valuable tool for the development of international master’s and Ph.D. programs in Energy Informatics. Increasing the visibility of energy informatics research to the stakeholders in the energy sector and its related industries is essential for establishing industrial collaboration and creating joint R&D projects. The creation of living labs provides an effective channel for promoting energy informatics research, by showcasing digital energy solutions in a real-life setting. Furthermore, living labs can facilitate international collaboration through the creation of international living lab networks that allow digital energy solutions to be researched and tested across different geographical regions with different climate conditions.

50  Research handbook on information systems and the environment The SDU Center for Energy Informatics is dedicated to the development of the energy informatics field and will continue to contribute through its many industrial, networking, conference, journal, and community activities. It is our conviction that energy informatics has an important role to play in the research and development of digital energy solutions that can mitigate climate change by accelerating a socially sustainable transition of the energy sector towards renewable and CO2-neutral energy resources. However, with the current pace of climate change, there is an urgent need for the energy informatics community to evolve much faster than it did in its first decade, as the challenges it shall help society to overcome in the coming decade are immense.

REFERENCES Billanes, J.D., Ma, Z., & Jørgensen, B.N. (2018). The Bright Green Hospitals: Case Studies of Hospitals’ Energy Efficiency And Flexibility in Philippines. Paper presented at the 2018 8th International Conference on Power and Energy Systems. Bjørnskov, J., Mortensen, L.K., Filonenko, K., Shaker, H.R., Jradi, M., & Veje, C. (2021). Optimization of district heating production with thermal storage using mixed-integer nonlinear programming with a new initialization approach. Energy Informatics, 4(2), 34. https://​doi​.org/​10​.1186/​s42162​-021​ -00150​-y. Bourque, P., Fairley, R.E.(D.), Abran, A., Garbajosa, J., Keeni, G., Shen, B., … Guillemette, K. (2004). Software Engineering Body of Knowledge. https://​www​.computer​.org/​education/​bodies​-of​ -knowledge/​software​-engineering. Brundtland, G.H., et al. (1987). Report of the World Commission on Environment and Development. https://​digitallibrary​.un​.org/​record/​139811. Christensen, K., Ma, Z., Demazeau, Y., & Jørgensen, B.N. (2020). Agent-Based Modeling of Climate and Electricity Market Impact on Commercial Greenhouse Growers’ Demand Response Adoption. Paper presented at the 2020 RIVF International Conference on Computing and Communication Technologies. Christensen, K., Ma, Z., Demazeau, Y., & Jørgensen, B.N. (2021). Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: An example of electric vehicles in the energy system. Energy Informatics, 4(2), 38. https://​doi​.org/​10​.1186/​s42162​-021​-00160​-w. Christensen, K., Ma, Z., & Jørgensen, B.N. (2021). Technical, economic, social and regulatory feasibility evaluation of dynamic distribution tariff designs. Energies, 14(10), 2860. https://​www​.mdpi​.com/​ 1996​-1073/​14/​10/​2860. Clausen, A., Maersk-Moeller, H.M., Soerensen, J.C., Jørgensen, B.N., Kjaer, K.H., & Ottosen, C.-O. (2015). Integrating Commercial Greenhouses in the Smart Grid with Demand Response Based Control of Supplemental Lighting. Paper presented at the Proceedings of the 2015 International Conference on Industrial Technology and Management Science. Danish Climate Agreement for Energy and Industry 2020 (2020). https://​kefm​.dk/​Media/​C/​B/​faktaark​ -klimaaftale​%20(English​%20august​%2014)​.pdf. Energy Informatics.Academy (2020). Energy Informatics.Academy: The global community for energy informatics. https://​www​.energyinformatics​.academy/​. European Commission (2019). A European Green Deal. https://​ ec​ .europa​ .eu/​ info/​ strategy/​ priorities​ -2019–2024/​european​-green​-deal​_en​#documents. Farhangi, H. (2010). The path of the smart grid. IEEE Power and Energy Magazine, 8(1), 18–28. https://​ doi​.org/​10​.1109/​MPE​.2009​.934876. Fatras, N., Ma, Z., & Jørgensen, B.N. (2021). Industrial consumers’ electricity market participation options: A case study of an industrial cooling process in Denmark. Energy Informatics, 4(2), 48. https://​doi​.org/​10​.1186/​s42162​-021​-00165​-5. Howard, D.A., Ma, Z., Aaslyng, J.M., & Jørgensen, B.N. (2020). Data Architecture for Digital Twin of Commercial Greenhouse Production. Paper presented at the 2020 RIVF International Conference on Computing and Communication Technologies.

SDU Center for Energy Informatics  51 Howard, D.A., Ma, Z., Veje, C., Clausen, A., Aaslyng, J.M., & Jørgensen, B.N. (2021). Greenhouse industry 4.0: Digital twin technology for commercial greenhouses. Energy Informatics, 4(2), 37. https://​doi​.org/​10​.1186/​s42162​-021​-00161​-9. IEA-EBC (1977). The International Energy Agency’s Energy in Buildings and Communities Programme. https://​www​.iea​-ebc​.org/​. IEA-IETS (2005). The International Energy Agency’s Technology Collaboration Programme on Industrial Energy-Related Technologies and Systems. https://​iea​-industry​.org/​about/​. Innovation Centre Denmark (2006). Innovation Centre Denmark. https://​ ufm​ .dk/​ en/​ research​ -and​ -innovation/​international​-cooperation/​global​-cooperation/​innovation​-centres​-and​-attaches​?set​ _language​=​en​&​cl​=​en. IPCC (2021). Climate Change 2021: The Physical Science Basis. https://​www​.ipcc​.ch/​report/​ar6/​wg1/​ #FullReport. Johra, H., Filonenko, K., Heiselberg, P., Veje, C., Dall’Olio, S., Engelbrecht, K., & Bahl, C. (2019). Integration of a magnetocaloric heat pump in an energy flexible residential building. Renewable Energy, 136, 115–26. https://​doi​.org/​10​.1016/​j​.renene​.2018​.12​.102. Jørgensen, B.N. (2018). Energy Informatics. Energy Informatics, 1(1), 7. https://​doi​.org/​10​.1186/​s42162​ -018​-0014​-6. Jørgensen, B.N., Kjærgaard, M.B., Lazarova-Molnar, S., Shaker, H.R., & Veje, C.T. (2015). Challenge: Advancing Energy Informatics to Enable Assessable Improvements of Energy Performance in Buildings. Paper presented at the Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, Bangalore, India. https://​doi​.org/​10​.1145/​2768510​.2770935. Jradi, M., Sangogboye, F.C., Mattera, C.G., Kjærgaard, M.B., Veje, C., & Jørgensen, B.N. (2017a). A world class energy efficient university building by Danish 2020 standards. Energy Procedia, 132, 6. https://​doi​.org/​10​.1016/​j​.egypro​.2017​.09​.625. Jradi, M., Veje, C., & Jørgensen, B.N. (2017b). Deep energy renovation of the Mærsk office building in Denmark using a holistic design approach. Energy and Buildings, 151, 306–19. https://​doi​.org/​10​ .1016/​j​.enbuild​.2017​.06​.047. Jradi, M., Veje, C.T., & Jørgensen, B.N. (2018). A dynamic energy performance-driven approach for assessment of buildings energy renovation: Danish case studies. Energy and Buildings, 158, 62–76. https://​doi​.org/​10​.1016/​j​.enbuild​.2017​.09​.094. Ki-moon, B. (2011). A Vision Statement by Ban Ki-moon, Secretary-General of the United Nations. https://​www​.seforall​.org/​system/​files/​gather​-content/​SG​_Sustainable​_Energy​_for​_All​_vision​.pdf. Ma, Z. (2019). Business ecosystem modeling: The hybrid of system modeling and ecological modeling – An application of the smart grid. Energy Informatics, 2(1), 35. https://​doi​.org/​10​.1186/​s42162​-019​ -0100​-4. Ma, Z., Asmussen, A., & Jørgensen, B.N. (2018). Industrial consumers’ smart grid adoption: Influential factors and participation phases. Energies, 11(1). https://​doi​.org/​10​.3390/​en11010182. Ma, Z., Billanes, J.D., & Jørgensen, B.N. (2017a). Aggregation potentials for buildings: Business models of demand response and virtual power plants. Energies, 10(10), 1646. https://​www​.mdpi​.com/​1996​ -1073/​10/​10/​1646. Ma, Z., Billanes, J.D., & Jørgensen, B.N. (2017b). A Business Ecosystem Driven Market Analysis: The Bright Green Building Market Potential. Paper presented at the 2017 IEEE Technology & Engineering Management Conference. Ma, Z., Christensen, K., & Jørgensen, B.N. (2021). Business ecosystem architecture development: A case study of electric vehicle home charging. Energy Informatics, 4(1), 9. https://​doi​.org/​10​.1186/​ s42162​-021​-00142​-y. Ma, Z., Clausen, A., Lin, Y., & Jørgensen, B.N. (2021). An overview of digitalization for the building-to-grid ecosystem. Energy Informatics, 4(2), 36. https://​doi​.org/​10​.1186/​s42162​-021​-00156​ -6. Ma, Z., Jørgensen, B.H., Chen, G., & Jørgensen, B.N. (2021). Welcome message from the organizers at the 1st Energy Informatics.Academy Conference Asia (EI.A Asia 2021). Energy Informatics, 4(2), 45. https://​doi​.org/​10​.1186/​s42162​-021​-00147​-7. Ma, Z., & Jørgensen, B.N. (2018). Energy Flexibility of the Commercial Greenhouse Growers: The Potential and Benefits of Participating in the Electricity Market. Paper presented at the 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference.

52  Research handbook on information systems and the environment Madsen, S.S., Santos, A.Q., & Jørgensen, B.N. (2021). A QR code based framework for auto-configuration of IoT sensor networks in buildings. Energy Informatics, 4(2), 46. https://​doi​.org/​10​.1186/​s42162​ -021​-00152​-w. Santos, A., Ma, Z., Agergaard, M., Rasmussen, S.F., & Jørgensen, B.N. (2020). Analysis of Energy Storage Technologies for Island Microgrids: A Case Study of the Ærø Island in Denmark. Paper presented at the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference. SDU CEI Cooperation (2021). Cooperation. https://​www​.sdu​.dk/​en/​forskning/​centref​orenergyin​ formatics/​cooperation. SDU CEI Publications (2021). Publications by SDU Center for Energy Informatics. https://​portal​ .findresearcher​.sdu​.dk/​en/​organisations/​sdu​-center​-for​-energy​-informatics/​publications/​. SDU CEI Research Projects (2021). Research projects by SDU Center for Energy Informatics. https://​ www​.sdu​.dk/​en/​forskning/​centref​orenergyin​formatics/​research+​projects. Springer Open (2018). Energy Informatics. https://​energyinformatics​.springeropen​.com/​. United Nations (1992). United Nations Framework Convention on Climate Change. https://​digitallibrary​ .un​.org/​record/​148994​?ln​=​en. United Nations (1997). Kyoto Protocol to the United Nations Framework Convention on Climate Change. https://​digitallibrary​.un​.org/​record/​250111​?ln​=​en. United Nations (2015a). Paris Agreement. https://​unfccc​.int/​sites/​default/​files/​english​_paris​_agreement​ .pdf. United Nations (2015b). Transforming Our World: The 2030 Agenda for Sustainable Development. https://​digitallibrary​.un​.org/​record/​1654217​?ln​=​en. Watson, R.T., & Boudreau, M.-C. (2011). Energy Informatics. Green ePress. Watson, R.T., Boudreau, M.-C., & Chen, A.J. (2010). Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Quarterly, 34, 23–38.

4. Data collection and exploitation strategies for Green Information Systems Vijaya Lakshmi, Jacqueline Corbett and Jane Webster

1. INTRODUCTION Green information technologies and systems (‘Green IS’) are information technology (IT) and information systems (IS) initiatives that address, directly and indirectly, environmental sustainability concerns faced by organisations (Jenkin et al., 2011). The direct effects relate to aspects of the technology or systems themselves; such impacts are typically negative due to the demands for resources in the production, use, and disposal of the artefacts (Dedrick, 2010). The indirect effects, which can be positive or negative, arise from using IS to support business processes and strategies. Positive indirect effects occur when IS-enabled business and production processes reduce the organisation’s negative environmental footprint in terms of pollution, natural resource use, or greenhouse gas emissions (Dedrick, 2010; Jenkin et al., 2011). Beyond organisational boundaries, Green IS can have transformative effects on social and economic structures (Dedrick, 2010) with the potential for building more sustainable human societies. Green IS increasingly rely on the collection and exploitation of diverse data. In particular, big data—that is, data stores whose size and characteristics require approaches, tools, and technologies that are beyond what are available and commonly used at a given point in time— are gaining significance in Green IS (Corbett & Webster, 2015). Traditional and big data provide exciting new opportunities for innovation and environmental sustainability because of the tremendous volumes of high-frequency (near-real-time) structured and unstructured data. However, the immense amount of new data is not easily handled by traditional data management approaches due to complexity, variety and multi-dimensionality; consequently, the characteristics and management of data pose important challenges to the realisation of effective Green IS strategies. For many organisations, opportunities and challenges associated with data are not well understood. Thus, the aim of this chapter is to elucidate the issues surrounding data collection and exploitation in the context of Green IS and the implications for organisations. Technology is an important medium through which people interact with nature, and human understanding of the world is largely influenced by representations provided by IS (Ahlborg et al., 2019). For instance, organisational sustainability reports rely on the integration and analysis of diverse organisational data, and climate change simulation models are enhanced with the availability of big data (Etzion & Aragon-Correa, 2016; Runting et al., 2020). However, in this discussion, we view data not just as a technical challenge, but as an eco-socio-technical phenomena. Boyd and Crawford (2012) suggest the phenomenon of big data has intertwined technical and social dimensions that must be taken into account. Technical dimensions include aspects such as storage, computation capabilities, and analytics found within big data systems (Boyd & Crawford, 2012), while social dimensions include beliefs and attitudes toward data 53

54  Research handbook on information systems and the environment and the consequences that derive from them. In this regard, the potential adverse impacts of big data on the environment have emerged as a growing concern. Thus, to the existing socio-technical perspective of big data, we add an ecological dimension and propose that big data and data analytics are eco-socio-technical phenomena. This perspective recognises the continuous interactions between the natural environment, technology, and social systems. For instance, the growth of big data has been driven by the promise of organisational and societal benefits (Loebbecke & Picot, 2015; Lucivero, 2020); however, to achieve these goals, the extensive infrastructure needed for big data systems creates high environmental demands in the form of natural resources, non-renewable energy, and pollution, which endanger sustainability (Lucivero, 2020). How organisations approach traditional and big data can significantly impact their ability to harness this valuable resource (Franks, 2012). Thus, it is essential for managers and researchers to have a good comprehension of the issues and develop strategies for developing and using data in an environmentally conscious manner. After explaining the notion of big data as an eco-socio-technical system, we present six use cases involving data within Green IS applications at the societal, organisational, and technological levels. Then we conclude the chapter with a discussion of implications for practice and research.

2.

BIG DATA AS AN ECO-SOCIO-TECHNICAL SYSTEM

Big data is often described with reference to five key characteristics: volume, velocity, variety, veracity, and value (Lycett, 2013). While ‘volume’ and ‘variety’ refer to the large quantity of diverse types of data from multiple sources, ‘velocity’ refers to the rapid rate at which data are generated (Lycett, 2013). The characteristic of veracity speaks to the unreliability inherent in certain data sources, which requires analysis to gain reliable predictions (Gandomi & Haider, 2015). ‘Value’ refers to the extent to which big data generate worthwhile insights and benefits through data analysis (Wamba et al., 2015). These five characteristics combine to offer substantial potential benefits to organisations. The large volume and variety of data create opportunities to study new problems with new perspectives. The velocity at which data are generated and used can provide a competitive advantage for organisations capable of generating real-time insights from big data compared to organisations with large, but lower-velocity data stores (Ghasemaghaei et al., 2018). Consideration of data veracity can enable organisations to assess the risks associated with big data use and create business value from data analytics (Demchenko et al., 2014). Data stores are growing every day. They are created or retrieved by integrating a variety of related data sources (Sharma et al., 2020). The main sources of big data include large-scale enterprise systems producing automated transaction records; online social media; mobile devices; the Internet of Things (IoT), including radio frequency identification (RFID) systems, sensors, and wearable technologies; and open and public data (Baesens et al., 2016; Jones, 2019; Kallinikos & Constantiou, 2015; Kitchens et al., 2018; Lyytinen & Grover, 2017). Satellite images and unmanned aerial vehicles (UAVs) are additional data sources as they capture images of the earth for use in a variety of applications, such as sustainable agriculture and natural resource management. The above description of big data focuses on the ‘data’ portion of the concept; that is, on the characteristics and provenance of very large and complex data stores. However, big data

Data collection and exploitation strategies for Green IS  55 have limited intrinsic value. They become relevant when data are converted into information that is usable by decision makers (Koch et al., 2021). The value and relevance of data are best understood when they are viewed as an eco-socio-technical phenomenon. The ecological component of the big data system is central to this view as big data need large amounts of energy and therefore environmental impacts must be considered and offset against the potential benefits of the Green IS. The second component recognises the social and organisational contexts within which data are used. Finally, the technological component encompasses the hardware, software, networks, and other physical components of big data systems. These three components are described in more detail next. 2.1

The Ecological Component of Big Data

The ecological component of big data systems relates to their interactions with the natural environment. There are three notable points in this relationship. First, the relationship between big data systems and the environment can be double-edged (Lucivero, 2020; Nishant et al., 2020). On the one hand, the information technologies required to collect, store, and process data are a source of negative environmental impacts. For example, the carbon footprint of training a single artificial intelligence (AI) model is estimated to be equivalent to 284 tonnes of CO2 (Hao, 2019). Additionally, new big data systems create toxic electronic waste and pollute natural resources. Data storage and acquisitions consume high amounts of energy, causing increased CO2 emissions and negative impacts on environments (Lucivero, 2020). On the other hand, as we present in the use cases below, Green IS that rely on traditional and big data can also contribute to solving environmental problems. Second, it has been noted that IT increasingly mediates the interaction between individuals and nature. Individuals’ understanding of the world is largely influenced by the representations furnished by technologies (Ahlborg et al., 2019), which are fuelled by data. At the individual level, this role of mediation can be seen in new gamified applications that encourage more sustainable energy use (Oppong-Tawiah et al., 2020). Third, information systems can transform human agency and capacity to act (Ahlborg et al., 2019). For example, big data systems have been implemented to increase the sustainability of agricultural practices, and sensors deployed in homes have made individuals more conscious about the environment and their sustainable behaviours (Bibri, 2018). However, as these systems become more embedded within individuals’ lives and organisational practices, they can lead to dependence (Anderson & Rainie, 2018) and create new constraints for changing environmental behaviours and practices. 2.2

The Social Component of Big Data

Interactions between people and data make up the social component. These interactions occur at multiple levels. At a high level, information systems using big data have the potential to improve a variety of social conditions, from health care (Wu et al., 2016), crime, threats, and public security (Loebbecke & Picot, 2015), to environmental concerns (Etzion & Aragon-Correa, 2016; Runting et al., 2020). For example, social media data have assisted humanitarian relief efforts and flood disaster recoveries (Monrat et al., 2018). On the other hand, big data can create and amplify many threats to society: privacy, informed consent, data biases, and environmental impacts, to name a few. For this reason, there is a growing interest

56  Research handbook on information systems and the environment in applied ethics to ensure that big data are collected and used in a beneficial way that respects human and societal values (Martin, 2015; Someh et al., 2019). The double-edged nature of big data analytics also creates social challenges for organisations. The transformation of an organisation into a data-driven enterprise requires new sensemaking processes (Lycett, 2013). Organisational sensemaking is an interpretive process in which members of the organisation (e.g., employees) seek to understand their environment and, through interactions with other members, create accounts that enable them to work collectively (Maitlis, 2005). An outcome of sensemaking is the emergence of big data frames, which are ‘knowledge structures, including the assumptions, expectations and knowledge itself, that individuals possess and use in order to understand organisational big data’ (Corbett & Webster, 2015, p. 4775–6). These frames play a key role in how organisational members adopt new technologies (Orlikowski & Gash, 1994) through cognitive processes, such as directing attention, providing problem-solving templates, and filtering inconsistent contextual information (Davidson, 2002). For instance, big data can be viewed as either an opportunity or a threat by organisations, depending on their position within the competitive environment. When organisations view big data as a threat, their actions may be more defensive, whereas organisations that consider big data as an opportunity are likely to be more open to big data experimentation and risk taking (Corbett & Webster, 2015). Another important social component of big data relates to the skills and human resources found within an organisation. Employees with adequate analytical capabilities, domain expertise and knowledge of data tools and techniques are essential to organisations because of their ability to extract useful information and apply it for organisational benefits (Ghasemaghaei et al., 2018). Differences between the analytical skills and expertise of organisations’ human resources can lead to differences in productivity: organisations with more internal expertise are more likely to leverage the value of data for organisational benefits. In sum, how organisations develop, use, and benefit from big data systems is shaped as much by the organisation’s sensemaking processes and human resources as it is by the technological components of these systems. 2.3

The Technological Component of Big Data

The technological component of big data refers to the mechanisms of data collection, storage, transmission, analysis, and diffusion of resulting information. These processes are enabled by three layers of technologies: the infrastructure layer (hardware components); the data organisation, analytics, and management layer (software components); and the service layer (data applications) (Hsu et al., 2017) The infrastructure layer, consisting of external storage systems, servers, data centre networking infrastructure, and cloud infrastructure, comprises the foundation. Big data systems start with one or more data sources: application data sources such as relational databases and real-time data sources such as IoT devices. The data captured through these sources are typically stored in distributed file stores, often called data lakes, that can hold large volumes of data in various formats. Azure Data Lake Store or blob containers in Azure storage represent several options that provide such storage. For most organisations, cloud computing represents an ideal method for providing storage, computing, and managing data generated by the IoT (Demchenko et al., 2014).

Data collection and exploitation strategies for Green IS  57 The data organisation, analytics, and management layer administers the storage, processing, and analysis of structured and unstructured data in real time, offline, or both. As the collected data sets are large, data processing often occurs via long-running batch jobs to filter, aggregate, and prepare the data for further analysis. Data processing may include reading source files, processing them, and writing the output to new files, facilitated through data repositories, such as Azure Data Lake Analytics or HDInsight Hadoop clusters. Following data processing, data analysis can be conducted. Data analysis comprises techniques to make sense out of the data. Commonly used techniques include image processing tools (e.g., Im toolkit, VTK toolkit), machine learning tools (e.g., Google TensorFlow, Cloudera), modelling and simulation statistical tools, time series analysis, and data visualisation techniques (Kamilaris et al., 2017). Data analytics infrastructure requires services and applications such as cluster services, database servers, SQL (structured query language), NoSQL (non-SQL), and massively parallel processing databases (Demchenko et al., 2014) to facilitate data applications. The services layer of big data systems refers to interfaces and applications, such as business consulting, project services, integration services, data storage services, security supports, and technical training (Hsu et al., 2017). This layer focuses on providing useful insights derived from the analytical tools to diverse organisational employees and external entities, such as customers, vendors, partners, and suppliers. For example, insights generated from data can be used by organisations to encourage pro-environmental behaviours (PEBs) (Oppong-Tawiah et al., 2020) and achieve energy savings (Zhang et al., 2018). Alternatively, financial organisations can use data-generated insights to detect fraud by intercepting transactions in real time and correlating them with the existing enterprise data (Jha et al., 2020). The service layer also provides the ability to understand, find, and navigate data within and outside the organisation. Moreover, through the ability to flexibly build reports and dashboards, employees can make more informed decisions and design appropriate business strategies (Chen et al., 2012). The key takeaway from this brief discussion is the idea that big data are not simply technological artefacts. They also have direct and indirect ecological and social implications. Big data systems are complex eco-socio-technical systems; thus, understanding these intertwined relationships is essential if we are to improve the use of data to achieve the objectives of Green IS initiatives.

3.

LEVERAGING DATA FOR GREEN IS

Both traditional and big data can support Green IS initiatives in addressing environmental issues. Data can be drivers of green innovation and can provide organisations with useful information, allowing managers to make highly informed decisions and optimise their approach to resource use and conservation. Organisations can use data to create highly sustainable supply chains. Furthermore, data have the potential to address grand societal challenges, such as those related to food security, sustainable development, and climate change. The following subsections present six use cases for technological, organisational, and societal applications of traditional and big data (summarised in Table 4.1).

58  Research handbook on information systems and the environment 3.1

Technological Applications

When considering the organisational implications of big data for environmental sustainability, organisations must be attentive to the impacts of the system itself. As described, big data requires a collection of technologies, tools, and techniques for collecting, generating, storing, analysing, and diffusing information. All information systems, particularly those that make use of big data, make demands on the natural environment. Sensors that are used to capture data require rare materials for fabrication. High-performance telecommunications and other networking technologies are required to transfer data from the point of capture to centralised cloud-based storage facilities. Then, big data analytics (including advanced statistical and AI techniques) are used to extract insights from the data. To inform decisions, the insights extracted from data need to be communicated, again through robust telecommunications and networking infrastructures, to the relevant decision makers and organisational members. 3.1.1 Use case: removing data waste in online reviews As discussed earlier, big data systems can exacerbate environmental sustainability concerns because of the high demands for energy to power data centres, which in turn can create CO2 emissions (Lucivero, 2020). Thus, a critical environmental goal is to reduce energy use and CO2 emissions throughout the entire data lifecycle. Organisations can use Green IS to tackle this problem by implementing a programme for automatically detecting and eliminating waste data. To understand how this could work, consider the market for online reviews, which has experienced exponential growth. Every day, consumers are asked to provide their opinions on a whole range of products, services, and experiences, creating a new source of data. However, research finds that anywhere from 25 per cent to 85 per cent of these reviews are superfluous (Savarimuthu et al., 2020) and have no meaning for the companies that collect them or for the consumers they are meant to inform. To combat this problem, machine learning techniques can be developed to separate the waste from useful reviews (Savarimuthu et al., 2020). By eliminating (e.g., deleting) waste reviews early in the data lifecycle (which includes storage, processing, transmission, and consumption phases), Savarimuthu and colleagues (2020) estimate that substantial environmental benefits could be achieved: over 252,000 kilograms of CO2 emissions, roughly equivalent to 627,000 thousand miles driven by an average passenger vehicle, could be saved if waste data reviews from 11 major app stores (comprising over 8.5 million applications) were eliminated at source before being stored and processed. Implementing machine learning solutions to remove data waste is a promising avenue for Green IS, but comes with additional challenges, particularly related to the data needed to train the programmes and the processing power needed to run them. To address this challenge, researchers and practitioners have started to turn their attention to Green AI which has favourable performance and/or efficiency trade-offs and strives to develop novel AI solutions that have lower resource and power requirements (Schwartz et al., 2020). 3.1.2 Use case: transitioning to low-carbon electricity systems In the United States, the electricity sector accounts for 25 per cent of the country’s total greenhouse gas emissions (EPA, 2021). Many countries still rely heavily on fossil fuels (e.g., coal, gas) to generate electricity and until this situation changes, achieving the reductions in CO2 emissions necessary to prevent catastrophic temperature rise and climate change will be difficult. One of the main ways to reduce reliance on fossil fuels is to integrate renewable

Data collection and exploitation strategies for Green IS  59 energy sources, such as solar photovoltaic and wind, into the electricity supply, and doing so on a smaller, distributed scale. In the past, integrating distributed renewable energy generation was nearly impossible because the electricity grid was built with large, centralised generation plants that sent electricity one way to the final consumers. In addition, renewable energies are more intermittent than fossil fuel-based generation. Dips and spikes in renewable energy generation can create havoc for electricity system operators who must maintain a constant balance between supply and demand. Enter the smart grid. ‘The vision of the smart grid is to use information technology to improve the performance of the electric grid’ (Dedrick et al., 2015, p. 18). By leveraging the IoT, the smart grid can be outfitted with sensors and smart meters at key points to capture real-time (continuous or high frequency) data on generation and demand. These measurement-related data can be consolidated with other data, such as meteorological, events, and marketing and strategic business data (Zhang et al., 2018). Collectively, these data provide electricity system operators with huge data stores that need to be processed in real time to ensure an optimal flow of electricity from where it is generated to where it is needed. In addition to the environmental demands of big data systems outlined earlier (material resources and energy use in data centres), other challenges related to big data in the smart grid are present. Concerns have been raised about electric utilities’ abilities to effectively use big data to achieve energy savings (Corbett et al., 2018a). This may be due to information overload leading to information-processing inefficiencies (Corbett, 2013) or sensemaking processes that cause organisations to view big data as a threat rather than an opportunity (Corbett & Webster, 2015). There is a need for intensive data processing and big data analytics specific to the context; for instance, to develop accurate and reliable renewable energy forecasting (Zhang et al., 2018). In addition, cybersecurity concerns are heightened in the smart grid because it represents a critical backbone for modern society. Thus, concerted efforts are required to secure the quality of the data and the networks to prevent unauthorised access and manipulation. To overcome these challenges, organisations should invest in green data centres and big data infrastructures; develop efficient big data analytics tools, processes, and skills; and adopt an informed strategic approach. 3.2

Organisational Applications

Organisations address environmental issues using data from both bottom-up and top-down perspectives. Considering bottom-up Green IS initiatives, employees may bring their own ideas to the workplace, influencing others in the organisation to incorporate sustainability considerations into their projects (Corbett et al., 2018b). Rather than bottom-up initiatives, most Green IS solutions have been implemented from a top-down perspective. Typically, organisational applications to support the environment generate big data through their increased use of sensors and tracking. Other applications include carbon management systems (e.g., Melville & Whisnant, 2014) and CSR (corporate social responsibility) systems (e.g., Barbeito-Caamaño & Chalmeta, 2020). 3.2.1 Use case: encouraging employees’ pro-environmental behaviours PEBs represent environmentally responsible behaviours in organisations; that is, any actions taken by employees that they believe will improve the environmental performance of the organisation (Ramus & Steger, 2000). To encourage PEBs, organisations have turned to IS to

60  Research handbook on information systems and the environment motivate and guide employees (Corbett, 2013). These systems collect data in a variety of ways, such as gamifying work tasks to encourage environmental behaviours (e.g., Oppong-Tawiah et al., 2020) or continuously tracking employees’ computer usage to provide feedback on desired behaviours (e.g., Staples et al., 2020). Thus, organisations can use data to mediate interactions between employees and nature and gain a variety of environmental benefits, such as through modifying employees’ driving behaviours (Foxx & Schaeffer, 1981) or saving energy in the office (Orland et al., 2014). Besides the positive environmental benefits of these employee-focused systems, the data generated by these systems can have unintended consequences. Specifically, because these systems involve tracking employees’ fine-grained interactions with software, they generate large quantities of data, with the negative environmental consequences of storage and processing outlined earlier. In addition, these data can result in new threats to employees, including privacy concerns (Jahn et al., 2011), counterproductive work (Diefenbach & Müssig, 2019), and perceptions of workplace monitoring (DeWinter et al., 2014). However, these challenges present opportunities for organisations and researchers to address these negative outcomes. For instance, involving employees in generating and communicating sustainable ideas (Smith & O’Sullivan, 2012) and participating in the design of gamified and tracking systems (Martin, 2020) would help to address some of these concerns. 3.2.2 Use case: communicating environmental actions Organisations communicate their environmental actions to encourage positive stakeholder reactions (Carlson et al., 1993) and to realise improved CSR (Balasubramanian et al., 2021) and financial (Walker & Wan, 2012) outcomes. Moving beyond traditional communication methods such as press releases, organisations now use social media to control environmental information presentation through the broadcasting of news-like messages (Etter, 2014; MacKay & Munro, 2012). These messages can increase the visibility of environmental issues and foster greater stakeholder involvement (Yang & Basile, 2021). Because stakeholders react better to organisations that act on environmental issues, this puts pressure on organisations to present themselves as sustainable entities, even when they are not ‘green’ (Siano et al., 2017). Unfortunately, social media data can lead to harmful organisational outcomes if the environmental communications are misleading. This is known as greenwashing (Gatti et al., 2021) and involves exaggerated, selective, or deceptive communications about organisations’ environmental performance (Lyon & Montgomery, 2015). Greenwashing is a widespread phenomenon (de Jong et al., 2018) and is particularly prevalent in organisations with significant environmental footprints, such as those in the oil and gas and automotive industries. The largest organisations in these highly polluting industries have vested interests in maintaining and even expanding their environmentally damaging operations. Some communicate half-truths using selective disclosure of green practices to mask their true environmental performance (Marquis et al., 2016), such as Exxon Mobil’s green advertising campaigns that encourage stakeholders to trust in industry solutions to sustainability problems (Plec & Pettenger, 2012). Thus, instead of outright disinformation, organisations may adopt sophisticated discourse strategies to conceal deceptive intent in their seemingly genuine sustainability actions. These organisations and their supporters use a range of strategies to promote and defend their positions and interests, engaging in control over information and communication (MacKay & Munro, 2012). Thus, an appropriate alignment between the data and the organisations’ sustainability initiatives is crucial to generate positive Green IS effects.

Data collection and exploitation strategies for Green IS  61 Greenwashed communications are difficult for stakeholders to detect (Torelli et al., 2020), especially in the mass of organisational communications on other topics. With greenwashing, stakeholders such as consumers and investors do not necessarily know the ‘ground truth’, so cannot verify whether organisations are deceiving them or not. To overcome this problem, researchers are beginning to explore the use of linguistic analyses to help detect greenwashing. For example, a study of oil and gas and automotive firms’ environmental Twitter communications examined communication cues (such as the level of detail) in tweets; it found that software can detect greenwashing through textual cues and this detected greenwashing relates negatively to organisations’ environmental and financial outcomes (Oppong-Tawiah & Webster, 2023). Thus, in the future, stakeholders may be able to use software-based deception detection methods to assess organisations’ communications. More generally, rather than greenwashing, organisations need to match their intended sustainability with their actual and communicated sustainability (Crilly et al., 2016). 3.3

Societal Applications

The use of big data extends beyond organisations and is permeating whole industries and societies. The growing concerns around CO2 emissions and climate change have increased the variety and scope of Green IS. Big data systems can help to reshape and transform traditional methods into digital methods and address the United Nations’ Sustainable Development Goals (SDGs; UN, 2021). For example, SDGs 2, 7 and 11 aim to address industrial and societal concerns related to reducing carbon footprints and encouraging sustainable modes of living. Data are viewed as a means to achieve these goals and foster a better life and a better planet by enabling better decisions and more informed choices. 3.3.1 Use case: building a sustainable food supply chain One societal application of big data in Green IS is found in the agricultural food supply chain (AFSC) (Sharma et al., 2020). Two major concerns associated with environmental sustainability in the AFSC are food waste and energy use during agricultural produce processing, storage, and transportation (Krishnan et al., 2020). To address these concerns, the industry is turning to data collected through IoT technologies. The use of data for a sustainable AFSC can be divided into three stages: production, processing, and distribution. In the production phase, big data systems help farmers make decisions around planting crops and using pesticides and weedicides, thereby reducing the risk of environmental contamination. Farmers deploy various IoT tools such as sensors, tractors, drones, and UAVs in fields, allowing large unstructured data collection in real time (Sarker et al., 2019). These huge data sets combined with pre-existing data related to weather and climate patterns are used to derive patterns and trends for making effective agricultural decisions using sophisticated analytics tools. For example, machine vision with pattern recognition algorithms and automatic classification techniques have been applied for crop planting and effective crop monitoring (Noyes, 2014; Zareiforoush et al., 2015), and automatic weed detection and recognition (Binch & Fox, 2017). These techniques contribute to reducing the use of weedicides and enhancing agricultural sustainability. Big data systems can also be used to predict crop yield (Haghverdi et al., 2018) and inform harvest decisions. Food processing activities consume fuel and release CO2 (Weaver et al., 2014), but big data systems have been used to reduce CO2 emissions in the processing of grains

62  Research handbook on information systems and the environment (Chandrasekaran & Ranganathan, 2017) and minimise food waste (Song et al., 2018). Finally, in the distribution phase, agricultural products are sent to distribution centres and warehouses. Here, big data systems have been used effectively for supply chain planning and inventory management (Singh et al., 2018), product shelf-life estimation (Larsen et al., 2010), and developing local food supply chains to ensure food safety and sustainability (Saetta et al., 2015). As evident from the above examples, big data can be used to tackle societal problems by considering nature, people, and technology simultaneously. However, technological challenges related to interoperability between different data sources, lack of proper infrastructure, and social challenges, like industry regulations and lack of adequate skills, limit the benefits of these technologies. The formulation of plans and policies that encourage a favourable coalition between the natural conditions of farms, farmers, and technology providers can be helpful in addressing some of these challenges. Further, providing easy access to technologies, data collection and sharing, skill development, and training workshops are potential steps to leverage data to address environmental concerns.

BOX 4.1 DATA AND SUSTAINABLE GREENHOUSESa Gull Valley Greenhouse, based in Lacombe, Alberta, produces tomatoes, bell peppers, lettuce, and herbs. In 2017, Gull Valley faced the challenge of increasing energy prices. The major source of energy for Gull Valley was natural gas, and the rising prices of natural gas became a bottleneck to running a profitable business. To reduce its energy consumption, Gull Valley decided to move to an innovative, sustainable and efficient way to reduce energy consumption and costs. In 2017, it installed LED lights and made significant investments in dehumidification and air exchange ventilators to minimise heat loss during winters. To achieve greater energy efficiency, Gull Valley contacted 360 Energy, an energy advisor and energy efficiency implementor company based in Canada. 360 Energy employs data mining, coaching, and procurement strategies to provide consultation to large energy-consuming companies. 360 Energy’s team analyses customers’ utility bills and forecasts their needs, energy usage, and utility markets. It advises customers on energy purchases (electricity, natural gas, water and CO2), energy optimisation, and budget forecasting. Over four years, Gull Valley contracted 360 Energy to help optimise its energy usage and costs. This collaborative venture has helped Gull Valley to gain operational efficiencies through a variety of different solutions. The intensive analysis and review conducted by 360 Energy also helped identify and resolve billing errors in Gull Valley’s utility bills. The collaboration with 360 Energy has provided Gull Valley with good economic returns. The electricity procurement strategy has helped Gull Valley achieve over $100,000 in cost avoidance, lower total electricity costs by 10 per cent, and pay nearly 50 per cent less than the market price for power in 2021. In addition, Gull Valley was able to deploy a strategic combination of market pricing and hedging during periods of lows and highs, resulting in significant cost savings.

QUESTIONS FOR REFLECTION • What types of data were collected and exploited by Gull Valley?

Data collection and exploitation strategies for Green IS  63 • To what extent did Gull Valley and 360 Energy consider ecological, social, and technological dimensions of the solutions? • Based on your reading of the chapter so far, can you suggest other data that Gull Valley could collect and use to improve the environmental performance of their greenhouse? a

For additional information on this case, see 360 Energy (2021).

3.3.2 Use case: creating smart, sustainable cities By 2050, more than two-thirds of the world’s population is expected to reside in cities (UN, 2018). Increasing urbanisation places significant demands on the planet’s natural resources and creates significant challenges to sustainability. Cities play an essential role in the realisation of many of the SDGs; thus, the construction and management of urban spaces must be rethought to achieve sustainable development. To address these challenges, so-called ‘Smart Cities’ are investing in information and communications technologies. Smart sustainable cities strive to achieve ‘a more livable and attractive urban environment within an intelligent and agile government’ (Corbett & Mellouli, 2017, p. 429). Big data are a key factor in the emergence of smart sustainable cities (Bibri & Krogstie, 2020) and they are being leveraged to improve all areas of city administration, enhance citizens’ quality of life, engage citizens, and provide more appropriate and resilient public services (Schaffers et al., 2011). For example, the city of Amsterdam collects over two petabytes of sensor data a year within its highly complex system of dykes, levees, and barriers, which aims to prevent flooding (Fitzgerald, 2016), a threat likely to become worse with continuing climate change. Using big data, the city and its partners are able to manage the dyke system and identify the weakest areas, leading to more targeted and cost-effective investments (Fitzgerald, 2016). The management of green spaces was the focus of efforts of one Quebec smart city (Corbett & Mellouli, 2017). Green spaces are an essential part of smart sustainable cities because they provide many aesthetic, environmental, financial, cultural, and health benefits. Urban trees also contribute to the improvement of local microclimates and offer long-term environmental benefits for cities due to their air-cleansing capacities (Jim & Chen, 2008). Managing urban green space requires the involvement of multiple stakeholders and trade-offs between benefits and risks (Corbett & Mellouli, 2017). Green IS support these management processes by consolidating diverse data from internal (e.g., citizen surveys on values associated with green spaces, sensor data captured from trees and soil) and external (e.g., trees’ CO2 absorption capabilities, climate conditions) sources and making them available for planning, management, and maintenance. Urban green space management has two additional fundamental characteristics: it is location-based, and thus integration of GPS data is also essential; and it is a long-term event-driven activity, meaning that data on the plant inventories and maintenance events (e.g., planting, treatments, replacement) must be retained in perpetuity (Corbett & Mellouli, 2017). The longevity of plants within an urban green space (imagine a tree that is expected to live a hundred years) creates challenges for maintaining data stores over the long term and requires forward-thinking big data system design and management. Another challenge relates to the complex eco-socio-technical relationships within this context. Existing approaches for data-based knowledge discovery of a city tend to focus on either the collection of data or a particular application (Mohammadi et al., 2020). Neither of these is sufficient: new approaches that consider the interdependent relationships between humans, infrastructure, and technol-

64  Research handbook on information systems and the environment ogies and aligning them to sustainability concerns are needed (Mohammadi et al., 2020). In their work, Corbett and Mellouli (2017) proposed the need for an integrated information ecosystem that sits at the centre of the sustainability, political, and administrative spheres and supports the realisation of smart sustainable cities.

Level

Table 4.1

Example applications of exploiting data in Green IS

Environmental goal

Opportunities for Green IS

Reduce energy use and Developing a machine

Sources of data Online reviews

● Machine learning tech-

CO2 emissions in the

learning application to

niques require data and

data lifecycle

identify and eliminate

computation power for

Transition to

Integrating distributed

Measurement

low-carbon electricity

renewable energy into

(sensors, smart

system

a smart electricity grid

meters), external

waste data at source Technological

Potential challenges

(meteorological, geographical), business (strategies and

big data systems ● Information overload and perceptions of data as threat ● Lack of analytical tools and skills ● Cybersecurity risks ● Unintended conse-

Gamifying tasks to

Fine-grained

pro-environmental

encourage PEBs

tracking of work

behaviours (PEBs)

● Adopt Green AI approaches

training and processing ● Environmental impacts of ● Choose green

performance) Increase employees’

Suggestions for addressing challenges

data centres and infrastructures ● Engage in positive sensemaking ● Invest in data analytics and cybersecurity ● Involve employees in

quences, such as employ-

the design of gamified

ees’ privacy concerns or

and tracking systems

Organisational

perceptions of workplace Social media

monitoring ● Greenwashed (deceptive)

● For stakeholders:

Communicating

Using IS (social

environmental

media, websites,

communications are

use software-based

actions to foster

online annual reports,

difficult for stakeholders

deception detection

greater visibility

etc.) to communicate

(consumers, investors)

methods to assess

and stakeholder

organisations’

to detect

involvement

environmental actions

● Greenwashing results in

communications ● For organisations:

lower financial and envi-

match green com-

ronmental performance

munications with

for organisations

actual sustainability

Societal

initiatives ● Interoperability between

Reduce CO2 emissions

Using data analytics

Sensors, drones,

in industry supply

techniques to model

UAVs, satellite

different data sources

chain

CO2 emissions in

images, tractors

● Industry regulations that

agricultural supply

discourage easy adoption

chains to make

and use of technology

● Open data adoption, designing data structures considering stakeholders’ needs ● Formulations of poli-

decisions regarding

cies to reduce cost of

reduced emissions

technology

Societal

Level

Data collection and exploitation strategies for Green IS  65 Environmental goal

Opportunities for Green IS

Sources of data

Potential challenges

Suggestions for addressing challenges

Smart, sustainable

Using Green IS to

Environmental

● Data must be retained

● Forward-thinking

cities

manage urban green

sensors, historical

in perpetuity, creating

design of big data

spaces

information,

challenges for data

citizen surveys,

management

GPS data,

● Potential of overlooking

meteorological

complex social, technical,

data, scientific

and ecological interac-

reports and models

tions due to focus on

systems ● Adoption of an integrated information ecosystem

specific applications

4.

IMPLICATIONS FOR ORGANISATIONS AND DECISION MAKERS

With technological advancements, organisations have entered the era of big data. The previous use cases give evidence of the interconnections between data and Green IS: Green IS both relies on data and generates new data in the hopes of delivering effective approaches to address environmental sustainability. As the use cases demonstrate, organisations must consider the technological components of their data. For example, organisations should be mindful in their choices of cloud providers and data centre locations (Lacoste et al., 2019) to ensure that data are optimally located and managed. Further, to reduce CO2, organisations can commit to using renewable, low-emission energy sources for data infrastructures and purchasing efficient hardware (Schwartz et al., 2020). Organisations could also devote efforts to reducing wasted resources, particularly data waste at source (Lacoste et al., 2019; Savarimuthu et al., 2020), which would reduce the environmental costs of storing, transmitting, and processing data elements needlessly. In conjunction with data waste reduction strategies, organisations could develop capacities to measure and monitor the efficiency of their data lifecycle. With this capacity, managers would be better informed about the main components or processes (e.g., storage, analytic routines and techniques) that generate the highest CO2 emissions (Lacoste et al., 2019; Schwartz et al., 2020) and be in a better position to reduce the harmful effects of IT on the environment. For their part, governments have the opportunity to formulate plans and policies to reduce the costs of data collection equipment and software. Governments could also subsidise investments in digital technologies to make them affordable for wider use and create industry standards and regulations to analyse data collection and storage (Ferris, 2017). The adoption of an integrated information ecosystem could facilitate data collection and analysis and enable collaboration at multiple levels to help advance sustainability objectives (Corbett & Mellouli, 2017). This type of approach would help to address the issue of information silos within organisations, supply chains, and industries. Furthermore, environmental policies’ formulation around the size, structure, and usage of renewable energy resources could be a promising step in minimising the adverse impacts of big data. With respect to the social dimensions of big data, the effective use of traditional and big data requires that an appropriate green culture is created and fostered within organisations. This culture should be nourished both from leaders (top-down) and from front-line employees

66  Research handbook on information systems and the environment (bottom-up). A green organisational culture would support employees’ efforts to incorporate PEBs while carrying out everyday business processes, including the implementation of Green IS solutions (Trid et al., 2019). Environmental reporting, aligning organisational communications with sustainability initiatives, and investing in green technologies could also be steps in incorporating pro-environmental values in the organisational culture (Wang, 2019). Furthermore, developing analytical capabilities and generating skills could be integral for organisations to maximise the value of big data. To improve firm decision making, organisations could initiate training programmes to improve employees’ analytical skills (Ghasemaghaei et al., 2018) and instill environment-friendly organisational practices. Considering the ecological components of big data systems, the adoption of advanced green strategies requires the use of more complex data and infrastructure, which can create more CO2 emissions. Hence, appropriate efforts are required on the part of organisations to facilitate adoption of innovative technologies. Organisations could co-create products with their members and collaborate with technology providers to help them improve their operations (Farley et al., 2018). Additionally, organisations could collaborate with external partners to develop the necessary architecture and complementary assets to develop proactive approaches to environmental sustainability (Farley et al., 2018). A promising area of collaboration is the development of platforms that allow standard workflows, analyses, and exchange of data sets among researchers, technicians, and systems developers (Alvar-Beltrán et al., 2021). Such collaborations could help address the intensity and pace of environmental change and improve the generation of scientific knowledge that can help decision makers understand, predict, and shape these changes. 4.1

Future Research

Data offer many promising opportunities for Green IS, but pursuing these opportunities comes with risks to society and the planet. Therefore, researchers must be engaged in meaningful work to inform practice and develop working solutions quickly (Gholami et al., 2016). We outline four main directions for Green IS research, with potential research questions listed in Table 4.2. First, there is need for transdisciplinary research on big data in the context of Green IS solutions. Environmental sustainability is a highly complex goal due to the interconnectedness of the natural and socioeconomic systems (Nishant et al., 2020). For instance, climate change, food security, and poverty necessitate the adoption of a perspective that provides a deeper understanding of the social, political, economic, technological, and ecological systems involved. Hence, addressing environmental sustainability requires knowledge integration from different disciplines to provide a holistic understanding of big data as an eco-socio-technical phenomenon. Transdisciplinary research and development approaches can help to provide more robust, appropriate, and effective solutions (Elliot, 2011; Hovorka & Corbett, 2012). Second, traditional and big data offer opportunities for ecological forecasting and improved decision making. Green IS embedded with big data analytics could provide significant benefits if they are tightly coupled with ongoing sustainability efforts. Often situated within business schools, IS researchers have traditionally focused on the business applications of Green IS and big data. Now is the time for the IS research community to embrace applications beyond traditional business and managerial boundaries. To ensure close links with environmental decision making, the acquisition and analysis of data must be solution-focused and address

Data collection and exploitation strategies for Green IS  67 sustainability challenges while engaging with decision makers and those affected by such decisions, including non-specialists and citizens. A third direction involves using data to foster more environmentally sustainable business models. In 2023, the world is at the crossroads of three major pressures: digital transformation driven by big data, urgency to deal with climate change, and the novel coronavirus pandemic. As countries emerge from the pandemic, there have been calls to ‘build back better’ or to ‘build back greener’. Thus, there is a great opportunity to rethink and redesign traditional business models, favouring innovative approaches that place a sincere emphasis on ecological concerns. A fourth direction for research focuses on using big data to help individuals develop stronger connections to the natural environment and engage in more PEBs, without introducing new ethical concerns or trade-offs. Table 4.2

Research directions and potential questions

Research direction

Application domain Technological

Organisational

Societal

Transdisciplinary

What are the critical design

How does organisational culture

What political levers can be

research

principles for Green IS big

influence the perceptions and use of used to support environmentally

data systems?

big data within Green IS?

responsible big data creation

Ecological forecasting

What are the data

What types of human resource

and use? How do non-specialists and

and decision making

requirements for ecological

capabilities, knowledge, and

citizens understand, perceive,

forecasting and decision

skills do governmental and other

and react to ecological

making?

organisations need to be able to

forecasting models based on

How can these data be

leverage data in their management

big data?

efficiently collected, stored,

of natural resources?

How can citizens’ trust in

analysed, and disseminated?

How might other ways of human

science-based decisions

learning, such as experiential

regarding the environment be

learning, help to improve the

improved?

exploitation of big data in Sustainable business

How can new data sources

environmental decision making? How can organisations leverage big

How can society (government,

models

enable new, green business

data to imagine and implement new

social movements, enterprises)

models? How can the

business models?

support the transition to the green economy using big data?

environmental impacts of big data be accurately measured? Individual green values

What new technologies can

What types of innovative

How can we overcome ethical

and behaviours

be developed to collect and

approaches, based on big data,

concerns, such as data privacy

protect personal data?

can be used to help individuals

and consent related to the use of

How can data be used

adopt PEBs and develop good

Green IS?

to enhance individuals’

environmental habits?

connections with the natural world?

68  Research handbook on information systems and the environment 4.2 Conclusion Green information systems rely on traditional and big data. Thus, managers and researchers must develop strategies for using their data in an environmentally conscious manner. To do so, they need to understand the key issues surrounding big data at the technological, organisational, and societal levels. We hope that our presentation of multiple use cases and our suggestions for future research directions will help spur organisations to address humanity’s most critical issue: environmental sustainability.

ACKNOWLEDGEMENTS We thank Divinus Oppong-Tawiah and Tony Savarimuthu for their feedback on this chapter. We also thank Gull Valley Greenhouse and 360 Energy for allowing us to share their story. Financial support was provided by the Natural Sciences and Engineering Research Council of Canada, grant RGPIN/05599–2019 to Jacqueline Corbett.

REFERENCES Ahlborg, H., Ruiz-Mercado, I., Molander, S., & Masera, O. (2019). Bringing technology into social-ecological systems research: Motivations for a socio-technical-ecological systems approach. Sustainability, 11, 1–23. Alvar-Beltrán, J., Elbaroudi, I., Gialletti, A., Heureux, A., Neretin, L., & Soldan, R. (2021). Climate Resilient Practices: Typology and Guiding Material for Climate Risk Screening. FAO, Rome. Anderson, J., & Rainie, L. (2018). Concerns about human agency, evolution and survival. https://​www​ .pewresearch​.org/​internet/​2018/​12/​10/​concerns​-about​-human​-agency​-evolution​-and​-survival/​. Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J., & Zhao, J.L. (2016). Transformational issues of big data and analytics in networked business. Management Information Systems Quarterly, 40(4), 807–18. Balasubramanian, S.K., Fang, Y., & Yang, Z. (2021). Twitter presence and experience improve corporate social responsibility outcomes. Journal of Business Ethics, 173, 737–57. Barbeito-Caamaño, A., & Chalmeta, R. (2020). Using big data to evaluate corporate social responsibility and sustainable development practices. Corporate Social Responsibility and Environmental Management, 27(6), 2831–48. Bibri, S.E. (2018). The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38, 230–253. Bibri, S.E., & Krogstie, J. (2020). The emerging data-driven Smart City and its innovative applied solutions for sustainability: The cases of London and Barcelona. Energy Informatics, 3(1). https://​doi​.org/​ 10​.1186/​s42162​-020​-00108​-6. Binch, A., & Fox, C.W. (2017). Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland. Computers and Electronics in Agriculture, 140, 123–38. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–79. Carlson, L., Grove, S.J., & Kangun, N. (1993). A content analysis of environmental advertising claims: A matrix method approach. Journal of Advertising, 22(3), 27–39.

Data collection and exploitation strategies for Green IS  69 Chandrasekaran, M., & Ranganathan, R. (2017). Modelling and optimisation of Indian traditional agriculture supply chain to reduce post-harvest loss and CO2 emission. Industrial Management & Data Systems, 117(9), 1817–41. Chen, H., Chiang, R.H., & Storey, V.C. (2012). Business intelligence and analytics: From big data to big impact. Management Information Systems Quarterly, 36(4), 1165–88. Corbett, J. (2013). Using information systems to improve energy efficiency: Do smart meters make a difference?, Information Systems Frontiers, 15(5), 747–60. Corbett, J., & Mellouli, S. (2017). Winning the SDG battle in cities: How an integrated information ecosystem can contribute to the achievement of the 2030 Sustainable Development Goals. Information Systems Journal, 27(4), 427–61. https://​doi​.org/​10​.1111/​isj​.12138. Corbett, J., Wardle, K., & Chen, C. (2018a). Toward a sustainable modern electricity grid: The effects of smart metering and program investments on demand-side management performance in the US electricity sector 2009–2012. IEEE Transactions on Engineering Management, 65(2), 252–63. Corbett, J., & Webster, J. (2015, January). Organizational sensemaking and big data frames: Opportunity, control, and data limitation. In 2015 48th Hawaii International Conference on System Sciences (pp. 4772–81). Corbett, J., Webster, J., & Jenkin, T. (2018b). Unmasking corporate sustainability at the project level: Exploring the influence of institutional logics and individual agency. Journal of Business Ethics, 147, 261–86. Crilly, D., Hansen, M., & Zollo, M. (2016). The grammar of decoupling: A cognitive-linguistic perspective on firms’ sustainability claims and stakeholders’ interpretation. Academy of Management Journal, 59(2), 705–29. Davidson, E.J. (2002). Technology frames and framing: A socio-cognitive investigation of requirements determination. Management Information Systems Quarterly, 26(4), 329–58. Dedrick, J. (2010). Green IS: Concepts and issues for information systems research. Communications of the Association for Information Systems, 27(1), 173–84. Dedrick, J., Venkatesh, M., Stanton, J.M., Zheng, Y., & Ramnarine-Rieks, A. (2015). Adoption of smart grid technologies by electric utilities: Factors influencing organizational innovation in a regulated environment. Electronic Markets, 25(1), 17–29. De Jong, M.D.T., Harkink, K.M., & Barth, S. (2018). Making green stuff? Effects of corporate greenwashing on consumers. Journal of Business and Technical Communication, 32(1), 77–112. Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the big data ecosystem. In 2014 International Conference on Collaboration Technologies and Systems (CTS) (pp. 104–12). IEEE, Piscataway. DeWinter, J., Kocurek, C.A., & Nichols, R. (2014). Taylorism 2.0: Gamification, scientific management and the capitalist appropriation of play. Journal of Gaming & Virtual Worlds, 6(2), 109–27. Diefenbach, S., & Müssig, A. (2019). Counterproductive effects of gamification: An analysis on the example of the gamified task manager Habitica, International Journal of Human-Computer Studies, 127, 190–210. Elliot, S. (2011). Transdisciplinary perspectives on environmental sustainability: A resource base and framework for IT-enabled business transformation. Management Information Systems Quarterly, 197–236. EPA (2021). Sources of greenhouse gas emission. https://​ www​ .epa​ .gov/​ ghgemissions/​ sources​ -greenhouse​-gas​-emissions. Etter, M. (2014). Broadcasting, reacting, engaging: Three strategies for CSR communication in Twitter. Journal of Communication Management, 18(4), 322–42. Etzion, D., & Aragon-Correa, J.A. (2016). Big data, management, and sustainability: Strategic opportunities ahead. Organization & Environment, 29(2), 147–55. Farley, S.S., Dawson, A., Goring, S.J., & Williams, J.W. (2018). Situating ecology as a big-data science: Current advances, challenges, and solutions. BioScience, 68(8), 563–76. Ferris, J.L. (2017). Data privacy and protection in the agriculture industry: Is federal regulation necessary? Minnesota Journal of Science & Technology, 18, 309.

70  Research handbook on information systems and the environment Fitzgerald, M. (2016). Data-driven city management: A close look at Amsterdam’s Smart City initiative. MIT Sloan Management Review. https://​sloanreview​.mit​.edu/​case​-study/​data​-driven​-city​ -management/​. Foxx, R.M., & Schaeffer, M.H. (1981). A company-based lottery to reduce the personal driving of employees. Journal of Applied Behavior Analysis, 14(3), 273–85. Franks, B. (2012). Taming the Big Data Tidal Wave. John Wiley & Sons, Hoboken. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–44. Gatti, L., Pizzetti, M., & Seele, P. (2021). Green lies and their effect on intention to invest. Journal of Business Research, 127, 228–40. Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. Journal of Strategic Information Systems, 27(1), 101–13. Gholami, R., Watson, R.T., Hasan, H., Molla, A., & Bjorn-Andersen, N. (2016). Information systems solutions for environmental sustainability: How can we do more? Journal of the Association for Information Systems, 17(8). https://​doi​.org/​10​.17705/​1jais​.00435. Haghverdi, A., Washington-Allen, R.A., & Leib, B.G. (2018). Prediction of cotton lint yield from phenology of crop indices using artificial neural networks. Computers and Electronics in Agriculture, 152, 186–97. Hao, K. (2019). Training a single AI model can emit as much carbon as five cars in their lifetimes. Technology Review. https://​www​.technologyreview​.com/​2019/​06/​06/​239031/​training​-a​-single​-ai​ -model​-can​-emit​-as​-much​-carbon​-as​-five​-cars​-in​-their​-lifetimes/​. Hovorka, D.S., & Corbett, J. (2012, December). IS sustainability research: A trans-disciplinary framework for a ‘grand challenge’. In 2012 33rd International Conference on Information Systems. Hsu, H.H., Chang, C.Y., & Hsu, C.H. (Eds.) (2017). Big Data Analytics for Sensor-Network Collected Intelligence. Morgan Kaufmann, Burlington. Jahn, M., Schwartz, T., Simon, J., & Jentsch, M. (2011, May). Energypulse: Tracking sustainable behavior in office environments. In 2011 2nd International Conference on Energy-Efficient Computing and Networking (pp. 87–96). Jenkin, T.A., Webster, J., & McShane, L. (2011). An agenda for ‘green’ information technology and systems research. Information and Organization, 21(1), 17–40. Jha, B.K., Sivasankari, G.G., & Venugopal, K.R. (2020, March). Fraud detection and prevention by using big data analytics. In 2020 4th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 267–74). IEEE, Piscataway. Jim, C.Y., & Chen, W.Y. (2008). Assessing the ecosystem service of air pollutant removal by urban trees in Guangzhou (China). Journal of Environmental Management, 88(4), 665–76. Jones, M. (2019). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 3–16. Kallinikos, J., & Constantiou, I.D. (2015). Big data revisited: A rejoinder. Journal of Information Technology, 30(1), 70–74. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F.X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced customer analytics: Strategic value through integration of relationship-oriented big data. Journal of Management Information Systems, 35(2), 540–574. Koch, H., Chipidza, W., & Kayworth, T.R. (2021). Realizing value from shadow analytics: A case study. Journal of Strategic Information Systems, 30(2), 101668. Krishnan, R., Agarwal, R., Bajada, C., & Arshinder, K. (2020). Redesigning a food supply chain for environmental sustainability: An analysis of resource use and recovery. Journal of Cleaner Production, 242, 118374. https://​doi​.org/​10​.1016/​j​.jclepro​.2019​.118374. Lacoste, A., Luccioni, A., Schmidt, V., and Dandres, T. (2019). Quantifying the carbon emissions of machine learning. https://​arxiv​.org/​pdf/​1910​.09700​.pdf.

Data collection and exploitation strategies for Green IS  71 Larsen, R.A., Schaalje, G.B., & Lawson, J.S. (2010). Food shelf life: Estimation and optimal design. Journal of Statistical Computation and Simulation, 80(2), 143–57. Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformations arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149–157. Lucivero, F. (2020). Big data, big waste? A reflection on the environmental sustainability of big data initiatives. Science and Engineering Ethics, 26(2), 1009–30. Lycett, M. (2013). ‘Datafication’: Making sense of (big) data in complex world. European Journal of Information Systems, 22, 381–6. Lyon, T.P., & Montgomery, A.W. (2015). The means and end of greenwash. Organization & Environment, 28(2), 223–49. Lyytinen, K., & Grover, V. (2017). Management misinformation systems: A time to revisit? Journal of the Association for Information Systems, 18(3), 2. MacKay, B., & Munro, I. (2012). Information warfare and new organizational landscapes: An inquiry into the ExxonMobil–Greenpeace dispute over climate change. Organization Studies, 33(11), 1507–36. Maitlis, S. (2005). The social processes of organizational sensemaking. Academy of Management Journal, 48(1), 21–49. Marquis, C., Toffel, M.W., and Zhou, Y. (2016). Scrutiny, norms, and selective disclosure: A global study of greenwashing. Organization Science, 27(2), 483–504. Martin, K. (2020). Breaking the privacy paradox: The value of privacy and associated duty of firms. Business Ethics Quarterly, 30(1), 65–96. Martin, K.E. (2015). Ethical issues in the big data industry. Management Information Systems Quarterly Executive, 14, 2. Melville, N.P., & Whisnant, R. (2014). Energy and carbon management systems. Journal of Industrial Ecology, 18(6), 920–930. https://​doi​.org/​10​.1111/​jiec​.12135. Mohammadi, N., Vimal, A., & Taylor, J. (2020, January). Knowledge discovery in smart city digital twins. In 2020 53rd Hawaii International Conference on System Sciences. Monrat, A.A., Islam, R.U., Hossain, M.S., & Andersson, K. (2018). Challenges and Opportunities of Using Big Data for Assessing Flood Risks. Springer, Cham. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. Noyes, K. (2014). Big data poised to change the face of agriculture. Fortune Data. http://​fortune​.com/​ 2014/​05/​30/​cropping​-up​-on​-every​-farm​-big​-data​-technology. Oppong-Tawiah, D., & Webster, J. (2023). Corporate sustainability communications as ‘fake news’: Firms’ greenwashing on Twitter. Sustainability, 15, 6683. https://​doi​.org/​10​.3390/​su15086683. Oppong-Tawiah, D., Webster, J., Staples, S., Cameron, A.-F., Ortiz de Guinea, A., & Tam, Y.H. (2020). Encouraging sustainable energy use in the office with a gamified mobile application. Journal of Business Research, 106, 388–405. Orland, B., Ram, N., Lang, D., Houser, K., Kling, N., & Coccia, M. (2014). Saving energy in an office environment: A serious game intervention. Energy and Buildings, 74, 43–52. Orlikowski, W.J., and Gash, D.C. (1994). Technological frames: Making sense of information technology in organizations. ACM Transactions on Information Systems, 12(2), 174–207. Plec, E., & Pettenger, M. (2012). Greenwashing consumption: The didactic framing of ExxonMobil’s energy solutions. Environmental Communication, 6(4), 459–76. Ramus, C.A., & Steger, U. (2000). The roles of supervisory support behaviors and environmental policy in employee ‘Ecointiatives’ at leading-edge European companies. Academy of Management Journal, 43(4), 605–26. https://​doi​.org/​10​.2307/​1556357. Runting, R.K., Phinn, S., Xie, Z., Venter, O., & Watson, J.E. (2020). Opportunities for big data in conservation and sustainability. Nature Communications, 11(1), 1–4.

72  Research handbook on information systems and the environment Saetta, S.A., Caldarelli, V., Tiacci, L., Lerche, N., & Geldermann, J. (2015). A logistic network to harmonise the development of local food system with safety and sustainability. International Journal of Integrated Supply Management, 9(4), 307–28. Sarker, M.N.I., Islam, M.S., Ali, M.A., Islam, M.S., Salam, M.A., & Mahmud, S.H. (2019). Promoting digital agriculture through big data for sustainable farm management. International Journal of Innovation and Applied Studies, 25(4), 1235–40. Savarimuthu, B.T.R., Corbett, J., Yasir, M., & Lakshmi, V. (2020, December). Using machine learning to improve the sustainability of the online review market. In 2020 41st International Conference on Information Systems. Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., & Oliveira, A. (2011). Smart cities and the future internet: Towards cooperation frameworks for open innovation. In The Future Internet Assembly (pp. 431–46). Springer, Berlin, Heidelberg. Schwartz, R., Dodge, J., Smith, N.A., and Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. Sharma, R., Kamble, S.S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926. Siano, A., Vollero, A., Conte, F., & Amabile, S. (2017). ‘More than words’: Expanding the taxonomy of greenwashing after the Volkswagen scandal. Journal of Business Research, 71, 27–37. Singh, A., Kumari, S., Malekpoor, H., & Mishra, N. (2018). Big data cloud computing framework for low carbon supplier selection in the beef supply chain. Journal of Cleaner Production, 202, 139–49. Smith, A.M., & O’Sullivan, T. (2012). Environmentally responsible behaviour in the workplace: An internal social marketing approach. Journal of Marketing Management, 28(3–4), 469–93. Someh, I., Davern, M., Breidbach, C.F., & Shanks, G. (2019). Ethical issues in big data analytics: A stakeholder perspective. Communications of the Association for Information Systems, 44(1), 34. Song, G., Semakula, H.M., & Fullana-i-Palmer, P. (2018). Chinese household food waste and its climatic burden driven by urbanization: A Bayesian Belief Network modelling for reduction possibilities in the context of global efforts. Journal of Cleaner Production, 202, 916–24. Staples, S., Webster, J., & Lv, S. (2020). Comparing goal setting approaches to boosting pro-environmental behaviors. Journal of Sustainability Research, 2(4), e200034. 360 Energy (2021). Greenhouse case study: Gull Valley. https://​360energy​.net/​greenhouse​-case​-study​ -gull​-valley/​. Torelli, R., Balluchi, F., & Lazzini, A. (2020). Greenwashing and environmental communication: Effects on stakeholders’ perceptions. Business Strategy and the Environment, 29(2), 407–21. Trid, S., Corbett, J., & Bouchard, L. (2019) Modèle théorique de projets de Green IS: une spécification des relations entre objectifs, compétences et culture environnementale. Systèmes d’Information et management, 24(1), 7–45. UN (2018). 68% of the world population projected to live in urban areas by 2050, says UN. Department of Economic and Social Affairs. https://​www​.un​.org/​development/​desa/​en/​news/​population/​2018​ -revision​-of​-world​-urbanization​-prospects​.html. UN (2021). Sustainable Development Goals. https://​www​.un​.org/​sus​tainablede​velopment/​. Walker, K., & Wan, F. (2012). The harm of symbolic actions and green-washing: Corporate actions and communications on environmental performance and their financial implications. Journal of Business Ethics, 109(2), 227–42. Wamba, S.F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–46. Wang, C.H. (2019). How organizational green culture influences green performance and competitive advantage: The mediating role of green innovation. Journal of Manufacturing Technology Management, 30(4), 666–83.

Data collection and exploitation strategies for Green IS  73 Weaver, C.M., Dwyer, J., Fulgoni, V.L., III, King, J.C., Leveille, G.A., MacDonald, R.S., Ordovas, J., & Schnakenberg, D. (2014). Processed foods: Contributions to nutrition. American Journal of Clinical Nutrition, 99(6), 1525–42. Wu, J., Li, H., Cheng, S., & Lin, Z. (2016). The promising future of healthcare services: When big data analytics meets wearable technology. Information & Management, 53(8), 1020–1033. Yang, J., & Basile, K. (2021). Communicating corporate social responsibility: External stakeholder involvement, productivity and firm performance. Journal of Business Ethics, 178, 501–17. Zareiforoush, H., Minaei, S., Alizadeh, M.R., & Banakar, A. (2015). Potential applications of computer vision in quality inspection of rice: A review. Food Engineering Reviews, 7(3), 321–45. Zhang, Y., Huang, T., & Bompard, E.F. (2018). Big data analytics in smart grids: A review. Energy Informatics, 1(1). https://​doi​.org/​10​.1186/​s42162​-018​-0007​-5.

5. How to unlock the potential of information systems for a circular economy Anne Ixmeier, Johann J. Kranz, Jan Recker and Roman Zeiss

1. INTRODUCTION Physical goods are so ubiquitous in our lives that we sometimes forget about them. Yet, the resources that make them up have enormous impacts on our personal and planetary well-being. To produce goods, natural materials need to be extracted, processed, and distributed. After consumption, goods and leftover materials are disposed of in landfills or incineration plants. This way of using resources characterises our current global economy as a cradle-to-grave model that inevitably will lead to a scarcity of material resources and exuberant waste streams (Forti et al., 2020). To illustrate: in 1970, global material use amounted to 27 billion tons (Gt); since then it has tripled and is projected to rise to 167 Gt in 2060 (OECD, 2019). Even more alarming, material use is increasing at a faster rate than population and economic output (United Nations, 2019) despite improvements in resource efficiency (see Figure 5.1). The material-intensive lifestyles of high-income countries and economic disincentives, such as virgin materials being cheaper than recycled ones, continue to accelerate the cradle-to-grave model to material consumption (OECD, 2019; United Nations, 2019). To decelerate this trend, decoupling economic growth from resource consumption must be a global imperative.

Source: United Nations (2019)

Figure 5.1

Population, material footprint and gross domestic product (GDP) growth index, 2000–2017 (baseline 2000 = 100)

74

How to unlock the potential of information systems  75 However, action to minimise resource consumption and waste production is within reach. The idea of a circular economy (CE) offers a chance to transform the current take-make-waste economic model into a circular model that helps to “decouple global economic development from finite resource consumption” (Ellen MacArthur Foundation, 2016, p. 17). The primary objective of a CE is to minimise the resource inputs to, and the negative environmental impacts of, economic operations. The CE’s main ambition is to improve the sustainability of consumption and production through reduced resource use, degradation, and pollution along the entire value chain. To unlock this potential, CE scholars have worked to formulate a range of principles and mechanisms to help economic actors to systematically narrow, slow, and close material loops by optimising production, distribution, and consumption processes, extending product lifespans, and reintegrating waste materials into supply chains (Geissdoerfer et al., 2017; Kirchherr et al., 2017; Potting et al., 2017). The transition from a linear to a circular economy has recently gained increased attention from policymakers and business practitioners alike as a facilitator of eco-industrial development and increased well-being (Ghisellini et al., 2016). And in the current era where environmental sustainability and digital transformation are becoming increasingly urgent and intertwined (e.g., Berg et al., 2021; Watson and Mathew, 2021; Weinhardt et al., 2021), and where business leaders have growing expectations toward digital technologies enabling sustainable development as well as making strategic contributions to business value (e.g., Vial, 2019; Watson and Mathew, 2021), unlocking the potential of digital technologies for the transition toward a CE has become a global priority on the agenda of information systems (IS) leaders and researchers (Kristoffersen et al., 2021; Zeiss et al., 2020). At present, however, research on the value and management of a circular economy and the associated digital transformation remains nascent (Weinhardt et al., 2021). Our aim in writing this chapter, therefore, is to deepen our understanding on how businesses can introduce and utilise digital technologies to initially minimise resource consumption and waste production, and ultimately to manage and scale up a CE. To mobilise impactful IS solutions for a CE, we describe four exemplary IS research streams that unlock the potential of selected digital technology solutions available at present or in the near future. We explore the potential of digital technologies to capture data to lower information asymmetries and coordinate action among CE market participants (see Section 3.1), to represent data about natural resources or physical goods through digital twins to facilitate their monitoring, analysis, simulation, and control (see Section 3.2), and to share data in order to ensure efficient and transparent data exchange in circular value networks (see Section 3.3). After outlining the potential of digital technologies for data capture, representation, and sharing, their dual role in simultaneously promoting and burdening the environment is discussed (see Section 3.4). We also review what we believe to be main roadblocks to establishing a productive interdisciplinary between IS and CE scholarship. We conclude with a few directions we deem promising for our field to follow. We proceed as follows. First, we briefly introduce the CE paradigm and recap why IS knowledge is relevant as an enabler of a CE. Next, we develop four concrete, actionable IS research directions about unlocking the potential of IS for a CE in an environmentally sustainable manner. Subsequently, we carefully reflect on challenges in doing actionable IS research on CE. In response to these opportunities and challenges, we conclude with a call for effectively leveraging digital technologies to support the transition toward a CE.

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2. BACKGROUND 2.1

What Is a Circular Economy and Why Does It Matter?

Many consider the CE model (see Figure 5.2) a promising strategy to address global sustainability challenges related to reversing ecosystem degradation by reconciling the economy and the natural environment (Haas et al., 2015; Van Schalkwyk et al., 2018). In a CE, the economic and ecological value of natural resources is preserved by keeping them in the economic system for as long as possible, either by lengthening the life of the products formed from them or by looping them back into the system to be reused (den Hollander et al., 2017; Gurewitsch, 2015). Thus, the CE is an economic model with the goal of minimising resource input as well as waste and emission leakage by narrowing, slowing, and closing material loops (Geissdoerfer et al., 2017; Kirchherr et al., 2017).

Source: EEA (2019)

Figure 5.2

Circular economy model

How to unlock the potential of information systems  77 The idea of a CE has not just recently begun to attract attention. Over the past 50 years of scientific conversations on waste and resource management, a CE has slowly crystallised as an umbrella concept reframing and organising a heterogeneous set of ideas on managing increasing pollution and extending material resource (Blomsma and Brennan, 2017). First, ideas with a focus on waste handling and prevention emerged in the 1970s (Boulding, 1966) and were refined during the 1980s (Stahel and Reday-Mulvey, 1981). In the following decades, the narrative moved away from waste handling and prevention (i.e., problem narrative) toward the retention of economic value (i.e., opportunity narrative), building upon the idea of systemic looping and cascading of materials (Blomsma and Brennan, 2017). Since the 2000s, the ideas have progressively extended beyond the boundaries of a single firm and its proximate supply chain actors toward a collaborative network of widely scattered stakeholders. CE gradually infused the business management context advancing the conversations from a so-far mainly technical (e.g., material flow analysis) to a sociotechnical discourse (Bocken et al., 2017; Bressanelli et al., 2018; Prendeville and Bocken, 2017). Budging from splintered initiatives, the CE model became more and more inclusive in terms of stakeholders, products, components, and material flows across all product lifecycles and is thus promising to address global sustainability challenges related to reversing ecosystem degradation by reconciling the economy and the environment (Haas et al., 2015; Van Schalkwyk et al., 2018). The benefits of a CE are powerful to address the environmental challenges relating to overconsumption, extraction, processing, and disposal of materials, as urged by the United Nations’ Sustainable Development Goals (SDGs) – in particular, but not limited to, goals 12 (Responsible Consumption and Production), 13 (Climate Action), 14 (Life Below Water), and 15 (Life on Land) (Schroeder et al., 2018; United Nations, 2015). In fact, the implementation of a CE is a core pillar of the European Union’s (EU’s) Green New Deal (European Commission, 2019). Although CE implementations are still in the development process (Ghisellini et al., 2016), there are key enablers that show promise for reshaping the linear into a circular economic model. There are four driving forces that have begun to prioritise a pathway toward a CE (see, for example, Ellen MacArthur Foundation, 2016; Geissdoerfer et al., 2017; Ghisellini et al., 2016; Jesus and Mendonça, 2018). First, shifts in financing and business models, have started to stimulate the transition to a CE by catalysing investments in reconsidered business models and innovations that create seminal value and build on the interaction between products and services (e.g., Geissdoerfer et al., 2018; Lüdeke-Freund et al., 2019). For example, the EU provides endowed funding programmes in order to support the transition to a CE, such as Horizon 2020 with a budget of €95.5 billion (CE Stakeholder EU, 2020). Further, the idea of intensified product use and collaborative consumption has been implemented in numerous sharing, lending, renting, or leasing business model innovations (Tunn et al., 2019); for instance, the bike-sharing provider Donkey Republic (2019), which networks with value-adding actors who take care of fleet relocation and maintenance. Second, new regulation and legislation in several regions, such as Europe or China, have started to drive change; for instance, via bans, product policies, and standards that set requirements for sustainable economic development (e.g., Lazarevic and Valve, 2017; McDowall et al., 2017). On a national level, Sweden was the first country to formulate an extended producer responsibility strategy in 1990 to achieve environmental objectives and increase producers’ responsibility for end-of-life products (Lindhqvist and Lidgren, 1990). In 2009, China passed

78  Research handbook on information systems and the environment the Circular Economy Promotion Law and is now pioneering CE beyond industrial systems by acknowledging CE as a national development goal by law (Mathews and Tan, 2011). In 2020, the EU adopted its Circular Economy Action Plan and passed directives for strengthening the “right to repair” of electronic products such as smartphones, and for banning single-use plastics such as straws to avoid obsolescence and waste (Circular Economy Action Plan, 2020). Third, advances in digital technology hardware (e.g., microprocessors, sensors, 5G, or new materials for transistors and chips) and software (e.g., predictive analytics, deep learning, or quantum instruction sets) have produced novel affordances for tracking and optimising resource use, strengthening trust between supply chain actors, or enabling feedback loops between end-user consumption, recycling, and original production, all of which make the implementation of circular business models increasingly technologically and informationally feasible (e.g., French and Shim, 2016; Wilts and Berg, 2017). For example, advances in sensor technologies now allow organisations to track and monitor products during use well beyond the point of sales. Advances in digital infrastructure allow pooling and sharing of data through application programming interfaces (APIs) across a heterogeneous and distributed set of actors (Zhang, 2016). Waste management IS, such as those based on SAP or Microsoft solutions (Burger et al., 2018; Microsoft, 2019; SAP, 2019), track and process information, such as real-time locations and routes of collection vehicles, records of user payments, and the history of waste collection on a grand scale (Kaza et al., 2018). Box 5.1 on the engineering intelligence technology offered by SPREAD (2019) illustrates functions and opportunities that digital technologies can offer for a CE (see Figure 5.3).

BOX 5.1 AUGMENTED ENGINEERING INTELLIGENCE OFFERED BY SPREAD The Berlin-based start-up SPREAD offers an engineering intelligence technology that helps customers, such as Mercedes, Porsche, and VW, to implement CE principles by optimising materials in production and extending product lifespans. SPREAD’s technology helps engineers to make more sustainable decisions by enabling immediate verification of design feasibility and repairability during the product lifecycle. Complex products such as personal and commercial vehicles, appliances, or industrial machinery require thousands of internal and external engineering experts to share specialised knowledge with each other for them to work. Managing this complexity is becoming an ever more resource-intensive and complex process that often leads to large-scale material waste and inefficiencies. SPREAD developed a proprietary augmented engineering intelligence technology to digitalise and connect engineering knowledge flows across the entire product lifecycle.

How to unlock the potential of information systems  79

Figure 5.3 Augmented engineering intelligence offered by SPREAD SPREAD uses 3D visualisation, machine learning, and digital twin technologies to present complex physical products in a way that engineers can intuitively understand. Thus, the founders claim that the platform allows engineering teams to make better engineering decisions and create more innovative and sustainable products at a lower cost. Based on results in pilot projects with car manufacturers, SPREAD estimates its platform’s CE potential as follows. 25 per cent reduction in duplicate parts SPREAD helps users to search and

˗40,000 tons of CO2 per year SPREAD identified that it could save

standardise parts & components within 1 kg of copper in a vehicle’s wiring

50 per cent faster repair SPREAD quickly identifies potential errors in the 3D product model and helps users repair products

products to reduce complexity und

harness, which scales to 2.5 million kg with 3D animated demount and assembly

unnecessary material waste.

and €20 million per year.

sequences. This helps customers promote circularity by repairing defect parts instead of scrapping entire products.

For more information, visit https://​www​.spread​.ai.

Fourth, advocacy and commitment have expanded. The notion of a CE is becoming not only more widely known but also more relevant to policy- and decision-makers on global, local, as well as individual scales. A rising share of stakeholders are becoming increasingly aware, interested, informed, and committed to engage in circular thinking and acting (Kirchherr et al., 2018; Reike et al., 2018). The release of the sixth assessment report of the Intergovernmental Panel on Climate Change, which concluded that human-induced climate change is severe, was a wake-up call that recently attracted considerable public interest (IPCC, 2021). Regular strikes for climate and collective action movements such as Fridays for Future or Extinction Rebellion show that individuals are increasingly fulfilling their collective duty to take action (Extinction Rebellion, 2021; Fridays for Future, 2020). 2.2

How Digital Technologies Can Support the Transition Toward a CE

Digital technologies play both a direct and indirect role in enabling a transition toward a CE. Not only are advances in IS and digital technology one of the main direct enablers of a CE (point three above), they are also enabling the other factors (i.e., shifts in financing and business models, new regulation and legislation, as well as expansions in advocacy and commitment).

80  Research handbook on information systems and the environment Transitioning to a circular economic system involves many heterogeneous actors who face individual and collective difficulties in envisioning, making sense of, and mobilising a shared understanding of the inherent social structures and material artefacts and is hence perceived as a sociotechnical challenge (Bauwens et al., 2020). Implementing and transitioning to a CE is primarily a challenge of effective information provision and use throughout the entire social and material value chain: improved resource use requires linking material flows with information flows to enable coordination between heterogeneous actors (Wilts and Berg, 2017; Zeiss et al., 2020). The IS discipline has a history of demonstrating how material flows, information flows, and social actors can be bridged with “technical artifacts for capturing, processing, transmitting, and representing information” (Grover and Lyytinen, 2015, p. 272; see also Gholami et al., 2016). While much of this research has traditionally explored purely economic impacts of IS, IS scholars have also established a stream of research that explores the potential of digital technologies to contribute to sustainable development (Malhotra et al., 2013; Seidel et al., 2017). It is therefore not surprising that CE scholars have long lauded digital technologies such as sensors, distributed ledgers, or digital platforms as key enablers to transforming the linear into a circular economic model (Antikainen et al., 2018; Berg et al., 2021; Casado-Vara et al., 2018; Reuter, 2016; Van Schalkwyk et al., 2018).

3.

UNLOCKING THE POTENTIAL OF DIGITAL TECHNOLOGIES FOR A CE

The transition to a CE offers abundant research challenges to achieve the goal of minimising resource input as well as waste and emission leakage by narrowing, slowing, and closing material loops (Geissdoerfer et al., 2017; Kirchherr et al., 2017). We outline four selected areas of research that we believe are fundamentally connected to IS scholarship. Our focus is on how digital technologies, such as sensors, distributed ledgers, artificial intelligence, or platforms, when forming part of suitable IS, provide affordances to capture, represent, and share information along value chains in a CE in an environmentally sustainable manner (French and Shim, 2016). We chose this focus because leveraging such affordances can assist overcoming hindrances, such as quality uncertainty, data availability, transaction costs, information asymmetries, or information security, that prevent market participants from realising a CE (Kirchherr et al., 2018; Ranta et al., 2018). Figure 5.4 summarises key functions and opportunities along value chains in a CE and selected IS research streams that could underpin IS research on topics relevant to a CE. In what follows, we expand in some depth on these research opportunities. We do so by exploring key research streams on each opportunity that is available already in the IS literature. Our understanding of research streams encompasses research programmes driven by theory (e.g., representation theory), phenomenology (e.g., open data), and technology (e.g., distributed ledgers).

How to unlock the potential of information systems  81

Figure 5.4 3.1

Opportunities for IS research to contribute to a CE

Reducing Information Asymmetry in Secondary Material Markets by Digital Platforms

Imagine a beverage producer who wants to buy plastics to produce bottles for its beverages. The producer has to decide whether to buy virgin plastics from a trusted supplier or to buy

82  Research handbook on information systems and the environment recycled granules from post-consumer plastics from unknown provenance and quality. Chances are that the beverage producer buys virgin plastics because quality uncertainty, transaction costs, customer acceptance risks, and frequently also prices, are lower. Hence, even if the beverage producer aims at reducing its carbon footprint by using virgin resources, several hindrances exist that prevent the company from participating in a circular plastics economy using secondary materials. As this example illustrates, the core challenges that companies face occur primarily because of information asymmetries between market participants. The situation in CE supply markets is similar to Akerlof’s (1970) classic example of “lemon markets” in which defective cars, called lemons, distort the functioning of the used car market. The problem is that buyers of used cars lack information about used cars’ quality which sellers have. This information asymmetry pushes reliable sellers out of the market as their high-quality used cars will be undervalued by buyers because of the unaddressed problem of substantial quality uncertainty in the market. Therefore, high information asymmetries in markets such as for used cars or recycled materials lead to market failure or collapse. Less informed buyers will only be willing to buy at a price at which only the worst sellers are willing to offer their used cars. To address these market failures, buyers seek to lower information asymmetries by gathering information themselves or using professional services, which increases transaction costs and delays transactions. At the same time, reliable sellers seek to decrease quality uncertainty by investing, for instance, in guarantees, third-party certifications or ratings, or brand reputation. Thus, as buyers and sellers have different ex ante information (a situation referred to as “adverse selection”), agency costs arise on both sides of the market. Buyers need to invest in acquiring additional information; sellers need to invest in signalling their trustworthiness and bear guarantee costs. Thus, markets characterised by high quality uncertainty need to lower buyers’ pre-purchase uncertainty, align interests, and create trust to function; else these markets may not be created in the first place or will not exist for long. Since Akerlof (1970) published his seminal paper more than fifty years ago, the Internet and the rapid diffusion and performance increases of digital technologies have led to the emergence of digital platforms which have transformed existing markets’ functioning and created new ones (Cusumano et al., 2019). Providers of digital platforms offer intermediary services like financial and reputational intermediation to decrease uncertainty regarding information, participants’ behaviour, and transactions, or in other words to “un-lemon” markets with information asymmetries. Research has shown that digital platforms alleviate market and information failures by lowering search costs (Kuruzovich et al., 2010) and decrease buyers’ uncertainty regarding product quality (Pavlou et al., 2007). Further, IS operating on such or other digital technologies can be used to decrease information asymmetries; for example, when they are used to improve the description quality of products and their actual condition (Dimoka et al., 2012). Such systems could be used to distinguish trustworthy sellers from dishonest sellers by providing detailed and reliable information about their products and benefit from investments in their reputation shown on platforms (Pavlou and Dimoka, 2008). Thereby, a safe and trusted environment could be created that decreases risks and transaction costs and enables value-creating interactions for market participants. Therefore, digital platforms offer great technological potential to address market and information failures in current CE markets for secondary materials. While some digital platforms for secondary materials such as Cirplus or Excess Materials Exchange have recently emerged, a wide adoption of secondary material marketplaces is complicated by the virtual non-existence

How to unlock the potential of information systems  83 of information about how much of which material is available when and in what quality (Wilts and Berg, 2017). Hence, matching supply and demand of secondary materials is a major issue (OECD, 2006) “because of the geographical dispersion of unrelated, heterogeneous actors and the asynchronous and irregular occurrence of material supply and demand” (Zeiss et al., 2020, p. 171). For example, potential sellers of secondary materials often lack easily accessible information on potential buyers (Aid et al., 2017; Golev et al., 2015), and buyers lack exact information on the recycled materials’ purity and composition, which is crucial as even the smallest contaminations can have severe consequences (Shen and Worrell, 2014). A wide array of digital technologies exist that can be recombined to alleviate the problem of unreliable information on digital platforms for secondary materials. Cyber-physical systems (CPS) integrate physical and software components via data networks such as the Internet. The integration allows software components to monitor and control physical processes and physical components to feed back into computational systems (Derler et al., 2012). An important CPS concept for CE secondary markets are digital twins, which we discuss in more depth in the next section. Two promising digital technologies which we will highlight in this chapter are digital watermarks and distributed ledger technology (DLT), because they offer useful properties to decrease information asymmetries and coordinate heterogeneous actor networks. Digital watermarks, for instance, are particularly suited to identify packaging material composition such as plastic bottles at a large scale and low costs. For instance, an industry consortium named HolyGrail 2.0 has launched an initiative with invisible digital watermarks on consumer goods packaging to improve the sorting accuracy and high-quality reuse of materials. Similarly, the logistics company Maersk started to develop cradle-to-cradle digital passports for its ships to identify and recycle steel components to a higher quality (Ellen MacArthur Foundation, 2021). This may also be a way forward to increase reuse and recycling for other products such as batteries. Similar to digital watermarks, DLT systems can be used to record and identify products’ material composition. Also, a material’s condition, location, or provenance can be tracked by DLT using so-called oracles which provide real-world data for assessment. Thus, DLT systems can increase transparency for market participants along entire supply chains (e.g., tamper-proof provenance, traceability, recycling quotas). In addition, DLT systems can coordinate numerous heterogeneous market participants and provide incentive mechanisms for information sharing and material recycling (Narayan and Tidström, 2020; Sunyaev et al., 2021). Using more energy-efficient consensus mechanisms and energy-saving hardware could even make DLT systems more efficient than centralised systems (Platt et al., 2021). To address current market and information failures in secondary materials markets by digital platforms, we see two major research needs. While we have developed robust knowledge on managing and governing heterogeneous actors across industries, blind spots in existing platform research exist about how to coordinate online (i.e., matchmaking) and offline (i.e., fulfilment) transactions in business-to-business (B2B) markets and how to deal with the asynchronous and spatially dispersed occurrence of supply and demand (Wilts and Berg, 2017). To date, research has focused on online-to-offline business-to-consumer (B2C) platforms. Thus, we lack an understanding of how local characteristics affect the adoption and scaling of online-to-offline B2B platforms (Li et al., 2018). We also need to understand better the effects of different operation models regarding geographical proximity of market participants and ownership. For instance, digital CE platforms that limit their operations to regional clusters (e.g., Kalundborg Symbiosis) or operate as a joint venture of participating

84  Research handbook on information systems and the environment firms could overcome problems such as information asymmetries, knowledge expropriation risks, lack of trust, or unreliable and energy-intensive long-haul logistics more effectively. On the other hand, local platforms may not scale and grow as fast as third-party CE platforms. In this respect, comparative case study research could help decision-makers in business and policy to assess the impact of geography, ownership, and recyclable material characteristics. Another major challenge is data standardisation, which currently complicates data sharing, ownership, and governance (see Section 3.3). For defining cross-industry standards, IS research is needed to avoid that power and politics delay standardisation processes and lock-in on inferior standards; e.g., regarding system architecture. Thus, analysing both standardisation processes (Roca et al., 2017) and standardisation effects on important metrics such as adoption, network externalities, costs, or social welfare is central (Liu et al., 2011; Lyytinen and King, 2006; Zhao et al., 2011). 3.2

Digital Twinning of Products and Materials

Another research opportunity that fundamentally speaks to IS scholarship is the question of how digital technologies allow natural resources or physical goods to be represented in so-called digital twins. Formally, digital twins are virtual counterparts (Dietz and Pernul, 2019) of physical and non-physical entities that capture the form, function, and operation of those entities at an unprecedented level of fidelity and timeliness. As such, they combine key representational features (e.g., real-time, data granularity) with information processing features (e.g., computational analysis of an object’s trace data), thereby offering new opportunities related to monitoring, analysis, simulation, and even control of their physical counterparts. Digital twins offer a host of affordances relevant to the implementation of a CE. For example, digital twins of products could enable companies to solve physical issues faster by detecting them sooner and allowing predictive maintenance, in turn prolonging the lifespan (i.e., reuse) of products. Digital twins during production could also help in predicting product design outcomes with a much higher degree of accuracy, leading to products that are better designed for reuse or recycling. Also, digital twins during consumption can allow recording of product location and use, facilitating product tracking, optimising reverse logistics, and maintaining compliance to CE regulations. Digital twins are by no means an emerging technology. They are in use in a variety of sectors. For example, in manufacturing digital twins are used to simulate complex products such as jet engines or large trucks in order to monitor and evaluate wear and tear and test stress levels experienced by products-in-use. These digital twins are used both for predictive maintenance and as input to future product design (Parrott and Warshaw, 2017). However, digital twins are not readily applicable to all product types consisting of any natural or composite material. To function appropriately as a virtual replica, digital twins need to be able to “tether” (Østerlie and Monteiro, 2020) – that is, connect – physical and digital dimensions of a product, its production and consumption, as seamlessly and synchronously as possible, ideally in a bilateral way (both to and from the physical object). But material restrictions limit the possibility of building and maintaining such a replica. Contrast the example of a jet engine to that of a sports shoe. Sports shoes are, by comparison to a jet engine, less complex and consist of materials such as fabric or leather for the shoe upper, and ethyl vinyl acetate and polyurethane for the soles. None of these materials by design feature electric let alone digital components. Jet engines by design comprise many materials that can be more

How to unlock the potential of information systems  85 readily or directly linked with digital components. For example, jet engines contain electric components such as turbines, afterburners, or compressors, all of which could easily connect to digital sensors or actuators. Moreover, not all forms of digital twinning are equally viable or desirable. In principle, any material composition of a physical product could be equipped with sensors and actuators at the level of each individual material component; however, the economic costs and material viability of tethering digital components required for twinning (in particular sensors and actuators) to singular components or materials involved in a product architecture hinges on the appropriate level of representational faithfulness required and the desired level of control. Neither question is trivial. Representational faithfulness of twins asks how complete, accurate, synchronous, and granular the virtual replica of some real-world object is or needs to be. For example, while it may be required for economic as well as safety reasons for the digital twin of a jet engine to transmit real-time data about each of its core components-in-use (e.g., inlets, turbines, afterburners, compressors, combustors nozzles, thrust reverses, and pumps) such that relevant controlling actions (e.g., adjustment, compensation, termination) can be performed in real time, it may neither be viable nor required to build a similarly granular and synchronous digital twin of a sports shoe. Perhaps, the replica only needs to represent the shoe as an aggregate module (e.g., the wear and tear of all soles together) and as a temporal composite (e.g., with daily or weekly changes in status). The question of the optimal balance in the design and use of digital twins to help establish a CE means reaching an equilibrium between faithful representation and appropriate control. This challenge speaks fundamentally to IS scholarship because the representation of real-world phenomena (through digital twins, for example) to then perform efficient operations on those representations (such as controlling) has been a central idea in the history of the IS field (e.g., Biller and Neuhold, 1978; Burton-Jones et al., 2017; Kent, 1978; Langefors, 1973; Recker et al., 2019; Rogers, 1986; Stamper, 1971; Suchman, 1995). Multiple opportunities exist to conduct research on digital twins in the context of a CE. Empirically, studies could be conducted to examine and compare the design and use of digital twins already available today. Such research could elicit design principles and also study the extent to which such principles favour or balance economic versus environmental (or social or other) performance implications. Theoretically, opportunities exist to develop theories of control–representation equilibriums that identify the relevant dialectics between characteristics of representation (such as completeness, accuracy, synchronicity, and granularity) and characteristics of control (such as adjustment, compensation, termination, and level or means of control), and connect the optimal configuration of these characteristics with desired performance implications across economic, environmental, and other value dimensions. Finally, in terms of design research, opportunities exist to develop both abstract design knowledge in the form of design principles that guide the development of digital twins for CE purposes as well as concrete design knowledge in the form of digital twin architecture and instantiations. 3.3

Data Sharing, Ownership, and Governance

Another research question that essentially relates to IS scholarship is what ownership and governance decisions CE companies need to make to manage reliable data sharing. To better understand circular material flows, CE relies on extensive IT-based connectivity and traceability that generate large amounts of heterogeneous data (Berg et al., 2021;

86  Research handbook on information systems and the environment Weinhardt et al., 2021). Considering the journey of one of the overly extracted materials, it becomes clear that the integrated acquisition, secure processing, and timely provision of information on the condition of materials throughout the entire product lifecycle creates data from a large number of inter-organisational actors (Khan et al., 2021). To effectively carry out CE practices, actors require sufficient and relevant product data, such as the provenance and composition of product systems, their condition, or instructions on how to disassemble them (Cong et al., 2017; Moreno et al., 2011). This information dynamically changes throughout the product lifecycle, and its availability to the different actors involved, such as producers, consumers, recyclers, and waste collectors, varies. This hampers efficient and transparent data exchange, keeps data in closed silos, and prevents companies from maximising data-based value (Parra-Moyano, 2020), which is a fundamental information challenge in a dynamic circular value network. To prevent data from remaining siloed at the corporate or departmental level, CE companies must facilitate data access to supply chain partners. However, issues around social complexities, such as poor information exchange, conflicting business interests, or low trust between actors, currently hinder networked collaboration (Fischer and Pascucci, 2017; Grant et al., 2010; Wilhelm et al., 2016) and need to be addressed to ensure first reliable data sharing and eventually concerted CE efforts. Multiple opportunities exist to conduct research on data sharing in circular value networks. One opportunity to help advance establishment of a functioning CE via data sharing is to leverage and extend IS research on data ownership and governance (Fadler and Legner, 2021; Khatri and Brown, 2010; Otto, 2011; Tallon, 2013). When designing data governance, both the locus of control and decision making are key features (Khatri and Brown, 2010). While IS literature has developed a thorough understanding of intra-organisational data governance, less is known about governing collaboration and data sharing in an inter-organisational setting (Abraham et al., 2019). Managing such data sharing requires ownership and governance decisions that maintain the trade-off between achieving scale to facilitate partners’ contributions and retaining control to prevent undesirable data use. While IS research has investigated data ownership for operational systems, where the purpose of data processing is known, the challenge of defining accountabilities for data that is stored at corporate level and used for previously unknown purposes is less addressed. As firms’ demand for data never diminishes (Watson and Mathew, 2021) and CE firms are increasingly data-driven (Berg et al., 2021; Fadler and Legner, 2021), the organisation inevitably evolves into a complex network of data producers and data consumers. In circular value networks, the distinction between the owner on the data supply side and the owner on the data demand side is blurring (Fadler and Legner, 2021). Our theoretical understanding of circular networks’ data governance is therefore very limited, and the traditional view that owner-management is the ideal governance form is no longer applicable (Schulze and Zellweger, 2021). Product system data shared across circular value networks can contain sensitive business information and valuable trade secrets (Fraccascia and Yazan, 2018), and to establish a CE, data providers would be asked to share these data with an unknown set of potentially competing actors. As data interacts with confidentiality (Parra-Moyano, 2020), assigning data ownership and governance plays an important role in managing data sharing and maintaining the trade-off between scale and control among CE companies. It is necessary to conduct research on data ownership and governance principles that help companies clarify the rights and responsibilities for decision control of data among supply chain partners. Reliable governance plays a crucial role in value

How to unlock the potential of information systems  87 creation and provides sufficient safeguard for actors in circular value networks to break down data silos, facilitate data sharing, and ultimately increase data-based value generation (Schulze and Zellweger, 2021). Another opportunity for IS research is to investigate common data standards and formats. In circular value networks, data need to be integrated from different systems to create and transfer information among partnering CE firms. If not standardised, heterogeneous actors in circular material flows follow local rules or language when providing data to the circular value network, and thereby risk the syntactic and semantic interoperability of decentralised data sources. CE data increasingly spans organisational boundaries and roles, and responsibilities extend to dispersed actors, requiring new capabilities related to common data formats and interfaces (Kerpedzhiev et al., 2020). To ensure data sharing between these actors and provide for the traceability of data provenance, agreed data formats and interfaces are mandatory. In the absence of standards that are at the same time sufficiently dynamic and adaptable to changing requirements, inefficient information exchange will jeopardise data-based value creation (Berg et al., 2021). The digital third-party market platform Cirplus (2021), for instance, connects geographically dispersed actors and provides certification and standardised data for secondary materials to overcome material (e.g., material purity) and social (e.g., trust) complexities associated with circular material flows. Another research opportunity to help advance establishment of a functioning CE via data sharing is to develop secure, decentralised data spaces. Decentralising capturing data across product, component, and raw material levels could enhance transparency in circular material flows if the data can travel virtually with the product systems across the entire lifecycle and become available to actors that require the data. Distributed ledger technology (DLT) (Beck et al., 2018) can support the design and implementation of secure CE data spaces. To name but a few, distribution and ledger sharing, openness, and transparency are properties featured in DLT implementations (Risius and Spohrer, 2017). Thus, DLT can well complement and improve data sharing by leveraging the decentralised and trusted coordination of inter-organisational process data (Kerpedzhiev et al., 2020). The Dutch start-up Circularise (2019), for instance, developed a blockchain-based decentralised communication protocol to enhance data availability and quality in circular value networks without disclosing datasets or actor identities. This solution addresses several social complexities, such as (1) fragmented product system data, (2) opaque circular value network structures, (3) non-willingness to share confidential product system data, and (4) unpredictable future data requirements. Through a so-called smart-questioning protocol, actors in need of product system data can pose questions to the entire distributed network (e.g., “Does the to-be-recycled product contain lead?”) and receive a confidence-weighted yes or no answer from the network. The data necessary for this response has been pre-recorded by data providers and verified in advance by trusted third parties. Thereby, DLT facilitates transactions and data-based value creation between heterogeneous actors in circular value networks and provides a technological pathway to decentral management of data across product, component, and raw material levels (Casado-Vara et al., 2018; Khan et al., 2021; Narayan and Tidström, 2020). Thus, secure data storage and exchange by leveraging DLT may help overcome both social and technical challenges involved in inter-organisational data sharing.

88  Research handbook on information systems and the environment 3.4

Unlocking the Potential of Digital Technologies for a CE in an Environmentally Sustainable Manner

While the three IS research streams presented above point to key functions that digital technologies can provide to enable a CE, we must not forget that digital technologies play a dual role in the relationship between IS and environmental sustainability. On the one hand, digital technologies carry potential to enable more sustainable human behaviour, such as circular practices. On the other hand, as material artefacts they are also a cause of environmental burden (Boudreau et al., 2008; Elliot, 2011). This environmental burden should not be underestimated. Recent calculations suggest that in 2020 the share of information communication technology (ICT) – that is, the use of computers and other electronic equipment and systems to collect, store, process, and transmit data electronically – in global greenhouse gas emissions ranged between 1.8 per cent and 3.9 per cent (Andrae and Edler, 2015; Belkhir and Elmeligi, 2018; Freitag et al., 2021; Malmodin and Lundén, 2018), which is comparable or even larger than the more widely discussed emission output of the aviation industry, estimated at 2.5–3.0 per cent (IEA, 2020; Our World in Data, 2020). Simultaneously, the demand for material resources required for ICT production, in particular rare-earth metals, as well as the disposal of e-waste, continue their skyrocketing growth. Annual demand for rare-earth metals doubled to 125,000 tonnes in 15 years and is expected to amount to 315,000 tonnes in 2030 (Alonso et al., 2012; Zhou et al., 2017). In view of increasing technology obsolescence, almost 45 million metric tons – equivalent to 6.1 kg per capita – of e-waste were generated globally in 2016. E-waste is expected to be the fastest-growing part of the world’s domestic waste stream with a further growth of 32 per cent by 2030 (Forti et al., 2020). Any IS research about the potential of digital technologies for a CE must therefore also reflect on possible negative environmental impacts that might arise throughout the entire lifecycle – that is, “design, production, application, operation, and disposal” (Elliot, 2011, p. 208) – of the digital technology. With regard to the three digital technologies discussed in the previous chapters, such a reflection might play out as follows. First, digital twins must be able to tether goods production and consumption as seamlessly and synchronously as possible (Østerlie and Monteiro, 2020). This tethering implies the use of digital sensors and actuators as well as appropriate data transmission, storage, and processing infrastructure, all of which require energy and material resources for production and operation and cause e-waste after disposal. Unlocking the potential of digital technologies for a CE through digital twins in an environmentally sustainable manner therefore means that the challenge of finding an equilibrium between faithful representation and appropriate control is not only one of economic or technical feasibility, but also one of ecological reason. Consider the digital twin example of a sports shoe and assume that both a QR code printed onto the shoe and a radio frequency identification (RFID) chip integrated into the shoe would be economically and technically feasible. While both could enable more efficient collection and reverse logistics processes, their ecological impact would differ, as the RFID chip required more energy and material resources during production and operation and caused more e-waste after disposal than the QR code. Therefore, when designing digital twins to support more circular material flows, questions like “What spatiotemporal data granularity is required to inform the intended circular practice?”, “How is the data captured, stored, and processed?”, and “How much energy and

How to unlock the potential of information systems  89 material resources does it take?” should be as important as “How much does it cost?” and “Does it work?” Second, digital platforms, online market platforms in particular, promise lower search costs that can mitigate market and information failures in CE markets for secondary products, components, and materials. However, research has shown that efficiency gains through digitalisation, such as lower search costs, can actually cause increased energy and resource consumption (Coroamă and Mattern, 2019; Lange et al., 2020; Rieger, 2021). It is by no means certain that more information leads to an ecologically better result. Several examples for such digital rebound effects (Coroamă and Mattern, 2019) – that is, unintended digitally induced countereffects that “reduce the potential energy [and resource] savings from improved energy [and resource] efficiency” (Sorrell, 2009, p. 1457) – exist. For instance, while e-commerce through digital platforms has been found more energy-efficient than brick-and-mortar retail due to the pooling of deliveries (Siikavirta et al., 2002), it does not apply to urban areas, where the pooled delivery with trucks still causes more emissions than customers collecting the groceries by foot and public transport (Williams and Tagami, 2002). Similarly, while digitally enabled collaborative consumption models, such as bike or car sharing, are typically lauded as environmentally friendly circular practices, scholars have repeatedly pointed out that shared products show greater wear and tear due to more careless consumption behaviour, which shortens products’ average lifetime and reduces potential material savings (Hildebrandt et al., 2018; Hollingsworth et al., 2019). Thus, future IS research could investigate how such digital rebounds can be “designed out” through deliberate design choices when building digital platforms. In a first step, different forms of digital rebounds must be empirically documented (Coroamă and Mattern, 2019; Lange et al., 2020) and underlying social and psychological mechanisms that explain these effects must be explored. In a second step, countermeasures in the form of IS design principles (Gregor and Hevner, 2013) should be discussed, implemented, and tested (Zeiss et al., 2020). Third, distributed ledger technology (DLT) could support the design and implementation of secure CE data spaces. However, while its distributed architecture offers unique capabilities for decentralised, tamper-resistant, and anonymous data sharing, it should be designed with care from an ecological perspective as its energy and resource consumption quickly outpaces that of traditional database systems (Sedlmeir et al., 2020). Briefly, a DLT’s energy and resource consumption depends on the accessibility of its architecture and the applied consensus algorithm (Sedlmeir et al., 2020). In terms of accessibility, DLT architectures can be permissioned (i.e., restricted, private access) or permissionless (i.e., unrestricted, public access). A permissionless DLT architecture, such as Bitcoin, typically contains more actors, so-called nodes, in its network than a permissioned one. As every node holds a copy of the ledger (i.e., the distributed database), the energy and resource consumption of permissionless architectures exceeds the energy and resource consumption of permissioned architectures by a magnitude due to greater data redundancy and its associated data storage and processing requirements. In terms of consensus algorithms, the so-called Proof-of-Work algorithm (i.e., a node is only allowed to write into the distributed ledger if it has solved a cryptographic, computationally intensive puzzle beforehand), which is common in permissionless DLT architectures, is by far the most energy-intensive one. Alternative algorithms, such as the “Proof-of-Stake” (i.e., the probability of a node to be allowed to write into the distributed ledger increases with the amount of the capital it possesses) or “Proof-of-Authority” (i.e., a node is allowed to write into the distributed ledger if it has been appointed authority by

90  Research handbook on information systems and the environment a voting of the entire network) algorithms, are less energy-intensive, yet still exceed the energy demands of traditional database systems (Sedlmeir et al., 2020). Therefore, while DLT has its perks when it comes to secure data spaces, it should not be considered a silver bullet. Again, it is the responsibility of the researcher to weigh in the negative environmental impact of DLT in the form of energy and material consumption, when investigating CE practices that are powered by this data sharing solution. In conclusion, research on IS for a CE is not only about maximising intended impacts positive for the environment. It is also about minimising or mitigating unintended negative impacts. So far, the latter objective has been widely ignored by our research community. With this chapter, we point to potential research opportunities that can explore and close this knowledge gap.

4.

ROADBLOCKS TO A PRODUCTIVE SYMBIOSIS OF IS–CE SCHOLARSHIP

We see three substantive challenges in doing actionable IS research on CE, which relate to (1) the heterogeneity of actors as well as (2) the interdisciplinarity and (3) the digital reality involved in researching the IS potential to a digital CE. We review each of these roadblocks that could undermine successful IS research on a digital CE and offer directions as pathways to building and evaluating the problem–solution pairing that could characterise a prolific CE–IS relationship. A first complexity stems from the heterogeneity and geographical dispersion of CE actors that extend beyond the structural boundaries of traditional supply chains. This transition from unidirectional and bilateral supply chains to multidirectional and multilateral value networks generates convoluted systems of heterogeneous and previously unrelated actors across multiple supply chains and industries. Considering the geographical dispersion of actors and the asynchronous and irregular occurrence of material supply and demand in CE value networks (Wilts and Berg, 2017), investigating CE value networks from a purely local or global perspective runs the risk of missing half of the story. On the one hand, platforms like Kalundborg Symbiosis (2019) bring together business actors located in close geographical proximity (e.g., within industrial parks) with predictable streams of by-products (Ashton, 2008; Bellantuono et al., 2017). On the other hand, digital platforms have to connect actors from different industrial sectors across larger geographical distance to develop synergies for the benefit of the global economy and environment (Aguiñaga et al., 2018; Mathews and Tan, 2011; Merli et al., 2018). In our view, IS scholars have to carefully balance between local and global solutions without losing sight of the big CE picture. Recent pleas for a consequent broadening of the IS discipline’s stakeholder scope (Clarke and Davison, 2020; Schultze et al., 2020) point to the IS potential for mastering multiple, interrelated stakeholders and exchanges. This leads us to the complexity that researching the digital CE is a true interdisciplinary challenge. Interdisciplinary collaboration has been both a buzzword and objective for many researchers for a long time. It has been practised successfully in many areas such as bioinformatics or geoinformatics. However, we think that interdisciplinarity is different in kind and in degree in the context of a digital CE. First, it connects not just two but at least three fundamentally different research fields – economics, environmental science, and IS. All three fields have different paradigm status and stand in stark contrast in terms of disciplinary core, shared

How to unlock the potential of information systems  91 assumptions, and preferred research paradigms. This forms a considerable challenge. Not only must scholars get acquainted with several large streams of literature, they must be willing and able to appreciate different research paradigms and methods, and make commensurable different assumptions and ontology and epistemology. In our view, IS scholars have to play a dual role in such collaboration. Topically, their objective is to describe and study the enabling and supplemental role of digital technologies in a CE. In a way, their role is that of a support actress, not a lead actress. But methodologically, we believe IS scholars must lead the effort to connect and integrate the views, assumptions, and approaches of the different scholars in such a collaboration. This role should be filled by IS scholars as the IS field is by comparison more diverse and inclusive of multiple research genres and traditions than economics or environmental sciences. We have come to accept and appreciate different modes and assumptions in our field, and we can demonstrate to others how different views can be reconciled, divergent assumptions be harmonised, and multiple methods be combined. Yet another complexity is that researching the digital CE involves physical as well as digital materiality perspectives. Research on the IS potential for sustainability largely takes the perspective that IS can shape the physical world (Baskerville et al., 2011). Yet, the IS research streams presented above rely on a digital reality; that is, on non-materiality of objects. The fact that the reality of a CE is first created digitally, and physical reality follows accordingly, implies a shift from a sociotechnical to a techno-social perspective (Baskerville et al., 2020). From the perspective that this ontological reversal poses new challenges for IS researchers, it may be purposeful to research how the sociotechnical systems can be complemented with a techno-social perspective to better understand assumptions about social actors, technology, and their causal interlinkage when the boundaries between the digital and the physical world dissolve (Geels, 2010; Vespignani, 2009). (Un)intended impacts on the environment and society are equally part of the sociotechnical and the techno-social perspective (Schultze et al., 2020). Future research on IS potential for sustainability transitions from the perspective of complex systems (Adepetu et al., 2014; Ketter et al., 2016; Venkatesh et al., 2016), for example, can play a key role in the effort to meet these challenges. Since the social and the technical system form an integral part of the complex CE challenge, future research needs to move beyond people- and technic-centricity and advance the understanding of their relational ontology (Schultze et al., 2020).

5. CONCLUSION Many grand challenges affecting economies, societies, and the environment strongly involve digital technologies and need attention from IS scholars (Davison and Tarafdar, 2018). Replacing the current take-make-waste economic model with a circular economic model is one of these. A CE model would enable the gradual decoupling of economic activity from the consumption of finite virgin resources and the building of economic, natural, and social capital (Ellen MacArthur Foundation, 2013). Though CE scholars have long lauded digital technologies as key enablers for transforming from a linear to a circular economic model (Antikainen et al., 2018; Casado-Vara et al., 2018; Reuter, 2016; Van Schalkwyk et al., 2018; Wilts and Berg, 2017), the IS discipline has to date not tapped the full potential of mobilising IS research on a digital CE. Against that background, we developed exemplary IS research streams in

92  Research handbook on information systems and the environment the context a digital CE. We explored the potential of digital technologies for data capture to lower information asymmetries and coordinate between CE market participants; for data representation of natural resources or physical goods to facilitate their monitoring, analysis, simulation, and control; and for data sharing to ensure efficient and transparent data exchange in circular value network. We based our research opportunities on the belief that IS can play a transformative, solution-oriented role (Corbett and Mellouli, 2017; Elliot and Webster, 2017; Hedman and Henningsson, 2016) in supporting the unlocking of the potential of digital technologies for a CE in an environmentally sustainable manner. Policymakers and market participants are well advised to capitalise on these findings and increasingly prioritise CE in their discussions. In light of the United Nations’ SDGs that claim the greening of IT, extension of IT replacement cycles, and substantial reduction of e-waste through prevention, reduction, repair, recycling, and reuse (United Nations, 2015), it seems almost imperative that organisations bring products designed for longer lifecycles to the market.

REFERENCES Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data Governance: A Conceptual Framework, Structured Review, and Research Agenda. International Journal of Information Management, 49, 424–38. https://​doi​.org/​10​.1016/​j​.ijinfomgt​.2019​.07​.008. Adepetu, A., Arnautovic, E., Svetinovic, D., & Weck, O.L. de (2014). Complex Urban Systems ICT Infrastructure Modeling: A Sustainable City Case Study. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(3), 363–74. https://​doi​.org/​10​.1109/​tsmc​.2013​.2257744. Aguiñaga, E., Henriques, I., Scheel, C., & Scheel, A. (2018). Building Resilience: A Self-Sustainable Community Approach to the Triple Bottom Line. Journal of Cleaner Production, 173, 186–96. https://​doi​.org/​10​.1016/​j​.jclepro​.2017​.01​.094. Aid, G., Eklund, M., Anderberg, S., & Baas, L. (2017). Expanding Roles for the Swedish Waste Management Sector in Inter-Organizational Resource Management. Resources, Conservation and Recycling, 124, 85–97. https://​doi​.org/​10​.1016/​j​.resconrec​.2017​.04​.007. Akerlof, G.A. (1970). The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics, 84(3), 488–500. https://​doi​.org/​10​.2307/​1879431. Alonso, E., Sherman, A.M., Wallington, T.J., Everson, M.P., Field, F.R., Roth, R., & Kirchain, R.E. (2012). Evaluating Rare Earth Element Availability: A Case with Revolutionary Demand from Clean Technologies. Environmental Science & Technology, 46(6), 3406–14. https://​doi​.org/​10​.1021/​ es203518d. Andrae, A., & Edler, T. (2015). On Global Electricity Usage of Communication Technology: Trends to 2030. Challenges, 6(1), 117–57. https://​doi​.org/​10​.3390/​challe6010117. Antikainen, M., Uusitalo, T., & Kivikytö-Reponen, P. (2018). Digitalisation as an Enabler of Circular Economy. Paper presented at the 10th Conference on Industrial Product-Service Systems (CIRP), Linköping, Sweden. Ashton, W. (2008). Understanding the Organization of Industrial Ecosystems. Journal of Industrial Ecology, 12(1), 34–51. https://​doi​.org/​10​.1111/​j​.1530​-9290​.2008​.00002​.x. Baskerville, R., Lyytinen, K., Sambamurthy, V., & Straub, D. (2011). A Response to the Design-Oriented Information Systems Research Memorandum. European Journal of Information Systems, 20(1), 11–15. https://​doi​.org/​10​.1057/​ejis​.2010​.56. Baskerville, R.L., Myers, M.D., & Yoo, Y. (2020). Digital First: The Ontological Reversal and New Challenges for IS Research. MIS Quarterly, 44(2), 509–23. https://​doi​.org/​10​.25300/​misq/​2020/​ 14418. Bauwens, T., Hekkert, M., & Kirchherr, J. (2020). Circular Futures: What Will They Look Like? Ecological Economics, 175, 1–14. https://​doi​.org/​10​.1016/​j​.ecolecon​.2020​.106703.

How to unlock the potential of information systems  93 Beck, R., Müller-Bloch, C., & King, J. (2018). Governance in the Blockchain Economy: A Framework and Research Agenda. Journal of the Association for Information Systems, 9(10), 1020–1034. https://​ doi​.org/​10​.17705/​1jais​.00518. Belkhir, L., & Elmeligi, A. (2018). Assessing ICT Global Emissions Footprint: Trends to 2040 & Recommendations. Journal of Cleaner Production, 177, 448–63. https://​doi​.org/​10​.1016/​j​.jclepro​ .2017​.12​.239. Bellantuono, N., Carbonara, N., & Pontrandolfo, P. (2017). The Organization of Eco-Industrial Parks and Their Sustainable Practices. Journal of Cleaner Production, 161, 362–75. https://​doi​.org/​10​.1016/​ j​.jclepro​.2017​.05​.082. Berg, H., Bendix, P., Jansen, M., Le Blévennec, K., Bottermann, P., & Magnus-Melgar, M. (2021). Unlocking the Potential of Industry 4.0 to Reduce the Environmental Impact of Production. Mol: European Environment Agency. Biller, H., & Neuhold, E.J. (1978). Semantics of Data Bases: The Semantics of Data Models. Information Systems, 3(1), 11–30. Blomsma, F., & Brennan, G. (2017). The Emergence of Circular Economy: A New Framing Around Prolonging Resource Productivity. Journal of Industrial Ecology, 21(3), 603–14. https://​doi​.org/​10​ .1111/​jiec​.12603. Bocken, N.M.P., Olivetti, E.A., Cullen, J.M., Potting, J., & Lifset, R. (2017). Taking the Circularity to the Next Level: A Special Issue on the Circular Economy. Journal of Industrial Ecology, 21(3), 476–82. https://​doi​.org/​10​.1111/​jiec​.12606. Boudreau, M.-C., Chen, A., & Huber, M. (2008). Green IS: Building Sustainable Business Practices. In R.T. Watson (Ed.), Information Systems (pp. 1–17). Athens: Global Text Project. Boulding, K.E. (1966). The Economics of the Coming Spaceship Earth. In H. Jarrett (Ed.), Environmental Quality in a Growing Economy (pp. 3–14). Baltimore, MD: Johns Hopkins University Press. Bressanelli, G., Adrodegari, F., Perona, M., & Saccani, N. (2018). Exploring How Usage-Focused Business Models Enable Circular Economy through Digital Technologies. Sustainability, 10(3), 1–10. https://​doi​.org/​10​.3390/​su10030639. Burger, C., Kalverkamp, M., & Pehlken, A. (2018). Decision Making and Software Solutions with Regard to Waste Management. Journal of Cleaner Production, 205, 210–225. https://​doi​.org/​10​.1016/​ j​.jclepro​.2018​.09​.093. Burton-Jones, A., Recker, J., Indulska, M., Green, P., & Weber, R. (2017). Assessing representation theory with a framework for pursuing success and failure. MIS Quarterly, 41(4), 1307–33. Casado-Vara, R., Prieto, J., La Prieta, F.D., & Corchado, J.M. (2018). How Blockchain Improves the Supply Chain: Case Study Alimentary Supply Chain. Procedia Computer Science, 134, 393–8. https://​ doi​.org/​10​.1016/​j​.procs​.2018​.07​.193. CE Stakeholder EU (2020). Financing the Circular Economy. https://​ circulareconomy​ .europa​ .eu/​ platform/​en/​financing​-circular​-economy. Circular Economy Action Plan (2020). Circular Economy Action Plan: The EU’s New Circular Action Plan Paves the Way for a Cleaner and More Competitive Europe. https://​ec​.europa​.eu/​environment/​ strategy/​circular​-economy​-action​-plan​_de. Circularise (2019). Transparency and Traceability for a Circular Economy. https://​www​.circularise​.com/​. Cirplus (2021). Cirplus Is Setting Standards for Recycled Plastics and You Can Be Part of It. https://​blog​.cirplus​.com/​cirplus​-is​-setting​-standards​-for​-recycled​-plastic​-and​-you​-can​-be​-part​-of​-it​ -50974ce2bdcf. Clarke, R., & Davison, R.M. (2020). Research Perspectives: Through Whose Eyes? The Critical Concept of Researcher Perspective. Journal of the Association for Information Systems, 21(2), 483–501. https://​doi​.org/​10​.17705/​1jais​.00609. Cong, L., Zhao, F., & Sutherland, J.W. (2017). Integration of Dismantling Operations into a Value Recovery Plan for Circular Economy. Journal of Cleaner Production, 149, 378–86. https://​doi​.org/​10​ .1016/​j​.jclepro​.2017​.02​.115. Corbett, J., & Mellouli, S. (2017). Winning the SDG Battle in Cities: How an Integrated Information Ecosystem can Contribute to the Achievement of the 2030 Sustainable Development Goals. Information Systems Journal, 27(4), 427–61. https://​doi​.org/​10​.1111/​isj​.12138.

94  Research handbook on information systems and the environment Coroamă, V.C., & Mattern, F. (2019). Digital Rebound: Why Digitalization Will Not Redeem Us Our Environmental Sins. Paper presented at the Proceedings of the 6th International Conference on ICT for Sustainability, Lappeenranta, Finland. Cusumano, M.A., Yoffie, D.B., & Gawer, A. (2019). The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power. New York, NY: Harper Business. Davison, R.M., & Tarafdar, M. (2018). Shifting Baselines in Information Systems Research Threaten Our Future Relevance. Information Systems Journal, 28(4), 587–91. https://​doi​.org/​10​.1111/​isj​.12197. Den Hollander, M.C., Bakker, C.A., & Hultink, E.J. (2017). Product Design in a Circular Economy: Development of a Typology of Key Concepts and Terms. Journal of Industrial Ecology, 21(3), 517–25. https://​doi​.org/​10​.1111/​jiec​.12610. Derler, P., Lee, E.A., & Vincentelli, A.S. (2012). Modeling Cyber–Physical Systems. Proceedings of the IEEE, 100(1), 13–28. https://​doi​.org/​10​.1109/​JPROC​.2011​.2160929. Dietz, M., & Pernul, G. (2019). Digital Twin: Empowering Enterprises Towards a System-of-Systems Approach. Business & Information Systems Engineering, 62(2), 179–84. https://​doi​.org/​10​.1007/​ s12599​-019​-00624​-0. Dimoka, A., Hong, Y., & Pavlou, P.A. (2012). On Product Uncertainty in Online Markets: Theory and Evidence. MIS Quarterly, 36(2), 395–426. https://​doi​.org/​10​.2307/​41703461. Donkey Republic (2019). Never Be Without a Bike. https://​www​.donkey​.bike/​. EEA (2019). Paving the Way for a Circular Economy: Insights on Status and Potentials. https://​www​ .eea​.europa​.eu/​publications/​circular​-economy​-in​-europe​-insights. Ellen MacArthur Foundation (2013). Towards the Circular Economy: An Economic and Business Rationale for an Accelerated Transition. Cowes: Ellen MacArthur Foundation. Ellen MacArthur Foundation (2016). Intelligent Assets: Unlocking the Circular Economy Potential. Cowes: Ellen MacArthur Foundation. Ellen MacArthur Foundation (2021). Using Product Passports to Improve the Recovery and Reuse of Shipping Steel: Maersk Line. https://​elle​nmacarthur​foundation​.org/​circular​-examples/​using​-product​ -passports​-to​-improve​-the​-recovery​-and​-reuse​-of​-shipping​-steel. Elliot, S. (2011). Transdisciplinary Perspectives on Environmental Sustainability: A Resource Base and Framework for IT-enabled Business Transformation. MIS Quarterly, 35(1), 197–236. https://​doi​.org/​ 10​.2307/​23043495. Elliot, S., & Webster, J. (2017). Editorial: Special Issue on Empirical Research on Information Systems Addressing the Challenges of Environmental Sustainability: An Imperative for Urgent Action. Information Systems Journal, 27(4), 367–78. https://​doi​.org/​10​.1111/​isj​.12150. European Commission (2019). The European Green Deal Sets out How to Make Europe the First Climate-Neutral Continent by 2050, Boosting the Economy, Improving People’s Health and Quality of Life, Caring for Nature, and Leaving No One Behind [Press release]. Extinction Rebellion (2021). Join the Rebellion! https://​rebellion​.global/​. Fadler, M., & Legner, C. (2021). Data Ownership Revisited: Clarifying Data Accountabilities in Times of Big Data and Analytics. Journal of Business Analytics, 5(1), 123–39. https://​doi​.org/​10​.1080/​ 2573234x​.2021​.1945961. Fischer, A., & Pascucci, S. (2017). Institutional Incentives in Circular Economy Transition: The Case of Material Use in the Dutch Textile Industry. Journal of Cleaner Production, 155, 17–32. https://​doi​ .org/​10​.1016/​j​.jclepro​.2016​.12​.038. Forti, V., Baldé, C.P., Kuehr, R., & Bel, G. (2020). The Global E-Waste Monitor 2020: Quantities, Flows, and the Circular Economy Potential. Bonn/Geneva/Rotterdam: United Nations University (UNU)/ United Nations Institute for Training and Research (UNITAR) – co-hosted SCYCLE Programme, International Telecommunication Union (ITU) & International Solid Waste Association (ISWA). Fraccascia, L., & Yazan, D.M. (2018). The Role of Online Information-Sharing Platforms on the Performance of Industrial Symbiosis Networks. Resources, Conservation and Recycling, 136, 473–85. https://​doi​.org/​10​.1016/​j​.resconrec​.2018​.03​.009. Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G.S., & Friday, A. (2021). The Real Climate and Transformative Impact of ICT: A Critique of Estimates, Trends, and Regulations. Patterns, 2(9), 1–18. https://​doi​.org/​10​.1016/​j​.patter​.2021​.100340.

How to unlock the potential of information systems  95 French, A.M., & Shim, J.P. (2016). The Digital Revolution: Internet of Things, 5G and Beyond. Communications of the Association for Information Systems, 38, 840–850. https://​doi​.org/​10​.17705/​ 1cais​.03840. Fridays for Future (2020). School Strike for Climate. https://​fridaysforfuture​.se/​en/​. Geels, F.W. (2010). Ontologies, Socio-Technical Transitions (to Sustainability), and the Multi-Level Perspective. Research Policy, 39(4), 495–510. https://​doi​.org/​10​.1016/​j​.respol​.2010​.01​.022. Geissdoerfer, M., Morioka, S.N., Carvalho, M.M. de, & Evans, S. (2018). Business Models and Supply Chains for the Circular Economy. Journal of Cleaner Production, 190, 712–21. https://​doi​.org/​10​ .1016/​j​.jclepro​.2018​.04​.159. Geissdoerfer, M., Savaget, P., Bocken, N.M.P., & Hultink, E.J. (2017). The Circular Economy: A New Sustainability Paradigm? Journal of Cleaner Production, 143, 757–68. https://​doi​.org/​10​.1016/​j​ .jclepro​.2016​.12​.048. Ghisellini, P., Cialani, C., & Ulgiati, S. (2016). A Review on Circular Economy: The Expected Transition to a Balanced Interplay of Environmental and Economic Systems. Journal of Cleaner Production, 114, 11–32. https://​doi​.org/​10​.1016/​j​.jclepro​.2015​.09​.007. Gholami, R., Watson, R.T., Hasan, H., Molla, A., & Bjørn-Andersen, N. (2016). Information Systems Solutions for Environmental Sustainability: How Can We Do More? Journal of the Association for Information Systems, 17(8), 521–36. https://​doi​.org/​10​.17705/​1jais​.00435. Golev, A., Corder, G.D., & Giurco, D.P. (2015). Barriers to Industrial Symbiosis: Insights from the Use of a Maturity Grid. Journal of Industrial Ecology, 19(1), 141–53. https://​doi​.org/​10​.1111/​jiec​.12159. Grant, G.B., Seager, T.P., Massard, G., & Nies, L. (2010). Information and Communication Technology for Industrial Symbiosis. Journal of Industrial Ecology, 14(5), 740–753. https://​doi​.org/​10​.1111/​j​ .1530​-9290​.2010​.00273​.x. Gregor, S., & Hevner, A.R. (2013). Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly, 37(2), 337–55. https://​doi​.org/​10​.2307/​43825912. Grover, V., & Lyytinen, K. (2015). New State of Play in Information Systems Research: The Push to the Edges. MIS Quarterly, 39(2), 271–96. https://​doi​.org/​10​.25300/​misq/​2015/​39​.2​.01. Gurewitsch, S. (Ed.) (2015). Growth Within: A Circular Economy Vision for a Competitive Europe. Cowes: Ellen MacArthur Foundation and McKinsey Center for Business and Environment. Haas, W., Krausmann, F., Wiedenhofer, D., & Heinz, M. (2015). How Circular is the Global Economy? An Assessment of Material Flows, Waste Production, and Recycling in the European Union and the World in 2005. Journal of Industrial Ecology, 19(5), 765–77. https://​doi​.org/​10​.1111/​jiec​.12244. Hedman, J., & Henningsson, S. (2016). Developing Ecological Sustainability: A Green IS Response Model. Information Systems Journal, 26(3), 259–87. https://​doi​.org/​10​.1111/​isj​.12095. Hildebrandt, B., Hanelt, A., & Firk, S. (2018). Sharing Yet Caring. Business & Information Systems Engineering, 60(3), 227–41. https://​doi​.org/​10​.1007/​s12599​-018​-0532​-6. Hollingsworth, J., Copeland, B., & Johnson, J.X. (2019). Are E-Scooters Polluters? The Environmental Impacts of Shared Dockless Electric Scooters. Environmental Research Letters, 14(8). https://​doi​.org/​ 10​.1088/​1748–9326/​ab2da8. IEA (2020). Aviation: More Efforts Needed. https://​www​.iea​.org/​reports/​aviation. IPCC (2021). Climate Change 2021: The Physical Science Basis. https://​www​.ipcc​.ch/​report/​ar6/​wg1/​. Jesus, A. de, & Mendonça, S. (2018). Lost in Transition? Drivers and Barriers in the Eco-Innovation Road to the Circular Economy. Ecological Economics, 145, 75–89. https://​doi​.org/​10​.1016/​j​.ecolecon​ .2017​.08​.001. Kalundborg Symbiosis (2019). Kalundborg Symbiosis. http://​www​.symbiosis​.dk/​en/​. Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. Washington, DC: World Bank. Kent, W. (1978). Data and Reality. New York, NY: North-Holland. Kerpedzhiev, G.D., König, U.M., Röglinger, M., & Rosemann, M. (2020). An Exploration into Future Business Process Management Capabilities in View of Digitalization. Business & Information Systems Engineering, 63, 83–96. https://​doi​.org/​10​.1007/​s12599​-020​-00637​-0. Ketter, W., Peters, M., Collins, J., & Gupta, A. (2016). Competitive Benchmarking: An IS Research Approach to Address Wicked Problems With Big Data and Analytics. MIS Quarterly, 40(4), 1057–89. https://​doi​.org/​10​.2139/​ssrn​.2700333.

96  Research handbook on information systems and the environment Khan, S.A.R., Razzaq, A., Yu, Z., & Miller, S. (2021). Industry 4.0 and Circular Economy Practices: A New Era Business Strategies for Environmental Sustainability. Business Strategy and the Environment, 30(8), 4001–14. https://​doi​.org/​10​.1002/​bse​.2853. Khatri, V., & Brown, C.V. (2010). Designing Data Governance. Communications of the ACM, 53(1), 148–52. https://​doi​.org/​10​.1145/​1629175​.1629210. Kirchherr, J., Piscicelli, L., Bour, R., Kostense-Smit, E., Muller, J., Huibrechtse-Truijens, A., & Hekkert, M. (2018). Barriers to the Circular Economy: Evidence From the European Union (EU). Ecological Economics, 150, 264–72. https://​doi​.org/​10​.1016/​j​.ecolecon​.2018​.04​.028. Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the Circular Economy: An Analysis of 114 Definitions. Resources, Conservation and Recycling, 127, 221–32. https://​doi​.org/​10​.1016/​j​ .resconrec​.2017​.09​.005. Kristoffersen, E., Mikalef, P., Blomsma, F., & Li, J. (2021). The Effects of Business Analytics Capability on Circular Economy Implementation, Resource Orchestration Capability, and Firm Performance. International Journal of Production Economics, 239, 108205. https://​doi​.org/​10​.1016/​j​.ijpe​.2021​ .108205. Kuruzovich, J., Viswanathan, S., & Agarwal, R. (2010). Seller Search and Market Outcomes in Online Auctions. Management Science, 56(10), 1702–17. https://​doi​.org/​10​.1287/​mnsc​.1100​.1216. Lange, S., Pohl, J., & Santarius, T. (2020). Digitalization and Energy Consumption. Does ICT Reduce Energy Demand? Ecological Economics, 176, 106760. https://​doi​.org/​10​.1016/​j​.ecolecon​.2020​ .106760. Langefors, B. (1973). Theoretical Analysis of Information Systems. Lund: Studentlitteratur. Lazarevic, D., & Valve, H. (2017). Narrating Expectations for the Circular Economy: Towards a Common and Contested European Transition. Energy Research & Social Science, 31, 60–69. https://​ doi​.org/​10​.1016/​j​.erss​.2017​.05​.006. Li, H., Shen, Q., & Bart, Y. (2018). Local Market Characteristics and Online-to-Offline Commerce: An Empirical Analysis of Groupon. Management Science, 64(4), 1860–1878. https://​doi​.org/​10​.1287/​ mnsc​.2016​.2666. Lindhqvist, T., & Lidgren, K. (1990). Modeller för Förlängt producentansvar [Model for extended producer responsibility]. In Ministry of the Environment, Från vaggan till graven - sex studier av varors miljöpåverkan [From the Cradle to the Grave: Six Studies of the Environmental Impact of Products] (pp. 7–44). Stockholm: Ministry of the Environment. Liu, C.Z., Gal-Or, E., Kemerer, C.F., & Smith, M.D. (2011). Compatibility and Proprietary Standards: The Impact of Conversion Technologies in IT Markets with Network Effects. Information Systems Research, 22(1), 188–207. https://​doi​.org/​10​.1287/​isre​.1090​.0255. Lüdeke-Freund, F., Gold, S., & Bocken, N.M.P. (2019). A Review and Typology of Circular Economy Business Model Patterns. Journal of Industrial Ecology, 23(1), 36–61. https://​doi​.org/​10​.1111/​jiec​ .12763. Lyytinen, K., & King, J.L. (2006). Standard Making: A Critical Research Frontier for Information Systems Research. MIS Quarterly, 30, 405–11. https://​doi​.org/​10​.2307/​25148766. Malhotra, A., Melville, N.P., & Watson, R.T. (2013). Spurring Impactful Research on Information Systems for Environmental Sustainability. MIS Quarterly Special Issue: IS & Environmental Sustainability, 37(4), 1265–74. https://​doi​.org/​10​.25300/​misq/​2013/​37:​4​.3. Malmodin, J., & Lundén, D. (2018). The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010–2015. Sustainability, 10(9), 3027. https://​doi​.org/​10​.3390/​su10093027. Mathews, J.A., & Tan, H. (2011). Progress Toward a Circular Economy in China. Journal of Industrial Ecology, 15(3), 435–57. https://​doi​.org/​10​.1111/​j​.1530–9290​.2011​.00332​.x. McDowall, W., Geng, Y., Huang, B., Barteková, E., Bleischwitz, R., Türkeli, S., … Doménech, T. (2017). Circular Economy Policies in China and Europe. Journal of Industrial Ecology, 21(3), 651–61. https://​doi​.org/​10​.1111/​jiec​.12597. Merli, R., Preziosi, M., & Acampora, A. (2018). How Do Scholars Approach the Circular Economy? A Systematic Literature Review. Journal of Cleaner Production, 178, 703–22. https://​doi​.org/​10​ .1016/​j​.jclepro​.2017​.12​.112. Microsoft (2019). Microsoft Dynamics NAV. https://​dynamics​.microsoft​.com/​en​-us/​nav​-overview/​.

How to unlock the potential of information systems  97 Moreno, A., Cappellaro, F., Masoni, P., & Amato, A. (2011). Application of Product Data Technology Standards to LCA Data. Journal of Industrial Ecology, 15(4), 483–95. https://​doi​.org/​10​.1111/​j​.1530​ -9290​.2011​.00353​.x. Narayan, R., & Tidström, A. (2020). Tokenizing Coopetition in a Blockchain for a Transition to Circular Economy. Journal of Cleaner Production, 263, 121437. https://​doi​.org/​10​.1016/​j​.jclepro​ .2020​.121437. OECD (2006). Improving Recycling Markets. Paris: OECD. OECD (2019). Global Material Resources Outlook to 2060: Economic Drivers and Environmental Consequences. Paris: OECD. Østerlie, T., & Monteiro, E. (2020). Digital Sand: The Becoming of Digital Representations. Information and Organization, 30(1), 100275. https://​doi​.org/​10​.1016/​j​.infoandorg​.2019​.100275. Otto, B. (2011). Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers. Communications of the Association for Information Systems, 29. https://​doi​.org/​10​.17705/​1cais​.02903. Our World in Data (2020). Climate Change and Flying: What Share of Global CO2 Emissions Come from Aviation? https://​ourworldindata​.org/​co2​-emissions​-from​-aviation. Parra-Moyano, J. (2020). What Managers Need to Know About Data Exchanges. MIT Sloan Management Review, 61(4), 39–44. https://​doi​.org/​10​.2139/​ssrn​.3670680. Parrott, A., & Warshaw, L. (2017). Industry 4.0 and the Digital Twin: Manufacturing Meets Its Match. https://​www2​.deloitte​.com/​content/​dam/​Deloitte/​kr/​Documents/​insights/​deloitte​-newsletter/​2017/​26​ _201706/​kr​_insights​_deloitte​-newsletter​-26​_report​_02​_en​.pdf. Pavlou, P., & Dimoka, A. (2008). Understanding and Mitigating Product Uncertainty in Online Auction Marketplaces. 2008 Industry Studies Conference Paper. SSRN Electronic Journal. https://​doi​.org/​10​ .2139/​ssrn​.1135006. Pavlou, P.A., Liang, H., & Xue, Y. (2007). Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal-Agent Perspective. MIS Quarterly, 31(1), 105–36. https://​doi​ .org/​10​.2307/​25148783. Platt, M., Sedlmeir, J., Platt, D., Tasca, P., Xu, J., Vadgama, N., & Ibañez, J.I. (2021). Energy Footprint of Blockchain Consensus Mechanisms Beyond Proof-of-Work. arXiv. https://​doi​.org/​arXiv:​2109​ .03667. Potting, J., Hekkert, M., Worrell, E., & Hanemaaijer, A. (2017). Circular Economy: Measuring Innovation in the Product Chain. The Hague: PBL. Prendeville, S., & Bocken, N.M.P. (2017). Design for Remanufacturing and Circular Business Models. In M. Matsumoto, K. Masui, S. Fukushige, & S. Kondoh (Eds.), Sustainability Through Innovation in Product Life Cycle Design (pp. 269–83). Singapore: Springer. Ranta, V., Aarikka-Stenroos, L., Ritala, P., & Mäkinen, S.J. (2018). Exploring Institutional Drivers and Barriers of the Circular Economy: A Cross-Regional Comparison of China, the US, and Europe. Resources, Conservation and Recycling, 135, 70–82. https://​doi​.org/​10​.1016/​j​.resconrec​.2017​.08​.017. Recker, J., Indulska, M., Green, P., Burton-Jones, A., & Weber, R. (2019). Information Systems as Representations: A Review of the Theory and Evidence. Journal of the Association for Information Systems, 20(6), 736–86. Reike, D., Vermeulen, W.J.V., & Witjes, S. (2018). The Circular Economy: New or Refurbished as CE 3.0? Exploring Controversies in the Conceptualization of the Circular Economy Through a Focus on History and Resource Value Retention Options. Resources, Conservation and Recycling, 135, 246–64. https://​doi​.org/​10​.1016/​j​.resconrec​.2017​.08​.027. Reuter, M.A. (2016). Digitalizing the Circular Economy. Metallurgical and Materials Transactions B, 47(6), 3194–3220. https://​doi​.org/​10​.1007/​s11663​-016​-0735​-5. Rieger, A. (2021). Does ICT Result in Dematerialization? The Case of Europe, 2005–2017. Environmental Sociology, 7(1), 64–75. https://​doi​.org/​10​.1080/​23251042​.2020​.1824289. Risius, M., & Spohrer, K. (2017). A Blockchain Research Framework. Business & Information Systems Engineering, 59(6), 385–409. https://​doi​.org/​10​.1007/​s12599​-017​-0506​-0. Roca, J.B., Vaishnav, P., Morgan, M.G., Mendonça, J., & Fuchs, E. (2017). When Risks Cannot Be Seen: Regulating Uncertainty in Emerging Technologies. Research Policy, 46(7), 1215–33. https://​doi​.org/​ 10​.1016/​j​.respol​.2017​.05​.010.

98  Research handbook on information systems and the environment Rogers, Y. (1986). Pictorial Representations of Abstract Concepts Relating to Human-Computer Interaction. ACM SIGCHI Bulletin, 18(2), 43–44. SAP (2019). Waste Management (EHS-WA). https://​help​.sap​.com/​viewer/​8a3​99f5444164​440ac4f426​ e847ec901/​6​.18​.11/​en​-US/​75​67bd534f22​b44ce10000​000a174cb4​.html. Schroeder, P., Anggraeni, K., & Weber, U. (2018). The Relevance of Circular Economy Practices to the Sustainable Development Goals. Journal of Industrial Ecology, 22, 1–19. https://​doi​.org/​10​.1111/​jiec​ .12732. Schultze, U., Van den Heuvel, G., & Niemimaa, M. (2020). Enacting Accountability in IS Research After the Sociomaterial Turn. Journal of the Association for Information Systems, 21(4). https://​doi​.org/​10​ .17705/​1jais​.00620. Schulze, W., & Zellweger, T. (2021). Property Rights, Owner-Management, and Value Creation. Academy of Management Review, 46(3), 489–511. https://​doi​.org/​10​.5465/​amr​.2018​.0377. Sedlmeir, J., Buhl, H.U., Fridgen, G., & Keller, R. (2020). The Energy Consumption of Blockchain Technology: Beyond Myth. Business & Information Systems Engineering, 62, 599–608. https://​doi​ .org/​10​.1007/​s12599​-020​-00656​-x. Seidel, S., Bharati, P., Fridgen, G., Watson, R.T., Albizri, A., Boudreau, M.-C.M., … Watts, S. (2017). The Sustainability Imperative in Information Systems Research. Communications of the Association for Information Systems, 40(3), 40–52. https://​doi​.org/​10​.17705/​1CAIS​.04003. Shen, L., & Worrell, E. (2014). Plastic Recycling. In E. Worrell & M.A. Reuter (Eds.), Handbook of Recycling (pp. 179–90). Boston, MA: Elsevier. Siikavirta, H., Punakivi, M., Kärkkäinen, M., & Linnanen, L. (2002). Effects of E-Commerce on Greenhouse Gas Emissions: A Case Study of Grocery Home Delivery in Finland. Journal of Industrial Ecology, 6(2), 83–97. https://​doi​.org/​10​.1162/​108819802763471807. Sorrell, S. (2009). Jevons’ Paradox Revisited: The Evidence for Backfire from Improved Energy Efficiency. Energy Policy, 37(4), 1456–69. SPREAD (2019). Your Product Intelligence Platform. https://​spread​.ai/​. Stahel, W.R., & Reday-Mulvey, G. (1981). Jobs for Tomorrow: The Potential for Substituting Manpower for Energy. New York, NY: Vantage Press. Stamper, R.K. (1971). Some Ways of Measuring Information. Computer Bulletin, 15(12), 432–6. Suchman, L.A. (1995). Making Work Visible. Communications of the ACM, 38(9), 56–64. Sunyaev, A., Kannengießer, N., Beck, R., Treiblmaier, H., Lacity, M., Kranz, J., … Luckow, A. (2021). Token Economy. Business & Information Systems Engineering. https://​doi​.org/​10​.1007/​s12599​-021​ -00684​-1. Tallon, P.P. (2013). Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost. Computer, 46(6), 32–8. https://​doi​.org/​10​.1109/​MC​.2013​.155. Tunn, V.S.C., Bocken, N.M.P., Van den Hende, E.A., & Schoormans, J.P.L. (2019). Business Models for Sustainable Consumption in the Circular Economy: An Expert Study. Journal of Cleaner Production, 212, 324–33. https://​doi​.org/​10​.1016/​j​.jclepro​.2018​.11​.290. United Nations (2015). Sustainable Development Goals. http://​www​.un​.org/​sus​tainablede​velopment/​ sustainable​-development​-goals/​. United Nations (2019). Ensure Sustainable Consumption and Production Patterns. https://​unstats​.un​.org/​ sdgs/​report/​2019/​goal​-12/​. Van Schalkwyk, R.F., Reuter, M.A., Gutzmer, J., & Stelter, M. (2018). Challenges of Digitalizing the Circular Economy: Assessment of the State-of-the-Art of Metallurgical Carrier Metal Platform for Lead and its Associated Technology Elements. Journal of Cleaner Production, 186, 585–601. https://​ doi​.org/​10​.1016/​j​.jclepro​.2018​.03​.111. Venkatesh, V., Rai, A., Sykes, T.A., & Aljafari, R. (2016). Combating Infant Mortality in Rural India: Evidence From a Field Study of E-Health Kiosk Implementations. MIS Quarterly, 40(2), 353–80. https://​doi​.org/​10​.25300/​misq/​2016/​40​.2​.04. Vespignani, A. (2009). Predicting the Behavior of Techno-Social Systems. Science, 325(5939), 425–28. https://​doi​.org/​10​.1126/​science​.1171990. Vial, G. (2019). Understanding Digital Transformation: A Review and a Research Agenda. Journal of Strategic Information Systems, 28(2), 118–44. https://​doi​.org/​10​.1016/​j​.jsis​.2019​.01​.003. Watson, R.T., & Mathew, S.K. (2021). Capital, Systems, and Objects: The Foundation and Future of Organizations from a South Asian Perspective. Singapore: Springer Nature.

How to unlock the potential of information systems  99 Weinhardt, C., Peukert, C., Hinz, O., & Van der Aalst, W.M. (2021). Welcome to Economies in IS! On the Plethora of IT-Enabled Economies. Business & Information Systems Engineering, 63(4), 325–8. https://​doi​.org/​10​.1007/​s12599​-021​-00705​-z. Wilhelm, M.M., Blome, C., Bhakoo, V., & Paulraj, A. (2016). Sustainability in Multi-Tier Supply Chains: Understanding the Double Agency Role of the First-Tier Supplier. Journal of Operations Management, 41, 42–60. https://​doi​.org/​10​.1016/​j​.jom​.2015​.11​.001. Williams, E., & Tagami, T. (2002). Energy Use in Sales and Distribution via E-Commerce and Conventional Retail: A Case Study of the Japanese Book Sector. Journal of Industrial Ecology, 6(2), 99–114. https://​doi​.org/​10​.1162/​108819802763471816. Wilts, H., & Berg, H. (2017). The Digital Circular Economy: Can the Digital Transformation Pave the Way for Resource-Efficient Materials Cycles? International Journal of Environmental Sciences & Natural Resources, 7(5), 555725. Zeiss, R., Ixmeier, A., Recker, J., & Kranz, J. (2020). Mobilising Information Systems Scholarship for a Circular Economy: Review, Synthesis, and Directions for Future Research. Information Systems Journal, 31(1), 148–83. https://​doi​.org/​10​.1111/​isj​.12305. Zhang, H. (2016). Planet of the Things. Computer Fraud & Security, 2016(3), 16–17. https://​doi​.org/​10​ .1016/​S1361​-3723(16)30027​-6. Zhao, K., Xia, M., & Shaw, M.J. (2011). What Motivates Firms to Contribute to Consortium-Based E-Business Standardization? Journal of Management Information Systems, 28(2), 305–34. https://​doi​ .org/​10​.2753/​MIS0742​-1222280211. Zhou, B., Li, Z., & Chen, C. (2017). Global Potential of Rare Earth Resources and Rare Earth Demand from Clean Technologies. Minerals, 7(203), 1–14. https://​doi​.org/​10​.3390/​min7110203.

6. From environmental towards sustainability management information systems Tyge-F. Kummer and Kenan Degirmenci

1. INTRODUCTION Information technology (IT) and related information systems (IS), as socio-technical systems utilising IT, contribute to environmental degradation as they consume resources and cause waste and emissions (Vom Brocke et al., 2013). However, the continuously growing stream of Green IT/IS research explores how IS can reduce the environmental footprint as they enable sustainable processes, services and products (Greengard, 2013; Kurp, 2008; Melville, 2010; Watson et al., 2012). While these approaches appear effective regarding their intention to reduce the environmental footprint (Cooper & Molla, 2017; Gholami et al., 2016; Hu et al., 2016; Leidner et al., 2022; Loeser et al., 2017; Watson et al., 2010; Yang et al., 2020), it is this focus that could limit their applicability in the real world as companies might regard ambitions to reduce environmental degradation via Green IS as expensive. While research in relation to Green IT often shows direct reductions in relation to energy or waste costs (Blevis, 2008; Jenkin et al., 2011; Watson et al., 2008), this does not necessarily count for Green IS. Cost effects in relation to Green IS are rather indirect due to required investments in IS and its deployment, use and management (Loeser et al., 2017). For instance, collaborative group software and telepresence systems to enable remote meetings and reduce travelling; environmental IS to track and monitor environmental variables such as waste, emissions, water consumption and carbon footprints; and supply chain systems to optimise logistics and reduce transportation efforts that in turn reduce the environmental impact (Watson et al., 2008). Related organisational initiatives to measure and reduce negative environmental effects may influence financial data, and can have implications on local communities as well. For instance, a reduction of water consumption can reduce costs while a reduction of emissions could benefit the health of workers and local residents. However, little is known about these interactions between the dimensions of the triple bottom line (TBL), and how they can be utilised strategically (Lago et al., 2015). In this chapter, we build up on the TBL and accounting framework that shapes current sustainability reporting and has a wider focus including three perspectives (environmental, social and economic) (Elkington, 2004). We then review sustainability reports and related media coverage from Australian Securities Exchange (ASX)-listed organisations and develop a mapping to classify TBL interactions to develop a better understanding of the relationships between the dimensions of the TBL. Real-world examples are used to illustrate the application of the mapping in different scenarios. We then review strategic management information systems (SMIS) solutions from leading sustainability software providers in order to examine to what extent they could utilise the suggested mapping. We derive recommendations for organisations as well as SMIS developers in order to enhance sustainability management and pave the 100

From environmental towards sustainability management systems  101 way towards a more comprehensive understanding of this topic. To that end, we argue that SMIS can serve as a strategic tool to enrich existing organisational sustainability management.

2.

THE LINK BETWEEN SUSTAINABILITY REPORTING AND SMIS

2.1

Foundations of Sustainability Reporting

In recent years, concerns within the international community regarding the social and environmental impact of organisational activities has increased (Kend, 2015; Muttakin & Subramaniam, 2015). A sustainability report conveys disclosures on the organisation’s positive and negative impacts on the environment, society and the economics that result from its activities (Global Reporting Initiative, 2015). Sustainability reporting plays an integral role in helping the entity understand, measure and communicate its environmental, economic, social and governance impact in the long term (White, 2009). It is important due to its ability to embrace the social activities of the business that promote the interest of the public in terms of financial growth and economic development. It also highlights the impacts that management activities have on employees, stakeholders, communities and the environment. The significance of sustainability reporting in an organisation is to ensure that it considers the outcome of sustainability issues and thus operates in a more transparent manner when it comes to dealing with the risks and opportunities that it is facing (Riley & Gadonniex, 2009). The Global Reporting Initiative (GRI) was established in 1997 to help provide guidelines on the preparation and transparent presentation of sustainability reports, and set the necessary procedures on how entities should establish their operations and activities in the pursuit of having a positive and sustainable impact on local communities (Savitz & Weber, 2013). While initially only a few companies complied with the GRI reporting guidelines, an increasing number of entities voluntarily produced sustainability reports and adopted the GRI Framework by the mid-2000s (Ernst & Young, 2013). Indeed, the GRI has become the leading source of guidance for sustainability reporting today (Guthrie et al., 2010). The GRI Framework is built upon the TBL, and is an accounting framework based on the idea that organisations should extend the existing traditional measures of corporate profit. Instead of having one bottom line determined by indicators derived from financial accounting, further aspects should be considered (Elkington, 1999, 2004). Thus, two additional bottom lines are suggested: social responsibility and environmental implications. Ideally, organisations should aim to achieve wins in all three bottom lines (Elkington, 1999, 2004). The three underlying dimensions (economic, social and environment) can also be summed up with the “3 Ps”: profit, people and planet (Elkington, 1999). Consequently, organisations are in addition to their financial obligations of maximising profits more and more accountable in terms of environmental impact and social responsibilities. In this regard, three linkages between the TBL dimensions are considered (Petrini & Pozzebon, 2009): 1. Environmental and social sustainability contributes to economic sustainability, which allows organisations to follow stakeholder strategies towards environmental or social sustainability goals (“business case”). 2. Economic and social sustainability contributes to environmental quality (“green case”).

102  Research handbook on information systems and the environment 3. Economic and environmental sustainability contributes to increasing social justice and equity (“social case”). In terms of interrelations between the TBL dimensions, a simultaneous optimisation of economic, social and environmental objectives is considered to generate lasting superior financial performance for the organisation (Elkington, 1999). Based upon a systematic and comprehensive literature review on Green IS, Wang et al. (2015) found that most of their identified papers were dealing with environmental and economic outcomes, but only few studies considered social outcomes. There are prominent examples of papers that focus on the environmental and economic dimensions, but disregard the social dimension; for example, the Belief–Action–Outcome Framework for Green IS which was proposed by Melville (2010). Additionally, IS research on sustainability has been criticised as being constrained in the realm of Green IS, which resulted in an appeal to make a transition from green to sustainability (Dao et al., 2011). Therefore, we will use in the following the term “sustainability management information systems” (SMIS). 2.2

Sustainability Management Information Systems

We define SMIS as systems that facilitate information processing in relation to the three dimensions of the TBL (Kerschbaum et al., 2011). Many organisations have implemented management programmes that require information systems “to monitor, evaluate, improve, and communicate environmental performance—including information baselines on inputs (energy, water, materials, etc.) and outputs (waste, emissions, etc.)” (Melville, 2010, p. 2). These systems focus primarily on the environmental perspective in relation to the ISO 14001 standard. ISO 14001 is an “internationally agreed standard that sets out the requirements for an environmental management system,” and which “helps organisations improve their environmental performance through more efficient use of resources and reduction of waste, gaining a competitive advantage and the trust of stakeholders” (International Organization for Standardization, 2015). However, previous studies show that the implementation of such environmentally focused SMIS has a positive impact on operations performance (e.g., Melnyk et al., 2003). We reviewed nine software providers (as of August 2017) and derived insights about SMIS solutions and their coverage of the TBL dimensions. Table 6.1 provides an overview which TBL dimensions are covered by selected SMIS solutions, and Table 6.A1 in the Appendix provides a list of SMIS and their software modules. It is noticeable that existing SMIS providers focus predominantly on environmental and social aspects. For example, prevalent environmental features comprise air emission reporting (all SMIS provide this feature), waste management (6 out of 9 SMIS), or water management (6 out of 9 SMIS). Other environmental features include, for example, fuel management, paper consumption tracking and radioactivity reporting. With regard to social features, we identified safety management (6 out of 9 SMIS) such as incident reporting, job hazard analysis and root cause; occupational health management (4 out of 9 SMIS) – more specifically, software modules that refer to chest X-ray monitoring, immunisation and office ergonomics; and external engagement (1 out of 9 SMIS) – in this case, donations. Some SMIS also focus on economic features; in our review, 4 out of 9 SMIS solutions involve economic-related software modules. For instance, software provider Ecometrica provides a module in its SMIS, which assists business travel costs. Hereby, it combines this economic feature with an environmental

Environment

Social

Economic

Sustainability

Sustainability management

X

X

X

Water management

Others

 

Chemical management

X

X

Air emission reporting

Energy management

X

Safety management

Waste management

X

 

External engagement

Occupational health management

management

X

X

X

Resource efficiency

Supply chain performance

X

 

X

X

 

X

X

 

 

 

 

X

 

Business travel costs

 

EHS Software

EHS Insight

Production

information system

Ecometrica

Overview of existing SMIS

Software company

Table 6.1 Enviance

ERA

ESdat

Medgate (Cority)

SAP

X

X

X

X

X

X

X

 

 

 

 

X

 

 

 

X

X

 

X

X

X

X

 

 

 

 

 

X

X

 

 

X

 

X

 

 

 

 

 

 

X

X

 

 

 

X

 

 

 

 

 

 

 

X

X

 

X

X

X

X

 

 

 

 

 

 

 

 

X

 

X

X

X

 

X

X

 

 

X

 

 

X

 

X

 

 

 

 

 

 

 

Management

Software

Management

Software

Management

System

Safety (EH&S)

System Software

and Energy Performance

Management

Data

Management

Sustainability

Verisae

Health and

Solutions Environmental Environmental Environmental Sustainability

Management

Environmental

Management

Environmental Environmental,

Emisoft

From environmental towards sustainability management systems  103

104  Research handbook on information systems and the environment feature by affording business travel costs along with air emission reporting (costs and carbon emissions generated by business travels). In addition, other software providers combine various modules from different TBL dimensions. For example, software provider Cority (formerly Medgate) offers a sustainability module that integrates diverse environmental features such as air emission reporting, and waste management, which also entails social features like incident reporting. Another example is Verisae, where “sustainability initiatives tracking” is provided, or Enviance, offering “corporate responsibility reporting”. Our review of SMIS solutions shows that there is a wide representation of environmental, health and safety (EHS)-related software modules. However, interaction effects between the TBL dimensions are underrepresented. Therefore, we will investigate them in the following section.

3.

A MAPPING FOR TBL INTERACTIONS

3.1 Method We utilise TBL to derive a mapping that outlines all possible dimension combinations. We argue that every business event has implications on at least one of the TBL dimensions. Therefore, the mapping can be used as a classification scheme to categorise every business event based on the underlying TBL dimension pattern. In order to illustrate the classification scheme, we examine annual reports of companies listed in the ASX 200. The ASX 200 was selected because: 1. The ASX Corporate Government Principles include expectations for listed companies to internally report on sustainability risks. This includes disclosure of material exposure to economic, environmental and social sustainability risks (ASX, 2014). As a result, corresponding data has to be collected and can be provided to external stakeholders. 2. Sustainability reporting is common among ASX listed companies. In 2016, only 16 out of 200 companies did not disclose sustainability information, and 101 provided it to a “leading” or “detailed” level (ASCI, 2017). 3. Australia’s economy depends highly on heavy industries such as mining (DIIS, 2016). As a result, environmental and social effects are common. In our analysis, we considered sustainability and annual reports. Additionally, news reports were considered in order to gather further information on the sustainability implications of events mentioned in the reports. Annual reports were considered based on market capitalisation in descending order. However, it needs to be emphasised that we apply deductive reasoning and use the empirical data to illustrate the approach. As a result, we only provide selected examples for every pattern combination in order to demonstrate the practicality of the approach. 3.2

Development of the Classification Scheme

In order to derive the classification scheme, we assume that three effects for each dimension exist. The effects are regarded as positive (+), neutral (0) or negative (–) in relation to the favourable or unfavourable implications on the company’s profit, the community or the natural

From environmental towards sustainability management systems  105 resources of the planet. We propose that an ideal state exists in which the effects on all three dimensions (economic, social and environment) are positive, which can be expressed as (+, +, +). This state is the centre of our classification scheme (see Figure 6.1). Other stages in which all dimensions have the same effect (0, 0, 0 and –, –, –) are not relevant for our model as either there is no effect at all or all effects are negative, which would not be rational for a company. Of course, it would be possible that, for example, a mining company with negative implications for the environment and the local communities suffers economic loss. However, this should not reflect the original intention of the mining company, which would expect at least some kind of economic benefit. Therefore, we assume that at least one of the three dimensions must be positive in every given situation. Figure 6.1 depicts the classification scheme.

Figure 6.1

TBL classification scheme

Within the classification scheme, the cells represent the different situations in which an organisation could be. The mapping is structured so that the distance towards a dimension on the peak of the triangle indicates whether the effect is positive or negative. Cells that contain positive effects for a particular dimension are placed closer to this dimension on the TBL triangle than neutral or negative effects. The shading indicates the total effect of the cell. Positive effects result in a darker shading. Consequently, (+, +, +) has the darkest shading in Figure 6.1, while cells with two negative effects, such as (–, –, +), have the lightest shading. In the following, we will classify examples obtained from publicly available sustainability reports to illustrate the mapping. 3.3

Classification of Real-World Examples

● Triple positive: An example of a so-called triple win (Elkington, 2004) with three positive effects is outlined by Woolworths (2021). In this example of a retailer, food waste that would usually be disposed in landfill is given to charities. This reduces the operational disposal costs (economic aspect), supports local communities (social aspect) and reduces the landfill which has a direct impact on the environment (environmental aspect). However,

106  Research handbook on information systems and the environment triple-win scenarios (+, +, +) seem to appear rarely. More common are examples in which only two aspects are positive. ● Double positive: There are six possible combinations with two positive factors. In three of them the remaining factor is neutral and in three the remaining factor is negative. An example for a scenario with positive economic and environmental effects is reported by Telstra (2017). The company trialled a new type of fuel cell that uses solar and wind energy for remote locations. These new cells are more efficient than diesel generators and replace the use of batteries on site, which require frequent substitution and careful disposal (Telstra, 2017). This technology provides a new revenue stream for the company while reducing the need for alternatives with worse environmental effects (+, 0, +). Also from Telstra comes an example of a scenario with positive economic and social implications. In this case, an information technology (Satellite Cells on Wheels – SatCOWs) is used to support disaster relief and discovery (Telstra, 2017). The cells boost coverage after a disaster and support the coordination of the emergency services in a disaster situation. While remote communities in particular benefit from this invention, it also adds to the company’s revenue (+, +, 0). In contrast, Computershare’s bicycle-sharing initiative combines a positive effect for the community by increasing its health with a positive effect on the environment as CO2 emissions are reduced (Computershare, 2017). However, the effect on the profit is unfavourable as the related sponsoring is an additional expense resulting in a (–, +, +) classification. We did not find any positive examples on social and environmental factors with neutral economic implications (0, +, +). A possible explanation is that there is always some kind of effect on the profit, whether positive or negative, as the other aspects implicate expenses or revenue. However, we found neither positive economic effects in combination with positive environmental and negative social implications (+, −, +) nor positive economic effects in combination with negative environmental and positive social implications (+, +, −). It needs to be noted that we do not claim that these interactions do not exist. Further research is required to determine whether and under what circumstances these combinations may occur. ● Single positive: The following combinations include single positive effects. This includes positive effects in one dimension with all other dimensions being neutral. We found examples in which the environmental or the social dimension is positively affected while the other two dimensions remain neutral. In relation to a Green Oscars project at Computershare, for instance, a team in Beijing committed to bring their own chopsticks and wash and re-use them for every meal they eat. Depending on the actual implementation and related external factors a difference between the costs to discard the chopsticks and the water and energy required to wash them could occur. However, we argue that this difference is likely marginal. The initiative is also unlikely to affect the social dimension, but a positive environmental effect could arise leading to a (0, 0, +) classification. Similarly the staff community fund at Commonwealth Bank (2017) affects only the social dimension positively (0, +, 0). In this project, the default for new employees is to be automatically signed up to make a donation each fortnight. The funds are used to support local communities. This is particularly interesting as the company uses funds from its employees for social projects, implicating that this is cost neutral for the company itself.

From environmental towards sustainability management systems  107 Other initiatives implicate a direct involvement for the company. Commonwealth Bank (2021), for instance, supported women’s cricket. In this example, a negative effect in relation to the profit arises in the short term as the positive social effects are an expense for the company (–, +, 0). However, it remains questionable whether such initiatives result in a reputational benefit for the bank that leads to increased profits in the long run. Maybe the most common relation is the positive economic effects that cause a negative effect on the environment (+, 0, −). At Woolworths, for instance, the operation as a retailer always requires energy and leads to waste (e.g., due to expired products). However, it is also possible that the generation of revenue has negative effect for the community and the environment. BHP Billiton, for instance, operate the world’s second-largest open-pit ferronickel mine at Cerro Matoso (Columbia) and recorded a significant community incident involving protests by local indigenous communities (BHP Billiton, 2014). The protesters claimed that the various health epidemics within their communities are a result of chemical residue from the mine, and that 30 years of pollution have rendered their lands inhabitable and poisoned their water (Colombia Reports, 2013). This combines negative effects for the community (social) and the environment (+, −, −). We were not able to identify empirical evidence for all combinations in the mapping. This raises the question whether a particular combination was simply not in the sample which we considered or if other reasons could prevent this specific combination. An example of a combination that is unlikely to occur in the real world is (+, 0, 0). The environment is usually somehow affected when a company tries to make profit. While companies can reduce their environmental footprint, some degree of waste is inevitable as it is often not possible to recycle everything. Thus, we were not able to identify any examples in this category and postulate that (+, −, 0) is the most common scenario in which companies operate. Other scenarios such as (0, −, +) appear possible but rare.

4.

CHANGING SOFTWARE REQUIREMENTS

Based on our review of sustainability reporting and SMIS, as well as the development of the classification scheme from a TBL perspective, we argue that SMIS software solutions should enable organisations to establish links between software modules comprising economic, environmental and social sustainability. This leads to the final question of how well existing software solutions support existing SMIS requirements. Table 6.2 outlines the relation between identified patterns and available software solutions. It appears as if the available SMIS struggle to support the identified initiatives. This is particularly remarkable as all of these initiatives have been derived from company disclosures and are therefore relevant for real-world companies. A general problem seems to be the fact that external engagement is limited to monetary contributions. However, in the real world companies engage with other types of contributions, including food donations (Woolworths) or specific products to support the community, such as Telstra’s disaster relief technologies. Additionally, the solutions do not acknowledge the interaction between different dimensions as the software only monitors a particular aspect (e.g., waste management), but not the implications on the other dimensions. However, in the real world the relations between these aspects are relevant. For instance, the food donations from Woolworths had positive effects on the resource efficiency, external engagement and waste management. A highly integrated

108  Research handbook on information systems and the environment Table 6.2

The reasons why available SMIS solutions are insufficient (EC = Economic, SO = Social, EN = Environment)

EC

SO

EN

Example

Available solutions

Comment

+

+

+

● Donate food waste

● Resource efficiency

● External engagement is limited to mone-

to the community

● External engagement

(Woolworths)

● Waste management

● Interaction between aspects is

● Energy management

● Available economic solutions focus only

tary contributions. insufficient.

+

0

+

● Sustainable fuel cells (Telstra)

on costs. Additional revenue due to more sustainable products is not monitored. ● Energy management focuses on company’s energy but not on energy savings based on more sustainable products.

+

+

0

● Disaster relief technolo-

● External engagement

gies (Telstra)

● Available economic solutions focus only on costs. Additional revenue due to more sustainable products is not monitored. ● External engagement is limited to monetary contributions.

0

0

+

● Green Oscars project

● Waste management

(Computershare)

● Waste management at the individual employee level is currently not supported.

0

+

0

● Staff community funding ● External engagement

● External engagement is included but

through employee dona-

voluntary employee donations are not

tions (Commonwealth

supported.

Bank) −

+

+

● Bicycle-sharing initiative ● Occupational health management ● Economic solutions focus on product (Computershare)

● Air emission reporting

costs and not employee-related initiatives. ● Occupational health test currently restricted to specific aspects (e.g., drug testing). General fitness levels and related health benefits are not monitored. ● Air emission reporting focuses on air emissions from value chain (not human resources).

system covering all three aspects is required to manage and report the related effects correctly. Another finding is that existing SMIS focus on expenses while revenue in relation to sustainability is not sufficiently recorded. However, Telstra’s sustainable fuel cells are an example that shows sustainable products can also generate profit. Similarly, the energy management focuses on a company’s energy consumption in relation to the value chain but not on energy savings in relation to more sustainable products. This makes it more difficult for companies to assess the positive effects of a product on the TBL. The same occurs in relation to human resources. Companies conduct sustainable initiatives utilising their human resources. Computershare’s Green Oscars project and Commonwealth Bank’s staff community funding are examples for an environmental and a social initiative without economic implications. The aim of the Green Oscars is to change the environmental behaviour of employees, while staff community funding encourages the employees to support the local community. However,

From environmental towards sustainability management systems  109 current systems do not support waste management in relation to employees nor their external engagement. To that end, the product focus in current SMIS should be extended by a customer and employee perspective. In addition, the available features do not meet sufficiently the actual requirements. Computershare’s bicycle-sharing initiative improves occupational health and reduces air emissions, with potential negative effects on the economic dimension. This relationship cannot be displayed correctly by existing SMIS due to their very specific focus on occupational health management (e.g., drug testing) and air emission reporting being restricted to the value chain. Consequently, it is not possible for a company to manage and report related initiatives correctly. This explains why companies often report sustainable initiatives but do not provide details in order to underpin their impact. Better and more detailed data could be of interest for stakeholders and may encourage other companies to adopt similar initiatives.

5. CONCLUSION In this chapter, we propose SMIS as a strategic tool based on the TBL perspective. We demonstrated that the three TBL dimensions (environmental, social and economic) interact and that the current value-chain focus is insufficient. To that end, we developed a mapping for TBL interactions and utilised real-world examples in order to illustrate existing deficiencies in SMIS. Future research should explore how companies can actively change the classification in the mapping. For instance, how a positive environmental aspect can be enriched by a positive social or economic effect. In this context, case studies would be particularly suitable. Our analysis further shows that the concept of environmental IS might be too narrow for organisations due to the sole focus on the environmental footprint as it does not consider economic or social implications. Our analysis suggests that companies already conduct a variety of projects in this domain, but the available software solutions do not match the organisational reality. SMIS will increase transparency for internal as well as external stakeholders and therefore help companies to become more sustainable.

REFERENCES ACSI (2017). Corporate Sustainability Reporting in Australia: An Analysis of ASX200 Disclosure. Australian Council of Superannuation Investors, Melbourne. ASX (2014). Corporate Governance Principles and Recommendations. Australian Securities Exchange Corporate Governance Council, Sydney. BHP Billiton (2014). Sustainability Report 2014. BHP Billiton, Melbourne. Blevis, E. (2008). Sustainability implications of organic user interface technologies: an inky problem. Communications of the ACM, 51(6), 56–7. Colombia Reports (2013). Cerro Matoso megamine temporarily closed over indigenous protests. https://​ colombiareports​.com/​cerro​-matoso​-megamine​-partially​-reopened​-indigenous​-protests/​. Commonwealth Bank (2017). Corporate Responsibility Report. Commonwealth Bank, Sydney. Commonwealth Bank (2021). Annual Report. Commonwealth Bank, Sydney. Computershare (2017). Corporate responsibility. https://​www​.computershare​.com/​corporate/​investor​ -relations/​corporate​-profile/​corporate​-responsibility. Cooper, V., and Molla, A. (2017). Information systems absorptive capacity for environmentally driven IS-enabled transformation. Information Systems Journal, 27(4), 379–425. Dao, V., Langella, I., and Carbo, J. (2011). From green to sustainability: Information technology and an integrated sustainability framework. Journal of Strategic Information Systems, 20(1), 63–79.

110  Research handbook on information systems and the environment DIIS (2016). Australian Industry Report. Department of Industry, Innovation and Science, Canberra. Elkington, J. (1999). Cannibals with Forks: Triple Bottom Line of 21st Century Business. Capstone, Oxford. Elkington, J. (2004). Enter the triple bottom line. In Henriques, A., and Richardson, J., eds, The Triple Bottom Line: Does It All Add Up? Earthscan, New York, 1–16. Ernst & Young (2013). Value of Sustainability Reporting: A Study by Ernst & Young LLP and the Boston College Center for Corporate Citizenship. EYGM, London. Gholami, R., Watson, R.T., Hasan, H., Molla, A., and Bjørn-Andersen, N. (2016). Information systems solutions for environmental sustainability: How can we do more? Journal of the Association for Information Systems, 17(8), 521–36. Global Reporting Initiative (2015). G4 Sustainability Reporting Guidelines: Reporting Principles and Standard Disclosures. Global Sustainability Standards Board, Amsterdam. Greengard, S. (2013). Vanishing electronics. Communications of the ACM, 56(5), 20–22. Guthrie, J., Ball, A., and Farneti, F. (2010). Advancing sustainable management of public and not for profit organizations. Public Management Review, 12(4), 449–59. Hu, P.J.-H., Hu, H.-F., Wei, C.-P., and Hsu, P.-F. (2016). Examining firms’ green information technology practices: A hierarchical view of key drivers and their effects. Journal of Management Information Systems, 33(4), 1149–79. International Organization for Standardization (2015). ISO 14001. https://​www​.iso​.org/​standard/​60857​ .html. Jenkin, T.A., Webster, J., and McShane, L. (2011). An agenda for “green” information technology and systems research. Information and Organization, 21(1), 17–40. Kend, M. (2015). Governance, firm-level characteristics and their impact on the client’s voluntary sustainability disclosures and assurance decisions. Sustainability Accounting, Management and Policy Journal, 6(1), 54–78. Kerschbaum, F., Strüker, J., and Koslowski, T. (2011). Confidential information-sharing for automated sustainability benchmarks. International Conference on Information Systems, Shanghai, China, 1–17. Kurp, P. (2008). Green computing. Communications of the ACM, 51(10), 11–13. Lago, P., Koçak, S.A., Crnkovic, I., and Penzenstadler, B. (2015). Framing sustainability as a property of software quality. Communications of the ACM, 58(10), 70–78. Leidner, D.E., Sutanto, J., and Goutas, L. (2022). Multifarious roles and conflicts on an interorganizational Green IS. MIS Quarterly, 46(2), 1–18. Loeser, F., Recker, J., Vom Brocke, J., Molla, A., and Zarnekow, R. (2017). How IT executives create organizational benefits by translating environmental strategies into Green IS initiatives. Information Systems Journal, 27(4), 503–53. Melnyk, S., Sroufe, R., and Calantone, R. (2003). Assessing the impact of environmental management systems on corporate and environmental performance. Journal of Operations Management, 21, 329–51. Melville, N.P. (2010). Information systems innovation for environmental sustainability. MIS Quarterly, 34(1), 1–21. Muttakin, M.B., and Subramaniam, N. (2015). Firm ownership and board characteristics: Do they matter for corporate social responsibility disclosure of Indian companies? Sustainability Accounting, Management and Policy Journal, 6(2), 138–65. Petrini, M., and Pozzebon, M. (2009). Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organisational context. Journal of Strategic Information Systems, 18(4), 178–91. Riley, T., and Gadonniex, H. (2009). The Complete Idiot’s Guide to Greening Your Business. Alpha, New York. Savitz, A.W., and Weber, K. (2013). The Triple Bottom Line: How Today’s Best-Run Companies Are Achieving Economic, Social and Environmental Success – And How You Can Too. Jossey-Bass, San Francisco. Telstra (2017). Bigger Picture 2017 Sustainability Report. Telstra Corporation, Melbourne. Vom Brocke, J., Loos, P., Seidel, S., and Watson, R.T. (2013). Green IS: Information systems for environmental sustainability. Business & Information Systems Engineering, 5(5), 295–97.

From environmental towards sustainability management systems  111 Wang, X., Brooks, S., and Sarker, S. (2015). Understanding Green IS initiatives: A multi-theoretical framework. Communications of the Association for Information Systems, 37. https://​doi​.org/​10​.17705/​ 1CAIS​.03732. Watson, R.T., Boudreau, M.C., Chen, A., and Huber, M.H. (2008). Green IS: Building sustainable business practices. in Watson, R.T., ed., Information Systems. Global Text Project, Athens, GA. Watson, R.T., Boudreau, M.-C., and Chen, A.J. (2010). Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Quarterly, 34(1), 23–38. Watson, R.T., Corbett, J., Boudreau, M.C., and Webster, J. (2012). An information strategy for environmental sustainability. Communications of the ACM, 55(7), 28–30. White, G.B. (2009). Sustainability Reporting: Managing for Wealth and Corporate Health. Business Expert Press, New York. Woolworths (2021). Annual Report. Woolworths, Bella Vista. Yang, X., Li, Y., and Kang, L. (2020). Reconciling “doing good” and “doing well” in organizations’ green IT initiatives: A multi-case analysis. International Journal of Information Management, 51, 1–12.

112  Research handbook on information systems and the environment

APPENDIX Table 6.A1

List of sustainability management information systems and their software modules

Software company

Ecometrica

SMIS

Sustainability

Website

https://​ecometrica​.com/​platform/​sustainability

Software modules

Business Travel, CSR, Donations, EHS, Energy, Ethical, GHG/Carbon, Paper, Science-Based Targets, Social, Spend, Supply Chain, Waste, Water

 

 

Software company

EHS Insight

SMIS

EHS Software

Website

https://​www​.ehsinsight​.com

Software modules

Incident Management, Audit Management, Training Management, Sustainability Management, Near Misses, Root Cause Analysis, Compliance Obligations, Work Observations, Environmental Spills, Nonconformance Management

 

 

Software company

Emisoft

SMIS

Environmental Management System

Website

https://​www​.emisoft​.com

Software modules

Discharge to Water, Fuel Management, Emissions to Air, Greenhouse Gas Reporting, Waste Management, Water Management, Chemical Management, Noise & Vibration Sampling, Production, Laboratory Reporting, Oil Drilling, Radioactivity Reporting, Incident Reporting, Energy Management

 

 

Software company

Enviance

SMIS

Environmental, Health and Safety (EH&S) Software

Website

https://​www​.enviance​.com

Software modules

Environmental: Air Compliance, Environmental Permit Management, Emissions Inventory, Greenhouse Gas, Water Compliance, Waste Management, Audits, Assessments & Inspections, Chemical Inventory Management, Incident Management, Management of Change, Action Tracking Health: Office Ergonomics, Behaviour-Based Safety: RSIGuard, Job Hazard Analysis, Action Tracking Safety: Job Hazard Analysis, Hazards Communication, Audits, Assessments & Inspections, Management of Change, Incident and Near Miss, Process Safety Management, Action Tracking Sustainability: Metrics & Corporate Responsibility Reporting, Carbon Management & Reporting

 

 

Software company

ERA Environmental Management Solutions

SMIS

Environmental Management System

Website

http://​www​.era​-environmental​.com

Software modules

Environmental: Water Management Solutions, Air Emission Solutions, Waste Management Solutions Compliance: Compliance Management Health & Safety: Integrated Health Safety Software SDS Authoring: SDS Authoring

 

 

Software company

ESdat

SMIS

Environmental Data Management Software

Website

http://​esdat​.net

Software modules

Air Management, Water Management, Soil Management

 

 

From environmental towards sustainability management systems  113 Software company

Cority (formerly Medgate)

SMIS

Environmental Management Software

Website

http://​www​.medgate​.com http://​cority​.com

Software modules

Environmental: Chemical Management, Compliance Management, Emissions, Environmental Audits & Inspections, Environmental Equipment Module, Environmental Event Reporting, Environmental Risk Assessment, Incident Management, Management of Change, Sustainability, Waste Management, Water Management Safety: Audits & Inspections, Compliance Management, COSHH, Equipment, Event Reporting, Incident Management, Management of Change, Respirator Fit Testing, Risk Assessment/JHA Occupational Health: Audiometric Testing, Body Fluid Exposure, Case Management for Occupational Health Professionals, Chest X-Ray, Clinic Visit, Clinical Testing, Drug Testing, Immunisation, Inventory, Medical Chart, Medical Surveillance Recalls, Occupational Health Equipment Module, Pulmonary Function Testing, Scheduling, SEG Management, Travel Clearance, Vision Testing Industrial Hygiene: Equipment, Hearing Fit Testing, Laboratory Requisition, Monitoring, Qualitative Exposure Assessment, Respirator Fit Testing, SEG Management, Survey

 

 

Software company

SAP

SMIS

Sustainability Performance Management

Website

https://​www​.sap​.com/​product/​financial​-mgmt/​performance​-management​.html https://​uacp2​.hana​.ondemand​.com/​viewer/​26e​5e39ad9bd4​af7a9f790e​3196dad70/​4​.0/​en​-US/​4b​ b83f9a0815​4925aac2b9​12419ed7cb​.html

Software modules

People Health & Safety, Product Safety & Stewardship, Energy Management, Resource Efficiency, Environmental Performance, Emissions Management, Green Computing, Green Supply Chain

 

 

Software company

Verisae

SMIS

Sustainability and Energy Management

Website

http://​www​.verisae​.com

Software modules

vx Sustain (Sustainability Management): GHG/Carbon Reporting, Refrigerant Management, Sustainability Initiatives Tracking vx Conserve (Energy Management): Energy Demand Management, Energy Supply Management

Notes: SMIS = sustainability management information system; CSR = corporate social responsibility; EHS = environmental, health & safety; GHG = greenhouse gas; RSI = repetitive strain injury; SDS = safety data sheet; COSHH = control of substances hazardous to health; JHA = job hazard analysis; SEG = similar exposure groups

7. Designing information systems that support environmental sustainability: a framework-based review Jan Recker

1. INTRODUCTION The information systems (IS) discipline has been challenged for several years now to provide an understanding of how IS can contribute to environmentally responsible human activity (Watson, Boudreau & Chen, 2010; Elliot, 2011). In response, the discipline has started to systematically explore the role that IS might play to solve the challenge of environmental sustainability (e.g., Melville, 2010; Elliot, 2011; Seidel et al., 2013; Ketter et al., 2016; Hasan et al., 2017; Loeser et al., 2017), with the hope that the IS discipline might produce solution-oriented research with impact potential; i.e., knowledge and artefacts that demonstrate how IS solutions and interventions could be designed and built that can mitigate imminent problems such as global warming and climate change (Gholami et al., 2016). This focus on solution-orientation and impact suggests that design science research (Hevner et al., 2004; Pfeffer et al., 2007; Sein et al., 2011; Gregor & Hevner, 2013; Baskerville, Kaul & Storey, 2015; Rai, 2017) could be a promising path to design IS that support environmental sustainability. My objective with this chapter is to assist design science researchers in their quest to develop IS solutions that help meet the challenges of environmental sustainability. As I will show, the community of design science researchers has become quite active, offering a variety of socio-technical IS artefacts, in the form of frameworks, models, algorithms, prototypes or situated instantiations, as solutions to assist individuals and organisations to make environmentally sustainable decisions and engage in environmentally sustainable work practices. Often, these types of artefacts are labelled as “Green IS” (e.g., Gholami et al., 2013; Hilpert, Schumann, et al., 2013; Leidner et al., 2022). In this spirit, I will also use the shorthand label “Green IS” to denote artefacts created through design science for the development of IS that support environmental sustainability. With that said, it is important not to confuse this use of the term “Green IS” as a denotation as socio-technical artefacts that support environmental sustainability and that are created through design science efforts from the use of the term “Green IS” to denote all research situated at the intersection of IS and sustainability (e.g., Butler, 2011; Loock et al., 2013; Loeser et al., 2017). My interest is only in design science research at that intersection. My specific goal is to review and advance Green IS design efforts. To meet these goals, I will develop a framework to analyse, synthesise and improve design knowledge accumulation about Green IS. The framework offers a structured conceptualisation of the Green IS design problem space, which is useful because existing solutions can then be positioned within this problem space, and the framework components can be used to guide design research to

114

Designing information systems that support sustainability  115 evolve by highlighting open Green IS design challenges and providing normative advice for improving and evolving existing Green IS designs. I start by reviewing briefly two kernel theories that I have used to construct the framework. I then report on the core elements of the framework, using a publication schema well known to design science researchers (Gregor & Jones, 2007). I then detail the procedures carried out to review the literature on design science for the development of IS that support environmental sustainability. I then illustrate how the framework can be used to (1) synthesise and (2) advance Green IS design knowledge by categorising the extant Green IS design literature and reviewing in depth two vignettes of promising Green IS design research. Last, I conclude this chapter with a discussion of implications.

2. PRELIMINARIES I start by noting that this chapter neither presents the only review of the Green IS literature as a whole (e.g., Wang et al., 2015; Gholami et al., 2016; Sedera et al., 2017; Singh & Sahu, 2020) in general, nor the only review of Green IS design research specifically. Specifically, Brendel et al. (2018) presented a review of Green IS design science papers. However, their focus was restricted to some selected IS outlets only and used exclusive search terms such as “design science”, which had to appear in the paper, restricting the sample to 23 papers. Moreover, their review was cumulative and descriptive, whereas the review in this chapter is framework-based. To that end, the framework I develop in this chapter is neither grounded inductively in data (Urquhart & Fernandez, 2013) nor is it systematically derived from a structured review of the literature (Boell & Cecez-Kecmanovic, 2015). Rather, I borrow deductively from premises of two existing theoretical perspectives to conceptualise a view of the problem space of IS solutions for environmental sustainability. This approach is common in design science research: design theories or frameworks are often based on kernel theories that govern meta requirements, boundary conditions and other assumptions that provide purpose and scope to the class of the artefacts being defined (Walls et al., 1992; Gregor & Jones, 2007; Baskerville & Pries-Heje, 2010). Because the purpose of my framework is to structure the literature of design science for the development of IS that support environmental sustainability, it needs to be general and abstract rather than situated and anchored in the development of one specific artefact or setting. Therefore, for developing the framework, I rely on two conceptual artefacts (Alter, 2017) at different levels of theoretical abstraction that enjoy widespread application in the literature on Green IS (Sedera et al., 2017): the Belief-Action-Outcome framework (Melville, 2010), as a basis to conceptualise the problem space of environmentally sustainable behaviours, and affordance theory (e.g., Markus & Silver, 2008), as a basis to conceptualise properties of IS solutions as technological objects. Needless to say, it should be clear that alternative conceptual models also exist that could serve as kernel theories; such as the energy informatics framework (Watson, Boudreau & Chen, 2010), or sensemaking theory (Weick, 1995; Seidel et al., 2013; Seidel et al., 2018). Both the Belief-Action-Outcome framework and affordance theory are conceptual devices to support theorising (Alter, 2017; Hassan et al., 2022). The main difference is in level of abstraction – affordance theory is mid-range and substantive, primarily on an individual level of analysis, stipulating what happens when goal-oriented actors interact

116  Research handbook on information systems and the environment with technological objects (Volkoff & Strong, 2018). Conversely, the Belief-Action-Outcome framework is broader and more inclusive and subsumes such individual-level (micro) actions as well as the composite macro actions that emerge from such micro-level actions. 2.1

Environmental Beliefs, Actions and Outcomes

The Belief-Action-Outcome framework (Melville, 2010) suggests that organisational behaviours are an outcome of belief and action formation on a macro and a micro level: people first develop beliefs (because of values, norms, attitudes or other factors) to engage in certain behaviours, then start forming action; that is, start doing things. Outcomes of belief and action formation can be measured, at both the micro and macro level. In my framework, macro and micro level are organisational system-level boundaries. The macro level denotes beliefs, actions and outcomes that occur on a social, collective level such as a project, team, department or firm. The micro level denotes beliefs, actions and outcomes that occur on an individual level. Importantly, these definitions limit the scope of salient environmental goals because micro and macro levels in this understanding do not include societal-level (such as the pollution level of different countries) let alone planetary-level systems (such as global warming). However, these goals match the general interest of IS scholarship on individual and organisational-level phenomena. Belief formation, action formation and outcome assessment in principle could relate to any behaviours; however, as is common in Green IS research, I use the framework to examine beliefs, actions and outcomes in light of environmental sustainability considerations: ● Belief formation captures how psychic states (beliefs, desires, opportunities, etc.) about the natural environment are formed. On the macro level, these include the ways an organisation coordinates and divides labour and how the organisation defines environmental expectations of its agents. These could include, for instance, the managerial interpretation of environmental issues in light of corporate identity (Sharma, 2000). On the micro level, belief formation captures environmentally relevant attitudes and norms and beliefs. For instance, individual environmentalism is dependent on ecological worldviews, awareness of consequences and ascription of responsibility (Steg & Sievers, 2000). ● Action formation describes how psychic states about the natural environment translate to actions. On the macro level, this includes actions taken by an organisation to affect the actions taken by its agents. For instance, organisations deploy IS to allow for sensemaking of environmental issues and use enterprise social networks to democratise sustainability information as well as critical environmental decisions amongst employees (Seidel et al., 2013). On the micro level, action formation describes what is done by individuals to improve environmentalism of behaviours. For instance, individuals may choose to use web portals that encourage energy consumption minimisation by setting individual goals (Loock et al., 2013), or they may choose to delocalise work practices by relying on file sharing and conferencing systems rather than physical travel (Seidel et al., 2013). ● Outcomes describe what the consequences of actions are, on a macro and/or a micro level. Originally, the Belief-Action-Outcome framework (Melville, 2010) defined outcomes as the functioning of organisations (or other social systems). In the context of environmental sustainability, for example, one outcome might be how much energy is consumed by an organisation or individual (e.g., Cappiello, Datre, et al., 2013; Loock et al., 2013), or how

Designing information systems that support sustainability  117 many carbon emissions are produced by engaging in certain behaviours (e.g., Corbett, 2013), or whether two choices differ in terms of environmental indicators such as pollution, energy consumption, water quality or other metrics. For the purposes of this chapter, I differentiate the outcomes of environmentally sustainable functioning into two core elements, viz. practices and decisions: environmentally sustainable work practices are those operations enacted by organisations or individuals that exhibit a minimal harmful impact on the natural environment. For instance, individuals may engage in sales meetings with clients using videoconferencing systems, which is environmentally a more sustainable practice than travelling to the client and meeting in person (Seidel et al., 2013). Environmentally sustainable decisions are those choices made by organisations or individuals that are characterised as having a better impact (typically less negative) on the natural environment than an alternative decision option. A simple example could be the choice of carbon-offsetting a business flight at an extra cost (MacKerron et al., 2009), or the choice of using black and white, duplex printing over coloured single-side printing (Hasan et al., 2013). 2.2

Action Possibilities and Technological Objects

Affordances describe possibilities for goal-oriented action that exist for specified user groups in relation to technological objects such as IS (Markus & Silver, 2008). In other words, affordances specify how actors relate to an object in terms of how it can be used (Gibson, 1977). While affordances exist in the relationship between users and technological objects (Stoffregen, 2003) the potential for action that is an affordance is vested in the attributes of the object (Zammuto et al., 2007); i.e., in material properties of IS. These properties could be concrete algorithms, data structures or other hard- and software components that define a system’s architecture. In IS research, affordance theory is often used to examine the processes that occur when goal-oriented actors interact with technological objects, such as how and why affordances are actualised (Strong et al., 2014; Anderson & Robey, 2017). This aspect of the theory focuses, so to speak, on effective information system use (Burton-Jones & Volkoff, 2017). This is not the way that I use affordance theory here. In what follows, I focus on the aspects of affordance theory that draw attention to several elements of designing IS artefacts (Norman, 1999; Ciavola & Gershenson, 2016), and which are important to ensuring that affordances can emerge in the interaction with specified user groups. First, affordances specify potential for actions, not actions or outcomes themselves (Strong et al., 2014). The outcomes of technology design are not deterministic; however, it is acknowledged that appropriate design can guide the actualisation of an affordance. For example, symbolic expressions describe how possibilities for action are communicated by the material properties of the system (Markus & Silver, 2008). For instance, the Windows key on PC keyboards (material property) featuring the Windows logo (symbolic expression) conveys that the start menu can be opened (the affordance). These symbolic expressions act as signs for the designers’ intentions for the system; yet of course in some cases symbolic expressions emerge through the interpretation of users – intended or not. Users perceive and interpret symbolic expressions depending both on their action-goals and their abilities for use. In the mentioned example of the Windows key, the relevant action possibility communicated has first to be learned by novice users (Grgecic et al., 2015).

118  Research handbook on information systems and the environment Second, the existence of an affordance does not necessarily mean that a user actually realises the offered action possibility. The actualisation of affordances depends on the symbolic expressions of the technology as well as actors’ abilities and goals. Abilities can act as constraints that limit what can be done with an object. Goals determine the ends for which users seek means to complete a task. For instance, Seidel et al. (2013) report that affordances for the reduction of paper consumption offered by a new printing system depended, amongst other things, on users’ goals and their correspondence with the new environmental targets at work. For the design of technical objects such as IS, this means that actions cannot be designed into an object, but objects can be designed such that affordances exist within the relationships that describe their use. Yet, symbolic expressions only have meaning for those with the same background as the designer; any designed symbolic expression therefore does not guarantee the same interpretation, let alone belief or value. Third, affordances can be related to technological objects at various levels of granularity (Volkoff & Strong, 2018), depending on the chosen level of generalisation of material properties or structural features of a technological artefact. In turn, theory around affordances (as action possibilities) and their connection to technology features can be developed on various levels of abstraction, which is useful to the purposes of my framework on a general level as well as its application to the design of all kinds of Green IS artefacts and specific action potentials vested in them.

3.

A FRAMEWORK TO STRUCTURE THE REVIEW OF IS DESIGN ARTEFACTS THAT SUPPORT ENVIRONMENTAL SUSTAINABILITY

With the conceptual background and vocabulary of the framework introduced, I now pursue my ambition to develop a new, abstract-level framework to reflect on the design of a class of socio-technical IS artefacts that assist individuals and organisations to make environmentally sustainable decisions and to enable environmentally sustainable work practices, rather than environmentally unsustainable ones. As mentioned above, I will call this class of artefacts “Green IS”. Different schemata exist (e.g., Walls et al., 1992; Markus et al., 2002; Gregor & Jones, 2007; Sein et al., 2011; Kuechler & Vaishanvi, 2012; Gregor & Hevner, 2013) that could be used to describe the framework. I decided to loosely follow the anatomy of eight components of design theory as advocated by Walls et al. (1992) and Gregor and Jones (2007) because, in my view, this schema aptly draws attention to design principles that are at the core of any design-oriented framework (Gregor, 2006, p. 620). 3.1

Purpose and Scope

The purpose of my framework is to give an abstract representation of a class of Green IS as socio-technical artefacts, and specifications for the development of kinds of IS that assist individuals and organisations to become more environmentally sustainable; i.e., as the outcomes of organisational actions at the micro or macro level that manifest as (1) environmentally sustainable work practices or (2) environmentally sustainable decisions. The framework stipulates that IS belong to the class of Green IS if they provide form and function to support belief

Designing information systems that support sustainability  119 formation, action formation and/or outcome assessment as they relate to these two particular components of environmental sustainability. The scope of the framework is set at an abstract, class level rather than the substantive, type level. Ontologically (Bunge, 1977; Weber, 1997), the framework is not concerned primarily with specific IS as existing, material things with particular properties but instead with the grouping of IS that share a set of common properties in general, which will be defined below as the principles of form and function and which characterise the class of IS that share these properties as Green IS. The representation offered by the framework is thus general and abstract rather than specific and nascent. It encompasses all kinds of socio-technical “Green IS”, both those already theorised (e.g., Hilpert et al., 2014) and/or developed (e.g., Hilpert, Schumann, et al., 2013) and those imagined and potential. Figure 7.1 provides an abstract representation of the Green IS design knowledge framework; that is, its principles of function and principles of form (Gregor & Jones, 2007). For illustration purposes, Figure 7.1 contains examples of IS that provide relevant affordances that embody the stipulated principles of function at either the macro or micro level. For example, interactive corporate sustainability reports describe IS that provide principles of the function “outcome assessment” at the macro level because they provide means to explore performance indicators for environmentally relevant outcomes from firm operations at the organisational level (e.g., Caldelli & Parmigiani, 2004). Conversely, modern carbon footprint management systems also allow assessing the environmental outcome of individual-level actions (e.g., Pourakbari-Kasmaei et al., 2020). The principle of function “action formation” on a micro level could be provided by an information system that provides work virtualisation affordances, such as computer-assisted virtual engineering tools that individuals could use (Jayaram et al., 1997). Or, individuals could log on to a video conferencing system such as Zoom (Hacker et al., 2020) to collaborate remotely. A system that provides action formation affordances on a macro level could be a fleet-level vehicle routing system that identifies optimal routes for a fleet of delivery trucks (as opposed to for individual drivers). Finally, examples of systems that support belief formation on a macro level include community platforms, such as the one developed by Seidel et al. (2018), which afford possibilities for entire groups to jointly discuss, vote and decide on sustainability initiatives to be implemented. Discussion forums (e.g., Degirmenci & Recker, 2023), on the other hand, could be seen as belief formation systems that allow individuals to explore arguments or debates pertaining to environmental actions or issues and thereby modify their own psychic states, attitudes or beliefs. While being a framework for Green IS as socio-technical artefacts, the framework here differs from other frameworks to guide design science efforts per se. For example, in contrast to Walls et al. (1992), the framework here does not specify meta requirements for user abilities and user goals that are relevant to the discussion of affordances. It also does not offer prescriptive knowledge, such as design propositions or implementation principles (Gregor & Jones, 2007). Instead, the purpose of the framework is to facilitate a framework-based review of the literature for understanding (Rowe, 2014); that is, to provide structural dimensions and categories that can guide material collection and structure the descriptive analysis, systematic coding and synthesis.

120  Research handbook on information systems and the environment

Figure 7.1 3.2

Framework for reviewing Green IS design artefacts

Principles of Function and Form

Following the Belief-Action-Outcome framework, my framework for Green IS distinguishes the level of operation (macro or micro) and the scope of operation (belief formation, action formation and outcome assessment) of a designed Green IS artefact. Both level and scope of operation together provide a conceptualisation of potential principles of function (Gregor & Jones, 2007). However, the Belief-Action-Outcome framework remains silent about principles of form; that is, the properties, functions, features or attributes of a technological object, or a class of technological objects, to inform these operations. These elements are covered in the framework by concepts from affordance theory, which postulates that systems need to provide material properties to allow for affordances to emerge in the relation between system and user, and that intended affordances can be more or less well communicated through symbolic expressions. The combination of both views allows the framework to determine (a) which affordances are required for belief formation, action formation and outcome assessment on either the macro or micro level, and (b) how these affordances can be communicated in IS

Designing information systems that support sustainability  121 design through appropriate symbolic expressions. This is because symbolic expression properties relate to affordance actualisation (Strong et al., 2014); i.e., from the interaction with instantiated Green IS objects, not from the design of these objects for these affordances. The architectural representation of the framework in Figure 7.1 is deliberately conceptual and abstract to allow examination of any kind of Green IS substantive-level design theory or artefact within the scope of the class-level framework. The framework stipulates that at least one of three functions must be performed by an information system to belong to the class of “Green IS” artefacts, namely either 1. belief formation about environmental sustainability, 2. action formation for environmental sustainability, and/or 3. outcome assessment of environmental sustainability. Each of these functions can operate at the micro (individual) level or the macro (organisation) level. At either level, each function can correspond to either environmentally sustainable practices (in terms of their assessment, performance or attitude formation) or environmentally sustainable decisions (in terms of their review, selection or sensemaking). The left-most “Function” column in the framework (Figure 7.1) specifies how belief formation, action formation and outcome assessment for decisions and practices must be supported by IS in order to belong to the class of Green IS. For example, on a micro level, belief formation for environmentally sustainable practices concerns the development of environmentally relevant attitudes such as ascription of responsibility and awareness of consequences (Steg & Sievers, 2000). As a second example, outcome assessment of environmentally sustainable decisions on a macro level concerns functionality of systems to allow for review of organisational-level decisions in light of green indicators, which is typically included in corporate sustainability reporting systems (Jindrichovska & Purcarea, 2011). As principles of form (Gregor & Jones, 2007), the framework in Figure 7.1 postulates that each of the functions can be embodied in IS through an appropriate combination of material properties and symbolic expressions. In turn, the principles of function at either the macro or micro level can be achieved by the provision of environmentally relevant functional affordances. Being a class-level framework, any kind of Green IS can operate at any one level, or several levels, of operations and can encompass one or many scope(s) of operation. Moreover, the instantiation of principles of form and function in any or even between any kind of Green IS may vary. The framework merely stipulates the meta-requirement for any kind of Green IS artefact to provide environmentally relevant functional affordances at the micro or macro level. Yet, it provides degrees of freedom regarding the design choices of form and function through which these affordances can be provided. Specifically, the choices of (a) relevant material properties in which environmentally relevant functional affordances are vested, and (b) suitable symbolic expressions to communicate the action possibilities for either environmentally sustainable practices or decisions, provide degrees of freedom to the designer such that, in principle, different material properties can be the basis for an affordance, and any one affordance can be communicated through different symbolic expressions. For example, consider an information system with the material property of an algorithm to track greenhouse gas (GHG) emissions of individuals’ actions at work. This property could be implemented with two different symbolic expressions (e.g., through a weekly graphical report, or via a real-time diagnostic tool such as a widget). These two expressions will likely signal different action pos-

122  Research handbook on information systems and the environment sibilities to users – one would signal affordances for weekly monitoring and ex post reflections whereas the widget might signal affordances for run-time adjustments of work behaviours. For illustration purposes, the framework in Figure 7.1 provides several examples of material properties (e.g., workflow engines or social networking technologies), and also forms of communicative possibilities through different symbolic expressions (e.g., action wizards, newsfeeds or traffic light systems). Thereby, the framework recognises that an environmentally relevant action possibility (say, output management affordances) can, in theory, be provided by systems that faithfully combine relevant material properties, such as configuration and controlling features (Seidel et al., 2013), together with an appropriate symbolic expression to visually communicate the intended action possibilities. For instance, in the study by Seidel et al. (2013) this affordance was communicated through a visual representation on the corporate intranet. By way of example, one could also imagine the affordance to be communicated through messaging services (say, regular text messages to the mobile phones of users). Which combinations most effectively guide the actualisation of a particular intended affordance is then an empirical question. It is worth noting here that any kind of Green IS may be adapted to signal functional affordances through different combinations of material properties and symbolic expressions in light of specified user groups’ abilities to receive and interpret different types of symbolic expressions. For instance, one might imagine that Green IS intended for digital native user groups (Vodanovich et al., 2010) use symbolic expressions such as infographics or mobile-friendly videos whereas Green IS for digital immigrants may choose to peruse more traditional symbolic expressions such as graphs and tables.

4.

USING THE FRAMEWORK TO ANALYSE AND ADVANCE DESIGN KNOWLEDGE ABOUT IS THAT SUPPORT ENVIRONMENTAL SUSTAINABILITY

In what follows, I use the framework introduced above to evaluate existing design science research on Green IS and offer advice for improvements, in two steps. First, I use the framework broadly for classification, positioning and evaluation of Green IS design artefacts in the extant literature; that is, for the goal of carrying out a cumulative, descriptive review (Paré et al., 2015; Templier & Paré, 2015). Second, I use the framework to demonstrate, by reviewing two Green IS design science studies in moderate detail, how it allows for inspection of acts of design knowledge creation and by identifying opportunities for evolution or advance of the designs. 4.1

Review Procedures

I searched 13 publication databases for contributions.1 No restrictions on disciplinary field or outlet (e.g., journals, books or conferences) were imposed because relevant contributions might appear in IS outlets, engineering outlets and business outlets as well as other outlets associated with environmental sciences. I searched titles, abstracts, keywords and whole papers when necessary. The search terms were “green information system”, “Green IS”, “information system (IS) + environmental sustainability” and meaningful combinations of these terms. I conducted a backward and a forward search. The backward search was realised

Designing information systems that support sustainability  123 by reviewing the references of the identified papers for further relevant literature, and the forward search was performed using Web of Science to find additional literature citing the identified papers (Webster & Watson, 2002). The timeframe for the review was 2010–2016: literature published in any year since Watson’s et al. (2010) inaugural call for Green IS research was included. In all, 416 papers were identified as relevant. I then examined which of these papers were artefact-centric, in the sense that they described a design artefact as part or an outcome of the research process. In total, 74 papers described, on some level and in some form, a Green IS design artefact as part or outcome of the research process. These 74 papers formed the final sample of Green IS design papers. In a second step, these papers were categorised by design contribution (Gregor & Hevner, 2013) and type of design artefact (Hevner et al., 2004). First, to categorise the papers by contribution type, I examined the artefacts in the paper and broadly classified them using Gregor and Hevner’s (2013) scheme: if they were stated to be instantiated technical objects in the form of products such as prototypes and platforms, or implemented processes such as procedures, routines or workflows, I classified them as level-1 contributions; if the artefacts were described in the form of abstract constructs, design principles, models, architectures, methods or technological rules, I classified them at level 2; and if the artefacts manifested as part of well-developed, mature design theories they would have been at level 3 (no such paper was found). In total, 62 level-2 contributions and 41 level-1 contributions were identified (several papers featured contributions on both levels). Second, I categorised the papers by type of artefact, using the typology by Hevner et al. (2004) as a basis to distinguish constructs (vocabulary and symbols) from models (abstractions and representations), methods (algorithms, procedures and practices) and instantiations (implemented prototype systems). Table 7.1 summarises the outcomes of this broad categorisation. Table 7.1 shows that 41 out of 74 Green IS design knowledge contributions in the extant literature describe instantiations – level-1 contributions to design knowledge in the forms of prototypes (e.g., a bikeability spatial mapping tool; Winters et al., 2013), technologies (e.g., a green fingerprint tablet; Ekman et al., 2015) or running systems (e.g., a geographic information system for the management of public inter-urban bus transport; Tibaut et al., 2012). Table 7.1 also shows that 62 papers embody design knowledge contributions situated at level 2 – broadly understood as knowledge about production rules of instantiations set at the level of classes of artefacts (Gregor & Jones, 2007; Sein et al., 2011). For example, it shows that the vast majority of these knowledge contributions are in the form of system architectures or mathematical equations on a substantive, idiographic level: the papers provide design knowledge in the sense that they identify specific requirements for a specific type of Green IS design. I found no paper that provides what Gregor and Hevner (2013, p. 342) label a level 3 design knowledge contribution: a well-developed (mid-range or grand) design theory. For the future, such a level 3 contribution would be imminently useful for the ongoing evolution and improvement of Green IS design science knowledge. Abstract-level design theorising allows examining, defining and bounding the design problem space, eliciting relevant field and phenomenological assumptions, establishing community-level definitions of design principles and problem-solution pairings, and identifying ways to examine the state of knowledge accumulation as well as ways to extend, expand or innovate existing and future designs.

124  Research handbook on information systems and the environment Table 7.1

Overview of Green IS design knowledge in the literature

Papers in total

416

Papers that discuss an IS artefact

74

Type of artefact discussed in paper (Hevner et al., 2004) Constructs (vocabulary and symbols)

46

Models (abstractions and representations)

61

Methods (algorithms, procedures and practices)

38

Instantiations (implemented prototype systems)

41

Papers that offer design knowledge contributions (Gregor & Hevner, 2013) Contribution type level 1 artefact

41

Contribution type level 2 artefact

62

Contribution type level 3 artefact

0

Next, I developed a coding scheme from the main concepts featuring in my framework (Figure 7.1). This coding scheme is summarised in Table 7.2 and explained below. Table 7.2

Summary of coding scheme

Form of artefact

What is the key Green IS artefact central to the paper?

Principles of

What are the salient functional affordances vested in the relationship between the instantiations and their

function

intended users? At which level do the affordances associated with the instantiations purportedly exist (macro or micro)? Which forms of action potential do the affordances associated with the instantiations primarily relate to (belief formation, action formation or outcome assessment)?

Principles of form

What are the salient material properties in which the identified affordances are vested? (the dominant components of the technical objects)? Which primary symbolic expressions do the designers of the instantiations use to communicate action possibilities of the technical objects?

To code principles of function, I evaluated the description of each Green IS instantiation in terms of the ascribed functional affordances (be they existent or potential) that would emerge in the relationship between the artefact and its users as discernible through the writing. I scanned each paper for descriptions of what the “possibilities for goal-oriented action afforded to specified user groups by technical objects” (Markus & Silver, 2008, p. 622) were that became apparent from descriptions of the artefact or its use in the paper. I then classified these affordances by scope of operation (belief formation, action formation and outcome assessment) and level of operation (macro and micro): to code scope of operation, I searched for action potentials that relate to the users’ “motivation, attitudes, or beliefs” and coded these as supporting belief formation; for action formation I searched for users’ “doing, performing, or acting” with the instantiations; and for outcome assessment I considered any “evaluation, monitoring, or controlling” action potentials. To code level of operation, I distinguished affordances that as per description related primarily to uses by individuals (micro level) from those that related primarily to uses by groups, communities, collectives or organisations (macro level). To that end, I searched for descriptions of intended or actual users as “groups, communities, or other collections of actors” (macro level) or “individual actors” (micro level). To illustrate this procedure, consider this extract from Cappiello, Datre, et al. (2013, p. 131, emphasis added):

Designing information systems that support sustainability  125 With the help of that, the ECO2Clouds Accounting Service will be able to extract data [about green metrics, described elsewhere in the same paper] from the monitoring databases into the Accounting Service database to provide monitoring information […] for the ECO2Clouds Portal.

I interpreted this description (together with the remainder of the paper not shown here) as suggesting that from the use of the monitoring infrastructure solution described in the paper, users will be able to realise the affordance of monitoring data about a set of defined “green” metrics of a federated cloud-based system. As per the framework, these potential actions relate to outcome assessment, and the level of operation (in the federated portal solution) related to organisations or other multi-user entities rather than individuals (hence macro). To code principles of form, where sufficient descriptive material was provided in the papers, for each identified functional affordance I ascertained the salient material properties and symbolic expressions as the principles of forms with which designers built their artefacts to provide for these affordances to emerge. To code material properties, I examined the papers for descriptions of salient structural features, attributes or characteristics “intrinsic to the technology in terms of matter and form and that are not part of the social context and endure across contexts and time” (Seidel et al., 2018, p. 225). The coding was exclusive rather than inclusive, meaning that properties were only listed when I felt that they were particularly salient to the artefact’s action potential to emerge. For example, for entirely software-based artefacts I typically did not consider properties of the underlying database structure unless it was a noteworthy condition to realise a particular affordance; an example is the linked dataspace for energy intelligence that forms part of the implementation of the Digital Environment Research Institute (DERI) Energy Observatory (Curry et al., 2012). I examined properties of software and hardware, as well as socio-technical artefacts where possible. For example: a key salient material property of the method artefact described by Cappiello, Plebani, et al. (2013) was its reliance on BonFire (a federated multi-platform cloud facility based on an infrastructure as a service delivery model). Finally, where possible, I coded symbolic expressions manifested in the instantiations. I searched for both functional and values-oriented symbols (Markus & Silver, 2008) purportedly used in the design to communicate to users about how to interact with the instantiations (as per functional symbols) to achieve a certain range of goals. When I found expressions relating to “standards, morals, beliefs, values or structures”, I considered them as values-oriented symbols (Markus & Silver, 2008; Grgecic et al., 2015). Again, I was exclusive rather than inclusive, and focused on particularly noteworthy salient symbolic expressions. For example, most artefacts offering outcome assessment functionality provided outcome data in raw format, or common tables and graphs, which I did not code as particularly salient symbolic expressions. As an example, consider the decision support system implementation prototype described by Bensch et al. (2015). By contrast, the SmartDriver artefact examined by Degirmenci and Recker (2016) involved particular salient symbolic expressions, namely gamified visual and audio objects (such as a cup and a ball, and “fail” sounds). Third, with this coding scheme in place, all papers were coded iteratively and perusing principles of interpretive inquiry (Klein & Myers, 1999): a research assistant was hired to perform the initial coding focusing on the broad categorisation of papers by contribution type and coding of principles of function. This stage proceeded iteratively: the research assistant coded a few papers (typically two or three to start with) and then had this coding critically reviewed by me, using principles of dialogical reasoning and suspicion to question both the

126  Research handbook on information systems and the environment mapping and the conceptual basis for the mapping (i.e., the coding scheme). Once agreement was reached, both the research assistant and I perused the same iterative steps for increasing subsets of the papers (from 3 to 5, to 10, to 20, to 50). We went through approximately five iterations until we reached an inter-subjective agreement about the coding procedures and the coding scheme, and thus we felt that the coding process on the basis of these steps would lead to plausible, dependable and credible outcomes. With procedures and coding scheme clarified this way, I then coded all papers myself. 4.2

Summative Review Outcomes

I now provide a summative, critical review (Paré et al., 2015) of the literature on Green IS design between 2010 and 2016, with the aim of describing (Rowe, 2014) current forms of Green IS design knowledge so that an understanding is provided of both design concepts strongly manifested in available Green IS artefacts and those that have been neglected so far in the accumulation of design knowledge. Table 7.3 summarises my interpretation of the literature with the help of the framework presented in Figure 7.1. With the framework as the conceptual basis of this “review for understanding” (Rowe, 2014; Bandara et al., 2015), I draw four main findings about the current accumulation of Green IS design knowledge from the results summarised in Table 7.3: 1. Most of the available design research around IS to support environmental sustainability operates both at the level of a situated implementation and at the level of design knowledge formulation: by the far the largest share of papers described both more abstract forms of knowledge – in particular architectural models and formal specifications (in mathematical terms) – as well as specific forms of knowledge; i.e., their implementations. Often in these papers, relatively less emphasis was placed on the actual artefact; however, this may also be a product of the publication schemes associated with design research (Gregor & Hevner, 2013). 2. In terms of principles of function supported by the Green IS artefacts, I note an imbalance between belief formation, action formation and outcome assessment: 36 of the 41 artefacts operated in the outcome assessment scope, with a majority (25) at the macro level. By contrast, only six artefacts operated in the belief formation scope; 11 artefacts operated in the action formation scope. Outcome assessment – i.e., the presentation of (environmental) data for consumption by users – is a natural scope of functionality for IS; however, to support sustainable behaviours, it is also a limited scope. Motivating the change of beliefs, values and orientations, and/or offering the possibility of choosing alternative actions and decisions would provide a more direct and potentially stronger transformative potential. One could say that the review shows that most artefacts are not strongly “green” in this sense. 3. The most mature artefacts in the sample (in terms of scope of functionality offered) were two solutions offered through commercial vendors: Power Manager, a commercial solution for smart green technology (Koo & Chung, 2014); and Velix, a web portal designed to motivate customers of a utility company to reduce their electricity consumption (Loock et al., 2013). Both offered outcome assessment together with belief formation and/or action formation functionality. This may be a result of being the outcome of a professional and commercial development cycle that may have had less restrictions than the typical resource base of design science research teams. However, their presence also indicates that it is quite possible to construct artefacts with extensive, and potentially very impactful, comprehensive “green” functionality.

An approach for Complex Urban Implementation of the

comprising industry standard equations, relations and key

two scenarios in a sustainable city

batteries

Principles for sustainable

Blevis, 2007

interaction design

DSS for reusing electric vehicle

et al., 2015

material composition

Beverungen  

 

Design principles

Nascent design theory architecture

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

data)

 

suite); MySQL database

x

of products based on their raw

 

None (raw

Decision model

 

business intelligence

DSS prototype

development commitments

 

 

  SpagoBI (open-source

 

(DSS) to assess sustainability

 

A decision support system

 

2015

 

Bensch et al.,

 

design

 

2015

 

visualisations  

Becker et al.,

Design principles and software

integrability and interactivity Manifesto for sustainability  

charts; listings;

extendibility, communicability,

reporting;

dashboard generators

The Sustainability Modelling and

indicators

configurability, scalability,

2012 Reporting (SMART) framework

models, design editors, report

adaptability, flexibility,

Various: Various: model

 

transformation (SBT) roadmap

environmental

 

simulation

modelling framework

result data)

None (raw

Functional and spatial

infrastructure that supports

x

 

Sundaram,

 

 

ICT system The sustainable business x

x

Prototype to monitor

 

 

expressions

Symbolic

The SMART system

 

 

Material properties

Principles of form

Ahmed &

ICT system or evaluate an existing

can be used to forecast a planned

performance indicators (KPIs) that

technology (ICT) model

purposes of simulating

 

Level 3

Modeling

Macro

 

Micro

information and communications

Macro

model in Matlab for

Micro

Systems ICT Infrastructure

Action Outcome Macro

Mathematical formulation of an

Level 2

Belief

Principles of function Micro

al., 2014

Adepetu et

Level 1

Contribution types

Artefact

Concept matrix of Green IS artefacts reported in the literature

Articles

Table 7.3

Designing information systems that support sustainability  127

(EV) charging strategies

The GAMES approach (green

active management of energy in

2013

Cappiello et

al., 2011

 

 

 

 

 

 

 

 

 

provided

No description

visualisation using colour

support visualisation tool

ValueCharts, a decision Interactive

BonFire Cloud facility

ECO-ARCH (design framework

and architecture analysis

method)

Chen &

Kazman,

2012

ECO-ARCH Method

 

 

exploration

economic) for

environmental,

 

x

 

(social,

 

 

x

wastewater solutions

 

 

 

representation

ULS Green IS Design Framework

 

 

social goals of alternative

 

implementation

DSS prototype

 

environmental, economic and

identify solutions which balance

to help community planners

of BonFire and Zabbix

infrastructure

et al., 2012

infrastructure on basis

the greenness of a cloud

2013

DSS prototype that is designed

Infrastructure architecture

of the monitoring

with a set of metrics to assess

Datre, et al.,

Chamberlain

Formalised metric

Experimentation) Implementation

A monitoring infrastructure

Cappiello,

 

testbed for Future

factors Internet Research and

result data)

None (raw

 

(Building service

BonFire Cloud facility

 

component

performance and environment

 

 

data)

experimental

 

 

the main software

Matlab code as

Vehicle-to-grid-capable None (raw

logic)

x

 

 

BonFIRE platform

 

 

 

optimisation in light of

A method to support

Plebani, et al., energy-aware process design

2013

x

 

 

set theory (first-order predicate

 

 

 

Formalisation of the method using

 

 

x

expressions

Symbolic

of the method on

Architecture

 

Material properties

Principles of form

Implementation

 

simulation

Level 3  

Macro

Mathematical model

Micro

Architecture

Macro

the model in a Matlab

Action Outcome Micro

Implementation of

Level 2

Belief

Principles of function Macro

IT service centres)

DSS to derive electric vehicle

Brandt et al.,

Level 1

Contribution types Micro

Cappiello,

Artefact

Articles

128  Research handbook on information systems and the environment

Architectures and models to

support the lifecycle of green

Chen et al.,

2015

Design principles for the

Corbett, 2013

intelligence based on dataspaces, a linked dataspace

2012

 

 

 

 

x

x

 

 

x

 

 

x

 

 

 

 

 

None (raw

 

 

Resource Description

Linked data architecture  

 

(RFID); iOS application

frequency identification data)

Sensor networks; radio

 

visual and audio representations functionality

information in

braking

acceleration and hard

reducing excessive

energy-efficiency by

order to drive more

application)

& Recker,

accelerometer

 

perusing ioS

 

provides real-time

 

application which

x

metaphors Gamified

Degirmenci

Smartphone app

 

imagery with ontology  

and tables, sensor network

management applications)

enterprise energy

awareness

 

graphs processing, semantic

clouds, support services, energy

intelligence at an

processing and situational

 

interface, complex event

linked energy information

for enterprise energy

linked data, complex event

observatory at the

widget dataspace model,

DERI Smooth Driver (gamified mobile Third-party iOS

2016

 

 

 

 

 

Mash-up

 

 

 

 

 

expressions

Symbolic

components (linked data wrappers,

 

 

 

 

 

Material properties

Principles of form

Framework (RDF) RDF, linked data,

Architecture including main

Implementation of

The linked dataspace for energy

Curry et al.,

Architecture for entity-centric sustainable IS

management system

carbon management systems

(Persuasive) design principles for

 

dashboard

Entity-centric Enterprise energy

2011

Web portal with KPI

 

Prototype

Curry et al.,

sustainable behaviours

promotion of environmentally

management systems for the

development of carbon

system

Level 3

Carbon emission monitoring

Macro  

Micro

 

Macro

 

Action Outcome Micro

2010

Architecture ,models, processes

 

Belief

Principles of function Macro

Choi et al.,

Level 2

Level 1

Contribution types Micro

products

Artefact

Articles

Designing information systems that support sustainability  129

Likely quantified data

simulation

model to quantify the economic

Micro

Macro

Level 3

certificates for services

Maestre,

Carbon-aware green cloud

architecture

Garg &

Buyya, 2012

2012

A methodology to create green

Fugini &

 

 

Architecture

Process diagram

criteria

Mathematical formulation of

Procedural model

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

x

 

 

 

 

 

 

 

bars

rating

vehicles

 

traffic light

 

DIALOG (software)

Architecture

consumption in buildings and

Prototype

Map metaphor;

comparison

system (hardware);

Ranking; email feedback system

 

Prototype GPS sensors;

 

and control of energy

 

 

al., 2012

x

 

Energy monitoring

 

x

A system for remote monitoring

 

 

Främling et

 

 

data)

E-bike usage feedback

x

 

None (raw sensor networks

bars GAMES infrastructure;

2014

 

 

 

feedback intervention providing

 

Mockup of dashboard layout

Architecture

 

An IS-enabled social normative

Email feedback system

 

x

Wortmann,

cloud services

monitor environmental impact of

modules

Architecture

architecture and

 

Flüchter &

2014

Ferreira et al., A cloud services dashboard to

Pseudocode

within the GAMES

 

framework

 

 

Framework

comparison

Graphs; visual

Pernici, 2014

Implemented prototype

 

 

 

An integrated energy-aware

Web portal

Ferreira &

Online portal

if any)

None (unclear

results)  

simulation

Discrete event

Dwyer, 2011

x

 

(simulation  

 

using excess capacity” OPOWER (home energy report)  

x

and environmental effects of  

Macro  

Micro

 

Macro

 

Micro

 

computer-aided event

optimisation problem

model in a discrete

provision: “an optimization

Mathematical model of the

Implementation of the

expressions

Symbolic

model for cloud service

Material properties

Principles of form

Capacity planning optimisation

Action Outcome

Häckel, 2012

Belief

Principles of function

Dorsch &

Level 2

Level 1

Contribution types

Artefact

Articles

130  Research handbook on information systems and the environment

Design space for Stepgreen

(a social comparison platform

for sustainable behaviour)

Printer feedback software

Grevet &

Mankoff,

2009

Hasan et al.,

middleware and a calculation

2011

processed material Green IS requirements

consumption backpacks backpack” (RCB) for each

and GHG emissions on product meat during processing

Concept of “resource consumption

tracking the resource

reporting energy consumption

al., 2013 of single batches of

calculation model

prototype capable of

of collecting, processing and

and process level

Mathematical formulation of

Initial target instance

Green IS artefact that is capable

Beckers, et

Mathematical model

Hilpert,

ADS-B

surveillance radar technique

from the upcoming secondary

actual flight trajectory data

management IS that derives

model for an environmental

A primary data collection

Hilpert et al.,

goals

requirements with efficiency Architecture

Mathematical model

resource consumption

 

Adaptation engine architecture

Prototype to monitor

2012

 

 

 

 

 

 

 

 

 

 

 

 

x

 

x

 

 

 

 

 

 

None (raw data)

database

 

data)

None (raw

Sensor networks; ERP

 

infrastructure

Self-developed test

emoticons

sends email

metaphors;

 

 

RDF

x

 

DERI database and

x

 

Statistics;

 

 

Linked dataspace

 

 

expressions

Symbolic

DERI database

 

 

Material properties

Principles of form

a software that queries

Hedwig et al., A novel adaptation framework

that harmonises operational

Level 3  

Macro  

Micro

 

Macro

 

Action Outcome Micro

 

Design space of social comparison

 

Belief

Principles of function Macro

Implemented as

Level 2

Level 1

Contribution types Micro

2013

Artefact

Articles

Designing information systems that support sustainability  131

Action Outcome

business process sustainability improvement

Environmentally Aware Process

Improvement

2012

procurement

Mobile application for building

energy prediction using

et al., 2015

Kim & Kang,

2016

performance prediction model

Green cloud broker for resource

Ketankumar

information-sharing problem Mathematical model

energy prediction

 

 

x

x

 

 

 

 

data)

None (raw

Smartphone application Gradient

mechanism

the Clarke-Pivotal

implementation of

Python-based

information-sharing

yellow–green)

(red–orange–

 

 

visual rating

 

 

Method

 

 

Architecture

Mobile app for building Mathematical model

model

Implementation of the

data) confidential

in Java

 

 

(SBS) to overcome the

 

 

data)

None (raw x

 

None (raw application; database

 

 

Linux server; software

 

 

MySQL database

Android application;

encryption for

 

 

 

 

 

 

Homomorphic

 

 

 

 

 

 

variant of the model

 

 

x

 

 

x

benchmarking service

 

 

 

 

 

 

A secure sustainability

Mathematical model

 

 

 

 

 

et al., 2011

Implementation of one

 

 

 

 

 

Kerschbaum

Pooja, 2013

Architecture

applications  

Software architecture

the energy efficiency of software model

et al., 2014

Green cloud architecture

Mathematical model

ME3SA, a model for evaluating

Kalaitzoglou

Kalange

 

 

 

Goal-question-metric design model  

Framework for semi-automated

An approach for Semi-automated  

Houy et al.,

Implementation of the

Design principles

sustainability reporting

2014

Meta requirements

A design theory for Green IS for

Hilpert et al.,

 

 

on-board diagnostics;

Prototype to track

vehicles

expressions

Symbolic

metaphors

Material properties

Principles of form

Bluetooth and GPS;

Level 3

parcel-service delivery

Macro

al., 2013

Micro Map

Macro smartphone with

Micro Sensor networks;

Level 2

Belief

Principles of function Macro

carbon emissions of

Carbon tracker

Hilpert,

Level 1

Contribution types Micro

Schumann, et

Artefact

Articles

132  Research handbook on information systems and the environment

metaphors; progress bar

consumption Implementation in Excel VBA

technology

DSS for offshore wind energy

projects

Koukal &

Breitner,

instantiation prototype

provides customers with feedback on their energy consumption

for energy-efficient SaaS

Velix, a web portal designed to

motivate customers of a utility

company to reduce their

electricity consumption

2015

Loock et al.,

2013

for IT-supported energy

Pietsch, 2013

terms of requirements needed for

al., 2008

designing a sustainable IS

A sustainability approach in

Maruster et

management

Maturity assessment

Manthey &

 

 

 

a sustainable information system

Requirements needed for designing  

Maturity assessment model

Procedural model

 

 

 

 

 

 

 

 

 

 

 

 

 

 

metaphors

transport simulation)

with map

 

 

visualisation

simulation (multi-agent

Simulation

in a MATSim

MATSim

rating; avatar for goal setting

Traffic light metering

provided

No description

data)

None (raw

and price)

Web portal; smart

antenna

network; ZigBee

Solar panel; sensor

Excel VBA application

revenue management

 

 

 

 

numbers are

smartphone application

displayed: Wh

None (only

Smart meter device,

Mathematical model

 

x

 

 

 

Architecture

x

 

x

x

x

agent-based modelling

 

 

 

 

 

Imagery with

Implementation of the

 

 

 

 

x

Mac OSX widget

DSS model for sustainable

 

x

 

 

 

 

Smart-card-enabled agent-based

 

 

 

 

x

x

2013

Architecture

Business model canvas

Federated cloud architecture

Mathematical model

Architecture

 

 

Lovrić et al.,

Web portal that

consumption)

collection of energy

(using sensor data

C-SenZ-IS:

Lawson et al., A customisable sensor IS model

2014

measure users’ energy

solution for smart green

Chung, 2014

 

Level 3

Smart meter device to

Macro

Power Manager: a commercial

Micro

Koo &

Macro  

Micro

 

Macro

x

Micro

 

in different visualisation styles

Design principles

Prototype

expressions

Symbolic

displays for sustainable lifestyle

Material properties

Principles of form

Coralog/Timelog: two ambient

Action Outcome

2010

Belief

Principles of function

Kim et al.,

Level 2

Level 1

Contribution types

Artefact

Articles

Designing information systems that support sustainability  133

collaboration platform

non-profit organisations,

model for green and sustainable

al., 2011

subsystems to allow employees

to monitor their power usage

Tawiah et al.,

2014

energy conservation practices

information regarding good

on their computers and share

An application with three

Oppong-

reengineering

to green business process

for a four-phased approach

2011

impact management system

Architecture of an environmental

Architecture

Procedure model

sustainable software”

A reference model for “green and

Guidelines

Smartphone application Adapted Persuasive Design

 

GitHub

power meters

Architecture and methodology

middleware toolkit on

for building software-defined

al., 2012

Nowak et al.,

Implementation as

 

Noureddine et PowerAPI: a middleware toolkit

software and its engineering

GREENSOFT: a reference

Naumann et

and smarter cities

will help create more sustainable

community related projects that

Level 3

governments to collaborate on

 

 

 

 

Macro

academic institutions and local

x

 

 

 

Micro  

 

 

 

 

 

 

 

x

Macro

 

 

 

 

 

 

Micro

 

Action Outcome

x

 

x

 

 

Macro

 

Belief

Principles of function

 

 

 

 

 

Micro

private businesses, foundations,

as a multi-sided

stakeholders, citizens,

 

artefact designed

be used by all community

al., 2015

Smart City: an

A single portal that can

Monteiro et

Level 2

Level 1

Contribution types

Artefact

Articles

metaphor Communication module

Garden display Feedback module;

 

 

Consumption module;

 

 

 

provided

 

No description

architecture

expressions

Symbolic

Multi-sided platform

Material properties

Principles of form

134  Research handbook on information systems and the environment

Extended process modelling notation perCues framework

business processes

A persuasive ambient application perCues mobile

of ecology in IT service

2013

Energy management software

prototype

Reiter et al.,

2014

management (ITSM)

A process for the management

Reiter et al.,

process

of a sample business

and software processes

information

 

to system-related

 

related IT components

 

 

consumption of the

 

 

metaphor

No description provided Heat map

data)

x

 

 

providing access

 

 

 

 

tracing the energy

 

 

x

 

None (raw  

 

 

 

Instrumentation

 

 

 

 

expressions

Symbolic

Windows Management

 

extended ITSM

Requirements for ecologically

 

 

Material properties

Principles of form

VB Express 2010

Prototype written in

 

the car

bus instead of using

caused by taking the

decrease in emissions

relevant bus and the

time of the next

such as the departure

pollution information,

personalised bus and

transportation

 

Level 3

prototype displaying

Micro  

Macro

 

Micro

to foster the usage of public

 

Action Outcome Macro

al., 2007

Macro

 

Belief

Principles of function Micro

Reitberger et

procedure

Activity-based emission analysis

measure carbon emissions in

2012

 

An approach to model and

Recker et al.,

Level 2

Level 1

Contribution types

Artefact

Articles

Designing information systems that support sustainability  135

for cloud-based services with

Simkin, 2014

a web-based prototype where

2014

Implementation (no detail provided)

energy efficiency portal

customers to sign up to an

probabilities of individual

algorithm that predicts

al., 2015

community platform

Web-based prototype

Solver for Excel 2010

Implementation in

Sodenkamp et A supervised classification

a sensemaking process

multiple users can engage in

Green e-community:

Seidel et al.,

aspects of the selection process

a focus on the environmental

A service selection model

Schrödl &

Simkin, 2013

 

 

 

Architecture

Design principles

 

x

Mathematical problem formulation  

activities in SCOR

Model of the resource-relevant

definitions  

performance measurement

Green SCOR

to distribution centre’s green

al., 2013

Schrödl &

indicators with mathematical

A holistic framework dedicated

Schödwell et

Framework of green performance

Process maps

into an organisation’s business  

Process architecture

sustainability-related topics

al., 2015

process management system

Business motivation model

 

An approach to how to integrate

Rozman et

 

Level 3

System architecture

 

 

 

 

 

 

 

Macro

Optimisation model

 

 

x

 

 

 

x

Micro

application

Action Outcome

 

 

 

 

 

 

 

Macro

OptCarShare 1.0: Java

Level 2

Belief

Principles of function

x

 

 

 

 

 

 

Micro

sharing stations

Level 1

Contribution types

 

 

 

 

 

 

 

Macro

al., 2013

Artefact

 

 

 

 

 

 

 

Micro

Rickenberg et DSS for the optimisation of car

Articles

(SVM)

learning algorithm

Supervised machine

 

Excel Solver

 

 

 

numerically

(MIP) model

data)

None (raw

imagery

chatroom

Topic forum/

data)

None (raw

 

 

 

Google map

integer programming

on top of

underlying mixed

Visualisation

expressions

Symbolic

which solves the

IBM ILOG CPLEX,

Material properties

Principles of form

136  Research handbook on information systems and the environment

A framework that contains

Stiel, 2014

Green IS research

governance practices

setting

in a business-to-business (B2B)

behaviour amongst individuals

dashboard

 

actions

 

user-friendly app

x

environmentally responsible

x

Speedometer,  

to complete desired

 

Checklist functionality

of the architecture  

 

as an interactive,

app-based DSS to promote

al., 2015

Implementation

Green Fingerprint: an

Concept of an instantiated artefact

Thompson et

 

IS

 

 

 

inter-organisational environmental

 

 

 

 

business networks

 

 

 

 

environmental IS for sustainable

 

 

 

 

2011

 

 

 

 

 

-Slabeba,

 

 

 

 

 

an architecture artefact for

 

 

 

 

 

for inter-organisational

 

 

 

 

 

An architecture artefact

Design requirements for

modules

Framework including methodology  

 

 

 

expressions

Symbolic

Stanoevska

 

 

Data model

Architecture for reverse logistics

 

 

Material properties

Principles of form

Thies &

dependencies

for inter-organisational

A framework for teaching

2011

logistics decision-making

IS for improving reverse

 

management

An environmental management

Management

Lifecycle analysis (LCA) framework for sustainable IS

 

Analysis in Sustainable IS

Stolze et al.,

Stindt, 2014

2013

Level 3

Stiel & Frank, A framework for Lifecycle

Macro  

Micro

 

Macro

 

Action Outcome Micro

discrete event simulation in

Framework

 

Belief

Principles of function Macro

Methodological toolkit

Level 2

Level 1

Contribution types Micro

research issues for the use of

Artefact

Articles

Designing information systems that support sustainability  137

Total articles: 74

Green IT systems

2010

  41

IT systems 62

Research methodology for Green

retrieval system

A research methodology for

system

Zhong & Liu,

construction of a green information

Four design principles for the

of a green information retrieval

 

 

 

0

 

 

1

 

 

5

 

 

6

5

 

 

25

 

 

 

 

11

  58

Assessment  

 

 

49

data from BC

map

Principles for the construction

topographical Property tax assessment maps

generates a bikeability

Yan, 2014

overlaying

 

a region

 

information system that

 

suitability for cycling

 

 

Heatmaps  

 

designated routes in

 

 

A shapefile for all

x

 

 

a geographic

 

 

 

characterise and map a region’s

 

 

 

2013

 

 

 

expressions

Symbolic

Bikeability:

to sustainability

outlining IS contribution potentials

Framework with principles

interfaces

 

Material properties

Principles of form

Winters et al., A bikeability index to

options

 

Level 3

through open web service

Micro  

Macro

 

Micro

2008

passenger IS across Europe

in Europe

 

Action Outcome Macro

Framework of sustainability

system, connecting autonomous

passenger information systems

Macro

 

Belief

Principles of function Micro

Watson et al.,

a decentralised peer-to-peer

Architecture of EPIS:

interoperability between

2012

 

A concept for the sustainable

Tibaut et al.,

Level 2

Level 1

Contribution types

Artefact

Articles

138  Research handbook on information systems and the environment

Designing information systems that support sustainability  139 4. Most artefacts within the action formation scope were instantiations of the class of decision support systems; i.e., IS that implemented calculation models that evaluated different decision alternatives. Only a few papers discussed artefacts that offered behavioural action potential – Koo and Chung’s (2014) paper again being an exception: the Power Manager tool allows one to actually modify (switch on and off) electrical devices connected via the software. Overall, this broad, summative evaluation illustrates that design science research on Green IS solutions remains at a nascent stage; however, it also pinpoints opportunities where improvements can be offered.

5.

TWO VIGNETTES TO ILLUSTRATE HOW GREEN IS DESIGN CAN BE ADVANCED

In what follows, my aim is to inspect in depth the knowledge contributions offered in two published cases of Green IS design, with the aim to identify how the framework introduced in this chapter might generate novel, prescriptive advice for how the solution potential of the Green IS designs might be improved. 5.1

The Carbon Tracker System

The first Green IS design science study is an example of an existing instantiated Green IS as a situated implementation of a technical object: the carbon tracker system (Hilpert, Schumann, et al., 2013). The carbon tracker system classifies as an instantiation of a system that operates at the outcome assessment macro level. It assists the assessment of the environmental sustainability of some work practices within an organisational unit (here: road transportation processes) by providing functional affordances, amongst others, for environmental indicator data analytics and information diffusion. In this particular instantiation, relevant material properties of the technological system include on-board diagnostics and sensor networks, to mention two salient structure features embodied in its hardware. Symbolic expressions used in the system include, for instance, map metaphors embedded in the user interface. Following the framework shown in Figure 7.1, there are two primary ways in which the solution potential of the carbon tracker system might be improved: first, an immediate increase in solution potential can be achieved by expanding its level of operation from macro to micro. Presently, the assessment functionality operates primarily at the group level (i.e., organisational units). But because macro-level operations depend on and consist of micro-level operations (Goodman, 2000), it would be logical to allow for the assessment functionality and reporting to also operate at the more granular, individual level. Second, by incorporating the flow-on effects from outcome assessment in the development of functionality to support belief formation. Belief formation can occur through IS that support sensemaking (Seidel et al., 2013; Seidel et al., 2018). Environmental sensemaking involves the sensing, weighing and synthesising of environmental data – such as those provided through the carbon tracker system’s outcome assessment functionality – in a way that leads to pro-environmental beliefs (Degirmenci & Recker, 2023). The incorporation of structural features for reflective disclosure

140  Research handbook on information systems and the environment and democratisation enables users to engage in a sensemaking process because these features allow individuals to reflect upon and participate in sustainability conversations on the basis of the outcome assessment data. 5.2

The Green e-Community Platform

A second Green IS design science study concerns an instantiation of a web-based community in a university setting called the green e-community (Seidel et al., 2014; Seidel et al., 2018). This system operates at the belief formation macro level. It provides material properties such as commentary and voting features, as well as symbolic expressions in the form of labels and rankings to enable sensemaking of environmental sustainable behaviours. For example, users of the green e-community platform are shown aggregated indicators in performance of the disposal of plastic cups at the university. To stimulate sensemaking, the environmental impact is depicted in terms of carbon emissions as a consequence of environmentally harmful behaviours. Following the framework shown in Figure 7.1, one primary way in which the solution potential of the green e-community platform might be improved relates to extending its scope of operation from belief formation to action formation: by including “actionable” functionality, the platform could allow users to immediately convert their newly shaped or revisited environmental beliefs by translating them into actions and decisions. This could be done in this particular case, for example, by converting the existing “action plan” (Seidel et al., 2014, p. 440) that displays a list of solutions to particular electronic services and platforms that enact some of these actions. In the example given by Seidel et al. (2014), for instance, a more action-oriented system implementation might not only give information about eco-effective water bottles but also include features to purchase these (through one-click functionality, for example) and/or request the collection of old bottles for purposes of recycling or disposal. The solution potential of the overall system would be enhanced substantially, primarily because outcomes of belief formation are not necessarily stable over time: newly shaped beliefs need to be reinforced through actions to become habitualised and “automatic” (Gollwitzer & Schaal, 1998).

6. CONCLUSIONS In this chapter, I defined a framework to help examine and advance the design knowledge about IS solutions for environmental sustainability. The framework specifies the class of Green IS as socio-technical artefacts that allow organisations to perform environmentally sustainable work practices and make environmentally sustainable decisions at the macro or micro level. Importantly, this so-denoted class of IS has explicit goals of affording environmentally sustainable practices and decisions. It allows designers to specify and implement systems that are true to the label “Green IS” as I define it in this chapter. With this explicit focus and its postulates, the framework I used for reviewing conceptualises only one kind of a class of IS artefacts that could be considered to be supportive of environmental sustainability. Other kinds of classes of artefacts do exist, and this review makes no statement about the relative advantages or disadvantages of different kinds of artefacts. For example, cyber-physical systems describe embedded automated solutions to environmental problems that operate on sensor networks

Designing information systems that support sustainability  141 and sensitised objects, amongst others (e.g., Watson, Boudreau, Li, et al., 2010). Other software systems might also be construed such that environmental sustainability impacts become non-functional requirements that are being considered, prioritised and perhaps implemented (Penzenstadler et al., 2014). These systems may generate positive first-, second- or third-order impacts (Loeser et al., 2017) yet also do not map to my framework’s definition of Green IS. It might be useful to evaluate the framework against progress criteria generally suggested for design theories (Aier & Fischer, 2011), a label that could be applied to the framework: ● Utility is provided because the framework is useful for classifying and evaluating existing Green IS artefacts and substantive-level design theories, as done in this chapter. ● Internal consistency is provided through the integration of two widely used kernel theories and through the clear definition of principles of form and function. ● External consistency is demonstrated, in part, by providing examples on the basis of both existing Green IS artefacts (Hilpert, Schumann, et al., 2013; Seidel et al., 2014), design theories (Hilpert et al., 2014) and empirical studies (Seidel et al., 2013), as well as through examples (such as those in Figure 7.1) that relate to instantiations that do not even yet exist. ● Evaluation of the simplicity of the framework will be the onus of fellow colleagues; however, I believe the framework is specified in a relatively parsimonious manner. ● Regarding fertility (fruitfulness of new research findings), I believe the framework is not only useful for review purposes as reported in this chapter; it could also potentially spawn two types of design science knowledge advancements: invention and exaptation (Gregor & Hevner, 2013). The framework allows for the invention of novel instantiations of IS as new solutions about the formation of environmentally sustainable beliefs, actions and/or outcomes. It allows for such inventions for beliefs, action and/or outcomes that do not yet exist or are not yet understood. The framework also contributes to exaptation: it allows for the development of Green IS for beliefs, actions and/or outcomes that already exist or that are already understood. Examples include systems that assist individuals in forming environmental sustainability beliefs such as awareness of consequences and ascription of responsibility (Steg & Sievers, 2000), or systems that allow individuals to enact environmentally sustainable behaviours such as output management and delocalisation (Seidel et al., 2013). For example, a videoconferencing system might not be specifically designed as a Green IS system but might be used as such (thus corresponding to exaptation) if, like in Seidel et al. (2013), it allows users to replace physical travel and thus carbon emissions through virtual meetings and collaborations. In closing, I want to express a word of caution about the scope of the review presented here and the implications one might draw from the review or the framework used to that end. First, I have used a narrow interpretation of environmental sustainability, one that is focused on resource efficiency rather than resource effectiveness (Chen et al., 2008). More environmentally sustainable work practices and decisions may turn out not to be effective (enough) for sustainable development as a whole. Second, the review here encompassed only one particular timeframe, 2010–2016. I am well aware that since then new papers have been published that present design-oriented IS solutions to environmental sustainability challenges (e.g., Degirmenci & Recker, 2023; Buhl et al., 2019; Benabdellah et al., 2021). Ideally, the review presented in this chapter will be updated from time to time to see if progress in the different areas of Green IS design science scholarship has been made.

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NOTE 1.

ACM Digital Library, AIS Electronic Library, EBSCOhost Business Source Elite, Emerald Insight, IEEE Xplore Digital Library, INFORMS PubsOnline, JSTOR, Palgrave Macmillan, ScienceDirect, SpringerLink, Taylor & Francis Online, Web of Science, Wiley Online Library.

REFERENCES Adepetu, A., Arnautovic, E., Svetinovic, D., & de Weck, O.L. (2014). Complex Urban Systems ICT Infrastructure Modeling: A Sustainable City Case Study. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(3), 363–74. Ahmed, M.D., & Sundaram, D. (2012). Sustainability Modelling and Reporting: From Roadmap to Implementation. Decision Support Systems, 53(3), 611–24. Aier, S., & Fischer, C. (2011). Criteria of Progress for Information Systems Design Theories. Information Systems and e-Business Management, 9(1), 133–172. Alter, S. (2017). Nothing Is More Practical Than a Good Conceptual Artifact … Which May Be a Theory, Framework, Model, Metaphor, Paradigm or Perhaps Some Other Abstraction. Information Systems Journal, 27(5), 671–93. Anderson, C., & Robey, D. (2017). Affordance Potency: Explaining the Actualization of Technology Affordances. Information and Organization, 27(2), 100–115. Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., & Beekhuyzen, J. (2015). Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. Communications of the Association for Information Systems, 37(8), 154–204. Baskerville, R., Kaul, M., & Storey, V.C. (2015). Genres of Inquiry in Design-Science Research: Justification and Evaluation of Knowledge Production. MIS Quarterly, 39(3), 541–64. Baskerville, R., & Pries-Heje, J. (2010). Explanatory Design Theory. Business & Information Systems Engineering, 2(5), 271–82. Becker, C., Chitchyan, R., Duboc, L., Easterbrook, S., Penzenstadler, B., Seyff, N., & Venters, C.C. (2015). Sustainability Design and Software: The Karlskrona Manifesto. 37th International Conference on Software Engineering, Florence, Italy. Benabdellah, A.C., Zekhnini, K., Cherrafi, A., Garza-Reyes, J.A., & Kumar, A. (2021). Design for the Environment: An Ontology-based Knowledge Management Model for Green Product Development. Business Strategy and the Environment, 30(8), 4037–53. https://​doi​.org/​10​.1002/​bse​.2855. Bensch, S., Kolotzek, C., Helbig, C., Thorenz, A., & Tuma, A. (2015). Decision Support System for the Sustainability Assessment of Critical Raw Materials in SMEs. 48th Hawaiian International Conference on Systems Sciences, Kauai, Hawaii. Beverungen, D., Klör, B., Bräuer, S., & Monhof, M. (2015). Will They Die Another Day? A Decision Support Perspective on Reusing Electric Vehicle Batteries. 23rd European Conference on Information Systems, Münster, Germany. Blevis, E. (2007). Sustainable Interaction Design: Invention & Disposal, Renewal & Reuse. 2007 SIGCHI Conference on Human Factors in Computing Systems, San Jose, California. Boell, S., & Cecez-Kecmanovic, D. (2015). On Being “Systematic” in Literature Reviews in IS. Journal of Information Technology, 30(2), 161–73. Brandt, T., Feuerriegel, S., & Neumann, D. (2013). Shaping a Sustainable Society: How Information Systems Utilize Hidden Synergies between Green Technologies. 34th International Conference on Information Systems, Milan, Italy. Brendel, A.B., Zapadka, P., & Kolbe, L.M. (2018). Design Science Research in Green IS: Analyzing the Past to Guide Future Research. 26th European Conference on Information Systems, Portsmouth, England. Buhl, J., Liedtke, C., Teubler, J., Schuster, S., & Bienge, K. (2019). A Material Footprint Model for Green Information Systems: Using Statistical Learning to Identify the Predictors of Natural Resource Use. Cogent Engineering, 6(1), 1616655. https://​doi​.org/​10​.1080/​23311916​.2019​.1616655.

Designing information systems that support sustainability  143 Bunge, M.A. (1977). Treatise on Basic Philosophy, Volume 3: Ontology I – The Furniture of the World. Kluwer Academic. Burton-Jones, A., & Volkoff, O. (2017). How Can We Develop Contextualized Theories of Effective Use? A Demonstration in the Context of Community-Care Electronic Health Records. Information Systems Research, 28(3), 468–89. Butler, T. (2011). Compliance with Institutional Imperatives on Environmental Sustainability: Building Theory on the Role of Green IS. Journal of Strategic Information Systems, 20(1), 6–26. Caldelli, A., & Parmigiani, M.L. (2004). Management Information System: A Tool for Corporate Sustainability. Journal of Business Ethics, 55(2), 159–71. https://​doi​.org/​10​.1007/​s10551​-004​-1899​ -5. Cappiello, C., Datre, S., Fugini, M., Melia, P., Pernici, B., Plebani, P., Gienger, M., & Tenschert, A. (2013). Monitoring and Assessing Energy Consumption and CO2 Emissions in Cloud-Based Systems. International Conference on Systems, Man, and Cybernetics, Manchester, England. Cappiello, C., Fugini, M., Pernici, B., & Plebani, P. (2011). Green Information Systems for Sustainable IT. In A. D’Atri, M. Ferrara, J.F. George & P. Spagnoletti (Eds.), Information Technology and Innovation Trends in Organizations (pp. 153–60). Physica-Verlag. Cappiello, C., Plebani, P., & Vitali, M. (2013). Energy-Aware Process Design Optimization. 3rd International Conference on Cloud and Green Computing, Karlsruhe, Germany. Chamberlain, B.C., Carenini, G., Öberg, G., Poole, D., & Taheri, H. (2012). A Decision Support System for the Design and Evaluation of Sustainable Wastewater Solutions. IEEE Transactions on Computers, 63(1), 129–41. Chen, A.J.W., Boudreau, M.-C., & Watson, R.T. (2008). Information Systems and Ecological Sustainability. Journal of Systems and Information Technology, 10(3), 186–201. Chen, H.-M., & Kazman, R. (2012). Architecting Ultra-Large-Scale Green Information Systems. First International Workshop on Green and Sustainable Software, Zurich, Switzerland. Chen, Y.-C., Chu, C.N., Sun, H.-M., Chen, R.-S., Chen, L.-C., & Chen, C.-C. (2015). Application of Green Collaboration Operation on Network Industry. International Journal of Precision Engineering and Manufacturing-Green Technology, 2(1), 73–83. Choi, H., Park, B., Lee, B., Park, Y., Lee, C., & Ha, J. (2010). The Logistics Equipment Carbon Emission Monitoring System for a Green Logistics. In T.-h. Kim, J. Ma, W.-c. Fang, B. Park, B.-H. Kang & D. Slęzak (Eds.), U- and E-Service, Science and Technology: UNESST 2010 (Vol. 124, pp. 147–53). Springer. Ciavola, B.T., & Gershenson, J.K. (2016). Affordance Theory for Engineering Design. Research in Engineering Design, 27(3), 251–63. Corbett, J. (2013). Designing and Using Carbon Management Systems to Promote Ecologically Responsible Behaviors. Journal of the Association for Information Systems, 14(7), 339–78. Curry, E., Hasan, S., & O’Riain, S. (2012). Enterprise Energy Management using a Linked Dataspace for Energy Intelligence. Sustainable Internet and ICT for Sustainability (SustainIT), Pisa, Italy. Curry, E., Hasan, S., ul Hassan, U., Herstand, M., & O’Riain, S. (2011). An Entity-Centric Approach to Green Information Systems. 19th European Conference on Information Systems, Helsinki, Finland. Degirmenci, K., & Recker, J. (2016). Winning the Environmental Challenge: Using Gamified Information Systems to Reduce Energy Consumption in Electric Vehicles. SIG Green Pre-ICIS Workshop: Digital Innovation for Sustainability – Directions for Policy and Practice, Dublin, Ireland. Degirmenci, K., & Recker, J. (2023). Breaking Bad Habits: A Field Experiment About How Routinized Work Practices Can Be Made More Eco-efficient Through IS for Sensemaking. Information & Management, 60(4), 103778. https://​doi​.org/​10​.1016/​j​.im​.2023​.103778. Dorsch, C., & Häckel, B. (2012). Matching Economic Efficiency and Environmental Sustainability: The Potential of Exchanging Excess Capacity in Cloud Service Environments. 33rd International Conference on Information Systems, Orlando, Florida. Dwyer, C. (2011). Socio-technical Systems Theory and Environmental Sustainability. Sprouts Working Papers on Information Systems, 11(3). Ekman, P., Raggio, R., & Thompson, S. (2015). The Green Fingerprint: Decreasing Energy Consumption with Decision Support Systems. 21st Americas Conference on Information Systems, Fajardo, Puerto Rico.

144  Research handbook on information systems and the environment Elliot, S. (2011). Transdisciplinary Perspectives on Environmental Sustainability: A Resource Base and Framework for IT-Enabled Business Transformation. MIS Quarterly, 35(1), 197–236. Ferreira, A.M., & Pernici, B. (2014). Managing the Complex Data Center Environment: An Integrated Energy-Aware Framework. Computing, 98(7), 709–49. Ferreira, L., Lopes, N., Putnik, G., Lopes, A., & Cunha, M. (2014). Monitoring Dashboard for Cloud Sustainable Greenhouses. 28th EnviroInfo 2014 Conference, Oldenburg, Germany. Flüchter, K., & Wortmann, F. (2014). Promoting Sustainable Travel Behavior through IS-Enabled Feedback: Short-Term Success at the Cost of Long-Term Motivation? 35th International Conference on Information Systems, Auckland, New Zealand. Främling, K., Nyman, J., Kaustell, A., & Holmström, J. (2012). Instance-Informed Information Systems: A Pre-requisite for Energy-Efficient and Green Information Systems. In A. Puiatti & T. Gu (Eds.), MobiQuitous 2011: Mobile and Ubiquitous Systems – Computing, Networking, and Services (Vol. 104, pp. 174–85). Springer. Fugini, M., & Maestre, J.A.P. (2012). Methodology for Green Certificates of Service Applications. 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, Toulouse, France. Garg, S.K., & Buyya, R. (2012). Green Cloud Computing and Environmental Sustainability. In S. Murugesan & G.R. Gangadharan (Eds.), Harnessing Green IT: Principles and Practices (pp. 315–39). Wiley. Gholami, R., Sulaiman, A.B., Ramayah, T., & Molla, A. (2013). Senior Managers’ Perception on Green Information Systems (IS) Adoption and Environmental Performance: Results from a Field Survey. Information & Management, 50(7), 431–38. Gholami, R., Watson, R.T., Hasan, H., Molla, A., & Bjørn-Andersen, N. (2016). Information Systems Solutions for Environmental Sustainability: How Can We Do More? Journal of the Association for Information Systems, 17(8), 521–36. Gibson, J.J. (1977). A Theory of Affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, Acting and Knowing: Toward an Ecological Psychology (pp. 127–43). Lawrence Erlbaum Associates. Gollwitzer, P.M., & Schaal, B. (1998). Metacognition in Action: The Importance of Implementation Intentions. Personality and Social Psychology Review, 2(2), 124–36. Goodman, P.S. (2000). Missing Organizational Linkages: Tools for Cross-Level Research. SAGE. Gregor, S. (2006). The Nature of Theory in Information Systems. MIS Quarterly, 30(3), 611–42. Gregor, S., & Hevner, A.R. (2013). Positioning and Presenting Design Science Research for Maximum Impact. MIS Quarterly, 37(2), 337–55. Gregor, S., & Jones, D. (2007). The Anatomy of a Design Theory. Journal of the Association for Information Systems, 8(5), 312–35. Grevet, C., & Mankoff, J. (2009). Motivating Sustainable Behavior through Social Comparison on Online Social Visualization [Summer 2008 CRA-Distributed Mentor Project Report]. http://​ dreuarchive​.cra​.org/​2008/​Grevet/​report​.pdf. Grgecic, D., Holten, R., & Rosenkranz, C. (2015). The Impact of Functional Affordances and Symbolic Expressions on the Formation of Beliefs. Journal of the Association for Information Systems, 16(7), 580–607. Hacker, J., vom Brocke, J., Handali, J., Otto, M., & Schneider, J. (2020). Virtually in This Together: How Web-Conferencing Systems Enabled a New Virtual Togetherness During the COVID-19 Crisis. European Journal of Information Systems, 29(5), 563–84. https://​doi​.org/​10​.1080/​0960085X​.2020​ .1814680. Hasan, H., Smith, S., & Finnegan, P. (2017). An Activity Theoretic Analysis of the Mediating Role of Information Systems in Tackling Climate Change Adaptation. Information Systems Journal, 27(3), 271–308. Hasan, S., Medland, R., Foth, M., & Curry, E. (2013). Curbing Resource Consumption Using Team-Based Feedback: Paper Printing in a Longitudinal Case Study. In S. Berkovsky & J. Freyne (Eds.), Persuasive Technology: 8th International Conference (Vol. 7822, pp. 75–86). Springer. Hassan, N.R., Lowry, P.B., & Mathiassen, L. (2022). Useful Products in Information Systems Theorizing: A Discursive Formation Perspective. Journal of the Association for Information Systems, 23(2), 418–46. https://​doi​.org/​10​.17705/​1jais​.00730.

Designing information systems that support sustainability  145 Hedwig, M., Malkowski, S., & Neumann, D. (2012). Efficient and Flexible Management of Enterprise Information Systems. 33rd International Conference on Information Systems, Orlando, Florida. Hevner, A.R., March, S.T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75–105. Hilpert, H., Beckers, C., Kolbe, L.M., & Schumann, M. (2013). Green IS for GHG Emission Reporting on Product-Level? An Action Design Research Project in the Meat Industry. In J. vom Brocke, R. Hekkala, S. Ram & M. Rossi (Eds.), DESRIST 2013: Design Science at the Intersection of Physical and Virtual Design (Vol. 7939, pp. 324–39). Springer. Hilpert, H., Friedemann, S., & Schumann, M. (2011). Improved Emission Data Collection in Air Cargo Processes using ADS-B. 22nd Australasian Conference on Information Systems, Sydney, Australia. Hilpert, H., Kranz, J., & Schumann, M. (2014). An Information System Design Theory for Green Information Systems for Sustainability Reporting: Integrating Theory with Evidence from Multiple Case Studies. 22nd European Conference on Information Systems, Tel Aviv, Israel. Hilpert, H., Schumann, M., & Kranz, J. (2013). Leveraging Green IS in Logistics: Developing an Artifact for Greenhouse Gas Emission Tracking. Business & Information Systems Engineering, 5(5), 315–25. Houy, C., Reiter, M., Fettke, P., Loos, P., Hoesch-Klohe, K., & Ghose, A. (2012). Advancing Business Process Technology for Humanity: Opportunities and Challenges of Green BPM for Sustainable Business Activities. In J. vom Brocke, S. Seidel, & J. Recker (Eds.), Green Business Process Management (pp. 75–92). Springer. Jayaram, S., Connacher, H.I., & Lyons, K.W. (1997). Virtual Assembly Using Virtual Reality Techniques. Computer-Aided Design, 29(8), 575–84. Jindrichovska, I., & Purcarea, I. (2011). CSR and Environmental Reporting in the Czech Republic and Romania: Country Comparison of Rules and Practices. Journal of Accounting and Management Information Systems, 10(2), 202–27. Kalaitzoglou, G., Bruntink, M., & Visser, J. (2014). A Practical Model for Evaluating the Energy Efficiency of Software Applications. 2nd International Conference on ICT for Sustainability, Stockholm, Sweden. Kalange Pooja, R. (2013). Applications of Green Cloud Computing in Energy Efficiency and Environmental Sustainability. IOSR Journal of Computer Engineering, 1(6), 25–33. Kerschbaum, F., Strüker, J., & Koslowski, T. (2011). Confidential Information-Sharing for Automated Sustainability Benchmarks. 32nd International Conference on Information Systems, Shanghai, China. Ketankumar, D.C., Verma, G., & Chandrasekaran, K. (2015). A Green Mechanism Design Approach to Automate Resource Procurement in Cloud. Procedia Computer Science, 54, 108–17. Ketter, W., Peter, M., Collins, J., & Gupta, A. (2016). A Multiagent Competitive Gaming Platform to Address Societal Challenges. MIS Quarterly, 40(2), 447–60. Kim, T., Hong, H., & Magerko, B. (2010). Design Requirements for Ambient Display that Supports Sustainable Lifestyle. 8th ACM Conference on Designing Interactive Systems, Aarhus, Denmark. Kim, Y.-R., & Kang, H.J. (2016). Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model. Energies, 9(3), 1–16. Klein, H.K., & Myers, M.D. (1999). A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems. MIS Quarterly, 23(1), 67–94. Koo, C., & Chung, N. (2014). Examining the Eco-Technological Knowledge of Smart Green IT Adoption Behavior: A Self-determination Perspective. Technological Forecasting and Social Change, 88, 140–155. Koukal, A., & Breitner, M.H. (2014). Offshore Wind Energy in Emerging Countries: A Decision Support System for the Assessment of Projects. 47th Hawaii International Conference on System Science, Waikoloa, Hawaii. Kuechler, W., & Vaishanvi, V. (2012). A Framework for Theory Development in Design Science Research: Multiple Perspectives. Journal of the Association for Information Systems, 13(6), 395–423. Lawson, V.J., Watson, R.T., & Ramaswamy, L. (2015). C-SenZ-IS: A Customizable Sensor IS Model for Energy Efficient SaaS. 48th Hawaii International Conference on System Sciences, Kauai, Hawaii. Leidner, D.E., Sutanto, J., & Goutas, L. (2022). Multifarious Roles and Conflicts on an Inter-Organizational Green IS. MIS Quarterly, 46. https://​doi​.org/​10​.25300/​MISQ/​2022/​15116.

146  Research handbook on information systems and the environment Loeser, F., Recker, J., vom Brocke, J., Molla, A., & Zarnekow, R. (2017). How IT Executives Create Organizational Benefits by Translating Environmental Strategies into Green IS Initiatives. Information Systems Journal, 27(4), 503–53. Loock, C.-M., Staake, T., & Thiesse, F. (2013). Motivating Energy-Efficient Behavior with Green IS: An Investigation of Goal Setting and the Role of Defaults. MIS Quarterly, 37(4), 1313–32. Lovrić, M., Li, T., & Vervest, P. (2013). Sustainable Revenue Management: A Smart Card Enabled Agent-based Modeling Approach. Decision Support Systems, 54(4), 1587–1601. MacKerron, G.J., Egerton, C., Gaskell, C., Parpia, A., & Mourato, S. (2009). Willingness to Pay for Carbon Offset Certification and Co-Benefits Among (High-)Flying Young Adults in the UK. Energy Policy, 37(4), 1372–81. Manthey, C., & Pietsch, T. (2013). Developing a Maturity Assessment Model for IT-Supported Energy Management. In B. Funk, P. Niemeyer & J.M. Gómez (Eds.), Information Technology in Environmental Engineering – ITEE2013 (pp. 105–17). Springer. Markus, M.L., Majchrzak, A., & Gasser, L. (2002). A Design Theory for Systems that Support Emergent Knowledge Processes. MIS Quarterly, 26(3), 179–212. Markus, M.L., & Silver, M.S. (2008). A Foundation for the Study of IT Effects: A New Look at DeSanctis and Poole’s Concepts of Structural Features and Spirit. Journal of the Association for Information Systems, 9(10), 609–32. Maruster, L., Faber, N.R., & Peters, K. (2008). Sustainable Information Systems: A Knowledge Perspective. Journal of Systems and Information Technology, 10(3), 218–31. Melville, N.P. (2010). Information Systems Innovation for Environmental Sustainability. MIS Quarterly, 34(1), 1–21. Monteiro, J., Austin, M., Mandilwar, G., & Sharman, R. (2015). Smart City App: Promoting Community Engagement and Collaboration for a Sustainable Future. In B. Donnellan, M. Helfert, J. Kenneally, D. VanderMeer, M. Rothenberger & R. Winter (Eds.), DESRIST 2015: New Horizons in Design Science: Broadening the Research Agenda (pp. 396–400). Springer. Naumann, S., Dick, M., Kern, E., & Johann, T. (2011). The GREENSOFT Model: A Reference Model for Green and Sustainable Software and Its Engineering. Sustainable Computing: Informatics and Systems, 1(4), 294–304. Norman, D.A. (1999). Affordance, Conventions, and Design. Interactions, 6(3), 38–43. Noureddine, A., Bourdon, A., Rouvoy, R., & Seinturier, L. (2012). A Preliminary Study of the Impact of Software Engineering on GreenIT. 1st International Workshop on Green and Sustainable Software, Zurich, Switzerland. Nowak, A., Leymann, F., Schumm, D., & Wetzstein, B. (2011). An Architecture and Methodology for a Four-Phased Approach to Green Business Process Reengineering. In D. Kranzlmüller & A.M. Toja (Eds.), ICT-GLOW 2011: Information and Communication on Technology for the Fight against Global Warming (pp. 150–164). Springer. Oppong-Tawiah, D., Webster, J., Staples, D.S., Cameron, A.-F., & de Guinea, A.O. (2014). Encouraging Sustainable Energy Use in the Office with Persuasive Mobile Information Systems. 35th International Conference on Information Systems, Auckland, New Zealand. Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing Information Systems Knowledge: A Typology of Literature Reviews. Information & Management, 52(2), 183–99. Penzenstadler, B., Raturi, A., Richardson, D., & Tomlinson, B. (2014). Safety, Security, Now Sustainability: The Nonfunctional Requirement for the 21st Century. IEEE Software, 31(3), 40–47. Pfeffer, K., Tuunanen, T., Rothenberger, M.A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77. Pourakbari-Kasmaei, M., Lehtonen, M., Contreras, J., & Mantovani, J.R.S. (2020). Carbon Footprint Management: A Pathway Toward Smart Emission Abatement. IEEE Transactions on Industrial Informatics, 16(2), 935–48. https://​doi​.org/​10​.1109/​TII​.2019​.2922394. Rai, A. (2017). Editor’s Comments: Diversity of Design Science Research. MIS Quarterly, 41(1), iii–xviii. Recker, J., Rosemann, M., Roohi Goohar, E., Hjalmarsson, A., & Lind, M. (2012). Modeling and Analyzing the Carbon Footprint of Business Processes. In J. vom Brocke, S. Seidel & J. Recker (Eds.), Green Business Process Management: Towards the Sustainable Enterprise (pp. 93–110). Springer.

Designing information systems that support sustainability  147 Reitberger, W., Ploderer, B., Obermair, C., & Tscheligi, M. (2007). The PerCues Framework and Its Application for Sustainable Mobility. 2nd International Conference on Persuasive Technology, Palo Alto, California. Reiter, M., Fettke, P., & Loos, P. (2013). Towards a Reference Model for Ecological IT Service Management. 34th International Conference on Information Systems, Milan, Italy. Reiter, M., Fettke, P., & Loos, P. (2014). Towards Green Business Process Management: Concept and Implementation of an Artifact to Reduce the Energy Consumption of Business Processes. 47th Hawaii International Conference on System Science, Waikoloa, Hawaii. Rickenberg, T.A., Gebhardt, A., & Breitner, M.H. (2013). A Decision Support System for the Optimization of Car Sharing Stations. 21st European Conference on Information Systems, Utrecht, The Netherlands. Rowe, F. (2014). What Literature Review Is Not: Diversity, Boundaries and Recommendations. European Journal of Information Systems, 23(3), 241–55. Rozman, T., Draghici, A., & Riel, A. (2015). Achieving Sustainable Development by Integrating It into the Business Process Management System. 22nd European Conference on Software Process Improvement, Ankara, Turkey. Schödwell, B., Erek, K., & Zarnekow, R. (2013). Data Center Green Performance Measurement: State of the Art and Open Research Challenges. 19th Americas Conference on Information Systems, Chicago, Illinois. Schrödl, H., & Simkin, P. (2013). A SCOR Perspective on Green SCM. International Conference on Information Resources Management (CONF-IRM), Natal, Brazil. Schrödl, H., & Simkin, P. (2014). Greening the Service Selection in Cloud Computing: The Case of Federated ERP Solutions. 47th Hawaii International Conference on System Science, Waikoloa, Hawaii. Sedera, D., Lokuge, S., Tushi, B., & Tan, F.B. (2017). Multi-disciplinary Green IT Archival Analysis: A Pathway for Future Studies. Communications of the Association for Information Systems, 41(28), 674–733. Seidel, S., Chandra, L., Reuter, N., Stieger, D., & Gau, M. (2014). Green E-community: Sensemaking in Environmental Sustainability Transformations. In M.C. Tremblay, D.E. VanderMeer, M.A. Rothenberger, A. Gupta & V. Yoon (Eds.), DESRIST 2014: Advancing the Impact of Design Science: Moving from Theory to Practice (Vol. 8463, pp. 438–42). Springer. Seidel, S., Chandra Kruse, L., Székely, N., Gau, M., & Stieger, D. (2018). Design Principles for Sensemaking Support Systems in Environmental Sustainability Transformations. European Journal of Information Systems, 27(2), 221–47. Seidel, S., Recker, J., & vom Brocke, J. (2013). Sensemaking and Sustainable Practicing: Functional Affordances of Information Systems in Green Transformations. MIS Quarterly, 37(4), 1275–99. Sein, M.K., Henfridsson, O., Purao, S., Rossi, M., & Lindgren, R. (2011). Action Design Research. MIS Quarterly, 35(2), 37–56. Sharma, S. (2000). Managerial Interpretations and Organizational Context as Predictors of Corporate Choice of Environmental Strategy. Academy of Management Journal, 43(4), 681–97. Singh, M., & Sahu, G.P. (2020). Towards Adoption of Green IS: A Literature Review Using Classification Methodology. International Journal of Information Management, 54(October), 102147. https://​doi​ .org/​10​.1016/​j​.ijinfomgt​.2020​.102147. Sodenkamp, M., Kozlovskiy, I., & Staake, T. (2015). Gaining IS Business Value through Big Data Analytics: A Case Study of the Energy Sector. 36th International Conference on Information Systems, Fort Worth, Texas. Steg, L., & Sievers, I. (2000). Cultural Theory and Individual Perceptions of Environmental Risks. Environment and Behavior, 32(2), 250–269. Stiel, F. (2014). On the Use of Discrete Event Simulation in Green IS Research: Developing A Conceptual Framework. 22nd European Conference on Information Systems, Tel Aviv, Israel. Stiel, F., & Frank, T. (2013). Towards a Conceptual Framework for Life Cycle Assessment in Sustainable Information Systems Management. 21st European Conference on Information Systems, Utrecht, Netherlands. Stindt, D. (2014). An Environmental Management Information System for Improving Reverse Logistics Decision-Making. ICCL 2014: Computational Logistics, Valparaiso, Chile.

148  Research handbook on information systems and the environment Stoffregen, T.A. (2003). Affordances as Properties of the Animal-Environment System. Ecological Psychology, 15(2), 115–34. Stolze, C., Boehm, M., Zarvić, N., & Thomas, O. (2011). Towards Sustainable IT by Teaching Governance Practices for Inter-Organizational Dependencies. TDIT 2011: Governance and Sustainability in Information Systems – Managing the Transfer and Diffusion of IT, Hamburg, Germany. Strong, D.M., Volkoff, O., Johnson, S.A., Pelletier, L.R., Bar-On, I., Tulu, B., Kashya, N., Trudel, J., & Garber, L. (2014). A Theory of Clinic-EHR Affordance Actualization. Journal of the Association for Information Systems, 15(2), 53–85. Templier, M., & Paré, G. (2015). A Framework for Guiding and Evaluating Literature Reviews. Communications of the Association for Information Systems, 37(6), 112–37. Thies, H., & Stanoevska-Slabeba, K. (2011). Towards Inter-Organizational Environmental Information Systems for Sustainable Business Networks. 17th Americas Conference on Information Systems, Detroit, Michigan. Thompson, S., Ekman, P., & Raggio, R. (2015). The Green Fingerprint: Decreasing Energy Consumption with Decision Support Systems. 21st Americas Conference on Information Systems, Fajardo, Puerto Rico. Tibaut, A., Kaučič, B., & Rebolj, D. (2012). A Standardised Approach for Sustainable Interoperability Between Public Transport Passenger Information Systems. Computers in Industry, 63(8), 788–98. Urquhart, C., & Fernandez, W.D. (2013). Using Grounded Theory Method in Information Systems: The Researcher as Blank Slate and Other Myths. Journal of Information Technology, 28(3), 224–36. Vodanovich, S., Sundaram, D., & Myers, M.D. (2010). Research Commentary: Digital Natives and Ubiquitous Information Systems. Information Systems Research, 21(4), 711–23. Volkoff, O., & Strong, D.M. (2018). Affordance Theory and How to Use It in IS Research. In R. Galliers & M.-K. Stein (Eds.), The Routledge Companion to Management Information Systems (pp. 232–46). Routledge. Walls, J.G., Widmeyer, G.R., & El Sawy, O.A. (1992). Building an Information Systems Design Theory for Vigilant EIS. Information Systems Research, 3(1), 36–59. Wang, X., Brooks, S., & Sarker, S. (2015). A Review of Green IS Research and Directions for Future Studies. Communications of the Association for Information Systems, 37(21), 395–429. https://​doi​ .org/​10​.17705/​1CAIS​.03721. Watson, R.T., Boudreau, M.-C., & Chen, A.J. (2010). Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community. MIS Quarterly, 34(1), 23–38. Watson, R.T., Boudreau, M.-C., Chen, A.J., & Huber, M. (2008). Green IS: Building Sustainable Business Practices. In R.T. Watson (Ed.), Information Systems (pp. 247–61). Global Text Project. Watson, R.T., Boudreau, M.-C., Li, S., & Levis, J. (2010). Telematics at UPS: En Route to Energy Informatics. MIS Quarterly Executive, 9(1), 1–11. Weber, R. (1997). Ontological Foundations of Information Systems. Coopers & Lybrand and the Accounting Association of Australia and New Zealand. Webster, J., & Watson, R.T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii. Weick, K.E. (1995). Sensemaking in Organizations. SAGE. Winters, M., Brauer, M., Setton, E.M., & Teschke, K. (2013). Mapping Bikeability: A Spatial Tool to Support Sustainable Travel. Environment and Planning B: Planning and Design, 40(5), 865–83. Yan, Z. (2014). Construction of Green Information Retrieval System Based on Cloud Computing. 2nd International Conference on Advances in Social Science, Humanities, and Management, Guangzhou, China. Zammuto, R.F., Griffith, T.L., Majchrzak, A., Dougherty, D.J., & Faraj, S. (2007). Information Technology and the Changing Fabric of Organization. Organization Science, 18(5), 749–62. Zhong, Y., & Liu, H. (2010). A Research Methodology for Green IT Systems Based on WSR and Design Science: The Case of a Chinese Company. In M. Zaman, Y. Liang, S.M. Siddiqui, T. Wang, V. Liu & C. Lu (Eds.), E-Business Technology and Strategy: CETS 2010 (Vol. 113). Springer.

8. Digital technology affordances for sustainable business practices Stefan Seidel, Jan Recker and Jan vom Brocke

1. INTRODUCTION Many organizations are investing effort into becoming more environmentally sustainable by lowering resource consumption and improving the environmental footprints of organizational outputs and processes (Loeser, Recker, vom Brocke, Molla & Zarnekow, 2017; Melville, 2010; Nishant, Teo & Goh, 2017; Seidel, Chandra Kruse, Szekely, Gau & Stieger, 2018; Watson, Boudreau & Chen, 2010). Such initiatives occur on many levels: intra-organizational (Seidel, Recker & vom Brocke, 2013), inter-organizational (Ansari & Kant, 2017; Zeiss, Ixmeier, Recker & Kranz, 2021), industry network (Eskandarpour, Dejax, Miemczyk & Péton, 2015), ecosystem (Corbett & Mellouli, 2017), and market (Kahlen, Ketter & Van Dalen, 2018). In this context, we understand sustainable business practices as those that exert minimal or no impact on the natural environment in terms of emissions and waste while using few resources, specifically non-renewable resources (Goodland, 1995). As this volume highlights, digital technologies can help implement sustainable business. While many examples show how digital technologies are used for various environmentally related purposes within and beyond the boundaries of single organizations, the question concerning how institutional elements, technologies, and actors interact to implement sustainable business practices remains. Answering this question can help to clarify how digital technologies can be designed, implemented, and used for sustainability in and across organizations. Drawing on extant research, including our own,1 we address this issue in this chapter. One valuable insight from information systems research is that digital technologies do not necessarily—let alone deterministically—lead to intended effects when the technologies are implemented (Leonardi, 2011; Markus & Silver, 2008; Orlikowski, 2000). Instead, they provide actionable spaces to organizational actors and groups of actors—that is, affordances (Leonardi, 2011; Markus & Silver, 2008; Volkoff & Strong, 2013). These affordances can be discovered and enacted when actors engage with the technology in their business practices as they pursue their goals (Leonardi, 2011), including when their goals are related to environmental sustainability. Typically, once an organization has implemented some new digital technology, the discovery and use of its affordances takes time as actors and digital technologies come to terms and as organizational change occurs (Leonardi, 2011). Therefore, we can view affordances as relationships between digital technologies and organizational actors (Markus & Silver, 2008) that may emerge or evolve over time. However, this interpretation of what a technology affords, such as when a video conferencing system affords new possibilities for collaborative and remote work, is contingent not only on organizational actors and their goals but also on the organizational context in which they are embedded. For example, while video conferencing systems have existed for a long time, their possibilities for enabling a variety of business practices that comply with demands 149

150  Research handbook on information systems and the environment for health, safety, and governmental regulation were realized and used widely only when the Covid-19 pandemic emerged (Hacker, vom Brocke, Handali, Otto & Schneider, 2020). Similarly, whether actors will use a digital technology for purposes that are beneficial to the specific cause of environmental sustainability also depends on the structures that pattern the business practices in an organization. One way to understanding the structural elements that govern business practices is as institutional logics (Essén & Värlander, 2019; Hultin & Mähring, 2017; Seidel & Berente, 2013). Institutional logics describe the material practices and symbols, including assumptions, values, and beliefs, that provide meaning and guide action (Thornton, Ocasio & Lounsbury, 2012). For instance, if organizational actors draw on a sustainability logic—one that comes with explicit environmental goals and scripts about appropriate behaviours (Dahlmann & Grosvold, 2017; Yan, 2017)—then they are more likely to interpret technologies in light of that logic and to find ways of using the technology in the service of improving their own behaviours and ultimately their organization’s environmental footprint (Seidel et al., 2013). Our own research (Seidel et al., 2013; Seidel, Recker, Pimmer & vom Brocke, 2014) highlights that whether one enacts technology in a way that helps implement environmental sustainability depends heavily on whether one draws on a sustainability logic. Still, while logics pattern behaviour and so are useful in understanding that behaviour, they do not determine behaviour. Actors can always choose to do otherwise (Orlikowski, 1992). It is the intertwining of technologies, logics, and actors, including their preferences and backgrounds, that provides actionable spaces—affordances—for business practices, but they do not deterministically lead to those business practices (Seidel & Berente, 2013). However, organizations typically do not singlehandedly follow a sustainability logic; instead they are institutionally plural (Besharov & Smith, 2014)—they espouse multiple, sometimes competing, logics on which actors can draw, such as when companies set out to maximize profits while pushing a sustainability agenda or when employees are instructed to improve their environmental footprint while their contractual key performance indicators (KPIs) stipulate non-sustainable behaviours. Therefore, a significant tension in contemporary organizations is that between an environmental logic and traditional market-based logics, where organizations pursue predominantly economic interests like profit maximization or cost efficiency (Dahlmann & Grosvold, 2017). Since most business organizations continue to pursue key economic imperatives in terms of profit and growth while also trying to be more sustainable (Schneider, 2015), actors that work in these organizations face dual, often conflicting objectives (Dahlmann & Grosvold, 2017). From an environmental point of view, this tension has important implications. First, organizational actors face tensions as they try to reconcile the traditional economic imperatives of a market-based logic with the sustainability goals that are associated with an environmental logic (Dahlmann & Grosvold, 2017; Gümüsay, Claus & Amis, 2020). Therefore, for digital technologies to afford sustainable behaviours, actors must find ways to satisfy these often contradictory logics. In addition, implementing digital technologies in some organizations necessarily involves the import of some institutional element from the field in which they were developed. For instance, if a digital technology was developed under an environmental logic with distinct environmental goals, using this technology in an organization involves the import of elements from that environmental logic, such as the practices that the technology supports (Berente & Seidel, 2022). This requirement has ramifications for those who design

Digital technology affordances for sustainable business practices  151 digital technologies for sustainability, as they must understand that actors in the target context may draw on competing logics. In this chapter, we use the lens of affordances and that of institutional logics to discuss the action potentials that digital technologies may provide to actors at the individual, organizational, and inter-organizational levels as they pursue sustainable business practices. We focus on how actors navigate the tensions between market-based and environmental logics (and, perhaps, other logics). We also discuss the implications of this view for designing and managing digital technologies for sustainability. Next, we provide an overview of the key concepts that are relevant to the topic pursued in this chapter: affordances, institutional logics, and digital technologies for sustainability. Then we discuss under what circumstances organizational members identify and use such affordances and how they may reconcile competing institutional logics. Next, we provide an overview of some known digital technology affordances for sustainable business practices at the individual, organizational, inter-organizational, and market levels. Finally, we highlight some implications for organizational practice.

2.

AFFORDANCES AND INSTITUTIONS

2.1

Notes on Digital Technologies and Sustainability

This volume presents several perspectives on “green information systems,” their design, their management, and their impacts. It is thus important that we highlight the role of digital technologies as we understand it in this chapter on affordances for sustainable business practices. In their seminal paper, Watson and associates (2010) highlight how green information systems help organizations implement sustainable business processes, focusing on the process and outcome of using digital technologies for the purpose of environmental sustainability. In line with this thinking, we examine how digital technologies, as the key technical element in information systems, facilitate environmentally sustainable processes and outcomes (Recker, 2023). Virtually any digital technology can facilitate environmental sustainability if the technology is interpreted as providing actionable spaces for business practices that are environmentally sustainable. For instance, using video conferencing systems like Zoom and Teams allows employees to stay at home during a pandemic and continue to work, but it also reduces travel and the resulting carbon emissions. Using an established enterprise resource planning (ERP) system to report on environmental measures like carbon emissions and the percentage of renewable energy used is a similar application of digital technology that serves the purpose of environmental sustainability. An insurance-sponsored driver-assistance application that was designed to help vehicle operators avoid costly accidents may also lower energy consumption, thereby improving the environmental footprint of driving (Degirmenci & Recker, 2016). Using printer drivers to pre-set printouts to double-sided using black ink reduces costs and is also beneficial for the environment (Seidel et al., 2013). Using enterprise social media for sensemaking around environmental issues allows organizational members to form shared mental models about the environmental consequences of their actions, thus preparing the ground for changing behaviour (Seidel et al., 2018). The point is that these technologies can lead to environmental benefits, but they neither necessarily do so nor are necessarily designed for such goals.

152  Research handbook on information systems and the environment 2.2 Affordances Affordances refer to “the potential for behaviours associated with achieving an immediate concrete outcome and arising from the relationship between an object (e.g., an IT artefact) and a goal-oriented actor or actors” (Volkoff & Strong, 2013, p. 823). Affordances are now the predominant way to think about how digital technologies and organizational actors are involved in organizational practice (Essén & Värlander, 2019; Faik, Barrett & Oborn, 2020; Markus & Silver, 2008; Volkoff & Strong, 2013; Zammuto, Griffith, Majchrzak, Dougherty & Faraj, 2007). Digital technologies afford practices to goal-directed users and groups of users (Markus & Silver, 2008) and are a key element in organizational change (Leonardi, 2011; Strong et al., 2014), as well as societal change (Essén & Värlander, 2019; Faik et al., 2020). Affordances describe the relations between technologies and actors or groups of actors (Markus & Silver, 2008) in that they describe how actors interpret what they can do with a technology to accomplish their goals (Leonardi, 2011; Seidel et al., 2013). At the same time, scholars have argued that affordances are dispositional (Fayard & Weeks, 2014); that is, that their features may pattern the practices that exist as relations between actors and the technology. This argument suggests that one can design for affordances, such as for affordances for environmentally sensitive practices in organizations (Seidel et al., 2018) or for implementing sustainable supply chains (Zampou, Mourtos, Pramatari & Seidel, 2022). However, it is not only the relationship between the technology and the actor that matters, or the designer’s intention; the organizational context in which this action is embedded also matters (Essén & Värlander, 2019; Seidel & Berente, 2013). All organizational practice is embedded in some broader institutional context (Scott, 2014). While structural elements pattern actions, actors can always choose to do otherwise (Orlikowski, 1992). To understand how and why organizational actors and groups of actors choose to use a technology to accomplish an environmental goal or implement an environmental business practice, one must understand the practice scripts, assumptions, values, and beliefs that guide their actions. 2.3

Institutional Logics and Environmentalism

The institutional logics perspective tries to reconcile the relationship between broader institutions and practices (Thornton et al., 2012). Institutional logics are sets of “material practices and symbolic constructions” (Friedland & Alford, 1991, p. 248). Contemporary organizations are institutionally plural, and organizational actors can draw on multiple institutional logics (Besharov & Smith, 2014), and these logics may be consistent or contradictory (Dunn & Jones, 2010; Kraatz & Block, 2008). The tension between established market-based logics (with practices and symbols related to maximising profit) and environmental logics (with practices and symbols related to preserving the environment) is an example of contradictory logics (Dahlmann & Grosvold, 2017). When actors draw on multiple institutional logics, such as both an environmental and a market-based logic, they may reconcile these logics by, for instance, embedding the environmental logic into the existing market-based logic (Dahlmann & Grosvold, 2017). This approach might work when environmental and managerial goals are congruent, such as when saving energy also saves money (Valogianni, Ketter, Collins & Zhdanov, 2020). Alternatively, the logics may co-exist, which will typically be at the cost of the logic that is not dominant, as

Digital technology affordances for sustainable business practices  153 when organizations follow the more dominant economic imperative at the cost of the environmental logic (Dahlmann & Grosvold, 2017). Even in such a case, environmental benefits can still accrue, such as when firms realize unplanned benefits in terms of demand growth or profitability growth from engaging in environmental sustainability initiatives that were not meant to be aligned with the market-based logic (Kranz, Fiedler, Seidler, Strunk & Ixmeier, 2021). As these examples demonstrate, actors can loosely couple practices that are consistent with competing logics, thus satisfying the demands that originate in those logics (Berente & Yoo, 2012; Meyer & Rowan, 1977). The prevailing reality is that only a few organizations, such as non-governmental organizations that have dedicated environmental missions, espouse environmentalism as a dominant logic, while most organizations remain dominated by economic imperatives and some form of managerial rationalism (Berente & Yoo, 2012). Since the primary goal of most businesses remains maximization of firm value in terms of revenue generation or shareholder value, environmental logics will necessarily compete with other logics. Next, we discuss how actors might navigate the pluralism of contemporary organizations using digital technologies that allow them to pursue environmental goals while also meeting economic imperatives.

3.

LEVELS OF INFORMATION SYSTEMS AFFORDANCES FOR SUSTAINABLE BUSINESS PRACTICES

Several studies have identified affordances vested in information systems that facilitate sustainable business practices and processes. However, some of the affordances we identify in this chapter have not been labelled as such because not all authors use the notion of affordances to describe technology-enabled actions but describe what technology features enable what environmentally sustainable practices and processes. Moreover, the action possibilities identified in the literature operate on different levels: among others, key areas in which affordances of digital technologies enable sustainable business practices and processes that have been identified include decision-making (Butler, 2011), sensemaking (Seidel et al., 2013), belief formation (Melville, 2010), automation (Dao, Langella & Carbo, 2011), output management (Seidel et al., 2013), and supply chain management (Zampou et al., 2022). In our own work (Seidel et al., 2013), we identified two key broad categories of affordances that relate to environmental practices: environmental sensemaking and sustainable practicing. “Environmental sensemaking” refers to the processes through which individuals and organizations create an ability to frame complex sustainability issues and prepare for sustainable action (Maitlis, 2005; Seidel et al., 2013; Thomas, Clark & Gioia, 1993; Weick, Sutcliffe & Obstfeld, 2005). “Sustainable practicing” refers to the individual and organizational behaviours that move environmentally harmful practices towards more sustainable ones (Seidel et al., 2013). Both categories of affordances are identified and enacted as organizational actors draw on an environmental logic with associated action goals that can manifest in measurable outcomes like reduced carbon emissions, energy consumption, or percentage of non-renewable energy used (Seidel et al., 2014). While environmental sensemaking and sustainable practicing are organizational-level affordances, they are rooted in individual-level actions. Sensemaking in organizational contexts is a collective process, and sustainable practicing involves groups of actors across an organization.

154  Research handbook on information systems and the environment More broadly speaking, we differentiate among (1) individual-level affordances for sustainable business practices (action potentials provided by digital technologies that help actors implement sustainable individual behaviour), (2) organizational-level affordances for sustainable business practices (actionable spaces provided by digital technologies that help actors implement sustainable organizational practices and processes), (3) inter-organizational affordances for sustainable business practices (action potentials that digital technologies provide to implement sustainable business practices across supply chains or ecosystems), and (4) market-level affordances for sustainable business practices (action potentials that digital technologies provide to change entire sectors’ sustainability processes or outcomes). Table 8.1 provides an overview of these four kinds of affordances, along with some examples. This categorization has implications for the involvement of institutional logics: at the individual level, an actor draws on environmental logics and perceives what a digital technology helps him or her to do in terms of more sustainable business practices, such as when the actor makes environmentally sensitive decisions about consumption (Loock, Staake & Thiesse, 2013). At the organizational level, a group of actors from across the organization draw on the same environmental logic and discover affordances that change some organizational practice or process and reduce the organization’s resource consumption and its associated waste and emissions (Seidel et al., 2013). At the inter-organizational level, groups of actors across organizational boundaries identify what they can do with digital technology to improve their inter-organizational practices and processes, such as in the case of supply chains and supply networks (Dao et al., 2011; De Camargo Fiorini & Jabbour, 2017; Zampou et al., 2022) or waste management (Borchard, Zeiss & Recker, 2021). Finally, at the market level, digital technologies can help actors manage energy markets (Ketter, Collins & Reddy, 2013) given the intermittency problem that is associated with the increasing use of renewable energy sources (Watson, Ketter, Recker & Seidel, 2022). As actors at various levels draw on an environmental logic over time, they may consistently identify and use those affordances across contexts and across time (Seidel & Berente, 2013). Continuously engaging in environmentally sensitive practices through the enactment of affordances may, in turn, impact the institutional context and reify the prevalence of the emergent environmental logic. Table 8.1

Selected digital technology-based sustainability affordances

Affordance level

Description

Examples

Individual

Action potentials for changing individual

● Green decision-making (Butler, 2011; Kranz & Picot,

behaviour as actors draw on an environmental logic

2011) ● Motivating energy-efficient behaviour, such as reducing energy consumption (Loock et al., 2013), water consumption (Tiefenbeck et al., 2018), or paper consumption (Degirmenci & Recker, 2023) ● Formation of environmental beliefs (Melville, 2010)

Organization

Action potentials for changing

● Organizational sensemaking (Seidel et al., 2018)

organizational practices and processes as

● Corporate sustainability reporting (Caldelli &

organizational actors and groups of actors collectively draw on an environmental logic

Parmigiani, 2004) ● Sustainable practicing (Seidel et al., 2013)

Digital technology affordances for sustainable business practices  155 Affordance level

Description

Examples

Inter-organization

Action potentials for changing

● Supply chain management (Björklund, Martinsen &

inter-organizational practices as organizational actors from different

Abrahamsson, 2012; De Camargo Fiorini & Jabbour, 2017; Zampou et al., 2022)

organizations collectively draw on an

● Waste management (Borchard et al., 2021)

environmental logic

● Wildlife management (Pan, Li, Pee & Sandeep, 2021) ● Energy supply and demand management (Watson et al., 2010)

Market

Action potentials for changing market mechanisms as market participants draw on an environmental logic

● Managing energy markets (Ketter, Collins, Gini, Gupta & Schrater, 2012; Ketter et al., 2013) ● Managing mobility markets (Valogianni et al., 2020)

The relationship between individual-level affordances and their organizational enactment deserves some attention (Strong et al., 2014). Authors often assume that affordances occur at the organizational level and are consistently enacted across contexts and time, but affordances, including the affordances for sustainable practicing we discuss in this chapter, originate from individual actors’ engagement with digital technologies in their material practices; that is, in a concrete situation with a concrete action goal in mind (Volkoff & Strong, 2018). It is the individual who identifies that remote work may reduce the environmental footprint of business travel, but only if this affordance is enacted throughout the organization as multiple actors draw on the same environmental logic when using digital technologies does it become an organizational-level phenomenon. Strong et al. (2014), for instance, highlight how individual-level affordances and their enactment contribute to higher-level affordances and associated outcomes. This process becomes visible, for instance, in work that focuses on sustainable individual-level practices like decision-making (Butler, 2011) and work that connects individual- and group-level behaviours (Melville, 2010). Moreover, organizations might introduce an environmental logic through policy definition and goal setting, leading individual actors to identify and enact affordances for sustainable business practices that then produce organizational-level outcomes. That is, the institutional context and its related material practices and symbolic elements influence whether actors and groups of actors identify an affordance (Seidel & Berente, 2013). Drawing on this idea that affordances are discovered and enacted as organizational actors interpret technology given the institutional logics they draw on, Figure 8.1 provides a schematic visualization of the relationship between logics and affordances vested in digital technology in the context of sustainable business practices. As actors and groups of actors interpret a digital technology’s properties in light of an environmental logic and its associated assumptions, values, beliefs, and practice scripts, they may identify affordances for sustainable business practices. Choosing to use these affordances in their practices could then lead to the reduction of environmental impacts, particularly in terms of resource consumption and its associated emissions and waste. Of course, we can conceive of situations in which actors do not explicitly draw on an environmental logic but still embark on environmentally sensitive behaviour. Before we discuss the tensions that may arise when market-based and environmental logics co-exist, we provide short descriptions of key affordances as identified and enacted by actors at each of the four levels. Specifically, we describe and provide examples of how digital technologies can afford sustainable practicing (at the individual or organizational level), environ-

156  Research handbook on information systems and the environment mental sensemaking (at the organizational level), waste management (at the inter-organization level), and rights management on energy markets (at the market level).

Figure 8.1 3.1

Affordances for sustainable business practices on four levels

Example 1: Sustainable Practicing Affordances at the Individual or Organization Level

Sustainable practicing broadly describes digital technology’s action potentials for implementing organizational practices and processes that reduce resource consumption and minimize emissions and waste. Sustainable practicing rests fundamentally on the individual level, where goal-driven actors can use digital technologies to engage in environmentally sustainable action. One example in this context concerns the use of digital technologies like self-tracking devices, feedback systems, and gamified applications to promote sustainable behaviour changes in terms of resource consumption. For example, digital real-time feedback technologies help individuals reduce water consumption while showering (Tiefenbeck et al., 2018), reflective disclosure technologies like reporting systems reduce paper use in printing (Degirmenci & Recker, 2023), and self-tracking devices promote sustainable short-distance mobility (e.g., walking) (Benbunan-Fich, 2019). These examples have in common that such technologies are often designed with an environmental logic in mind and often function appropriately only when individuals are already seeking to pursue environmental action goals by means of their actions. Sustainable practicing affordances can also emerge at the organizational level. Our previous work, for instance, identified sustainable output-management affordances as one such set of affordances (Seidel et al., 2013), as it describes using digital technology to manage resource consumption and its related waste and emissions. For instance, drawing on an environmental logic may demand that an organization reduce its consumption of non-renewable energy or environmentally harmful resources like unrecycled paper. A simple example of output management is setting the organization-wide printer setting to double-sided instead of one-sided or using black ink only instead of colour printing. The material properties of digital technologies

Digital technology affordances for sustainable business practices  157 that allow for output management include configuration features, features for access control, and other controlling features. Another example of sustainable practicing affordances that received particular attention during the Covid pandemic is delocalization affordances (Hacker et al., 2020; Seidel et al., 2013). Delocalization affordances of digital technology allow for performing work independent of the worker’s location, which reduces negative environmental impacts by limiting the movement of resources, including travel. Clearly, digitally enabled remote work has many benefits, increasing resources’ resilience, such as in the case of the Covid pandemic, as well as reducing travel and its related costs. However, only if such affordances are interpreted using an environmental logic do their environmental benefits surface. Reduced travel and its costs are examples of where digitally enabled material practice serves an environmental logic and a market-based logic simultaneously, although opportunity costs, such as those related to not meeting clients face-to-face or decreased quality of work outcomes if actors do not meet in person, may emerge. The material properties of digital technologies that provide the ground for delocalization include such features as file sharing, application sharing, phone conferencing, video conferencing, and instant messaging. 3.2

Example 2: Sensemaking Affordances at the Organizational Level

Sensemaking affordances are possibilities for cognitive activities through which individuals across the organization can frame, interpret, and understand the complex, multi-layered issues related to sustainability transformations (Seidel et al., 2013). For these cognitive activities to occur at the organizational level (i.e., among groups of actors, not just individuals), means for information dissemination and interaction among many individuals across an organization must be available. The key is that digital technologies for environmental sensemaking provide stakeholders with information that helps them reflect on their current practices (reflective disclosure affordances) and provide means for interaction and information dissemination at the organizational level to facilitate inclusion (information democratization affordances). The material properties of digital technologies that provide the basis for sensemaking include features like monitoring and presentation of environmental indicators, data analytics (to allow for reflective disclosure), information access and interaction, and online discussion and content-sharing (to allow for information democratization) (Seidel et al., 2013). Stimulating sensemaking for social issues has been a prevalent concern during the pandemic and has been much influenced by how information is presented, such as through dashboards (Recker, 2021). In line with previous work on sensemaking in general (Weick, 1995, 2001; Weick et al., 2005), our work highlights how this sensemaking process can trigger the implementation of environmentally sustainable business practices. 3.3

Example 3: Supply Chain Management Affordances at the Inter-Organizational Level

If organizations team up in partner networks with a view, for example, to implement sustainable supply chains (Zampou et al., 2022), they can also draw on an environmental logic or even sets of logics to help them identify what certain technologies may allow them to do (afford) at the inter-organizational level. Doing so may mean that the environmental logic creates conflict

158  Research handbook on information systems and the environment with long-held inter-organizational collaboration logics that relate, for example, to trust and data-sharing, and that traditionally prohibited the use of environmental practices (Zeiss et al., 2021). As an example of inter-organizational affordances, consider key technology affordances for managing sustainable supply chains, which include data collection, energy monitoring, supply chain coordination, workflow management, and estimation of carbon footprints. Providing and enacting these affordances requires the integration of key information flows related to transactions, context, energy, and products’ environmental impacts (Zampou et al., 2022). 3.4

Example 4: Pricing Management Affordances at the Market Level

At the market level, digital technologies can provide affordances for sustainable practices like digital mirror actions (Watson et al., 2020), which are technology-based interventions that can be used to manage over- and under-supply from the use of renewable energy sources (Wee, Yang, Chou & Padilan, 2012). Managing deferrables like household appliances whose operation can be deferred to non-peak times, rights markets, and dynamic pricing (Watson et al., 2020) can support the transition to renewable energy. Such management will have to co-occur with the introduction of an environmental logic at the market level, such as in the form of policy, regulation, or subsidy. Carbon pricing (Boyce, 2018) might be one appropriate element to implement such a logic. Key material features to afford the associated practices require digital technologies for management and control, data analytics, and data and communications.

4.

AFFORDANCES FOR SUSTAINABLE PRACTICES AND THE MARKET–ENVIRONMENT TENSION

Earlier in this chapter, we explained how digital technologies can afford sustainable practices and processes when actors and groups of actors draw on an environmental logic that requires actions and behaviours that increase sustainability by reducing resource consumption (input) and waste and emissions (outputs). We also highlighted how these goals may be in conflict with other logics, particularly with a market-based logic, such as when environmental behaviour incurs investments that have no immediate economic benefit. Therefore, to enact sustainable material business practices, organizational actors may have to reconcile and navigate demands that originate from multiple institutional logics that govern their business practices and the relationships between them (e.g., cooperative, congruent, or competitive). The key is that when organizational actors reinterpret a current or new digital technology in light of an environmental logic, they have to find ways of using this technology to satisfy two or more logics. To that end, they embark on institutional work, which is what actors do to create, maintain, and disrupt businesses and institutions (Lawrence & Suddaby, 2006). In some cases, institutional work may mean embedding the environmental logic alongside the dominant market-based logic, which can lead to blended logics over time (Dahlmann & Grosvold, 2017). In other cases, the status quo is preserved and logics and associated material practices are congruent in principle but conflict in practice (Dahlmann & Grosvold, 2017). In such cases, digital technologies’ affordances for sustainable business practices may not be enacted even when they are discovered.

Digital technology affordances for sustainable business practices  159 Continuing from this view, we can conceive of using digital technologies to implement sustainable business practices and processes as institutional work, and we can conceive of affordance identification and enactment as how digital technologies allow organizational actors to create, reproduce, and modify business practices. Sustainable material practices result from actors’ engagement with an environmental logic as they identify actionable spaces that are consistent with this logic. In some situations, market-based and environmental logics can be blended successfully, such as when digitally enabled material business practices simultaneously serve economic and ecological goals. In other cases, these logics may co-exist, but the centrality of the market-based logics is not compromised at the cost of sustainability (Dahlmann & Grosvold, 2017). An organizational actor who implements digital technologies with the goal of improving the organization’s sustainability must be aware of this tension. Moreover, at the inter-organizational level, it is plausible that this tension will be amplified because multiple organizations are likely to draw on different dominant logics. (Think of actors in a non-governmental organization who see an environmental logic as dominant and collaborate with private organizations, such as in private–government partnerships.) Therefore, at a strategic level, organizations must consider how they can introduce an environmental logic and also make this logic more dominant if they want their actors to implement digitally enabled sustainable business practices successfully. In our own research, we have seen such a move implemented through explicit goal setting, organizational policy definition, and the use of sustainability champions (Seidel et al., 2013). When organizational actors interpreted digital technologies under this “shifted logic,” they identified affordances for sustainable business practices, such as when they “realized” that videoconferencing technology allowed them to maintain client contact without physical travel. At an operational level, organizations must seek opportunities to identify when practices that serve multiple coexisting logics, particularly the market-based and the environmental logics, can be implemented. From an affordance perspective, such implementation requires that the technology provide actions that are consistent with both logics, as is the case for reducing travel through the delocalization affordances videoconferencing solutions offer (cost reduction and smaller environmental footprint). Still, it is also clear that focusing only on reconciliation against the background of a dominant market-based logic will not allow organizations and inter-organizational networks to move fully to sustainable business practices. This insight has implications for the design of digital technologies for sustainability and for their adoption and use in organizational practice. We discuss these issues next.

5.

DESIGNING TECHNOLOGY TO HAVE AFFORDANCES FOR SUSTAINABLE PRACTICES

Our research (Recker, 2023; Seidel et al., 2018), along with that of others (for instance, Hilpert, Kranz & Schumann, 2013), highlighted the possibility of designing technologies to have affordances for sustainable business. However, some challenges exist. Most notably, it is long held in the information systems field that one must distinguish the development context from the use context of information technology (Orlikowski, 1992). One way to address this difference is through the lens of institutional logics, as the institutional context of developing

160  Research handbook on information systems and the environment a technology is different from the context of its use. This approach has two primary implications for the design of digital technologies for sustainability. First, when designing technology that has affordances for sustainable business practices, designers will necessarily draw on their own experiences in their fields of practice. When the designed technology is used in its target context, this development logic is, at least to a certain extent, imported to that context (Berente & Seidel, 2022). Information systems designers must be aware of this shift; that is, for a certain digital technology to be useful in a target context, its developers must anticipate the logics that actors are likely to draw on in that context (Berente & Seidel, 2022). Key questions include what features are likely to afford sustainable business practices in the target context, and what institutional elements, such as policies or norms, may prevent users from using the system? There will be differences based on whether one develops a digital technology for sustainable business practices in a manufacturing context or in a software development context. Designers must be aware that the new environmental logic carried by the digital technology they are designing might create tensions with the incumbent market-based logic in the target context. Finally, designers must learn from experiences with the technology in other target contexts and determine under what conditions what digital technology feature affords what environmental action and what that means for future designs. Second, the organization or network of organizations must be aware that importing a sustainability logic may be at odds with incumbent logics. Our research suggests that implementations must be accompanied by explicit institutional efforts to introduce structural change, such as through policy definition and the use of sustainability agents. When investing or implementing digital technologies for sustainability, organizations must ask to what extent the new practices will be compatible with the established market-based practices that are associated with the market-based logic. Therefore, adoption and use will depend on the tensions that are created as the new digital technology is implemented in its target context.

6.

CONCLUSION AND OUTLOOK

At a time when research on the green information systems field was still in its infancy, we asked (Seidel et al., 2013), how do information systems contribute to the implementation of sustainable business practices? We found at that time that digital technologies can provide key affordances for sensemaking and sustainable practicing if they are interpreted while considering new, environmentally related action goals. One key insight was that the material properties of digital technologies lead to implementation of sustainable business practices only if actors draw on an environmental logic with appropriate norms, values, beliefs, and practice scripts (although we did not use that language at that time). We highlighted the role of management in shaping the organization’s institutional set-up through, for instance, goal setting and the use of “sustainability agents” as a type of change agent. This insight about the role of context in interpreting digital technologies to provide environmental benefits was the logical point of departure for this book chapter. While our original work focused on organizational-level business practices, we broadened our view in writing this chapter by arguing that these affordances for sustainable business practices are no mere organizational-level phenomena but can occur at multiple levels, including the individual, organizational, inter-organizational, and market levels. Indeed, the literature on green information systems now provides a plethora of applications that highlight this view. Seen through

Digital technology affordances for sustainable business practices  161 the lens we used in this chapter these applications are based on the interpretation of digital technologies while actors draw on an environmental logic. However, we also highlight the naivety of assuming that actors simply draw on an environmental logic, identify and enact affordances for sustainable business practices, and magically reduce their environmental footprint. Instead, organizations are pluralistic, and tensions between the emergent environmental logic and the prevalent logic(s) occur. Consequently, organizational actors will have to reconcile this logic with other logics that typically revolve around economic imperatives like time, cost, quality, and (ultimately) profit. Recent movements in response to climate change, as well as political agendas, are likely to strengthen the institution of environmentalism and, hence, the associated environmental logic. Therefore, we anticipate that accommodating for environmental logics and implementing material practices that are consistent with such logics, particularly in terms of values related to reduced resource consumption, emissions, and waste, will be increasingly prevalent. At the same time, digital technologies have the potential of “distributing” environmental logics through their role as carriers and will be involved in this change towards environmentalism.

NOTE 1. We have conducted qualitative, quantitative, and design-oriented research on how green information systems can be designed, how they allow organizations to implement sustainable business practices, and with what effect.

REFERENCES Ansari, Z.N., & Kant, R. (2017). A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management. Journal of Cleaner Production, 142, 2524–43. Benbunan-Fich, R. (2019). An affordance lens for wearable information systems. European Journal of Information Systems, 28(3), 256–71. Berente, N., & Seidel, S. (2022). Digital technologies: Carrier or trigger for institutional change in digital transformation? In T. Gegenhuber, D. Logue, C.R. Hinings & M. Barrett (Eds.), Digital Transformation and Institutional Theory (pp. 197–210). Bingley: Emerald Group. Berente, N., & Yoo, Y. (2012). Institutional contradictions and loose coupling: Postimplementation of NASA’s enterprise information system. Information Systems Research, 23(2), 376–96. Besharov, M.L., & Smith, W.K. (2014). Multiple institutional logics in organizations: Explaining their varied nature and implications. Academy of Management Review, 39(3), 364–81. Björklund, M., Martinsen, U., & Abrahamsson, M. (2012). Performance measurements in the greening of supply chains. Supply Chain Management: An International Journal, 17(1), 29–39. Borchard, R., Zeiss, R., & Recker, J. (2022). Digitalization of waste management: Insights from German private and public waste management firms. Waste Management & Research, 40(6), 775–92. Boyce, J.K. (2018). Carbon pricing: Effectiveness and equity. Ecological Economics, 150, 52–61. Butler, T. (2011). Compliance with institutional imperatives on environmental sustainability: Building theory on the role of green IS. The Journal of Strategic Information Systems, 20(1), 6–26. Caldelli, A., & Parmigiani, M.L. (2004). Management information system: A tool for corporate sustainability. Journal of Business Ethics, 55(2), 159–71. Corbett, J., & Mellouli, S. (2017). Winning the SDG battle in cities: How an integrated information ecosystem can contribute to the achievement of the 2030 Sustainable Development Goals. Information Systems Journal, 27(4), 427–61. Dahlmann, F., & Grosvold, J. (2017). Environmental managers and institutional work: Reconciling tensions of competing institutional logics. Business Ethics Quarterly, 27(2), 263–91.

162  Research handbook on information systems and the environment Dao, V., Langella, I., & Carbo, J. (2011). From green to sustainability: Information technology and an integrated sustainability framework. Journal of Strategic Information Systems, 20(1), 63–79. De Camargo Fiorini, P., & Jabbour, C.J.C. (2017). Information systems and sustainable supply chain management towards a more sustainable society: Where we are and where we are going. International Journal of Information Management, 37(4), 241–49. Degirmenci, K., & Recker, J. (2016). Winning the Environmental Challenge: Using Gamified Information Systems to Reduce Energy Consumption in Electric Vehicles. Paper presented at the SIG Green Pre-ICIS Workshop: Digital Innovation for Sustainability: Directions for Policy and Practice, Dublin, Ireland. Degirmenci, K., & Recker, J. (2023). Breaking bad habits: A field experiment about how routinized work practices can be made more eco-efficient through IS for sensemaking. Information & Management, 60(4), 103778. Dunn, M.B., & Jones, C. (2010). Institutional logics and institutional pluralism: The contestation of care and science logics in medical education, 1967–2005. Administrative Science Quarterly, 55(1), 114–49. Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, 54, 11–32. Essén, A., & Värlander, S.W. (2019). How technology-afforded practices at the micro-level can generate change at the field level: Theorizing the recursive mechanism actualized in Swedish rheumatology 2000–2014. MIS Quarterly, 43(4), 1155–76. Faik, I., Barrett, M., & Oborn, E. (2020). How information technology matters in societal change: An affordances-based institutional logics perspective. MIS Quarterly, 44(3), 1359–90. Fayard, A.-L., & Weeks, J. (2014). Affordances for practice. Information and Organization, 24(4), 236–49. Friedland, R., & Alford, R.R. (1991). Bringing society back in: Symbols, practices and institutional contradictions. In W.W. Powell & P.J. DiMaggio (Eds.), The New Institutionalism in Organizational Analysis (pp. 223–62). Chicago, IL: University of Chicago Press. Goodland, R. (1995). The concept of environmental sustainability. Annual Review of Ecology and Systematics, 26, 1–24. Gümüsay, A.A., Claus, L., & Amis, J. (2020). Engaging with grand challenges: An institutional logics perspective. Organization Theory. doi:10.1177/2631787720960487. Hacker, J., vom Brocke, J., Handali, J., Otto, M., & Schneider, J. (2020). Virtually in this together: How web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis. European Journal of Information Systems, 29(5), 563–84. Hilpert, H., Kranz, J., & Schumann, M. (2013). Leveraging green IS in logistics. Business & Information Systems Engineering, 5(5), 315–25. Hultin, L., & Mähring, M. (2017). How practice makes sense in healthcare operations: Studying sensemaking as performative, material-discursive practice. Human Relations, 70(5), 566–593. Kahlen, M.T., Ketter, W., & Van Dalen, J. (2018). Electric vehicle virtual power plant dilemma: Grid balancing versus customer mobility. Production and Operations Management, 27(11), 2054–70. Ketter, W., Collins, J., Gini, M., Gupta, A., & Schrater, P. (2012). Real-time tactical and strategic sales management for intelligent agents guided by economic regimes. Information Systems Research, 23(4), 1263–83. Ketter, W., Collins, J., & Reddy, P. (2013). Power TAC: A competitive economic simulation of the smart grid. Energy Economics, 39, 262–70. Kraatz, M.S., & Block, E.S. (2008). Organizational implications of institutional pluralism. In R. Greenwood, C. Oliver, R. Suddaby & K. Sahlin (Eds.), The SAGE Handbook of Organizational Institutionalism (pp. 243–75). Thousand Oaks, CA: SAGE. Kranz, J., Fiedler, M., Seidler, A., Strunk, K., & Ixmeier, A. (2021). Unexpected benefits from a shadow environmental management information system. MIS Quarterly Executive, 20(3), 6. Kranz, J., & Picot, A. (2011). Why Are Consumers Going Green? The Role of Environmental Concerns in Private Green-IS Adoption. Paper presented at the European Conference on Information Systems 2011.

Digital technology affordances for sustainable business practices  163 Lawrence, T.B., & Suddaby, R. (2006). 1.6 institutions and institutional work. In R. Clegg, C. Hardy, T.B. Lawrence, & W.R. Nord (Eds.), Handbook of Organization Studies (pp. 215–54). London: SAGE. Leonardi, P.M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147–68. Loeser, F., Recker, J., vom Brocke, J., Molla, A., & Zarnekow, R. (2017). How IT executives create organizational benefits by translating environmental strategies into green IS initiatives. Information Systems Journal, 27(4), 503–53. Loock, C.-M., Staake, T., & Thiesse, F. (2013). Motivating energy-efficient behavior with green IS: An investigation of goal setting and the role of defaults. MIS Quarterly, 37(4), 1313–32. Maitlis, S. (2005). The social processes of organizational sensemaking. Academy of Management Journal, 48(1), 21–49. Markus, M.L., & Silver, M.S. (2008). A foundation for the study of IT effects: A new look at DeSanctis and Poole’s concepts of structural features and spirit. Journal of the Association for Information Systems, 9(10), 609–32. Melville, N.P. (2010). Information systems innovation for environmental sustainability. MIS Quarterly, 34(1), 1–21. Meyer, J.W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340–363. Nishant, R., Teo, T.S.H., & Goh, M. (2017). Do shareholders value green information technology announcements? Journal of the Association for Information Systems, 18(8), 542–76. Orlikowski, W.J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427. Orlikowski, W.J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–28. Pan, S.L., Li, M., Pee, L., & Sandeep, M. (2021). Sustainability design principles for a wildlife management analytics system: An action design research. European Journal of Information Systems, 30(4), 452–73. Recker, J. (2021). Improving the state-tracking ability of Corona dashboards. European Journal of Information Systems, 30(5), 476–95. Recker, J. (2023). Designing information systems that support environmental sustainability: A framework-based review. In V. Cooper, J. Kranz, R. Watson, & S. Matthews (Eds), Research Handbook on Information Systems and the Environment (pp. 114–48). Cheltenham, UK and Northampton, MA: Edward Elgar Publishing. Schneider, A. (2015). Reflexivity in sustainability accounting and management: Transcending the economic focus of corporate sustainability. Journal of Business Ethics, 127(3), 525–36. Scott, W.R. (2014). Institutions and Organizations: Ideas, Interests, and Identities (4th ed.). Thousand Oaks, CA: SAGE. Seidel, S., & Berente, N. (2013). Toward “Third Wave” Information Systems Research: Linking Sociomaterial Practice with Broader Institutional Logics. Paper presented at the 34th International Conference on Information Systems (ICIS 2013), Milan, Italy. Seidel, S., Chandra Kruse, L., Szekely, N., Gau, M., & Stieger, D. (2018). Design principles for sensemaking support systems in environmental sustainability transformations. European Journal of Information Systems, 27(2), 221–47. Seidel, S., Recker, J., & vom Brocke, J. (2013). Sensemaking and sustainable practicing: Functional affordances of information systems in green transformations. MIS Quarterly, 37(4), 1275–99. Seidel, S., Recker, J.C., Pimmer, C., & vom Brocke, J. (2014). IT-enabled sustainability transformation: The case of SAP. Communications of the Association for Information Systems, 35(1), 1–17. Strong, D.M., Volkoff, O., Johnson, S.A., Pelletier, L.R., Tulu, B., Bar-On, I., … Garber, L. (2014). A theory of organization-EHR affordance actualization. Journal of the Association for Information Systems, 15(2), 53–85. Thomas, J.B., Clark, S.M., & Gioia, D.A. (1993). Strategic sensemaking and organizational performance: Linkages among scanning, interpretation, action, and outcomes. Academy of Management Journal, 36(2), 239–70.

164  Research handbook on information systems and the environment Thornton, P.H., Ocasio, W., & Lounsbury, M. (2012). The Institutional Logics Perspective: A New Approach to Culture, Structure, and Process. Oxford: Oxford University Press on Demand. Tiefenbeck, V., Goette, L., Degen, K., Tasic, V., Fleisch, E., Lalive, R., & Staake, T. (2018). Overcoming salience bias: How real-time feedback fosters resource conservation. Management Science, 64(3), 1458–76. Valogianni, K., Ketter, W., Collins, J., & Zhdanov, D. (2020). Sustainable electric vehicle charging using adaptive pricing. Production and Operations Management, 29(6), 1550–1572. Volkoff, O., & Strong, D. (2013). Critical realism and affordances: Theorizing IT-associated organizational change processes. MIS Quarterly, 37(3), 819–34. Volkoff, O., & Strong, D.M. (2018). Affordance theory and how to use it in IS research. In B. Galliers & M.-K. Stein (Eds.), The Routledge Companion to Management Information Systems (pp. 232–45). Abingdon: Routledge. Watson, R.T., Boudreau, M.-C., & Chen, A.J. (2010). Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Quarterly, 34(1), 23–38. Watson, R.T., Ketter, W., Recker, J., & Seidel, S. (2022). Sustainable energy transition: Intermittency policy based on digital mirror actions. Journal of the Association for Information Systems, 23(3), 631–38. Wee, H.-M., Yang, W.-H., Chou, C.-W., & Padilan, M.V. (2012). Renewable energy supply chains, performance, application barriers, and strategies for further development. Renewable and Sustainable Energy Reviews, 16(8), 5451–65. Weick, K.E. (1995). Sensemaking in Organizations (Vol. 3). Thousand Oaks, CA: SAGE. Weick, K.E. (2001). Making Sense of the Organization. Malden, MA: Blackwell. Weick, K.E., Sutcliffe, K.M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16(4), 409–21. Yan, S. (2017). When a Competing Logic Arises: How Environmental Investment Funds Make Firms Greener. Paper presented at the Academy of Management Proceedings. Zammuto, R.F., Griffith, T.L., Majchrzak, A., Dougherty, D.J., & Faraj, S. (2007). Information technology and the changing fabric of organization. Organization Science, 18(5), 749–62. Zampou, E., Mourtos, I., Pramatari, K., & Seidel, S. (2022). A design theory for energy and carbon management systems in the supply chain. Journal of the Association for Information Systems, 23(1), 329–371. Zeiss, R., Ixmeier, A., Recker, J., & Kranz, J. (2021). Mobilising information systems scholarship for a circular economy: Review, synthesis, and directions for future research. Information Systems Journal, 31(1), 148–83.

9. Green IS: an imperative and an opportunity for IT services Saji K. Mathew and Thillai Rajan

1. INTRODUCTION Amidst the rising concerns over a future climate disaster, development without adverse effects on the environment remains a global challenge (Gates, 2021). As the quality of life and productivity of human beings are a function of access to modern technologies, the production and consumption of the latter must go green to affect the former. Sustainable development is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (Brundtland et al., 1987, para. 1). Corporate policies play a critical role towards such sustainable development. However, building a culture of sustainability appears to be in conflict with the primary motive of corporations, which is to generate shareholder wealth. This is because switching to sustainable practices would require corporations to pay a green premium (Gates, 2021). On the other hand, environmental, social and governance (ESG) responsibility has become a priority today for several corporations (Economic Times, 2021). What is driving this corporate concern for the environment? In particular, do for-profit business entities construe environmental responsibility as a cost to comply with, or as an opportunity to create new business or as an imperative for business sustainability? This chapter addresses these questions with a special focus on India’s IT services industry. Sustainable development seeks to bring a balance between a country’s economic development and the environment. India ranks third in the world in greenhouse gas (GHG) emissions, after China and the US. However, the per capita GHG emissions are comparatively lower due to the large population of the country. According to some estimates the environmental impact of economic development in India has contributed to losses of US$79.5 billion from extreme climate events and $100 billion due to vector-borne diseases in the last two decades (Shah, 2021). The gross domestic product (GDP) of India grew from $468.4 billion in 2000 to $2.87 trillion in 2019 (World Bank, 2021). These figures reflect how the growth aspirations of a country could invite damage to its environment, unless moderated by considered sustainable development policies and practices. India’s IT sector has made significant contributions to the country’s economic development. The sector grew from $1 billion in 2000 to $194 billion in 2021 (NASSCOM, 2021). With legally binding global frameworks like the Kyoto Protocol, the industry became increasingly aware of the need to comply with emission standards (Misra & Srivastava, 2012). The IT sector, which employs about 4.5 million people (Statistica, 2020) also loses $1.3 billion due to pollution-induced productivity losses every year (Shah, 2021). Although the rationale for compliance with environmental regulations is obvious in this context, information systems (IS) research on the role of IT industry for environmental sustainability is quite limited. 165

166  Research handbook on information systems and the environment The purpose of this chapter is to explore how IT services as an industry could contribute to environmental sustainability while also supporting client organisations through automation and decision support. In particular we investigate a leading IT services organisation in India with specific focus on the evolution of its sustainability practice into a multi-fold strategy. We review extant literature pertaining to Green IS and organisations, and formulate a research methodology to investigate the case of Infosys Technologies Ltd. We chart the evolution of the company and its sustainability strategy using primary data conducted through an on-site case study in 2012. We subsequently employ secondary data sources to analyse the growth of sustainability practices with specific focus on the period from 2012–2020. We conclude the chapter with insights for Green IS studies, particularly in relation to IT firms and how their beliefs developed from environmental changes drive certain actions and outcomes.

2.

GREEN IS AND THE IT SERVICES INDUSTRY

In the 1960s, and for the several decades thereafter, IS research focused predominantly on how IT contributes to organisations and business performance, and much less on the environment and sustainability (Dedrick, 2010; Watson et al., 2010). Recognising this critical gap in IS research, Green IS has been intently fostered as a research stream distinct from Green IT (Vom Brocke et al., 2013; Hasan et al., 2017; Melville, 2010; Seidel et al., 2013; Watson et al., 2010). Green IS turns the spotlight for IS research on environmental sustainability (Watson et al., 2010), which refers to designing, implementing and transforming IS to contribute towards sustainable business processes. IT infrastructure as a whole contributes relatively less to global GHG emissions – a meagre 2 percent compared to 27 percent by energy production (Gates, 2021). In this context, it is all the more important to turn attention from Green IT to IS as a whole, to make the world green. IS enable business organisations to address environmental degradation in two ways. First, adopting eco-efficient practices through automation enhances operational efficiency and reduces costs to organisations (Chen et al., 2008). Second, IS generates relevant information for internal and external stakeholders to ensure eco-equity in decision-making (Dyllick & Hockerts, 2002; Seidel et al., 2013; Watson et al., 2010). “Eco-equity” refers to the “equity between peoples and generations and, in particular, the equal rights of all peoples to environmental resources” (Gray & Bebbington, 2000, p. 7). IS enable sustainable organisational practices and processes to connect information, organisations and the natural environment to improve environmental and economic performance (Melville, 2010; Molla et al., 2011). A review of Green IS literature (Vom Brocke et al., 2013) has shown that prior Green IS studies have highlighted the role of IS for behavioural change in organisations (e.g., Kranz & Picot, 2011), IS as a source of solution for eco-effectiveness (e.g., Watson et al., 2011) and IS for sustainability transformations through affordances (e.g., Seidel et al., 2013). Following the Belief–Action–Outcome framework for IS sustainability studies proposed by Melville (2010), Loeser et al. (2017) conducted an empirical investigation on the nature of the benefits organisations could accrue from sustainability initiatives. This study reported three key benefits: cost reductions, corporate reputation enhancement and green innovation capabilities. These studies highlight IS as a solution to bring transformational changes in organisations. However, the role of solution agents like the consulting and services organisations in influencing other organisations for sustainable business practices has not been addressed sufficiently in extant

Green IS  167 literature. This study focuses on one such IT services provider in India and the evolution of the company’s focus on sustainability.

3.

RESEARCH METHODS

The purpose of our study is to develop understanding about the motivations of an IT services organisation in pursuing environmental sustainability as a practice. We adopted an in-depth longitudinal case study approach (Yin, 2015) to understand the journey of sustainability initiative at Infosys, a globally respected Indian IT services provider. We use “longitudinal” to mean the long duration of 2012–2021, during which we collected data from multiple sources to analyse sustainability policy interventions and their outcomes. We collected data predominantly through the interview method, person to person, telephonic and focus group discussion (FGD). Our contact with the Sustainability Group of Infosys was established through the CEO of the organisation. We conducted two FGDs with the Sustainability Group of Infosys during 2012. They were spaced three months apart. The three-month gap helped us to understand information received during the first interaction and ask more specific questions during the second. The respondents included the VP of Education & Research, Associate VP of Education & Research, and three executive members of the Sustainability Group. We also conducted individual interviews with the executives to understand the rationale behind Infosys’ specific sustainability initiatives. In total we conducted two FGDs and 10 face-to-face interviews. An early version of the case study was published in 2013 (Mathew & Rajan, 2013). After 2012, we maintained contact with the Sustainability Group through occasional telephonic interviews and meetings at IT/IS events. We also referred to secondary sources such as the company website, published cases about the organisation, press releases and annual reports. We have used data from sustainability reports from 2007 to 2020, available at https://​ www​.infosys​.com/​about/​corporate​-responsibility/​sustainability​-reports​.html. This case study presents the progressive evolution of Infosys from its beginnings through different stages of innovation in the IT services industry. Senior executives at Infosys believed that the sustainability initiatives at the company were not a new movement, but a logical extension of the company’s long-standing commitment to society and the environment. Sustainability was a key agenda at Infosys and was deeply ingrained in the company’s ethos and the way in which it operated. The case study also articulates the company’s commitment to sustainability, as evidenced by the involvement of the top management in providing leadership.

4.

BACKGROUND OF THE COMPANY

The belief in sustainability is reflected in the statements of Infosys’ senior management in 2008 (Mathew & Rajan, 2013): Sustainability is the only future for the world and for business. The urgency is to advance this reality rather than wait for time to take its course. (Former CEO and Co-Founder, Infosys) Sustainability is not a reaction to our risks. It is our core value. (Former COO and Co-Founder, Infosys)

168  Research handbook on information systems and the environment Infosys, headquartered in Bengaluru, India, is the second largest IT services company in India. For several years, it was considered the bellwether of the Indian IT services industry, and its performance and guidance were keenly watched by not only other IT firms but by other businesses in India as well (Kumar et al., 2009). The founders of Infosys were driven by the vision of being a globally respected corporation (Narus & Seshadri, 2007). They felt that their vision could be realised only by consciously following strong value systems and ethics. Sustainability was a core component of that value system and the overall business philosophy of the company. That business philosophy rested on four major tenets: Predictability, Sustainability, Profitability, and De-risking (which was referred to as PSPD within the organisation). These components of the business philosophy formed the basis of the business decisions taken by the company from its inception (Seshadri & Narus, 2007). Including sustainability as an integral part of its business philosophy indicated the importance that the company attached to this activity. In the 1980s sustainability did not galvanise the attention and imagination of policy makers and business leaders as it did during the first decade of the 21st century. A company which was founded with the vision of earning global respect from all its stakeholders, Infosys conceived its environmental sustainability initiative in the larger context of business performance and social responsibility (Abdelal et al., 2007). Sustainability actions at Infosys rested on three pillars: a social contract, resource intensity and green innovation. In the initial years, much of the sustainability activities were centred around the social contract theme, which was to meet the ethical, social and environmental expectations of local communities, social organisations and society. However, over time, the footprint of sustainability-related activities expanded. New areas such as resource intensity and green innovation, which enabled a reduction in consumption, became important priority areas for top management. While Infosys had been doing several activities related to sustainability in an informal way for a long time, the formal rollout of the environmental sustainability initiatives was started in January 2008 (Infosys, 2008). Since then progress has been steady. Parallel to the annual report, Infosys started publishing a sustainability report from financial year 2008 (FY08) which outlined the previous year’s performance and achievements of the company on various sustainability initiatives and the goals for the forthcoming year. This report has been extended to cover ESG aspects starting from FY21. The sustainability reports complied with the Global Reporting Initiative (GRI), a widely accepted industry standard to report sustainability initiatives. In 2012, Infosys was identified as one of the top 25 performers in the Caring for Climate initiative1 by the United Nations Global Compact (UNGC) and UN Environment Programme. As part of the top 25 performers Infosys had achieved the greatest absolute emission reduction percentage during the 2009–10 period (Infosys, 2012). In FY12, the company used 47.83 million units of green power, while achieving a reduction of 12 percent in per capita per month electricity consumption. Further, the company was able to achieve a reduction of 18.27 percent in per capita per month freshwater consumption (Infosys, 2013). Besides building significant resources with industry and academia, the Green Innovation initiative of Infosys also helped some of its clients save substantial costs. For example, Infosys Green Innovation helped a UK-based client to reduce energy consumption by up to 90 percent through the implementation of cloud solutions. The company also won prestigious awards and recognition, including the National Award for Excellence in Energy Management from the Confederation of Indian Industry in 2010 and the Green IT Company

Green IS  169 of the Year at the 19th Annual ITsAP (IT and ITES Industry Association of Andhra Pradesh) Awards (Infosys, 2012). Sustainability activities, specifically those pertaining to resource intensity and green innovation, gained additional momentum from 2008 under an able top management leadership. Apart from being a core value system of the company, Infosys’ top management believed that sustainability represented a large business opportunity for both the domestic and export markets (Mathew & Rajan, 2013). From an IS perspective, this strong belief drove key initiatives and actions towards Green IS in Infosys (Loeser et al., 2017; Melville, 2010). The year 2020 marked 13 years of publishing Infosys’ annual sustainability reports. Infosys became the first company in India to certify its carbon neutrality against PAS 2060:2014 (Infosys, 2021), thirty years ahead of the timeline set by the Paris Agreement. It was also an appropriate time for the company to assess its progress and achievements, and how it could leverage its expertise to capitalise on the evolving opportunities, as it moved to an ESG framework. 4.1

Evolution of Infosys

Infosys Technologies Ltd was founded in 1981 by N.R. Narayana Murthy and six other professionals with an initial investment of $250 at Pune, near Mumbai. By 1983 Motor Industries Co Ltd (MICO), a Bangalore-based spark plug manufacturing company and a member of the Bosch Group, had become one of the first clients of Infosys, and subsequently the company moved to Bangalore in the Karnataka state of Southern India. During the same decade the company tried several businesses, including telecom equipment manufacturing, manufacturing automation solutions and software (Abdelal et al., 2007). During 1984–1989 Rajiv Gandhi, then Prime Minister of India, recognised the role of science and technology in building modern India. His government introduced two landmark pieces of legislation which had a significant impact in the development of India’s IT industry: the New Computer Policy of 1984 and the Policy on Computer Software Export, Development and Training in 1986. The New Computer Policy protected domestic producers from foreign competition, while the latter policy facilitated the importation of foreign software and provided Indian IT companies with exposure to multinationals (Sen & Frankel, 2005). During this period, when Infosys seemed to be losing its core business focus in software development, the company explored several innovative methods in software development comparable to the assembly line process in manufacturing. 4.2

Global Delivery Model

By 1987 Infosys had an office in Boston, USA, and by 1994 the company convinced some multinational corporations (MNCs) to move maintenance and migration project activities to India. The advent of Internet and information and communications technology (ICT) infrastructure providing faster and cheaper means for international communication helped Infosys grow its offshoring revenue. Almost at the same time, Infosys leveraged the economic liberalisation policy advanced by the Indian government in 1991, and steadily progressed in its global operations, particularly in the US. Infosys’ focus on quality led to its Capability Maturity Model (CMM) Level 5 certification. This further enabled the company to build trust and confidence with several Fortune 500 companies. By 1999 the company was listed in NASDAQ, which further gained it the confidence of US clients. Furthering its fortunes, the imaginary “Y2K

170  Research handbook on information systems and the environment bug” as the new millennium was approaching found many MNCs placing their confidence in India’s technical talent to prepare for the new millennium. By 2004, Infosys employed about 32,000 people and crossed the landmark $1 billion in revenue (Figure 9.1). In another two years, the company reached $2 billion. By 2008 Infosys’ client base included over 500 global Fortune 2000 companies and revenue increased to $4 billion. North America contributed 60 percent of its revenue, and Europe about 26 percent (Infosys, 2008). Infosys was one of the first software companies to successfully pioneer the Global Delivery Model (GDM), distributing development activities between onsite and offshore locations during the development process (Martinez-Jerez et al., 2009). According to one of the founders of Infosys, GDM at the company was about “sourcing capital where it was cheapest, producing where it is most cost effective, and selling where it is most profitable, all without being constrained by national boundaries.” In software development, it involved assembling different parts of the software developed in various geographical locations and delivering it to the client at its desired location. According to another founding member of Infosys, this innovation was called Infosys 1.0. 4.3

Infosys Consulting and Beyond

As Infosys’ client base increased, the nature of its engagements in IT services widened to cover package software implementation, R&D, infrastructure management, system integration, testing as a service and business process outsourcing. With the expansion of the bouquet of services to its clients, the company’s relationship with clients became more complex. The company wanted to receive repeat projects from its clients and at the same time widen its engagements with them. By the end of the 1990s, Infosys identified a key opportunity to enter a client relationship early in the lifecycle to help the client define its problem, identify solutions and further implement a solution as a natural extension of this engagement (Capur, 2006). The company thus wanted to foray into IT consulting, in direct competition with existing IT consulting firms such as Accenture and Tata Consultancy Services (TCS). Infosys chose an organic way of developing this capability when the company founded Infosys Consulting in 2004 as its fully owned subsidiary in the US. This phase of the company was termed Infosys 2.0 by one of its founding members, wherein Infosys “integrated GDM with consulting, brought in vertical focus and expanded beyond ADM [application development and maintenance] to offer end-to-end services.”

5.

THE SUSTAINABILITY INITIATIVE AT INFOSYS: THE BEGINNING OF A JOURNEY

In 2007, Infosys decided to examine long-term risks to the company. For a company that followed PSPD as a central theme in engaging with business, the exercise was deeply significant (Infosys, 2008). How is the global business environment changing? What would its impact be on Infosys’ business? A significant change that could not miss the attention of Infosys leadership was the environment itself. Active debate advanced by the UN on sustainable development, particularly after the UN 2005 World Summit and the backdrop of global warming, was catching the attention of countries and corporations at that time, together with the growing emphasis on sustainability, emission and triple bottom line reporting. Infosys’

Green IS  171 top management developed a strong belief on the imperative of sustainability, leading to very considered actions towards Green IS strategy. Green IT buzz seemed to herald an era of environmentally sensitive information technology. In 2007, market research firm Gartner highlighted Green IT as one of the top 10 strategic technologies for 2008 (Mathew & Rajan, 2013), which continued to occupy the top 10 list until 2010. A 2009 Business Council and Conference Board survey indicated that almost two-thirds of CEOs in the US believed sustainability to be a mainstream concern for businesses (Mathew & Rajan, 2013). Infosys did not miss the opportunity to focus its attention and resources on sustainability as a key area of future strategy. Around 2010, Infosys realised the business potential for providing sustainability-related services. It was felt that implementing sustainability-related solutions “lends a ‘green’ quotient to brands that influences stock prices and market value” (Mathew & Rajan, 2013, p. 3). From 2009, Newsweek started publishing an annual ranking of global companies based on their environmental performance. Leading companies aspired for the top rankings in that list as it was perceived to enhance their brand equity and generate substantial goodwill among stakeholders (Mathew & Rajan, 2013). In addition to contributing to brand value, Green IT solutions directly impacted the competitiveness of companies by reducing costs through means such as energy and water conservation, and waste reduction. For example, Yahoo opened a data centre in New York State that consumed 40 percent less electricity and 94 percent less water than previous centres. Dell had reduced the energy consumption of its desktop and laptop computers by 25 percent during 2010–2012, saving its customers more than $5 billion in electricity costs (Mathew & Rajan, 2013). Newsweek’s green ranking indicated that the move to embrace sustainability was seen across sectors and was not restricted to just the information technology and services sector. There was increasing evidence that many companies that implemented Green IT solutions had started realising measurable benefits (Jaswal, 2011). Academic research also provided support for adopting sustainability as a business imperative. A study undertaken by the Harvard Business School tracked the performance of 180 companies over 18 years and found that firms that adopted environmentally and socially responsible policies significantly outperformed their peers. The study estimated that every dollar invested in a portfolio of sustainable companies in 1993 would have grown to $22.60 by 2011, as compared to the $15.40 for a portfolio of companies less focused on sustainability (Mathew & Rajan, 2013). Literature on competitive strategy shows that corporate social responsibility (CSR) can provide various benefits, like enhancing a firm’s relationships and reputation among various stakeholders such as customers, employees, regulators, and suppliers (e.g., Porter & Kramer, 2006). It is also argued that CSR activities can be a source of innovation and further help firms to improve their competitive context. Against the backdrop of the studies that showed the positive influence of CSR activities on firm performance, several organisations started viewing environmental sustainability as an extension of CSR. Leading organisations such as McKinsey, Gartner, HP and the WWF found that though the ICT industry is responsible only for 2 percent of the global CO2 emissions, it has the potential to develop solutions that can significantly enable the reduction of the remaining 98 percent of the CO2 emissions by non-ICT industries (Mathew & Rajan, 2013). Sensing the market opportunity, software companies started offering various Green IS solutions that provided a variety of benefits. The following quote during our interviews captures the enabling mood of the pro-sustainability environment:

172  Research handbook on information systems and the environment In the case of Infosys sustainability initiative, I did not have to go and get the top management buy-in anytime. It was initiated and supported by the founding fathers of the company. (Head, Green Initiatives, Infosys, 2008)

Infosys’ policy on sustainability did not emerge as a knee-jerk reaction to the environment, but was formulated purposefully by the top management, by thoughtfully recognising that sustainability also makes business sense and could be embedded in the strategic purpose of the organisation. Infosys identified “sustainable tomorrow” as a key theme among seven strategic themes for the future and recognised that a smart enterprise can grow sustainably through a focus on social contracts, effective resource utilisation and green innovation. According to the sustainability policy adopted by Infosys, social contracts stand for the company’s implicit responsibility to the larger society and seek to include the social and environmental dimensions of the company’s business. Resource intensity focuses on doing more business with fewer resources, covering energy, water or materials. Green innovation seeks to leverage the opportunity for business leadership through sustainability. Notwithstanding the potential benefits that the company could derive from implementing a sustainability programme, there were also concerns about the costs involved and how benefits could be justified financially. Prior IS studies have highlighted the difficulties corporations encounter in the financial justification of Green IS initiatives (e.g., Loeser et al., 2017). Several benefits of sustainability initiatives were intangible in nature and not useful for an objective cost–benefit analysis. In this context, top management of the company had to take a very considered view of the sustainability initiative to steer it forward. 5.1

Top Management: Leading by Example

Infosys top management was committed both professionally and personally to the realisation of Infosys’ sustainability goals. Formulated under the leadership of its Chairman Emeritus, N.R. Narayan Murthy, Infosys’ sustainability policy focused not only on making its business sustainable, but also those of its clients, as well as Infosys’ ecosystem and lifestyles (Infosys, 2012). The Co-Chairman and CEO of the company took responsibility for sustainability initiatives at the board level. His passion for championing the cause of sustainability was evident even at the time of his tenure as CEO: I am fortunate to be able to lead Infosys at a time when it has created a sustainability policy and has taken concrete steps to build a sustainable tomorrow. I am personally committed to communicating this message to all stakeholders, especially to Infoscions and their families. I walk the talk and lead by example in my personal life too. (Co-Chairman and CEO of Infosys, 2010)

This commitment to the environment was echoed across top management during our interviews in 2012: As leaders, we need to lead by example and imbibe sustainability in our personal lives such that it becomes part of who we are and what we stand for. (Head of Administration and Human Resources, Infosys, 2012)

The leaders of Infosys started championing the cause of sustainability at leading international organisations. The CEO of the company was a member of the executive committee of the World Business Council for Sustainable Development (WBCSD). This and similar initiatives

Green IS  173 helped the company build a public green image. The company also formulated measurable green goals which were evidenced during our personal interviews in 2012: This public commitment reflects the ethos of the leadership and the organization. We have now translated this into goals and actions that form part of the Corporate Scorecard of the corporation. (Associate VP, Infosys, 2012)

Infosys was honoured when its Co-Chairman and CEO was invited to chair the UNGC’s Business Action for Sustainable Development. In his address at the UNGC’s Global Conference at Rio de Janeiro in June 2012, he asserted: Most solutions for sustainable development will [have to] come from business. Solutions exist with the ability to have profound impacts on areas including energy and climate, water, biodiversity, agriculture and food, corruption and gender equality. Despite positive developments and shifting trends, corporate sustainability as practised today is insufficient – a quantum leap is needed. With the right incentives and enabling environments, business can make significant and lasting contributions to sustainable development.

5.2

Internal Focus: Green Solutions Development

The internal focus of Infosys’ Green initiatives resonated with the context in which the company operated. Infosys owned and operated a large proportion of real estate assets in India, and the total fixed cost pertaining to landholding, buildings, plant and machinery, computer equipment and furniture was $1.56 billion in 2011 (Infosys, 2011). The company recognised that resource intensity would serve to protect the environment and also lead to large cost savings. These key insights formed the guiding principles for the company’s internal focus on environmental sustainability, leading to a number of programmes to optimise the use of energy, water, materials and waste. Infosys’ campus in Bengaluru was developed as an environmentally friendly and energy-efficient facility, and employed eco-friendly methods such as renewable energy, low-pollution transport facilities and efficient water and waste management. Infosys identified and engaged with suitable partners in the development of energy management solutions for its building infrastructure. The company partnered with IIT Bombay in co-developing energy monitoring and management solutions for its buildings. It also conducted a number of experiments in its campus to identify appropriate solutions for optimal energy use. For example, it conducted experiments to switch between air conditioning and ceiling fans for optimal energy consumption. It also restricted the amount of white paper that could be used by an employee to a certain pre-defined quantity based on the position of the employee in the organisation. The company also focused on developing Green IT solutions such as cloud computing and server utilisation management for reducing its carbon footprint. As the company engaged more in identifying and implementing solutions that led to less energy consumption and more protection of the environment, the executives involved in the projects recognised that green solutions involved some trade-off with comfort. Since employee comfort was involved in the initiative it was imperative to involve all employees for the successful implementation of green solutions. This was echoed in the voice of the leadership, as evidenced in our interview with the company’s Associate VP of Education & Research in 2012:

174  Research handbook on information systems and the environment Sustainability in Infosys is everybody’s business.

With employee involvement, the company initiated an awareness drive called “ecolittles” and also formed “ecoclubs” with a core team of 25–50 people. These groups met periodically to spread employee awareness about sustainability and involved tracking the progress made in the green solutions projects. Reflecting on the achievements of Infosys’ green programmes, the Associate VP observed: An important aspect of earning the respect of your stakeholders is walking the talk and Infosys has done just that. The implementation of our Resource Intensity initiatives [has] given us considerable saving while creating an engaged workforce and allowed us to become an integral part of our client’s green supply chain. A committed leadership under the guidance of our executive co-chairman enables us to engage in an informed and sustainable manner in these initiatives.

5.3

The Sustainability Business Unit

Encouraged by the successes of its internal efforts with resource intensity and Green IT, Infosys perceived excellent market potential for its internally tested solutions among the clients of the company. It started a separate business unit to provide solutions that met the sustainability needs of its clients. The Sustainability Business Unit was organised to develop, test and implement sustainability projects internally and then convert the learning from the internal projects as solutions for clients. The unit was headed by a VP who reported to the Senior VP of the Products, Platforms and Services (PPS) division. PPS was one among the three divisions of the horizontal structure of the company in 2012; the other two were Consulting & Systems Integration (CSI) and Business IT Solutions (BITS). Four specific activity groups consisting of Consulting, Delivery, Research, and Sales reported to the VP & Unit Head of the Sustainability Group. The Infosys Sustainability Executive Council (ISEC) monitored the planning and progress of all sustainability initiatives. Not only did the sustainability business unit go to market with the solutions that Infosys had implemented internally, it also started to offer other solutions and system integration services based on the various OEM (original equipment manufacturer) products available in the marketplace tailored to the needs of the client. During our interviews in 2012, the VP of Education & Research observed: The perspective gets different when looked at from the point of sustainability. While clients previously talked about reducing costs, when looking from a sustainability perspective they talked about reducing their carbon footprint. While both might at the end of the day achieve the same purpose, the pathways might be different. For example, if we look at sustainability under product lifecycle management, we not only design a product, but we also ensure that it has been designed to recycle. By starting the sustainability business unit, we wanted to address the sustainability needs of the client from a technology perspective.

The composition of the Sustainability Business Unit team was also very different when compared to other business units of Infosys. While 80–85 percent of the team had technology backgrounds, as was seen in other business units, the nature of their experience was very specific to the unit. They had experience in implementing and integrating products made by OEMs such as Siemens, Schneider Electric, and so on. Consultants, who accounted for the remaining 15–20 percent of the team, had very different backgrounds from peers in other business units.

Green IS  175 In keeping with the business focus of the unit, the consultants in the sustainability unit had previous experience in facilities management, building management, and so on. Table 9.1  

Service offerings of Infosys’ Sustainability Business Unit

Name of the

Description

service 1

Corporate

This helps organisations to define their sustainability strategy and formulate an organisational

Sustainability

structure and governance model that supports the enterprise’s sustainability strategy and

Services

execution. This also includes helping enterprises with CSR reporting using established principles, procedures and tools.

2

Energy

This includes energy consumption baselining, enterprise energy audit services, use of

Management

technologies that capture energy consumption data from multiple third-party building

Services

management systems, helping to set optimal consumption limits and policies to manage energy use, and so on.

3

Green IT Services

These services range from strategy formulation to implementation advice to re-assess the IT infrastructure from the perspective of reducing CO2 and GHG emissions. It helps clients to reduce the energy consumption of their IT infrastructure, resulting in a lower carbon footprint, lower total cost of ownership of IT assets and lower IT operational expenses, leading to a reduction in the overall cost of business operations.

4

Supply Chain

GRI has introduced new standardsa for measuring Scope 3 emissions that largely apply to supply

Services

chains. While there are clearly defined standards and methods for quantifying Scope 1 and 2 emissions, organisations that do provide sustainability reporting under GRI have to assess the sustainability of their supply chain as well. Infosys services were designed to evaluate and create sustainable procurement strategies.

Note: a https://​www​.globalreporting​.org/​standards/​media/​1012/​gri​-305​-emissions​-2016​.pdf.

Table 9.2  

Solution offerings of Infosys’ Sustainability Business Unit

Name of the

Description

solution 1

Enterprise

The ESRS is an enterprise-wide solution that manages workflow, integrates with business

Sustainability

transaction systems and provides real-time reporting and enhanced operational efficiency to

Reporting

improve sustainability. The ESRS solution manages sustainability data such as incidents, waste

Solution (ESRS)

management, water management, energy consumption and GHG emissions with a comprehensive mechanism for data collection, aggregation, analysis and reporting, which complies with rules and regulations. The solution reduces the time and effort required to calculate the environmental footprint because of the integration with the existing ERP systems of the organisation. For example, Infosys developed and implemented a sustainability reporting system for a global independent energy company with revenue exceeding $29 billion. The project took 12 months and 46 person months of effort for completion.

2

Utility Bill

The UBAP solution aimed to transform and streamline utility bill processing, by completely

Automation

digitizing and automating the entire cycle of such processing – starting from invoice receipt to

and Processing

posting data in enterprise resource planning (ERP), resulting in reduced efforts, quick processing

Solution (UBAP)

and high flexibility. It provides seamless integration with the existing ERP systems and enterprise carbon, energy and resource management (ECERM) tools of the client organisation. The solution helped clients optimize the utility bill payment process, improve energy management and reduce environmental impacts through sustainability initiatives.

176  Research handbook on information systems and the environment In 2012, the business unit focused on four service offerings and two solution offerings (Mathew & Rajan, 2013). The service offerings of Infosys are given in Table 9.1 and the solution offerings in Table 9.2.

6.

SUSTAINING THE SUSTAINABILITY INITIATIVE

Infosys, as a leading IT services company with a global footprint, integrated environmental sustainability into its business strategy before many other IT services companies. The various achievements of the company from the early years of the inception of its sustainability initiative (Infosys, 2020) is given in Table 9.3. Table 9.3

Key historical achievements of Infosys Sustainability Initiative

Year

Key green achievements

2011

● Won the CII National Award for Excellence in Energy Management ● Committed to Carbon Neutrality Goals in the UN ● Employee volunteer-led ecoclubs formed at various development centres

2012

● Implemented radiant cooling technology for the first time in a commercial building in India ● Solar photovoltaic (PV) plants installed with 250 kW capacity ● Best in industry in water management at the World Water Summit

2013

● One of the top 25 performers in the Caring for Climate initiative by the UNGC and the UN Environment Programme

2014

● 288,065 trees on campus, almost double the number in 2009

2015

● Winner of the International Ashden Award (Green Oscars) for Sustainable Buildings

● World’s eighth greenest company in a ranking published by Newsweek ● Infosys case study showcased at the WBCSD conference in Montreux ● Energy Efficiency in Buildings (EEB) 2.0 guide launched in partnership with the WBCSD ● First Indian company to join RE100, a global platform for major companies committed to 100 percent renewable power 2016

● Carbon offset projects worth $9.37 million introduced, a step towards achieving the stated carbon neutrality goal ● Initiated three community-based carbon offset projects in rural India ● 6.6 MW solar PV farm installed in Pocharam campus at Hyderabad, India

2017

● Received Microsoft Supplier Program Climate Change Leadership award instituted by Microsoft in collaboration with the US Environmental Protection Agency (EPA) ● Joined Carbon Pricing Leadership Coalition (CPLC); announced an internal carbon price, fixed at $10.5 per ton of CO2e ● Achieved a goal of a 50 percent reduction in per capita electricity since starting its journey in 2008

2018

● Total installed capacity solar power reached 46.2 MW ● Collaborated with Leibniz University in Germany to conceptualize and implement a fully automated solar-heat-assisted dryer for treating sewage sludge ● European patent granted for radiant cooling solution developed by Infosys

2019

● Recognised at the Global Green Future Leadership Awards for the Best Climate Change Program

2020

● Infosys became the first company in India to certify its carbon neutrality against PAS 2060:2014

● Winner of the Greenbuild Leadership Award by the US Green Building Council ● ESG programme initiated

The pioneering sustainability initiative helped Infosys become net carbon zero in 2020, thirty years ahead of the timeline set by the Paris Agreement. Infosys became the first company in India to certify its carbon neutrality against PAS 2060:2014 (Infosys, 2021). On 5 June 2021, celebrated as World Environment Day, the CFO of Infosys reflectively noted:

Green IS  177 the entire planet needs to transition to net zero CO2 emissions by 2050 – however, Infosys is a pioneer in this space. In 2011, well before the Paris Agreement, Infosys voluntarily committed to becoming net zero. Through our efforts to reduce or remove CO2 emissions from our operations via energy efficiency and renewable energy, Infosys turned net zero in FY2020 – that’s 30 years ahead of the timeline under the Paris Agreement. (Infosys, 2021)

Source: Sustainability reports of Infosys, 2008–2020

Figure 9.1

GHG emissions and company growth

Figure 9.1 shows Infosys’ growth over the years and the company’s steady performance in reducing GHG emissions during 2008–2020 period. GHG emissions have been measured in tonnes of CO2 emission equivalent (tCO2e).2 Following international GHG protocols, Infosys classified emissions into direct (emissions from sources controlled by the company) and indirect (emissions as a consequence of the activities of Infosys, at premises controlled by another entity). Further, GHG emissions have been accounted under three headings: Scope 1 (emissions from sources and equipment owned or controlled by Infosys), Scope 2 (emissions due to electricity purchased by Infosys at the premise of the electricity producer) and Scope 3 (emissions due to business travel, employee commuting, and transmission and distribution losses). By 2020, the company had created 25 million sq ft of LEED (Leadership in Energy and Environmental Design) platinum green building certified infrastructure. The campuses of Infosys across India have 44.3 percent of energy coming from renewable energy sources. Using 2008 as a baseline, the company reduced per capita electricity consumption by 55 percent (Infosys, 2021). The Green IS and sustainability journey of Infosys was not always smooth sailing, with several challenges emerging along the way. In 2011 and 2012, the leadership position of Infosys was under threat like never before (Mathew & Rajan, 2013). Cognizant, a competing IT services company, was growing very fast and was expected to replace Infosys as the second largest IT services company in terms of revenue, a position that Infosys had occupied for several years. But more importantly, Infosys, which used to be the industry leader in terms

178  Research handbook on information systems and the environment of revenue and profit growth rates, started lagging behind TCS, the new leader. While the profitability of Infosys continued to be the highest when compared to its peers, questions were being asked about the importance of a sustainability-driven business model in an increasingly competitive scenario. These developments had a short-term bearing on the sustainability agenda of the company. Further, sustainability and its relation to competitive advantage and profitability, and sustaining the momentum of the sustainability initiative, became huge challenges for the organisation (Mathew & Rajan, 2013). Furthermore, although Infosys was one of the early movers in the Indian IT industry in formulating a clear sustainability strategy and building a sustainability business unit, the company’s competitors followed similar strategies. Infosys’ major competitor, TCS, did continue to outperform it in the global Newsweek green rankings of companies.3 Despite the challenges, by 2020 the sustainability strategy of Infosys had paid off richly. The company also became cognizant of the changing environmental conditions locally and globally. This extended the sustainability initiative to a more wholesome ESG programme. Responding to the emerging challenges of gender equality, protection of human rights and data privacy, the company embraced social sustainability as another dimension of its sustainability initiative. Further, the company recognised governance mechanisms for ethical business practices, sustainable supply chains, and so on (Economic Times, 2021). The economic sense of the three-fold sustainability strategy has been highlighted by the CFO: Financial capital is the lifeblood of corporations and ESG-focused funds are expected to have over $50 trillion of assets by 2025, which you can only ignore at your peril. The markets also endorse environmental stewardship, rewarding companies doing steadily well in ESG with consistently better returns. At Infosys, I strongly believe our efforts at ESG over the years have helped us become both a responsible and successful brand. (CFO, Infosys, 2021)

7. CONCLUSION Our study set out to understand how the beliefs of top management develop and drive an IT services firm to pursue Green IS and a sustainability strategy, and how such actions led to certain specific outcomes (Loeser et al., 2017; Melville, 2010). Consistent with prior IS research findings (Loeser et al., 2017) we found that sustainability initiatives in Infosys led to cost reductions, corporate reputation enhancement and green innovation capabilities in the long run. We found a further effect on reputation enhancement, leading to higher global competitiveness in attracting global clients. The case of Infosys also throws light on the multi-fold strategy firms pursue in the IT services sector. Infosys’ strategy for sustainability initiative had three dimensions: (a) compliance with global standards for GHG emissions, (b) building a green image to attract global clients and (c) creating a separate business to offer Green IT solutions to clients. Green IT for internal efficiency was a starting point for this organisation. On one side, such corporations need to comply with certain global standards for reducing carbon footprint for environmental sustainability. Reducing GHG emissions in different categories (scope 1, 2 and 3) included both Green IT and non-IT projects. For example, green initiatives in Infosys reduced per capita electricity consumption by 55 percent over a 12-year period. The number of employees in the organisation grew from 91,200 in 2008 to 242,371 in 2020. This shows the major cost-saving potential of the strategy for a company which is labour-intensive.

Green IS  179 Beyond compliance and cost savings, IT service providers like Infosys also perceived focus on sustainability as a business opportunity. Early adoption of global standards like triple bottom line reporting, and reporting sustainability separately from financial and annual reporting, helped Infosys position itself as a company practising global standards for environmental sustainability. This image helped the company to work with global clients at an increasing scale. The company had 538 clients in 2008 with about 97 percent of its orders received from existing clients. By the year 2020 the number of clients increased to 1,411 and the repeat order percentage remained at the same rate of 97 percent. Infosys continued to retain and grow its global clientele. We posit that the image of the firm built through environmental sustainability practice helped it in retaining and expanding its clientele, as also explained by top management (Economic Times, 2021). Finally, Infosys provided green solutions to client firms to comply with global standards. The Green IT experiments were monetized by the unit to create a revenue stream. This is a unique aspect of a green value chain where one IT firm supports a client firm in reducing its carbon footprint and supporting environmental sustainability compliance. As highlighted by the CFO of Infosys recently, “ESG focussed funds are expected to have over USD50 trillion of assets by 2025, which you can ignore only at your peril. The markets also endorse environmental stewardship” (Economic Times, 2021). This study is limited to one organisation, and more studies, particularly multi-level comparative case studies, would generate additional insights on the Green IS strategies of IT services firms. However, this single in-depth case has highlighted the need to expand the scope of Green IS from IS for sustainable business processes to IS in environmental sustainability-based business strategy.

NOTES 1.

2. 3.

The Caring for Climate initiative is aimed at advancing the role of businesses in addressing climate change. It recognises companies that demonstrate leadership in reducing greenhouse gas emissions that improve their efficiency and that of their supply chain, set targets and transparently report verifiable data on current emissions, further the understanding of the corporate sector’s environmental impact and its role in developing leading solutions to build a low-carbon, climate resilient economy, and work to engage stakeholders and policy makers to encourage scaled-up climate action. https://​ghgprotocol​.org/​corporate​-standard. https://​www​.livemint​.com/​Companies/​HUd​oPA2qJap7e​2dkBufo0N/​TCS​-and​-Infosys​-among​-top​ -green​-companies​-globally​-Newsweek​.html.

REFERENCES Abdelal, R.A.W.I., Di Tella, R., & Kothandaraman, P. (2007). Infosys in India: Building a software giant in a corrupt environment. Harvard Business School Case 707-030. Brundtland, G.H., Khalid, M., Agnelli, S., Al-Athel, S., & Chidzero, B.J.N.Y. (1987). Our Common Future. New York: United Nations and Oxford University Press. Capur, A.(2006). Infosys Consulting in 2006: Leading the Next Generation of Business and Information Technology Consulting. Stanford Graduate School of Business, Case No. SM-151. Chen, A.J., Boudreau, M.C., & Watson, R.T. (2008). Information systems and ecological sustainability. Journal of Systems and Information Technology, 10(3), 186–201.

180  Research handbook on information systems and the environment Dedrick, J. (2010). Green IS: Concepts and issues for information systems research. Communications of the Association for Information Systems, 27(1), 11. Dyllick, T., & Hockerts, K. (2002). Beyond the business case for corporate sustainability. Business Strategy and the Environment, 11(2), 130–141. Economic Times (2021, June 05). Environmental concerns and corporate bottom lines are aligned: Infosys became net zero in 2020 – CFO Nilanjan Roy. https://​economictimes​.indiatimes​.com/​news/​et​ -evoke/​environmental​-concerns​-and​-corporate​-bottom​-lines​-are​-aligned​-infosys​-became​-net​-zero​-in​ -2020​-cfo​-nilanjan​-roy/​articleshow/​83241444​.cms. Gates, B. (2021). How to Avoid a Climate Disaster: The Solutions We Have and the Breakthroughs We Need. Penguin Random House. Gray, R., & Bebbington, J. (2000). Environmental accounting, managerialism and sustainability: Is the planet safe in the hands of business and accounting? In Advances in Environmental Accounting & Management, Vol. 1, pp. 1–44. Emerald Group. Hasan, H., Smith, S., & Finnegan, P. (2017). An activity theoretic analysis of the mediating role of information systems in tackling climate change adaptation. Information Systems Journal, 27(3), 271–308. Infosys (2008). Sustainability Report 2007–08. https://​www​.infosys​.com/​about/​corporate​-responsibility/​ sustainability​-reports​.html. Infosys (2010). Sustainability Report 2009–10. https://​www​.infosys​.com/​sustainability/​Documents/​ infosys​-sustainability​-report​-2009–10​.pdf. Infosys (2011). Sustainability Report 2010–11. https://​www​.infosys​.com/​about/​corporate​-responsibility/​ sustainability​-reports​.html. Infosys (2012). Sustainability Report 2011–12. https://​www​.infosys​.com/​about/​corporate​-responsibility/​ sustainability​-reports​.html. Infosys (2013). Sustainability Report 2012–13. https://​www​.infosys​.com/​about/​corporate​-responsibility/​ sustainability​-reports​.html. Infosys (2020). Sustainability Report 2019–20. https://​www​.infosys​.com/​about/​corporate​-responsibility/​ sustainability​-reports​.html. Infosys (2021). Sustainability Report 2020–21. https://​www​.infosys​.com/​about/​corporate​-responsibility/​ sustainability​-reports​.html. Jaswal, A. (2011). Adopting and implementing green. SETLabs Briefings, 9(1). Kranz, J., & Picot, A. (2011). Why are consumers going green? The role of environmental concerns in private Green-IS adoption. 19th European Conference on Information Systems (ECIS). Kumar, N., Mohapatra, P.K., & Chandrashekhar, S. (2009). India’s Global Powerhouses: How They Are Taking on the World. Harvard Business Press. Loeser, F., Recker, J., vom Brocke, J., Molla, A., & Zarnekow, R. (2017). How IT executives create organizational benefits by translating environmental strategies into Green IS initiatives. Information Systems Journal, 27(4), 503–53. Martinez-Jerez, A., Kaplan, R., & Miller, K. (2009). Infosys’s Relationship Scorecard: Measuring Transformational Partnerships. Harvard Business School Case 9-109. Mathew, S.K., & Rajan, T. (2013). Sustainability as an imperative and an opportunity: The case of Infosys Limited. Emerald Emerging Markets Case Studies, 3(2), 1–16. Melville, N.P. (2010). Information systems innovation for environmental sustainability. MIS Quarterly, 34(1), 1–21. Misra, N., & Srivastava, M. (2012). Environmental sustainability in industry. Mint. https://​www​.livemint​ .com/​Companies/​f22​B5yVJ1GXpC​lDL0eZheK/​Environmental​-sustainability​-in​-industry​.html. Molla, A., Cooper, V., & Pittayachawan, S. (2011). The Green IT readiness (G-Readiness) of organizations: An exploratory analysis of a construct and instrument. Communications of the Association for Information Systems, 29(1), 4. Narus, J.A., & Seshadri, D.V.R. (2007). Infosys Technologies Ltd.: Growing share of a customer’s business. Vikalpa, 32(4), 127–46. NASSCOM (2021). Reimagining Indian enterprises’ tech landscape in a digital-first world, a new order out of chaos. https://​nasscom​.in. Porter, M.E., & Kramer, M.R. (2006). The link between competitive advantage and corporate social responsibility. Harvard Business Review, 84(12), 78–92.

Green IS  181 Seidel, S., Recker, J., & Vom Brocke, J. (2013). Sensemaking and sustainable practicing: Functional affordances of information systems in green transformations. MIS Quarterly, 37(4), 1275–1299. Sen, S., & Frankel, F.R. (Eds.). (2005). India’s Strategy of IT-Led Growth: Challenges of Asymmetric Dependence. Center for the Advanced Study of India. Seshadri, D.V.R., & Narus, J.A. (2007). Value chain migration at Infosys (A). https://​store​.hbr​.org/​ product/​value​-chain​-migration​-at​-infosys​-a/​imb319​?sku​=​IMB319​-PDF​-ENG. Shah, R. (2021, May 26). Industry 4.0: India’s urgent need to make the revolution green. Forbes India. https://​www​.forbesindia​.com/​article/​sustainability​-special/​industry​-40​-indias​-urgent​-need​-to​-make​ -the​-revolution​-green/​68165/​1. Statistica (2020). Employment of the IT-BPM industry in India from financial year 2011 to 2020. https://​ www​.statista​.com/​statistics/​320729/​india​-it​-industry​-direct​-indirect​-employment/​. Vom Brocke, J., Watson, R.T., Dwyer, C., Elliot, S., & Melville, N. (2013). Green information systems: Directives for the IS discipline. Communications of the Association for Information Systems, 33(1), 30. Watson, R.T., Boudreau, M.C., & Chen, A.J. (2010). Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Quarterly, 34(1), 23–38. Watson, R.T., Boudreau, M.C., Chen, A.J., & Sepúlveda, H.H. (2011). Green projects: An information drives analysis of four cases. Journal of Strategic Information Systems, 20(1), 55–62. World Bank (2021). GDP (current US$). https://​data​.worldbank​.org/​indicator/​NY​.GDP​.MKTP​.CD. Yin, R.K. (2015). Qualitative Research from Start to Finish. Guilford Publications.

10. The persuasive potential of digital nudging for eco-sustainable behaviour1 Anne Ixmeier, Anna Seidler, Christopher Henkel, Marina Fiedler, Johann J. Kranz and Kim Strunk

1. INTRODUCTION Climate change and environmental pollution are grand challenges of our time (Melville 2010; Seidel et al. 2017). The United Nations Framework Convention on Climate Change (2018) states that “climate change presents the single biggest threat to sustainable development everywhere and its widespread, unprecedented impacts disproportionately burden the poorest and most vulnerable” and highlights the importance of alleviating climate change and environmental pollution in nine out of the 17 Sustainable Development Goals. To achieve the United Nations’ Sustainable Development Goals, such as responsible consumption and production as well as sustainable cities and communities, urgent action to curb carbon emissions is crucial. To address these challenges, individuals, organisations, and societies need to adopt more eco-sustainable behaviour (ESB) (Elliot 2011, Zeiss et al. 2020). ESB comprises all behaviours that reduce the use of collective, limited natural resources, halt global warming, and protect the biosphere (Stern 2000). Individual ESB is strongly related to the awareness of environmental consequences (Henkel and Kranz 2018, McDonald 2014). Hence, to motivate individual ESB, it is commonplace to provide information conveying norms about a specific behaviour and its environmental outcomes (Unsworth et al. 2013). Although environmental awareness is important for fostering ESB (Kranz and Picot 2011, Tiefenbeck et al. 2018), normative information provided by governments, non-governmental organisations, or companies has been shown to have very little effect on actual ESB (Gardner and Stern 2002). Prior information systems (IS) research has shown IS’ persuasive potential to encourage ESB by using digital nudges, which make use of heuristics to change behaviour (Thaler and Sunstein 2008). Digital nudging is defined as “the use of user-interface design elements to guide people’s behaviour in digital choice environments” (Weinmann et al. 2016, p. 433) and can be applied to guide real-world behaviours, such as encouraging energy-efficient behaviour in private households using IS feedback systems (e.g., Loock et al. 2013, Tiefenbeck et al. 2018). The concept of nudging refers to adapting the choice architecture, i.e. the environment in which decisions are made, to encourage decision making without restricting decision options (Thaler and Sunstein 2009); e.g., by sorting options, setting defaults, or framing information in a specific way (Dolan et al. 2012, Johnson et al. 2012). Highly beneficial effects are generated when the nudging strategy builds on gamification, which aims at activating individual motivations to influence users’ attitudes and behaviours by including game-design elements in non-game contexts (Blohm and Leimeister 2013, Lowry et al. 2013, Treiblmaier et al. 2018). In the context of ESB, the literature has extensively relied on stimulating competition, fun, or enjoyment as motivating mechanisms (e.g., Loock et al. 2013, Oppong-Tawiah et al. 2014). However, environmental psychology literature indicates that ESB is simultaneously affected 182

The persuasive potential of digital nudging  183 by multiple, often competing goals (i.e. normative, hedonic, and gain goals), reflected in the goal framing theory (GFT; Lindenberg and Foss 2011; Lindenberg and Steg 2007, 2013). GFT postulates that goals “govern or ‘frame’ to what people attend, what knowledge and attitudes become cognitively most accessible, how people evaluate various aspects of the situation, and what alternatives are being considered” (Lindenberg and Steg 2007, p. 119). Essentially, all three goals can generate ESB, but only one goal is focal in attention, and thereby salient in consciousness, and thus likely to influence behaviour – referred to as the governing goal frame (Lindenberg and Steg 2007). ESB is strongly related to the normative goal, which refers to behaving appropriately and following social norms (Lindenberg 2017), and can be supported by normative information, which allows individuals to anchor their behaviour in normative expectations (Koo and Chung 2014, Unsworth et al. 2013). However, it is challenging to maintain the salience of the normative goal frame (Lindenberg 2006). Additionally, the normative goal can be reinforced by a consistent hedonic goal – improving personal feelings (Lindenberg 2017) – or a gain goal: improving one’s resources through competition (Lindenberg 2017, Lindenberg and Foss 2011). Beyond strengthening the normative goal through normative information (strategy 1: support of normative goal), aligning hedonic and gain goals with normative goals (strategy 2: alignment of gain and hedonic goals with normative goals) could promote ESB (Lindenberg and Steg 2007, 2013). Alignment according to strategy 2, hence, refers to making gain and hedonic goals less incompatible with normative goals in order to overcome seemingly contradicting goals and promote normative behaviour such as ESB. In our case, alignment of hedonic (gain) and normative goals was operationalised by providing not only normative information but also compatible hedonic (competitive) feedback that simultaneously activates normative and hedonic (gain) goals to promote ESB. To date, however, the literature has not studied how gamification addresses behaviour, beyond making a system more competitive or enjoyable. Hence, we still do not know how to make gamification more effective in order to increase the compatibility of multiple, often competing goals regarding ESB. Thus, we address the following research question: What is the effect of gamified nudges to promote eco-sustainable behaviour through (a) strengthening the normative goal frame? (b) aligning the normative goal with potentially competing gain and hedonic goals? Guided by these overarching questions, we investigate how normative goals can best be supported and how hedonic or gain goals can best be aligned with normative goals to hinder competing individual goals from being “pushed into the cognitive background” (Lindenberg and Foss 2011, p. 506). Using gamified nudges to strengthen the normative goal frame by aligning it with the often competing hedonic and gain goals opens up a new avenue for research on gamification and normative behaviours such as eco-sustainability. Since traditional, mostly information-related methods of encouraging positive environmental behaviours have had limited effect (Abrahamse et al. 2005), the application of gamified IS to influence behaviour is promising because the general desirability of protecting the environment makes individuals more receptive to persuasion approaches (Corbett 2013, Ixmeier and Kranz 2020, King and Tester 1999). We thus address an important void in IS literature, as we consider the support of the normative goal and the complex interactions of multiple goals, while past research has one-sidedly focused on

184  Research handbook on information systems and the environment either competition (i.e., addressing a gain goal frame) or fun and enjoyment (i.e., addressing a hedonic goal frame) as explanations for the motivational impact of gamification on ESB. To elicit the effects of gamified IS in aligning potentially competing goals to trigger ESB, we conducted a controlled, randomised laboratory experiment with N = 160 participants, using a four-group between-subject design ([1] normative goal frame condition, [2] hedonic and normative goal frames condition, [3] gain and normative goal frames condition, [4] control condition). We designed the experiment to further assess the potential of IS designs to motivate eco-sustainability and operationalised ESB in terms of participants’ use of an eco-friendly search engine (Henkel et al. 2019). Our results show that participants who are provided with normative information which frames eco-sustainability as the appropriate behaviour, and who receive additional gamified treatments that support the hedonic goal (via emotional feedback) as well as the gain goal (via competitive feedback) respectively, behave more eco-sustainably than participants who did not receive gamified treatments. Further, we found no differences in ESB between the groups that were provided with gamified treatments supporting hedonic or gain goals (i.e., groups 2 and 3), which indicates that both gamified treatments are equally effective in stimulating ESB. To the best of our knowledge, our study is the first to experimentally investigate how the normative goal frame can be strengthened and aligned with hedonic and gain goals to promote ESB. By showing that gamified nudges are effective in strengthening and aligning the normative goal frame, which encourages normative behaviour such as ESB, our study extends existing research on gamification. Thus, our study is one of the first to demonstrate gamification’s persuasive potential to overcome barriers to ESB and, more broadly, to promote normative altruistic behaviours. Thereby, gamified IS opens up a new avenue for practically attenuating the attitude–behaviour gap and promoting ESB in organisational and societal contexts. We proceed as follows. First, we elaborate on the theoretical background rooted in the literature on goal frames and digital nudges for enhancing ESB. Next, we present our experimental study on the alignment of competing goal frames, including hypotheses, experimental design, and data analyses. We then discuss our results and contributions, the study’s limitations, and potential avenues for further research on digital nudging and ESB.

2. BACKGROUND 2.1

Goal Framing Theory

Rooted in environmental psychology literature, GFT postulates that salient goals govern people’s attention, and how people are receptive to knowledge and attitudes, evaluate situations, and consider alternatives (Lindenberg and Steg 2007). Generally, three overarching goals drive behaviour: normative, hedonic and gain goals (Lindenberg and Foss 2011, Lindenberg and Steg 2013, Ruepert et al. 2017). First, a normative goal considers appropriate behaviour, following social norms, and furthering collective goals (Lindenberg 2017). In a normative goal frame with ESB framed as appropriate behaviours, individuals are more willing to act eco-sustainably, even if doing so is unpleasant or burdensome (Steg et al. 2014). This normative goal frame is stronger when people are aware of environmental problems and behavioural consequences, as individuals are then particularly sensitive to information about what is expected from influential others (Lindenberg and Steg 2007, 2013).

The persuasive potential of digital nudging  185 Second, a hedonic goal is related to improving one’s feelings in the present moment. In a hedonic goal frame, individuals are more likely to engage in eco-sustainability when their actions provide instant gratification, such as fun or enjoyment (Lindenberg and Steg 2007). Hence, a hedonic goal frame refers to immediately improving personal feelings (Lindenberg 2017). Since personal feelings are focal for emotional states (Russell 2003), this goal frame is closely related to positive emotions. Emotions crucially influence one’s beliefs about what is good or bad, right or wrong (McGrath 2006) and thus energise and guide behaviour (Csikszentmihalyi 1975, McGrath 2006, Ryan and Deci 2000). Third, a gain goal is related to guarding and improving one’s resources. When a gain goal is salient, individuals will behave eco-sustainably only if this behaviour increases resources, such as money, status, or time (Steg et al. 2014). Since individuals in a gain goal frame are highly sensitive to opportunities for, and threats to, the improvement of their resources, offers of individual rewards and competition support the goal frame (Lindenberg and Foss 2011). Individuals in a gain goal frame feel attracted to competition in which increasing ESB and outperforming others is the objective (Lindenberg and Foss 2011, Matallaoui et al. 2017). Only one of the goals, the governing goal frame, is in focal attention, thereby salient in consciousness, and thus likely to guide behavioural decision making (Lindenberg and Steg 2007). The other, potentially competing goals are pushed to the background and either stabilise (if they are congruent with the focal goal) or destabilise (if they are incongruent with the focal goal) the goal frame (Lindenberg and Steg 2007). 2.2

Digital Nudging and ESB

Research on sustainable behaviour change has gained much popularity in the past few years and the motivational approach has proven particularly effective (Groening et al. 2018). Motivated behaviour is (un)deliberately purposeful; i.e., directed toward a goal. As a result, behavioural insights into nudges and choice architectures designed to mobilise goals and change behaviours have in recent years increasingly made their way into persuasion and intervention strategies (Slattery et al. 2020, Wensing et al. 2020). The concept of nudging, defined by Thaler and Sunstein (2009), refers to the adaptation of choice architecture that “can change people’s behaviour in a predictable way without excluding any options or significantly changing economic incentives” (p. 15). A nudge is simple, easy to avoid, and as such only an impulse, neither a restriction nor an order (Thaler and Sunstein 2009). “Choice architecture” refers to the environment in which decisions are made. Per se, every way of presenting an option influences the decision-maker (Johnson et al. 2012, Thaler and Sunstein 2009) and nudges present an opportunity to shape the choice environment; e.g., by sorting options, setting defaults, embedding game-design elements, providing incentives, or framing information in a specific way (Dolan et al. 2012, Johnson et al. 2012). An often-quoted example of a default nudge is organ donation. One study indicates that changing the choice environment by adjusting the default setting can significantly impact participants’ choice. With an opt-in default (participants actively consent), 42 per cent agreed to donate organs. With an opt-out default (participants actively reject), it was 82 per cent. Thus, the number of organ donors could be doubled simply by changing the default setting (Johnson and Goldstein 2003). While behavioural interventions have existed for a long time, the use of pervasive digital technologies in behavioural interventions has more recently gained increased attention

186  Research handbook on information systems and the environment (Geissdoerfer et al. 2017, Krishnan and Teo 2011). Digital technologies can be deliberately designed to encourage ESB, particularly with digital nudging elements that acknowledge decision makers’ bounded rationality (Kahneman 2003, Simon 1972) and make use of heuristics to change behaviour (Thaler and Sunstein 2008, Weinmann et al. 2016). Digital nudges designed into digital applications have shown potential to encourage behaviour; e.g., through increasing system interaction (Halttu and Oinas-Kukkonen 2017), changing context (Shevchuk and Oinas-Kukkonen 2016), providing feedback (Jung et al. 2010), or augmenting self-efficacy (Cichy et al. 2021). Such behavioural interventions take effect by thwarting people’s decision biases and nudging them into potentially beneficial actions (Thaler and Sunstein 2008, Weinmann et al. 2016). Eco-sustainable interventions are a major area of application for nudging (Huber et al. 2018, Stefansdotter et al. 2016, Steg and Vlek 2009, Thaler and Helmig 2013). Highly beneficial effects are generated when the nudging strategy builds on gamification, which aims at activating individual motivations to influence users’ attitudes and behaviours by including game-design elements in non-game contexts (Blohm and Leimeister 2013, Lowry et al. 2013, Treiblmaier et al. 2018). Gamification is a high-scale, low-cost means of influencing human actions. As such it has been applied in a large variety of different contexts in IS research to activate individuals’ motivation and influence users’ attitudes and behaviours by incorporating gamification design elements (e.g., images, leaderboards, animations, or visual assets) into software design (Blohm and Leimeister 2013, Liu et al. 2017). Gamification has also been successfully applied to foster individuals’ eco-sustainable attitudes and behaviours (Flüchter and Wortmann 2014, Loock et al. 2013). For instance, the automobile manufacturer Renault designs gamification design elements into the multimedia screens of its cars to inform drivers about their driving style and motivate energy-saving driving. The more petals are displayed (see Figure 10.1), the more flexible and economical the driving style, and the lower (by up to 10 per cent) the fuel consumption of the vehicle (Renault 2019).

Source: Renault (2019)

Figure 10.1

Renault’s gamified nudge on eco driving

The persuasive potential of digital nudging  187 Table 10.1

Summary of goal frames and related studies using gamified IS interventions

Goal frame Definition

Illustrative example

Effective gamified IS

Gain

Gain goal frame is related to guarding

Individuals are more likely to take the

Goal setting (Brewer et al.

and improving resources. Individuals

train instead of a car, if it saves them

2011, Loock et al. 2013);

act eco-sustainably if behaviour

time and/or money.

scores (Brewer et al. 2011,

operationalisation

increases resources, such as money,

Gnauk et al. 2012), real-time

status, or time (Steg et al. 2014).

feedback (Brewer et al. 2011); point systems (Liu et al. 2017, Lounis et al. 2014); performance

Hedonic

Hedonic goal frame is related to

Individuals are more willing to change

feedback (Liu et al. 2017) Visualisation (Liu et al. 2017,

improving the way one feels in the

their car driving behaviour, if more

Oppong-Tawiah et al. 2020),

present moment. Individuals act

eco-sustainable car driving is connected emoticons (Berengueres et al.

eco-sustainably if behaviour results in

to positive emotional feedback that

2013)

satisfaction or pleasure (Lindenberg and leads to satisfaction or pleasure. Steg 2007). Normative

Individuals in a normative goal frame

In some communities, garbage has to

None (mostly non-gamified

consider appropriate behaviour,

be separated into various containers;

normative information)

following social norms, and furthering

in others, this is not necessary. People

collective goals (Lindenberg 2017).

may use this confusing situation to

Individuals act eco-sustainably if

be excused from making any effort

behaviour is approved by others

with regard to garbage. Authorities

and if environmental problems and

and environmental organisations

behavioural consequences are concrete

need to define concrete norms about

(Lindenberg and Steg 2007).

what people should do to behave pro-environmentally.

Prior literature on ESB has mainly focused on a gain goal frame in the form of competition as a motivating mechanism to effectively leverage ESB (see Table 10.1). Competitions are characterised by rival parties that attempt to achieve a goal (Weiser et al. 2015) and are thus closely connected to IS designs that allow for challenges and the provision of timely and positive feedback (Zhang 2008). Existing IS studies indicate that gamification design elements, such as point systems, goal setting, or performance feedback, promote individuals’ environmental awareness and intentions to behave eco-sustainably (Loock et al. 2013, Tiefenbeck et al. 2018). Despite the popularity of gamification, little is known about how gamified IS align multiple goals and help to keep the normative goal foregrounded and push potentially competing individual goals into the background. Thus, to date our understanding of addressing and aligning multiple goals with different game mechanisms in order to overcome seemingly contradicting goals and strengthen norm-guided behaviours is limited. We seek to address these issues to increase our understanding of ways gamification can be used to promote normatively driven ESB through aligning competing goals.

188  Research handbook on information systems and the environment

3.

HYPOTHESES DEVELOPMENT

Since GFT postulates that ESB is influenced by three overarching, potentially competing goals, our study aims to test the effectiveness of gamified IS in aligning these goals to promote ESB. To elaborate on the potential effects of gamification, we build on existing research and relate a normative goal frame to normative information (Koo and Chung 2014, Unsworth et al. 2013), a hedonic goal frame to emotions (Russell 2003), and a gain goal frame to competition (Lindenberg and Foss 2011). As a normative goal frame refers to behaving appropriately, supporting collective goals, and following social norms (Lindenberg 2017), ESB is strongly related to individuals’ awareness of environmental consequences (McDonald 2014). To increase problem awareness and trigger ESB, normative information needs to be linked to concrete behaviour (Unsworth et al. 2013) and be readily available when decisions about behaviour are made (DeYoung 2000). Normative information includes expectations about the appropriateness and desirability of behaviours (Cooke and Rousseau 1988). As people in a normative goal frame feel connected to a collective, the collective’s expectations about appropriate behaviour are incorporated into individual behavioural decision making (Lindenberg 2017). Individuals with high normative concerns feel obliged to follow the expectations of others and react negatively to violations of collective normative expectations (Lindenberg 2017, Ruepert and Steg 2018). Thus, normative information addresses people’s normative obligation to act in line with expectations regarding desired behaviour, such as ESB. Providing individuals with normative information on appropriate behaviour should therefore strengthen the normative goal frame, since it increases problem awareness and individuals’ normative obligation to act according to social expectations (Lindenberg and Steg 2007). We therefore hypothesise: H1: Strengthening the normative goal frame through normative information regarding eco-sustainability will be more effective in triggering ESB than providing no information. A hedonic goal frame refers to immediately improving personal feelings, which are central to emotions (Russell 2003). Emotions strongly impact behaviour, since they energise and guide behaviour (Csikszentmihalyi 1975, McGrath 2006, Ryan and Deci 2000). Emotions are mainly understood as affective states directed at a specific object (Russell 2003, Sun and Zhang 2006) and are considered “a critical factor in human decisions and behaviours within many social contexts” (Zhang 2013, p. 247). Providing people with visually induced positive emotions increases their willingness to donate to environmental organisations, compared to people who experience negative or no emotion (Ibanez et al. 2016). Furthermore, positive emotions are found to positively affect personal norms’ influence on ESB (Elgaaied 2012, Onwezen et al. 2013). Thus, existing research has indicated that emotions influence intended normative behaviours (McGrath 2006), such as ESB. However, existing research has mainly focused on emotions’ effect on the intention to change behaviour. Thus, we lack insight regarding the effects of positive emotions on actual behaviour. This is of particular importance, as people’s concern for the environment is not necessarily reflected in their actual behaviour, a phenomenon known as the attitude–behaviour gap (Blake 1999). Gamification research has proposed visual cues, such as graphics, colours, sounds, or emoticons (Gerow et al. 2013), to make IS usage more enjoyable and fun (Hamari 2013, Scheiner et al. 2017). Using gamified IS to promote enjoyment when acting eco-sustainably may make ESB more appealing, even if the behaviour itself is not enjoyable (e.g., recycling). Thus, using

The persuasive potential of digital nudging  189 emotions to bring individual hedonic motives in line with normative goals should increase individuals’ inclination to act eco-sustainably. Hence, we argue that compared to providing no information, gamified IS that provide emotional feedback and access to normative information align hedonic and normative goals, so that the likelihood of ESB increases. Further, we expect that combining normative information with emotional feedback is more effective in triggering ESB compared to providing normative information only. Thus, we hypothesise: H2a: Aligning hedonic and normative goals through both gamified emotional feedback and normative information will be more effective in triggering ESB than providing no information. H2b: Aligning hedonic and normative goals through both gamified emotional feedback and normative information will be more effective in triggering ESB than providing normative information only. Individuals in a gain goal frame are highly sensitive to opportunities that increase their resources, such as money, time, or status (Lindenberg and Foss 2011). This need can be satisfied by competition that allows for comparing one’s performance with that of others (Matallaoui et al. 2017). Thus, for example, a contest during which participants strive for victory or superiority to increase personal resources (e.g., prizes or enhanced status) appeals to the gain goal frame (Liu et al. 2013). A key mechanism to activate the gain goal frame is to set clear performance goals to provide “the user with an idea of how the service is meant to be used and what is expected of the user” (Hamari 2017, p. 470). To satisfy users’ motivational needs related to competition, IS that allow comparisons of individual and group performance, setting individual goals, and assigning default goals have been shown to be successful in triggering behavioural changes in diverse contexts (Jung et al. 2010, Loock et al. 2013). Competitive performance feedback reduces the gap between self-selected goals and actual behaviour, since competitive feedback enables individuals to check the achievement of their behaviour and adjust it toward the set goal (Karlin et al. 2015, Loock et al. 2013). Studies have also shown that challenging competition leads to higher levels of attention (Novak et al. 2000), learning (Skadberg and Kimmel 2004), and information processing (Sicilia et al. 2005). Thus, gamification that affords competition among individuals should make ESB more appealing and increase individuals’ cognitive efforts (Santhanam et al. 2016, Schöbel et al. 2016). Accordingly, we argue that strengthening the normative goal frame by combining gamified competitive feedback with normative information (i.e., making gain and normative goal frames more compatible) will increase ESB, compared to receiving (a) no information or (b) normative information only. Thus, we hypothesise: H3a: Aligning gain and normative goals through both gamified competitive feedback and normative information will be more effective in triggering ESB than providing no information. H3b: Aligning gain and normative goals through both gamified competitive feedback and normative information will be more effective in triggering ESB than providing normative information only. GFT postulates that the salient goal – i.e., the goal frame – guides behavioural decision making, while other goals are pushed to the background and either stabilise (if they are congruent with the focal goal) or destabilise (if they are incongruent with the focal goal) the salient goal (Lindenberg and Steg 2007). The hedonic goal frame is considered the strongest, since from an evolutionary perspective, hedonic goals satisfy the most fundamental human needs (Lindenberg 2017). The normative goal frame is considered to be the weakest, because the

190  Research handbook on information systems and the environment collective good involved provides less immediate individual advantages. The gain goal frame is considered to lie somewhere in between (Lindenberg 2017). When a hedonic goal frame guides behaviour, people seek positive emotions and immediate improvement in their well-being. Showing participants an emotionally appealing image before bidding in an internet auction resulted in inferior financial performance, because individuals concentrated not only on their bidding performance, but also on regulating their emotions (Adam et al. 2016). Performance goals were thus less influential and hedonic appeals dominated cognitive and motivational processes. Similarly, highly stimulating game environments were found to distract students from learning (Young et al. 2012). Thus, an overly high emotional involvement level in gamification can weaken performance. In contrast, gamified IS that induce competition leverage users’ motivation for greater performance (Mekler et al. 2017, Song et al. 2013). Besides, gamified IS that allow the comparison of performance via comparative scores satisfy individuals’ innate need for performance feedback (Deci and Ryan 2000, Liu et al. 2017). Such competitive feedback motivates individuals to adapt their behaviour to outperform the reference group, which can self-reinforce overall group performance (Liu et al. 2017, Santhanam et al. 2016). Regarding norm-guided behaviour, such as ESB, comparative feedback could also result in individuals being motivated to keep up or be superior to others’ performance (Lindenberg and Steg 2013). Further, the presence and behaviours of other people foster ESB, since individuals tend to compare their own behaviour to the behaviour of others (Lo et al. 2012). The likelihood of people acting eco-sustainably significantly increases when people think their behaviour is being observed (Keizer et al. 2008). Also, observing others’ norm-guided behaviour activates norms, because acting against these norms shows a lack of moral integrity (Aquino et al. 2009). Thus, people tend to behave more eco-sustainably when choices for associated behaviours are made in public (Griskevicius et al. 2010). Therefore, we assume that gamified IS that allow for competition among individuals are more effective in activating social norms and guiding behaviour than gamified IS that provide emotional stimuli. Thus, we hypothesise: H4: Aligning gain and normative goals through both gamified competitive feedback and normative information will be more effective in triggering ESB than aligning hedonic and normative goals through both gamified emotional feedback and normative information.

4.

EXPERIMENTAL DESIGN AND DATA COLLECTION

4.1

Participants and Design

To test our hypotheses, we conducted an experiment with management students (undergraduate and graduate students) at a German university. In total, N = 160 management students voluntarily participated in our study for a monetary compensation of EUR 8.00 each. The amount of the compensation was independent from the participant’s performance to not add a confounding factor and not divert any attention from the treatments. Their mean age was 22.81 years (SD = 3.08). As described above, the study followed a randomised four-group between-subjects design ([1] normative goal frame, [2] hedonic and normative goal frames, [3] gain and normative goal frames, [4] control). We randomly assigned participants to one of the four conditions.

The persuasive potential of digital nudging  191 4.2 Procedure The participants were randomly assigned to an individual cubicle with a computer, and we explained that during the experiment all necessary information would be provided on the screen. Following this, all participants completed a survey on eco-sustainable values, beliefs, and their willingness to act eco-sustainably. These details served as control variables. Following the survey, the participants were forwarded to the introduction screen on which the task was explained. The participants’ task was to find correct answers to validated multiple-choice questions, adopted from Sparrow et al. (2011). These questions are considered difficult and cannot be answered by common knowledge (Sparrow et al. 2011). To avoid any bias, none of the questions were related to eco-sustainability. To answer these questions, the participants had to choose between a standard, non-eco-friendly, but user-friendly search engine (Google, www​ .google​.com) and an eco-friendly, but less user-friendly search engine (Blackle, www​.blackle​ .com). Blackle is a search engine that works in the same way as Google, but has a black background to reduce screens’ energy consumption by approximately 35 per cent. Reduced brightness and the lower contrast ratio make Blackle less optically appealing and less convenient to use. Participants’ decision between both search engines reflects ESB in real-life settings, because eco-sustainability is regularly associated with a higher investment in resources, such as time and effort, and with altering habits. Similarly, using Blackle requires participants to both alter behaviour by choosing an unfamiliar search engine and invest additional efforts to actively engage in ESB through reduced energy consumption. Therefore, Blackle provides an ideal scenario in which participants experience the immanent challenge of avoiding habits and investing additional resources to benefit the environment (Andersson and Von Borgstede 2010). After receiving an introduction to the task and information about both search engines’ functionalities, all participants had five minutes to get used to the experimental setting and the search engines by answering two trial questions (trial session; see Figure 10.2).

Figure 10.2

Course of the experiment

Adopted from Sparrow et al. (2011), respondents had to, for instance, answer the trial question “What is a quince?” by choosing between the answer options Fruit, Vegetable, Animal, or Technology. Before starting the performance session of the experiment, the participants had the opportunity to clarify any remaining questions. The authors responded, ensuring that all

192  Research handbook on information systems and the environment participants received exactly the same information across all sessions. During the performance session, we asked the participants to answer three multiple-choice questions over four rounds (12 questions in total) with the help of the search engines. For instance, one question, adopted from Sparrow et al. (2011), was “What is Krypton’s atomic number?” After each round of the performance session, the participants in all groups, besides the control group, received one type of treatment. After the performance session, we asked the participants to complete a survey that included manipulation checks and control variables. All participants were then formally debriefed. 4.3 Conditions Participants in the normative goal frame condition received information regarding the energy consumption of both search engines, including details on the amount of energy conserved if they used Blackle on a global scale. They were further informed that their performance was measured based on the screen’s electricity consumption, which was dependent on the search engine they used, the correctness of their answers, and their response time. This normative information appeals to eco-sustainable values, framing ESB as the most appropriate behaviour (Steg and Vlek 2009), and was provided in the form of normatively framed facts, following the work of Seidel et al. (2017). Participants in the combined hedonic and normative goal frames condition received the same information as the participants in the normative goal frame condition at the beginning of the experiment. The information was also available during the performance session by hovering over an information button at the top of the page. To support this information and to activate the hedonic goal frame, emotions were evoked by showing the participants an apple tree along with a supportive message after each round (see Figure 10.3). For every question correctly answered via Blackle, a red apple was added to the tree. We designed this manipulation in line with prior research which has shown that aesthetic impressions are important in triggering emotions (Blake 1982, Van der Heijden 2004) and that positive emotional states influence the desire to perform a given behaviour (Loock et al. 2013). Thus, to strengthen the normative goal frame and align it with hedonic goals, normative information was reinforced by a consistent hedonic goal, namely improving personal feelings through emotional feedback. In the combined gain and normative goal frames condition, participants received the same normative information as the normative goal frame condition at the beginning of the experiment and they could access this information via an information button during the treatments. To activate the gain goal frame, this information was supported by a functionality that allowed one to assess one’s own performance in relation to the average performance of others (see Figure 10.3). To design this stimulus, we built on Loock et al.’s (2013) manipulation by including the following functionalities that enabled for the intended behaviour: (1) a display of the participant’s own score and the score of all other participants in the same group, thus allowing social comparison; (2) a scale-based efficiency rating that provided individual feedback; and (3) a goal-setting functionality that allowed participants to set a goal for collecting points by using Blackle during the next search. After each round, participants received positive and timely feedback, as well as functionalities that allowed goal setting and social comparison. Thus, to strengthen the normative goal frame and align it with gain goals, normative information was reinforced by a consistent gain goal, namely improving participants’ resources through competitive feedback.

The persuasive potential of digital nudging  193

Figure 10.3

Treatments in the hedonic and normative goal frames condition and the gain and normative goal frames condition

Participants in the control condition received only neutral task-related instructions, and no additional information on appropriate behaviour, performance, or any gamified treatments. This allowed us to carry out isolated evaluation of possible treatment effects. 4.4 Measurements We developed a cumulative score to measure ESB performance as our dependent variable. The development of this score was based on a thorough literature review, and included an assessment of the final score by five experienced scholars in the field of experimental research. We calculated the ESB performance for each question based on the following formula (see Table 10.2 for the measurement of each factor): energy efficiency time

​ESB performance  ​=  correctness  *  ​ ____________      ​ 

Table 10.2

Factors to calculate ESB performance

Factor

Measurement

Points

Correctness

Correctness of the answer

False: 0 points

Energy efficiency

Energy efficiency of the search engine

Time

Response time

Correct: 1 point Google: 1,000 points Blackle: 1,350 pointsa One point per second needed

Note: a The points reflected that Blackle reduces the electricity consumption of the screens by 35 per cent.

194  Research handbook on information systems and the environment Calculating the score to measure ESB performance based on these three factors reflects ESB in real-life settings, as it (1) includes the attainment of a certain goal (i.e., answering questions correctly), and (2) reflects the direct environmental outcome of the behaviour (i.e., energy efficiency, usage time) in consideration. Thus, the score reflects the need to answer the questions correctly, and considers that the usage of Google indicates a 35 per cent higher energy consumption compared to Blackle; however, finding answers in Blackle is more difficult, and thus more time-consuming than finding answers in Google. To ensure that all participants understood the measurement for ESB, each participant had to calculate three scores of fictional examples and then list each person in the correct order based on the scores. After each of the four rounds (each comprising three questions), the score for each question was added up to show participants in the gain and normative goals conditions a cumulative round-based score. 4.5

Manipulation Checks and Controls

As a manipulation check for the emotional treatment in the hedonic and normative goal frames condition, we used the positive and negative affect schedule (PANAS; Watson et al. 1988; α = .862), which is a widely used indicator for an individual’s prevailing emotional state. As a manipulation check for the competitive treatment in the gain and normative goal frames condition, we adapted Landers et al.’s (2017) manipulation check by asking whether and to what extent a performance goal was determined by the experimental setting. Participants had to make a single choice from a list of three options, in which option three (“The goal could be set and adapted by myself”) indicated the correct answer. This manipulation check follows previous literature (Landers et al. 2017) and addresses participants’ attention for the competitive treatment. All manipulation checks proved their reliability in prior studies (e.g., Blay et al. 2012, Landers et al. 2017, Zhang et al. 2016). As control variables for ESB performance, we built on established scales on environmental beliefs and attitudes (EBA; Gatersleben et al. 2002; α = .850), personal norms (PN; Steg et al. 2005; α = .848), and personal ecological norms (PEN; Hunecke et al. 2001; α = .897) to control for inclination to ESB before starting the experiment. We further included scales on personal innovativeness regarding information technology (PIIT; Agarwal and Prasad 1998; α = .877), computer playfulness (CP; Agarwal and Prasad 1998; α = .842), and attitudes to using technology (ATUT; Venkatesh et al. 2003; α = .868) to control for participants’ affinity for dealing with technology and new technical features, such as Blackle. For all scales, the items were measured on a seven-point Likert scale (anchored between strongly disagree [1] and strongly agree [7]). We further controlled for participants’ sex, age, student status (undergraduate or graduate student), and prior experience with similar experiments.

5.

ANALYSIS AND RESULTS

5.1

Descriptive Statistics

Table 10.3 shows the means, standard deviations, and correlations of all study variables. The demographics and participants’ values and experiences were equally distributed among participants in the four conditions.

0.626

1.066

M

SD

M

Control SD

1

 

3.425

0.589

0.460

5.250

4.843

3.856

4.958

4.864

5.738

1.575

0.793

0.890

0.752

1.035

0.901

0.699

1.325

23.125 3.236

1.700

3.400

5.756

4.982

4.181

4.817

4.867

5.756

2.000

0.886

0.820

0.698

1.521

1.000

0.800

1.780

0.737 0.381

5.575

4.832

3.794

5.038

4.881

5.900

1.800

0.905

0.884

0.916

1.074

0.967

0.712

1.474

22.600 2.455

1.825

0.6653 3.400 0.491

22.525 3.740

1.600 -0.134*

0.049

0.069

0.085 -0.048

-0.111*

-0.131*

-0.016 0.038

-0.026 0.059

-0.051 0.014

0.009

-0.094 -0.045*

-0.089 0.032

-0.013 1

 

2

 

-0.045

-0.157*

-0.146*

0.242*

0.238*

0.246*

-0.105*

-0.190*

1

 

 

3

 

 

 

 

 

5

 

-0.073 1

 

 

 

 

 

6

 

-0.072

-0.064 0.531* 0.037

0.113* 0.126* -0.090*

-0.016 0.173* -0.149*

0.010

0.033

-0.030 -0.062 0.708*

0.035

0.129* 1

1

 

 

 

4

 

-0.031

-0.138*

-0.083

0.694*

1

 

 

 

 

 

 

7

 

-0.059

-0.194*

-0.032

1

 

 

 

 

 

 

 

8

 

0.402*

0.353*

1

 

 

 

 

 

 

 

 

9

 

0.433*

1

 

 

 

 

 

 

 

 

 

10

 

1

 

 

 

 

 

 

 

 

 

 

11

 

Notes: * p < .01. ESB: ESB performance, Exp: prior experimental experience (5 times or more = 1, 3–4 times = 2, 1–2 times = 3, never = 4), Sex: Gender (male = 1, female = 2), Age: age in years, Status: student status (undergraduate student = 1, graduate student = 2), EBA: environmental beliefs and attitudes (Gatersleben et al. 2002), PN: personal norms (Steg et al. 2005), PEN: personal ecological norms (Hunecke et al. 2001), PIIT: personal innovativeness regarding information technology (Agarwal and Prasad 1998), CP: computer playfulness (Agarwal and Prasad 1998), ATUT: attitudes toward using technology (Venkatesh et al. 2003).

1.002

4.761

5.381

10 CP

1.033

0.872

0.698

1.631

11 ATUT

4.908

4.967

5.829

1.825

22.975 2.678

0.401

0.784

PEN

PIIT

8

9

3.400

1.800

3.906

EBA

PN

6

7

Age

Status

4

5

Exp

Sex

2

3

SD

normative

normative

M

Gain and

Hedonic and

51.820 27.722 74.850 43.773 83.440 47.031 49.448 33.488 1

SD

M

Variables

ESB

 

Normative

Mean, standard deviation, and correlation matrix

 

1

 

Table 10.3

The persuasive potential of digital nudging  195

196  Research handbook on information systems and the environment We checked for homogeneity of variances between all conditions and conducted a t-test for equal and unequal variances accordingly (Mertens et al. 2017). To check for multicollinearity issues and uncover relationships between variables, we conducted a correlation analysis of ESB performance and all independent variables. Since we found no high correlations between variables (all |r| < .708) and no high variance inflation factors (all VIF ≤ 2.920), multicollinearity was not problematic (Diamantopoulos et al. 2008, Diamantopoulos and Winklhofer 2001). We carried out a Mann–Whitney U test to assess differences in ESB performance between the four conditions. All manipulation checks were effective. In comparison to the control group (M = 4.950, SD = 0.111), participants in the hedonic and normative goal frames condition receiving emotional feedback reported a higher positive emotional state (M = 4.910, SD = 0.099, t(78) = 2.212, p < .050), measured according to the PANAS scale (Watson et al. 1988). Participants in the gain and normative goal frames condition, receiving competitive feedback, indicated that they could set a goal and were able to adapt it themselves, and thus paid attention to the competitive treatment; 75 per cent of the participants provided the correct answer (M = 2.730; SD = 0.506). 5.2

Mann–Whitney U test

As the dependent variable was not normally distributed,2 we applied a non-parametric Mann– Whitney U test to compare the four conditions after receiving the treatments (see Table 10.4). Table 10.4

Mann–Whitney U test after treatments

Condition

Normative goal

Hedonic and normative goal

Gain and normative goal

frame

frames

frames

Hedonic and normative goal frames

4.733 (*)

 

 

Gain and normative goal frames

6.105 (*)

-1.540

 

Control

1.134

5.413 (*)

6.689 (*)

Note: * p < 0.001, with s-values.

The results indicated that there was no difference in ESB performance between participants in the normative goal frame condition and participants in the control condition (p = 0.257). However, we found significant differences in ESB performance between the non-gamified and the gamified conditions: participants in the hedonic and normative goal frames condition receiving emotional feedback indicated significantly more ESB than participants who received only normative information (normative goal frame condition; p < 0.001) or participants who received neither a gamified treatment nor normative information (control condition; p < 0.001). Participants in the gain and normative goal frames condition also showed significantly more ESB than participants who received only normative information (normative goal frame condition; p < 0.001) or participants who received neither a gamified treatment, nor normative information (control condition; p < 0.001). Thus, participants in the two gamified conditions had a significantly higher actual ESB performance compared to participants in the non-gamified conditions. The results from the Mann–Whitney U test indicated that competitive feedback strengthening the gain goal frame, and emotional feedback strengthening the hedonic goal frame, were equally effective in triggering ESB (p = 0.124).

The persuasive potential of digital nudging  197 5.3

Post-Hoc Analysis

In addition, we analysed the effect of gamification on closing the attitude–behaviour gap – a mismatch between what people say and actually do (Blake 1999). The attitude–behaviour gap is specifically relevant in the context of norm-driven ESB as individuals’ positive attitude toward eco-sustainability is not reflected in their actions. This mismatch has often been observed and discussed in prior literature over the last few decades (Blake 1999, Frederiks et al. 2015, Kollmuss and Agyeman 2002). To analyse the effectiveness of gamification in closing the attitude–behaviour gap, we aggregated the three environment-related variables (EBA, PN, PEN) and used the aggregate value to split the participants using a median-split procedure (median = 5.20). In a Mann– Whitney U test, we compared the ESB performance of the participants that indicated higher levels of environmental concerns in the non-gamified (groups 1 and 4) with the gamified groups (groups 2 and 3) to ascertain whether gamified treatments could significantly decrease the attitude–behaviour gap. The results show that the gamified groups achieved significantly (z-value: – 5.746, p < 0.001) higher levels of ESB performance than the non-gamified groups.

6.

DISCUSSION AND IMPLICATIONS

We analysed and compared the effectiveness of different gamified nudging mechanisms which addressed the hedonic, gain, and normative goals that guide human behaviour (Lindenberg 2017; Lindenberg and Steg 2007, 2013). Our results show that participants who are provided with normative information which frames eco-sustainability as the appropriate behaviour, and who receive additional gamified treatments that support hedonic goals (via emotional feedback) as well as gain goals (via competitive feedback) respectively, behave more eco-sustainably than participants who did not receive gamified treatments. Thus, our results indicate that gamified IS can be leveraged to promote ESB. Further, we found no differences in ESB between groups that were provided with gamified treatments supporting hedonic or gain goals (i.e., groups 2 and 3), which indicates that gamified IS addressing hedonic or gain goals are equally effective in stimulating ESB. This finding implies that both hedonic and gain goals can principally trigger ESB (Lindenberg and Steg 2007), since individual feelings and resources can be related to eco-sustainability. 6.1

Theoretical Implications

Our study contributes to research on gamified IS in the context of norm-guided behaviour in three major ways: first, our study is the first to consider multiple goals that trigger human behaviour using gamified IS. Our study shows that gamified IS that afford emotional or competitive stimuli can stimulate ESB by increasing the compatibility of multiple goals that guide human behaviour. We thereby provide novel insights about how IS can influence and align often competing goals to promote ESB in a relevant and highly realistic scenario (Benitez-Amado and Walczuch, 2012, Seidel et al. 2013, Watson et al. 2011). As designing gamified IS for norm-guided behaviours can make normative goals more compatible with hedonic or gain goals, gamification mechanisms enable individuals to overcome barriers to ESB. Because individual behaviour is considered a key enabler for sustainability programmes

198  Research handbook on information systems and the environment (Seidel et al. 2010), behavioural changes toward more environmentally friendly practices can actively be supported by aligning potentially competing goals with gamified IS. Our findings thus provide effective game mechanisms that increase the understanding of how gamification can be used to promote normatively driven ESB through aligning competing goals. Second, we found that gamified IS that stimulate emotions are equally effective as gamified IS that stimulate competition in improving individuals’ ESB. However, we found significant differences between both conditions after the first round. The effect of competitive feedback in round two was greater than the effect of emotional feedback. This finding implies a transient, short-term effect of competition-inducing IS, showing that in short periods of time gamified IS that provide competitive feedback are more effective than gamified IS that provide emotional feedback. Yet, we found no evidence for differences between both stimuli during the further rounds of the study. Thus, even though in gamified IS stimulating a gain goal frame via competition might have a stronger short-term effect than stimulating a hedonic goal frame via emotions, gamified IS allowing for an activation of both a gain or a hedonic goal provide great potential for supporting ESB. Third, by integrating GFT in gamification research, this study provides insights into how gamification design elements can be used to improve task feedback (Liu et al. 2017). As the focal goal influences which information is absorbed and processed, what knowledge and attitudes become cognitively accessible, and how people evaluate aspects and alternatives of different situations, information and feedback in line with the salient goal frame is more effectively processed (Lindenberg and Steg 2007, 2013). Our results show that gamified IS providing competitive and emotional feedback are effective in strengthening the relative weight of normative goals if they are framed as congruent. Thus, we extend prior research that demonstrates the effectiveness of direct and immediate feedback to support ESB (e.g., Fischer 2008, Loock et al. 2013) by showing that presenting normative information with gamified feedback is effective in conveying feedback to increase individuals’ ESB. As such, our study provides rigorous empirical evidence that gamified nudging is a useful method to trigger behavioural change toward more norm-guided behaviour, such as ESB. The insights on gamified nudging provide a new perspective to systems design by expanding the knowledge on how to leverage the motivational affordance in favour of sustainable behaviour (Jung et al. 2010, Zhang 2008). Moreover, we extend research on GFT by experimentally investigating how the normative goal frame can be strengthened and aligned with hedonic and gain goals to promote ESB, as suggested by Lindenberg and Steg (2007). By demonstrating that gamification is effective in strengthening and aligning the normative goal frame, our study answers Lindenberg and Steg’s (2007) call for research on lowering the competition of potentially competing goals in order to promote normative behaviour such as ESB. We also address their call for policy viability (Lindenberg 2017). 6.2

Practical Implications

Beyond theoretical implications, our study thus also provides insight for organisations and society. We have shown that gamification that considers the interplay between multiple goals is effective in triggering ESB, which is increasingly imperative for the future of our planet. Many governments and organisations aim to find new ways to encourage individual ESB to decrease carbon footprints. Our study demonstrates that gamification is an effective way to attenuate the attitude–behaviour gap in organisational and societal contexts. Our results are

The persuasive potential of digital nudging  199 also useful in a broader context of norm-guided and altruistic behaviours, such as complying with information security policies or supporting volunteering work. Given the turbulent times we live in, it is also of utmost importance to align normative information which frames social sustainability as the appropriate behaviour with additional gamified treatments that support hedonic goals (via emotional feedback) as well as gain goals (via competitive feedback) to promote socially inclusive behaviour. Important implications for app developers and policy makers can be drawn on how behaviour change could occur through the introduction of ethically carefully designed digital nudges (Schmidt and Engelen 2020) that activate individual motivations to influence users’ attitudes and behaviours by including game-design elements in non-game contexts (Blohm and Leimeister 2013, Lowry et al. 2013, Treiblmaier et al. 2018). Furthermore, our study implies that the traditional way of providing normative information to promote norm-guided behaviours has little effect in creating behavioural change. However, when normative information is combined with gamified IS that afford hedonic or gain goals it is more effective. Thus, organisations and governments should consider increasing the effectiveness of providing normative information, as included in policies, rules, or guidelines, by blending it with gamified IS that make it compatible with individual hedonic and gain goals. 6.3 Limitations Even though we designed our study with care, our study has limitations. First, a laboratory experiment limits generalisability and realism. However, research has shown that there is no significant difference between laboratory and field experiments (Remus 1986), which holds especially true for behavioural responses to goals, feedback, and incentives (Locke 1986). Using a controlled experimental laboratory study allowed us to test our hypotheses precisely, while controlling for organisational influences that can impair the results of field experiments (Jung et al. 2010, Ruchala 1999). As we could vary one factor at a time, our experimental setting’s internal validity can be considered as high. Yet, students might be more prone to gamified IS, as younger adults exhibit higher affinity toward technology and particularly games (Liu et al. 2017) as well as higher concern toward eco-sustainability (Diamantopoulos et al. 2003). Thus, a demographically representative follow-up study could increase validity across age groups. Additionally, controlled, randomised field experiments could help to increase external validity outside of laboratory settings. Also, our experimental setting did not allow measurement over a longer period of time. Thus, we encourage future research to apply longitudinal experimental designs to better understand the interventions’ short- and long-term effects, as well as the interplay of multiple gamified interventions that were sequentially introduced. This could provide further insight into how to design gamified IS to encourage ESB in the long term. Moreover, despite its effectiveness, enthusiasm for the nudging concept is not universal. A common criticism is that nudging manipulates in an unethical way and restricts personal freedom of choice (Schmidt and Engelen 2020). Although persuasive systems could potentially be designed to manipulate the user in opaque ways, the concept of nudging is explicitly based on easily avoidable stimuli that maintain freedom of choice without prohibiting behavioural options (Thaler and Sunstein 2008). A second criticism relates to the static, one-size-fits-all approach of nudging interventions, which is not tailored to the individually governing personal values for ESB (e.g., Altmann et al. 2018, Brandsma and Blasch 2019), although it has been shown that activating these values makes the governing goal salient and triggers compliant

200  Research handbook on information systems and the environment ESB (Steg et al. 2014). Thirdly, digital nudging is criticised for only encouraging short-term behaviour change without triggering a long-term learning effect. This means that the effect of nudging stops as soon as the intervention is removed. In response, the concept of boosting has gained attention as a type of intervention strategy that enhances people’s personal competences instead of behaviours (Hertwig and Grüne-Yanoff 2017). The “vision behind boosting is to equip individuals with […] competences that are applicable across a wide range of circumstances” (Hertwig and Grüne-Yanoff 2017, p. 981). Whereas nudges aim to change behaviour, boosts are designed to enhance individual competences and thus have a long-term behavioural effect. Therefore, we encourage future research to compare behavioural interventions with nudging and boosting effects, as this could provide an unprecedented opportunity to encourage ESB with lasting impact.

7. CONCLUSION The numbers of wildfires, droughts, blizzards, floods, and other natural disasters worldwide has been skyrocketing in recent years, making the impact of climate change more evident than ever before. To mitigate the exceedingly dangerous consequences of climate change, it is imperative to find novel ways of motivating individual eco-sustainable behaviours. We found that digital nudging, and gamification in particular, can be an effective way of promoting ESB if the gamified IS design takes competing goal frames into account. Our study investigated and compared the effectiveness of gamified IS affording competitive and emotional feedback. Our results show that gamified IS that align hedonic or gain goals with normative goals led to more ESB. Further, our study demonstrates that providing aligned hedonic or gain goals is equally effective in promoting normative-driven ESB. Our study addresses an important void in the IS literature by analysing gamification’s effects in the complex interactions of multiple, often competing goals, while past research has chiefly focused on single goal frames to motivate ESB. Thus, building on GFT, this study, first, demonstrates that gamification can influence goal frames by aligning otherwise competing goals. Second, it highlights that combining normative information with competition and emotion-based gamification design elements is effective in conveying direct and immediate feedback to increase ESB. Thus, if gamified IS are designed to increase the compatibility between normative, hedonic, and gain goals regarding norm-guided behaviours, gamification’s persuasive potential can be an effective way of overcoming individual barriers to ESB and secure the future of the planet.

NOTES 1. This work is a derivative of Promoting Eco-Sustainable Behavior with Gamification: An Experimental Study on the Alignment of Competing Goals © 2020 by Anna-Raissa Seidler, Christopher Henkel, Marina Fiedler, Johann Kranz, Anne Ixmeier, and Kim Strunk. 2. A Skewness/Kurtosis test for normality (p < 0.001), the Shapiro–Wilk test (p < 0.001), the Shapiro– Francia test (p < 0.001), and the visual inspection with Q-Q plots reveal a non-normal distribution of our data. Thus, parametric tests could not be used (Mertens et al. 2017).

The persuasive potential of digital nudging  201

REFERENCES Abrahamse, W., Steg, L., Vlek, C., and Rothengatter, T. 2005. “A Review of Intervention Studies Aimed at Household Energy Conservation,” Journal of Environmental Psychology (25:3), pp. 273–91. Adam, M.T.P., Astor, P.J., and Krämer, J. 2016. “Affective Images, Emotion Regulation and Bidding Behavior: An Experiment on the Influence of Competition and Community,” Journal of Interactive Marketing (35), pp. 56–69. Agarwal, R., and Prasad, J. 1998. “A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology,” Information Systems Research (9:2), pp. 204–15. Altmann, S., Falk, A., Heidhues, P., Jayaraman, R., and Teirlinck, M. 2018. “Defaults and Donations: Evidence from a Field Experiment,” in DICE Discussion Paper, Düsseldorf Institute for Competition Economics (DICE). Andersson, M., and Von Borgstede, C. 2010. “Differentiation of Determinants of Low-cost and High-cost Recycling,” Journal of Environmental Psychology (30:4), pp. 402–8. Aquino, K., Freeman, D., Reed, A., Lim, V.K.G., and Felps, W. 2009. “Testing a Social-cognitive Model of Moral Behavior: The Interactive Influence of Situations and Moral Identity Centrality,” Journal of Personality and Social Psychology (97:1), pp. 123–41. Benitez-Amado, J., and Walczuch, R.M. 2012. “Information Technology, the Organizational Capability of Proactive Corporate Environmental Strategy and Firm Performance: A Resource-based Analysis,” European Journal of Information Systems (21:1), pp. 664–79. Berengueres, J., Alsuwairi, F., Zaki, N., and Ng, T. 2013. “Gamification of a Recycle Bin with Emoticons,” in ACM/IEEE International Conference on Human-Robot Interaction, Tokyo, Japan, pp. 83–84. Blake, I. 1982. “Graphical User Interfaces for Business Information Systems,” MIS Quarterly (33:6), pp. 15–47. Blake, J. 1999. “Overcoming the ‘Value-action Gap’ in Environmental Policy: Tensions between National Policy and Local Experience,” Local Environment (4:3), pp. 257–78. Blay, A.D., Kadous, K., and Sawers, K. 2012. “The Impact of Risk and Affect on Information Search Efficiency,” Organizational Behavior and Human Decision Processes (117:1), pp. 80–87. Blohm, I., and Leimeister, J.M. 2013. “Gamification: Design of IT-based Enhancing Services for Motivational Support and Behavioral Change,” Business & Information Systems Engineering (5:4), pp. 275–8. Brandsma, J.S., and Blasch, J.E. 2019. “One for All? The Impact of Different Types of Energy Feedback and Goal Setting on Individuals’ Motivation to Conserve Electricity,” Energy Policy (135), pp. 1–11. Brewer, R.S., Lee, G.E., and Johnson, P.M. 2011. “The Kukui Cup: A Dorm Energy Competition Focused on Sustainable Behavior Change and Energy Literacy,” in Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii, USA, pp. 1–10. Cichy, P., Salge, T.-O., and Kohli, R. 2021. “Privacy Concerns and Data Sharing in the Internet of Things: Mixed Methods Evidence from Connected Cars,” MIS Quarterly (45:4), pp. 1863–1892. Cooke, R.A., and Rousseau, D.M. 1988. “Behavioral Norms and Expectations: A Quantitative Approach to the Assessment of Organizational Culture,” Group & Organization Studies (13:3), pp. 245–73. Corbett, J. 2013. “Designing and Using Carbon Management Systems to Promote Ecologically Responsible Behaviors,” Journal of the Association for Information Systems (14:7), pp. 339–78. Csikszentmihalyi, M. 1975. “Play and Intrinsic Rewards,” Journal of Humanistic Psychology (15:3), pp. 41–63. Deci, E.L., and Ryan, R.M. 2000. “The ‘What’ and ‘Why’ of Goal Pursuits: Human Needs and the Self-determination of Behavior,” Psychological Inquiry (11:4), pp. 227–68. DeYoung, R. 2000. “Expanding and Evaluating Motives for Environmentally Responsible Behavior.” Journal of Social Issues (56:3), pp. 509–26. Diamantopoulos, A., Riefler, P., and Roth, K.P. 2008. “Advancing Formative Measurement Models,” Journal of Business Research (61:12), pp. 1203–18. Diamantopoulos, A., Schlegelmilch, B.B., Sinkovics, R.R., and Bohlen, G.M. 2003. “Can Socio-Demographics Still Play a Role in Profiling Green Consumers? A Review of the Evidence and an Empirical Investigation,” Journal of Business Research (56:6), pp. 465–80.

202  Research handbook on information systems and the environment Diamantopoulos, A., and Winklhofer, H.M. 2001. “Index Construction with Formative Indicators: An Alternative to Scale Development,” Journal of Marketing Research (38:2), pp. 269–77. Dolan, P., Hallsworth, M., Halpern, D., King, D., Metcalfe, R., and Vlaev, I. 2012. “Influencing Behaviour: The Mindspace Way,” Journal of Economic Psychology (33), pp. 264–277. Elgaaied, L. 2012. “Exploring the Role of Anticipated Guilt on Pro-environmental Behavior: A Suggested Typology of Residents in France Based on their Recycling Patterns,” Journal of Consumer Marketing (29:5), pp. 369–77. Elliot, S. 2011. “Transdisciplinary Perspectives on Environmental Sustainability: A Resource Base and Framework for IT-enabled Business Transformation,” MIS Quarterly (35:1), pp. 197–236. Fischer, C. 2008. “Feedback on Household Electricity Consumption: A Tool for Saving Energy?,” Energy Efficiency (1:1), pp. 79–104. Flüchter, K., and Wortmann, F. 2014. “Promoting Sustainable Travel Behavior through IS-enabled Feedback: Short-term Success at the Cost of Long-term Motivation?,” in Proceedings of the 35th International Conference on Information Systems, Auckland, New Zealand, pp. 1–17. Frederiks, E.R., Stenner, K., and Hobman, E.V. 2015. “Household Energy Use: Applying Behavioural Economics to Understand Consumer Decision-Making and Behaviour,” Renewable and Sustainable Energy Reviews (41), pp. 1385–94. Gardner, G.T., and Stern, P. 2002. Environmental Problems and Human Behavior, Boston, MA: Pearson Custom. Gatersleben, B., Steg, L., and Vlek, C. 2002. “Measurement and Determinants of Environmentally Significant Consumer Behavior,” Environment and Behavior (34:3), pp. 335–62. Geissdoerfer, M., Savaget, P., Bocken, N.M.P., and Hultink, E.J. 2017. “The Circular Economy: A New Sustainability Paradigm?,” Journal of Cleaner Production (143), pp. 757–68. Gerow, J.E., Ayyagari, R., Thatcher, J.B., and Roth, P.L. 2013. “Can We Have Fun @ Work? The Role of Intrinsic Motivation for Utilitarian Systems,” European Journal of Information Systems (22), pp. 360–380. Gnauk, B., Dannecker, L., and Hahmann, M. 2012. “Leveraging Gamification in Demand Dispatch Systems,” in Proceedings of the 2012 Joint EDBT/ICDT Workshops, Berlin, Germany, pp. 103–10. Griskevicius, V., Tybur, J.M., and Van den Bergh, B. 2010. “Going Green to Be Seen: Status, Reputation, and Conspicuous Conservation,” Journal of Personality and Social Psychology (98:3), pp. 392–404. Groening, C., Sarkis, J., and Zhu, Q. 2018. “Green Marketing Consumer-Level Theory Review: A Compendium of Applied Theories and Further Research Directions,” Journal of Cleaner Production (172), pp. 1848–66. Halttu, K., and Oinas-Kukkonen, H. 2017. “Persuading to Reflect: Role of Reflection and Insight in Persuasive Systems Design for Physical Health,” Human-Computer Interaction (32:5–6), pp. 381–412. Hamari, J. 2013. “Transforming Homo Economicus into Homo Ludens: A Field Experiment on Gamification in a Utilitarian Peer-to-peer Trading Service,” Electronic Commerce Research and Applications (12:4), pp. 236–45. Hamari, J. 2017. “Do Badges Increase User Activity? A Field Experiment on the Effects of Gamification,” Computers in Human Behavior (71), pp. 469–78. Henkel, C., and Kranz, J. 2018. “Pro-Environmental Behavior and Green Information Systems Research: Review, Synthesis and Directions for Future Research,” in 39th International Conference on Information Systems, San Francisco, USA, pp. 1–17. Henkel, C., Seidler, A.-R., Kranz, J., and Fiedler, M. 2019. “How to Nudge Pro-Environmental Behavior: An Experimental Study,” in Proceedings of the 27th European Conference on Information Systems, Stockholm & Uppsala, Sweden, pp. 1–16. Hertwig, R., and Grüne-Yanoff, T. 2017. “Nudging and Boosting: Steering or Empowering Good Decisions,” Perspectives on Psychological Science (12:6), pp. 973–86. Huber, R.A., Anderson, B., and Bernauer, T. 2018. “Can Social Norm Interventions Promote Voluntary Pro Environmental Action?,” Environmental Science & Policy (89), pp. 231–46. Hunecke, M., Blobaum, A., Matthies, E., and Hoger, R. 2001. “Responsibility and Environment: Ecological Norm Orientation and External Factors in the Domain of Travel Mode Choice Behavior,” Environment and Behavior (33:6), pp. 830–852.

The persuasive potential of digital nudging  203 Ibanez, L., Moureau, N., and Roussel, S. 2016. “How Do Incidental Emotions Impact Pro-environmental Behavior? Evidence from the Dictator Game,” Journal of Behavioral and Experimental Economics (66), pp. 1–6. Ixmeier, A., and Kranz, J. 2020. “Green Your Own Device! A Study on Consumers’ Preferences for Eco-Design Principles,” in 28th European Conference on Information Systems, Marrakesh, Morocco, pp. 1–20. Johnson, E.J., and Goldstein, D. 2003. “Do Defaults Save Lives?,” Science (302), pp. 1338–9. Johnson, E.J., Shu, S.B., Dellaert, B.G., Fox, C., Goldstein, D.G., Häubl, G., Larrick, R.P., Payne, J.W., Peters, E., and Schkade, D. 2012. “Beyond Nudges: Tools of a Choice Architecture,” Marketing Letters (23), pp. 487–504. Jung, J.H., Schneider, C., and Valacich, J. 2010. “Enhancing the Motivational Affordance of Information Systems: The Effects of Real-time Performance Feedback and Goal Setting in Group Collaboration Environments,” Management Science (56:4), pp. 724–42. Kahneman, D. 2003. “A Perspective on Judgment and Choice: Mapping Bounded Rationality,” American Psychologist (58), pp. 697–720. Karlin, B., Zinger, J.F., and Ford, R. 2015. “The Effects of Feedback on Energy Conservation: A Meta-analysis,” Psychological Bulletin (141:6), pp. 1205–27. Keizer, K., Lindenberg, S., and Steg, L. 2008. “The Spreading of Disorder,” Science (322:1681), pp. 1681–5. King, P., and Tester, J. 1999. “The Landscape of Persuasive Technologies,” Communications of the ACM (42:5), pp. 31–8. Kollmuss, A., and Agyeman, J. 2002. “Mind the Gap: Why Do People Act Environmentally and What Are the Barriers to Pro-Environmental Behavior?,” Environmental Education Research (8:3), pp. 239–260. Koo, C., and Chung, N. 2014. “Examining the Eco-Technological Knowledge of Smart Green IT Adoption Behavior: A Self-Determination Perspective,” Technological Forecasting and Social Change (88), pp. 140–155. Kranz, J., and Picot, A. 2011. “Why Are Consumers Going Green? The Role of Environmental Concerns in Private Green-IS Adoption,” in Proceedings of the 19th European Conference on Information Systems, Helsinki, Finland, pp. 1–12. Krishnan, S., and Teo, T.S.H. 2011. “The Effect of Information Systems Capabilities on Sustainability: A Country-Level Analysis,” Proceedings of the 15th Pacific Asia Conference on Information Systems (PACIS), Brisbane, Australia, pp. 1–10. Landers, R.N., Bauer, K.N., and Callan, R.C. 2017. “Gamification of Task Performance with Leaderboards: A Goal Setting Experiment,” Computers in Human Behavior (71), pp. 508–15. Lindenberg, S. 2006. “Prosocial Behavior, Solidarity, and Framing Processes,” in Solidarity and Prosocial Behavior, D. Fetchenhauer, A. Flache. A.P. Buunk, and S. Lindenberg (eds.), Boston, MA: Springer, pp. 23–44. Lindenberg, S. 2017. “The Dependence of Human Cognitive and Motivational Processes on Institutional Systems,” in Social Dilemmas, Institutions, and the Evolution of Cooperation, B. Jann and W. Przepiorka (eds.), Oldenburg: De Gruyter, pp. 85–106. Lindenberg, S., and Foss, N.J. 2011. “Managing Joint Prediction Motivation: The Role of Goal-framing and Governance Mechanisms,” Academy of Management Review (36:3), pp. 500–525. Lindenberg, S., and Steg, L. 2007. “Normative, Gain and Hedonic Goal Frames Guiding Environmental Behavior,” Journal of Social Issues (63:1), pp. 117–137. Lindenberg, S., and Steg, L. 2013. “Goal-framing Theory and Norm Guided Environmental Behavior,” in Encouraging Sustainable Behavior, H. van Trijp (ed.), New York, NY: Psychology Press, pp. 37–54. Liu, D., Li, X., and Santhanam, R. 2013. “Digital Games and Beyond: What Happens When Players Compete?,” MIS Quarterly (37:1), pp. 111–24. Liu, D., Santhanam, R., and Webster, J. 2017. “Towards Meaningful Engagement: A Framework for Design and Research of Gamified Information Systems,” MIS Quarterly (42:4), pp. 1011–34. Lo, S.H., Peters, G.-J.Y., and Kok, G. 2012. “A Review of Determinants of and Interventions for Pro-environmental Behaviors in Organizations,” Journal of Applied Social Psychology (42:12), pp. 2933–67.

204  Research handbook on information systems and the environment Locke, E.A. 1986. Generalizing from Laboratory to Field Settings: Research Findings from Industrial Organization, Organizational Behavior, and Human Resource Management, Lexington, MA: Lexington Books. Loock, C.-M., Staake, T., and Thiesse, F. 2013. “Motivating Energy-Efficient Behavior with Green IS: An Investigation of Goal Setting and the Role of Defaults,” MIS Quarterly (37:4), pp. 1313–32. Lounis, S., Pramatari, K., and Theotokis, A. 2014. “Gamification Is All About Fun: The Role of Incentive Type and Community Collaboration,” in Proceedings of the 22nd European Conference on Information Systems, Tel Aviv, Israel, pp. 9–11. Lowry, P.B., Hammer, B., Gaskin, J., Roberts, T.L., and Twyman, N. 2013. “Taking ‘Fun and Games’ Seriously: Proposing the Hedonic-motivation System Adoption Model (HMSAM),” Journal of the Association for Information Systems (14:11), pp. 617–71. Matallaoui, A., Koivisto, J., Hamari, J., and Zarnekow, R. 2017. “How Effective is ‘Exergamification’? A Systematic Review on the Effectiveness of Gamification Features in Exergames,” in Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii, USA, pp. 3316–25. McDonald, F. 2014. “Developing an Integrated Conceptual Framework of Pro-environmental Behavior in the Workplace through Synthesis of the Current Literature,” Administrative Sciences (4:3), pp. 276–303. McGrath, K. 2006. “Affection Not Affliction: The Role of Emotions in Information Systems and Organizational Change,” Information and Organization (16:4), pp. 277–303. Mekler, E.D., Brühlmann, F., Tuch, A.N., and Opwis, K. 2017. “Towards Understanding the Effects of Individual Gamification Elements on Intrinsic Motivation and Performance,” Computers in Human Behavior (71), pp. 525–34. Melville, N. 2010. “Information Systems Innovation for Environmental Sustainability,” MIS Quarterly (34:1), pp. 1–21. Mertens, W., Pugliese, A., and Recker, J. 2017. Quantitative Data Analysis: A Companion for Accounting and Information Systems Research, Heidelberg: Springer. Novak, T.P., Hoffman, D.L., and Yung, Y.F. 2000. “Measuring the Customer Experience in Online Environments: A Structural Modeling Approach,” Marketing Science (19:1), pp. 22–44. Onwezen, M.C., Antonides, G., and Bartels, J. 2013. “The Norm Activation Model: An Exploration of the Functions of Anticipated Pride and Guilt in Pro-environmental Behaviour,” Journal of Economic Psychology (39), pp. 141–53. Oppong-Tawiah, D., Webster, J., Staples, S., Cameron, A.-F., and Ortiz de Guinea, A. 2014. “Encouraging Sustainable Energy Use in the Office with Persuasive Mobile Information Systems,” in Proceedings of the 35th International Conference on Information Systems, Auckland, New Zealand, pp. 1–11. Oppong-Tawiah, D., Webster, J., Staples, S., Cameron, A.F., Ortiz de Guinea, A., and Hung, T.Y. 2020. “Developing a Gamified Mobile Application to Encourage Sustainable Energy Use in the Office,” Journal of Business Research (106), pp. 388–405. Remus, W. 1986. “Graduate Students as Surrogates for Managers in Experiments on Business Decision Making,” Journal of Business Research (14:1), pp. 19–25. Renault. 2019. Driving Advice, Eco Driving. https://​gb​.e​-guide​.renault​.com/​eng/​Clio​-5/​DRIVING​ -ADVICE​-ECO​-DRIVING. Ruchala, L. 1999. “The Influence of Budget Goal Attainment on Risk Attitudes and Escalation,” Behavioral Research in Accounting (11), pp. 161–91. Ruepert, A.M., Keizer, K., and Steg, L. 2017. “The Relationship between Corporate Environmental Responsibility, Employees’ Biospheric Values and Pro-environmental Behaviour at Work,” Journal of Environmental Psychology (54), pp. 65–78. Ruepert, A.M., and Steg, L. 2018. “Environmental Considerations as a Basis for Employee Pro-environmental Behaviour,” in Research Handbook on Employee Pro-Environmental Behaviour, V.K. Wells, D. Gregory-Smith, and D. Manika (eds.), Cheltenham, UK and Northampton, MA: Edward Elgar Publishing Ltd., pp. 106–27. Russell, J.A. 2003. “Core Affect and the Psychological Construction of Emotion,” Psychological Review (110:1), pp. 145–72. Ryan, R.M., and Deci, E.L. 2000. “Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions,” Contemporary Educational Psychology (25:1), pp. 54–67.

The persuasive potential of digital nudging  205 Santhanam, R., Liu, D., and Shen, W.C. 2016. “Gamification of Technology-mediated Training: Not All Competitions are the same,” Information Systems Research (27:2), pp. 453–65. Scheiner, C., Haas, P., Bretschneider, U., Blohm, I., and Leimeister, J.M. 2017. “Obstacles and Challenges in the Use of Gamification for Virtual Ideas Communities,” in Gamification: Using Game Elements in Serious Contexts, S. Stieglitz, C. Lattemann, S. Robra-Bissantz, R. Zarnekow, and T. Brockmann (eds.), Cham: Springer, pp. 65–76. Schmidt, A.T., and Engelen, B. 2020. “The Ethics of Nudging: An Overview,” Philosophy Compass (15:4), pp. 1–13. Schöbel, S., Söllner, M., and Leimeister, J.M. 2016. “The Agony of Choice: Analyzing User Preferences Regarding Gamification Elements in Learning Management Systems,” in Proceedings of the 37th International Conference on Information Systems, Dublin, Ireland, pp. 1–21. Seidel, S., Chandra Kruse, L., Székely, N., Gau, M., and Stieger, D. 2017. “Design Principles for Sensemaking Support Systems in Environmental Sustainability Transformations,” European Journal of Information Systems (27:2), pp. 1–38. Seidel, S., Recker, J., and Pimmer, C. 2010. “Enablers and Barriers to the Organizational Adoption of Sustainable Business Practices,” in Proceeding of the 16th Americas Conference on Information Systems, Lima, Peru, pp. 1–10. Seidel, S., Recker, J., and Vom Brocke, J. 2013. “Sensemaking and Sustainable Practicing: Functional Affordances of Information Systems in Green Transformations,” MIS Quarterly (37:4), pp. 1275–99. Shevchuk, N., and Oinas-Kukkonen, H. 2016. “Exploring Green Information Systems and Technologies as Persuasive Systems: A Systematic Review of Applications in Published Research,” Proceedings of the 37th International Conference on Information Systems (ICIS), Dublin, Ireland, pp. 1–11. Sicilia, M., Ruiz, S., and Munuera, J.L. 2005. “Effects of Interactivity in a Web Site,” Journal of Advertising (34:3), pp. 31–44. Simon, H.A. 1972. “Theories of Bounded Rationality,” in Decision and Organization, C.B. McGuire and R. Radner (eds.), Amsterdam: North Holland, pp. 161–76. Skadberg, Y.X., and Kimmel, J.R. 2004. “Visitors’ Flow Experience while Browsing a Web Site: Its Measurement, Contributing Factors and Consequences,” Computers in Human Behavior (20:3), pp. 403–22. Slattery, P., Vidgen, R., and Finnegan, P. 2020. “Persuasion: An Analysis and Common Frame of Reference for IS Research,” Communications of the Association for Information Systems (46), pp. 30–69. Song, H., Kim, J., Tenzek, K.E., and Min, K. 2013. “The Effects of Competition and Competitiveness upon Intrinsic Motivation in Exergames,” Computers in Human Behavior (29:4), pp. 1702–8. Sparrow, B., Liu, J., and Wegner, D.M. 2011. “Google Effects on Memory: Information at our Fingertips,” Science (333), pp. 776–8. Stefansdotter, A., Steen-Knudsen, J., Flack, M., and Hansen, P.G. 2016. Nudging for Sustainable Consumption of Electronics, Copenhagen: Nordic Council of Ministers. Steg, L., Bolderdijk, J.W., Keizer, K., and Perlaviciute, G. 2014. “An Integrated Framework for Encouraging Pro-environmental Behaviour: The Role of Values, Situational Factors and Goals,” Journal of Environmental Psychology (38:4), pp. 104–15. Steg, L., Dreijerink, L., and Abrahamse, W. 2005. “Factors Influencing the Acceptability of Energy Policies: A Test of VBN Theory,” Journal of Environmental Psychology (25:4), pp. 415–25. Steg, L., and Vlek, C. 2009. “Encouraging Pro-environmental Behaviour: An Integrative Review and Research Agenda,” Journal of Environmental Psychology (29:3), pp. 309–17. Stern, P.C. 2000. “Toward a Coherent Theory of Environmentally Significant Behavior,” Journal of Social Issues (56:3), pp. 407–24. Sun, H., and Zhang, P. 2006. “The Role of Affect in Information Systems Research: A Critical Survey and a Research Model,” in Human–Computer Interaction and Management Information Systems: Foundations, P. Zhang and D. Galletta (eds.), Armonk, NY: M.E. Sharpe, pp. 295–329. Thaler, J., and Helmig, B. 2013. “Promoting Good Behavior: Does Social and Temporal Framing Make a Difference?,” Voluntas: International Journal of Voluntary and Nonprofit Organizations (24:4), pp. 1006–36. Thaler, R.H., and Sunstein, C.R. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness, New Haven, CT: Yale University Press.

206  Research handbook on information systems and the environment Thaler, R.H., and Sunstein, C.R. 2009. Nudge: Wie man kluge Entscheidungen anstößt, Berlin: Ullstein eBooks. Tiefenbeck, V., Goette, L., Degen, K., Tasic, V., Fleisch, E., and Lalive, R. 2018. “Overcoming Salience Bias: How Real-time Feedback Fosters Rresource Conservation,” Management Science (64:3), pp. 1458–76. Treiblmaier, H., Putz, L., and Lowry, P.B. 2018. “Setting a Definition, Context, and Theory-Based Research Agenda for the Gamification of Non-Gaming Applications,” Association for Information Systems Transactions on Human-Computer Interaction (THCI) (10:3), pp. 129–63. United Nations Framework Convention on Climate Change. 2018. “Achieving the Sustainable Development Goals through Climate Action.” https://​unfccc​.int/​achieving​-the​-sustainable​ -development​-goals​-through​-climate​-action. Unsworth, K.L., Dmitrieva, A., and Adriasola, E. 2013. “Changing Behaviour: Increasing the Effectiveness of Workplace Interventions in Creating Pro-environmental Behavior Change,” Journal of Organizational Behavior (34:2), pp. 211–29. Van der Heijden, H. 2004. “User Acceptance of Hedonic Information Systems,” MIS Quarterly (28:4), pp. 695–704. Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. 2003. “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly (27:3), pp. 425–78. Watson, D., Clark, L.A., and Tellegen, A. 1988. “Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales,” Journal of Personality and Social Psychology (54:6), pp. 1063–70. Watson, R.T., Boudreau, M.C., Chen, A.J., and Sepulveda, H.H. 2011. “Green Projects: An Information Driven Analysis of Four Cases,” Journal of Strategic Information Systems (20:1), pp. 55–62. Weinmann, M., Schneider, C., and Vom Brocke, J. 2016. “Digital Nudging,” Business & Information Systems Engineering (58:6), pp. 433–6. Weiser, P., Bucher, D., Cellina, F., and De Luca, V. 2015. “A Taxonomy of Motivational Affordances for Meaningful Gamified and Persuasive Technologies,” in Proceedings of the ICT for Sustainability (ICT4S), Copenhagen, Denmark, pp. 1–10. Wensing, J., Caputo, V., Carraresi, L., and Bröring, S. 2020. “The Effects of Green Nudges on Consumer Valuation of Bio-Based Plastic Packaging,” Ecological Economics (178), p. 106783. Young, M.F., Slota, S., Cutter, A.B., Jalette, G., Mullin, G., Lai, B., and Yukhymenko, M. 2012. “Our Princess Is in Another Castle: A Review of Trends in Serious Gaming for Education,” Review of Educational Research (82:1), pp. 61–89. Zeiss, R., Ixmeier, A., Recker, J., and Kranz, J. 2020. “Mobilising Information Systems Scholarship for a Circular Economy: Review, Synthesis, and Directions for Future Research,” Information Systems Journal (31:1), pp. 148–83. Zhang, P. 2008. “Motivational Affordances: Reasons for ICT Design and Use,” Communications of the ACM (51:11), pp. 145–47. Zhang, P. 2013. “The Affective Response Model: A Theoretical Framework of Affective Concepts and their Relationships in the ICT Context,” MIS Quarterly (37:1), pp. 247–74. Zhang, X., Zuo, B., Erskine, K., and Hu, T. 2016. “Feeling Light or Dark? Emotions Affect Perception of Brightness,” Journal of Environmental Psychology (47), pp. 107–11.

11. Comfort vs money: influencing the energy user for sustainable consumption Silpa Sangeeth L.R., Saji K. Mathew and Richard T. Watson

1. INTRODUCTION Sustainable consumption remains a very debatable issue. Some scholars doubt if the discourse has made any substantial progress towards environmental sustainability. They argue that the very notion of sustainable consumption is unsustainable due to the lack of contextualisation of the social, cultural, and historical premises of consumption (Dolan, 2002). Others find the existing strategies and policy instruments related to sustainable consumption quite inadequate in shifting “material-intensive consumer culture (Abdullah et al., 2011) to a society with less materialistic aspirations” (Mont & Plepys, 2008, p. 536). Economists further argue that without a steady growth in household consumption levels the economic development of a country would stall. This makes the choice between economic growth and sustainable consumption dichotomic. To this, a middle path has been suggested to decouple economic consumption from material consumption to generate economic growth while pursuing environmental goals (Jackson, 2008). Although the issue of sustainable consumption has been contentious at a philosophical level, its policy history shows sustained collective attention to influencing consumer behaviour to achieve environmental goals. As the modern era unfolded to offer higher standards of living through the consumption of materials and electrical energy, there also arose a collective concern on the adverse effect of irresponsible consumption on the environment. The United Nations (UN) Conference on Environment and Development held in Rio de Janeiro in 1992 advocated sustainable consumption of the Earth’s rare resources while pursuing higher standards of living (McCammon, 1992). This was a clarion call towards revising consumption behaviour by rethinking choices, expectations, and lifestyles, and it echoed in the years that followed (Jackson, 2008). Electrical energy consumption, which is a fundamental necessity for health and productivity, increases with national per capita income (Mont & Plepys, 2008; Gates, 2021). Production of electricity using fossil fuels accounts for 27 per cent of greenhouse gas (GHG) emissions. Around 2020, solar and wind became the cheapest methods for generating electricity.1 However, there is an environmental impact of building massive renewable generation capacity, and energy conservation remains a necessity. What will motivate energy consumers to conserve energy? How can people continue to enjoy the comforts of using electricity, while at the same time minimising the adverse impact of electricity production and consumption on the environment? The renewable sources of energy suffer from intermittency (Watson, 2020; Gates, 2021) and hence require investment in energy storage. A strategy towards sustainable electricity production implies sustainable energy consumption. This involves changing consumption patterns through load shifting such that more electricity is used when it is cheapest to produce, 207

208  Research handbook on information systems and the environment such as when there is plentiful sunshine or wind. Demand response (DR) programmes driven by utilities aim at the active participation of electricity consumers in responding to electricity prices as the prices change over time (Torriti, 2015). DR has evolved as an intervention strategy to address intermittency in electricity demand and supply (Watson, 2020). In other words, sustainable energy consumption implies demand and supply responses. This chapter contributes to the ongoing debate on sustainable consumption from an information systems (IS) perspective. Sustainable consumption has been studied from a range of viewpoints: economic, sociological, psychological, and environmental. Our work aims to understand sustainable consumption in the domain of energy. We first review the literature related to sustainable consumption with a special focus on energy. We bring out limitations in the conceptual understanding of consumer behaviour from the IS and behavioural economics perspectives. We further articulate DR as the engine for sustainable energy consumption, and how an economic analysis using the concepts of Willingness to Accept (WTA) and Willingness to Pay (WTP) (Table 11.1) helps to develop informational strategies for DR. An exploratory study in the Indian energy consumer market with a sample of 117 consumer responses further enriches our understanding of energy consumers through behavioural segmentation based on consumer response and demography. We conclude the chapter with a discussion on policy. Table 11.1

Measures for economic behaviour analysis for demand response programmes in household consumers

Measures for economic behaviour of energy users

Demand response strategies

1. Willingness to Accept (WTA)

The minimum incentive for household consumers to reduce energy consumption during peak demand hours.

2. Willingness to Pay (WTP)

The maximum penalty for household consumers who increase energy consumption during peak demand hours.

2.

CONCEPTUAL FOUNDATIONS OF SUSTAINABLE ENERGY CONSUMPTION

Sustainable consumption is defined as “patterns of consumption that satisfy basic needs, offer humans the freedom to develop their potential, and are replicable across the whole globe without compromising the Earth’s carrying capacity” (Hertwich, 2005, p. 4674). The World Summit for Sustainable Development (WSSD) calls for a life-cycle-based approach where sustainable production and consumption strategies are implemented together (WSSD, 2002). Here sustainable consumption is concerned with raising awareness and changing consumer behaviour, values, and motivations, and sustainable production involves providing radically innovative products and services and making their production process more resource-efficient (Barber, 2007). For instance, the transition of consumer demand to a sustainable pattern should encourage producers to meet the demand with a sustainable production process, such as the choice of green products. Sustainable consumption seems to contradict many related themes, such as human needs, equity, quality of life, resource efficiency, waste minimisation, life-cycle thinking, and consumer sovereignty, due to their apparently divergent objectives (Mont & Plepys, 2008). For example, economic progress typically results in higher levels of household consumption of

Comfort vs money  209 goods and services. On the other hand, conservative sustainable consumption requires reduced demand. This would imply adjusting lifestyles to find happiness in less material ways of living. It could also imply paying premiums to buy and consume green products and services. However, consumer policy is generally governed by the consumer sovereignty principle based on assumptions of rational choice (Jackson, 2008), which allows people to decide their consumption. In such a context, what would motivate an energy consumer to consume less energy, pay higher prices for electricity (green premiums), or shift their time of use of electricity to off-peak hours? This question requires an understanding of the theoretical foundations of consumer behaviour, ranging from economics, psychology, and strategy to marketing. Prior studies have proposed concepts from rational choice, adjusted expected value theory, moral, normative conduct, and habit models (Jackson, 2005) to analyse and explain sustainable consumption. As sustainable consumption can cut across multiple disciplines, the synthesis of models has also been attempted. Spaargaren (2003) proposes a social practices model derived from Gidden’s structuration theory (Giddens, 1984, 1991) to provide a sociological perspective of sustainable consumption. In this approach, Spaargaren departs from the usual method of treating consumption as an individual phenomenon to be explained using models such as sociopsychological attitude–behaviour (Fishbein & Ajzen, 1975). In the social practices model, an individual consumer is not an isolated entity who makes independent choices for consumption. On the contrary, this theory construes consumption as a process that happens amidst a group of interconnected human actors and social institutions. A consumer makes use of the “possibilities offered to them in the context of specific systems of provisioning” (Southerton et al., 2004, p. 145). An individual is characterised by a lifestyle and is situated amidst prevailing social practices. Therefore, the system of provisioning, lifestyle, and social practices interact and evolve in structure and together determine the sustainable consumption behaviour of individuals and households. An economic analysis of sustainable consumption has been the dominant focus of several studies (Jackson, 2005; Seyfang, 2009), predominantly based on the rational choice premises. Irrational premises of human behaviour based on the concepts of bounded rationality, habit, and emotion have also been proposed to counter the rational choice approach (Jackson, 2005). Motivational and attitudinal factors continue to remain a strong predictor towards contemporary sustainable technology adoption in households (Malhotra et al., 2008; Wunderlich et al., 2019). However, to the best of our knowledge, more recent concepts of behavioural economics such as loss aversion and the endowment effect, which predominate irrational economic behaviour of humans, have not been employed to study sustainable consumption behaviour. Some studies have suggested that policies based on information and price signals to consumers have received limited success in changing their unsustainable energy consumption behaviour (Jackson, 2008, p. 262). However, more scientific studies are required to examine how the concepts from behavioural economics and information systems could explain sustainable energy consumption. 2.1

Demand Response for Sustainable Energy Consumption

DR initiatives have evolved in the domain of energy production and distribution as a sustainable consumption approach. In DR initiatives, an energy consumer actively participates in adjusting their consumption, particularly for peak load management.

210  Research handbook on information systems and the environment Peak load management employs the automated metering infrastructure (AMI) functionality for improving power quality through load curtailment programmes at consumer premises to balance demand and supply. Here the objective of DR is event-based, altering consumer load patterns to reduce peak electricity demand. The utilities send curtailment signals to the consumers to reduce their maximum sanctioned power temporarily to the minimum threshold value. Consumers on receiving this signal volunteer to switch off some of their non-essential appliances or uses to attain a mutually agreed minimum threshold reduction. Dynamic pricing provides efficient usage of energy by employing time of use (ToU) tariffs facilitated through smart meters in combination with consumer engagement. Accordingly, consumers choose to curtail their load in the peak hours when prices are high and consume more power in the off-peak hours when prices are low. The overall effect of dynamic pricing and metering technology will serve to flatten the demand curve by shifting loads from peak periods to low-demand periods. ToU-based DR programmes have been developed to encourage consumers to shift their loads to off-peak hours and thus regulate their consumption. DR is used for reducing power consumption so that both energy providers and consumers benefit, mutually. As discussed previously, DR programmes rely on IS to implement a two-fold strategy of reducing consumption during peak hours or shifting peak loads to off-peak hours (Gellings, 2009). With smart metering, utilities can capture and provide real-time information about household energy usage to consumers. When utilities provide information on DR events, consumers’ active involvement is imperative to the success of a DR strategy’s implementation. Thus, demand flexibility is the motivation, willingness, and ability of the electricity consumers to engage in DR strategies, referred to as “all intentional adjustments in electricity consumption pattern by end-use customers that are intended to modify the timing, level of instantaneous demand, or total electricity consumption” (IEA, 2003, p. 20; see also Albadi & El-Saadany, 2008, p. 1990). Figure 11.1 illustrates the interaction of intervention strategies in a residential behavioural DR system. However, little is known about how consumers might respond to potential cues available in DR information (Reiss & White, 2005, 2008; Allcott & Mullainathan, 2010).

Figure 11.1

Intervention strategies in residential behavioural DR system

Early studies related to household energy consumption reported that informational interventions raise individuals’ concern for energy problems at large. Engineering and behavioural interventions motivate US households to change their residential lifestyle to enable energy conservation (Geller, 1981). Psychological cues and behavioural interventions can bring

Comfort vs money  211 changes in energy consumption patterns (Reiss & White, 2005, 2008; Allcott & Mullainathan, 2010). It is estimated that households can save 10 per cent of their energy consumption through the application of motivational techniques and feedback (Darby, 2001). Further, consumers’ responsiveness towards prices informs DR interventions (Valogianni & Ketter, 2016). DR programmes expect that consumers will respond favourably to DR messages and recommendations to effect sustainable consumption. However, behavioural economists have shown that people react to messages of gain and loss differently (Goes, 2013; Gupta et al., 2018). DR interventions involve messages of monetary gains for compromising comfort from energy usage and penalties for consuming energy for gaining comfort during peak hours. In a given socio-economic environment, an energy consumer’s choice to participate in a DR programme is contingent on their decision criteria. Studies from green electricity adoption in households have found that WTP is salient (Zarnikau, 2003; Arkesteijn & Oerlemans, 2005). The economic value attributed by energy consumer segments could differ based on willingness to pay money to gain comfort and willingness to accept money to compensate for the loss of comfort, which forms the decision criteria in consumers’ cost–benefit analysis (Tversky & Thaler, 1990; Kahneman, 1991). This chapter, thus, aims to develop an understanding of a consumer’s energy usage behaviour in response to pricing information in DR programme outcomes. 2.2

Economic Behaviour of Energy Users

Behavioural scientists have demonstrated that non-normative factors affect the choice preferences of consumers’ decision making under uncertain conditions (Slovic, 1995). Decisions made with cognitive biases deviate from the assumption of rational choice in neo-classical economic theory. Prospect theory (Kahneman & Tversky, 1979) states that for decisions involving risk, the expected outcomes are coded as gains or losses, and losses loom larger than gains. Prospect theory also applies to the concept of an endowment effect (Thaler, 1980) which further explains how people attach greater value to the goods and services they own. When people experience an endowment effect, the pain of giving up an owned good is stronger than the gain of getting an equally valued good. The endowment effect results from a difference between the relative preferences between goods and money. This difference is assessed by the discrepancy of two components – WTA and WTP (Tversky & Thaler, 1990; Kahneman et al., 1991). The analysis of the discrepancy between WTA and WTP has two components – exchanging a good or service for money and exchanging money for a good or service. The economic behaviour of energy consumers has been analysed to a limited extent in the extant literature. Two main psychological intervention strategies proposed to promote behavioural changes by energy consumers are: informational strategies targeting motivational factors and structural strategies aimed at changing the contextual factors under which behavioural choices are made (Steg & Vlek, 2009). Information includes house-specific data on energy usage, price signals, appliance specific energy usage, and goal-oriented messages. Here information on actual behaviour is used as a means of motivation for commitment towards reducing energy consumption. On the other hand, structural strategies aim at changing the circumstances, such as costs or benefits in the form of incentives or penalties for consumers who participate in DR programmes. DR programmes are broadly classified into two types – incentive-based and time-based. Incentive-based DR is a structural strategy that offers payments for customers to reduce

212  Research handbook on information systems and the environment their electricity usage. The utilities must modify their tariff design to give energy consumers their preferential choices to trade-off between getting an incentive to reduce consumption or a penalty to continue enjoying comfort in using more electricity during peak hours. Time-based DR like ToU rates is an informational strategy that provides customers with different tariffs according to the time of day with the objective of encouraging consumers to decrease their energy consumption during those periods. Here, utilities display information to the energy consumers in the form of price signals that could motivate them to change their behavioural actions towards curtailing electricity usage. ToU rates charge higher prices for electricity at peak demand times and lower prices at off-peak times than flat-rate tariff rates, which always impose the same price per kilowatt hour (kWh) of electricity. In the energy domain, several studies have highlighted the value of applying concepts from behavioural science to explain, predict, and change consumer behaviour (Pollitt & Shaorshadze, 2011; Frederiks et al., 2015). Cost-reflective tariff design strategies apply cognitive biases like status quo (Hobman et al., 2016), loss aversion (Farsi, 2010; Nicolson et al., 2017), and framing effect (Bager & Mundaca, 2017). Status quo is a common anomaly in human decision making where people resist change and prefer to retain the current default setting. One of the strategies that could be deployed to exploit status quo bias is introducing cost-reflective pricing offers as an opt-in scheme at the time where consumers are open to changes, like moving homes, switching to a new supplier, or complaining about a high bill. Loss aversion is another prominent cognitive bias where consumers tend to focus more heavily on losses than on comparable gains. The design strategies in cost-reflective tariff rates where an off-peak reduction in price (i.e., a potential gain with much larger cost savings) is greater than the peak increment in price (i.e., a potential loss) would aim to minimise the risk of financial loss from a high bill. Further, experimental evidence demonstrates how framed marketing messages encourage positive behaviours in reducing energy consumption (Cheng et al., 2011; Dolan & Metcalfe, 2013; Fell et al., 2015). Prior research has also estimated WTP and WTA and examined how they relate to other factors influencing energy consumers’ behaviour. In residential electricity consumption, factors like prior notice of power outages, meter ownership, household size, years of residence, monthly income, bank account holding, employment status, and education have a significant and positive impact on mean WTP (Abdullah & Mariel, 2010; Taale & Kyeremeh, 2016). Further, some authors have studied the impact of socio-demographic (Roe et al., 2001; Amador et al., 2013), behavioural, and attitudinal factors (Du Preez et al., 2012) on customers’ preferences and WTP for electricity from renewable resources. In sum, the result suggests that regarding consumer-driven purchases in deregulated electricity markets, many population segments are willing to pay significantly more for green energy with reduced emissions. 2.3

Energy Informatics and DR

As information, and its creation, dissemination, and consumption, plus responses to it, play a critical role in DR systems, we now review the contribution of IS research towards DR. Information is the pivot in designing systems recognising the interdependencies among energy system functional elements: flow networks, sensor networks, and sensitised objects. Recognising this, energy informatics (EI) seeks to innovate, design, implement, and evaluate sustainable smart energy-saving systems in order to match uncertain supply and flexible demand in the distribution network (Watson et al., 2010; Goebel et al., 2014). EI is an inte-

Comfort vs money  213 grated framework that combines flows in energy consumption and information systems to improve energy efficiency. Digital technologies enable the communication and coordination of large-scale spatially distributed objects. The EI framework provides an IS perspective of energy systems for the development of environmentally sustainable practices in reducing energy consumption, and thus CO2 emissions (Watson et al., 2010). Following the EI framework, Feuerriegel and Neumann (2014) studied supply- and demand-side parameters that affect DR decisions. Demand–supply uncertainties in the grid could alter the information processing need for decisions. Smart meters generate granular electricity consumption data that can be used to derive the energy usage patterns of energy consumers during peak demand periods (Dedrick, 2010). Load profile analyses of the consumption data are used in decision support systems (Flath et al., 2012; Gupta et al., 2018) to identify consumer segments and offer innovative service products (for instance, segment-specific tariff design) to manage consumers’ energy usage. Load profiling based on clustering techniques has been extensively used to understand the factors affecting household energy use at finely resolved timescales (Liao, 2005; Chicco, 2012). For instance, hierarchical clustering (Kim et al., 2012) has been used to analyse electricity usage using 15-minute load data. Similarly, K-means clustering (Benítez et al., 2014; Rhodes et al., 2014) has been used to categorise temporal demand profiles. Other feature-extraction techniques that are employed in the electricity consumer research range from self-organising maps (Rodrigues et al., 2003; Figueiredo et al., 2005; Räsänen et al., 2010) to fuzzy clustering (Zhang & Sun, 2008). While prior energy consumer segmentation studies are based on load patterns and behavioural characteristics, demand management patterns in these studies were elicited from socio-demographic criteria (Rowlands et al., 2003; Sütterlin et al., 2011). Although several researchers have used WTP and WTA for analysing the economic behaviour of individuals (e.g., Acquisti et al., 2013), the use of these measures has not been explored in understanding energy usage behaviour.

3.

THEORY DEVELOPMENT FOR BEHAVIOURAL DEMAND RESPONSE

Consistent with behavioural economic theories, this study assumes that consumers sometimes exhibit non-rational behaviour in their energy usage. Situated in the DR context, the WTA/ WTP values can show how someone who enjoys the comfort of using power at a particular level, when asked to pay extra during the peak, could be deterred from doing so by the prospect of a loss of money. Along similar lines, someone who is asked to switch off an appliance for a monetary gain could also be reluctant to make a change of behaviour because of the inconvenience. Therefore, in this research, the minimum incentive to be given to consumers for decreasing energy usage during peak demand (WTA mode) and the maximum penalty to be given to consumers for increasing energy usage during peak demand (WTP mode) across different segments of electricity consumers is estimated. Following Acquisti et al. (2013) and Oikonomou et al. (2009), the WTA/WTP trade-off arising from disutility, where a consumer can be deprived of sustaining a certain level of effort (comfort) in exchange for compensation, is modelled. Consider a household consumer with a utility function ​U​ (m, c) ​,  m​ representing the money one pays for comfort of ​c​ (from using appliances consuming electricity). The two levels of money are ​m​ +​ (money gain) and​

214  Research handbook on information systems and the environment m​​ −​​ (money loss) and the two comfort levels are ​c​ +​​ (comfort gain) and ​c​ −​​ (comfort loss). According to Edgeworth’s (1881) indifference curve, utilities of non-independent goods result in a constant utility curve that yields equal satisfaction or utility to an individual. Applying an indifference curve in the WTA mode, the energy consumer faces a choice between an initial condition ​ (m, ​c​ +​) ​​and the choice of gaining extra money (WTA) in exchange for losing comfort to a level of c​ ​ −​, which is given by equation (1). ​U​ (m + WTA, ​c​ −​) ​  = ​   U​ (m, ​c​ +​) ​

(​ ​1​)​

In the case of WTP, the energy consumer faces a choice between an initial condition ​ (m, ​c​ −​) ​​ and the choice of losing money in exchange for increasing comfort, as expressed in equation (2). ​U​ (m − WTP, ​c​ +​) ​  ​=  U​ (m, ​c​ −​) ​

(​ ​2​)​

The WTA/WTP measures can be analysed to understand how households exercise a binary trade-off between money and comfort. In the case of the binary trade-off between energy consumption and monetary schemes, a consumer also faces an endowment effect while making a choice depending on the starting point. A household consumer with an initial condition of​ U​​ 1​ (m, ​c​ +​) ​​ would accept WTA to be at the same utility level ​U​ 1​ by an energy consumption reduction that changes comfort from ​c​ +​ to ​c​ −​ to reach ​U​ 1​ (​m​ +​, ​c​ −​) ​​. In the WTP mode a consumer will choose to pay the extra money to consume more electricity and move to the level​ U​​ 0​ (​m​ −​, ​c​ +​) ​​, ​​U​ 0​