The Routledge Companion to Accounting Information Systems [First edition.] 9781138125865, 1138125865, 9781315647210, 1315647214, 9781317297321, 1317297326, 9781317297345, 1317297342

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The Routledge Companion to Accounting Information Systems

Information technology has permeated all walks of life in the past two decades. Accounting is no exception. Be it financial accounting, management accounting, or audit, information technology and systems have simplified daily tasks and routine work, simplified reporting, and changed how accounting is done. The Routledge Companion to Accounting Information Systems provides a prestige reference work which offers students and researchers an introduction to current and emerging scholarship in the discipline. Contributions from an international cast of authors provides a balanced view of both the technical underpinnings and organisational consequences of accounting information systems. With a focus on the business consequences of technology, this unique reference book will be a vital resource for students and researchers involved in accounting and information management. Martin Quinn is Associate Professor and Head of Accounting at DCU Business School. He has published widely on management accounting and accounting change. His research interests include accounting information systems and cloud computing. Erik Strauss is Professor of Accounting and Control at Witten/Herdecke University. His research interests are in the influence of technology on management accounting and control, management accounting change and the role of the management accountant. He is a member of the editorial board of Qualitative Research in Accounting and Management and Corporate Ownership and Control.

Routledge Companions in Business, Management and Accounting Routledge Companions in Business, Management and Accounting are prestige reference works providing an overview of a whole subject area or sub-discipline. These books survey the state of the discipline including emerging and cutting-edge areas. Providing a comprehensive, up-todate, definitive work of reference, Routledge Companions can be cited as an authoritative source on the subject. A key aspect of these Routledge Companions is their international scope and relevance. Edited by an array of highly regarded scholars, these volumes also benefit from teams of contributors who reflect an international range of perspectives. Individually, Routledge Companions in Business, Management and Accounting provide an impactful one-stop-shop resource for each theme covered. Collectively, they represent a comprehensive learning and research resource for researchers, postgraduate students and practitioners. Published titles in this series include: The Routledge Companion to Accounting and Risk Edited by Margaret Woods and Philip Linsley The Routledge Companion to Wellbeing at Work Edited by Sir Cary L. Cooper and Michael P. Leiter The Routledge Companion to Performance Management and Control Edited by Elaine Harris The Routledge Companion to Management Information Systems Edited by Robert D. Galliers and Mari-Klara Stein The Routledge Companion to Critical Accounting Edited by Robin Roslender The Routledge Companion to Trust Edited by Rosalind Searle, Ann-Marie Nienaber and Sim Sitkin The Routledge Companion to Tax Avoidance Research Edited by Nigar Hashimzade and Yuliya Epifantseva The Routledge Companion to Intellectual Capital Edited by James Guthrie, John Dumay, Federica Ricceri and Christian Neilsen The Routledge Companion to Behavioral Accounting Research Edited by Theresa Libby and Linda Thorne The Routledge Companion to Accounting Information Systems Edited by Martin Quinn and Erik Strauss

The Routledge Companion to Accounting Information Systems

Edited by Martin Quinn and Erik Strauss

First published 2018 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 711 Third Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business. © 2018 selection and editorial matter, Martin Quinn and Erik Strauss; individual chapters, the contributors. The right of Martin Quinn and Erik Strauss to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Screenshots of Microsoft Excel used with permission from Microsoft. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book has been requested ISBN: 978-1-138-12586-5 (hbk) ISBN: 978-1-315-64721-0 (ebk) Typeset in Bembo By Keystroke, Neville Lodge, Tettenhall, Wolverhampton

Contents

List of figures List of tables List of contributors Introductory reflections on Accounting Information Systems Martin Quinn and Erik Strauss

ix xi xiii xvii

PART A

The Accounting Information Systems discipline

1

1 Introduction to Accounting Information Systems Peter Cleary

3

2 Development of Accounting Information Systems over time Pierangelo Rosati and Victoria Paulsson

13

3 Technologies underpinning Accounting Information Systems Gerhard Kristandl

24

4 Systems planning, design and implementation Noel Carroll

39

5 Change management Krister Bredmar

55

PART B

Organisational effects of Accounting Information Systems

67

6 Accounting Information Systems and how to prepare for Digital Transformation Sven-Volker Rehm

69

7 Accounting Information Systems and decision-making Markus Granlund and Henri Teittinen

81

v

Contents

8 Changing the speed and format of information provision: examining the temporal decoupling of accounting numbers and their analysis Nicolás J. B.Wiedemann and Leona Wiegmann

94

9 Accounting Information Systems outputs: XBRL, AI and in-memory technologies Ting Sun

108

10 Outsourcing of Accounting Information Systems Benoit Aubert and Jean-Grégoire Bernard

120

11 Accountants’ roles and accounting-related technologies João Oliveira

133

12 Big Data and knowledge management with applications in accounting and auditing: the case of Watson Daniel E. O’Leary

145

13 Issues with Big Data Bernhard Gärtner and Martin R.W. Hiebl

161

14 Accounting for capital investment appraisal: time for a radical change? Elaine Harris,Thinh Hoang and George Ngan

173

PART C

Controlling Accounting Information Systems

191

15 Data security and quality W. Alec Cram

193

16 AIS auditing: audit tools for a continuous auditing approach Maria Céu Ribeiro

205

17 Leveraging Accounting Information Systems for standardization: a case study of a SOX compliance journey Ulrike Schultze 18 Supporting business strategy Victoria Paulsson and Malcolm Brady

vi

218 235

Contents

PART D

Future directions of Accounting Information Systems

249

19 Integration with other data and systems Miranda Kajtazi and Olgerta Tona

251

20 Technology, the future and us Tadhg Nagle

262

21 Challenges to technology implementation Pierangelo Rosati and Theo Lynn

272

22 New developments in information technology: a call for action Joan Ballantine and Robert D. Galliers

292

Index

302

vii

Figures

3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 6.1 6.2 7.1 7.2 7.3 8.1 10.1 12.1 12.2 12.3 12.4 14.1 14.2 14.3 14.4 14.5 15.1 17.1 17.2 17.3

Client-server architecture Local area network Wide area network Network topologies Factors which influence the success of AIS Overview of system layers System development lifecycle model Waterfall model Iterative model Spiral model V-model Agile model Basic objects and modelling pattern for an event-driven process chain Sample process for developing an APD Data environment and decision-making environment framing decision-making Uncertainty, decision-making and the roles of accounting practice Summary of AIS and decision-making Illustration of a dashboard Outsourcing and offshoring decisions Watson tone analysis of an email Loan grading process Data quality What drives Days Late? A typical spreadsheet A spreadsheet may have custom functions Spreadsheets can add basic comments but they do not encourage active information and opinion sharing Excel has basic optimisation tools, but managers need to have advanced skills to use them Modern spreadsheets have basic scenario management tools but a lack of dynamic models to study the interactions among various parameters Data security and quality objectives, controls and outcomes Revenue by lines of business (2000 and 2011) Control summary on SharePoint Individual control in SharePoint (across three screens)

27 27 28 29 41 42 44 48 49 50 51 52 75 78 84 85 92 99 121 149 153 155 156 176 176 177 178 179 195 222 227 227 ix

Figures

17.4 18.1 19.1 19.2

x

Audit trail portion of the individual control on SharePoint Matrix for AIS strategy Shifting between the states SoR (safety of resources) and VoR (vulnerability of resources) Users versus providers

229 239 254 258

Tables

3.1 3.2 3.3 4.1 6.1 6.2 6.3 6.4 10.1 13.1 13.2 14.1 14.2 14.3 14.4 15.1 17.1 17.2 19.1 20.1 21.1 21.2 21.3

The OSI and TCP/IP reference models Examples of computer hardware Programming language categories Outline of a Software Requirements Specification document Contextual factors of the BPM context and potential changes through Digital Transformation Diagonal positioning of IT controls against general IT management functions Basic principles for designing controls for digital business objects and their challenges through Digital Transformation Example of an operational procedures documentation of security concept implementation Risk factors Opportunities with Big Data for management accounting Challenges with Big Data for management accounting Principles of good practice in investment appraisal Integrated reporting framework The principles of IR IIRC definitions of the six capitals Data security and quality objectives Compliance effort summary Summary of controls by type List of interviewees Conflict of visions on Digital Natives Categories and determinants of user resistance in information systems projects Software-as-a-Service security issues and challenges High-level contractual issues and challenges in cloud computing agreements

31 32 36 46 70 72 72 77 128 165 168 181 182 183 184 195 223 226 254 264 276 281 283

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Contributors

Benoit Aubert is Head of School, School of Information Management and Professor at Victoria

University of Wellington (New Zealand). Previously he was Professor at HEC Montréal (Canada). His main research areas are outsourcing, innovation and risk management. Aubert has been President and Chief Executive Officer of the CIRANO and Director of Research at HEC Montréal. He is currently on the editorial board of IEEE Transactions on Engineering Management and the Asia Pacific Journal of Information Systems. Joan Ballantine is an accounting graduate and Fellow of the Association of Chartered Certified

Accountants (FCCA). Ballantine has previously held lecturing positions at Warwick Business School and Queen’s University. Joan joined the Ulster University Business School as a Professor of Accounting in 2008. Jean-Grégoire Bernard’s research focuses on the adoption, implementation and governance

of IT-enabled organisational innovations within and across organisations. His work sits at the intersection of information systems and organisation studies. Before joining the Victoria University of Wellington in 2010, Bernard taught at HEC Montréal. Malcolm Brady is Senior Lecturer in Management at DCU Business School. His research is

in the area of business competition and business processes. Dr Brady has published widely in high-ranking international journals including Managerial and Decision Economics, System Dynamics Review, Production Planning and Control, Strategic Change, International Journal of Managing Projects in Business, Knowledge and Process Management, Journal of Management History and Industrial Relations Journal. Krister Bredmar has been teaching and researching within the field of management control and

information systems for more than 20 years. His work has been published in international journals and in books. Noel Carroll is a Research Fellow at the Applied Research for Connected Health (ARCH)

Technology Centre, University of Limerick. Noel is the Data Lead within ARCH and enjoys working on industry-led projects and collaborating with academic partners. A full list of publications is available at: https://noel-carroll.com/research-2/publications/. Peter Cleary lectures in Accounting at University College Cork. His primary research interest

examines the role of management accounting in knowledge-intensive firms. Dr. Cleary’s research has been published in the Journal of Intellectual Capital and the International Journal of Accounting, Auditing and Performance Evaluation. xiii

Contributors

W. Alec Cram is an Assistant Professor of Information and Process Management at Bentley

University. His work has been published in a variety of outlets, including the Information Systems Journal, European Journal of Information Systems and the Journal of the Association for Information Systems. Robert D. Galliers became Bentley University’s inaugural University Distinguished Professor in

2009, having served as Provost since 2002. He also holds a chair at Loughborough University. Previously at the LSE, Warwick Business School, and Curtin University, he is editor-in-chief of the Journal of Strategic Information Systems. Bernhard Gärtner is a Postdoctoral Researcher at the Institute of Management Control and

Consulting at Johannes Kepler University, Linz, Austria. Before his academic work, he worked as a management accountant at Raiffeisenverband Salzburg. His research focuses on the interactions between information systems and management accounting. Markus Granlund is a Professor of Management Accounting. His research interests cover a wide

range of technical and behavioural issues in management accounting and information systems. He has published widely in respected scientific journals and also serves such journals in many editorial roles. Elaine Harris is Professor of Accounting and Management at Roehampton University, having also

been the Director of the Business School from 2010 to 2014. She started her career in accountancy practice and has held various management positions in higher education in the last 30 years. Elaine is chair of the Management Control Association and has published books and papers on strategic investment appraisal and project risk. She is associate editor of the British Accounting Review. Martin R. W. Hiebl is Professor of Management Accounting and Control at the University of

Siegen. His research primarily focuses on strategic management and finance functions in family businesses and the role of CFOs and management accountants. Thinh Hoang is a PhD student in Business and Management Studies at the University of

Roehampton. His research interests focus on the fields of management accounting, integrated thinking and integrated reporting. Miranda Kajtazi is an Assistant Professor at Lund University and Örebro University. Her

research interest concerns one of the most crucial resources of our human and social affairs: information. Her focus is on information security. She has published her research in international journals and presented at international conferences. Miranda currently serves as a committee member for a special issue on information security at the European Journal in Information Systems. Gerhard Kristandl is a Senior Lecturer in Management Accounting and ERP systems at the

University of Greenwich, London. He has recently published papers in management accounting and cloud technology with CIMA, in Zeitschrift fuer KMU und Entrepreneurship and in Controlling and Management Review. Theo Lynn is Professor of Digital Business at DCU Business School. He is Principal Investigator

of the Irish Centre for Cloud Computing and Commerce. His research interests focus on the impact of technology on business practice. xiv

Contributors

Tadhg Nagle is a lecturer in the Department of Accounting, Finance and Information Systems

at University College Cork (Ireland). With a primary focus on delivering business value through technologies, Tadhg’s attention is currently on developing practitioner-research capabilities through design research methods. He has published internationally, both at conferences and in journals such as Information Systems Journal (ISJ) and Information and Software Technology (IST). George Ngan is a graduate student in accounting and finance at Roehampton University.

He started his career in investment banks and was the CEO of KBC Financial Products Asia Pacific when he retired in 2006. George obtained his MBA at University of Southern California in 1992. His research interests are in corporate social responsibility and performance measurement. Daniel E. O’Leary is a Professor in the Marshall School of Business, University of Southern

California, focusing on information systems, artificial intelligence and knowledge management systems. He is the former editor of IEEE Intelligent Systems and current editor of Intelligent Systems in Accounting, Finance and Management. João Oliveira is Assistant Professor at the Porto University School of Economics and

Management (Portugal) and former visiting professor at HEC Paris. He has published a textbook, Management Accounting (McGraw-Hill), and articles and chapters on ERPs, shared services centres, rules, routines and roles in management accounting and power in management accounting research. Victoria Paulsson is a Postdoctoral Researcher at IC4, Dublin City University. She is a specialist

in business decision-making and her research interests include decision-making, ERP systems and cloud computing. Her research is featured in top-ranked international conferences such as those for ECIS and Springer Books. Sven-Volker Rehm is Assistant Professor of Information Management at WHU – Otto Beisheim

School of Management, Germany. His research focuses on transformations of networked enterprises and has been published in journals such as Information & Organization, MIS Quarterly Executive, International Journal of Information Management and others. Maria Céu Ribeiro, CFE, CIA, CRMA, is a certified accountant with more than 20 years of

accounting experience in auditing. She is an audit Senior Manager in Oporto PwC office, responsible for the carrying out, coordination and control of several auditing and statutory engagements, consolidation, internal control and accounting procedures manuals. She teaches Forensic Auditing at postgraduate level. Pierangelo Rosati is a Postdoctoral Researcher based in the Irish Centre for Cloud Computing

and Commerce. His research focuses on accounting, finance and data analysis in the cloud and for cloud computing companies. Dr Rosati received a BA (business) and MBA from the University of Bologna and a PhD (MBA) from the University of Chieti-Pescara. Ulrike Schultz is Associate Professor in Information Technology and Operations Management.

She is also visiting professor at Lund University, Sweden. Her research explores the impact of information technology on work practices. Her research has been published in leading xv

Contributors

information systems journals, including Information Systems Research, Management Information Systems Quarterly, European Journal of Information Systems, Journal of Information Technology and Information and Organization. Ting Sun graduated from Southwestern University of Finance and Economics (China) with a

doctoral degree in Accounting. She is an AIS PhD candidate in Rutgers Business School and a research assistant at the Continuous Auditing & Reporting Lab in Rutgers Business School. Her research interests include continuous auditing and monitoring, audit data analytics and artificial intelligence in auditing. She has published several papers and one book. Henri Teittinen is a Postdoctoral Researcher at Turku School of Economics. His expertise

covers management control systems, performance measurement systems and Accounting Information Systems. He has published in International Journal of Accounting Information Systems, Nordic Journal of Business and Electronic Journal of Organization and Management. Olgerta Tona is a PhD candidate at Lund University. Her research interests comprise decision

support systems, business intelligence, mobile business intelligence, big data and analytics. Currently she is investigating how the rapid growth of mobile technologies is challenging traditional technologies such as business intelligence (BI). Her work has been published in journals, at conferences and as book chapters. She continuously serves as a reviewer to different IS journals and conferences, contributing, therefore, in the IS community. Nicolás J. B. Wiedemann is a PhD candidate at the Institute of Management Accounting and

Control at WHU – Otto Beisheim School of Management, Germany. His research interest is organizational change, currently focusing on the dynamics of truces in organisational routines. In addition, Wiedemann researches the processes and effects of change in organisations, specifically management accounting, due to new information technologies. His research is funded by the Hanns Seidel Foundation. Leona Wiegmann is an Assistant Professor and a PhD graduate of the Institute of Management

Accounting and Control at WHU – Otto Beisheim School of Management, Germany. Wiegmann’s research focuses on the roles and practices of management accountants and the impact of Accounting Information Systems on management accounting. Her current research concentrates on organisational change, particularly the change in organisational routines.

xvi

Introductory reflections on Accounting Information Systems Martin Quinn and Erik Strauss

Information technology has permeated all walks of life, especially in the past two decades. Accounting is no exception to this. Be it financial accounting, management accounting, or audit, information technology and systems have simplified daily tasks and routine work, simplified reporting and changed how accounting is done. Against this background, the aim of this Companion is to offer a comprehensive and contemporary guide to students and academics that reveals the state of current knowledge on accounting information systems (AIS). Particular attention is paid to providing students and academics a balanced view of both the technical underpinnings and organisational consequences of accounting information systems, with emphasis on the latter. To achieve this aim, the book in structured in 22 chapters, which we outline below. The chapters are organised into four parts. Part A (Chapters 1 to 5) explores the basics of what we term the AIS discipline; Part B (Chapters 6 to 14) explores the organisational effects of AIS; Part C (Chapters 15 to 18) look at the controlling of AIS; and finally, Part D (Chapters 19 to 22) looks to the future. Chapter 1 provides a general introduction to AIS. The chapter begins by defining accounting before outlining the potential use of accounting-based information. It then proceeds to introduce the concept of information system (IS) before specifically addressing AIS. After providing a critical analysis of AIS, the chapter concludes by pondering the future for both accountants and accounting in the continually evolving technology-based global economy. Chapter 2 deals with the development of AIS over time. Although mostly associated with information technology, rudimentary AISs existed long before the development of computers. As time passed, AISs evolved to include more features, which led to increased complexity. The evolution of AISs has been anything but linear. Therefore, Chapter 2 elaborates on the development of AIS from (1) the introduction of double-entry bookkeeping, which gained particular importance in the industrial age (1700–1940); (2) the development of computers, which opened the so-called Information Age in the 1950s; and (3) the adoption of the Internet, which allowed the Integration Phase (1990s) to commence and enabled the development of AIS 2.0 (2000s–present) and gives a potential outlook to the future. Chapter 3 explains the technologies underpinning AIS. After clarifying the general relationship between AIS and technology, the chapter reveals networks as a necessary technological feature of AIS before the components that are aligned to build and run an AIS are xvii

Martin Quinn and Erik Strauss

discussed, namely hardware and software. As such, this chapter adopts a drill-down approach to illustrate the technologies that underpin modern AIS. Chapter 4 details the links between systems planning, design and implementation. It elaborates various system development lifecycle models available and gives an overview about the key processes and decisions to support developers. Within the development models, particular emphasis is placed on planning, designing and implementing. In this chapter, AIS system development approaches in “Systems Planning, Design and Implementation” are examined. It also examines how systems analysts assess user interaction with existing or new business technologies while identifying specific functions, such as user requirements to support the organisational strategy. Chapter 5 deals with change management in the context of AIS. In this chapter, the emphasis is on describing the background behind AIS and its role in changing organisations. Even though advanced IS in general, and modern AIS in particular, bring new opportunities to an organisation and the management function, it is important to first understand the new context in which AIS is situated. It is also important to understand the challenges managers are facing when trying to benefit from more advanced systems and try to grasp where AIS trends are heading and what kind of change that brings. Chapter 6 deals with the questions of AIS and how to prepare for digital transformation. To answer this question, the chapter concentrates on guidelines for implementing Accounting Procedure Documentations as a vehicle to conceptually integrate AIS, business process management, IT Management and future business planning. A practice-oriented view is provided on how to approach business process management for AIS in the context of new legal requirements for AIS in Germany. Chapter 7 links accounting information systems and decision-making. It outlines how AIS can support managerial decision-making in contemporary organisations. The authors suggest some future trends regarding this topic by describing the factors constituting different decisionmaking environments and what they call data environments. This lays a foundation for describing what kind of information is needed in different contexts and how AIS can support managers in making informed decisions. Chapter 8 examines the temporal decoupling of accounting numbers and their analysis due to the changing speed and format of information provision by AIS. As managers rely on provision of information by management accountants generated through AIS, substantial changes in this kind of system might also impact decision-making processes. Particularly, increases in data processing speed, and the resulting continuous and instant availability of accounting numbers, challenges the relevance of reporting periods. Also, improvements in data visualisation enable non-accounting experts to use those systems – potentially putting an end to the primacy of information provision by management accountants. Thus, this chapter discusses how new capabilities of AIS can affect the process of providing managers with decision-making information as well as what role management accountants play therein. Chapter 9 focuses on AISs outputs – particularly XBRL reporting. By providing a standard business communication language, XBRL improves the effectiveness and efficiency of data preparing, analysing and exchanging from incompatible sources. In-memory computing enables real-time analysis of large volumes of data to support timely decision-making. Additionally, artificial intelligence helps solve the dilemma of big data processing that, on one hand, decisionmakers have had to deal with mountains of data, forcing them to employ data analytical tools; and on the other hand, reliance on human experts to identify features of the real-world big data which is semi-structured (e.g., text) or unstructured (e.g., video or audio). Chapter 10 discusses options for outsourcing AIS. It starts by defining outsourcing, clarifying the difference to offshoring and providing examples of numerous ways outsourcing can be used xviii

Introductory reflections on AIS

for AIS. Following this definition, key decisions associated with outsourcing are described. When trying to appreciate issues associated with the outsourcing of AISs, the chapter reveals that it is important to realise that a large proportion of issues are the same as the ones associated with outsourcing of information systems in general. Therefore, elements presented in this chapter are not limited to accounting and might also provide insights to outsourcing more generally. Chapter 11 elaborates the links between accountants’ roles and accounting-related technologies. To do so, the chapter discusses how the roles of accountants in organisations have been, and may continue to be, affected by developments in information systems, and accounting and integrated systems in particular. The examination spans both financial accountants’ and management accountants’/controllers’ roles. Chapter 12 presents the case of IBM’s Watson as an application of big data and knowledge management in accounting and auditing. Therefore, the chapter investigates big data and knowledge management in the context of so-called cognitive systems, with a particular focus on IBM’s Watson as a case to illustrate both an emerging form of knowledge management and how firms are analysing big data to generate knowledge from data. This analysis includes a number of applications that have been developed to address accounting and auditing problems. In particular, this chapter analyses how Watson has been brought into business settings and used or proposed for use in accounting, auditing and finance. Chapter 13 continues with the big data topic, discussing opportunities and challenges which arise through its use for management accounting. The chapter starts with a clarification of the underlying understanding of big data and management accounting. It then focuses on opportunities for management accounting from big data, after which possible challenges are examined and potential avenues for further research suggested. Chapter 14 discusses the question: Is it time for a radical change in accounting for capital investment appraisals? To do so, the chapter considers problems with the type of AIS typically used in capital budgeting. It identifies issues with the use of spreadsheets and the lack of formal systems for evaluating or integrating non-financial information. It explores the emergence of integrated reporting and the challenges for AIS to incorporate integrated thinking into strategic investment appraisal and proposes a new meaning for capital investment that incorporates all capitals, not just financial capital. Finally, it considers how AIS practice could develop to bring capital investment appraisal techniques up-to-date to tackle modern day decision-making in organisations. Chapter 15 critically reflects on data security and quality, providing a broad overview of the role of data within AIS, with a primary focus on the risks to security and quality. It examines a range of security and quality risks, corresponding internal controls and the organisational implications of a data security program. For practitioners, this chapter can help to establish a holistic, integrated understanding of the links between data security and quality risks, corresponding countermeasures and organisational outcomes. For researchers, this chapter draws on a range of AISs, management information systems and cybersecurity research in order to highlight the integrative nature of accounting processes, technology and information security as it pertains to the data used within today’s organisations. Chapter 16 addresses a topic which has profoundly affected auditing research during recent years, i.e. the links between information and communication technology, particularly AIS and auditing. It covers the advent and subsequent development of continuous auditing, a new approach to monitoring and auditing information, following the transformative impact of the technological advances on business practices. Chapter 17 explores how AIS can be leveraged for standardisation by showing a case study of a SOX compliance journey. After introducing the Sarbanes-Oxley Act of 2002 and the case xix

Martin Quinn and Erik Strauss

(Trinity Industries), the case firm’s first four years of SOX compliance are described. The role of AIS (e.g. General Ledger systems, Manufacturing Resource Planning systems and spreadsheets) during this time is highlighted. Years five through seven of the company’s compliance journey are discussed in detail, with a particular focus on case firm’s standardisation of the internal audit process through the development and use of a SharePoint workflow management system. Finally, the chapter concludes with some reflections on the multi-level nature of accounting standards, the potential for organisational transformation motivated by mandatory standards and the role of AIS in complying with them. Chapter 18 presents key ideas on how firms can use their AIS to support business strategy. The chapter outlines a two-by-two matrix representing a strategic AIS framework based upon two fundamental dimensions: the strategy type (cost leader versus differentiator) and the software acquisition mode (build versus buy). Four distinct AIS strategies are identified and discussed: Differentiator & Build; Differentiator & Buy; Cost Leader & Build; and Cost Leader & Buy. Chapter 19 suggests how information security measures can be implemented and integrated into a mobile business intelligence (m-BI) system both from the providers’ (integrated in the product) and organisations’ perspectives (integrated by policies and rules). Organisations that have implemented m-BI take a more data-centric strategy to protect their information, such as higher data aggregation, no publishing of sensitive data and no local data. Providers embrace a more technical perspective to make sure that their customers’ resources are protected from external security threats, such as authentication algorithms and encryption, aspects that users often disregard. The findings are explained by portraying that safety of resources versus vulnerability of resources depends on the level of data sensitivity. Chapter 20 discusses a number of perspectives on how technology is shaping “us”, both individually and as a society. A number of biases are outlined, such as whether we as human beings think technology is shaping us in a good way and if we think our technological future is going to be a good place for us as a society overall. The motivation for the chapter comes from a need to be mindful of the impact of technology and possibly get us to recognise our own biases and, as a result, help us make more rational decisions for the future. However, this mindfulness only comes about when we examine a wide spectrum of perspectives – from the contrarian to conformist, technophile to luddite, and utopian to dystopian. This chapter might build the starting point for more fruitful discussions that will provide a more rounded view on the sociotechnical nature of IS. Chapter 21 explores the challenges to technology implementation. It starts with a discussion of more traditional challenges in information systems adoption based on examining the empirical literature from five common perspectives, i.e. project management, technology, user resistance, organisational/environmental and outsourcing challenges. The chapter extends the traditional challenges by elaborating emerging challenges for AIS adoption related to cloud computing, big data analytics and mobile technologies. This includes a discussion on challenges related to migration to the cloud, security, data protection and ICT governance, and contractual issues. Chapter 22 presents new developments in IT and calls for action. This chapter particularly considers major developments taking place in new information technologies such as Enterprise Resource Planning (ERP) systems and the more recent hyperbole surrounding big data. It also considers the implications of such developments, in terms of organisational capabilities, with particular reference to the accounting and finance professions. It commences with a reflection on ERP systems as a potential strategic asset and then considers big data in a similar light. Implications for accounting practice and for accounting and finance professionals are then considered. xx

Introductory reflections on AIS

We hope these 22 chapters provide a good scholarly overview of the current state of AIS, with some eye to the future. Having said that, the pace of technological change today is quite fast and we cannot hope to keep up with all changes, nor can we hope to predict them. Despite this, we can take from these chapters and point to two major areas which will, we think, be the subject of much future research on the organisation consequences of AIS. First, whereas prior technological developments mainly supported accountants by facilitating ex-post data gathering and analysis, current developments might also have an influence on more future-oriented accounting tasks. For example, predictive analytics based on big data is already used to forecast future business developments, such as in sales. Albeit this technology is (more or less) in its infancy, one could easily see how it will develop in the near future and, thereby, “unburden” accountants even more than currently and allow them to move to more value-adding activities like predicting and planning future business developments. Second, if the acceptance of analytics based on technology increases over time, the question arises: Who will challenge the suggested courses of action from these systems? However, as the algorithms will become more and more complex, it might be very difficult to understand them or to identify mistakes. Against this backdrop, we suggest that the technological development will not reduce the relevance of human actors in organisational decision-making but quite the opposite, i.e. the human side of the human being in the sense of emotions and gut feelings become even more relevant as humans might detect or avoid wrong decisions.

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Part A

The Accounting Information Systems discipline

1 Introduction to Accounting Information Systems Peter Cleary

Introduction The use of accounting information has been traced back many thousands of years and originally emerged as a means to allow wealthy individuals to keep track of their assets as they bought, sold and/or traded them. Double-entry bookkeeping was first described in 1494 by an Italian monk named Luca Pacioli, when he referred to the system of debits and credits used by merchants in Venice. This system of recording still underpins modern (financial) accounting systems to the present day. Over time, the commercial environment gradually evolved from primarily family-owned firms to increasingly large and complex entities encompassing a wide variety of stakeholders (e.g. investors, suppliers, customers, etc.) each with their own information (including accounting) requirements. This necessitated the need for more sophisticated accounting information than heretofore. Consequently, two distinct forms of accounting (i.e. financial and management) were developed in an attempt to satisfy these informational requirements. In recent times, rapid advances in technology and the emergence of the Internet, as both a means of communication and a trading platform in its own right, have spawned entire industries and have led to the creation of a society where the provision of data, information and knowledge are now heralded as amongst the key drivers of economic success. It is against this backdrop that accounting exists within organisations today, in the form of a suite of interlinked information technology (IT) enabled systems. Indeed, such is the reliance on information systems (IS) nowadays, that it is difficult to envisage modern organisations in all their forms (i.e. private, public, profit-oriented, not-for-profit, small, large, etc.) being able to function effectively without them. As the first chapter of this volume, the aim here is to provide a general introduction to Accounting Information Systems (AIS). The chapter begins by defining what is meant by accounting before outlining the potential use of accounting-based information. It then proceeds to introduce the concept of IS, before specifically addressing AIS. After providing a brief but critical analysis of AIS, the chapter concludes by pondering the future for both accountants and accounting in the continually evolving technology-based global economy. Subsequent chapters will provide much more detailed insights from contemporary research on various topics mentioned in this chapter. 3

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Accounting Accounting has been defined as: The process of identifying, measuring and communicating economic information to permit informed judgements and decisions by users of the information. American Accounting Association, 1966, p. 1 Accounting is generally deemed to consist of two distinct disciplinary areas, financial accounting and management accounting. Financial accounting aims to provide a summary of the financial performance of an entity for a particular period of time (e.g. quarterly, annually, etc.), and is primarily used by external parties (e.g. financial institutions, shareholders, suppliers, etc.) as a means to evaluate an organisation’s recent financial performance. The information provided within the remit of financial accounting, and how it is to be reported upon, is dictated by specific accounting rules and standards. In this regard, the two primary standard setting bodies in financial reporting are the International Accounting Standards Board (IASB) who are responsible for the International Financial Reporting Standards (IFRS) and the Financial Accounting Standards Board (FASB) who are responsible for the US Generally Accepted Accounting Principles (GAAP). Both bodies refer to the provision of “useful” information to potential decision-makers (e.g. investors), which in itself must demonstrate the qualitative characteristics of relevance and faithful representation. Relevant information can facilitate a user to make a different decision to the one they considered prior to being aware of the relevant information. Faithful representation refers to the scenario where an organisation’s financial statements reflect their actual performance during the reporting period, i.e. they are complete, neutral and free from error. From a legal perspective, the provision of financial accounting information is mandatory for organisations. As the information provided within the realm of financial reporting is relied upon by a diverse range of stakeholders, it is legally subject to an annual external audit for the vast majority of firms. This is to ensure that – amongst other objectives – the accounting records are accurate, that they have been completed in accordance with the relevant accounting rules and regulations and that the financial statements presented fairly represent the firm’s financial position at a particular point in time. At the same time, management accounting attempts to forecast and plan the consequences of future organisational events (e.g. the budgeted total profit for the coming financial year or the expected payback period on an item of new machinery). The information underpinning it (both financial and non-financial) is generally used internally by management to facilitate them in making well-informed planning and control-based decisions. Unlike the standardised reporting requirement inherent in financial accounting, no such requirements apply in management accounting, the use of which within organisations is elective. In the conduct of their duties, all accountants are required to adhere to various rules and regulations, including the rules of the professional accounting bodies and the commercial laws of the country in which they are employed. In this regard, the enactment of the Sarbanes-Oxley Act (SOX) in the USA in 2002 significantly increased the level of regulation and subsequent compliance tasks that many employed in financial roles globally were subject to. This act was introduced in response to a series of high-profile financial scandals, including Enron, WorldCom and Tyco, which collectively shook investor confidence in financial statement information. A radical overhaul of the incumbent regulatory system was therefore required in an attempt to restore the faith of investors by protecting them from further fraudulent accounting-based activities. As compliance with SOX is viewed by many as “best practice” in the area of corporate governance, many firms, both private and public, have implemented the terms of the Act, despite 4

Introduction to AIS

not being legally required to do so. According to the terms of the Act, senior management (i.e. the Chief Executive Officer (CEO) and the Chief Financial Officer (CFO)) are required to personally review all of the financial information provided and to confirm that it does not contain any misrepresentations. They are also collectively responsible for developing and maintaining an “adequate” internal control structure and all annual financial reports must include a report stating that this is the case coupled with an assessment by management of its effectiveness. From the external auditors’ perspective, it is now their responsibility to confirm management’s assertions in this regard coupled with reporting on the state of the overall financial control system. Collectively, the introduction of SOX has significantly increased attention on the accuracy of both the inputs and outputs of various accounting-based IS. As illustrated above, the discipline of accounting continues to evolve in response to changing organisational requirements. This has led some to speculate that financial accounting will ultimately become more forward-looking and, facilitated by advances in IT (Taipaleenmäki and Ikäheimo, 2013), will ultimately converge with management accounting (Hemmer and Labro, 2008). Recent research suggests that this convergence has already begun and has resulted in an enhanced level of consistency in financial reporting and better cooperation between those engaged in what traditionally was viewed as distinct financial accounting and management accounting roles (Weißenberger and Angelkort, 2011). Furthermore, as there can often be significant costs associated with maintaining distinct financial and management accounting systems, many smaller organisations maintain a single system whereby their financial accounting information is often used internally by management to inform their decision-making. From an IS perspective, it has been suggested that the accounting function was one of the first within organisations to begin to regularly use computers from the 1950s onwards. Since then, those employed in this area have continued to embrace technology as a means of performing their duties in a more effective and efficient manner. However, it has also been argued that the claim that developments in IT have transformed the accounting function into a knowledgebased service provider have not been supported by concrete and sustained evidence (Granlund, 2011). The next section therefore outlines the role occupied by IS in organisations.

Information systems In the knowledge environment where organisations now compete, information is a resource that must be effectively managed to be of value, as without it, it would be extremely difficult to survive. An IS refers to a collection of IT-enabled hardware and software which has been designed to provide specific information to selected users. There are various types of information systems that can exist within organisations. Examples include, but are not limited to, management information systems, marketing information systems, human resource information systems and accounting information systems. Information differs from data as it has been put in a usable form, whereas data essentially refers to facts and statistics. For example, factual sales data can be analysed according to customer, product, etc., therefore allowing management to make informed decisions on how they might attempt to increase sales/profits on a customer and/or product basis. As this example illustrates, those engaged in accounting-based roles both create and transform data and information. Within an organisational setting, accounting information flows in different directions. For example, information flows downwards from senior management to mid-level and low-level management in the form of budgetary targets, key performance indicators, non-financial targets, etc., whereas summarised operational information is communicated upwards from lower levels and can then be used by senior management for planning and control purposes. Furthermore, as 5

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organisations do not exist in a vacuum, external parties such as suppliers, customers, shareholders, etc., continually exchange data/information with the firm. This externally originating data/ information ultimately becomes part of the firm’s internal information system for further analysis and use. Irrespective of the type of information system, according to McLaney and Atrill (2010) the output (i.e. information) generated from it must possess certain characteristics for it to be of value. These include relevant (i.e. it must have the ability to influence subsequent decisions), timely (i.e. it needs to be provided promptly so as to inform the decision being considered), accurate (i.e. it should not contain any material errors), complete (i.e. all pertinent elements included), understandable (i.e. it must be presented to the user in a manner in which they can easily comprehend), comparable (i.e. it needs to facilitate the user in making comparisons where applicable), objective (i.e. any bias on behalf of the compiler(s) should be excluded) and finally, the cost of collecting and storing the information should not exceed the likely benefit to be derived from its use. The type of information required by the end-user will dictate the choice of IS best equipped to satisfy their particular requirements. The use, scale and reliance on organisational IS has dramatically evolved over the course of the last 20 years due to the seismic developments that have occurred in technology. Indeed, the role occupied by IT (which underpins IS) within organisations has increased to such an extent that those employed in this area are now commonly regarded as indispensable to the successful operation of IS within such firms. In terms of the evolution of IS usage within organisations, Bodnar and Hopwood (2004) report that data processing systems were initially designed to conduct repetitive transactionoriented tasks and consequently provided little usable information for decision-making purposes. These were followed by Management Information Systems (MIS) which attempted to provide information to managers in support of their decision-making remit. Decision Support Systems (DSS) emerged next and sought to process organisational data into a decision-making format suitable for the end-user. Specifically, these systems were primarily aimed at supporting the ad hoc and non-routine nature of managerial decisions. More recently, Executive Information Systems (EIS) were promoted as a necessity for senior management as they were capable of customisation to specific strategic informational requirements, many of whom were external to the organisation. Many EIS allowed management to “drill-down” from aggregated data to more specific and detailed data, and in many instances the data could be displayed in graphical form (Bodnar and Hopwood, 2004). Since the 1990s, IS usage within organisations has included Enterprise Resource Planning (ERP) systems. Prior to their development, each organisational function (including accounting) often had their own individual information system, which existed in isolation from each other. ERP systems were therefore developed to collect data from all parts of the organisation and to feed it into an application supporting all (or a majority) of a firm’s activities. As the information contained within an ERP system is held within one central database, each piece of information need only be stored once, thereby eliminating the possibility of different files containing different information about the same item. In essence, an ERP system is a set of integrated software application modules that aims to control all information flows within an organisation, including accounting (Rom and Rhode, 2006). As all modules are fully integrated, users can access real-time information concerning various aspects of the business, a facility that was not previously available with the IS that preceded the development of ERP systems. Cloud computing has recently emerged as a means of consolidating an organisation’s IT/IS resources (including accounting) via a shared provider of such services, access to which is generally facilitated through an internet browser. The potential advantages for a firm to adopt cloud computing include lower IT costs/barriers to entry, global data/systems availability, automatic access to IT updates and ease 6

Introduction to AIS

of IT scalability. Potential limitations include issues surrounding data protection, reliability, disaster recovery of lost data, privacy and security. A major issue with all IS projects relates to the fact that many of the subsequent benefits realised are intangible in nature and, hence, difficult to quantify. Consequently, there has been sustained criticism over the years surrounding the amount of capital invested in IS projects compared with the actual “visible” benefits realised. Furthermore, IS projects have in the past also been criticised as being over-sold by the respective IT champion in an attempt to ensure that the proposed project was ultimately approved for development (Brynjolfsson, 1993; Masli et al., 2011). However, as employees and managers have become more IT/IS savvy over the years, it seems reasonable to conclude that this latter criticism has in all likelihood been significantly reduced. As the majority of investments in IS/IT are aimed at automating particular processes with a potential reduction in, for example, staff numbers and costs, they are effectively attempting to increase organisational productivity. However, as noted above, the perceived lack of tangible results has resulted in what has been referred to as the IT productivity paradox. Indeed, this has led to the suggestion that investments in IS/IT may only be fully realised with a corresponding restructuring of the organisation itself (Han et al., 2011). It is within this context that the next section discusses AIS.

Accounting Information Systems An AIS is a technology-based system that enables an organisation to collect, store and process accounting data (financial and non-financial) and convert it into information that is then capable of supporting subsequent decision-making (Bodnar and Hopwood, 2004). Examples of such decisions include how best to allocate scarce resources, plan cash flows, determine human resource requirements, etc. As accounting information permeates most business decisions taken, the potential role and impact of the AIS can be significant. The size and scale of the organisation will determine the most appropriate AIS suitable for their needs. For example, a relatively simple manual system may be sufficient for a sole trader, whereas from the perspective of a global conglomerate, a sophisticated and integrated suite of AIS would most likely be required. In terms of their composition, an AIS generally consists of three main elements: (1) a transaction processing system that supports the organisation’s daily operations with the provision of relevant reports, etc.; (2) a general ledger/financial reporting system which produces the traditional financial reporting statements, i.e. income statement, balance sheet, cash flow statement; and (3) a management reporting system which produces reports and any other ad hoc information required by management to assist them in running the organisation. The primary objective of an AIS is to provide the relevant stakeholders with timely and reliable accounting-based information to support their subsequent decision-making. Therefore, to become an intrinsic element of the successful operation of an organisation, the AIS needs to be, at least, functional (i.e. every task requested of it needs to be undertaken in an appropriate timeframe), reliable (i.e. it must function correctly as and when required), usable (i.e. it must have the capability of being tailored to the end-user’s individual requirements) and maintainable (i.e. it possesses the necessary functionality to allow it to adapt to new organisational circumstances as and when required) (Buljubasic and Ilgun, 2015). According to Granlund (2011) prior research conducted in the IS arena has not considered the impact of AIS within organisations to any great extent. Indeed, those studies have tended to pursue a technical orientation with a corresponding shortage of research in areas such as management decision-making and control (Granlund and Mouritsen, 2003). This fact seems at odds with the development of IS generally, as their very existence requires the provision of 7

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relevant information to support the decision-making remit of management. In this regard, it has been suggested that the interface between management accounting and IT represents a complex area and arguably the most unpredictable in the realm of AIS (Sutton, 2006). At this point, it should be noted that there has been an on-going debate as to where AIS research is most appropriately situated. Some have argued the case for IS, others have suggested that accounting represents a more natural setting, while another cohort have suggested that as AIS has its own “unique identity”, it should contribute to both areas, but not necessarily at the same time (Steinbart, 2009). This questioning of the AIS research agenda is not new. Previously, Sutton (1992) had challenged much of the prior AIS research. This criticism included suggestions that the discipline appeared to lack an identity, many of the renowned AIS scholars were researching in areas that were considered outside of the AIS domain, and a shortage of skilled researchers in the area along with a lack of appropriate levels of academic rigour in the research that was conducted. Although it was acknowledged some years later that research in the AIS area had evolved (Sutton, 2010), questions about its long-term sustainability as a viable research area continue to be raised. Nevertheless, AIS are an essential infrastructural component in modern business environments with organisations continuing to invest significant sums of money to try and improve their efficiency and effectiveness. The next section offers a critical analysis of the use of AIS within organisations.

Critical analysis of AIS Due to the development of the Internet and e-commerce, many organisations now compete on a global rather than local basis. The advent of globalisation, including the liberalisation of capital markets, has meant that the basis of competition between firms has also recently changed. Access to capital has become less restrictive, meaning that firms are now competing against each other in various ways. One of them is based on superior customer service, suggesting that satisfied customers are more likely to become repeat customers and hence more profitable for the organisation. In this regard, information generated from an AIS can play a major role in ensuring that customer satisfaction is maintained and/or enhanced by allowing management to act quickly in addressing any issue that potentially affects customer satisfaction in a negative manner. A further potential advantage of using an AIS is enhanced efficiency, as computerised systems are faster at processing data than humans. Consequently, the information generated by the AIS is immediately available for use by management to support their subsequent decisionmaking. This capability would not be possible by using a manual system. It has also been suggested that if an AIS is operated in an effective manner, it can potentially prevent/mitigate the occurrence of possible crises by allowing management to take appropriate action quickly (Ceran et al., 2016). Cost effectiveness represents another potential benefit of an AIS. By investing the necessary resources (financial and otherwise) needed for an AIS, an organisation can potentially reduce the number of staff required for manual accounting-based work and redeploy these employees into higher value-adding roles instead. As discussed earlier, ERP systems (the majority of which incorporate an accounting module) have the potential to allow management to regularly report predetermined key performance metrics. The ability to drill down into accounting data using an ERP system may facilitate the auditing of the firm with a subsequent improvement in their internal control and hence, corporate governance. It has been further suggested that ERP systems can potentially support an organisation’s incumbent management accounting practices as such firms were found to be better off than those without (Rom and Rhode, 2006). It has also been suggested that organisations who decide to migrate some of their AIS to a cloud-based environment may increase the 8

Introduction to AIS

flow of information internally, thereby making it more accessible and facilitating a more strategic orientation for their accounting/finance function. The net result of this could be enhanced decision-making with a subsequent positive impact on organisational performance. Indeed, Quinn et al. (2014) refer to improved efficiency in business processes as a result of using cloud computing in their sample of firms studied. Furthermore, Cleary and Quinn (2016) have shown enhanced business performance in a sample of Irish SMEs due to their use of cloud-based accounting/finance infrastructures. Other benefits that may be derived from the implementation of AIS include an improvement in the quality and a reduction in the cost of products by effectively monitoring the good/defective output generated by production machinery coupled with enhanced knowledge-sharing throughout the organisation and beyond. For example, the organisation may allow their customers to directly access their inventory levels and sales order entry systems, which would reduce the cost of both their sales and marketing activities (Romney and Steinbart, 2006). Although large firms may have considerable resources at their disposal to invest in new AIS, for many smaller firms, a lack of time, resources and expertise may hinder their ability to follow suit. Indeed, in some cases, an attitude seems to exist that if the incumbent accounting systems have not been subject to sustained criticism, no obvious reason to replace them with more advanced alternatives exists. If this is the attitude that prevails for rudimentary AIS, then it seems reasonable to suggest a lower degree of adoption of more sophisticated and expensive AIS. This argument is supported by the fact that research has shown low adoption rates for the implementation of sophisticated management accounting information systems such as activity-based costing, target costing, etc., despite the potential advantages that organisations could realise from their use (Kennedy and Affleck-Graves, 2001). Indeed, it has been reported that more basic systems such as standard costing and job costing appear to dominate in reality (Cleary, 2015). Additionally, research conducted by Van der Steen (2009) has claimed that employees overly familiar with incumbent management accounting systems makes it difficult for management to implement new and potentially more beneficial ones. This scenario indicates that organisations may be managing their increasingly complex operations with basic AIS and are therefore not considering the potential advantages that more advanced and robust AIS could possibly deliver. In any AIS, once raw data is captured within the system, it will then be used to generate outputs. However, if the input data is inaccurate, the output data will inevitably follow suit, with potentially negative consequences for subsequent organisational decision-making. As accounting data contains much of an organisation’s commercial and highly sensitive information, it is imperative that adequate internal controls are implemented to ensure that, for example, suppliers are not provided with details of other suppliers’ credit terms or that employees’ pay levels are not made public. For any of these possibilities to occur would create major “trust” issues for both the AIS and the organisation concerned, which may take a considerable amount of time and effort to resolve, if ever. Other reservations surrounding AIS include the possibility that firms who invest in this area may subsequently discover that the levels of creativity and innovation that previously existed within their accounting functions have declined due to increased standardisation and automation (Gordon and Tarafdar, 2007). It has also been suggested that the use of cloud computing in the area of AIS may not be currently appropriate due to on-going issues surrounding security and confidentiality (Quinn et al., 2014). However, with the installation of adequate protection, e.g. firewalls and anti-virus software, the risk of, for example, a virus/ malware or authorised access can be significantly reduced. As time evolves, one would expect that these particular issues will ultimately be resolved. Following on from this critical analysis of AIS, the next section considers the impact of AIS on both accounting and accountants. 9

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Role of accounting/accountants Due to the rapid pace of advances in IT, the nature of accounting and the role of accountants continues to evolve. However, it should be remembered that accountants have always performed numerous activities associated with developing AIS or their equivalent. These have included: assessing end-user’s information requirements, developing report formats and content, identifying sources and the reliability of input data, etc. Indeed, as articulated by Malmi and Brown (2008), IT systems are not developed or do not exist in isolation, and in the context of AIS are primarily developed in partnership between accountants and IT personnel. The integrated nature of modern AIS indicates that accounting information is now more readily available to more employees within the organisation than previously, such as the sales or logistics functions. This has created threats to the role of the accountant, but also new opportunities, as modern AIS provide a “drill down” facility into aggregate information to a much greater extent and detail than before. A potential positive consequence of this is the revelation of insights previously hidden that could make a significant difference to the organisation’s future trading performance. As the IT skills required by accountants continues to increase, professional global accounting bodies such as the Chartered Institute of Management Accountants (CIMA) and the Association of Chartered Certified Accountants (ACCA) now explicitly require their students to study IT/ IS modules in preparation for their future employment in accounting roles. Once qualified, it is a requirement for accountants to maintain and enhance their skills through a process known as Continuous Professional Development (CPD). This is to ensure that their IT skills (amongst others) are continually upgraded to realise potential benefits that may accrue from the introduction of new technologies as quickly as possible. This view has been endorsed by professional accounting bodies such as the American Institute of Certified Public Accountants, who have stressed the importance of increasing the knowledge and use of IT to improve organisational performance (Dillon and Kruck, 2004). In terms of its impact on accounting, it has been suggested that organisations who have implemented ERP systems could rationalise some of their existing management accounting procedures. By doing so, it may allow their accountants to perform routine tasks in a more efficient manner, whilst simultaneously allowing them to re-focus their time and energy on performing higher value-added activities. Indeed, studies have shown that the implementation of ERP systems can facilitate a change in the role of accountants from traditional score-keepers to informed business analysts (Scapens and Jazayeri, 2003). Additionally, research conducted by Kanellou and Spathis (2013) found that the benefits to accounting from the introduction of ERP systems, such as quicker gathering of data, a reduction in the time needed to close off accounts and improved decision-making, have facilitated a reduction in staff in the accounting department. Post-ERP implementation, research conducted by Chen et al. (2012) found that the role occupied by management accountants changed to incorporate additional managerial-type functions such as training, education and financial analysis. The researchers also reported that this was facilitated by the use of ERP systems to replace repetitive accounting tasks.

Summary This chapter has provided a general introduction to AIS. It began by defining accounting in its various forms before outlining the potential use of accounting-based information from an organisational perspective. The concept of IS was then introduced, with a focus on AIS. A critical analysis of AIS followed before the chapter concluded by considering the future role for both accounting and accountants in the rapidly evolving IT-based global economy. Within 10

Introduction to AIS

accounting, there is an on-going movement towards the harmonisation of global accounting standards so as to facilitate investors in making more informed capital allocation decisions. Simultaneously, there is also a suggestion that organisations may in the future engage in what has been termed “integrated reporting”. This proposal is in response to the view that as some elements of financial reporting rules and regulations are considered to be overly restrictive, it would allow firms to publish “extra” information (both financial and management accounting based) as a supplement to their annual report. By doing so, it will allow them to communicate with interested stakeholders as to how they are planning to manage their limited resources so as to create value in the short, medium and long term. From an AIS perspective, all of this accounting information (both quantitative and qualitative) will need to be generated and provided in a timely manner so as to facilitate stakeholders in making informed decisions. As the preceding represents just some examples of the possible future use of AIS, it seems reasonable to conclude that the role and scale of AIS within organisations will continue to grow. Consequently, the role currently occupied by both accounting and accountants will also inevitably evolve, with multiple opportunities and threats likely to emerge. Continual advances in technology will be central to this development.

References American Accounting Association (1966). A Statement of Basic Accounting Theory. Evanston, IL, USA: American Accounting Association. Bodnar, G. H. and Hopwood, W. S. (2004). Accounting Information Systems. Hoboken, NJ, USA: Pearson Prentice Hall. Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 66–77. Buljubasic, E. and Ilgun, E. (2015). Impact of accounting information systems on decision making: The case of Bosnia and Herzegovina. European Researcher, 96(7), 460–469. Ceran, M. B., Gungor, S. and Konya, S. (2016). The role of accounting information systems in preventing the financial crises experienced in businesses. Economics, Management and Financial Markets, 11(1), 294–302. Chen, H. J., Huang, S. Y., Chiu, A. A. and Pai, F. C. (2012). The ERP system impact on the role of accountants. Industrial Management & Data Systems, 112(1), 83–101. Cleary, P. and Quinn, M. (2016). Intellectual capital and business performance: An exploratory study of the impact of cloud-based accounting and finance infrastructure. Journal of Intellectual Capital, 17(2), 255–278. Cleary, P. (2015). An empirical investigation of the impact of management accounting on structural capital and business performance. Journal of Intellectual Capital, 16(3), 566–586. Dillon, T. W. and Kruck, S. E. (2004). The emergence of accounting information systems programs. Management Accounting Quarterly, 5(3), 29–36. Gordon, S. R. and Tarafdar, M. (2007). How do a company’s information technology competences influence its ability to innovate? Journal of Enterprise Information Management, 20(3), 270–290. Granlund, M. (2011). Extending AIS research to management accounting and control issues: A research note. International Journal of Accounting Information Systems, 12(1), 3–19. Granlund, M. and Mouritsen, J. (2003). Problematizing the relationship between management control and information technology, introduction to the special section on “management control and new information technologies”. European Accounting Review, 12(1), 77–83. Han, C., Hsieh, C., Lai, F. and Li, X. (2011). Information technology investment and manufacturing worker productivity. Journal of Computer Information Systems, 52(2), 51–60. Hemmer, T. and Labro, E. (2008). On the optimal relation between the properties of managerial and financial reporting systems. Journal of Accounting Research, 46(5), 1209–1240. Kanellou, A. and Spathis, C. (2013). Accounting benefits and satisfaction in an ERP environment. International Journal of Accounting Information Systems, 14(3), 209–234. Kennedy, T. and Affleck-Graves, J. (2001). The impact of Activity-Based Costing Techniques on firm performance. Journal of Management Accounting Research, 13(1), 19–45. 11

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Malmi, T. and Brown, D. (2008). Management control systems as a package: Challenges, opportunities and research directions. Management Accounting Research, 19(4), 287–301. Masli, A., Richardson, V. J., Sanchez, J. M. and Smith, R. E. (2011). The business value of IT: A synthesis and framework of archival research. Journal of Information Systems, 25(2), 81–116. McLaney, E. and Atrill, P. (2010). Accounting: An Introduction. Essex, UK: Financial Times Prentice Hall. Quinn, M., Strauß, E. and Kristandl, G. (2014). The effects of cloud technology on management accounting and decision-making. Financial Management, August, 54–55. Rom, A. and Rhode, C. (2006). Enterprise resource planning systems, strategic enterprise management systems and management accounting: A Danish study. Journal of Enterprise Information Management, 19(1), 50–66. Romney, M. and Steinbart, P. (2006). Accounting Information Systems. Hoboken, NJ, USA: Pearson Education International. Scapens, R. and Jazayeri, M. (2003). ERP systems and management accounting change: Opportunities or impacts? A research note. European Accounting Review, 12(1), 201–233. Steinbart, P. J. (2009). Thoughts about the future of the Journal of Information Systems. Journal of Information Systems, 23(1), 1–4. Sutton, S. G. (1992). Can we research a field we cannot define? Towards an understanding of the AIS discipline. Advanced Accounting Information Systems, 1, 1–13. Sutton, S. G. (2006). Enterprise systems and the re-shaping of accounting systems: A call for research. International Journal of Accounting Information Systems, 7(1), 1–6. Sutton, S. G. (2010). A research discipline with no boundaries: Reflections on 20 years of defining AIS research. Advanced Accounting Information Systems, 11(4), 289–296. Taipaleenmäki, J. and Ikäheimo, S. (2013). On the convergence of management and financial accounting: The role of information technology in accounting change. International Journal of Accounting Information Systems, 14(4), 321–348. Van der Steen, M. (2009). Inertia and management accounting change: The role of ambiguity and contradiction between formal rules and routines. Accounting, Auditing & Accountability Journal, 22(5), 736–761. Weißenberger, B. E. and Angelkort, H. (2011). Integration of financial and management accounting systems: The mediating influence of a consistent financial language on controllership effectiveness. Management Accounting Research, 22(3), 160–180.

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2 Development of Accounting Information Systems over time Pierangelo Rosati and Victoria Paulsson

Introduction Accounting is typically seen as a slow-paced and conservative industry. Despite such a reputation, both the accounting discipline and the role of accountants have changed significantly over time (Baldvinsdottir et al., 2009). Boyns and Edwards (1997a, p. 21) state that “accounting is a discipline which may be seen, at a particular point in time, to encompass a body of ideas, a number of conventions, a set of available tools/techniques and a variety of actual practices”. All these components are embedded in Accounting Information Systems (AIS). The scope of AIS has expanded throughout history with the role of accountants, and as a consequence technological development has shaped how accountants perform their work. AIS have expanded from mere record keeping systems to complex systems encompassing technical, organisational, and cognitive factors (Mauldin and Ruchala 1999). AIS are not used only by accountants, but also by other decision-makers within organisations who need to make their decisions based on accounting data, as well as in tasks that involve an application of accounting data (Reneau and Grabski 1987). Among the available definitions1 of AIS in the literature, this chapter adopts the one proposed by David et al. (1999) who define AIS as a discipline: that captures, stores, manipulates, and presents data about an organisation’s value-adding activities to aid decision makers in planning, monitoring, and controlling the organisation. p. 8 This definition is preferred over others for several reasons. First, this definition of AIS does not make any reference to information technology per se. Therefore, it fits particularly well in a discussion about the evolution of AIS, which is the aim of this chapter. Readers should not be confused between the terms information systems and information technology since they are not synonymous; furthermore, the former does not imply the latter since information systems existed well before the development of computers (Alikhani et al., 2013). Information technology, on the contrary, is simply a use of computers to manage information systems. Thus, considering AIS only as computerised systems, or simply information technology, would underestimate the extent of changes occurred over time. Second, the definition proposed by David et al. (1999) 13

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refers to the essential role of AIS, which is to provide both internal and external decision-makers with timely and valuable information about the organisation. Although the essential role of AIS has been substantially untouched, the extent to which such information has been used to monitor, control, or plan an organisation’s activities has changed over time, leading to a change in AIS design (Anandarajan et al., 2004). Leitch and Davis (1983) identified four main drivers behind the evolution of AIS: •

•

•

•

Information requirements. Information needs can be classified in two categories, i.e. internal and external information requirements. Internal information requirements have been growing over time due to the increase in firm size and business complexity, as well as changes in ownership structure. For example, a merchant who runs his/her own local business has lower information requirements than a firm with offices in many different countries. External information is related to financial reporting. Regulators in different parts of the world have substantially increased the information a firm has to disclose in order to enhance the information value of financial statements, which thereby increases the level of investor protection. Technology development. AIS do not always imply the use of computers. However, it would be hard to imagine modern information systems without any support from computers (i.e. information technology). Information technology has been gradually introduced to aid AIS in many aspects. The first computerised AIS were adopted to handle day-to-day operations in large organisations. Many technological developments have led to increasing computing power and rapidly decreasing cost, and the advent of the Internet represents quantum changes within AIS. Systems approach. The systems approach is a way of structuring and coordinating the activities and operations within an organisation. This approach is based on the idea that different parts of a system may, and should be, interrelated so that the value of the system as a whole is greater than the sum of its components. The systems approach is clearly visible in modern AIS, but it was extremely challenging to put it in to practice prior to the 1990s, when the enterprise resource planning (ERP) system was invented. In those days, AIS typically did not interact with any other information systems adopted in other departments and vice versa. The systems approach was a result of the integration need that emerged inside companies that had grown substantially in terms of size and complexity. With the systems approach, AIS started to provide timely performance feedback to other functions. Scientific approach to management. The application of mathematical, statistical, and analytical models to organisational management has significantly increased the complexity of AIS. As a response to the growing complexity of companies and the business environment, AIS are required to handle increasingly larger volumes of data in different formats as well as to integrate, manipulate and analyse this data. As a result, AIS have experienced a constant increase in the number of tasks they can execute and in performance.

Even though the accounting discipline is typically considered slow-paced and conservative, it, the role of accountants, and AIS have changed significantly over time. The innovation process has been anything but linear and this chapter aims to describe these main stages in the AIS evolution. In a similar attempt, Anandarajan et al. (2004) divide the AIS history into five periods: Ancient Times, Pre-RenAISance, RenAISance, Industrial Age, and Information Age. The characteristics of the first four periods are briefly summarised in the next section, and after this, we extend the discussion on the post-1950s era in three main phases i.e. the Information Age (1950s–1980s), the Integration Phase (1990s), and AIS 2.0 (2000s–present). 14

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Where it all began The first example of AIS can be traced back to ancient Jericho around 8,000 BC where merchants kept track of inventories and transactions using tokens (Anandarajan et al., 2004). For 5,000 years, there were just small improvements until the advent of the abacus (Brown 2014). Originally invented in China, the abacus made its way to western civilisation and became a valuable tool for accounting calculations. Around the same period in Egypt and Mesopotamia, accounting transactions were being recorded on papyrus rolls and clay tables. The role of these rudimentary AIS consisted only in “list making”, with transactions being recorded in terms of items exchanged. Three major changes then occurred and changed the course of accounting practices. First, the invention of banking and coins attributed to the Greeks (575 BC) enabled commercial transactions to be recorded in money instead of weights or other measures (De Soto 2009); second, the development of a basic income statement and balance sheet in ancient Rome (Anandarajan et al., 2004); and third, the development of an initial concept of corporation (Brown 2015). Roman authorities introduced a regulated system of accounting and taxation, and merchants were required to “register” their business and report profits for taxation purposes. During the Middle Ages there was a period from 1000–1200 that saw small incremental advancements in the AIS field. The Normans invented the tally stick, which displayed the equivalent of accounts receivables and payables in our modern financial statements (Anandarajan et al., 2004). Italian merchants improved single-entry bookkeeping practices to effectively manage the increased number of transactions generated by the Crusades (Gleeson-White, 2011). The period between 1250 and 1350 saw the so-called commercial revolution (Bryer, 1993). During those years, merchants’ businesses grew so significantly they formed partnerships in order to raise funding. Furthermore, between 1400 and 1600 large joint-stock companies2 emerged to fund transoceanic expeditions, and for the first time there was a real separation between owners and managers. In such a context, efficient and reliable AIS were essential to ensure transparency between investors (owners) and administrators (managers), and to make appropriate decisions for the benefit of the companies. Similarly to single entry bookkeeping, double-entry bookkeeping3 allowed transactions to be measured and recorded, but it also explained how profit and loss statements and a balanced sheet were generated (Littleton, 1933). The enhanced level of transparency and accountability increased the level of trust in commercial and investment ventures. Thus, investments in commercial and investment opportunities increased and enabled further economic developments. It was during the Industrial Age (1700–1940) that double-entry bookkeeping gained importance on a global scale (Anandarajan et al., 2004; Hoskin and Macve, 1986). This period was also characterised by a significant change in AIS. AIS had to provide more information to both external and internal stakeholders. With the separation between ownership and control becoming more common in different industries (Johnson and Kaplan, 1987), financial reports had to provide owners with complete and reliable information about how financial results were generated. At the same time, internal stakeholders were constantly looking for mechanisms to reduce inefficiencies in the production process (Fleischman and Tyson, 1993). AIS had to support management activities by providing more granular information about the production process and relevant performance measures. This gave rise to a new accounting field known as management accounting.4 Management accounting is closely related to the development of scientific management, and a main concern at this time was to identify and understand costs for the purposes of control. AIS had to be adapted to manage the increased volume of information to be processed. The same logic adopted for other operating processes was applied to AIS where the workflow was divided into minute operations performed by a worker or by a small group; 15

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in this way the performance of each worker and process could be controlled by mathematical checks. Despite all the changes to AIS up to and during the industrial age, the impact of technology in the office was mostly confined to various forms of office mechanisation (i.e. typewriters). It was the so-called Information Age that represented a real breaking point in AIS development.

The Information Age: 1950s to 1980s The Information Age spans a 40-year period in which there were many technological developments. For ease of reference, the Information Age is divided into two distinct phases: the mainframe phase (1950s–1960s) and the personal computer (PCs) and decision support system (DSS) phase (1970s–1980s). The mainframe phase started with the development of the first computer with electronic circuits by Eckert and Mauchly in 1943. A decade later, IBM released the IBM 702 Electronic Data Processing Machine. The machine’s ability to process large amounts of data and handle alphanumeric variables of varying size made it suitable for accounting workloads. Thus began a revolutionary phase in AIS history (Anandarajan et al., 2004). At this time, the main role of computers was to improve the efficiency and accuracy of manual accounting work performed by accountants in voluminous and repetitive accounting activities such as inventory tracking, general ledger transactions, accounts receivable/payable, and payroll. The traditional role of accountants was to summarise transactional details of how a business operated in a past period, but this time with an aid from computerised AIS. AIS were no longer operating through faith in accountants and their locally developed manual routines, but through faith in computers (Baldvinsdottir et al., 2009). Despite the slowly growing importance of computerised AIS, their functions were limited to routine data gathering and processing. The introduction of computerised AIS had three main impacts. First, speed and volume increased rapidly with the introduction of machines in the accounting function. Managers could introduce new methods for performance monitoring and management control. For example, management by exception, a practice in which only a significant deviation from a budget/plan is brought to the attention of the management, was made possible by computerised AIS (Brownell, 1983). Second, there was a swift change of skill sets required for senior accountants. Before the introduction of computers in the AIS function, senior accountants had only needed to perfect the art of manualbased AIS. With the introduction of computers to their work routines, senior accountants faced a dual role of not only running the AIS, but also the computer system so that the traditional manual-based AIS could work in perfect harmony with the computerised system (Anandarajan et al., 2004). Demand for senior accountants was high and they were in a good position to negotiate higher salaries. Conversely it had an adverse impact on junior accountants. This is the third point that we will discuss. The introduction of computerised AIS meant that basic accounting clerks were no longer required and were made redundant since the work was more efficient and effective when performed by computers than humans. Only simple tasks like data entry were left to junior accounting staff. For example, a US-based multi-branch bank reported a 75 percent reduction of bookkeeping staff within the first 18 months of an electronic bookkeeping machine installation (Braverman, 1974). The typical bean counter role of accounting profession, who provided only transactional and historical information of the business, eroded for the first time. Despite the undeniable benefits that the mainframes introduced, the adoption rate was initially extremely low. By 1955 there were only about 240 mainframes in use in the US (Campbell-Kelly and Garcia-Swartz, 2009). The main reasons for such a low adoption were the complexity of such systems and the high investment required. 16

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The 1970s marked the beginning of PCs and DSS phase. By this time, the number of companies adopting computerised AIS began to increase. The turning point was the development of personal computers (PCs) and accounting software, which first became available during this period. With personal computers, the required investment to adopt computerised AIS dropped dramatically. The MITS Altair 8800 was the first PC to be introduced to the market in 1975 at a retail price between $275 and $395, followed by the Apple I in 1976 at $666 (ComputerHistoryMuseum.org, 2016). In comparison, the cost of mainframe computers was around $4.6 million (Perry, 2007). Such a low cost represented a significant incentive for companies to adopt PCs in their daily activities. The software side grew in harmony. The ICP Quarterly in 1974 listed 324 accounting software packages available on the market for core accounting functions such as general ledger, account receivable/payable, billing, budgeting and costing within the quoted price range of $5,000–$8,400 (Campbell-Kelly, 2004). The following decade saw the emergence of the business controller role within the accounting profession. The significance of this role grew over time (Baldvinsdottir et al., 2009). In this role, accountants are thought of as business advisors who analyse accounting data and provide direction to middle management on how to best make a business decision at a strategic level. It should be noted that the 1980s marked only a very early onset of the business controller role. It was not until the 1990s that technological advancement, like the ERP system, which will be discussed in the next section, was sufficient enough to fully support accountants in this role. To aid the early but steadily growing business controller role, a new kind of computerised AIS called the decision support system (DSS) was invented (Baldvinsdottir et al., 2009). DSS is a term that refers to any kind of computer system that can be used to support complex decision making and problem solving (Shim et al., 2002). Many kinds of DSS are identified in the literature, such as (1) data-driven DSS, (2) model-driven DSS, (3) knowledge-based DSS, (4) communicationbased DSS, and (5) document-driven DSS (Alter, 2004). The relative importance of a particular type of DSS compared to the others depends on how decision-makers make use of the systems in question to support decisions at hand. For example, to perform a sales forecast for the next quarter, an accountant might need access to all kinds of DSS for information about past sales, current knowledge about macro economy and insights about a particular market. These data and information could be available throughout all kinds of DSS. One of the software revolutions that marked the DSS era of 1980s was VisiCal, the first electronic spreadsheet software launched in 1978 (Cunha et al., 2015). VisiCal played a significant role in transforming the perception of PCs into that of a useful business tool. It had most of the features of electronic spreadsheets available on the market today, e.g. a column/row tabulation program with a What You See Is What You Get (WYSIWYG) interface, a cell reference (A1, A2, etc.), and recalculation of formulas (Mendes, 2012). An electronic spreadsheet provided an excellent platform to perform a what-if analysis, which involved combining historical accounting data with up-to-date and historical facts and knowledge to forecast how a future business condition might play out. A simulation of several different business conditions (e.g. best-case, most-likely case and worst-case scenarios) could be run through electronic spreadsheets and possibly through other statistical software. Despite the gradual growing significance of the business controller role, the role did not dominate the AIS landscape until the 1990s, as the technology required to support decision-making tasks was not fully developed. For example, VisiCal was only the first attempt at a DSS in which much manual work was left to accountants, particularly manual data entry into spreadsheets. In summary, the information age can be divided into two main phases. The initial mainframe phase (1950s–1960s), in which AIS ran on complex mainframes adopted by a limited number of big companies, and the second – PCs and DSS phase (1970s–1980s) – in which revolutionary technologies, such as PCs and electronic spreadsheets, gradually reshaped AIS. 17

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The Integration Phase: 1990s The 1990s was a decade of great volatility in the global economy. Developed countries experienced rapid economic growth from international trades, but there were also many major financial crises in several parts of the world (Nankani, 2005). In terms of technological advancement, many key IT developments were invented during this period to aid computerised AIS. The Internet, data warehousing, and ERP systems are some key technologies that define the computerised AIS landscape of the 1990s (Rashid et al., 2002). These technologies were complemented with advancements in cost and management control techniques, which were highly popularised throughout the decade (Meyer, 2003); these include activity-based costing (ABC) (Cooper and Kaplan, 1991), and balanced scorecard (BSC) (Kaplan and Norton, 1995). All of these challenges – macro-economic conditions, technological advancement, along with advanced cost and management control techniques – required accountants to integrate all of these aspects together into their work function. Thus we label this decade as the Integration Phase. The 1990s posed significant challenges to accountants. The increasing globalisation of business (Nankani, 2005), tighter controls imposed by ABC and BSC, and growing business uncertainty as signalled by various financial crises worldwide (Nankani, 2005). Accountants faced such challenges by adopting the new IT tools and management accounting techniques, which became available during those years. There was also a shift in the role of accountants. The business controller emerged as the dominant role over the bean counter role. The business controller role was fully developed by this decade, while the 1980s was only the introduction of the role. This progressive expansion of the business controller role in the 1990s was due to the arrival of the ERP system and the remainder of this section will explain the impact of ERP systems on the changing role of accountants. An ERP system is, in essence, an integrated cross-functional business support system (Grabski et al., 2011) that is often available off-the-shelf and ready to be implemented in any business organisation. ERP systems comprise of functions (or modules as they are commonly referred to in the business literature) that correspond to a function or department within an organisation. Common functions that were available in a typical ERP package in the 1990s consisted of: (1) Financial Accounting, (2) Managerial Accounting, (3) Human Resources, (4) Supply Chain Management (SCM), (5) Project Management, and (6) Customer Relationship Management (CRM) (Rashid et al., 2002). To achieve these all-in-one functions through single software, the ERP itself is comprised of four key layers. An elaboration of these layers allows us to understand how the ERP system integrates with other key technologies of the 1990s, like a data warehouse and the Internet, to make a strategic impact on organisations (Kale, 2016; Møller, 2005; Paulsson, 2013). The four layers of an ERP system consist of: •

•

•

18

The foundation layer is built upon data warehousing technology. It provides an integrated database for different modules and outlines an application framework to make ERP modules work together in perfect harmony. The process layer provides core transaction-based functions of the ERP system, such as financial accounting and material and production planning. These are the core components of an ERP system from its early days (Rashid et al., 2002). The analytical layer combines transaction data in the process layer with other databases, which might not be directly synchronised with the core-integrated database in the foundation layer, to provide a decision support system to users. The Internet, and other network technologies in the 1990s, like local area network (LAN) and wide area network (WAN), are the main technologies that make the connection between different databases possible. Modules like SCM and CRM are the hallmarks of the analytical layer.

Development of AIS over time

•

The e-business layer utilises internet technology to provide electronic collaborations with other business parties outside an organisation, such as customers, business partners, and employees. The layer enables the ERP system to integrate with other business systems through enterprise application integration (EAI) technology.

The ERP system in the 1990s had fully working foundation and process layers, but it also started to embrace the analytical layer into the overall ERP design. However, it was not until the 2000s that the analytical layer was fully commercialised and the e-business layer started to take off (Møller, 2005). The ERP system of the 1990s played a significant part in supporting the business controller role for accountants. To effectively utilise the ERP system, all business processes must run according to an accepted industry standard, regardless of location and local cultures – this is commonly known as best practice (Hong and Kim, 2002). In any ERP implementation, best practice is required alongside the technological part of the ERP system in order to improve a process efficiency (Hong and Kim, 2002; Wagner and Newell, 2004). Best practice applies a standardised data definition throughout business branches. For example, a definition for an invoice overdue is anything over thirty days throughout business branches (as opposed to 15 days in New York branch and 30 days in London branch as it had been prior to best practice). Therefore, accountants spend less time reconciling data definitions and allow the process layer of the ERP system to do the drudgery of financial data processing and consolidation. With the ERP system and best practice, accountants had more time to focus on the analytical layer of the ERP system. Accountants could integrate transactional accounting data with qualitative and quantitative insights available from other business functions through the integrated database in ERP system and the Internet to forecast and control business performance.

AIS 2.0: 2000s–present The term AIS 2.0 is inspired by the term Web 2.0, where user-generated content and compatibility with other IT systems are key to success. The decade from 2000 onward is labelled AIS 2.0 to highlight a significantly growing impact of accounting-generated business insights through ERP systems and business intelligence (BI), as well as AIS’ ability to integrate with other innovations, like cloud computing, and big data, to generate even more accounting-driven business insights. The AIS landscape from the 2000s onward has been shaped by many socioeconomic factors (e.g. accounting scandal and legislation), and technological advancement (e.g. ERP system and business intelligence system). Major accounting scandals such as the Enron case in 2001 caused many legislative bodies to enforce tighter controls on accounting practice and AIS. The SarbanesOxley Act (SOX) of 2002 passed by the US congress includes sections that specifically address computerised AIS (Brown and Nasuti, 2005). For example, Section 302 mandates that officers of a company to confirm that internal controls, procedures, and assurance are put in place. Since a large amount of financial information is prepared by automated, computerised AIS like ERP systems, the burden to comply with Section 302 expands beyond the Chief Financial Officer (CFO) function to that of the Chief Information Officer (CIO) (Brown and Nasuti, 2005). CIOs, whose primary responsibilities are to direct and control ERP projects, are required to sign off a company’s financial statements as they are deemed responsible for the financial information processing through the systems that they control. Section 404 focuses on an annual assessment of effectiveness in internal control procedures. Clearly, IT internal controls received particular attention in Section 404. In such a context, ERP systems continue to be the technology that many believe is key to operationalising and satisfying the compliance requirements posed by legislation (Brown and Nasuti, 2005; Panko, 2006). 19

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Apart from the growing importance of ERP systems for compliance, the analytical layer and the e-business layers of ERP systems are now a prospering business. BI is now the common term referring to the analytical layer of business operations. BI is commonly connected to the ERP system, but it is not an absolute prerequisite, as some BI might be better positioned to gather data and information elsewhere (Elbashir et al., 2011). The most significant function of BI is to perform “data gathering, data storage and knowledge management with analysis to evaluate complex corporate and competitive information” (Negash and Gray, 2008, p. 176 ). Similarly, the e-business layer integrated with the ERP system begins to grow from 2000s onward as more and more businesses exchange values over the Internet (Hsu, 2013) and via cloud computing (Marston et al., 2011). In a nutshell, cloud computing is a new technology enabled via the Internet for users to access an on-demand network of shared computing resources, e.g. networks, servers, storage, applications and services (Mell and Grance, 2011). The challenges that cloud computing creates from an AIS perspective are discussed in other chapters of this volume. The trend now is that most business applications will be migrated to the cloud, including ERP systems (Raihana, 2012) and BI (Mircea et al., 2011). The e-business layer also holds the promise of enabling information produced through ERP systems and many other online databases to be shared among companies in a supply chain, in a timely yet cost-effective fashion. This gives rise to a new technology called big data. Big data exploits a vast amount of data available on the Internet, other online databases and internally gathered historical data from an ERP system, along with the scalable information processing power of cloud computing, to answer any data-driven question when traditional methods to data analysis could not provide such an answer in a timely, cost-effective, and satisfactory fashion (Kraska, 2013). Other chapters in this volume provide a deeper discussion about the impact of big data on AIS. The technological advancement in this period defines a new way of business operation as we have highlighted about how businesses are connected through the e-business layer of ERP system. The power of the Internet, cloud computing and big data defines a new landscape for business operations in many ways. For example, consumer trends are changing at a much faster rate than before due to the explosion of information sharing via social web, e-commerce sites, and the Internet. Businesses must constantly refine and redefine their competitive advantage (Barney, 1991) to survive constantly fluctuating market trends. In such a context, the analysis coming from AIS and accountants has risen to a strategic level. The business controller role in the 1990s is now escalated to the new role of business partner (Järvenpää, 2007; Scapens and Jazayeri, 2003). The role is similar to the business controller role, however, with a focus on data-driven analytics to shape effective and strategic decision-making, not just control decisions at the tactical level like it was before. The primary activities of accountants in organisations are to analyse information and advise management on how to navigate new and existing business ventures. They analyse information clues from multiple sources, both online and offline, to create a futuristic business vision (Järvenpää, 2007). Accountants are now considered the experts in information analysis. The term business partner reflects their main role in working alongside top management to shape strategic business decisions.

Conclusion The accounting discipline has played a critical role in economic history. AIS have the merit of having supported accountants in activities since ancient times. Although mostly associated with information technology, rudimentary AIS existed long before the development of computers. As time passed, AIS evolved to include more features, which led to increased complexity. The evolution of AIS has been anything but linear. Indeed, it is possible to identify at least three 20

Development of AIS over time

significant breakthroughs that led to the AIS that we know today: (1) the introduction of double-entry bookkeeping, which gained particular importance in the industrial age (1700– 1940); (2) the development of computers which opened the so-called Information Age in the 1950s; and (3) the adoption of the Internet which allowed the Integration Phase (1990s) to commence and enable the development of AIS 2.0 (2000s–present). The double-entry bookkeeping represents the essence of AIS. The increased level of transparency enabled the separation between ownership and control, a characteristic of the modern enterprise. Computers and the Internet represent the instruments that brought AIS to a superior level of sophistication. They have significantly enlarged the scope of AIS, which is not confined in the mere reporting or in the analysis of historical data, but also extends to the provision of forward-looking information to help top management in their decision-making. AIS are now able to convey timely and valuable information to both internal and external stakeholders, and are well recognised as a key component of companies’ information systems. The effort to continuously adapt AIS to the constantly evolving business environment has brought AIS from a mere supportive role to that of a strategic one, and such a role is not likely to change in the future.

Acknowledgement The research work described in this chapter was supported by the Irish Centre for Cloud Computing and Commerce, an Irish National Technology Centre funded by Enterprise Ireland and the Irish Industrial Development Authority.

Notes 1 See for example the ones suggested by Belfo and Trigo (2013) and Poston and Grabski (2000). 2 A well-known example of a joint-stock company is the East India Company founded in 1600 (Lawson, 2014). 3 The origin of double-entry bookkeeping is uncertain. However, it was attributed to Italian mathematician Luca Pacioli who wrote, in 1494, the treatise Summa de Aritmetica, Geometrica, Proportione et Priportinalite where he explained how to effectively and efficiently record financial information. 4 Sometimes management accounting is also referred to as cost accounting. It emerged in Britain around 1870s (Boyns and Edwards, 1997b).

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Mendes, J. C. (2012). Evolution of Model-Driven Spreadsheets. PhD dissertation, Universidade do Minho. Retrieved September 19, 2017, from http://repositorium.sdum.uminho.pt/bitstream/1822/27889/1/ eeum_di_dissertacao_pg16490.pdf. Meyer, M. W. (2003). Rethinking Performance Measurement: Beyond the Balanced Scorecard. Cambridge: Cambridge University Press. Mircea, M., Ghilic-Micu, B. and Stoica, M. (2011). Combining business intelligence with cloud computing to delivery agility in actual economy. Journal of Economic Computation and Economic Cybernetics Studies, 45(1), 39–54. Møller, C. (2005). ERP II: a conceptual framework for next-generation enterprise systems? Journal of Enterprise Information Management, 18(4), 483–497. Nankani, G. (2005). Economic Growth in the 1990s: Learning from a Decade of Reform. Washington, DC: The International Bank for Reconstruction and Development/The World Bank. Negash, S. and Gray, P. (2008). Business Intelligence. New York: Springer. Panko, R. R. (2006). Spreadsheets and Sarbanes-Oxley: regulations, risks, and control frameworks. Communications of the Association for Information Systems, 17(1), 29. Paulsson, W. V. (2013). The Complementary Use of IS Technologies to Support Flexibility and Integration Needs in Budgeting. PhD in Information Systems Monograph, Lund University, Lund. Retrieved from https:// lup.lub.lu.se/search/publication/4022792. Perry, M. (2007). Computer Prices and Speed: 1970 to 2007. Retrieved September 19, 2017 from http:// mjperry.blogspot.ie/2007/08/ibm-mainframe-computer-in-1970-pictured.html. Poston R. S. and Grabski, S. V. (2000). Accounting Information Systems research: is it another QWERTY? International Journal of Accounting Information Systems, 1(1), 9–53. Raihana, G. F. H. (2012). Cloud ERP: a solution model. International Journal of Computer Science, Information Technology & Security, 2(1), 76–79. Rashid, M. A., Hossain, L. and Patrick, J. D. (2002). The evolution of ERP systems: a historical perspective. In H. Liaquat, P. Jon David and A. R. Mohammad (Eds.), Enterprise Resource Planning: Global Opportunities and Challenges. Hershey, PA: IGI Global, 1–16. Reneau, J. H. and Grabski, S. V. (1987). A review of research in computer-human interaction and individual differences within a model for research in accounting information systems. Journal of Information Systems, 2(1), 33–53. Scapens, R. W. and Jazayeri, M. (2003). ERP systems and management accounting change: opportunities or impacts? a research note. European Accounting Review, 12(1), 201–233. Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R. and Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126. Wagner, E. L. and Newell, S. (2004). “Best” for whom? the tension between “best practice” ERP packages and diverse epistemic cultures in a university context. The Journal of Strategic Information Systems, 13(4), 305–328.

23

3 Technologies underpinning Accounting Information Systems Gerhard Kristandl

Introduction It is now widely accepted that Information Technology (IT) has been, and still is, a major driver for accounting to become a knowledge service profession (Granlund, 2011). Accounting Information Systems (AIS) have grown into complex decision-support systems whilst increasing the speed and accuracy of more traditional accounting tasks (Mauldin and Ruchala, 1999). IT impacts the quality of the AIS (measured in terms of scope, timeliness, aggregation, reliability, flexibility and usefulness), which in turn impacts the quality of accounting information (Wisna, 2013). To become the enabling and empowering tool AIS are considered to be, they require technological underpinnings that facilitates their smooth operation (Choe, 1998; Gelinas et al., 2015; Ghasemi et al., 2011; Wisna, 2013). Inadequate technology underpinning AIS can burden the company with extra maintenance and data recovery costs, and issues with data reliability, security and privacy. Thus, inadequate technology can potentially corrupt the very outcomes of an AIS, namely reports and decision-relevant information (Ghasemi et al., 2011), leading to incorrect, unreliable decisions. In the remainder of this chapter, the general relationship between AIS and technology is detailed. Further, networks are revealed as a necessary technological feature of AIS. Then, the components that are aligned to build and run an AIS are discussed, namely hardware and software. As such, this chapter adopts a drill-down approach to illustrate the technologies that underpin modern AIS. It needs to be stated at this point that networks, hardware and software should not be seen as separate classifications – they have to work together to provide a reliable basis for any AIS to perform and provide reports and decision-relevant information, as the technology defines the scope and limitations of what an AIS can and cannot do (Curtis and Cobham, 2005). The users of an AIS (such as managers and accountants) need to be certain that the system works as intended, as information from AIS shape its users’ perception of reality (O’Donnell and David, 2000).

Accounting Information Systems and technology Accounting has experienced many improvements due to the computerisation of accounting processes (Ghasemi et al., 2011). Traditional paper-based ledgers and bookkeeping processes have been automated and mirrored in AIS, which eventually morphed into full 24

Technologies underpinning AIS

decision-making systems (O’Donnell and David, 2000). An AIS in this context is a cohesive organisational structure (Boczko, 2007); a set of processes, functions, interrelated activities, documents and technologies (Hurt, 2016) that captures, processes and reports data, and as such provides information for decision-making and control purposes to internal (Quinn and Kristandl, 2014) and external parties (Hurt, 2016). Historically speaking, an AIS was a specialised subsystem of Management Information Systems (MIS), and thus integrated with other information systems in firms. With the rise of material resource planning (MRP) and subsequently enterprise resource planning (ERP) systems, AIS have become even more integrated with other information systems (Gelinas et al., 2015). This has an important implication for the view taken in this chapter – the technologies that enable AIS to run are the same for other types of information systems. They share the same networks, hardware, software and other components that make up the technological basis for them to be efficiently and effectively operated. This view is in line with Gelinas et al. (2015, p. 14) who explain that the distinctions between separate information systems have become somewhat blurred, and a clear differentiation between IS and AIS has been given up today. As discussed in more detail in other chapters of this companion, an AIS fulfils not only a decision-oriented, but also a controlling function. The cohesiveness of the AIS structure is achieved through prudent, integrated system design and the interactions of the human actors along a network of computerised resources that capture, process and deliver the required information (Boczko, 2007; Gelinas et al., 2015; Ghasemi et al., 2011). These resources can be determined as a collection of computer hardware and software, connected to one another within a network (Ghasemi et al., 2011; Quinn and Kristandl, 2014), and need to be implemented and maintained to support business processes (Gelinas et al., 2015). Although non-computerised AIS exist (Quinn and Kristandl, 2014), modern businesses that employ such a system can rarely do so without the use of computers. In very simple terms, to capture – ideally – all relevant, transaction-based information for accounting purposes in an organisation, the resources need to be linked to one another to input information, send the information to the right addressee (another computer or person) for processing, and finally onwards to the party that requires the processed data for decision-making, reporting or control (including audit).

Networks As of the time of writing, almost every business is connected to and uses networks, particularly the Internet. As of 2014, 97% of enterprises in the EU had access to the Internet, 92% used a fixed broadband connection, and 66% furnished their employees with mobile internet devices for business purposes (Eurostat, 2015). Using mobile technologies such as mobile payment systems and capturing document data (e.g. via a scan of invoices) have a major impact on AIS and the opportunities to collect and report data in real time (Brandas et al., 2015; Trigo et al., 2014). This illustrates the importance and – to some extent – implicitness of networks in today’s corporate environment. The technological view on networks is that of a so-called hard network (as opposed to social soft-type, or the logical, more abstract semi-soft type (see Boczko, 2007). A hard network is the physical representation of a group of devices (e.g. computers and servers) connected to one another via a network interface card (NIC) and wired/wireless links, managed by software allowing data exchange (Hall, 2016). Wired connections (e.g. copper wire, twisted pair, coaxial, fibre optic) connect the various computers in a permanent manner, typically via point-to-point links (Tanenbaum and Wetherall, 2011). Wired connections, once set up, are difficult to reconfigure. Hardware links provide the physical means and infrastructure to enable networking; 25

Gerhard Kristandl

this includes computer-to-computer, computer-to-server, server-to-server or computer-toperiphery (e.g. shared network printers; Quinn and Kristandl, 2014) connections. Wireless networks connect via broadcast links, such as high-frequency radio signals, infrared, electromagnetic signals or laser for short-distance networks; or mobile telephony, microwaves or satellite for long-distance networks (Boczko, 2007; Richardson et al., 2014; Tanenbaum and Wetherall, 2011). Wireless networks provide the advantages of mobility, rapid deployment, flexibility and scalability, low-cost setup and easy maintenance (Boczko, 2007; Richardson et al., 2014). However, they can be limited by the distance to the access point, as well as the number of wireless devices using the existing bandwidth at the same time. Wireless network access typically requires access to a wired intranet or internet, linked via a wireless network card (WNIC). All types of computer networks can be described using the following three attributes (Boczko, 2007), which are now elaborated: • • •

Architecture Topology Protocols

Architecture The architecture describes the technological layout and configuration of a network, and includes the definition of intra-network and inter-network relationships, the physical configuration, the functional organisation, the operational procedures employed, as well as the data used (Boczko, 2007; Magal and Word, 2012). It also determines the geographical distribution of the network resources and the scale of the network (Tanenbaum and Wetherall, 2011). Network architecture can either be described in systems or hardware terms. A typical example of the former is a client-server model (C-S). The purpose of this type of systems architecture is to interconnect and distribute software and hardware efficiently and effectively across a network (Boczko, 2007). Here, a client is a computer or workstation that uses services (programs, applications, data processing), whereas a server is a computer that manages and allocates these services (Curtis and Cobham, 2005; Hall, 2016). Clients require servers to access network resources to process data in an AIS. The C-S model is an example of a multi-tier architecture (see Figure 3.1), where presentation, data/application processing and data storage/management are separated into different layers (see Chapter 4 – Systems planning, design and implementation). Information systems such as SAP ERP are based on a C-S architecture, with only the graphical user interface (GUI) running at the user end. The C-S model is widely used in online commerce, and forms the underlying idea behind cloud computing with the main difference that the data is stored on a server that is owned by a cloud provider instead of the company, accessed via the Internet (Lin and Chen, 2012; Zissis and Lekkas, 2012). An evolution of the standard C-S architecture is service-oriented architecture (SOA), based on the concept of designing and developing inter-operable functions and applications (services) that are reusable (Hall, 2016; Magal and Word, 2012). SOA enables a company to create composite applications such as AIS functions without needing to change the underlying application. This modular approach provides a cost-efficient way to lower cost and complexity during integration programmes, whilst simultaneously offering access to more complex software. Many providers of AIS, such as SAP, have service-enabled their applications to permit businesses to create composite information systems. Where C-S and SOA are examples for systems architecture, hardware architecture supports the distribution of software, data and processes. Typical examples are LANs, WANs and VPNs, which are now discussed. 26

Technologies underpinning AIS

Figure 3.1 Client-server architecture

A LAN (local area network; see Figure 3.2) is a network within geographically close confines, often within the same room or building, privately owned by a single organisation (Quinn and Kristandl, 2014; Tanenbaum and Wetherall, 2011). Within this type of network, computers (nodes), servers and peripheral devices, such as printers, are connected either wired or wirelessly (WLAN) (Richardson et al., 2014). Hubs and switches (see hardware later) interconnect

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Figure 3.2 Local area network 27

Gerhard Kristandl

the devices and send packets (formatted, small units of data; Richardson et al., 2014) over the network. LANs allow use of a common network operating system, centralisation of shared data and programs from a central server, as well as their downloading for local processing, communication of personal computers with outside networks (e.g. the Internet), sharing of scarce resources, email, as well as access to and use of a centralised calendar and diary (Curtis and Cobham, 2005). Sharing resources over a network is cost-efficient since there is less need for large-storage hard disks or programs for every computer within the network. Computers may act as both clients and servers in smaller LANs – such a network is then called a peer-to-peer network (Boczko, 2007) due to the equivalent responsibilities that each workstation fulfils. In larger LANs, workstations act as clients only, and are then linked to a server (server network). A WAN (wide area network; see Figure 3.3) is a network over a larger geographical area (e.g. a country), connected via public (e.g. phone lines) or private (e.g. leased lines or satellite) communication facilities (Hall, 2016; Shinder, 2001). WANs provide remote access to employees or customers, link two or more separate LANs at sites within a company, and provide the business with access to the Internet (Richardson et al., 2014). Depending on the location of a central hub, there are two types of WAN, namely centralised and distributed. In a centralised WAN, all major functions (e.g. accounting, procurement, sales order processing) are carried out at the central hub. The computers in the network do not process transactions locally, but send data processing requests remotely to the central hub (Boczko, 2007; Curtis and Cobham, 2005). All data traffic can be closely monitored, but it puts a heavy burden on the network itself. The central hub needs to queue and prioritise all concurrent requests, rendering the network much more vulnerable to a complete standstill. A distributed WAN, on the other hand, is decentralised in terms of data processing (Boczko, 2007; Curtis and Cobham, 2005), and thus better able to transmit and process individual transactions simultaneously. In a distributed environment, LANs are connected to one another and/or to larger WANs.

Figure 3.3 Wide area network 28

Technologies underpinning AIS

Depending on the type of LANs linked, this is achieved via bridges (linking same-type LANs) or gateways (linking different-type LANs; Hall, 2016). The largest distributed WAN to date is the Internet, and the World Wide Web is – simply put – a WAN that uses a client/(web) server architecture to transmit data and process tasks (Shinder, 2001). Variations of WANs are MANs (metropolitan area networks) and CANs (campus area networks) that can be quite large, but are confined within a city or campus (Shinder, 2001; Tanenbaum and Wetherall, 2011). A VPN (virtual private network) is created using a secure tunnel between a corporate WAN and (home) offices via virtual links over the Internet rather than leased lines (Richardson et al., 2014; Tanenbaum and Wetherall, 2011). VPNs came to prominence due to the larger bandwidth availability, enabling remote access to corporate WANs from outside the business premises, such as salespersons, home offices and business partners that require access. Companies that use cloud technology for their networks are particularly in need of secure access points, which a VPN provides. This type of network provides a cheap way of connection, but due to their use of the Internet, suffer a lower quality-of-service (QoS) than corporate WANs (Richardson et al., 2014).

Topology The term topology denotes the shape of a network, determining how network devices are connected to one another, and how they communicate (Boczko, 2007). Figure 3.4 shows the most common topologies utilised in networks.

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a. linear bus topology

b. Daisy-chain bus topology

e. Star topology

f. Hybrid star-bus topology

Figure 3.4 Network topologies 29

Gerhard Kristandl

A bus topology is a linear topology where clients share a central line connection (Boczko, 2007; Hall, 2016), either linear or in a daisy-chain (see Figure 3.4 a and b). When data is sent along the network, it contains a unique network address for the desired destination, and will thus be delivered to the correct network resource. Although a bus topology is easy to set up and extend, the connected devices are competing for connection resources, as only one line is available to them (Hall, 2016). In cases where two or more clients want to use the network at the same time, this might lead to queuing and slow operation of the network – a situation that is exacerbated by every additional node added to the bus. Thus, this topology is limited in size, as it may become difficult to operate and manage (Boczko, 2007). In a ring topology, each node is connected to two other nodes, and represents a peer-to-peer arrangement (see Figure 3.4 c). As opposed to a bus topology in a daisy-chain configuration, a ring topology creates a closed loop of nodes, meaning that if a signal is sent along the network, and no destination node accepts it, it returns to the sending node (Boczko, 2007; Hall, 2016). Each node has equal status, but only one node can communicate at a time. Unlike a bus topology, the nodes along a ring topology move the signal along rather than ignore it. This improves network speed; the scalability of the network is also superior to a bus topology, as additional nodes do not significantly impact network speeds. It requires more connections however than a bus network, is more costly to implement, and if one single node fails, it will impact the entire network. Both ring and bus typologies have become side-lined in favour of the more stable star typology (see below; Quinn and Kristandl, 2014). A mesh topology (see Figure 3.4 d) is a variation of the bus topology, where every node is connected with every other node (Boczko, 2007). Although providing a more stable and reliable network than the ring topology, especially for small networks, its complexity increases considerably when the network grows. This in turn can render network management and reconfiguration difficult and costly (Boczko, 2007). Mesh topologies are often used in WANs to connect various LANs to one another. In a star topology (see Figure 3.4 e), all devices are linked to a central computer which acts as a transmission device (Boczko, 2007; Hall, 2016). In this case, signals are transmitted via the central host computer rather than along the entire network. It is relatively easy to implement, extend and monitor, and if a device fails, it typically does not have an impact on the entire network (Quinn and Kristandl, 2014), unless the central computer fails. Other disadvantages lie in higher costs (maintenance, security) and higher risk of virus infection (as all data runs via the central hub; Boczko, 2007). The star topology is often used in both centralised and distributed WANs where the central host computer is a mainframe (Hall, 2016). The topologies above can be combined based on business needs. Examples of such hybrid topologies are star-bus (or tree) or star-ring (or token ring) topologies (Boczko, 2007) that aim to combine the advantages and eliminate the drawbacks of their individual contributors. Figure 3.4 f shows an example of a star-bus topology that is easier to extend and more resilient than a pure bus topology.

Protocols Without instructions to manage the communication and flow of data between the devices, the network that an AIS is running on would merely be a physical arrangement of computers and cables. A network requires protocols – formalised and uniform set of rules and standards that govern the syntax, semantics and synchronisation of communications between nodes – to enable network devices to communicate (Hall, 2016; Quinn and Kristandl, 2014). AIS offerings need to comply with these standards – they define the formal rules of conduct and etiquette to avoid misinterpretation and discord. 30

Technologies underpinning AIS

Hall (2016) states that: Establishing a standard of conduct through protocols, which all members of the community understand and practice, minimizes the risk of miscommunications between nations of different cultures. p. 505 Standardised reference models for network protocols help ensure that interconnection is achieved in a seamless manner. The Open Systems Interconnection (OSI) standard is a model of seven-layered protocols (Tanenbaum and Wetherall, 2011) that define standards that govern protocol development and intra-layer and inter-layer protocol communication. This model (see Table 3.1) also provides a clear distinction between data manipulation (layers 1–4) and data communication (layers 5–7). Table 3.1 The OSI and TCP/IP reference models

7 6 5 4 3 2 1

OSI

TCP/IP

Application Presentation Session Transport Network Data link Physical

Application Not present in model Not present in model Transport Internet Link Not present in model

Source: Adapted from Tanenbaum and Wetherall, 2011, p. 46

The OSI model is not commonly used today, and has been criticised for being flawed, too complex and inefficient. Instead, the four-layered TCP/IP (Transfer Control Protocol/Internet Protocol; see Table 3.1) reference model is considered more practical and pragmatic (Tanenbaum and Wetherall, 2011), avoiding many issues that the OSI model brought with it.1 Many of the better-known network protocols today, come from the TCP/IP model, such as: • • • • •

Ethernet (link layer); Internet Protocol (network layer); TCP (transport layer); Hypertext Transfer Protocol (HTTP, application layer); or FTP (File Transfer Protocol, application layer).

Tanenbaum and Wetherall (2011) emphasise that in spite of its criticism, the OSI model is still very valid for today, although many of its protocols are not in use anymore. For the TCP/IP model, the reverse is true: whilst the model is rarely used, the protocols are widely employed.

Hardware Hardware in IT comprises all physical computing machinery and equipment used to capture, process and store data (Curtis and Cobham, 2005). This includes computers and their components (e.g. keyboards, disk drives, etc.) and servers, but also cloud-enabled devices such as tablets and smartphones (Quinn and Kristandl, 2014). A computer can be defined as a workstation that 31

Gerhard Kristandl

provides a network-human interface; an access point to the AIS for both input and output of the required accounting data. The role of a server (see also Architecture earlier) in a network is to process and manage the flow of information between the nodes, and allocate processing resources to the task at hand. From a common systems model point of view, a computer comprises (Curtis and Cobham, 2005): • •

• •

Input devices that accept, convert and transmit data; A central processing unit (CPU) that executes program instructions, controls and coordinates data movement, and carries out arithmetic and logical operations whilst storing programs and data; A secondary (backing) storage that maintains a permanent record of data and programs beyond execution and for security; Output devices that receive information from the CPU and convert it into the required format.

This common systems model as illustrated by Curtis and Cobham (2005) can be detailed further. Table 3.2 lists examples of hardware that are typically present in an individual computer. However, from an organisational perspective where a higher degree of computing and communications power is required, it can be separated into individual devices which are connected via network links (Quinn and Kristandl, 2014). The connections between nodes and servers require communication devices (see below) that create and manage these links, e.g. network cards, repeaters and hubs (Boczko, 2007; Richardson et al., 2014). As discussed earlier, these connections can either be wired or wireless. Input devices accept data, convert them into a machine-readable form, and transmit them within a computer system (Curtis and Cobham, 2005). Keyboards are a typical input device, where information is entered into the system and converted into binary code whilst being shown on a screen. Other input devices (see Table 3.2) also capture the initial data entered into the system. Scanners, for instance, are a widely used device, using technology like optical character recognition (OCR) or magnetic ink character recognition (MICR) to identify relevant data Table 3.2 Examples of computer hardware Hardware Type

Function/s

Examples

Input devices

To input/capture data

Keyboard, touchscreen, mouse, barcode reader, microphone, pointing devices, scanners

Processors

To performs calculations, to execute tasks

CPU, Intel range of microprocessors, A5 chip (Apple iPhone)

Storage devices

To retain data when power is switched off

Hard disks, CD/DVD/Blu-ray disks, USB sticks

Output devices

To provide/display data in an understandable and useful format

Display screens, printers, speakers

Communication/ network devices

To allow network devices to communicate and exchange information with each other across networks

Routers, cabling, switches, network interface cards (wired/wireless), hubs, firewalls, modems

Source: Adapted from Quinn and Kristandl, 2014, p. 15

32

Technologies underpinning AIS

from a source document. OCR is often used by utility companies, credit card companies or government departments. Documents to be scanned using OCR typically come with specific instructions on how to fill in the data, such as writing in capital letters, black ink and within a confined box; this is to enable the OCR to correctly identify the characters written (Curtis and Cobham, 2005). A common use of MICR is in processing cheques in banks, where the cheque number, account and sort codes are written in magnetic ink on the cheque. Barcode readers are another widespread type of input device, particularly in logistical processes to record the movement of goods. A good example of an industry that relies on data input via barcode scanners is food retailing (e.g. supermarkets). Voice recognition via microphones are also widely used for data entry, for instance in call centres or customer service to screen and route calls. Lastly, pointing devices such as a mouse is a commonplace feature in computers nowadays. These various types of input devices have advantages and disadvantages related to accuracy and cost. Keyboards, for instance, are by and large inexpensive input devices, but are subject to error, since data is typically entered by a person (Curtis and Cobham, 2005). At the same time, data entry can be quite slow when keyboards are being used – this is different with scanners or barcode readers where data entry is quick and less prone to error, but this comes with the disadvantage that they are costlier when acquired and operated. Processors enable a computing device to decode and execute program instructions, control and coordinate data movements, and perform arithmetic and logical operations (Curtis and Cobham, 2005; Quinn and Kristandl, 2014). Examples of processor manufacturers are Intel (Pentium), AMD or Apple (A5 chip). Processors comprise the arithmetic and logic unit for calculations and data comparisons, and the control unit for data movements. Together with the main memory unit (random-access memory, RAM), used for storage of currently used data/ programs and the operating system, processors are the heart of the computing functions of hardware (Curtis and Cobham, 2005). The history of processors has shown an exponential increase in processing power, which in turn allows for quicker program execution and larger RAM for program-multitasking in computers today. Storage devices serve to maintain the input and processed data as well as programs on a permanent basis (Curtis and Cobham, 2005), for immediate or later use. Storage devices can also provide backup for data in case of security and integrity issues. As opposed to the main memory, where the CPU only stores data whilst the computer is switched on, storage devices hold the data even if powered off. Different types of storage devices differ in speed of data retrieval, capacity, cost and robustness. Hard disks are a typical storage device in most computers systems. Types of hard disks are magnetic drives (hard drive disk, or HDD, where a laser records the data on and reads it off the disk), optical disks (Blu-ray, DVD/CD), flash drives (USB sticks, external drives) or more recently, cloud storage where the stored data is accessed via the Internet. The latter type in particular experienced a rapid increase in usage, as it allows not only large organisations, but also small and medium-sized companies to acquire and operate hitherto unaffordable AIS technology (Brandas et al., 2015). Older types of storage devices such as magnetic tapes or floppy disks still exist in some organisations (such as the US Nuclear Weapons Force, see BBC, 2016), but nowadays do not feature in modern AIS technology. Output devices are used to display information in the required format. Typical examples are computer monitors, tablet and smartphone screens, printers and speakers (Curtis and Cobham, 2005; Quinn and Kristandl, 2014). Screens in general are the most common type. They are either connected to a desktop computer, embodied in laptops, tablets, smartphones, machinery, vehicles – the Internet of things has enabled internet connectivity to the most unusual devices, and thus creates more opportunity for collecting data (Mazhelis et al., 2012). Printers are another output device that issue information by means of laser, ink, dot-matrix or thermal printing 33

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technology (Curtis and Cobham, 2005). Larger types of printers are plotters, chain and drum printers, but these are mostly attached to larger mainframe computers and do not provide the quality and flexibility suitable for AIS information output. Of course, hard disks can also serve as output devices if the information stored is later used as an input in another computer system. In this case, the information is not issued to an end-user. Communication/network devices are hardware that allow network resources to interconnect with one another. As discussed earlier, the actual connection between a node and network is either wired (cabling) or wireless (NIC). However, the cabling or connection alone is not enough – data that is transmitted along the network needs to find the right address. This is done by a switch, a specialised computer that determines an outgoing line for incoming data, sending it to the right address (Richardson et al., 2014; Tanenbaum and Wetherall, 2011). Routers act in a similar manner to switches inasmuch as they choose the most efficient communication path through a network to the required destination (Richardson et al., 2014). Routers use the Internet Protocol (IP) address of both the sender and the receiver of the data, and decide which path is the most direct. Where switches and routers determine where an incoming data packet needs to go, hubs merely transmit them. Packets that arrive at one port are copied to all other ports, so that all other equipment connected to the LAN receive the packet (Richardson et al., 2014). Networks that use hubs instead of switches are called non-switched networks, where communication links are shared by all devices (Curtis and Cobham, 2005). Typically, the performance on switched networks is higher than non-switched, as there are no data collisions, data can be transmitted simultaneously, and the capacity is used more efficiently (Tanenbaum and Wetherall, 2011). Not just from a performance, but also a data security point of view, switched networks are preferable, as data traffic is only sent to the address where it is required (Tanenbaum and Wetherall, 2011). They are also more efficient to monitor, as corporate firewalls are a security system comprising hardware like switches, routers, servers and software, to allow or deny a data packet that enters or exits a company LAN to continue on their transmission path (Richardson et al., 2014).

Software Although hardware and networks are essential in enabling a smooth-running and purpose-driven AIS, without software it would not work. Software is the general term used to describe the instructions that control the operations of hardware (Curtis and Cobham, 2005; Quinn and Kristandl, 2014).2 Software can either be categorised as operating systems (OS), database systems or applications software, and requires the use of programming languages to design and create them.

Operating systems software This type of software comprises programs that enable an efficient and smooth operation of the computer system (Curtis and Cobham, 2005), and is considered the “most important piece of software” (Richardson et al., 2014, p. 242). It is the basis for the hardware to function, controls the flow of multiprogramming, schedules tasks and provides a way for applications software (see below) to work with the hardware. It further allocates computer resources to users and applications, and manages the human-computer interface and access points to the network. An OS provides the following four functions (Curtis and Cobham, 2005): • • 34

Handling of data interchange between input/output devices and the CPU; Loading of data and programs into and out of the main memory;

Technologies underpinning AIS

• •

Allocating main memory to data and programs as needed (managing processes and memory, so that all programs receive a share of the available resources); Handling job scheduling, multiprogramming and multiprocessing.

Examples of OS available on the market are Microsoft Windows, Apple OS, Linux, Unix, Chrome and Android and iOS for mobile devices. Note that not all of these incur acquisition costs – Linux distributions like Ubuntu and openSUSE are free, whereas Windows may have a cost based on the licence model. All of these OS provide a Graphical User Interface (GUI) as opposed to text-based interfaces that require the entry of command lines to work with the system – an example is MS-DOS which has been superseded by the more user-friendly Windows systems. Due to its crucial role in the smooth running of an AIS, the OS needs to be secured against internal and external threats to its integrity. This includes intended or unintended security threats by users, computers, applications, the OS itself, as well as hacking or data leaks of sensitive information to the outside (Richardson et al., 2014). As such, the OS requires clear IT governance policies that control who can access the system, system and network resources, and actions that are allowed by users. These security features are even more relevant in a cloudcomputing environment, where several “virtual” (rather than actual) OS share the same hardware (such as the same server), and could potentially become permeable, allowing access to resources between two instances of an OS running on the same platform.

Database systems software In an integrated AIS, corporate accounting data is typically stored on a central database to ensure that all relevant applications access the same kind of information when processing data. As such, databases are another crucial component in an AIS, and require a database system that is able to record, manage and store a massive amount of day-to-day accounting data. A database system comprises two main software components, namely a data warehouse (a centralised collection of company-wide data for a long period of time), and operational databases that draw data from the data warehouse (Richardson et al., 2014). Operational databases contain the data for current/ recent fiscal years, updated whenever a transaction is processed. Periodically, data is uploaded from the operational databases to the data warehouse to provide decision-useful information to identify trends and patterns – the process of analysing them as such is called data mining, using Online Analytical Processing (OLAP).

Applications software Applications software are programs that fulfil specific user functions (Quinn and Kristandl, 2014). In an accounting context, this may mean functions like sales ledger processing, budgeting, forecasting or reporting (Curtis and Cobham, 2005). During their execution, programs and the data required are stored in the RAM. Most AIS are offered as applications packages that include functional modules such as sales ledgers, accounts receivable, accounts payable, payroll, credit and payment systems. These desktop accounting packages are offered and distributed by ERP software providers such as SAP or Oracle, or mid-level accounting package providers such as Sage. The same accounting packages are also provided via a subscription-based cloud offering by the same companies. If applications packages are unable to fulfil a specific business need, a company could commission development (or develop it using their in-house resources). However, such specific 35

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software developments require lengthy analysis, design and testing phases (Curtis and Cobham, 2005). Specially commissioned software can become very costly as opposed to applications packages; if specially commissioned, the cost of software development, testing and subsequent updates needs to be absorbed in full by the commissioning business (Curtis and Cobham, 2005). Further considerations stem from the frequent requirement to run the commissioned software on different types of hardware (portability), and the existence of professional documentation that enables adequate IT support. On the other hand, specially commissioned software provides a perfect fit to the corporate requirements; this includes the elimination of redundant functions that may not be needed (but paid for), and the compatibility with existing specially commissioned software (Curtis and Cobham, 2005). Whether a company is able to commission specially designed AIS software is more often than not a question of affordability, which by and large rules out the ability of smaller businesses to commission them. Interestingly, it appears that most companies prefer packaged software to self-developed or commissioned ones (Granlund, 2011). To avoid compatibility issues between programs, businesses often acquire entire software suites, where several programs are integrated and sold together. Suites provide inter-program compatibility in data exchange and user interface – good examples are Microsoft Office (e.g. Word, Excel, Access, Outlook) or the SAP Business Suite (containing ERP, Customer Relationship Management, Supplier Relationship Management, Supply Chain Management, Product Lifecycle Management).

Programming languages Software is written as a set of instructions to control computer operations. These instructions are written in a formal language to communicate them to the computer – a programming language. Table 3.3 details in brief three main categories of programming languages, as well as their characteristics (Curtis and Cobham, 2005): Machine-oriented programming languages have been by and large superseded by higher-level ones due to their dependence on the source program and the computer architecture it is written on/for. Task-oriented languages that require compiling can be used over and over, independent of the source program, and provide portability between computer systems and architectures, as well as a certain level of security against tampering with the compiled code (Curtis and Cobham, 2005). However, task-oriented language programs operate slower due to inefficiencies in the Table 3.3 Programming language categories Category

Characteristics

Examples

Machine-oriented

Written in strings of 0s and 1s, either directly or via mnemonics (assembler), for the specific architecture the program is run on

Machine code, assembly languages

Task-oriented

Written for task rather than machine; relatively straightforward to learn using Near-English expressions; requires translation into machine code using an interpretation or compiling process

COBOL, FORTRAN, C

Object-oriented

Using objects with properties that stand in relationship to other objects and interact with them

C++, C#, Java, PHP, Python, Perl, ABAP Objects

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compiling process, as well as the exclusion of the individual CPU structure. Object-oriented programming languages (OOPL) apply a logic that is different to the previous task-oriented ones, in that they do not define complex operations, but rather the objects and their (changeable) attributes that take part in the operations. OOPLs produce a more natural way of reflecting the real world, objects that are reusable (saving programming time and complexity) and a simpler syntax (Curtis and Cobham, 2005). A good example for an OOPL used in AIS is ABAP Objects, the programming language of the SAP ERP software.

Summary This chapter detailed the technological underpinnings of AIS that are predominantly the same as for general corporate information systems. It detailed and discussed the technological perspective on networks, hardware and software that enable an AIS to record, process and display accounting information for reporting and decision-making. A main emphasis was placed on computer networks that not only enable AIS to record transactions in any part across a company, but also furnish the business with the computing power needed to process large amounts of data. Connected within a network are hardware that provides the physical resources, as well as software that enables the smooth operation of an information system. Common standards like protocols facilitate the inclusion of various accounting software packages to create an efficient technological environment for running an AIS. The future of AIS will continue to be inextricably linked to their technological underpinnings. With cloud computing offering processing power hitherto unavailable to many businesses (Strauss et al., 2015), the spread of AIS will increase with technological developments at an unprecedented growth rate. Smaller businesses will be able to employ AIS as efficiently as larger businesses have been able to for decades. At the same time, new developments like SAP HANA will enable business analytics based on the information in AIS for businesses of all shapes and sizes. Data can be gathered from more than just the typical user input, but from internetenabled devices that were not linked to networks before – the Internet of things will provide businesses with data from the most unusual of places, to be used for accounting purposes (Mazhelis et al., 2012). Finally, the rise of mobile smart devices enables decision-makers to access and retrieve accounting information from their AIS on the go, at any time, in any place. As such, the technological future of AIS seems a promising and bright one.

Notes 1 The details of OSI v TCP/IP is beyond the scope of this chapter. For a detailed discussion, please see Tanenbaum and Wetherall, 2011. 2 The term firmware also exists, indicating an inseparable combination of hardware and software, it being a set of instructions that is permanently encoded on a microchip.

References BBC (2016). US Nuclear Force Still Uses Floppy Disks. Retrieved June 16, 2016, from www.bbc.co.uk/ news/world-us-canada-36385839. Boczko, T. (2007). Corporate Accounting Information Systems. Harlow, UK: FT Prentice Hall. Brandas, C., Megan, O. and Didraga, O. (2015). Global perspectives on accounting information systems: mobile and cloud approach. Procedia Economics and Finance, 20, 88–93. Choe, J. M. (1998). The effects of user participation on the design of Accounting Information Systems. Information & Management, 34(3), 185–198. Curtis, G. and Cobham, D. (2005). Business Information Systems: Analysis, Design and Practice, 5th ed. Harlow, UK: FT Prentice Hall. 37

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Eurostat (2015). Information Society Statistics: Enterprises. Retrieved June 16, 2016 from http://ec.europa.eu/ eurostat/statistics-explained/index.php/Information_society_statistics_-_enterprises. Gelinas, U. J., Dull, R. B. and Wheeler, P. R. (2015). Accounting Information Systems, 10th ed. Stamford, CT: Cengage Learning. Ghasemi, M., Shafeiepour, V., Aslani, M. and Barvayeh, E. (2011). The impact of Information Technology (IT) on modern accounting systems. Procedia Social and Behavioral Sciences, 28, 112–116. Granlund, M. (2011). Extending AIS research to management accounting and control issues: a research note. International Journal of Accounting Information Systems, 12(1), 3–19. Hall, J. A. (2016). Accounting Information Systems, 9th ed. Boston: Cengage Learning. Hurt, R. L. (2016). Accounting Information Systems: Basic Concepts and Current Issues, 4th ed. New York: McGraw-Hill Education. Lin, A. and Chen, N.-C. (2012). Cloud computing as an innovation: perception, attitude, and adoption. International Journal of Information Management, 32(6), 533–540. Magal, S. R. and Word, J. (2012). Integrated Business Processes with ERP Systems. Hoboken, NJ: John Wiley & Sons. Mauldin, E. G. and Ruchala, L. V. (1999). Towards a meta-theory of accounting information systems. Accounting, Organizations and Society, 24(4), 317–331. Mazhelis, O., Luoma, E. and Warma, H. (2012). Defining an internet-of-things ecosystem. In S. Andreev, S. Balandin and Y. Koucheryavy, (Eds.), Internet of Things, Smart Spaces, and Next Generation Networking. Berlin: Springer, 1–14. O’Donnell, E. and David, J. S. (2000). How information systems influence user decisions: a research framework and literature review. International Journal of Accounting Information Systems, 1(3), 178–203. Quinn, M. and Kristandl, G. (2014). Business Information Systems for Accounting Students. London: Pearson. Richardson, V. J., Chang, C. J. and Smith, R. (2014). Accounting Information Systems. New York: McGrawHill Education. Shinder, D. L. (2001). Computer Networking Essentials. Indianapolis, IN: Cisco Press Core Series. Strauss, E., Kristandl, G. and Quinn, M. (2015). The effects of cloud technology on management accounting and decision-making. CIMA Research Executive Summary Series, 10(6). Tanenbaum, A. S. and Wetherall, D. J. (2011). Computer Networks, 5th ed. Boston: Pearson Education. Trigo, A., Belfo, F. and Estebanez, R. P. (2014). Accounting Information Systems: the challenge of the real-time reporting. Procedia Technology, 16, 118–127. Wisna, N. (2013). The effect of Information Technology on the quality of Accounting Information System and its impact on the quality of accounting information. Research Journal of Finance and Accounting, 4(15), 69–75. Zissis, D. and Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592.

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4 Systems planning, design and implementation Noel Carroll

Introduction Information systems (IS) are developed for different purposes, largely depending on human and business needs. The variety of systems that may be developed range from, for example, operational level transaction processing systems (TPS), to managerial planning control level using office automation systems (OAS), to strategic level using decision support system (DSS). In addition, enterprise resource planning (ERP) is an important system type that provides integrated applications to manage the business and automate many back-office functions related to technology, services and human resources. The key point is that different IS serve different purposes, often directed at various management levels within an organisation. As users adopt new technologies, system analysts are typically tasked with integrating traditional systems with new solutions, e.g. web systems, cloud-based systems or wireless systems. The systems analyst uses analysis and design techniques to solve specific business problems using IT solutions. With any new system, there are several questions which managers may question, for example: How many systems development approaches are there to addressing our core problem? What is the best approach to implement AIS? How can it be ensured that system output will meet user needs? Where should AIS data be stored, e.g. on-site or in “the cloud”? How should data be organised and accessed? What privacy concerns could our customers have with different data access options? Should mobile accessibility options be considered? These are merely a sample of questions managers may explore with key stakeholders and with systems analysts. To address some of these questions this chapters examines AIS system development approaches. Systems analysts systematically assess how users interact with existing or new business technologies while identifying specific functions, for example, accounting and user needs to gather and process data to support the organisational strategy. Any change to an organisational information system requires a systematic methodology to support the design, planning and implementation of a new system. In this chapter, we examine the systems development lifecycle to identify how it guides the design, planning and implementation of AIS.

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Accounting Information Systems (AIS) AIS has been well documented in the literature (for example, Hall, 2012; Rom and Rohde, 2007; Moscove et al., 1998; Gordon and Miller, 1992) and defined in numerous ways, all sharing many commonalities. Here, AIS is defined as a specialised subset of an organisational information system that accumulates, classifies, processes, analyses and communicates relevant financial transaction information to internal and external parties and supports management in decisionmaking tasks. Thus, AIS are designed to facilitate both financial and non-financial analysis and reporting by supporting various accounting tasks. Whatever the key focus is of an AIS client (i.e. financial and non-financial data), this does not largely affect the systems development but rather informs the system requirements and the desired functionality of the AIS. AIS have three basic functions: 1 2 3

Efficient and effective collection and storage of financial data and activities; Ensure controls are in place to accurately record and process data; Supply accurate and timely information to support decisions through various reports and financial statements.

Considering the importance of accurately collecting, storing and processing financial data, adopting an AIS automates and streamlines reporting and data mining capabilities of an accounting function. An AIS also ensures that managers can accurately summarise data and present it in an improved manner to support decision-making. Extracting information from a large volume of data in a database through the process of consolidation is critical to ensure analysts and key decision-makers can easily consume information. To understand “the system” structure for applications, such as an AIS, a good starting point is the various layers which communicate with each other to offer various functionalities on separate servers, computers, networks and remote locations. These layers include presentation, business, service and data (see Figure 4.2). The desired functionality of the AIS influences the design, planning and implementation processes.

Aligning people, processes and technology There are three key factors to the success of AIS to drive organisational strategy: people, processes and technology (Figure 4.1). AIS researchers often examine how accounting and IT systems are designed (technology) to support strategic and operational decision needs (people) that are necessary for the implementation of an organisation’s strategy (processes) (Macintosh and Quattrone, 2010; Hall, 2012). In fact, AIS can provide a structure that reflects how an organisation uses technology to collect, store and analyse financial data. These three factors have a significant impact on the type of AIS an analyst designs, plans and implements. It also provides insight to influence how the AIS best aligns with the organisation’s operations, strategy and required return-on-investment. As part of the AIS design phase, systems analysts need to examine human-related problems or barriers towards completing key tasks and enhancing productivity that contribute to business success. For example, addressing barriers towards communication and customer relationships, data gathering, data monitoring and analysis may be areas which present opportunities to support accounting practices. Understanding the end-user requirements and the business needs is key to finding a balance between people, processes and technology. 40

Systems planning, design and implementation

Technology

Figure 4.1 Factors which influence the success of AIS

AIS business processes A business process can be described as a structured set of activities designed to achieve a specific business goal or objective. Within the business and IS domain, there has been considerable research and development on improving business processes – for example, business process engineering and business process management (Hantry et al., 2010). Thus, defining how specific processes align with a business strategy to deliver a required organisational output is a key factor for the implementation of AIS. Specifically, understanding how the AIS brings about improved structures and procedures to execute key processes allows a business to enjoy improved efficiencies, transparency, visibility, audibility and overall improved business performance. This requires end-users to understand the purpose of business processes to identify how AIS improve the likelihood of achieving specific goals and enables key stakeholders to prioritise specific aspects of the AIS, e.g. cost, time, design or functionality. This also provides greater clarity in balancing business and software requirements and how the systems analyst begins to define the AIS design and supports the requirements analysis phase (discussed later). Finally, understanding how the technology can support business processes requires technical knowledge on how the technology capabilities are tailored to achieve business goals and non-technical knowledge on the success and credibility of technical solutions from case studies or testimonials that support evidence of successful implementation. Technical knowledge examines core areas such as information flows, system architecture, hardware and software platforms and examining how the AIS needs to align with the organisation’s people and processes. Therefore, the advantages and disadvantages of various AIS solutions should be discussed with key stakeholders with particular emphasis on how they will impact people, processes and technology to achieve defined benefits within a business and specifically in various functions. Accounting is one such function that requires solutions, such as AIS to support key processes of measurement, processing and communication of financial information within a business.

AIS application layers The AIS application layers (Figure 4.2) provide a logical view on the groupings of key software components that make up a system. Each layer has a specific purpose to execute various processes. This supports the developers’ ability to differentiate key system requirements and identify how components are reusable. This also influences the overall design process of the AIS. The discrete 41

Noel Carroll

u. .

Figure 4.2 Overview of system layers

components of an AIS are grouped into sub-layers to perform specific tasks and to provide a business service which ultimately becomes the AIS design blueprint. Identifying the functionalities for each layer of the system enables developers to optimise the system deployment and influences design decisions. At an abstract level, the logical architecture view illustrated in Figure 4.2, indicates a set of cooperating services grouped into the three key layers of the AIS: 1

2 3

Presentation: comprising the user interface components to deliver user-orientated services, which enables user interaction with a system. This layer acts as a bridge to the core business services. Business: comprising the business workflows, business components, business entities to implement the core functionality of the system and execute key business processes. Data: comprising data access components, data utilities, service agents which are accessed to support the business layer typically access through defined business services.

Each layer of an application contains various components that play a role in the functionality of that layer. Across all three layers are crosscutting services including security, operational management and communication services. For example, identifying and clarifying the nature of rules and routines in a management accounting context (Quinn, 2011) suggests what may be useful for less formalised (or, less rules-based) organisations. Capturing both the formal and 42

Systems planning, design and implementation

informal concept of routines within an organisation is important to influence the design and functionality of AIS. To support the development of AIS, there are a number of key phases the systems analyst and software development team will complete. For example, the analyst will have to undertake a detailed requirements analysis to identify the core problems and capture both how the current system operates and what desired process change and functionality is required by the organisation, from end-user data entry to the senior management level of the organisation to support decisions. The analyst will propose a systems design that requires a thorough analysis of the current system, processes and business rules with a view of optimising processes through the design, planning and implementation of a new AIS. The analyst must identify what data is required, any processing algorithms and reporting formats. Suitable internal controls must be made available to management for key decision-making tasks whereby accounting information is clear, accurate, meaningful, reliable and timely. Therefore, a system must capture all current transactions in order to make information available to managers online. When all of the AIS requirements are clearly identified, a request for proposal (RFP) is typically submitted to a bidding process, whereby a software development company submits their development proposals. In most cases, the software vendors will be required to provide demonstrations of the product prototype to meet the needs of the organisation. It is important to maintain proper documentation of the design process, procedures and various key processes. This is vital to the success of the AIS. The documentation supports the end-users learning about the system’s capabilities and functionalities. The design process must be clearly documented to describe various processes and procedures to guide the implementation and adoption process including software instructions to support training activities. The next important phase comprises testing processes from input to output and is critical to ensure that the software product functions are correct and consistent with the customer’s requirements. Reports from the testing phase must be reviewed and verified supporting traceability for future system audits. There must be adequate training and support to ensure that the end-user can successfully execute new processes and procedures. Training must be provided to ensure end-users can operate the new system and operate various functionalities of end-to-end processes. Additional support must be provided in a flexible manner online or via telephone. After some negotiation with various software developers, an agreement is formed to deliver an AIS to meet organisational needs within a specific timeframe and budget. The software development company will then commence work on the solution through the systems development lifecycle (SDLC). Alternatively, systems development may be an internal function within the organisation. Depending on the size of the company, financial resources to support software development and the availability of internal technical staffing resources, internal software development is often the most preferred option. In many cases, however, internal software development of an AIS is less common as organisations may prioritise to need to focus on enhancing their competitiveness through the optimisation of business processes rather than consuming resources on AIS development.

Developing AIS within a system development lifecycle In recent years, organisations have increasingly adopted cloud-based AIS to enjoy the benefits of reduced cost and improved accessibility. In other cases, organisations will seek system analysts to design, plan and implement bespoke AIS. This requires a systems analyst to develop an understanding of the organisation and identify what the ideal configuration of the system components are to align with an organisation’s operations. This requires the analyst to undertake a number of key steps (Figure 4.3) within the system development lifecycle (SDLC). 43

Noel Carroll

. . . Planning

Deployment

\

I Testing

\

Defining

Building

......... 4

I Designing

Figure 4.3 System development lifecycle model

The SDLC framework defines steps within the software development process. The framework is guided by ISO/IEC 12207 – an international standard for software lifecycle processes which defines the tasks required for developing and maintaining software. Thus, the SDLC provides a process used by the software industry to design, plan and implement software systems such as AIS. Through the use of the SDLC, software developers are guided to produce quality software that meets clients’ requirements and achieves business goals. The SDLC provides a detailed plan to develop or alter software through a specific methodology while maintaining quality standards throughout the development process. As illustrated in Figure 4.3, the SDLC comprises the following key stages to develop AIS: • • • • • •

Stage 1: Planning and requirement analysis Stage 2: Defining requirements Stage 3: Designing the AIS architecture Stage 4: Building the AIS Stage 5: Testing the AIS Stage 6: Deployment in the market and maintenance of the AIS

Each of these stages is now detailed to identify the role they play in the design, planning and implementation of AIS.

Stage 1: Planning and requirement analysis Gathering and analysing requirements is considered to be the most important stage of the SDLC as it provides the very foundation that the entire AIS project will be based on. This phase requires input from senior stakeholders, market analysis and a feasibility study to determine the scope of a project. There are typically several key influences on project development, including regulation, economics, business goals and technology trends – for example, consider how the emergence of cloud computing solutions (Lynn et al., 2014) and data regulations have influenced planning and 44

Systems planning, design and implementation

requirements analysis of AIS. Ensuring that quality standards are adhered to through the identification of risks is also factored into the planning and requirements analysis stage. Therefore, finding a balance between business and technical requirements while mitigating risks is a key part of this development stage. This leads on to the second phase: defining requirements.

Stage 2: Defining requirements Having gathered information from key stakeholders during the requirements analysis process, it is critical that the systems analysis begins to define and document the AIS requirements. Through the identification of common themes and the prioritisation of key stakeholder requirements, the analysts will record requirements on a Software Requirements Specification document. This document will list all requirements that must be met in the design and development stages of the SDLC. Building on the requirements analysis process that studies and analyses the customer and the user needs to arrive at a definition of the problem domain and system (and software) requirements, the key outcome is to detect and resolve conflicts between (user) requirements and negotiate priorities of key stakeholders. This can be achieved using various requirements modelling techniques to specify AIS requirements by elicitation to a more structured approach. Modelling languages such as Goal-Oriented Modelling (GRL), Unified Modelling Language (UML), Systems Modelling Language (SysML) or Use Case Maps (UCM) can be employed to support this process.

Stage 3: Designing the AIS architecture The Software Requirements Specification document acts as a vital reference source to guide the AIS product architects on the most suitable solution for the client. For example, the Software Requirements Specification document describes the key factors as per Table 4.1 overleaf. While this is merely for demonstrative purposes to highlight the importance requirements play in influencing the final AIS product, there would typically be more than one design approach proposed and documented in the Design Document Specification. To confirm that the requirements align with the organisation’s needs, the Design Document Specification is reviewed by key stakeholders to ensure that the project is within budget, can be delivered on-time, the system is robust, the system does not present any risks to existing business operations and that the design is acceptable and meets their expectations. Specifically, the design process defines the architectural components that prescribe for various communication and data flows of the AIS.

Stage 4: Building or developing the AIS The fourth stage focuses on how the AIS is built and emphasises the importance of having a quality Design Specification Document to reference and guide the development process. As the phase suggests, this largely involves generating programs to develop the product as specified by the Design Document Specification. The programming language used to develop the product is typically influenced by the software requirements.

Stage 5: Testing the AIS Testing is a critical process throughout each of the key phases of the SDLC. Within this specific phase requires testing on various AIS defects through tracking, reporting, fixing and retesting in accordance with software quality standards. 45

Noel Carroll Table 4.1 Outline of a Software Requirements Specification document Focus

Key Document Components

Introduction to the document and product

Describe the purpose of the document Describe the target audience Outline the product scope

Description of key product details

Description from the product perspective Detail on the product functions List various classes and characteristics Provide detail on the operating environment Highlight the design and implementation constraints Provide details on the user documentation Describe the key assumptions and dependencies

External interface requirements

Describe the user interfaces Describe the hardware interfaces Describe the communication interfaces

System features

Provide details on each of the key (separate) system features

Other non-functional requirements

Define the performance requirements Provide details on the safety requirements Highlight the key security requirements Outline the software quality attributes Define the business rules

Other aspects to be determined

Identify other key factors which must be determined with key stakeholders

Stage 6: Deployment in the market and maintenance of the AIS When the testing phase of the AIS is complete, it can be deployed and released in the market in accordance with the organisational strategy. This may be achieved by targeting a specific segment of the market to gather feedback through user-acceptance testing.

AIS software prototyping Software prototyping refers to building software application prototypes that provide the functionality of the product under development, although it does not present the sophistication or exact logic of the planned AIS. This enables the developer to gain a rich understanding of requirements at early or numerous stages of development. The customer can provide rich feedback on various aspects of the software product, ranging from the user interface, the experience, the functionality and the desired outcomes. This may follow a process such as: 1 2 3 4 5 6

46

Identify customer AIS requirements; Develop the initial AIS prototype for the customer to examine; Review and adapt the prototype according to customer feedback; Build prototype to meet revised customer expectations; Examine whether changes meet time and budgetary resources; Repeat process until a final product is agreed upon.

Systems planning, design and implementation

There are different software prototyping types widely used, such as: •

• • •

Throwaway/rapid prototyping: this requires minimum effort to establish customer requirements whereby the developer launches into building a prototype. From this prototype, the actual system is developed and a greater understanding of user requirements emerges from the development process. Evolutionary prototyping: developing prototypes with minimal functionality to allow requirements be well understood and evolved throughout the development process. Incremental prototyping: developing numerous functional prototypes of the sub-systems and integrating the prototypes to develop a finished system solution. Extreme prototyping: typically conducted within the web development domain using HTML format, simulating data processing, implementing services and integrating all components into a final prototype.

There are a number of well-established software development lifecycle models that offer different variations of the development processes, which are now detailed.

SDLC models There are a number of different SDLCs. Each model influences the unique approach for each of the software development process. The most common software development process models are: • • • • • • •

The waterfall model (Royce, 1970) The iterative model (Zurcher and Randell, 1968) The spiral model (Boehm, 1988) The V-model (Forsberg and Mooz, 1991) The big bang model (Eason, 1988) The agile model (Agile Alliance, 2001) The rapid application development (RAD) model (Martin, 1991)

Waterfall model The waterfall model is represented by six key sequential phases as shown in Figure 4.4 overleaf. These sequential design processes depict a downward flow (hence the name “waterfall”) through the software development processes phases of requirements analysis, system design, implementation, testing, deployment and maintenance. These key phases are: • •

• •

Requirements analysis: identifying and documenting the needs of various stakeholders on a Requirements Specification Document within the organisation. System design: examining the Requirements Specification Document and preparing the system design to align requirements and design of a system. This ensures that both hardware and software requirements are captured in the design process which ultimately influences the system’s architecture. Implementation: units of the program are developed, tested (i.e. unit testing), implemented and integrated into the next phase of the development lifecycle. Integration and testing: during the implementation phase, all units are developed and integrated into the system after unit testing is successful. Additional functional and non-functional 47

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Figure 4.4 Waterfall model

• •

testing is also carried out after full integration to remove any failures that may be identified within the system as a whole. Deployment of the system: when the testing is complete, the software product may be the deployed in the client environment or released within the wider market place. Maintenance: the system may experience some issues within the clients’ environment and require patches to address the problems. In addition, new versions of the software may be released, for example, to improve the software functionality and security. New releases may be an on-going service level agreement to ensure that the software evolves with the organisation over a specific period of time.

Each of these phases are often executed when specific goals in previous phases are successfully completed. None of the phases are designed to overlap and the progress is described as flowing downwards through each phase.

Iterative model The iterative model encourages a series of software development phases that adopt a number of iterations or revisions of the product (see Figure 4.5). The requirements are typically clear and well defined but require some alterations to various functionalities based on the client’s experience and feedback with the system. The requirements may be enhanced over time. However, there is typically a deadline for the project and the various iterations are executed within a defined timeframe. In some cases, there may be some uncertainty of the actual technology and there is a learning process for the development team and each iteration informs the team on what functional and non-functional units best address the clients’ requirements. The iterative model is also the preferred development approach when there are limited staffing resources or expertise for various development roles and allows developers to contract developers for specific iterations of the product, e.g. user interface designers. 48

Systems planning, design and implementation

Requirements

Productv.2

Design

Design

Design

Test

Test

Test

r-

Implement

Implement

Implement

~

Analyse

r-

Design

Test

Implement

-

Analyse

,__;

Productv.n

Productv.l

l-)

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l-)

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Figure 4.5 Iterative model

Spiral model The spiral model promotes repetitive cycles (or spirals) of software development iterations (Figure 4.6). The spiral passes through a number of key phases that are summarised as follows: •

•

•

•

Determine requirements: the spiral process is initiated by identifying and documenting the key AIS business requirements. Based on the key business processes, sub-requirements are also identified when the project evolves and matures. There is also a learning process between the system analyst and client as the system evolves and the requirements become increasingly defined. Towards the end of this spiral the project is finally deployed in the market or within a specific silent environment. Risk analysis: this phase requires the system analyst to identify, estimate and monitor technical feasibility and management risks to the system development process. For example, if there are continuous changes to a system’s requirements, this would impact the planned cost and schedule of developing the product. This needs to be monitored on an on-going basis to ensure that such risks are minimised and mitigated. The customer also evaluates the software and provides feedback on an on-going basis. The feedback is also taken into consideration to determine how it could impact factors such as the project timeframe and costs. Design for improvements: at the baseline spiral, a conceptual design commences this phase through the development of an architectural design, logical design of modules, physical product design and the overall final design as the process evolves through the spiral. Build based on feedback: this is producing the actual software through each spiral evolution moving from concept, proof-of-design and towards an actual product that is influenced by continuous customer feedback. Greater clarity on the software requirements and design details are addressed through each spiral and version. Each version should increasingly meet customer expectation and requirements to the point where they are completely satisfied with the final software product. 49

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Determine Requirements

Risk Analysis

,_,.,,

Operational

AJsess Developmentli

Build based on Feedback

Design for Improvements

Figure 4.6 Spiral model

The big bang model Adopting the big bang model suggests that there is no specific process in place to follow and development is influenced by the availability of capital and human resources to develop a product. This model is not typically suitable if there is a specific software need and there are significant recourses (staff or money) readily available to generate a quick (small) solution. It is a quick model in the sense that there is little planning, scheduling or formal development process involved in the big bang model. Much of the effort goes into generating the code for the final software product. Such an approach has its benefits and drawbacks. For example, the benefits include its simplicity as a model, it requires little planning, it is relatively easy to manage and requires few resources. It also offers developers a greater sense of flexibility and encourages a learning approach to find a “best-fit” solution. Some of the drawbacks include the relative high-risk associated with this model due to the uncertainty of the requirements and therefore the final product. It is a useful model for simple projects but does not provide the necessary structures for complex and object-orientated development projects. The model is also weak in terms of supporting long-term projects since it lacks planning, formal structures and may become misguided by the lack of requirements analysis that could generate high project costs.

V-model Another popular SDLC model is the “V-model” or verification-validation model. This model is named after it’s unique V-shape that executes processes in a specific sequence (see Figure 4.7). The V-model is considered an extension of the waterfall model whereby testing is carried out for each 50

Systems planning, design and implementation

Systems

Sub-Systems

Figure 4.7 V-model

corresponding development stage. As this suggests, testing plays a critical role in this development model and each phase is only instated when the previous phase has been successfully completed. The V-model is a product-development approach and has become a common standard in software development. As illustrated in Figure 4.7, the processes are mapped out as a flowchart that takes the form of the letter V. The development process flows from the upper-left of the V towards the right, and continues up along the upper-right endpoint. On the left-hand, downward-sloping direction of the V, business requirements, AIS design parameters and design processes are defined. At the bottom of the V, developers work on writing the actual code for the AIS. On the right-hand ascending branch of the V, testing and debugging is carried out on the product. The unit testing is carried out first, followed by bottom-up integration testing. On the upper-right point of the V the product release and on-going support is represented. The V-model has been a popular model largely because of its simplicity. However, in more recent years, many developers report that it is too rigid for the evolving nature of modern IT business systems and environments.

The agile model The agile development model may be described as an incremental model that develops software through rapid cycles and is often used for time critical applications (see Figure 4.8 overleaf). The focus of this approach is to develop in small incremental releases, whereby each release goes through a thorough testing process and builds on previous functionality. Throughout this process, it is critical that software quality is maintained. Currently, extreme programming (XP) is one of the most well-known and widely used agile development life cycle models. Agile has become an increasingly popular software development approach due to its flexibility and adaptability throughout the rapid development cycles. Various methods include, scrum, dynamic systems development method, extreme programming, crystal clear and the rational unified process. All of these methods require collaborative networks to develop software and respond to 51

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•

.EJa El

1st 2nd Development L_--~---------------Devel ---.o~,m _e_n_t________________-T cyc, ~e-------------T I

I

' I_ '

_1

Deployment?

I

I

' 'I _

_1

Deployment?

I

I

I _1

I

t _

Deployment?

Figure 4.8 Agile model

change in a short timeframe based on dynamic interactions and evolving requirements. There are several advantages and disadvantages to employing an agile model. Some advantages include: • • • •

•

Considerable focus on software quality and design excellence; Improved customer satisfaction through rapid development lifecycles; Faster turnaround timeframes to deliver a software product (e.g. within weeks); Human interaction is strongly encouraged (e.g. peer interaction in teams, cooperation between stakeholders and regular customer interaction) rather than placing emphasis on tools; Provides a dynamic and readiness for change environment at all stages of the development lifecycle.

There are some disadvantages in adopting an agile model, for example: • • •

The pace of development iterations shifts the focus away from documentation and the need to invest time in the design stages; Difficult to plan for the required effort and resources since requirements are evolving throughout the software development lifecycle; The lack of clarity on a vision for the final product can often lead to time management problems since there is no defined end product.

Therefore, agile methods are ideal in situations whereby new changes are required on a frequent basis and implement at relatively low costs. Software developers must be adaptive to change and customer requirements on a regular basis. Agile caters for the ever-changing dynamic business and IT world based on open channels for regular feedback.

Summary This chapter detailed the systems planning, design and implementation of an AIS. It detailed various SDLC models available and an overview of the key processes and decisions to support developers. Within the development models, particular emphasis is placed on planning, designing and implementing. Much emphasises is now being place in the design aspects of system development. In this chapter, we explored how systems analysts assess how users interact with existing 52

Systems planning, design and implementation

or new business technologies while identifying specific functions, such as user requirements to support the organisational strategy. We also examined the systems development lifecycle to identify how it guides the design, planning and implementation of AIS and various approaches to support these processes. From this chapter, we learn that as the application of systems development becomes increasingly engrained and interwoven into the fabric of accounting in organisations, the complexities of the accounting processes and value chain define the needs of users and the requirements of AIS. Thus, while technological advancements play a key role in the planning, design and implementation of AIS, so to do behavioural aspects of formal and informal accounting routines which interplay on a regular basis. It is this interplay that ultimately influences the AIS acceptance within an organisation. Employing techniques from software engineering and other fields supports the systems analysts understanding of the organisation, the users and the need for AIS. Some research has been undertaken in other fields, such as technology and design to examine the socio-technical aspects of IT innovation (Carroll, 2012). For example, there are research developments that examine the design science research methodology and its application to AIS research (Winter and Aier, 2016; Kiesow, Zarvić and Thomas, 2015; Geerts, 2011). In conjunction with these approaches, systems analysts and programmers use a number of documentation techniques to specify the key features of systems. Thus, auditors will typically collaborate with systems professionals during IT audits to develop a thorough insight of a new system, e.g. AIS within an organisation. Identifying the correct documentation techniques and systems design is critical to the implementation of a new AIS.

References Agile Alliance (2001). Manifesto for Agile Software Development. Retrieved September 19, 2017, from www. agilemanifesto.org. Boehm, B. W. (1988). A spiral model of software development and enhancement. Computer, 21(5), 61–72. Carroll, N. (2012). Service Science: An Empirical Study on the Socio-Technical Dynamics of Public Sector Service Network Innovation (Doctoral Dissertation). University of Limerick. Eason, K. (1988). Information Technology and Organizational Change. London: Taylor & Francis. Forsberg, K. and Mooz, H. (1991). The relationship of system engineering to the project cycle. INCOSE International Symposium, 1(1), 57–65. Geerts, G. L. (2011). A design science research methodology and its application to accounting information systems research. International Journal of Accounting Information Systems, 12(2), 142–151. Gordon, L. A. and Miller, D. (1992). A contingency framework for the design of accounting information systems. In C. Emmanuel, D. Otley and K. Merchant (Eds.), Readings in Accounting for Management Control. London: Chapman & Hall, 569–585. Hall, J. (2012). Accounting Information Systems. London: Cengage Learning. Hantry, F., Papazoglou, M., van den Heuvel, W. J., Haque, R., Whelan, E., Carroll, N., Karastoyanova, D., Leymann, F., Nikolaou, C., Lammersdorf, W. and Hacid, M. S. (2010). Business process management. In M. Papazoglou, K. Pohl, M. Parkin and A. Metger (Eds.), Service Research Challenges and Solutions for the Future Internet. Berlin: Springer, 27–54. Kiesow, A., Zarvić, N. and Thomas, O. (2015). Design science for future AIS: transferring continuous auditing issues to a gradual methodology. In B. Donnellan, M. Helfert, J. Kenneally, D. VanderMeer, M. Rothenberger and R. Winter (Eds.), New Horizons in Design Science: Broadening the Research Agenda. Cham, Switzerland: Springer International Publishing, 311–326. Lynn, T., Carroll, N., Mooney, J., Helfert, M., Corcoran, D., Hunt, G., Van Der Werff, L., Morrison, J. and Healy, P. (2014). Towards a framework for defining and categorising business process-as-a-service (BPaaS). Proceeding of the 21st International Product Development Management Conference. University of Limerick, Limerick, Ireland. Macintosh, N. B. and Quattrone, P. (2010). Management Accounting and Control Systems: An Organizational and Sociological Approach. Hoboken, NJ: John Wiley & Sons. Martin, J. (1991). Rapid Application Development (Vol. 8). New York: Macmillan. 53

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Moscove, S. A., Simkin, M. G. and Bagranoff, N. A. (1998). Core Concepts of Accounting Information Systems. Hoboken, NJ: John Wiley & Sons. Quinn, M. (2011). Routines in management accounting research: further exploration. Journal of Accounting & Organizational Change, 7(4), 337–357. Rom, A. and Rohde, C. (2007). Management accounting and integrated information systems: a literature review. International Journal of Accounting Information Systems, 8(1), 40–68. Royce, W. W. (1970). Managing the development of large software systems. Proceedings of IEEE WESCON, 26(8), 1–9. Winter, R. and Aier, S. (2016). Design science research in business innovation. In C. Hoffman, S. Lennerts, C. Schmitz, W. Stolzle and F. Uebernickel (Eds.), Business Innovation: Das St. Galler Modell. Wiesbaden: Springer Fachmedien, 475–498. Zurcher, F. W. and Randell, B. (1968). Iterative multi-level modelling: a methodology for computer system design. IFIP Congress, 2, 867–887.

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5 Change management Krister Bredmar

Introduction Accounting has always played an information system function in an organisation. When Luca Pacioli wrote his Summa in 1494, a chapter described how merchant families in Venice used a double-entry bookkeeping methodology to keep track of goods and money (Thompson, 1994). At that time, the accounting function played the role of documenting juridical transactions, but also was a means of showing ethical actions. In addition to this, the geometrical dimension was important, which helped the user to document a transaction in more than one dimension. In many ways, financial reports and information have always been about understanding the business and operations (Littleton, 1953). Through the gathering and structuring of accounting information, these reports add another dimension of understanding, which in many ways help management in their work with planning and controlling the performance. Technology in different forms drives the renewal of how things are done in an organisation (Peppard and Ward, 2016). This can be understood by considering how technologies, as in machines, change how products or services are realised. However, technology also deals with how the work is achieved, how processes are run and who does what. When exploring how accounting as a function has changed over time, the most important development arose when computerised systems were introduced. Simple, well-defined, routine tasks were moved to transaction-based systems that did the simple calculations that accounting is based on (Simkin et al., 2015). By this time, the concept of accounting information systems was born and new ways of structuring reports, as well as making supporting decisions, were emerging. The way an organisation chooses to work with its accounting information system (AIS) informs the ability management has to plan for and control operations (Emmanuel et al., 1990). The new ways of working with accounting issues in the form of AIS did not in itself bring anything new to the organisation. The accounting function was still working in the same way, but automation shortened the time and increased the possible variations when compiling financial reports. By doing so, the technology, regarding computers as machines and the way work was conducted, changed in many organisations when AIS was introduced. In a more structured way, AIS could act as the catalyst for changing how operations are planned for and monitored, focusing the organisation on expected performance and measuring whether or not the performance is achieved. When changing how things are done in an 55

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organisation, a more structured process of how change itself should be done could be applied. This is usually referred to as change management and can be understood as the process where different aspects of organisations operations, such as technical areas, personnel aspects and cultural dimensions, could be altered (Chaffey and White, 2010). From an AIS perspective, change is also dealing with how the business as such is transformed into more efficient and improved operations, bringing an improved performance as a result. In order to understand how AIS delivers improved performance through change management it is important to first understand the development of AIS as such, into what is has become today, and then also try to understand how these new forms of AIS contribute to change in organisations. In this chapter, the emphasis is on describing the background behind AIS and its role in changing organisations. Even though advanced information systems in general, and modern AIS in particular, bring new opportunities to an organisation and the management function, it is important to first understand the new context in which AIS is situated. It is also important to understand the new challenges managers are facing when trying to benefit from the new, more advanced systems and try to grasp where AIS trends are heading and what kind of change that brings. These areas will be covered in this chapter, namely: AIS in a new context, AIS and the management challenge, and AIS and trends that drive change.

AIS in a new context Over the years AIS has become more complex, but also increased in ability. Earlier systems would more or less “just” do the work of the accountant, but in more recent systems a widely expanded ability to do advanced analyses has become possible (Simkin et al., 2015). We could say there are three rough phases in accounting. The first was when tasks were done by hand in books and the focus was on summarising columns and checking that the figures were correct. In the next phase, the computer entered the scene and routine-based work was automated whereby one of the immediate effects was that the need to check numbers was less important. It was however, still important to check that the numbers entered into the system were correct. In the third and current phase, the accounting function has a more integrated role within a broader, more comprehensive system, usually called an enterprise resource planning (ERP) system. It is still an information system and deals with accounting issues, but it is more or less integrated into an even more complex context.

Information systems When trying to understand what drives change and how systems such as AIS contribute to this, a natural first step is to try to understand information systems as such. One of the first scholars who coined the phrase in a research conference in New York during the mid-1960s was Börje Langefors (Langefors, 1995). At that time, the ability to process data in order to process information was discussed and this new concept was called an information system. Later, additional meaning was added, such as capturing, storing, processing and communicating information within an organisation, usually with the help of some kind of technology (Alter, 1999). It is important however to remember that an information system in general, and an AIS in particular, contributes to the management function by offering different forms of performance reports that in various ways help managers with decision-making, e.g. to plan and control operations (Davis and Olson, 1984). The more abilities the information system brings, the more the management function needs to understand how to use its features in managing the business. This idea is something that Peter 56

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Checkland and his colleagues in Lancaster worked on (Checkland and Holwell, 1998). Their approach was that instead of looking at a system as something merely technical, and in a way “hard”, they wanted to add a “soft” dimension by also considering how real-world management problems were addressed. One insight they elaborated was that people in general, and managers in particular, wanted to act in a purposeful way. In order to do this, they needed some sort of information support and it is here that information systems enter – “information systems exist to serve, help or support people taking action in the real world” (Checkland and Holwell, 1998, p. 10). Translated to an AIS context, this means that these systems are built to help managers act in a purposeful way, by supporting them with information aimed at actions within the organisation.

Stand-alone systems When computers were introduced on a larger scale in business sometime around the early 1980s, the software companies used was generally limited to one narrow task or function (Bradford, 2014). Accounting is an early and good example of this. The software was characterised as narrowly focused on a single function, and as a result data and/or information were partitioned between the departments dealing with different functions. Another consequence from this was that the same data could be stored in two different systems, making it harder to work with in decision-making. One way of describing and also understanding this type of information system is that it was a stand-alone solution, a system that did not communicate with others. In a way, it focused on a single user’s needs, for example, a specific manager’s request. Another way of describing these systems or software is that they supported what is called enduser computing (Chaffey and White, 2010). The end-user of the information system used the system as a tool, an optional source of information, or as merely a transaction processor that kept the records in order. Advanced calculations could be done with these systems but as digital communication outside and inside an organisation was not that well developed, it was the user that formed and controlled the way the systems were used and the role they played. A simple example is that of a spreadsheet, where different forms of information systems such as management and executive ones, calculated data from the early AIS, and by doing so transformed it into information. In this way accounting data became management information. Even though such software are early examples of information systems, their development over the years has been enormous.

ERP – enterprise resource planning Up to and including the 1990s there was increased development in various forms of more advanced information systems. Especially within the manufacturing industry, a new standard was set where the specific focuses were on inventory, materials planning, and later, resource planning (Bradford, 2014). In a way, this allowed managers to implement management control in the way influential authors such as Robert Anthony (1965) stated it should be – about obtaining the resources needed to achieve organisational goals. Still, some of the early systems, such as material requirements planning and manufacturing resource planning, were stand-alone systems. Nevertheless, their data came from different sources and departmental borders were somewhat erased. When the information systems used today, e.g. ERP systems, were introduced, they – in different forms and ways – integrated various functions, and by doing so expanded the possibility for management to have information systems support (Simkin et al., 2015). A typical ERP system includes several different facets of business functions (Bradford, 2014). It therefore integrates data from not only accounting, but also from customers, human resources, 57

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supply chain, inventory, and sales and marketing, to mention a few. The accounting function in general, and AIS in particular, have through this integration come to play a new role where it is not only the traditional financial statements that are interesting to report, but rather more detailed commentary on, for example, a specific product or customer, some parts of a process, or strategic information about different scenarios. As AIS has a new role in a more advanced information system context, and its focus on management information support, systems have begun to drive a change in the way management is using AIS information. Scholars such as Kaplan and Norton (1996) have arrived at the conclusion that even though financial information in the new era of advanced information systems is not enough, it is still crucial for the business to be able to reach its strategic goals and to stay competitive. Accounting information still dominates, but in a different context.

A critical challenge – information overload What information consumes is rather obvious: it consumes the attention of its recipients. Hence, a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it. Nobel Prize winner, Herbert Simon

When working with modern, advanced ERP systems and AIS it has become a huge challenge to navigate the immense amount of data that are exponentially increasing around and within organisations. In some cases, the volume of data produced and stored doubles within six to twelve months, which makes data and information almost impossible to work with in a structured way. Information, as described earlier, is intended to support managers’ decisions and actions, and therefore the value of it decreases when too much unstructured information is presented. There are several different ways of dealing with this overload. One is to work with aggregated information, the bigger picture, and when there is a deviation between goals and performance a deeper drill-down is needed. This is usually possible with modern AIS. Another way of managing information overload is through the use of different filters – for example, via key indicators or specific performance measures, which focus a manager’s attention on specific results. An advanced AIS brings a multitude of opportunities, but also comes with some important challenges, especially relating to the amount of information produced.

What the future brings The ability to present a broader picture has, in many ways, facilitated managers with new tools and hopefully new insights into what is occurring in a business. This makes it even more important for the management function to be able to interpret and incorporate new information into their everyday work. One of the challenges for the future is then for managers to learn and change the way things are conducted within an operation in order to stay competitive. As the new form of AIS and its role in ERP systems are transforming into becoming the metaphorical backbone, or nerve system, in an organisation, the ability to use the system to create increased customer value or growth is what separates the “wheat from the chaff” per se. In the future, these new systems will play an increasingly important role, and organisations that manage to understand their new functions will be able to benefit from the opportunities they bring. Accounting will still be accounting, and information systems still information systems, but in a new context they will bring added value to management in ways and forms that were not there 58

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before (Simkin et al., 2015). As an illustration of how information systems such as AIS can contribute to change, the case of Philz Coffee is a good illustration. Business case – Philz Coffee

In San Francisco there is a chain of coffee shops, Philz Coffee. Founded by Phil Jaber, it is a family-owned and managed business. Over the years they have established themselves as a quality supplier of a blend of real authentic coffee, which is served both in the coffee shops and sold to those who want to make it at home. When Phil’s son Jacob became CEO, one of his challenges was to expand the business and scale up what was a known success to a larger setting. They wanted to increase the number of coffee shops and also expand coffee sales. As the core business was fundamentally about everything, from making the different flavours to selling a hot cup of coffee, there was both a demand on point-of-sale happenings, but also regarding inventory management in a productive way. In order for the business to be able to grow, Philz Coffee needed to change the way things were done. Jacob decided to contact NetSuite, an ERP systems provider. They offered Philz Coffee a system solution that was cloud-based, making it possible to connect to over the Internet, and by doing so, accessible from different locations. It also connected all major functions in the operations, from production, packaging and inventory, to marketing and sales. In the middle of everything was the accounting module. It kept track of all the financial consequences from running the operation. Through the system, Jacob was able to plan for and measure the performance in different parts of the business, both financial performance as well as other forms. NetSuite helped Philz Coffee expand its business through changes in how integrated information systems were used to manage the operations. Even though AIS is still a crucial function, it has become an even more important part in an ERP system, providing managers with financial information and, if needed, adding other integrated information into the management function. In many ways a new information system, and in particular a new and advanced AIS, brings with it new ways of working and running an operation. In this way implementing an AIS also comes with change in the organisation, change that needs to be managed in order to be successful. How management deals with change lays the foundation for the coming success of a system implementation and the technical side of the AIS implementation becomes a socio-technical challenge. This is in many cases dealt with through different forms of change management, where the technical side of an AIS implementation is accompanied by a softer organisational implementation endeavour.

AIS and the management challenge Changing how things are done and processes are run in an organisation are typical management challenges. These affect how people in the organisation should behave and do their work, but also are a matter of how different technological solutions are implemented, where one of the most obvious is the information system. From a work-centred perspective, operations and also changes in, for example, a process, are therefore about people, information and technology. Albeit from another perspective, change in an organisation is about changing the organisation itself. This is an important distinction because changing an organisation can be both of more strategic importance and, when it is operationalised, of a lower level importance. Change in many cases comes down to how managers alter their way of taking decisions and acting upon them. Different actors in an organisation, those who could be described as change agents, then drive change. 59

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Work-centred analysis A fruitful way of looking at an organisation and its processes is through work-centred analysis, a model developed by Steven Alter (Alter, 1999). This focuses on an information system’s role in an organisation. In doing so, there is an initial need to understand what kind of product or service the organisation tries to compete with and the customers are willing to pay for. Thus, from one perspective it is important to understand and follow what could be described as the result that is generated from the core business process, or what the organisation does. At the centre of the model are the business processes that produce the goods or services and this is, on the other hand, built on three equally important parts: people, information and technology. In order to understand what is occurring within an organisation and how technology affects the core processes, one can look at the organisation from a behavioural perspective, where people and information are important to be able to describe a process. Yet, from another perspective, the technological solution that produces information is also equally important to understand. These two perspectives are considered as two sides of the same coin. When used to analyse a system within an organisation, it becomes important to try to understand what kind of problem the system is supposed to solve. When Checkland (1981, Checkland and Scholes, 1990) discussed the softer side of systems, he noted there were some organisational problems that needed addressing. It is important when doing a systems analysis to describe the situation where the problem occurs in enough depth, and as a result a specific management problem is contextualised. Then, different solutions to the problem within the context are presented and the ability to implement a solution is described. Altogether, a good systems analysis, with the focus on actual ambitions or problems within an organisational context, should form the basis for a blueprint of future possible changes. Analysing the value and contribution from systems such as AIS then forms the basis for this change to occur.

Organisational change Processes that bring change in an organisation can be understood from different perspectives. From one perspective, change could be about incremental change, where on-going adjustments to new circumstances, or a fine-tuning of operations, are the focus. On the other hand, change in an organisation can also be about more strategic decisions that have longer-term consequences. Whatever way change occurs, it is important to note whether the organisation wants to be proactive or reactive. When an incremental or minor change is decided upon it could very well be reactive, but if the change discussed in an organisation is more of strategic importance it might be crucial to be proactive. When working with ERP systems in general, and AIS in particular, it is important to think through what consequences the system will have for the organisation. In many cases, it is of strategic importance and senior management needs to be proactive in facilitating the needed change that a new system brings with it. Synonymous to organisational change is organisational learning (Senge, 1990). When an organisation changes the way it does things, usually based on some kind of experience or new insight, there is an inherent element of learning. This is effectively what happens when a new information system is implemented, such as an AIS. The organisation needs to learn how to take advantage of the new possibilities that the system brings, which are made concrete when conducting operations in a new way. Sometimes the new system forces employees to do things differently, but learning and change frequently come from employees themselves. However, often there is a discrepancy between what people in an organisation know and what they, on the other hand, do (Argyris, 1982). Many of the opportunities that a new system offers are not 60

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fully learned until it changes the way work is done in a permanent way. As such, organisational learning and organisational change are intertwined.

Change management and change agents Managing change in an organisation is usually thought of as something realised over several phases (Jay and Smith, 1996). In the first phase, there is a need to understand why change is required, resulting in some sort of plan for the change process, where different milestones are identified and a cost-benefit analysis occurs. In the next phase, different forms of preparation are realised, including environmental analysis, critical success factors and different forms of threat to change. Then, in the third phase the change is implemented. This also involves thinking through if there is an additional need to change working procedures or to test the new system in one department. In the final fourth phase, the change needs to be stabilised through support in different forms, which also might include fine-tuning and additional training. Altogether, there is usually the need to think the entire project through so that the desired change will be made. Usually, there is also a need to have a change agent who is in charge of the entire process of working through the change phases. This is of course done with the help and mandate of senior management. The change agent could be understood as a form of project manager that runs the change process, monitoring and ensuring goals are met and that necessary resources are available. In addition, the change agent might also be responsible for communicating why change is needed. Communication is often something that is crucial for an organisation to adapt to changes, and in many cases an organisation needs to believe, not only understand, why change is needed. When long-term, strategic change is about to happen, this could be described as a transformational change and the ability of the transformational leadership is important. If this leadership is not working as it should, it could contribute to a change project failing. To paraphrase Brown (1994), transformational leadership is a key to successful management of technology change, involving the instilling of a sense of purpose in staff and encouraging them to identify emotionally with the organisation and its goals.

A critical perspective on change management challenges There seems to be a never ending search in many organisations for the next change. The structures, forms and ways of doing the job seem to be continually questioned, and something new is needed, something that the others have. This ongoing strive to find the new “Holy Grail” per se sometimes makes it difficult for an organisation to mature in the latest organisational change, and therefore they miss out on the benefits they initially hoped to achieve. It then becomes important to ask the question within senior management if the need for change matches the opportunities that it brings. When it comes to changing AIS, it is even more crucial to ask that question as the accounting function is imbedded in so much of what is done and changing such a system, especially to an ERP, brings many additional alterations. If the information processed and used today is close to what is needed from a management perspective, then the additional value of changing a system might not outweigh the cost, or the effort, of doing so. In some cases, a new AIS also means a new internal structure or process, and the cost of changing a system must also include that of business process.

An even faster change pace in the future There is a problem with the rate of change in many organisations. This problem has to do with the fact that a lot of the earlier change theories were developed under circumstances that were 61

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more stable. Through technological innovation and new systems, such as advanced AIS and integrated ERP, old models are not applicable in the same way. As such, a modern form of change management also focuses on how to change an organisation’s culture, getting employees to understand that change will benefit them, altogether forming a more dynamic view on the constant state of change (Goldberg, 1992). The concept of business agility has come to encompass this new state of constant change, where the focus is on the organisation’s ability to rapidly change when new expectations occur in the environment around the organisation and its ability to change in a cost-efficient manner. Changes in an organisation come with a cost and it has become even more important for senior managers to be able to estimate the benefit and value from changing the organisation compared to the cost. Implementing a new AIS is no longer only about hardware and software costs, there is a lot more to it than that. In many cases the new technical opportunities drive the ability to use advanced information systems and data analysis to improve efficiency and increase innovation. New systems and advanced analyses drive how management understands and make decisions within a company, decisions and actions that in the long-term end up in the AIS. Complex and advanced integrated systems, where AIS plays an important part, today drive change when it comes to how operations are achieved and how businesses gain competitive advantage.

AIS and trends that drive change In the majority of companies there is a never ending quest for increasing revenue, growth and/ or increased profitability. Through modern, advanced AIS there is an opportunity to track and monitor various parts of an organisation that contribute to these aforementioned parameters. In some industries, there is a saying that 20% of the customers generate 80% of the revenue. With the help of modern AIS, this can be monitored and acted upon. For example, performance that deviates from targets could be tracked through a detailed AIS, which brings new opportunities for management. Moreover, a change in how the management accounting function works can be facilitated through implementing a new AIS, especially if it automates routine work and frees time for more advanced analysis. Analysis, decisions and actions are all possible through advanced digital initiatives in general, and complex analysis of financial data in particular, and in many ways may contribute to a more productive business.

Expecting the unexpected When planning for any new system, there is an implied idea that managers know what they are planning for, that is, what they want the system to contribute with. However, in many cases it might be hard to overview all possible contributions that a system might bring, especially if it also means changing and improving the way things are done in an organisation. One of the ideas behind an ERP implementation is that it facilitates an increased ability to plan and control operations. This could be done through advanced analysis and calculation, and integrating and analysing data in a new way may also reveal findings not previously known (Chapman, 2005). Such findings could be out of line with the intentions behind implementing a specific system, but when discovered they transpire to be valuable. From a management control perspective, these insights and discoveries might be quite valuable and, in different ways, drive the renewal and transformation of how management control is conducted. As the accounting function is based on norms and rules, these norms and rules are also reflected in the way an AIS is built. Nevertheless, AIS is a technically driven solution which may not contribute to a strategic purpose. However, if the new system is integrated within an ERP, 62

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and therefore both contribute to a new technical platform whilst implying a strategic move, there is also a possibility that the organisation changes its operations (Hyvönen, 2003). Several of the AIS software packages offered on the market are what could be called best-of-bread (BoB) systems that are more or less developed to comply with accounting standards and they have also been developed with the needs of a certain industry in mind. This makes it more difficult to use systems to facilitate major changes within an organisation, as they are standardised. In order for an organisation to experience business value from such a system, there needs to be a clear strategic intent, which also drives and tries to maximise a positive business change (Davenport, 2000).

Adopting or not adopting a new feature Within management accounting and control research there has been an on-going development for some years now when it comes to new ways of managing an organisation based on financial and non-financial information. It started with a discussion regarding the ways management accounting and control developed since industrialisation. Scholars such as Johnson and Kaplan (1987) suggested that the methods and techniques formed during industrialisation, and still used at that time, had lost relevance. One of the new methods developed as an answer to this problem was the balanced scorecard (Kaplan and Norton, 1996) that used both financial and non-financial information in the work of managing an organisation in general and to transform a strategy into actions in particular. One of the problems with that model was that it depended heavily on an advanced information system support, and at that time the systems were not that developed and difficult to use (Olve et al., 1999). Because of a poor system support, it became hard to adopt new models, such as the balanced scorecard. When more advanced ERP solutions with clear and advanced AIS function were developed, it became easier to support new needs from management (Spathis and Constantinides, 2004). These new systems also changed accounting processes as such, making them to some extent automatic. The empirical evidence confirms a number of changes in the accounting processes introduced with the adoption of ERP systems. The most frequently quoted ones involve the introduction of an internal audit function, the use of non-financial performance indicators, and profitability analysis at segmental/product level. It is noteworthy though that these changes stem from the main advantages of ERP system, which have also been the driving force for managers adopting them. Spathis and Constantinides, 2004, p. 243 Through the use of advanced AIS like an ERP system, there is an opportunity to be more flexible and also to improve the quality of usefulness within financial reports. In many ways, this facilitated an improved competitive position, through the modern use of accounting information together with the non-financial, bringing a more efficient management function via the adoption of the ERP solution and changing the business processes.

Resistance to change: changing AIS but keeping the operations the same It is common that an organisation shows resistance to change, especially when it comes to changes in structure and how work is done. This has, in a way, to do with the comfort and stability of existing processes as what organisations strive for. When implementing a new AIS/ERP 63

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system there may be resistance to learning the new techniques, as well as accepting the new responsibilities (Gupta, 2000). This may be traced back to poor training, yet there may also be additional costs associated with new systems which give them a negative character within an organisation. This could be due to different ambitions and how the projects shift in focus and in scope (Bredmar et al., 2014). Different phases in an AIS or ERP system project also focus on different areas and new needs might be targeted that also bring new costs and decisions. When dealing with resistance in an organisation, it can be described as barriers that need to be addressed in a conscious way. An opportunity could become a barrier if the organisation does not look at it as an opportunity but a problem. There might be some initial ideas or suggestions that bring changes to operations and processes that on implementation all of a sudden become problems that the organisation does not want or think they need (Bredmar et al., 2014). They instead merely wanted a modification change, not a total remake (Sulaiman and Mitchell, 2005). One of the problems with change is that in the first phases of a change project, for example driven by an implementation of a new AIS, there might be some value or benefits pinpointed and communicated as a rhetorical argument as to why the change is needed. Nevertheless, at the end, the change that was actually implemented may look different and might not deliver the value or benefit initially thought of or argued for.

The accountants’ new role and future implications Changing the way things are done in an organisation is not something easily done. This makes it even more important to understand what new role the accountant might play in this process and an organisation’s quest for additional value by managers and as competitive advantage. In many cases, there is an increased need among managers to get additional support from accountants just to understand newly packaged financial information that advanced systems deliver (Scapens and Jazayeri, 2003). Even though new systems usually bring change, this is not always the case. A stand-alone AIS might not per se mean that functions or structures are changing, as this is, to an extent way, up to the accountant and the managers, especially when it comes to how the systems are used. Similarly, implementing new management accounting techniques, which could be understood as a change process, is not necessarily driven by introducing new ERP or AIS systems (Booth et al., 2000). Effectively, the accountants’ work has become broader today, with more of an educational and informational function, as the routine work of day-to-day transactions has been narrowed (Scapens and Jazayeri, 2003). The accountant and the AIS function have thus come to play a new and increasingly important part in delivering value to the organisation, and by doing so change is arising in a slower, more indirect way. New software and technical solutions which bring opportunities to be evaluated, as well as increase competitive advantage and the management function’s abilities, are the ones who will find their way into the established practices of an organisation.

Summary The form of technical development that organisations have faced over more than 20 years, especially when it comes to new administrative, advanced information systems, is in many ways remarkable. It has forced organisations to change the way they handle their operations, in some cases due to the demand of the customer, in some cases due to the possibilities that arise from new technical solutions. New AIS features have also created a need for more thought through change management, driving the urgency for understanding how change in administrative 64

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processes comes about and how these new opportunities can work as a force that creates a competitive advantage. AIS constitutes more than ever a system that can create the basis for an efficient business, a system that drives renewal and a system that in the long run makes a company profitable by showing what parts of an operation are most profitable. In a way advanced AIS have come to play the role of the catalyst that more or less forces the organisation to think through how operations are done and in what ways it could be improved in order to stay competitive. AIS are more and more becoming the backbone and nerve system that it once was envisioned to be, back then as a metaphor, and today literally is.

References Alter, S. (1999). Information Systems: A Management Perspective. Reading, MA: Addison-Wesley. Anthony, R. N. (1965). Planning and Control Systems: A Framework for Analysis. Boston, MA: Harvard University. Argyris, C. (1982). Reasoning, Learning and Action: Individual and Organizational. San Francisco, CA: Jossey-Bass. Booth, P., Matolcsy, Z. and Wieder, B. (2000). The impacts of enterprise resource planning systems on accounting practice: the Australian experience. Australian Accounting Review, 10(3), 13–29. Bradford, M. (2014). Modern ERP: Select, Implement, & Use Today’s Advanced Business Systems. Raleigh: North Carolina State University. Bredmar, K., Ask, U., Frisk, E. and Magnusson, J. (2014). Accounting Information Systems implementation and management accounting change. Business Systems Research, 5(2), 125–138. Brown, A. (1994). Transformational leadership in tackling technical change. Journal of General Management, 19(4), 1–12. Chaffey, D. and White, G. (2010). Business Information Management: Improving Performance Using Information Systems. Harlow, UK: Pearson Education. Chapman, C. S. (2005). Not because they are new: developing the contribution of enterprise resource planning systems to management control research. Accounting, Organizations and Society, 30(7/8), 685–689. Checkland, P. (1981). Systems Thinking, Systems Practice. Hoboken, NJ: John Wiley & Sons. Checkland, P. and Holwell, S. (1998). Information, Systems and Information Systems. Hoboken, NJ: John Wiley & Sons. Checkland, P. and Scholes, J. (1990). Soft Systems Methodology in Action. Hoboken, NJ: John Wiley & Sons. Davenport, T. H. (2000). Mission Critical: Realizing the Promise of Enterprise Systems. Boston, MA: Harvard Business School Press. Davis, G. B. and Olson, M. H. (1984). Management Information Systems. New York: McGraw-Hill. Emmanuel, C., Otley, D. and Merchant, K. (1990). Accounting for Management Control. London: Chapman and Hall. Goldberg, B. (1992). Managing change, not the chaos caused by change. Management Review, 81(11), 39. Gupta, A. (2000). Enterprise resource planning: the emerging organizational value systems. Industrial Management & Data Systems, 100(1), 114–118. Hyvönen, T. (2003). Management accounting and information systems: ERP versus BoB. European Accounting Review, 12(1), 155–173. Jay, K. E. and Smith, D. C. (1996). A generic change model for the effective implementation of information systems. South African Journal of Business Management, 27(3), 65–70. Johnson, H. T. and Kaplan, R. S. (1987). Relevance Lost: The Rise and Fall of Management Accounting. Boston, MA: Harvard Business School Press. Kaplan, R. S. and Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Boston, MA: Harvard Business School Press. Langefors, B. (1995). Essays on Infology. Lund, Sweden: Studentlitteratur. Littleton, A. C. (1953). Structure of Accounting Theory. Sarasota, FL: American Accounting Association. Olve, N.-G., Roy, J. and Wetter, M. (1999). Performance Drivers. Hoboken, NJ: John Wiley & Sons. Peppard, J. and Ward, J. (2016). The Strategic Management of Information Systems: Building Digital Strategy. Hoboken, NJ: John Wiley & Sons. Scapens, R. W. and Jazayeri, M. (2003). ERP systems and management accounting change: opportunities or impacts? A research note. European Accounting Review, 12(1), 201–233. 65

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Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. London: Century Business. Simkin, M. G., Norman, C. S. and Rose, J. M. (2015). Core Concepts of Accounting Information Systems. Hoboken, NJ: John Wiley & Sons. Spathis, C. and Constantinides, S. (2004). Enterprise resource planning systems’ impact on accounting processes. Business Process Management Journal, 10(2), 234–247. Sulaiman, S. and Mitchell, F. (2005). Utilising a typology of management accounting change: an empirical analysis. Management Accounting Research, 16(4), 422–437. Thompson, G. (1994). Early double-entry bookkeeping and the rhetoric of accounting calculation. In A. G. Hopwood and P. Miller (Eds.), Accounting as Social and Institutional Practice, 24th ed. Cambridge: Cambridge University Press, 40–66.

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Organisational effects of Accounting Information Systems

6 Accounting Information Systems and how to prepare for Digital Transformation Sven-Volker Rehm

Introduction The recent appearance of the term “Digital Transformation” has fundamentally impacted the discourse on business practices (Bharadwaj et al., 2013; Majchrzak et al., 2016). Popular perception defines Digital Transformation as an endeavour of integrating and exploiting new digital technologies, bringing about changes to a firm’s business model, product/service offerings, and business processes (Mathrani et al., 2013; Hess et al., 2016). Among the various changes that might appear, including technology use and modifications to organizational structures and processes, changes to the value creation model and its related financial tenets are particularly imminent (Hess et al., 2016). In this way, Digital Transformation provides the impetus to reconsider the very nature of AIS in their purpose to manage data and information to be used by decisionmakers in accounting and finance functions, and to contribute to strategy implementation and internal control of the enterprise. Today, with an increasingly digital foundation to organizing business operations, “the traditionally presumed sequential and linear links among corporate strategy, firm structure and information systems design are no longer in play” (Bhimani and Willcocks, 2014, p. 469). This insight is exacerbated by new opportunities for data use and analytics that new technologies bring about (see e.g., Anaya et al., 2015). The potential impacts on the accounting and finance functions are numerous: Cost structures will need to become adaptive to more agile data analysis techniques across structured and unstructured data; internal control and documentation will benefit from new analysis techniques; and the distance between analysis and execution by managers will be shortened or even eliminated. In consequence, an accountant’s role will become more focused on redesigning and adapting a “fluid” system of performance metrics, as well as responsibility, incentive and reward structures (Bhimani and Willcocks, 2014, p. 480). As part of a Digital Transformation, managing business processes will take on a new relevance for designing and managing AIS with a perspective on implementing and controlling business strategy and operations. Cloud computing for instance, as a digital enabler for business, has become a means to experiment with new business processes, while monitoring involved risk factors and maintaining the opportunity to discard unsatisfactory process variants (Bhimani and Willcocks, 2014, p. 484). Such novel process design options bring about a new potential for 69

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achieving innovativeness at an operational level while keeping an eye on accounting and finance functions. Business Processes Management (BPM) has developed over the past decades as a management approach that helps establish a comprehensive view of an enterprise’s operations, defining key performance indicators (KPIs) for measuring and monitoring process performance, and implementing means for continuous process improvement and innovation (vom Brocke et al., 2016). BPM also has its place in the field of AIS, contributing to the basics of AIS design and implementation (see e.g., Hall 2011). However, the contemporary perspective on BPM is mainly focused on relatively stable processes with a consistent and well-defined information systems’ (IS) support. Related process and IS life cycles are considered to be long term. It has only been recent that research on BPM starts to acknowledge the multiple and changing contexts in which BPM operates, calling for a contextual view of BPM and taking into account situational factors related to goal, process, organization, and environment dimensions (vom Brocke et al., 2016). As part of a Digital Transformation, these dimensions might change more flexibly, which makes it necessary to make the design of AIS and its management adaptive to the particular characteristics of the enterprise processes’ context (see Table 6.1). It also requires having a look at the enterprise’s business environment, e.g., with regards to outsourcing (Christ et al., 2015). Repeatedly achieving fit between (a) the business environment and business processes, as well as between (b) business processes and technology will become increasingly prominent despite posing a constant challenge (Trkman, 2010). In this context, approaching the design of AIS and their management requires a shift in perspectives – one that (a) is focused on enabling technical functions that allow for a (compliant) implementation of AIS and other enterprise systems, and (b) considers AIS-enabled accounting and finance functions and activities as constraints in the design and implementation of enterprise Table 6.1 Contextual factors of the BPM context and potential changes through Digital Transformation Dimension

Factors

Potential changes induced by Digital Transformation

Goal

Focus

The distinction between processes for exploitation (improvement, compliance) and exploration (innovation) might dissolve, and more flexible, prudent experimentation becomes necessary.

Process

Value contribution, Repetitiveness, Knowledgeintensity, Creativity, Interdependence, Variability

Core, management and support processes might become more closely interrelated and their variability increases, e.g., through linkages to external business partners. Knowledge intensity rises because automation is extended, repetitiveness reduced, and focus is directed towards more creative (effective, agile) process design.

Organization

Scope, Industry, Size, Culture, Resources

Inter-organizational processes become a necessity, and servicebased business aspects gradually replace product-oriented data management. Business processes include partners of different sizes (start-ups, small, medium and large firms).

Environment

Competitiveness, Uncertainty

Due to alternative digital strategies of competitors and business partners, uncertainty in process design as well as risk-taking mentality increase. Measuring performance in relation to competition becomes more difficult.

Source: Adapted from vom Brocke, Zelt and Schmiedel, 2016

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systems in general. In other words, just like for any other set of business processes, the potential for digitalization as part of the accounting and finance functions and activities needs to be re-evaluated. Doing so provides for a novel perspective on AIS in the enterprise because the “manifestation” of AIS, as we have experienced in the past, for instance in form of Enterprise Resource Planning (ERP) systems or other dedicated financial and management reporting IS, might eventually dissolve due to blurring system boundaries. Organizational resources might regain a more important role than IT resources (see e.g., Hsu, 2013) in this respect. Further, the distance between temporary technical implementation within temporary business processes on the one side, and the firm’s presumably relatively stable organizational culture and boundary conditions for defining business functions on the other side, increases. This is an observation not uncommon in the contemporary IS literature (see e.g., Besson and Rowe, 2012). It implies that “IS managers . . . must apply new skills . . . and new leadership modes which challenge the IS department and require profound adaptation” (Besson and Rowe, 2012, p. 117). It also implies that accountants might need to re-focus on maintaining an efficient and effective operating model for guiding the implementation of control decisions that are, to some extent, implementation-agnostic. This chapter will therefore concentrate on guidelines for implementing Accounting Procedure Documentation (APD) as a vehicle to conceptually integrate AIS, BPM, IT Management and future business planning. A practice-oriented view is provided on how to approach BPM for AIS in the context of Digital Transformation when considering the new legal regulations on AIS in Germany.

Dealing with digital business objects in AIS Information management and AIS The underlying tenet of Digital Transformation is that all business operations and transactions will increasingly become digitized. While this trend towards digital business objects is not new, its interpretation in today’s business context may be. It might lead to a more streamlined execution of related digital processes, objects and representations, as well as information and IT management in general. From an IT management (ITM) perspective, AIS-specific management functions and activities can be positioned diagonally to other enterprises’ ITM activities (see Table 6.2). Combining two popular models (Krcmar, 2005; Hall, 2011) show that particular requirements emerge in the Digital Transformation context as the result of three major areas of IT controls (A, B, C in Table 6.2) relating to anticipated changes and four major areas of ITM (1 to 4 in Table 6.2). Together they lead to a set of complex management tasks. As part of Digital Transformation, specific problematic aspects appear for designing and implementing internal controls that require further discussion, and that potentially include various systems apart from AIS. Such aspects include:1 • • • •

Retaining compliance with higher velocity of system changes and shortened IT application lifecycles; Achieving and retaining compliance to requirements for documenting of business transactions, storage and record keeping in distributed IS and across various IS; Implementing guidelines on data accessibility and preservation set by external public authorities (according to domestic legislation); Digitization of paper-based documents and future accessibility of electronic records.

Focusing on internal controls, the expected increased volatility of IS suggests reconsidering how digital business objects are managed and handled throughout the various systems involved in 71

Sven-Volker Rehm Table 6.2 Diagonal positioning of IT controls against general IT management functions IT management (ITM)

Effects of Digital Transformation

IT controls

1. Leadership tasks of ITM: IT strategy, IT governance and IT processes (IT service management), IT organisation and IT human resources

➞

Increasing requirements to align business and IT strategies . . .

A. IT security and access concepts, e.g. transaction authorization and validation, backup controls, risk controls (threats, failure)

➞

Information life cycles shorten, types of data diversify (structured/unstructured; internal/external etc.) . . .

➞

Application life cycles shorten; outsourcing and cloud-based software provision models increase . . .

➞

Technology (hardware) ownership decreases; data processing complexity and communication increase . . .

2. Information Life Cycle: Information Supply and Demand, and Utilization 3. IS Management: Data, Processes, IT Applications and Software Lifecycle, Outsourcing 4. Management of ICT: Storage, Processing, Communication, Technologies

B. IT Governance controls and compliance, e.g., internal control framework and IT control systems, computing security/ controls, disaster recovery planning C. Continuous systems development controls, e.g., program changes, and application controls, testing techniques

finance and accounting functions and activities. To this end, various basic principles exist that help in designing internal controls; these principles however are challenged by Digital Transformation, as outlined in Table 6.3. In order to cope with these outlined aspects and anticipated challenges, an analysis and, eventually, revision of AIS – or respectively, the accounting and finance functions and activities – on the process level becomes a prerequisite. Such a comprehensive analysis can be conceptualized as APD. The underlying facets of such an APD are now outlined. Table 6.3 Basic principles for designing controls for digital business objects and their challenges through Digital Transformation Principles

Interpretation and potential changes through Digital Transformation

– Verifiability

With shortening application lifecycles, new processes (and related controls) need to be defined in order to allow to chronologically re-examine the validity of transactions across IS. Particularly the availability of “raw” data and master data used by replaced previous IS needs to be safeguarded. For documents, transparent measures for indexing need to be introduced.

– Transparency and clarity; – Completeness, particularly complete documentation of all transactions

With increased business process flexibility, each documentation or reporting process requires a transparent and easily comprehensible description including involved systems, data sources, data types, and processing at the time of reporting. Such information should be integrated into business process intelligence applications in order to automatically verify and control the proper recording of changes. Time limits for documenting all related transactions and provisions (controls) will have to be complied too.

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Principles

Interpretation and potential changes through Digital Transformation

– Accuracy and truth; – Timely booking and record storage; – Immutability of records and stored data

With increased diversity of data and larger numbers of data processing IS, securing timely and immutable recording requires additional processes dedicated to design controls on process level that are sufficiently flexible to interpret and record the used application systems’ data access and management functions (including plans for distributed disaster recovery).

– No record without evidence; – No record without offsetting entry (contra account); – Abbreviations must be comprehensible

With increased diversity of data formats (e.g., file types) as well as number and distribution of systems, documenting (historiography) requires dedicated new systems for storage of electronic documents for the defined record retention periods in formats which are readable even after major application software changes. (Thus, some file formats will not fulfil related compliance criteria). Some countries (e.g., Germany) require to enable direct access options to such records. Related master data, accounts and additional information such as abbreviations and descriptions need to be made transparently available to third parties in such cases, too. (This might not hold for emails or other types of communication message formats which can often be considered carriers of relevant data and which can thus be deleted).

APD – a business process view on AIS Requirements and boundary conditions for procedural documentations Following the verifiability principle, independent from the technical implementation, reporting, documenting and control processes need to be made transparent – which is contributing to both, design of internal controls and management processes as well as meeting compliance requirements. Procedural documentations in general cover both organizational processes as they are carried out and technical processes as they should be instantiated. In view of the Digital Transformation context, particularly the latter aspect deserves attention: APD in this regard needs to be chronologically versioned and stored just as thorough as the reported data itself. The technical process documentation for electronic documents, for instance, involves the information origin (data input event), indexing, processing and storage, measures to safeguard unambiguous retrieval and machine-based analysability, and protection against loss and manipulation/falsification as well as reproduction. It has been only recently, in November 2014, with effect from January 2015, that the German federal ministry of finance has introduced “principles to ensure the due maintenance and preservation of books, records and documents in electronic form, as well as for data access (BMF Germany, 2014)”.2 These principles extend previous formal requirements for IS-based handling of electronic records,3 and give some provisions that predominantly motivate the introduction of APDs as a systematic method to safeguard compliance to various legislations.4 In the case of Germany, missing or insufficient APD documentations potentially lead to unfavourable circumstances for the entire accounting system and, hence, the enterprise. For instance, this situation can lead to sanctions, such as estimation of tax bases by authorities, assigning the burden of proof to the firm, possibly consequences within criminal tax law, fines, determination of missing effective annual financial statements, elimination of subsidies, or withdrawal of the legal basis for profit distributions. Such sanctions – if in effect in a particular business setting – in themselves provide a good incentive to engage in design of an appropriate APD. Nevertheless, a second impetus might be the improvement and maybe evolutionary adaption of AIS, with 73

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regard to an increasingly changing business context, and requisite adaptions on the IS. This second aspect will be discussed further below. The following paragraphs outline implementation aspects of APD. While the presented suggestions are informed by recent German legislation, they provide guidelines for firms in any legal or business setting, as they can be considered minimum requirements to capture relevant “adjusting screws” during change and particularly, Digital Transformation processes.

Implementing procedural documentations Hence, the objectives of creating an APD are first of all the proof of completion of the regularity principles as defined by the local legislation. All AIS-based finance and accounting procedures need to be available in a way that third-party authorities or external consultants can validate them with appropriate effort. This requires a comprehensive documentation that covers all specific procedures relevant to accounting and reporting. In particular, the management of transactions and documents as pieces of evidence needs to become transparent; the description of how documents are received and recorded, processed, and stored/retrieved is defining the core of an APD. Basic questions an APD needs to answer are (adapted from AWV, 2015, p. 4): • • • • • • • • •

How are document input and document identification organized? How is the completeness of the collected documents ensured? How are documents ordered and where are the documents stored? How is the processing of conventional paper documents and born digital documents paralleled? How is the storage location (e.g., conventional file folder or archival system) secured against unauthorized access and against loss? Who has access to the storage location and to sorting functionalities? How often and in what way do accountants, managers, or third parties (e.g., corporate accounting or tax consultancies) receive the documents? How do you ensure that all affected persons know about and support above aspects? How do you ensure that the evidence is not destroyed before the end of the retention periods?

Following these examples, an APD needs to consider several important aspects. In principle, each IS involved to data processing, both AIS and related systems, require their own dedicated representation in an APD stating in which way or to what extent above questions have been implemented on the respective individual technical basis. Also, the Internal Control Systems (ICS) need to be part of the APD. Further, two basic processes need to be documented, i.e. the documentation function including capturing, processing, displaying and storing documents; and the scanning function, describing the capture of paper-based input information. Usually, the components, or building blocks, of APD are: 1 2 3 4 74

General description of the documentation business process (what is in, what is out); APD accountant user manual (to be used also by other stakeholders in the accounting and finance context); Technical system documentation; Operational procedures documentation.

AIS and Digital Transformation

In order to reach the verifiability requirement, it will be beneficial to link these components through an integrated business process management approach. In the following paragraphs, the components are presented in short. Component 1: general description of the documentation business process

This description should use a visual and easily understandable approach to document and represent all relevant business processes, i.e. comprising activity flows and related events, the linked application systems, as well as documents and risks. For instance, the event-driven process chain (EPC) of the ARIS methodology (Scheer, 1992; Rosemann and van der Aalst, 2007; van der Aalst, 1999) can provide a suitable instrument that is also software-supported and can inform IT Management (Rahimi et al., 2016). Figure 6.1 illustrates core elements of an EPC: Positioning activities in the centre of the business process, organizational units or roles associated to or responsible for the activity can be defined; input and output (generated) information can be explicated, and related risks and controls can be documented. Using software support to BPM each of these elements is represented as a database entry which enables easy handling and retrieval, and is the basis for business process intelligence, i.e. the automatic detection of flaws in the actually instantiated processes, if the APD is linked with the operative systems. In this way, in principle, the entire Internal Control System (ICS) can be represented (and eventually controlled). Particular controls that can be modelled are for instance: Access rights and related security concepts, separation of functions (e.g., the originator of a document does not approve it), validity checks, data entry coordination/checks, processing controls, IT system failure controls, safety measures against data and software manipulation (which need to be

Document Activity

Organizational unit/Role

Figure 6.1 Basic objects and modelling pattern for an event-driven process chain (created with ARIS Express software/software AG) 75

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adapted to system complexity), and others. Using software-based business process modelling, it is easier for third parties to access and validate related accounting and reporting procedures – including direct links to their implementation, also in case of procedural or implementation variants. Contemporary BPM software often also enables the automatic generation of process models on basis of search/retrieval functionality, e.g., when searching for activities affected by failures in particular system functions. Component 2: APD accountant user manual

When using a software-based APD, a user manual should be provided for all roles involved, in order to safeguard proper handling of the system by users. While for some cases the software provider’s manual will suffice, in many cases configurations or customizations will make an enterprise-specific manual necessary. The manual should cover: • • • • • •

General description of the IT application, its objectives and use cases; Listing and explanations of all application systems, their interrelations and sub-systems; Types and meaning of input masks including data entry fields; Visualizations of all application-internal processing steps/procedures; Explanation of modalities that facilitate data collection and analysis; as well as Separate documentation and explanation of the Internal Control System (ICS), e.g., to understand and reproduce configurations (parameters) and defined procedures.

Component 3: technical system documentation

The technical system documentation has two objectives: First, it defines boundary conditions for and measures to maintain ordinary (undisturbed) system operation. Second, it serves as provision of guidelines and measures in case of disturbances, e.g. to ensure ordered emergency operations. It should predominantly contain IT-specific hints and parameters for technical staff, and allow for system maintenance for third-party providers. In this respect, it is helpful to provide for easily comprehensible descriptions, because in emergency cases qualified external experts may need to reproduce the applications’ processing steps in an adequate timeframe without expertise in the used programming languages. This relates particularly to processing functions and rules, which, for reasons of verifiability, exclude pure source code documentations from the APD. The following areas and factors need to be described: • • • • • • •

Objective of the application system; Data organization and data structures; Changeable table content used in the booking process; Processing rules including input and processing controls; Program internal error handling procedures; Code lists; and Interfaces to other systems or modules.

Component 4: Operational procedures documentation

The subject of the operational procedures documentation is to explain the correct application of the accounting procedures. Among other factors it covers: 76

AIS and Digital Transformation

• • • • •

Security concept; Data backup strategies and procedures; Processing evidence (protocols of processing and synchronization); Nature and contents of the release procedure for new and modified applications; and Listing of available programs including versioning evidence.

Table 6.4 provides an example of an operational procedures documentation of the security concept implementation. The principle process of creating an APD is illustrated in Figure 6.2. The figure shows the principal sequence of steps; single steps usually need to be iteratively detailed along the process. Initial step should be the definition of responsibilities and process owners, i.e. describing the distribution of roles responsible for particular business processes and/or functions. Further steps are the creation of business process models of relevant parts of the enterprise, particularly the accounting and finance processes and activities; then, descriptions of hardware, software and technical procedures follow and detail the business process models. When creating an APD, in practical settings it has proven helpful to first create a master document that serves as a navigation portal to all other related information sources. Such secondary sources comprise (1) procedural documentations (including business process models), (2) manuals Table 6.4 Example of an operational procedures documentation of security concept implementation Chapter/Aspect

Contents

1

Introduction

(Scope and guidelines for use of the manual)

2

Inventory analysis

Overview: Facilities, IT systems, IT applications, network plan, etc.; (Aspects to cover: availability, integrity, confidentiality)

3

Vulnerability/ Risk analysis & measures

3.1 3.2

Physical security: fire protection, security systems, power supply, emergency power supply, etc. Logical security 3.2.1 3.2.2

4

Emergency management

Hardware (Intra) security: servers, workstations, etc. Software (Inter) security: email (incl. spam), Internet, virus scanners, etc.

3.3

Organisational security: IT policy, authorization concept, data backup concept, etc.

4.1

Risk analysis 4.1.1 4.1.2 4.1.3 4.1.4

4.2 4.3 4.4 4.5 4.6

Analysis of the core processes of the company Analysis of IT services and failure scenarios Threat/effects analysis on failure of individual core processes Legal requirements to be considered

Process-oriented emergency planning Maintenance of timeliness Organisational contingency plans Other analyses (of the specific emergency setting) Emergency exercises and emergency manual

5

Liability agreement with employees on proper data handling

6

Restoration exercises (intermittent)

7

Change history

77

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on~\

Modelling description of \ Definition of

\\

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\ \c--------------------------c------------,-------------'

p~:S=and \\ activities

Hardware

\

Oelicrlptlon of

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Maintenance

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~=:·::,~d/)( \ ·~i:::~l \.~·~=~~~p~==~d~J::~oni~•ndu=~o~ I

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Figure 6.2 Sample process for developing an APD

and technical documentations, (3) contracts and protocols, and (4) operating procedures and functional descriptions. This has proven efficient for maintenance of the APD. On the one hand, when changes need to be recorded throughout various linked documents and when responsibilities need to be assigned for particular parts of the APD. In this respect, it is important to notice that the APD needs to mirror the correct application software versions or respective variants of procedures as they are applied/used in practice. Vice versa, employees need to be briefed in order to actually “live”, i.e. consciously comply to the provisions of the APD, in their day-to-day work. Thus, during the maintenance phase, regular control and updating of all elements of an APD are required. Regarding the lifetime of an APD, its retention time is generally equivalent to those of the applied AIS and the relevant data as legally prescribed. Therefore, each update and change needs to be documented through a reproducible versioning history of the entire APD documentation.

AIS, BPM, ITM, and future business planning At first glance, the requirements for creating an APD might seem bureaucratic, and promising significant additional effort to customary reporting practices. In fact, in order to make IT Management more agile also with respect to accounting and finance functions, the APD establishes a crucial instrument that can be used to diagnose the enterprise for issues in compliance and IT security, and to design measures for preventing damages. On another level, BPM is the basis to allow for an evolutionary design of enterprise systems, and particularly, AIS. With Digital Transformation, the agile, ad hoc design, configuration and deployment of application software for business needs becomes increasingly prominent (Pahlke et al., 2010; Neumann et al., 2014). The software development and deployment process then needs to be conceived as an iterative cycle that predominantly relies on participative improvement of application software (Abrahamsson et al., 2009; Cao et al., 2009). BPM – particularly through its visual representations – thus builds a basis to discuss process improvements, alternatives or variants between operationally involved staff, accountants and other company stakeholders. It also enables simulations on basis of process designs and operational process data with a view on business and operations strategies – without letting risks and compliance out of sight.

Acknowledgements The author would like to expressly thank Mr. Thomas Martin and Mr. Helmut Heimfarth for their support on German legislation and their insights to practical applications of accounting procedure documentations. 78

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Notes 1 These aspects complement on a process level the well-known risks inherent to AIS and financial reporting systems (FRS), such as incorrect general ledger accounts, etc., which may result in misstated financial statements and other reports, and which are subject to sanctions through Sarbanes-Oxley legislation, for instance (see e.g. Hall, 2011, p. 362). 2 “Grundsätze zur ordnungsmäßigen Führung und Aufbewahrung von Büchern, Aufzeichnungen und Unterlagen in elektronischer Form sowie zum Datenzugriff (GoBD)”. 3 Previous important regulations and/or amendments were published in 1984, 1995, 2001, 2012. 4 See BMF Germany, 2014, p. 32.

References Abrahamsson, P., Conboy K. and Wang X. (2009). Lots done, more to do: the current state of agile systems development research. European Journal of Information Systems, 18(4), 281–284. Anaya, L., Dulaimi, M. and Abdallah, S. (2015). An investigation into the role of enterprise information systems in enabling business innovation. Business Process Management Journal, 21(4), 771–790. AWV (2015). Arbeitskreis für wirtschaftliche Verwaltung e. V., Auslegung der GoB beim Einsatz neuer Organisationstechnologien, Muster-Verfahrensdokumentation zur Belegablage, Version: V1.0 19. October 2015. Retrieved September 19, 2017, from www.awv-net.de/cms/front_content.php? idcat=286. Besson, P. and Rowe, F. (2012). Strategizing information systems-enabled organizational transformation: a transdisciplinary review and new directions. The Journal of Strategic Information Systems, 21(2), 103–124. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A. and Venkatraman, N. (2013). Visions and voices on emerging challenges in digital business strategy. MIS Quarterly, 37(2), 633–634. Bhimani, A. and Willcocks L. (2014). Digitisation, ‘Big Data’ and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490. BMF Germany, Bundesministerium der Finanzen, Grundsätze zur ordnungsmäßigen Führung und Aufbewahrung von Büchern, Aufzeichnungen und Unterlagen in elektronischer Form sowie zum Datenzugriff (GoBD), 14. November 2014. Retrieved September 19, 2017 from www.bundesfinanzmini sterium.de/Content/DE/Downloads/BMF_Schreiben/Weitere_Steuerthemen/Abgabenordnung/ Datenzugriff_GDPdU/2014-11-14-GoBD.pdf?__blob=publicationFile&v=1. vom Brocke J., Zelt, S. and Schmiedel, T. (2016). On the role of context in business process management. International Journal of Information Management, 36(3), 486–495. Cao, L., Mohan, K., Xu, P. and Ramesh, B. (2009). A framework for adapting agile development methodologies. European Journal of Information Systems, 18(4), 332–343. Christ, M. H., Mintchik, N., Chen, L. and Bierstaker, J. L. (2015). Outsourcing the information system: determinants, risks, and implications for management control systems. Journal of Management Accounting Research, 27(2), 77–120. Hall, J. A. (2011). Accounting Information Systems, 7th ed., Mason, OH: South-Western Cengage Learning. Hess, T., Matt, C., Benlian, A. and Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2), 123–139. Hsu, P-F. (2013). Commodity or competitive advantage? Analysis of the ERP value paradox. Electronic Commerce Research and Applications, 12(6), 412–424. Krcmar, H. (2005). Informations Management. Berlin/Heidelberg: Springer-Verlag, Majchrzak, A., Markus, M. L. and Wareham, J. (2016). Designing for digital transformation: lessons for information systems research from the study of ICT and societal challenges. MIS Quarterly, 40(2), 267–278. Mathrani, S., Mathrani, A. and Viehland, D. (2013). Using enterprise systems to realize digital business strategies. Journal of Enterprise Information Management, 26(4), 363–386. Neumann, G., Sobernig, S. and Aram, M. (2014). Evolutionary business information systems. Business & Information Systems Engineering, 6(1), 33–38. Pahlke, I., Beck, R. and Wolf, M. (2010). Enterprise mashup systems as platform for situational applications. Business & Information Systems Engineering, 2(5), 305–315. 79

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Rahimi, F., Møller, C. and Hvam, L. (2016). Business process management and IT management: the missing integration. International Journal of Information Management, 36(1), 142–154. Rosemann, M. and van der Aalst, W. (2007). A configurable reference modelling language. Information Systems, 32(1), 1–23. Scheer, A. W. (1992). Architektur integrierter Informationssysteme: Grundlagen der Unternehmensmodellierung, 2nd ed. Berlin: Springer-Verlag. Trkman, P. (2010). The critical success factors of business process management. International Journal of Information Management, 30(2), 125–134. van der Aalst, W. (1999). Formalization and verification of event-driven process chains. Information and Software Technology, 41(10), 639–650.

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7 Accounting Information Systems and decision-making Markus Granlund and Henri Teittinen

Introduction Decision-making plays a major, many times crucial, role in managers’ work as they aim to make their organizations prosper. Some of the decisions are operative ones and need to be made on monthly, weekly or even daily basis. Strategic decisions, on the other hand, are made more seldom, but they may be once in a lifetime type of decisions, framing the future of the organization. Decisions also form chains, as most decisions lead to new decision-making situations in the near or more distant future (Mouritsen and Kreiner, 2016). In order to be able to make “wise” and justified decisions, managers need something to build their decisions on: experience, intuition, information and their combinations, depending on the situation. During the last decade the idea of fact-based decision-making has gained more momentum. This means that decisions could and should be based more on “cold facts” instead of intuition, beliefs and feelings. This development is supported by recent developments in information and communications technology (ICT), such as Big Data and business analytics (CGMA, 2013). The need for more and better information derives from the complexity and dynamics of the global markets and its implications for “success recipes”. In brief, the whole existence of modern ICT and Accounting Information Aystems (AIS) therein is based on information needs. But what are these needs today and in the future? And how can AIS meet such needs? In this chapter, we outline how modern AIS can support managerial decision-making in contemporary organizations. We also aim to outline some future trends regarding this topic. We start by describing the factors constituting different decision-making environments and what we call data environments. This lays a foundation for describing what kind of information is needed in different contexts and how AIS can support managers in making informed decisions.

Decision-making and its environment Decision-making contexts vary depending on many issues. Typical contingency factors (Vaassen, 2002; Chenhall, 2003) offer a valid starting point for the analysis of such contexts and the related mechanisms. Such factors include size, lifecycle stage, line of business, technology, strategy, culture and so on. These factors define what kind of information is deemed relevant and valid 81

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in each context. As soon as we know about the information needs – i.e. what are the decisions to be made and in what environment – we can start designing information systems (IS) to produce relevant decision support. Decision-making is a process that can be organized in many ways. Some organizations have defined structured procedures for certain decision categories, whereas some rely more or less on ad hoc practices. Also decision-making styles vary by individuals: some rely more on experience and intuition, while some need and want figures, calculations and systematic analyses on which to base decision-making. It is also interesting to ponder on the human/computer relationship here. We know that automatization and robotics are taking over many things that used to be carried out by people. Decision support is one of the fields where developments in ICT obviously play a big role in this regard. Many routine-like decisions have been handed over to IT, which can calculate the best choice under defined circumstances. Yet, even in these cases, it is people who define the parameters and algorithms and, in most cases, verify the decisions, like in case of loan decisions in banks. In most cases, it is still people who make decisions, as decision-making often requires experiencebased, analytical thinking, as well as interpretation of information. Bigger decisions are typically made in groups, where different forms of expertise come together to inform decision-making in a versatile and more comprehensive way. Altogether, decision-making involves human action and interaction, which brings along different perceptions, interpretations, misunderstandings, disputes about estimates and emotions. This is also the reason why there is a tendency to go for automatized, fact-based decision-making; it is thought to reduce the number and magnitude of human errors in the process. In this regard, it is also important to recognize that if the decision is made by a single person, there may emerge problems with cognitive and motivational biases. Decisions are not made in a vacuum, but they are influenced by a number of factors in the decision-making environment. Information systems, such as AIS, can support people in these tasks by offering answers to specified questions, creating scenarios for learning purposes, as well as supporting decision influencing and legitimation (Burchell et al., 1980; Chong and Eggleton, 2003). An important issue to consider here is also the style with which decisions are made. Some people prefer fast and aggressive decision-making styles, whereas others act more slowly and as followers. This has natural implications for how they use AIS and other IS. A relevant generic concept affecting management in organizations relates to institutionalized, taken-for-granted assumptions and beliefs, i.e. culture. Organizational culture tells “how things are done here”. Gradually, also, IS start carrying such assumptions and values, as they reflect managerial cognitions and logics (Kaplan, 2008, 2011). This way IS may also standardize behaviour towards common ways of operating. As mentioned, all these issues alone and together define what decisions are needed and made, and especially what kind of information is needed to facilitate decision-making. We will also elaborate later the fact that in addition to what information is produced, the successfulness of decision-making ultimately depends on how that information is used. Getting information of good quality seems to be a common challenge of contemporary organizations. This is due to many things, not least because of the large number and fast pace of changes in the networked and global operating environment. This is also the reason why ICT has been put into the core of the development agenda of most organizations: how to get more valid, timely and relevant information to support decisions to be made regarding the short and long term. Vaassen (2002) has defined the quality of information being composed of two main characteristics: economy, i.e. the cost of producing information, and effectiveness. In this framework, effectiveness is further divided into reliability (validity and completeness) and relevance (accuracy, timeliness, understandability). This is a comprehensive basis against which to analyze contextual information needs and the design of AIS and other IS. 82

AIS and decision-making

Data environment/ICT domain In order to produce information that fulfils the above mentioned quality criteria, organizations have to carry out careful IS design. The simple fact is that you cannot analyze and report something on which you do not collect data. Without going into the technical details in this chapter, we simply describe the process of producing relevant information and further knowledge as the information is applied in decision-making situations. As Taipaleenmäki and Ikäheimo (2013) explicate, this process starts from the configuration of metadata and proceeds into data collection/registration procedures and further to data storage (databases and data warehouses) through ETL (extract, transform, load) technologies. The process where these datasets are transformed into information takes place in enterprise software, stand-alone software and spreadsheets. AIS technologies include all these elements, and can be used alone or together. Some of the calculations that are run through software are automated, some made ad hoc by user requirements. We draw here on the definition of AIS by Vaassen (2002, p. 3). He distinguishes four related elements of AIS: •

•

• •

Information systems: an information system is a set of interrelated components working together to collect, retrieve, process, store and disseminate information for the purpose of facilitating planning, control, coordination and decision making in businesses and other organizations. Managerial information provision: the systematic gathering, recording and processing of data aimed at the provision of information for management decisions (choosing among alternative applications) for entity functionality and entity control, including accountability. Accounting and administrative organization. Internal control.

Of these four elements we focus on the two first ones, i.e. IS and managerial information provision. However, technical details in this regard are beyond the scope of this chapter. Concrete examples of this sphere are budgeting systems, performance measurement/management systems (e.g. balanced scorecard), costing systems and various ad hoc analyses to support specific decisions. Regarding informed decision-making, these systems alone or in various combinations support managers. The general idea of such AIS is to provide financial and non-financial (numeric) information that is reported in desired forms to the decision-makers. Different applications from spreadsheets to enterprise systems offer nowadays a number of ways to visualize the information. The people producing such information also increasingly apply sophisticated techniques, including data mining and simulation technologies.

Framework of decision-making and AIS Figure 7.1 summarizes the “big picture” of AIS and decision-making. The starting point is that managers face decision-making situations for which they need specific information; sometimes detailed, sometimes more “rough”. This sets requirements for AIS design that are mediated by the context where the decision is being made. AIS designers then aim to design and develop systems that meet the requirements. In a similar vein, the decision-making environment mediates the information channels as relevant and important information is filtered in the process towards the actual decisions, which themselves entail complex filtering and interaction mechanisms. The decisions made and the experience gained in the decision-making process feed to learning and consequent development initiatives that influence, for example, perceptions 83

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Decision-making/decisions • Requirements: what information is needed? • Future implications of decisions • Decision-makers o Cognitions, experience, emotions--interpretation of information o Interaction, group dynamics 0 Machines vs. people 0 Role of information vs. intuition Design requirements

Framing of decision-making through the context

I

Learning & I development

Decision-making environment • Economic/functional factors: size, lifecycle stage, line of business, etc. • Institutional factors/culture, politics • Systems and processes • Role ofAIS Design requirements

Information

Data environment and AIS • • • • •

Metadata Registration ETL Storages Software-- reporting and visualization

Data to information

Figure 7.1 Data environment and decision-making environment framing decision-making

regarding the role of AIS and, in the end, system development. The AIS needs to be constantly evaluated and developed to keep up with and fit the operating environment and technological development, the question being: under the specific circumstances, how to produce efficiently and effectively information that is reliable, comprehensive (yet accurate) and offered in a timely manner in an understandable visualized format (Vaassen, 2002).

Decision-making situations and AIS In this section, we are discussing the roles AIS may play in different decision-making situations. First, we will take a look at the Burchell et al. (1980) framework and then discuss its implications for managerial practice. Although the framework was presented in the 1980s, it is still today highly relevant for analysing the role of AIS in organizational decision-making, as the premises regarding uncertainty related to decision-making have not changed.

The Burchell et al. (1980) framework Burchell et al. (1980) (see also Figure 7.2) argued that the uses of accounting information were extended and accounting systems were not used any longer only for taxation purposes, but also for enabling more detailed financial management of the firm. They explained this by the 84

AIS and decision-making

uncertainty of objectives Low

Low Uncertainty of cause and effect High

Answer machines Decision by computation

Answer machines/learning machines Decision by judgement

High Ammunition (dialogue) machines Decision by compromise Rationalization machines (idea machines)

Ex post legitimation (decision by inspiration)

Figure 7.2 Uncertainty, decision-making and the roles of accounting practice Source: Adapted from Burchell et al., 1980; Boland, 1979

emergence of new organizational practices and forms, which include coordinating, centralized and functional control, as well as divisional, matrix and project organizations. Organizations were also required to fulfil more extensive needs of reporting for capital markets. They stated that accountants were more and more involved in different types of management activities, such as budgeting and standard costing, planning and resource allocation, and thus they became central actors in organizational management. They also argued that these internal organizational changes and external pressures resulted in changes in accounting and institutionalization of accounting. Accounting had become more than only a “machine” responding to preconceived organizational needs; the role of accountants was becoming more like searching for new opportunities regarding accounting practice. In addition, new professional institutes and bodies of accounting and accountants were established. The transformation also implied that accounting procedures were increasingly defined and documented in all kinds of organizations (Burchell et al., 1980). In addition, Burchell et al. (1980) stated that the relationship between accounting and organizational decision-making had been conceived as only normative; the role of accounting is simply to provide relevant information for decision-making and improve the decision-making processes. However, they argued that this perspective had been taken for granted and rarely examined critically. For that reason, understanding better the role of accounting in organizations, they used the framework of Thompson and Tuden (1959) to elaborate on the roles of accounting in decision-making in practice. The Thompson and Tuden (1959) framework is divided into categories by the uncertainty regarding casue-and-effect relations (either low or high) and uncertainty regarding objectives (either low or high). Burchell et al. (1980) presented that when objectives are clear and the consequences of actions are known/certain, decision-making is possible by automation (computation). Under such circumstances decision-making may be designed and programmable, and thus accounting can serve as an “answer machine”. When the relations between cause and effect become more uncertain, decision-making will be more judgemental and subjective by the participants in decision-making. In practice, this means ad hoc analyses and what-if models. Burchell et al. (1980) call this role of accounting in decision-making process as “learning machine”. In times of Big Data, though, the IS may find relevant cause-and-effect relations without users’ knowledge. We may say that sometimes the IS “knows” more than the user even in uncertain environments. However, decision-making under such circumstances typically requires human cognition and capability to interpret whether the suggested relations are valid 85

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and reliable. When the relation between causes and effects are certain but the objectives include political rather than computational rationales, the role of accounting in the decision-making process is seen to be an “ammunition machine” (or “dialogue machine”). And finally, when both uncertainty regarding objectives and uncertainty regarding cause-and-effect relations is high, the decision-making tends to be of an inspirational nature, and accounting has been used for legitimizing and justifying actions. This role of accounting Burchell et al. (1980) describes as a “rationalization machine”.

The role of AIS in different decision-making situations We have chosen four basic decision-making situations for further analysis, including pricing, product mix, equipment replacement and outsourcing decisions. While it is impossible to include all kinds of decision-making situations in such an analysis, we can consider these decision types to be relatively commonly relevant (capital budgeting is also excluded from the analysis as there is a chapter in this book analysing the role of AIS in investment appraisal). Overall, our approach in this analysis derives from the fact that in contemporary organizations decision-making situations have been presented as information management questions (for more detailed descriptions and calculations, see for example Burns et al., 2013). Pricing decisions

Pricing is one of the most important decision-making situations in every organization. Too low pricing requires higher sales volumes, whereas too high pricing means higher profit margin per unit, but typically results in lower sales volume. Thus, to adjust pricing to the level where it meets the pricing strategy so that it is in line with the company’s financial objectives is a demanding task. Porter (1980) noted early on that corporate strategy has to be targeted either to cost leadership or to differentiation. If the target is to offer the lowest price, the company follows a cost leadership strategy. If the customer segment is not price-sensitive, a differentiation strategy should be applied. In cost leadership strategy, the purpose is to win the market share having the lowest prices, or at least having the lowest price regarding value for customers. We argue that a cost leadership strategy, in particular, requires continuous search for cost reductions in all business activities, and thus there also exists a continuous need to make decisions, which can be supported by AIS and its automatic process controlling. Sometimes decisionmaking models in pricing can be pre-programmed. This means that pricing models with different types of demands can be estimated. By giving the parameters, AIS will calculate the prices or control the processes. For example, regarding flight tickets, decision-makers have pre-programmed the pricing model for the tickets and the prices will be changed based on the designed demand curve. Similar situations can be found in banks regarding credit lending decisions. By giving the parameters on borrowers, IS will produce a risk assessment and the interest rate (price) for the customer. If some of the parameters are changed, a new price or risk rate will be generated. In this way, decisions on price can be automatized and the role of AIS is to support the decisionmaking and work as an “answer machine”. In particular, the digital environment facilitates the design of the pricing process to be computational, as it enables the cost-efficient collection of data for future pricing modelling. However, it is rarely possible to pre-program pricing decisions totally. Even if we would be able to pre-program our pricing model, we are not able to pre-program our customer buying behaviour. When there is no uncertainty regarding the objectives but there is uncertainty regarding causes and effects, managers face questions such as: What have the prices and volumes been previously? How did our actions affect demand and profitability? And, what can be expected if 86

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we set prices now and in the near future in a particular way? Combining different data sets concerning these questions, AIS can support decision-making as a “learning machine” by producing relevant pricing scenarios for decision-makers. Accountants and managers can learn about the potential consequences of decisions by experimenting with various alternatives in the pricing model and thus generate an understanding of the pricing mechanisms. The capacity of AIS to make simulations is supported by functionality combining future plans with different types of historical data from the databases. Naturally, in the end, a decision simply needs to be made based on this understanding, which may later prove to be a good one or not, depending on our ability to estimate the consequences of the actions taken. Pricing may many times also include subjective elements. In situations where the objectives are not clear (e.g. regarding the pricing strategy in general), but we have certain ideas regarding causalities, the role of AIS may become an “ammunition machine”, as decision-makers may use different scenarios offered by the AIS to influence other people involved with the decision of their view regarding the “correct price”. This role has also been labelled as a “dialogue machine” (Boland, 1979), meaning that AIS may in this role help managers to develop and argue different points of view which are conflicting (but consistent with the underlying facts). This view emphasises the role of AIS to encourage exploration and debate. Regarding the “rationalization machine” or role, first of all, it may be that AIS information is used afterwards to demonstrate that the made decision was a good one. We may also state that AIS themselves may work as “rationalization machines”. Contemporary AIS include different types of simulation methods and embedded knowledge for pricing which may as such legitimise the role of AIS in decision-making vis-à-vis other bases of decision-making, such as experience and even intuition. Product mix decisions

Product mix decisions include typically four dimensions: (1) the number of different product lines of the company, (2) the total number of items within the product lines, (3) versions of each product in one product line and (4) consistency, which tells about the relation between the product lines. Decision-making in this context may relate, for example, to the following kinds of questions: (1) Should we establish new product lines or remove some of the existing ones; (2) Should we add more items into certain product lines; (3) How many versions should we have regarding each product; or (4) Should the product lines be more consistent with each other (see for example Cooper and Kaplan, 1991; Drury, 2008)? As we may infer from the above questions, product mix decisions are closely related to the market situation, capacity issues, cost structure, as well as the lot sizes of production (see for example Lea and Fredendall, 2002). The role of AIS is to connect sales, purchasing, logistics and marketing together in order to provide data on financial and cost management issues. A sales order may start all the transactions in the supply chain: when a sales order has been entered into the system, deliveries, production or purchasing orders may be generated. This illustrates that, particularly in real time and digital environment, IS can support decision-making even automatically. Uncertainty of objectives and causality are pretty low in this context, and AIS works as an “answer machine” in short-term planning. In long-term planning, the role of AIS is to present historical data on demand. Managing the product mix and the supply chain is not based on sales orders, but more on mixed subjective interpretations of historical data. In this type of decision-making situation, AIS can support decision-making in the learning role (simulations) or, depending on the level of uncertainty regarding objectives, as an “ammunition machine” as the different potential choices are discussed 87

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and debated. Like in pricing decisions, contemporary AIS may also be connected to different types of production planning methods, like Lean Manufacturing, Just-in-Time production, or Activity-Based-Management. Regarding the legitimizing role, the same premises apply here as in the case of pricing decisions illustrated above. Equipment replacement investments

Replacement investments differ from other investments in that those involve the displacement or scrapping of an existing investment. Typically, investments are made when new assets are required for the expansion of the company. We can say that replacement investments are less complex to make and they do not require such a massive search for alternatives as new expansion investments. One key characteristic in replacement investments is also the timing, referring here to optimizing: how long to keep existing machinery before it starts to be generally more cost-beneficial to replace it (see for example Dobbs, 2004; Merret, 1965). An interesting question also is what the replacement investments are like in contemporary organizations: in addition to parts or modifications regarding existing product lines they may increasingly concern service type of investments, like new versions of software. This makes the evaluation of optimal replacement even more challenging, as it is very difficult to evaluate how “worn out” the original investment is. Today, the Internet has brought the sourcing of products and services globally available. This means that comparisons can be made easily in real time worldwide. Sometimes the sourcing is based on sourcing catalogues, which may make it possible to even automatize the decisions on replacement investments. In such a case, the IS gives the decision-makers clear guidelines on where to source and what the item and transaction costs will be. In these situations, AIS works as an “answer machine”. When replacement investments involve high uncertainty regarding causalities but there is a clear vision on objectives (at least costs of investments can be gathered and reported), AIS include all the needed data in the database which can be used in the decision-making process for simulations and to learn about the optional paths to take. Then, we can say that AIS works like a “learning machine”. Similarly, as in the other examples above, also here AIS can be used as an “ammunition machine” (even in a political way) as alternative calculations are produced to back up one’s own views and debate the options concerning the decisions to be made and actions to be taken. AIS databases typically include a lot of historical data, which can be used for such influencing purposes in situations where there is disagreement regarding the objectives of action. Typically, in postauditing of investments, AIS can be used as a “rationalization machine” to evaluate and justify the decisions made. However, regarding replacement investments, this role is less apparent than in case of decisions on new expansion investments. Outsourcing

The outsourcing decision is also known as the make-or-buy decision. This means that there may exist needs to decide whether to manufacture items or produce services in-house or purchase those from an external supplier. Two main issues have to be considered in these situations: (1) cost of outsourcing in relation to own production and (2) availability of production capacity (see for example Burns et al., 2013). Contemporary organizations are more and more part of global alliances and networks. For example, regarding manufacturing type of organizations, they increasingly work either as 88

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subcontractors or manage their own subcontractor networks. In the leading-edge firms, the subcontractor works just like the purchaser’s own product line. In this context, modern IS need to enable control of inputs, outputs and capacity in real time. Even if the supply chain as such could be automatized, there is always an unavoidable need for supervision and control. Only people are able to manage and make decisions when something goes wrong and unexpected things happen (which are not pre-programmed into the IS). An AIS may work automatically in a “controller” role, as an “answer machine”, when we know the consequences of actions and there is no uncertainty involved with our objectives. In situations when the objectives are clear but there is uncertainty around the cause-and-effect relations, AIS with its database can support decisions as a “learning machine”. It can be used to produce different simulations on the optional paths regarding make-or-buy, and facilitate the generation of future scenarios as the decision-makers want to speculate on the outcomes of the different paths; the possible different future states of affairs. Also, when causalities are clear (e.g. outsourcing will advance the organization’s operations) but it is not clear which products, product lines or services should be outsourced, AIS can act as an “ammunition machine” in debates around the specific actions to be taken. Finally, we may suggest that the role of AIS in decision-making regarding outsourcing when both the objectives and causalities are unclear (high uncertainty) is more to build the decision-making context. Contemporary AIS may thus act in a constituting role in trying to offer as many clues as possible concerning the uncertain environment; at least it may serve this role in a way to help the decision-makers realize the high uncertainty surrounding the decision to be made.

Conclusion – the role of AIS in decision-making To summarize, for the first, we may conclude that the task of AIS in supporting decisionmaking, in its technical form, is to collect, retrieve, process, store and disseminate managerially relevant information to decision-makers (see Vaassen, 2002). In the digitalized environment, AIS may collect and build on massive databases in serving this task. However, as we have described, this is only one dimension in the big picture as it comes to how the decision-making environment is constituted and what factors are involved before the final decision is reached and executed. In this process, the AIS may act in different roles depending on uncertainty related to the objectives and cause-and-effect relations surrounding the particular decision situation: giving answers, facilitating learning, serving as a basis for argumentation and legitimizing the decisions (Burchell et al., 1980). When objectives and causalities of decisions are known – in other words, when uncertainties of both objectives and causalities are low – AIS are able to present the correct options within the framework of specified parameters. Then it is possible to automatize and pre-program the decision-making models, and AIS may work as an “answer machine”. If the objectives are clear but causalities are not well-known, AIS are not able to give clear answers to the questions the decision-makers may have, but rather options and directions through simulations. AIS works then as a “learning machine”. When decision-makers have different opinions regarding the objectives but the causalities are relatively clear, AIS with its capability to produce reliable figures and facts can offer a valid basis on which to build arguments as the decision-makers try to influence the decision situation and convince each other about a specific option. AIS acts then as an “ammunition machine” as it is used for purposeful prioritizing. When there is uncertainty involved with both the objectives and causalities, AIS may work as a basis with which decisions are legitimized (ex post), i.e. as a “rationalization machine”. 89

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Decision-making situations can be categorized either as operational or strategic. Operational decisions are of short-term and repeatable nature, and they do not require huge amounts of monetary or other resources. On the other hand, strategic decision-making situations are of long-term nature and they typically affect the whole organization. Uncertainty in operational decisions is known to be lower than in strategic decisions. This distinction also affects the roles AIS may have. In operational decisions, the decision-making model can sometimes be preprogrammed and automated, whereas with strategic decisions the situation typically exists only once; they do not occur repeatedly, but are more or less unique. Strategic decisions also typically involve higher amounts of uncertainty and risk, which means that the “ammunition” and “rationalizing” roles of AIS often get activated. Nevertheless, both operative and strategic decisions involve behavioural aspects, the first typically less, the latter even quite a lot, as the long-term orientation makes the situation more difficult; whenever we look further into the future, the amount of uncertainty inevitably increases. This further relates to the amount of subjectivity and interpretation of information involved with both individual and group decision-making. We may assume that decision-making models that are more or less programmable imply that interpretation of information is kind of in-built into the AIS model. There is not much room or even need for interpretation, although the final decisions always require human consideration. Anyway, when decisions are made for the first time, it always requires human understanding. Later, as we have learnt from mistakes and causal effects of our actions, we can improve decision-making and information models to be increasingly programmable, especially as comes to operational decisions. AIS can neither make interpretations, nor can they interact with their environment. The supporting role of AIS is important to note as we increasingly discuss artificial intelligence and robotics. Not even the best systems can over time accurately foresee the consequences of decisions; how customers, competitors or employees may act in the short or, particularly, long term. Burchell et al. (1980) recognized early on the interpretative nature of decision-making and thus the complex nature of AIS therein. In this context, we should also appreciate the role of decision-making styles. Sometimes decisions are required to be made in unexpected situations and quickly, whereas in some situations decisions can be made slowly, following activity in the markets; the behaviour of competitors and consumers, for example, as comes to pricing decisions. Some decision-makers have more trust in figures whereas some may base their decisions more on intuition and feelings. AIS are part of the decision-making process, where decision-makers and AIS work together. However, the role of AIS depends on the style of decision-making. People’s sense making and cognitive characteristics are different, which makes the design of AIS always challenging. We should also always appreciate the different organizational contexts where decision-makers operate. Each organization has its own policies and processes to prepare and make decisions. Furthermore, different decision-making processes may exist also within the same organizations, in their different business units, or divisions in different geographical locations. This is a further challenge for AIS, in the design of which it is often cost-effective to aim for standardization, which, on the other hand, may mean contextual trade-offs concerning the validity and relevance of the information produced. We have illustrated also the role AIS themselves play in constituting decision-making environments. AIS design is often used to document and systematize previously undocumented or unsystematic (not transparent) processes with the aim of standardization of practices. In this way AIS also work as security checks for decision control: the decision-making processes are expected to be followed as documented and programmed into AIS. The intention at the more general level then is to secure that the processes are in line with the organizational rules and policies. In that 90

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sense, AIS are not only technical tools, but also kind of “social actors” in organization, as they carry common rules and procedures regarding decision-making practice. Somewhat paradoxically, the subjectivity involved with decision-making may undermine or lead to bypassing of such rules, as people follow routines that may be only loosely coupled to the formal rules. Overall, the context of decision-making cannot be emphasized enough. The contingency factors framing AIS design and use are crucial to understand. For example, in small organizations decision-making processes are naturally different than in large ones. In small organizations, there may not exist decision-making policies. It may be that those are not even needed, if the entrepreneur-CEO is responsible for all the decisions and is responsible for their outcomes only to himself/herself. When companies grow, the need for more comprehensive systems emerges. Thus, the role of AIS in decision-making is always depending on the size, line of business, lifecycle stage, etc. of the organization. We have been discussing the role of AIS in supporting decision-making. However, in some situations AIS may have less favourable effects; they may slow down or even create obstacles in the decision-making process. AIS are nowadays relatively complex and multidimensional, which requires a lot of knowledge from their users. The process required by the rules embedded in AIS and related systems and policies may sometimes lead to unnecessarily cumbersome and complicated practices, resulting in frustration, dissatisfaction and shortcutting. Sometimes these may be unintended consequences of the AIS design; the purpose has been something else, but for several reasons, for instance, the end-users of the systems have not been engaged in the design process sufficiently to support decision-makers in their real-life tasks. In this way, the AIS may have become, at least partly, an obstacle to efficient and effective decision-making that not only slows down the process, but may also lead to “random behaviour” where nobody can any longer separate the facts from fiction. This is also a further indication of the role AIS may play as a part of the broader socio-technical decision-making context. We may conclude that the role of AIS should be explored through the following perspectives: (1) data and information context, i.e. decisions are based on “naked” facts and/or interpretation, (2) decision-making and organizational context, i.e. the decision-making process is either systematic and documented or not and (3) decision-makers’ context, i.e. the decision has been made individually or in a group. These elements have been presented in Figure 7.3 overleaf summarizing the role of AIS in decision-making (cf. Figure 7.1).

Future trends This chapter has explored how AIS can support decision-making in organizations. We also briefly described how AIS may hinder or slow down decision-making. The sophistication of AIS for decision-making obviously varies a lot in practice due to different contingency factors. Our general illustration holds for most organizations, especially the larger ones. On the other hand, technology is never ready and the most advanced companies and public sector organizations have already moved to the era of Big Data and its analytics (Warren et al., 2015). When we include also non-structured data in the sphere of AIS and other IS, we enter a new world of decision support. Hadoop and other applications, not to mention data collection through the Internet of Things, facilitates already now (on-line) decision-making regarding not only pricing and operative activities, but they also transform whole business models and processes how business is being made. Robotics and automatization are inseparable parts of this phenomenon. Interestingly, Burchell et al. (1980) pointed out the political and influencing roles of AIS partly questioning the existence of a pure “answer machine”. However, with the technological development this role gets increasingly emphasized, as the modern technology seems to enable better and better fact-based and automated decision-making. This is an important observation also 91

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Data and information context

Decision-making based on facts

Decisions based on interpretation

The role of AIS:

The role of AIS:

Answer machine

learning machine, ammunition machine Facts and figures am support interpretations

Giving parameters, AIS generates results

/ Decision-makers' context

Decision is made individually

Decision is made in a group

The role of AIS:

The role of AIS:

learning machine

learning machine, ammunition machine

Facts and figures can support interpretations

Facts and figure can .support interpretations

AIS can be used for simulation

AIS can support communication and discussion AIS can be used for simulation

Decision making and organizational context

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Formalized decision-making processes exist

Formalized decision-making proceses do not exist

The role of AIS:

The role of AIS:

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Easy to follow !he processes

Everyone works as they like in decision-making

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AJS can be used for creating the decision-making culture and context

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Figure 7.3 Summary of AIS and decision-making

because this is becoming possible even if the decision-making environment (including causalities) has become more and more uncertain and turbulent, in general. Furthermore, regarding the lower right-hand corner of the framework, also an “idea machine” role has been suggested for AIS (Earl and Hopwood, 1981): a role enabling creative solutions to be found to messy problems as objectives are ambiguous and predictive models are poor. This role was earlier considered to be unrealistic to expect from a formal AIS. We suggest that with the new technologies (Big Data, analytics, etc.), if embedded in the AIS, make this a realistic role to expect. With the developing ICT also comes risks. One such risk relates to the huge amount of data being generated every second. There is a real risk of “drowning” in data and information. Having more data and more versatile data, and data scientists to handle it, the role of knowledgeable managers is yet of utmost importance. This is because data/information tells little without real 92

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understanding and insight of the business. Technological complexity sets new requirements for businesses and even to existing business models. We will increasingly face radical transformations in this regard, which also implies that decision-making will change: what we will decide upon and how. Regarding Figure 7.1, this also means that the design requirements for AIS gradually change. And who knows if in the future we will any longer see AIS as we know them today, but rather embedded technologies that together form massive decision-making systems including all kinds of data collected on-line. In any case, increasingly, developments in ICT enable the delivery of different software and platforms for AIS for everyone in a cost-effective way through cloud services. Previously the bigger companies have been the pioneers in developing AIS, but this may be changing.

References Boland, R. J. (1979). Control, causality and information system requirements. Accounting, Organisation and Society, 4(4), 259–272. Burchell, S., Clubb, C., Hopwood, A., Hughes, J. and Nahapiet, J. (1980). The roles of accounting in organisations and society. Accounting, Organisation and Society, 5(1), 5–27. Burns, J., Quinn, M., Warren, L. and Oliveira, J. (2013). Management Accounting. Berkshire, UK: McGrawHill Education. CGMA. (2013). From Insight to Impact: Unlocking the Opportunities in Big Data. London: CGMA. Chenhall, R. (2003). Management control systems design within its organisational context: findings from contingency-based research and directions for the future. Accounting, Organisation and Society, 28(2), 127–168. Chong, V. K. and Eggleton, I. R. (2003). The decision-facilitating role of management accounting systems on managerial performance: the influence of locus of control and task uncertainty. Advances in Accounting, 20, 165–197. Cooper, R. and Kaplan, R. (1991). Profit priorities from activity-based costing. Harvard Business Review, 69(3), 130–135. Dobbs, I. M. (2004). Replacement investment: optimal economic life under uncertainty. Journal of Business Finance & Accounting, 31(5–6), 729–757. Drury, C. (2008). Management and Cost Accounting, 7th Ed. London: Cengage Learning. Earl, M. J. and Hopwood, A. G. (1981). From management information to information management. In H. C. Lucas Jr, F. F. Land, T. J. Lincoln and K. Supper (Eds.), The Information Systems Environment. Amsterdam: North Holland. Kaplan, S. (2008). Cognition, capabilities and incentives: assessing firm response to the fiber-optic revolution. Academy of Management Journal, 51(4), 672–695. Kaplan, S. (2011). Research in cognition and strategy: reflections on two decades of progress and a look to the future. Journal of Management Studies, 48(3), 665–695. Lea, B.-R. and Fredendall, L. D. (2002). The impact of management accounting, product structure, product mix algorithm, and planning horizon on manufacturing performance. International Journal of Production Economics, 79(3), 279–299. Merret, A. J. (1965). Investment in replacement: the optimal replacement method. Journal of Management Studies, 2(2), 153–166. Mouritsen J. and Kreiner, K. (2016). Accounting, decisions and promises. Accounting, Organisations and Society, 49, 21–31. Porter M. E. (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: Free Press. Taipaleenmäki, J. and Ikäheimo, S. (2013). On the convergence of management accounting and financial accounting: the role of information technology in accounting change. International Journal of Accounting Information Systems, 14(4), 321–348. Thompson, J. D, and Tuden, A. (1959). Strategies, structures and processes of organisational decision. In J. D. Thompson, P. B. Hammond and R. W. Hawkes (Eds.), Comparative Studies in Administration. Pittsburgh, PA: University of Pittsburgh Press. Vaassen, E. (2002). Accounting Information Systems: A Managerial Approach. Hoboken, NJ: John Wiley & Sons. Warren, J. D., Moffitt, K. C. and Byrnes, P. (2015). How Big Data will change accounting. Accounting Horizons, 29(2), 397–407. 93

8 Changing the speed and format of information provision Examining the temporal decoupling of accounting numbers and their analysis Nicolás J. B. Wiedemann and Leona Wiegmann

Introduction Managers are “people who make decisions about a business, department, [. . .], etc.” (MerriamWebster Online Dictionary, n.d.) and communicate information to internal and external stakeholders (Barnard, 1938). For those purposes, they rely on managerial information provided by various sources (McLeod and Jones, 1986; Jones et al., 1989) such as management accountants. Historically, management accountants have been recognized by scholars as crucial sources of information due to their prominent professional role (Burns and Baldvinsdottir, 2005). This role has undergone a major shift over the last decades, as several studies have observed (e.g. Granlund and Lukka, 1997): from “bean counters”, who primarily gathered historical data and measured past performance, to internal consultants or “business partners”, who provide more in-depth insights such as analytical interpretation and scenario analyses (Atkinson et al., 2012; Hansen and Mowen, 2007; Siegel et al., 2003). This development was primarily facilitated by innovations in information technology (IT) during the 1990s (Booth et al., 2000; Sánchez-Rodríguez and Spraakman, 2012; Scapens and Jazayeri, 2003). Information systems (IS) that were up and coming at the time, such as enterprise resource planning (ERP) systems, made it possible to harmonize multiple stand-alone systems across different functions into a single organization-wide system (Spathis and Ananiadis, 2005; Davenport, 1998). This new opportunity empowered management accountants to develop holistic digital representations of organizations; consequently, they were able to provide managers with more recent, accurate and in-depth analyses (Sánchez-Rodríguez and Spraakman, 2012). Now, circa 20 years later, another surge of innovation in the IT industry is taking place (Stodder et al., 2015). State-of-the-art AIS are taking advantage of in-memory technology, which promises real-time data processing and the automation of more sophisticated analyses (Stodder et al., 2015). This increase in speed means that accounting numbers can be continuously and instantaneously available rather than – as common in current reporting practices – only at the end of a period (typically month end). Consequently, this challenges the relevance of period deadlines at which management accountants provide information. Furthermore, the format of 94

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how information is displayed offers new opportunities by replacing inflexible graphs and extensive tables of figures with interactive visualizations (Dilla et al., 2010), which makes dealing with accounting numbers more intuitive (Stodder et al., 2015). Interactive visualizations provide the possibility to “amplify cognition or the acquisition and use of knowledge” to a more expansive group of people (Card et al., 1999, p. 6). Due to such improvements in usability, management accountants and, in particular, non-accounting experts such as managers can grasp accounting numbers with less difficulty. In this way, managers can be empowered to perform analyses themselves to gain insights about the business. Thus, technologically-advanced AIS may – like their predecessors, the ERP systems – facilitate another change in management accounting. In particular, it can result in a transfer of previous accounting activities to managers, which could, on the one hand, threaten the role of management accountants as the primary providers of accounting information but, on the other, strengthen their role as business partners within the organization (cf. Caglio, 2003) and, subsequently, allow more time for more profound analyses. Therefore, we addressed the following research question: How can the opportunities that AIS provide affect the process of providing managers with information relevant for decision-making and what role do management accountants play therein? To address the study’s aims, empirical evidence was collected and analyzed from an in-depth case study in a multinational organization. Its management accounting department had been distinguished by an international consulting firm as the most efficient and effective among their peers. The organization attributed the success to their technologically-advanced AIS, which exploited opportunities of increased speed when providing information as well as new interactive formats to visualize that information. Methodically, data was gathered from semi-structured interviews, observations and internal documents, and was analyzed based on a thematic coding (Flick, 2014). Our analysis showed that faster data processing led to a temporal decoupling of accounting numbers and their corresponding analysis. Although faster and automated data processing allowed for the generation of accounting numbers in real-time – thus making them available on a continual basis – the analysis of such information still required preparation time. In this context, we could observe changes in both management accountants’ and managers’ work as well as in the interaction between managers and management accountants that resulted from that temporal decoupling. On the part of the management accountants, the automated data generation saved time, which they then used for more profound analyses as well as the analysis of issues arising from ad hoc events. Because of this, management accountants were able to develop a better understanding of the business and strengthen their role as business partners in the organization. On the part of the managers, it became evident that the new interactive formatting opportunities translated into a temporal decoupling of accounting numbers and corresponding analyses. This was because the intuitive and interactive visualizations enabled managers, as non-accounting experts, to grasp and interpret accounting numbers on a daily basis without having to wait until month’s end to receive accounting numbers. Consequently, managers were more concerned with the accounting numbers and developed a better understanding of the business. Nevertheless, in addition to managers’ individual analysis of accounting numbers, management accountants still provided comprehensive analyses at month’s end. Despite this temporal decoupling, managers and management accountants in our case described the associated discussions as more intense as their focus changed from conferring about what the status of the business actually is to discussions about the underlying reasons for the provided numbers as well as solutions to related problems. In the remainder of this chapter, we will first review the literature on management accountants’ tasks and then discuss both how a shift in the ratio of these tasks were enabled by new information technologies as well as recent developments of information technologies that could cause another 95

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shift. Subsequently, we will describe the empirical setting and the applied data collection and analysis in the method section. Finally, we will present our empirical findings and a concluding discussion.

Literature review Management accountants provide managers with decision relevant information (Byrne and Pierce, 2007), which is why they are entrusted with several tasks that can be categorized into three areas: transaction processing, reporting and decision support (Booth et al., 2000). Transaction processing refers to data preparation, which includes entering, collecting and cleansing raw data (cf. Madsen, 1963). Reporting builds upon this raw data to account for past developments and the status quo (Madsen, 1963). Finally, to support decisions both historical and future-related information is considered in the form of forecasts and evaluated options for actions (Madsen, 1963; Stodder et al., 2015). These three fields have been supported in varying degrees by IS and, in particular, AIS (Granlund and Mouritsen, 2003). The dependency of organizations on IS (including AIS) has grown over the last decades, thus forcing IT managers to improve the quality of IS (Gorla et al., 2010). The further standardization and automation of already integrated IS has led to an even stronger harmonization of systems across functions (Spathis and Ananiadis, 2005; Davenport, 1998). Based on this, AIS were able to effectively support management accountants in their transaction processing tasks (Granlund and Malmi, 2002). The harmonization of systems principally affected the collection, entry and cleaning of raw data, thus increasing the quality of the output and thereby automating previously manual tasks (Scapens and Jazayeri, 2003). The reduction of manual tasks again led to several benefits (cf. Shang and Seddon, 2000, 2002) – in particular, efficiency gains (Granlund, 2007; Burns and Baldvinsdottir, 2005). This increase in transaction processing efficiency (Grabski et al., 2011; Vakalfotis et al., 2011) allowed management accountants to put more effort on analyses for reporting and decision-support (Granlund and Malmi, 2002). The role of the management accountants then tilted towards internal consultancy (Anastas, 1997). In addition, management accountants had to develop IT competencies to design and manage the related information flows of IS (Caglio, 2003), meaning that they also took over tasks that traditionally belonged to the IT department. Although this, in sum, led to improvements in data collection, Fahy and Lynch (1999) have found that spreadsheets were still used to generate analytical explanations and handle managers’ unpredictable information demands. In other words, reporting and decision support have seen only modest direct benefits from AIS in the last decades. Recent technology, used in AIS, aims to further standardize and automate tasks (Stodder et al., 2015) in reporting and decision support in order to achieve efficiency gains in these areas, as well. Thus far, management accountants have provided information to aid managers in their problem-solving and decision-making activities. However, the technological changes make it worthwhile to review how managers’ capabilities to succeed in these tasks are affected by different presentations of information. Ramarapu et al. (1997) have found that IS that present information in a strict linear sequence are inferior to IS that allow users to follow their own logic of connecting the information. Their reasoning is based on cognitive fit theory (Vessey, 1991), which argues that a pre-set sequence fits the specific cognition of the user less well and is therefore more challenging to understand (Bush, 1945; Shneiderman and Kearsley, 1989). These findings emphasize the need for possibilities to break through a sequence and go back and forth to better mirror managers’ cognition. Regarding management accounting information and the visualization thereof, interactive dashboards with hyperlinks can thus be considered favourable 96

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to PowerPoint decks that present information sequentially. Furthermore, these dashboards rely on graphical visualizations that facilitate the encoding of information (Stodder et al., 2015). The reason being that even when visualizations contain the same information as tables, they better fit managers’ cognition, which subsequently increases decision-making quality (Hirsch et al., 2015) by enabling them to be more accurate (Umanath and Vessey, 1994) and efficient (Huang et al., 2006). Consequently, research suggests that changes in AIS that incorporate features that are more aligned with the requirements of managers as final recipients may again change the role of management accountants and their interaction with managers. In addition, with a stronger consideration of managers, organizations must also deal with an increasing proportion of unstructured data (i.e. textual data) and an exponentially growing amount of data (Russom, 2011). This influx of data creates a need for greater data processing speed. In-memory technology (e.g. SAP HANA) and real-time reports are powerful technical prerequisites to address these new demands (Russom, 2011). In summary, there is a call for faster, more intuitive and recipient-specific information provision to realize the opportunity of a powerful means for understanding data (Dilla et al., 2010), which points to real-time and self-service formats. Concomitant with the increasing importance in practice, these developments have also caught the attention of scholars (Chen et al., 2012), who have also pointed to some critical points. Hodge (2001) and Elliott et al. (2012) have found that information presented in an interactive way are perceived as more reliable and trustworthy. In other words, the possibility to interact with accounting numbers involves the risk of an overconfidence bias. This bias points to the necessity of management accountants as an intermediary to mitigate this effect (Hodge, 2001). In addition to a potential bias, what are further consequences of the implementation of real-time and self-service formats for the provision of information by management accountants? Scholars have argued that the analysis of accounting numbers is more valuable than the pure provision of accounting numbers, as the process and argumentation that lead to the results of analysis are decisive for the quality and effectiveness of decisions (Merchant and Van der Stede, 2007). In this line of reasoning, Galbraith (1973) has questioned whether everything could be measured and represented in numbers and figures, as would likely be necessary for a self-service information provision. Similarly, Quattrone (2016) has stressed the relevance of a process of communicating, discussing and understanding accounting numbers and, thus, underlines Galbraith’s argumentation. This indicates that numbers do not speak for themselves, as they can be considered incomplete translations of reality (Chapman, 1997) and underlines the need for intermediaries to provide a more complete picture of the reality. Scholars have argued that management accountants can act as such intermediaries and enable reasonable choice (Busco and Quattrone, 2015; Quattrone, 2015). In conclusion, prior research underlines the importance of IS for management accountants and indicates various potential effects regarding the provision of information, which we will analyze in more detail in the following sections.

Method For the empirical evidence in this chapter, we conducted a qualitative, in-depth, single-case study at PolymerCo (fictitious name), which specializes in material solutions and generated approximately €15 billion in 30 manufacturing plants worldwide in the 2013 fiscal year. The management accounting function comprises approximately 120 people, who are assigned to three different departments for each organizational business unit as well as a central department of approximately ten people. In the course of a general savings program, a team consisting of people from the business intelligence and management accounting department started an 97

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initiative to improve the provision of information within the organization in 2013. Thereupon, a new AIS, an automated dashboard solution, was implemented, which replaced the prevailing manual Excel-based calculations and PowerPoint-based slide decks. This new dashboard solution could process all organization data in near real-time and generate interactive visualizations for any aggregation level that facilitated the analysis of accounting numbers. It was accessible by both management accountants and managers in the form of aggregated accounting numbers. Since the data was directly loaded in the tool and displayed in graphs on the dashboard, it made the combination of Excel and PowerPoint obsolete. In addition to the individual use of such dashboards, management accountants and managers also shared dashboards to distribute the same accounting numbers among different people. Data gathering started in July 2013, upon contacting PolymerCo, and ended in September 2016. To guarantee data reliability and reduce a potential informant bias (Gioia et al., 2013), a confidential agreement with PolymerCo was signed in advance (Miles et al., 2014). During the first six months, the primary objective of data collection was to become familiar with the organization, particularly the people and processes related to the dashboard solution. After this familiarization, we aimed at collecting process and interpretive knowledge of the participants (Bogner and Menz, 2009) to understand the course of actions as well as how participants perceived them. For this purpose, we conducted semi-structured interviews (Meuser and Nagel, 2009), which were held with the head of the business intelligence team (who was responsible for the implementation of the dashboard solution), eight executive managers, and eleven management accountants. The interviews were audio recorded and transcribed verbatim immediately afterwards to ensure data integrity. Some of the interviews were conducted in German. Quotes from these interviews were translated as accurately as possible in order to convey their general meaning. In addition, informal talks were documented, especially those that took place when we visited the team responsible for the operational implementation. Furthermore, observations (e.g. software presentations) enabled us to gain an understanding about how the new dashboard solution works. The content of these informal talks and observations was carefully noted (Emerson et al., 2011). Moreover, we had access to internal documents, such as reference databases and presentations, as well as a duplicate of the dashboard solution. Both documentation and experimentation helped us to gain a deeper understanding of the features and use of the dashboard solution. We began an ongoing data analysis simultaneous to data collection (Miles et al., 2014). In particular, we alternated between discussions in regular case analysis meetings (Miles et al., 2014) and cross-examinations of the gathered data. Both were based on the results of thematic coding of the data (Flick, 2014). This iterative approach allowed us to carve out conclusions regarding how the opportunities regarding speed and format affected information provision at PolymerCo.

Empirical analysis Prior to the implementation of the new AIS, reporting and decision support at PolymerCo, according to involved management accountants, was characterized by a high degree of monthly manual work (which will be outlined in more detail later). This monthly procedure provided managers with an opportunity to directly discuss the accounting numbers and corresponding analysis with the responsible management accountants. In this way, they not only received the status of the business (represented by the visualized numbers) but also learned more about the underlying reasons due to the annotations and additional interpretations that they obtained in personal interactions with management accountants. As a result, managers could deepen their understanding of the business. 98

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The objective pursued by PolymerCo with the implementation of a new AIS, an automated business intelligence system with an interactive dashboard, was to improve the provision of information by eliminating recurring manual work. This new AIS could process data in near real-time, which allowed both management accountants and managers to have direct access to the latest accounting numbers. In addition, the new AIS visualized these accounting numbers automatically in pre-defined graphs and allowed the user, via its intuitive user interface – as described by users – to filter, drill down, etc. to multiple organizational levels. The faster data processing and automated visualization of various metrics (mostly key performance indicators such as revenues, volume, margins, production figures, downtimes, savings and safety figures) allowed an instant interaction with the accounting numbers. This opened new avenues for use, such as a trial-and-error-search with almost no effort in comparison to the previous Excel and data warehouse procedure. In interviews, the developers of the new AIS explained that the design of the system and, in particular, the usability of the user interface as a dashboard was inspired by Apple’s highly intuitive software. This comparison referred to the ease that the new AIS provided in selecting applications from a catalogue as well as in arranging and customizing them in a personal screen according to users’ preferences. Following this logic, users were able to set up individual dashboards that could consist of several pages. Each page could contain multiple frames, which could represent and visualize various accounting numbers. For instance, one could depict three metrics (e.g. revenues for business units A, B and C) in a chart and compare their progress over time for different regions and product categories. A selected frame could provide various functions, such as drill-downs and filters, which allowed the user to apply assumptions in simulations that compare against other bases (e.g. periods, metrics). These features facilitate the monitoring and analysis of historical trends, current performance, and scenario analysis (see Figure 8.1). During our interviews, it became evident that the technological-advances described in terms of faster data processing and intuitive visualization of information had implications for the work of both management accountants and managers as well as the interaction between them. In the following two sections, we will examine the resulting implications, first for management accountants and then for managers. In the subsequent section, we will then elaborate on the

PolymerCo

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Figure 8.1   Illustration of a dashboard 99

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implications for the provision of information by management accountants and the interaction between managers and management accountants in the long term.

Implications of faster data processing Due to the new AIS, accounting numbers were continuously and almost instantaneously available for managers, whereas they were previously provided in specific reporting cycles. The head of global product management described the implications of the change in data processing for managers as follows: I am no longer dependent on using a management accountant to give me an interim report or information on a region. I can now simply have a look, see how the US is going, and if it is not going [how I expect it to be], I can pick up the receiver and say, “Listen, what’s going on? I see here that sales have been going down for two days.Volume is going down. Something is going on.” That is significantly easier. Now, we have near real-time information for everything that you want to look at for this enterprise on a global basis from everywhere in every detail, and therefore, it’s amazing. He pointed out that in comparison to the previous process, the faster data processing eliminated the time lag of data availability. Previously, more than a hundred organizational entities delivered their data after the sixth working day of the month. The management accounting teams then started with the preparation, consolidation and subsequent analysis. This meant that, after the consolidation, they first analyzed the relevant data in Excel and subsequently created visualizations of their analysis in PowerPoint, which they then annotated. Due to limited resources, management accountants were more likely to fill a previous PowerPoint deck instead of adapting it continuously to the current situation or making additional visualizations. Nevertheless, according to one head of a management accounting department in a business unit, this procedure took almost two weeks and thus resulted in outdated data. The new technological-advancement minimized this time lag, leading to instantaneously available data that was generated independently from reporting cycles. As the quote above also indicates, in addition to an independence from reporting cycles, an independence of managers from management accountants emerged, because data were now accessible not only directly to management accountants but also to all persons concerned with the respective numbers. The management accountants thus no longer served as information intermediaries. Instead, information became democratized so that managers in our case study perceived an increased transparency due to the direct access to all accounting numbers. That increased the awareness of what was selected by management accountants, to whom they could refer more easily, if needed. Despite the increased transparency, the independence from management accountants had a further consequence regarding the kind of information provided. Previously, management accountants provided analytical interpretations of accounting numbers in annotations and face-to-face meetings and, hence, a story about what the accounting numbers represent from their perspective (e.g. a story about the recent development of a business unit). With the new AIS, managers were able to and/or had to interpret the accounting numbers on their own. This implicated that managers had more degrees of freedom to tell a specific story that favours their interests in, for instance, the leadership team meetings. This shows that the role of the management accountants as providers of accounting numbers lost relevance. 100

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Given the technological opportunities, PolymerCo could have restricted the availability of accounting numbers by updating the numbers only in specific reporting cycles. However, they opted not to implement these restrictions, as they feared that the system would lose acceptance among managers and they saw the advantages of having data that were more recent. The new AIS gained acceptance not only by providing information faster but also by providing information in a more easily graspable format, which we will elaborate on in the following.

Implications of intuitive visualizations Whereas faster data processing primarily had implications for managers, the new format also impacted the work of the management accountants. A high number of static visualizations that management accountants provided previously in the form of a PowerPoint deck of approximately two hundred slides changed to an automated interactive dashboard that put both managers and management accountants in the position to inform themselves more intuitively. Three characteristics of the system were crucial: formatting automation, formatting flexibility to select the content according to individual specifications and features of the new system that facilitated collaboration by sharing information. In terms of the first format-related aspect (automation), the AIS automatically generated, in contrast to the previous Excel and PowerPoint procedure, visualizations of over 100 different predefined graphs. Users could select these graphs from a catalogue based on their needs and preferences. As one management accountant explained, this implied that management accountants no longer needed to “dive deeply into endless tables of numbers and pick the right ones” for their analyses. This increased the accuracy and, in the beginning of an analysis, facilitated an understanding of the data, as our informants reported. In addition, the direct visualization allowed users to more easily identify “certain trends and correlations, but also outliers and mistakes”, as one head of management accounting at one business unit recounted. Based on an easier trend identification, it empowered management accountants to be more proactive and communicate certain issues early on. Concerning managers, it implied that, due to the visualization, it was not necessary to extract (uncleansed and non-aggregated) data out of a data warehouse. Instead, managers had instant and intuitive access to (cleansed) accounting numbers that could be aggregated for any level of analysis. In other words, the dashboard solution allowed access to accounting numbers with little or no knowledge about data warehouses and data preparation – thus extending the scope of possible users to managers. A second aspect was the flexibility to select the content according to users’ specific needs and/or interests by simply selecting a graph (e.g. revenue development against the previous year and revenue development against the forecast) from the catalogue. Moreover, it allowed users to filter the content of the dashboard according to different levels of analysis. The implication of this flexibility was that managers and management accountants could compile accounting numbers exactly according to their needs and did not have to go through two hundred PowerPoint slides. The head of procurement and trading stressed this in an interview: I think each user, of course, has a different need, depending on how the technology helps them to do their jobs. In addition, everybody gets the chance to have their relevant metrics served in a way that is digestible and actionable. I think that’s what is very important. In addition to facilitating users’ work on an individual level, the new AIS also facilitated the work on a collective level, which also nurtured the acceptance of the AIS. Two features were crucial for supporting effective collaboration between users: Due to the shared dashboard feature, 101

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users could subscribe to templates that contained pre-existing compilations of visualizations. Moreover, the padlock feature could be used to lock specific views in these shared dashboards (by locking a view, a subscriber could not change the comparison bases or assumptions, so that a shared view among all subscribers could be ensured). The combination of these two features allowed users to provide information in a standardized way for specific purposes (e.g. leadership team meetings), which gave managers a common discussion basis. However, as with PowerPoint decks, this feature also implicated that managers could refer solely to the shared view in meetings despite possible individual interests. Overall, the features of the new AIS provided managers at PolymerCo an opportunity to retain the shared view on the business in specific teams (e.g. leadership team meetings), while at the same time allowed users to analyze data flexibly according to individual preferences. While the flexibility led to the situation that managers increasingly informed themselves and thus developed a better individual business understanding, the shared views helped teams develop a shared business understanding and subsequently use that to make decisions.

Temporal decoupling of accounting numbers and their analysis The faster data processing and the interactive visualizations not only had implications for the work of managers and management accountants but also for the interaction between them. The first implication was that management accountants and managers recognized advantages for their information basis. As described above, management accountants could directly delve deeper into the data based on a faster identification of trends or outliers, whereas managers had continuous and independent access to the latest accounting numbers. A head of a management accounting department of a business unit described in an interview: “The manager does not have to wait anymore to get a report, a sheet of paper, or slides from a management accountant. He can proactively open the system whenever he wants. This allows him to deal with issues much earlier”. In our case, managers sometimes had a look at the accounting numbers even before the management accountants had even dealt with them. The head of the central management accounting department illustrated the changed interaction with an example: “Due to the fact that information is now accessible to everybody at an earlier time, the CFO can send you an email regarding specific topics as soon as he enters the office in the morning [i.e. possibly earlier than the management accountant], or he questions you directly when you pick up your coffee in the kitchen in the morning”. We thus conclude that management accountants did not necessarily have to initiate interaction with management at the end of the reporting period. Rather, the AIS created a situation that stimulated both management accountants and managers to trigger discussions of accounting numbers. Furthermore, the new AIS also had implications for the kind of inquiries that managers made to management accountants. The CFO described the implication as follows: “This is an essential change. Previously, our [managers’] inquiries were 90% of a transactional nature. Now, however, these are ‘business partner’ questions”. Our respondents described this as a boon for the organization – previously, many transactional questions about an exact figure on specific events had to be answered, whereas now questions about the underlying reasons are more important. The head of business planning and administration described the change in the interaction as follows: “Discussions are more intense, because you do not have to ask anymore what the result is, because everybody has access to it. Instead, it is a discussion regarding the topic”. Thus, management accountants were no longer expected to merely forward numbers but, instead, to contribute to mutual and deep discussions about underlying reasons. 102

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This increased desire on the part of the managers to discuss business issues in-depth came with an increased demand for more in-depth analytical interpretations about the underlying reasons and argumentation. Managers were able to carry out simple analyses; however, as they dealt more with the accounting numbers, they developed greater interest in more complex analyses and analytical interpretations such as cause-and-effect analyses and scenario analyses, which are more time-consuming. Both managers and management accountants felt that these more complex analyses enabled them to conduct more profound discussions with one another. The head of global product management explained this in an interview: Well, everybody has the same numbers, but then the real work starts in interpreting them, doing variance analysis, making follow up calls and reviewing closely why specific effects exist. You need somebody who is able to explain in an hour precisely the drivers and influences that affected the business in each region, in each strategic entity in the last two months, and to derive implications in order to steer [the organizational entity you are responsible for] effectively in the upcoming months. Although accounting numbers were continuously available, the time-consuming analyses remained to be provided at the end of a reporting period. Based on this observation, we argue that, in the context of providing information to managers, accounting numbers and their corresponding analysis became temporally decoupled, as the former were continuously available, whereas the latter were reported in specific cycles (e.g. at month end). Nevertheless, we also observed tendencies to limit this temporal decoupling. Informants reported that, overall, the frequency of inquiries increased, especially as managers used more often informal occasions (e.g. conversations in the office kitchen) to quickly discuss certain numbers. In addition, due to the outlined change of the type of inquiries from managers to management accountants, informants described the interactions as more intense because they were conducted with more analytical depth. We interpret this increased and more intense interaction as a kind of countermovement to limit the temporal decoupling in order to be still able to combine accounting numbers and analytical interpretations. In summary, we thus observed that these changes affected the relevance of management accountants. Seemingly, the time that management accountants spent on transaction processing and reporting tasks decreased further. In turn, the time spent on profound analyses and interpretations for decision support increased. Based on this, we suggest that AIS have the potential to enable another shift in the tasks of management accounts towards decision support.

Concluding discussion Scholars proposed transaction processing, reporting and decision support as the tasks of management accounting (Booth et al., 2000). To fulfil these tasks, management accountants make use of AIS (Granlund and Mouritsen, 2003), which were already subject to innovations in the IT industry in the 1990s (Booth et al., 2000; Sánchez-Rodríguez and Spraakman, 2012; Scapens and Jazayeri, 2003). Now, approximately 20 years later, another surge of innovations is about to influence information provision in organizations (Stodder et al., 2015). In particular, technological-advanced AIS are taking advantage of faster data processing and interactive visualizations (Stodder et al., 2015) and, thus, provide the potential to introduce changes to management accountants’ work. Consequently, we set out in this chapter to examine how the capabilities that such systems bring along may affect the process of providing managers 103

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with information relevant for decision-making as well as what role management accountants play therein. Based on an implemented new AIS that made advantage of the described capabilities with regards to speed in data processing and the format of provided information, we could observe that it allowed making accounting numbers continuously and instantaneously available. This finding is consistent with studies that showed that managers are dissatisfied with a time lag in receiving information (van der Veeken and Wouters, 2002). Furthermore, we showed that the new formatting possibilities allowed intuitive access to accounting figures by people who have less knowledge about data warehouses or who are non-accounting experts, such as managers. This finding is in line with previous studies that showed that formatting is an important aspect to reach a broader target group (Card et al., 1999). In our case, both factors contributed significantly to the acceptance of the system and facilitated, in this way, the described changes in the work of both management accountants and managers. Regarding management accountants’ work, prior research evaluated the effectiveness and quality of management accountants output and emphasized that analytical interpretation is more valuable (Merchant and Van der Stede, 2007; Quattrone, 2016) in contrast to stand-alone accounting numbers (Busco and Quattrone, 2015; Quattrone, 2015). We observed that, with the help of the new AIS, management accountants were able to analyze data faster and, in this way, more easily identify trends, outliers, etc. Moreover, we observed a shift in the time devoted to specific tasks, in that management accountants focused more on analysis for reporting and decision support. In addition, managers are informed about the figures in shorter intervals (near real time), which enables more profound discussions and intense interactions. Therefore, we conclude that the relevance of management accountants increased due to a higher proportion of more highly valued tasks. In light of this, we did not observe a downsizing of the management accounting department, which can also be a result of the implementation of new information systems (cf. Agliati et al., 2001). Nevertheless, managers’ direct access to accounting numbers implied that management accountants’ role as an intermediary of information changed. It allowed managers to create individual interpretations of the accounting numbers. However, prior research described accounting numbers as an incomplete translation of reality (Chapman, 1997). In this vein, Quattrone (2016) emphasized the importance of listening, discussing and understanding numbers, which underlines the significance of a critical reflection that may get lost if the intermediary role becomes less relevant. This raises the question of whether the user of such accounting numbers (e.g. managers) has the capacity to be objective and able to separate between me and myself (Hoskin and Macve, 1986). It underlines the important role that management accountants can play as a platform for reasonable discussions (Busco and Quattrone, 2015; Quattrone, 2015). Whereas, in our case, management accountants were partially able to play this role in the frequent discussions with the management, it remains open for future research if and how management accountants succeed in this role in light of such democratization of accounting numbers when managers do not seek the discussion with management accountants. In conclusion, we showed how accounting numbers and corresponding analyses can become temporally decoupled by AIS that take advantage of faster data processing and interactive formats. Nevertheless, we could also show how managers and management accountants tried to limit this temporal decoupling by intensifying their interaction, which was concomitant with an increase in the relevance of management accountants. Furthermore, we observed a shift in the time spent on management accountants’ tasks towards more analyses for decision support. This allowed us to conclude that new AIS have the potential to improve the information provision of management accountants. 104

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Acknowledgement We would like to sincerely thank the Hanns Seidel Foundation, which supported this research through a scholarship to the first author.

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Granlund, M. (2007). On the interface between management accounting and modern Information Technology. A literature review and some empirical evidence. SSRN Electronic Journal. Retrieved September 19, 2017, from https://ssrn.com/abstract=985074. Granlund, M. and Lukka, K. (1997). From bean-counters to change agents: the Finnish management accounting culture in transition. LTA, 3, 213–255. Granlund, M. and Malmi, T. (2002). Moderate impact of ERPS on management accounting: a lag or permanent outcome? Management Accounting Research, 13(3), 299–321. Granlund, M. and Mouritsen, J. (2003). Special section on management control and new Information Technologies. European Accounting Review, 12(1), 77–83. Hansen, D. R. and Mowen, M. M. (2007). Managerial Accounting, 8th ed. Mason, OH: Thomson/ South-Western. Hirsch, B., Seubert, A. and Sohn, M. (2015). Visualisation of data in management accounting reports. Journal of Applied Accounting Research, 16(2), 221–239. Hodge, F. D. (2001). Hyperlinking unaudited information to audited financial statements: effects on investor judgments. Accounting Review, 76(4), 675–691. Hoskin, K. W. and Macve, R. H. (1986). Accounting and the examination: a genealogy of disciplinary power. Accounting, Organisations and Society, 11(2), 105–136. Huang, Z., Chen, H., Guo, F., Xu, J. J., Wu, S. and Chen, W.-H. (2006). Expertise visualisation: an implementation and study based on cognitive fit theory. Decision Support Systems, 42(3), 1539–1557. Jones, J. W., Saunders, C. and McLeod, R. (1989). Information media and source patterns across management levels: a pilot study. Journal of Management Information Systems, 5(3), 71–84. Madsen, V. (1963). The Tasks and Problems of Accounting. Copenhagen: Gyldendal. McLeod, R. and Jones, J. W. (1986). Making executive information systems more effective. Business Horizons, 29(5), 29–37. Merchant, K. A. and Van der Stede, W. A. (2007). Management Control Systems: Performance Measurement, Evaluation and Incentives. London: Pearson Education. Merriam-Webster Online Dictionary (n.d.). Retrieved July 22, 2016, from www.merriam-webster.com/ dictionary/management. Meuser, M. and Nagel, U. (2009). The expert interview and changes in knowledge production. In Bogner, A., Littig, B. and Menz, W. (Eds.), Interviewing Experts. London: Palgrave Macmillan, 17–42. Miles, M. B., Huberman, A. M. and Saldaña, J. (2014). Qualitative Data Analysis: A Methods Sourcebook, 3rd ed. London: Sage Publications. Quattrone, P. (2015). Value in the age of doubt: accounting as a maieutic machine. In Kornberger, M. (Ed.), Making Things Valuable, 1st ed. Oxford: Open University Press, 38–61. Quattrone, P. (2016). Management accounting goes digital: will the move make it wiser? Management Accounting Research, 31, 118–122. Ramarapu, N. K., Frolick, M. N., Wilkes, R. B. and Wetherbe, J. C. (1997). The emergence of hypertext and problem solving: an experimental investigation of accessing and using information from linear versus nonlinear systems. Decision Sciences, 28(4), 825–849. Russom, P. (2011). Big Data Analytics, TDWI Best Practice Report, 4, TDWI. Sánchez-Rodríguez, C. and Spraakman, G. (2012). ERP systems and management accounting: a multiple case study. Qualitative Research in Accounting & Management, 9(4), 398–414. Scapens, R. W. and Jazayeri, M. (2003). ERP systems and management accounting change. Opportunities or impacts? A research note. European Accounting Review, 12(1), 201–233. Shang, S. and Seddon, P. B. (2000). A comprehensive framework for classifying the benefits of ERP systems. American Conference on Information Systems (AMCIS). Long Beach, California. Shang, S. and Seddon, P. B. (2002). Assessing and managing the benefits of enterprise systems: the business manager’s perspective. Information Systems Journal, 12(4), 271–299. Shneiderman, B. and Kearsley, G. (1989). Hypertext Hands-on! An Introduction to a New Way of Organizing and Accessing Information. Reading, MA: Addison-Wesley. Siegel, G., Sorensen, J. E. and Richtermeyer, S. B. (2003). Are you a business partner? Strategic Finance, 85(3), 38–43. Spathis, C. and Ananiadis, J. (2005). Assessing the benefits of using an enterprise system in accounting information and management. Journal of Enterprise Information Management, 18(2), 195–210. Stodder, D., Halper, F. and Russom, P. (2015). Emerging Technologies: For Business Intelligence, Analytics, and Data Warehousing, TDWI Best Practice Report, TDWI. 106

Changing speed and format

Umanath, N. S. and Vessey, I. (1994). Multiattribute data presentation and human judgment: a cognitive fit perspective. Decision Sciences, 25(5/6), 795–824. Vakalfotis, N., Ballantine, J. and Wall, A. (Eds.) (2011). A literature review on the impact of enterprise systems on management accounting. Proceedings of the Eighth Conference on Enterprise Systems, Accounting and Logistics, Thassos, Greece. van der Veeken, H. J. and Wouters, M. J. (2002). Using Accounting Information Systems by operations managers in a project company. Management Accounting Research, 13(3), 345–370. Vessey, I. (1991). Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decision Sciences, 22(2), 219–240.

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9 Accounting Information Systems outputs XBRL, AI and in-memory technologies Ting Sun

Introduction The objective of this chapter is to explore the role of three cutting-edge technologies – XBRL (eXtensible Business Reporting Language; including iXBRL), in-memory computing technology and Artificial Intelligence (AI) – in data analytics for internal decision-making. These technologies are chosen as XBRL solves the issue of data preparation and integration and enhances data usefulness; in-memory computing enables real-time data processing; AI helps extract abstract patterns of data and provides guide for data prediction. XBRL is a financial reporting markup language that provides the financial community with a standard approach to prepare, publish in a variety of formats, reliably extract and automatically exchange information from financial statements. With XBRL tags, data items on financial statements and related footnotes can be automatically identified and described. More importantly, XBRL can play a role in decision-making. Research conducted by Hodge et al. (2004) indicates that XBRL-enhanced search engines assist investors in acquiring and integrating relevant financial information. In a similar way, the improved data usefulness provided by XBRL facilitates data analytics and provides insight for management and internal auditors. The need for real-time access to all of an entity’s data for improved internal decision-making calls for in-memory computing technology like SAP HANA, which provides more efficient and robust data processing and analysis. Using this technology, entities no longer need to access information stored in a data warehouse. Transactional data is maintained in memory, allowing data analytics to be conducted in real time. The SAP HANA platform, therefore, can be used to conduct realtime data analytics based on huge size of data. Artificial Intelligence provides an intelligence layer for data to efficiently process tedious and complex analytical tasks and has become one of the hottest trends in business. AI capabilities are spreading into nearly all industrial applications, ranging from banking systems that detect attempted credit card fraud, to software agents that track and pay for orders for thousands of goods, to marketing analysis systems that predict sales for products in a specific area. This chapter explore all three in terms of analytics and internal decision-making. The final part of the chapter proposes the possible applications of Machine Learning and Deep Learning as two important AI technologies to business decision-making. 108

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eXtensible Business Reporting Language (XBRL) and Inline XBRL (iXBRL) XBRL To internal business decision-makers, a key to achieve advantageous position against competitors is the availability of insightful data and the efficiency of obtaining the data (Reyes et al., 2007). Although it was originally designed for financial statements of public companies, XBRL tags can be attached to financial documents of varying formats, allowing internal decision-makers to acquire and integrate relevant financial information. Executives may need to compile information from different departments into a single spreadsheet to analyse the performance of a business strategy. When performing analytical procedures for audit planning, an internal auditor may want to compare the number of unfilled orders to inventory and cost of goods sold, for example. However, the financial information within or without the entity usually has varying forms (i.e., HTML, Excel, Word document, PDF and Edgar filings in plain text) and some forms do not allow their content to be indexed by search engines or other automated analytic tools. Due to the inconsistency of information format among different systems, it is inefficient and vulnerable to errors when collecting/extracting data fields and when preparing it for automated data analysis. As a result, the need for a uniformed and flexible language for business communication with high semantic value has grown (Debreceny et al., 1998) and the potential benefits that XML (eXtensible Markup Language) plays in facilitating the retrieval of rich datasets on the web has been discussed (Debreceny and Gray, 2001). eXtensible Business Reporting Language (XBRL) developed by the AICPA (and other international accounting bodies) provides a standard method for preparing, analysing and exchanging financial information. It is a standard set of specifications for web-based business reporting. XBRL is based on XML, a markup language that defines the way in which the documents can be encoded to be human-readable and machine-readable. As a global standard for encoding semantic information, XBRL (with XML-based tags attached to the reported financial items) provides financial information users with free and interactively-available data (DataTracks, 2012) as well as structured metadata1 (text, graphic, or video data) at the most primitive level to intelligently search, read, share, exchange, analyse, validate and present (Harris and Morsfield, 2012). The XBRL instance document is a XML file that contains the values and contexts, such as the measurement units and reporting period of financial reporting data, providing data and structure to enable machine and human readability. Thus, an instance document could be the financial records of a company displayed in an HTML file with various XBRL items embedded. The instance is linked to the XBRL taxonomy. There are two categories of taxonomy, the standard taxonomy and the extension taxonomy. The XBRL standard taxonomy defines financial reporting concepts (also called “elements,” such as “cash and cash equivalent”) used to report (tag) facts about specific financial statements line items. However, as extant research reveals that the standard taxonomy cannot fulfil the needs of companies with specific characteristics for financial reporting (Bonsón et al., 2009), taxonomy extensions were developed, allowing organizations to disclose concepts that are not included in the standard taxonomy by creating elements unique to their own business. The extensibility distinguishes XBRL from most other XMLbased standards. XBRL elements consists of schema and a linkbase. While the schema defines underlying taxonomy elements that are being reported, involving element name, data type, balance type and period type, the linkbase combines labels and references to the elements and defines the relationships between those elements as well as relationships between elements and other sources. 109

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Despite those benefits that XBRL brings, concerns remain relative to the quality of the information reported in XBRL, including filing errors and XBRL-based financial statements’ comparability. For instance, it was reported that one-quarter of all SEC-XBRL filings under rule 33-9002 up to September 1, 2009 contained calculation errors (Debreceny et al., 2010). In July 2014, the SEC highlighted the need to eliminate calculation errors by sending a letter to alert CFOs of missing calculation relationships in the XBRL files, asking them to “take necessary steps to ensure that you are including all required calculation relationships”.2 Software systems were thus developed to identify XBRL errors. For example, a Consistency Suite tool that covers more than 30 categories of XBRL filing errors is provided by XBRL.US to examine problems in XBRL files before submission. In addition, some errors cannot be identified by a software. For instance, the incorrect positive or negative sign of the data value, the use of an old extension element, the tag selected represents a different meaning than what is disclosed in the traditional paper-based document, etc. The detection of such errors relies on the expertise of the XBRL individual (Financial Execution Research Foundation, 2014). The flexibility provided by an XBRL extension taxonomy results to a compatibility issue (Cohen, 2004; Zhu and Wu, 2011; Chen and Sun, 2011). It is reported that almost two-thirds of the elements in XBRL financial reports are not comparable with those of similar companies (Zhu and Wu, 2011). Researchers pointed out that the preparing process of XBRL-tagged information should be cost-effective and XBRL extensions make automated comparisons across companies and industries inefficient as it may need human intervention to understand the elements in individual XBRL disclosures (Debreceny et al., 2011). There are discussions on the importance of how to balance the trade-off between the flexibility and the issue of comparability, costs and reliability (Cohen, 2004; Piechocki et al., 2009). Since 40% of XBRL extensions are found unnecessary (Debreceny et al., 2011), regulators need to carefully address XBRL taxonomy extensions to guide filers in reducing the number of extensions in financial statements. The information integrity within XBRL-based documents is another critical problem (Perdana et al., 2014). No and Boritz (2004) suggested the concept of XBRL assurance to ensure the information integrity by implementing assertions to comply with the XBRL taxonomy and guidance. Boritz and No (2008) observed the lack of assurance of XBRL documents in the SEC’s voluntary XBRL filing program and to address this issue, they implemented mock assurance as a surrogate to provide assurance on XBRL filing for United Technologies Corporation (Boritz and No, 2009). Srivastava and Kogan (2010) proposed a set of assertions, consisting of statements for ensuring XBRL assurance and assurance processes. Plumlee and Plumlee (2008) addressed three issues: assurance guidance, XBRL-tagging validation and audit risks, for XBRL assurance.

iXBRL Since 2009, the SEC has required listed companies to report two versions of financial information – a plain-text document and a XBRL document – due to the difficulties of the human readability in XBRL documents. A XBRL document often presents poorly when converted to a human viewable format, as the standard rendering solutions are unable to effectively replicate the presentation layout and formatting of traditional paper-based financial report. The problem becomes even worse when the published taxonomies need to be extended to include facts that were not predefined in the company’s extension taxonomy. Currently, the audited or reviewed financial filings (e.g., 10-K and 10-Q) are stored in HTML format in the “Document Format File” section of the EDGAR database for human consumption, while the unaudited XBRLbased documents are stored in the “Data Files” section of the EDGAR database and for 110

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consumption by software. This separate reporting leads to unnecessary duplication of work for filers (Basoglu and White, 2015) as well as extensive training and detailed technical knowledge required of financial statement users (Harris and Morsfield, 2012). As a result, researchers argue that the current implementation of XBRL-based financial reporting is not as useful and transparent to analysts and investors as it was expected (Basoglu and White, 2015). Fortunately, Inline XBRL (iXBRL), a recent development of XBRL-based reporting, tackles this problem. iXBRL embeds XBRL metadata within an HTML document to integrate both human- and machine-readable instance documents (Cohen et al., 2014). Unlike the XBRLbased document that can be viewed only with specialized viewers and focuses on automated machine readability of data, the iXBRL document can be viewed on standard browsers and emphasizes data rendering (DataTracks, 2012). The preparer of an iXBRL document is able to control the presentation format of the document to make it look like a printed financial report or an interactive website. For example, the preparer is able to make the text bold or italicized, and the order and alignment of facts in a table can also be controlled by the preparer. As a result, preparing financial documents with iXBRL could benefit the user by making the data much more readable for humans. Furthermore, the implementation of iXBRL opens new topics of future research, such as the validation of the potential benefits and values that iXBRL could bring, as well as the assurance of the integrity of iXBRL instance document (Basoglu and White, 2015).

Data analytics and internal decision-making By improving information relevance, representation, comparability, consistency and understandability, XBRL3 enhances the usefulness of financial information and more disaggregated data (Vasarhelyi et al., 2012) and facilitates data analytics, helping entities investigate past performance, optimize business processes and gain insight for internal decision-making. On the other hand, thanks to the frequent use of GPS-enabled devices, wireless sensors and RFIDs, business is in a real-time connected world where the real-time data captured by the embedded sensors communicates to data centre networks for processing. In such a digital world, business growth is sustained on a real time (or near real time) basis by speed decisions made from complete information, with insights based on real-time data analytics (Russom, 2013). The real-time data analytics refers to data that is able to capture, process and analyse the instant it flows, or streams, into the system (Pittman, 2013). The benefit of real-time data analytics is obvious: the manager is able to make informed business decisions rapidly and with more complete information. After shortening the response time for data analysis, a company can take prompt strategy to prevent customers from churning. Based on the responses of 2,106 senior executives, PwC’s Global Data and Analytics Survey of 2016 indicates that by 2020 executives expect more data-driven, faster and more sophisticated decision-making with a good mix of human judgment and machine algorithms, rather than merely rely on intuition and experience (PwC, 2016).

Real-time analysis of large volumes of data: modern in-memory database technology Given today’s exponential growth of real-time data collected by sensors during business operations, speed is crucial for a company to succeed. Executives have to make timely decisions based on the real-time data. To maximize the profit of the entity, the manager needs to obtain competitive products or services sooner, exploit market opportunities faster, identify business risk and threats more rapidly and manage the customer lifecycle at every touchpoint more efficiently (Hunley and Foley, working paper). The internal auditor needs a real-time monitoring 111

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of the accounting information system and detects anomalies more promptly. Therefore, the need for deeper, mobile and real-time access to all the data of the entity for improved internal decision-making at an operational, tactical and strategic level call for more efficient and robust data management infrastructures as opposed to traditional architectures of database systems, where it relies on hardware refreshes and improved memory caching to support complex data management tasks. With the ability of replicate, store and perform analysis on real-time data, in-memory computing technology has great potential to enable real-time decision-making on digitized business operations. Businesses formerly relied on traditional relational database management systems (RDBMS) with hard drive based memory to store our data. With the new in-memory computing technology we are able to store the data in “connected” main memory (RAM) across a cluster of computers and process it in parallel. This technology performs roughly 5,000 times faster than the traditional way. SAP HANA4 serves as an outstanding example of modern in-memory computing technology and presents itself as a first step towards a holistic data management platform supporting high performance analytics applications at unprecedented speed. It is fully in-memory, which means it keeps data in a server’s RAM, with both row stores and column stores supporting transactional (OLTP5) and multidimensional (OLAP6) workloads. Its OLAP capabilities are virtual, having no requirement on caches, aggregation, indexes or physical cubes. It is unnecessary to duplicate the reporting process by posting data to a separate system. Data analytics are conducted simultaneously when AIS transactions are performed, providing instant feedback on AIS transactions and data (Smith, 2016). SAP HANA and other in-memory computing techniques are driven by technology innovations including (1) high-speed processing due to hardware advances and increasingly affordable memory, (2) increased amount of memory as a result of the mainstream availability of 64-bit processors, (3) the popularity of multicore processor (SAP, 2013). Since SAP HANA has evolved into a platform in 2013, organizations are able to build and deploy a growing number of on-demand applications in areas of customer engagement, finance, human resources, manufacturing, procurement, logistics, IT, etc., and perform retrospective and predictive analysis and deliver real-time insights from both transactional and analytical processing. As data is stored and analysed in local memory, the latency of data transferring and loading is eliminated, which leads to more prompt reactions to anomalies and irregularities. For example, the SAP Fraud Management analytic application powered by SAP HANA processes large volumes of real-time data, helps analysts detect, prevent and deter fraud and provides continuous monitoring on fraud. SAP HANA platform also enables millions of fans around the world to visit NBA’s statistics website (www.NBA.com/Stats) to instantly access and interact with the NBA’s entire history of official statistics. Premium automotive tuning company Mercedes-AMG GmbH employs the SAP HANA platform to support real-time analysis of data from five different engine tests, enabling ad hoc analyses and simulations at any time. Whenever a potential engine issue is detected, the system reports the corresponding patterns, enabling AMG to immediately terminate expensive tests and start analysing the situation. In the academic arena, researchers analysed the capabilities of SAP HANA for analytical processing. Rudny et al. (2014) examine the analytical possibilities of SAP HANA by applying it to forecast the energy demand in the energy sector based on a number of experiments, and demonstrated its effectiveness in time series forecasting as compared to classical solutions. Bracher et al. (2015) apply SAP HANA to marketing by scoring churn risk within a telecommunication industry. Keshava et al. (2015) focus on audience discovery and targeting and identify prospective clients for sales agents based on data from various sources (such as market trends, blogs, news, marketing catalogues) and demonstrate the superior text analysis capability of SAP HANA. 112

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The application of AI to internal decision-making AI is “the effort to make computers think” (Haugeland, 1989). In other words, AI is a machine which uses cutting-edge techniques to competently perform or mimic “cognitive” functions that are usually performed by human minds, such as “learning” and “problem solving” (Norvig and Russell, 2009). Internal business decision-makers have an urgent demand for insights from realworld big data to assist them in obtaining better knowledge of the operational and financial situation and to serve their customers. Nevertheless, on one hand, management and internal auditors have to deal with mountains of data, forcing them to employ data analytical tools to process and analyse it. On the other hand, they have to rely on human experts to identify the features of the real-world big data as most of the data is semi-structured (e.g., text) or unstructured (e.g., video or audio). Fortunately, current advances in AI may help them solve this dilemma and support faster and better decision-making (MIT Technology Review, 2017). In this section, two key AI technologies, Machine Learning and Deep Learning, are discussed. While Machine Learning assists us in predicting patterns by the model trained with historical data, Deep Learning focus on information extraction of big data.

Machine Learning Machine Learning is defined as a “field of study that gives computers the ability to learn without being explicitly programmed” (Samuel, 1959). It evolved from the research of pattern recognition and computational learning theory in AI. Machine Learning attempts to make predictions through the construction of algorithms that learn from data. The algorithms are generated by developing a model from a subset of data called “training set” and testing the model with the “test set,” another subset of data. By recognizing complex patterns of data, Machine Learning enables cognitive systems to automatically learn, make intelligent decisions and improve itself through interactions with data, devices and people (Lakshmi and Radha, 2011). Machine Learning has been prevalently applied to data analysis in fields of finance, marketing, telecoms, taxation, litigation, insurance and web analysis. To date, Machine Learning has been widely adopted to predict events, especially in the field of finance. Examples include forecasting bankruptcies (Wilson and Sharda, 1994; Ben Jabeur and Fahmi, 2014), defaulting loans (Messier and Hansen, 1988; Moin and Ahmed, 2012), stock prices (Barr and Mani, 1994; Huang and Lin, 2014), interest rate (Kanevski and Timonin, 2010) and credit ratings (Buta, 1994; Hájek, 2011). More importantly, Machine Learning has been incorporated into fraud detection, which is of special interest to auditors. As financial fraud perpetrators attempt to conceal fraud and financial fraud often involves collusion, it is difficult to discover these types of fraud tactics merely by employing traditional analytical modelling methods. Various Machine Learning algorithms were applied to solve the fraud detection problem. Lin et al. (2003) evaluate an integrated fuzzy neural network for financial fraud detection and conclude that it outperforms most statistical models and prior artificial neural networks. Whiting et al. (2012) explore the role of statistical learning and data mining in detecting financial fraud, with the goal of advancing fraud discovery performance and proactively detecting or even mitigating financial fraud. They establish four different models, including Probit, Logit, partially adaptive and ensemble models and compare their predictability. Using endogenous financial data through the evaluation of analytical procedure expectations, an early study conducted by Green and Choi (1997) develops a neural network fraud classification model to detect the risk of management fraud. Bell and Carcello (2000) propose a logistic regression model for estimating the likelihood of fraudulent financial reporting for an audit client. 113

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Cecchini et al. (2010) propose a methodology for detecting management fraud based on support vector machines. The results show that the support vector machine based methodology is a useful method for discriminating between fraudulent and non-fraudulent companies. Machine Learning applies pattern matching and deviations or variations detection to evaluate risks (Ramamoorti et al., 1999). Issa and Kogan (2014) propose a methodology for reviewing the assessment of internal controls risk by internal auditors and business owners. This methodology is based on developing an ordered logistic regression model that employs historic data on internal controls risk assessments. The Machine Learning applications that have been addressed in academia could be used to leverage internal decision-making in several areas, which are now outlined. Financial planning and budgeting

Business planning, especially financial budgeting, is critical for a company. Although modern budgeting software solutions such as ACCPACCFO, BIZBENCH and iLumen can assist management to create budgets without the hassle of managing error-prone spreadsheets, these products provide limited analytical capability in discovering potential unviable budgets (Yip, 2012).7 Chief executives are responsible for the overall process of a company’s financial planning and budgeting, determining the effectiveness of compliance with corporate policies and procedures and ensuring the financial planning and budgeting process operates as planned. Facing the problem of how to determine the accuracy of budget, Machine Learning may be a good approximation approach. Previous research shows that certain financial variables can indicate the future performance and sustainability of a business. Examples of these variables include cash flow, profit margin, net profit and return on equity, etc. They can be used to predict future events of a business in a particular industry (Nijhuis and Westerhuis, 2013) and, therefore, are associated with the company’s budget. As a result, by investigating a company’s historical variation between the forecast and actual data on these budget-related financial variables, internal auditors are able to uncover the characterized change pattern of them and use this knowledge to label every historical forecast as a viable or unviable budget8 (Yip, 2012). Machine Learning techniques, by analysing the historical data, could effectively help recognize the existence of pattern change and establish a classification model for the accuracy inference of the company’s business budgeting and planning. This model is designed to alert management for budget inaccuracy, so that a company can adjust its method and policy of financial planning and budgeting. Operational process monitoring, control and diagnosis

The current economic climate encourages cost-cutting activities, increases risk exposure and fosters organizational changes. Organizations are implementing Continuous Monitoring, a feedback mechanism used by management, to ensure that controls are operated as designed and transactions are processed as prescribed (Vasarhelyi et al., 2010). To enable continuous monitoring, an organization’s operational process should be controlled and the data from the process should be analysed to obtain insights on the patterns underlying the data. A possible solution to this could be the combination of a Machine Learning technique (such as Artificial Neural Network) as an expert system, which is a knowledge-based AI technology (Uraikul et al., 2007). Since Machine Learning can automatically extract knowledge (e.g., the pattern of data) from data, symbolic information can be integrated into an artificial neural network learning algorithm (Kasabov, 1996), rules can be generated (this is the bottleneck of Machine Learning in knowledge acquisition application) (Uraikul et al., 2007), knowledge modelled and extraction achieved. 114

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Retrieving information from weblogs

Weblogs providing commentary on businesses and companies have rapidly gained in popularity over the past few years. Information from weblogs can be an important and more objective source of data. The problem regarding how to effectively search and collect related data from countless weblogs opens up new opportunities for developing blog-specific search and mining techniques. A study by Chen et al. (2008) proposes probabilistic models for blog search and mining using two Machine Learning techniques, latent semantic analysis (LSA) and probabilistic latent semantic analysis (PLSA). The former applies Machine Learning to business blogs. It demonstrates that the proposed model can present the blogosphere in terms of topics with measurable keywords, hence tracking popular conversations and topics in the blogosphere. Potential applications of this stream of research may include retrieving data from other sources, such as newspapers and analysts’ reports. In addition, since this study focuses on information retrieving, more advanced Machine Learning techniques can be used to design a system to automatically monitor and identify trends in data from these sources.

Deep Learning The value of Deep Learning in internal decision-making primarily lies in the fact that it is able to extract highly abstract data features from unstructured or semi-structured data without human intervention, providing a great volume of analysable, real-world big data to support decisionmaking. Deep Learning (also called Deep Neural Network) is a new area of Machine Learning invented in 2006 by Hinton et al. (2006). Its objective is to represent input data and generalize the learned patterns for the future use (Hinton et al., 2006; Najafabadi, 2015). Instead of teaching machines what to do, Deep Learning technology allows them to learn how to do it for themselves based on the data provided and ultimately they will tell us what to do (Maycotte, 2014). In simpler terms, Deep Learning is about learning multiple levels of representation and abstraction, which helps acquire the meaning from data such as images, voice and text. Both Deep Neural Network and Artificial Neural Network contain an input layer, hidden layer and output layer, whereas Deep Neural Network has multiple hidden layers and Artificial Neural Network only has one or two hidden layers. During the past ten years, we have observed numerous successes of Deep Learning in diverse applications of image feature coding, natural language processing, handwriting recognition, audio processing, information retrieval and multitask learning and robotics (Deng and Yu, 2013; Mezghani et al., 2010; Xiong and Zhao, 2014). The successful applications of Deep Learning to voice, image and text processing and analysing benefit the internal decision-making of entities by providing valuable insights from additional sources of data. Speech recognition is one of the first successful applications of Deep Learning methods at an industrial scale. It is the process of translating spoken words into text, which is challenging due to the high viability in speech signals, such as accents, dialects, pronunciations, emotion and speech styles. The presence of environmental noise, reverberation, different microphones and recording devices results in additional variability. For a machine to correctly recognize speech, it needs cognitive computing – a system with architecture that imitates how the human brain understands information. Deep Learning aims to automatically identify features from raw input data typically through analysing the primitive spectral or possibly waveform features (Deng et al., 2013). With speech recognition tools boosted by Deep Learning, such as IBM Watson, at hand, conversations and speech from a company’s call centre, management meetings and other sources could be identified and translated automatically and the result can be used to establish trends or clusters of opinion with similar characteristics. 115

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Empowered by deep conventional neural networks (CNN), a classic Deep Learning model, along with the mainstream availability of prodigious data sets, image recognition is gaining momentum. Image recognition involves two core tasks: image classification and object detection. In the “image classification” task, the system is taught to recognize object categories, such as “book,” “tree,” or “car,” while in the “object detection” task, precise position of the object in the image should be identified (He et al., 2014). A deep neural network, in this way, recognizes visual patterns of objects. It begins with the input layer, where the images are input to the model, followed by the first hidden layer where the very essential components such as pixels are identified. As the layer inside the deep neural network proceeds further, it recognizes more advanced and abstract features of the image, such as edges and then shapes. Each successive layer in a neural network uses features in the previous layer to learn more complex features. Each hidden layer going further into the network is a weighted, non-linear combination of the layers in the lower level. The entire Deep Learning process is about refining the weights representing what was learned during unsupervised training. In 2013, Google announced its visual search engines. The basic idea of this search engine is to “use the visual content of an image to generate searchable tags for photos combined with other like text tags and EXIF metadata to enable search across thousands of concepts like a flower, food, car, jet ski, or turtle” (Rosenberg, 2013). One possible source of data for business analysis is the video captured from surveillance cameras. A face detection system based on image recognition technology can conduct a serious of tasks – face detection, parsing, verification and face attribute recognition. Such a system is helpful for gathering evidence for fraud detection as investigators could easily identify the perpetrator or search for a particular individual’s activities from sequential digital images of a CCTV system. In addition, entities may use electronic commerce or image processing systems to scan and convert source documents (e.g., purchase orders, bills of lading, invoices and checks) into electronic images to facilitate storage and reference. Text is another source of data. Examples include emails, conference calls, newspaper articles, conversations and comments like Tweets and Facebook posts, product reviews from Amazon and so on. Such types of data contain useful information that could provide insights for business decision-making in customer targeting and segmentation and other areas. Since the vast majority of the text is semi-structured or un-structured, it is difficult to extract the feature with regular text mining or Machine Learning techniques (i.e., traditional artificial neural networks). Deep Learning is a solution to unlock the potential of text data. With a deep neural network, text data can be easily analysed and the feature underlying can be extracted without human intervention, thanks to its multiple hidden layers where huge numbers of neurons are connected with each other based on complex mathematical calculation. The deep neural network is capable to identify “who,” “what,” “when,” “where,” “why” and the sentiment from the text data and transforms the unstructured text into structured data with identified features, which can then be incorporated into the organization’s existing data analytics applications.

Summary This chapter discusses three technologies that facilitate internal decision-making in data analytics: XBRL, in-memory computing and Artificial Intelligence. By providing a standard business communication language, XBRL improves the effectiveness and efficiency of data preparing, analyzing and exchanging from incompatible sources. In-memory computing enables real-time analysis of large volumes of data to support timely decision-making. Lastly, AI solves the dilemma of big data processing that, on one hand, decision-makers have had to deal with mountains of data, forcing them to employ data analytical tools; on the other hand, it has to rely on human 116

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experts to identify the features of real-world big data as most of the data is semi-structured (e.g., text) or unstructured (e.g., video or audio). In addition to introducing the basic idea of the three technologies, this chapter analyzes their benefits and shortcomings and explores the current (or possible) applications in different aspects of internal decision-making.

Notes 1 2 3 4 5 6 7 8

The metadata defines the reported terms and the relationships between the terms. See the sample letter at www.sec.gov/divisions/corpfin/guidance/xbrl-calculation-0714.htm It also includes iXBRL and XBRL GL. HANA stands for High-Performance Analytic Appliance. OLTP stands for Online Transaction Processing. OLAP stands for Online Analytical Processing. For methodology details see Yip, 2012. For example, label “0” is given if the actual value of a test variable is worse than the predicted one, indicating an inaccurate forecast of budget. A label “1” is given if the actual value of a test variable is better than the predicted one, indicating an accurate forecast of budget.

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Nijhuis, S. and Westerhuis, J. (2013). What factors determine long-term solid financial performance? Performance, 5(1): 54–61. No, W. G. and Boritz, J. E. (2004). Assurance reporting for XML-based information services: XARL (eXtensible Assurance Reporting Language). Canadian Accounting Perspectives, 3(2), 207–233. Norvig, P. and Russell, S. J. (2009). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall. Perdana, A., Robb, A. and Rohde, F. (2014). An integrative review and synthesis of XBRL research in academic journals. Journal of Information Systems, 29(1): 115–153. Piechocki, M., Felden, C., Graning, A., and Debreceny, R. (2009). Design and standardisation of XBRL solutions for governance and transparency. International Journal of Disclosure and Governance, 6(3), 224–240. Pittman, D. (2013, December 16). Learn about real-time analytics through real-world examples. IBM Big Data & Analytics Hub. Retrieved from www.ibmbigdatahub.com/blog/learn-about-real-timeanalytics-through-real-world-examples. Plumlee, R. D. and Plumlee, M. A. (2008). Assurance on XBRL for financial reporting. Accounting Horizons, 22(3), 353–368. PwC (2016). PwC’s Global Data and Analytics Survey 2016: Big Decisions. Retrieved from www.pwc.com/ us/en/advisory-services/data-possibilities/big-decision-survey.html. Ramamoorti, S., Bailey, J. and Traver, R. O. (1999). Risk assessment in internal auditing: a neural network approach. International Journal of Intelligent Systems in Accounting, Finance & Management, 8(3), 159–180. Reyes, E., Rodríguez, D. and Dolado, J. (2007). Overview of XBRL technologies for decision making in Accounting Information Systems. Journal of Information Systems, 19(4), 19–41. Rosenberg, C. (2013). Improving photo search: a step across the semantic gap. Google Research Blog. Retrieved from https://research.googleblog.com/2013/06/improving-photo-search-step-across.html. Rudny, T., Kaczmarek, M. and Abramowicz, W. (2014). Analytical possibilities of SAP HANA – on the example of energy consumption forecasting. Advances in Systems Science, 141–150. Russom, P. (2013). Operational intelligence: real-time business analytics from big data. TDWI Checklist Report, 1–8. Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210–229. SAP (2013). Detect, Prevent and Deter Fraud in Big Data Environments. Retrieved from www.sap.com/bin/ sapcom/en_us/downloadasset.2013-09-sep-17-10.detect-prevent-and-deter-fraud-in-big-dataenvironments-pdf.html. Smith, J. (2016). Accounting Information Systems: ethics, fraudulent behavior and preventative measures. University Honors Program Thesis. Georgia Southern University. Retrieved from http://digital commons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1188&context=honors-theses. Srivastava, R. P. and Kogan, A. (2010). Assurance on XBRL instance document: A conceptual framework of assertions. International Journal of Accounting Information Systems, 11(3), 261–273. Uraikul, V., Chan, C. W. and Tontiwachwuthikul, P. (2007). Artificial intelligence for monitoring and supervisory control of process systems. Engineering Applications of Artificial Intelligence, 20(2), 115–131. Vasarhelyi, M. A., Chan, D. Y. and Krahel, J. P. (2012). Consequences of XBRL standardization on financial statement data. Journal of Information Systems, 26(1), 155–167. Vasarhelyi, M. A., Alles, M. and Williams, K. T. (2010). Continuous Assurance for the Now Economy: A Thought Leadership Paper for the Institute of Chartered Accountants in Australia. Queensland, Australia: Institute of Chartered Accountants. Whiting, D. G., Hansen, J. V., McDonald, J. B., Albrecht, C. and Albrecht, W. S. (2012). Machine learning methods for detecting patterns of management fraud. Computational Intelligence, 28(4), 505–527. Wilson, R. L. and Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545–557. Xiong, H. Y. and Zhao, J. (2014). An image retrieval method based on Machine Learning and SVM. Applied Mechanics and Materials, Trans Tech Publications, 631, 474–477. Yip, K. L. (2012). Determining the accuracy of budgets: a machine learning application for budget change pattern recognition. Master’s thesis. Unitec Institute of Technology. Zhu, H. and Wu, H. (2011). Interoperability of XBRL financial statements in the US. Mobile Applications and Knowledge Advancements in E-Business, 129.

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10 Outsourcing of Accounting Information Systems Benoit Aubert and Jean-Grégoire Bernard

Introduction Outsourcing of information systems activities continues to grow. Companies regularly use a combination of employees, local vendors and offshore providers for their information systems services. In this way they can create the optimal combination to take advantage of best prices and talents worldwide. While offshore outsourcing is the form that seems to dominate the headlines, domestic outsourcing is also growing at a very fast pace (Overby, 2015). The demand for outsourcing services is notably driven by advances in cloud computing and the provision of business processes as a service (Deloitte, 2014). This same study noted that while information systems services are becoming a relatively mature outsourcing segment, finance and accounting services outsourcing is expected to grow significantly over the next few years. In light of these facts, it is important to understand what outsourcing is. It is also essential to recognise the key decisions a manager has to make when considering outsourcing of Accounting Information Systems activities. This chapter starts by defining outsourcing, clarifying the difference with offshoring and providing examples of the numerous ways outsourcing can be used for Accounting Information Systems. Following this definition, the key decisions associated with outsourcing are described. The first is the decision to outsource an activity. Managers have to assess whether an activity is suitable for outsourcing. The second is the contract. Once outsourcing is chosen, the contract has to be designed to extract the best possible outcome from the vendor while protecting the client. Third, management of the relationship is explored. Parties have to actively communicate and exchange information to collaborate effectively. Fourth, the risk assessment of outsourcing contracts is discussed. Outsourcing is a major business decision requiring active risk assessment and appropriate risk mitigation. Finally, new managerial dilemmas posed by recent technological advances are outlined. The move of Accounting Information Systems to a cloud-based ecosystem of service providers is forcing managers to rethink how the accounting function is organised in the 21st century firm. When trying to appreciate issues associated with the outsourcing of Accounting Information Systems, it is important to realise that a large proportion of issues are the same as the ones associated with outsourcing of information systems in general. Therefore, elements presented in 120

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this chapter that pertain to the outsourcing decision are not specific to accounting. These elements are as relevant for Accounting Information Systems as they would be for other types of systems. However, outsourcing of Accounting Information Systems, because of the importance of accounting data in the organisation, brings some specific challenges. These are discussed in the section detailing the managerial dilemmas.

Defining outsourcing Outsourcing is fundamentally a make-or-buy decision. Companies can opt to use their own employees to perform a task or rely on a vendor to get the work done. When considering IT activities, companies can use their own infrastructure and staff to perform the activities or rely on suppliers to provide these as a service. This last option is called outsourcing. Formally, outsourcing is the decision to rely on an external party for a product or service instead of conducting the corresponding activities inside the organisation. Outsourcing takes many forms. In some cases, activities are performed by a supplier’s employees inside the client’s premises. In those cases, the supplier emulates the behaviour of internal employees, ensuring daily interactions with internal staff. In other circumstances, services are provided from a different location. In those cases, companies are usually seeking economies of scale, letting their supplier pool activities with similar ones for other clients. In these situations, the activities can be conducted in the same region, or at great distance. When companies select suppliers abroad to take advantage of other countries’ lower cost structures, outsourcing becomes offshore outsourcing (often shortened to “offshoring”). In those situations, services are offered from foreign locations. It is important to remember that while outsourcing always refers to the use of suppliers, offshoring may involve using the company’s own employees offshore. As shown in Figure 10.1, the decision to use a supplier and the decision to go offshore are two different decisions, even if they are made at the same time.

A brief history of outsourcing While the popularity of IT outsourcing has grown significantly since the late 1980s, it is not a new phenomenon. As early as the 1960s time-sharing services were in place to allow different companies who did not have their own computers to use computer resources in shared mode (Abbate, 2001). By the early 1980s, there was a large group of suppliers offering IT outsourcing Geographical distance - localisation decision Activities performed at or near the client's main premises

Organisational distance - make or buy decision

Activities performed off;hore

Activities performed by a supplier

Outsourcing

Offshore outsourcing

Activities performed by employees of the dient organisation

Internal organisation

Offshoring (offshore branch or centre)

Figure 10.1 Outsourcing and offshoring decisions 121

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services, though these services were not very visible in the business world. In 1989, Kodak, which was seen as a sophisticated user of IT at the time, announced that its IT services would be outsourced (Wilder, 1989). The contract was large for those times and entailed the transfer of over 500 employees to the three chosen suppliers (Brown, 1990). This contract became highly publicised and led to several more contracts, often influenced by the legitimacy given to outsourcing by the Kodak-IBM arrangement (Loh and Venkatraman, 1992). It also signalled the entry of IBM into the IT service industry (IBM, 2002). In the following years, the outsourcing industry saw the signing of several multi-billion dollar contracts. The trend that followed outsourcing in the 1990s was offshoring. As shown in Figure 10.1, offshoring means that activities are transferred offshore. During that period companies were not necessarily seeking to transfer activities to suppliers. They were mostly assessing where to locate their activities. Taking advantage of rapidly falling telecommunication costs and an increasingly qualified workforce in low-wage countries, companies moved several of their IT activities and business processes overseas. While encountering some resistance in developed countries, this contributed to the overall productivity of organisations (Blinder, 2006). In several ways, the offshoring of IT services parallels the relocation of manufacturing activities observed in the second half of the 20th century. As the skills of workers in developing countries increased, offshore companies were able to offer very competitive services to companies worldwide. Because IT services are purely digital, moving them offshore was especially easy, facilitated by the development of the Internet. Currently, outsourcing and offshoring have become part of the daily lives of managers. Client firms routinely use suppliers for IT services. This has been reinforced by the growth of cloud computing and cloud services. In general, most firms now eschew all-encompassing and very large contracts. These contracts, observed in the late 1990s, are not seen as ideal anymore. Companies use outsourcing more selectively, targeting the most appropriate services for this type of governance. They also call on multiple suppliers, taking advantage of the specific expertise of each one to gain access to expertise while avoiding lock-in problems.

Outsourcing of accounting systems Outsourcing takes many forms and is used for many different types of activities, including Accounting Information Systems. Outsourcing can be used to develop and implement new Accounting Information Systems or to operate them once they are implemented. These arrangements for outsourcing accounting systems operations range from the mere provision of computer hosting services to contracting the whole accounting business process to an external provider. These various options are discussed in the following paragraphs.

Outsourcing the development of Accounting Information Systems Outsourcing is often used to develop and implement new information systems. It comes in two main varieties: using a provider for the software and/or consultants to configure the software. It is important to note that companies rarely rely on custom-made software nowadays. There is little need to develop software from scratch unless very specific needs must be addressed inside the organisation. This is rarely the case with most business activities. Companies mostly select off-the-shelf software packages from established vendors and configure them as required. There are many applications available, often embedded in larger toolkits like ERP packages. It is relatively easy for suppliers to develop a system that will serve the accounting needs of a myriad 122

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of firms (Zentz, n.d.). The fact that every company has accounting needs, combined with the standardisation provided by accounting standards, makes production of accounting software packages financially viable. For normal use, packaged solutions provide a faster and cheaper alternative to the development of new accounting systems (Cohn, 2014). Companies can purchase a package and configure it to their needs. In so doing, they also gain access to systems having been extensively tested for reliability and accuracy. The configuration of the software is the second way outsourcing is used. When companies select a software package, they have to ensure that it is configured to fit their specific needs. This means that specific codes have to be created to reflect the activities of the company. Areas of business have to be established to enable the company to produce the required profit and loss statements, controlling documents (used by auditors) and all other required reports. Configuring software requires a good understanding of the software itself, of accounting principles and of company operations. Adequate configuration of the accounting software will ensure that performance tracking and the information provided to managers will be correct and that regulatory requirements will be met.

Outsourcing the operation of Accounting Information Systems Once the systems are configured and ready to use, outsourcing can be used to support operation of the systems. The first form of outsourcing for accounting systems simply consists of using a supplier to operate, on behalf of the client, the accounting systems. This form of outsourcing is probably the simplest. When the system is hosted by an external provider, it can still be used by employees of the firm who may not even know that the system is hosted externally. Cloud-based solutions are used more and more for Accounting Information Systems. Cloud companies offer standardised solutions at very competitive prices through a remote infrastructure accessible through the Internet, in comparison to an on-premise infrastructure maintained by the client (Armbrust et al., 2010). This type of solution is typically less flexible than a traditionally configured one. A cloud solution is configurable, but to a lesser extent than traditional solutions (Weinhardt et al., 2009). Cloud systems will cover the basic needs of most companies, although solutions are becoming increasingly sophisticated. Gaps in functionality are usually filled through an ecosystem of applications that can be interconnected through the cloud (Rickmann et al., 2014). Finally, operation of the system can also be outsourced through accounting intermediaries. These firms primarily offer accounting services, but use software (often from a cloud provider) to supply the accounting service to the company. This is usually referred to as business process outsourcing, in which a whole business process is performed by a third party (Lacity et al., 2011). In these situations, the client does not select the software, leaving this choice to the accounting service provider. Comparison between business process outsourcing and information systems outsourcing shows that both strategies share common motivations and patterns (Lacity et al., 2011).

Growth of outsourcing for accounting systems Because they are well-documented and well-structured, accounting activities are easier to outsource than other, less-structured management activities. The trend of outsourcing accounting and financial activities and processes continues to grow. Analysts indicate a growth rate of 8% (Byer, 2013). This growth is in part fuelled by the expanding needs of clients. Outsourcing these activities enables clients to use the expertise of their suppliers to access analytics capabilities and 123

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make better use of financial information, which facilitates the standardisation of processes across multiple sites and countries (Mullich, 2013). In light of the variety of outsourcing choices and the growth of outsourcing, it is important to understand how outsourcing decisions are made. What elements should be considered before deciding to outsource an activity?

The outsourcing decision There have been numerous studies looking at the decision to outsource IT activities. Very thorough reviews of the outsourcing literature, outlining what variables can influence the outsourcing decisions, can be found in Dibbern et al. (2005) and Lacity et al. (2010). The following paragraphs present the key elements explaining the choice of outsourcing as a governance mode for an information systems activity.

Economic considerations As mentioned earlier, outsourcing is the use of a supplier to perform an activity instead of relying on employees. One approach that has been extensively used to analyse this type of decision is from economics, more precisely Transaction Costs Economics. This approach looks at the costs associated with outsourcing. When a company contracts out an activity, it has to search for the best supplier, negotiate a contract, monitor the contract to ensure compliance, etc. All these activities are time-consuming and generate transaction costs. Even if a supplier is specialised and has a production cost advantage over the client, there will be situations in which the transaction costs will be too high for the transaction to be profitable to outsource. In those situations, the client reverts to internal governance. Traditionally, transaction costs have been linked to two sources: asset specificity and uncertainty (Williamson, 1979). A specific asset is one that is unique to a specific transaction and that has no residual value for another transaction. If a firm controls a specific asset, it can ask a higher price of the company that needs the asset. The client firm will try to protect itself against such opportunistic behaviour, thus generating transaction costs (Williamson, 1979). The second element generating transaction costs is uncertainty. It takes many forms. First, there can be uncertainty about the nature of activities to perform. This means that the contract will need to establish contingencies, which are costly to develop (Williamson, 1979). The second form is uncertainty about the measurement of the activities. Activities that are difficult to measure entail additional transaction costs since much effort is needed to monitor the supplier to ensure it delivers the services promised (Alchian and Demsetz, 1972). There is one exception to this logic of avoiding transaction costs. Companies will willingly pay higher transactions costs for activities that are infrequent in nature. Transactions organised within a firm have to be recurrent to ensure that employees and assets are used on a continuous basis. This means that companies are likely to outsource activities that are occasional to avoid hiring employees just for that purpose. In this case, they will be willing to accept higher than usual transaction costs (Williamson, 1979). Asset specificity has not been found to be a significant factor in IT outsourcing decisions (Aubert et al., 2012; Lacity et al., 2010). It seems that most assets linked with IT activities are generic (hardware, software) and can be repurposed easily. The few assets that could be specific are likely to be associated with knowledge. In this case, they are difficult to include in contracts since they are not owned by the companies but instead are kept by employees. It is difficult to include the control of knowledge in a contract, just as it is difficult to control knowledge inside 124

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the minds of employees (Aubert and Rivard, 2016). This suggests that asset specificity does not seem to play a significant role when deciding to outsource an IT activity. The variables that have been found to be the best predictors of outsourcing of information systems activities are all related to uncertainty in its various forms. Uncertainty around the type of activities to be delivered is a deterrent to outsourcing. If activities cannot be specified in advance in a contract, it becomes very difficult to outsource them. On the other hand, when activities are well standardised, they are much easier to detail in a contract and thus easier to outsource (Aubert, et al., 2012). This means that companies considering outsourcing have to be able to predict the nature of their activities over a period of time at least as long as the contract. In the case of accounting activities, there is a core group of activities that are easy to predict. These activities are required for the organisation on an ongoing basis. Other activities may be more difficult to predict, notably when they are associated with changes in the organisation (mergers, acquisitions, spinoffs) that are not expected. Typically, organisations will sign contracts covering the core needs and will use separate arrangements to cover unexpected events as those events unfold. Measurability is also very important. When a contract is signed, the client has to be able to assess the work of the supplier and the supplier has to be able to demonstrate that the services delivered meet specifications. When client and supplier cannot agree on the services delivered, both in quality and quantity, they cannot enforce a contract. This is easier said than done. In outsourcing contracts, the client cannot easily monitor services performed by the supplier and is usually unable to tell whether a problem is due to an unforeseeable event or to some negligence on the part of its supplier. Suppliers, knowing that their behaviour cannot be observed easily, can avoid responsibility for poor performance, or exaggerate their level of effort (and thus their costs) when informing their client (Aubert et al., 2003). Therefore, clients will try to outsource activities for which measurement is as easy as possible.

Political considerations Transaction Costs Economics offers guidelines for identifying activities that should be outsourced. It is a rational method for determining the governance of IT activities in the organisation. However, there are cases in which organisational logic and best interest do not always align with individual logic and best interest. When assessing why different managers choose (or not) to outsource a set of activities, it is important to realise that they make those decisions in a way that often reflects their own personal benefit. In some instances, the use of outsourcing was introduced by managers to create impetus for a change they were promoting, to justify a budget increase for their department, to increase the visibility of their role, etc. (Lacity and Hirschheim, 1993). In some instances, outsourcing is also used to benchmark activities. By asking suppliers to offer a service, companies obtain a cost comparison and can see how their own internal services are performing (Lacity and Hirschheim, 1993). Outsourcing can also be used to transform services. For instance, when trying to integrate different systems, some managers use outsourcing to increase the push for integration. It can serve as a means to raise awareness of the proposed change in other departments (finance, marketing, production) and draw attention on the possibilities offered by information systems. By bundling services in a large contract, IT costs suddenly become visible and receive more attention from the company’s board and leadership team.

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Designing the contract When outsourcing is used, it is important to remember that services will be managed through a contract between the client and its supplier. Agency theory provides key insights on the incentives, pitfalls and strategies that can be associated with such contract (Sappington, 1991). When designing a contract, the client hopes that suppliers will work in a way that is in the client’s best interests. However, clients are aware that suppliers tend to work in their own best interests. Clients have to be aware of three challenges when trying to develop outsourcing contracts: adverse selection, imperfect commitment and moral hazard (Sappington, 1991). Adverse selection is the first challenge to overcome when considering awarding an outsourcing contract to a supplier. If asked about its ability, a supplier is likely to overstate its capabilities to ensure it gets the contract. No supplier will admit being incompetent. This suggests that contracts should be designed in a way that discourages the less capable suppliers and encourages the competent ones. In IT, contracts have traditionally been labelled “time and material”, in which the client and the supplier agree on hourly rates for work, but for which the number of hours remain flexible. These contracts are attractive for less competent suppliers, since they are likely to need more hours (and make more money) than efficient suppliers. There are also fixedprice contracts, for which the total price is agreed in advance and for which the supplier becomes responsible to exert the efforts required to deliver the service promised. These contracts are more likely to attract efficient suppliers, since efficient suppliers are more likely to make a profit (by requiring less hours) than inefficient ones. The caveat is that the deliverables have to be clearly specified for such contracts to be feasible. In some cases, this is relatively easy to do. In other circumstances, the definitive specifications may not be available when the contract is signed. Imperfect commitment represents the difficulty any party faces in promising something and honouring its promise. Very often, in contracts, unexpected events arise and parties will use these surprises to renege on their promises. For example, suppliers can claim that requirements were not clear enough and seek to increase the price initially agreed upon for a piece of software. Contractual measures preventing imperfect commitment are difficult to implement. They often rely on a long-term horizon. If a supplier hopes to secure additional contracts in the future, it may be more “honest” in its early engagements with the client. Bonds are sometimes used for information systems projects, but not very frequently. In IT outsourcing, the promise of future contracts and the protection of reputation are probably the strongest incentives for a vendor to remain committed to its promises (Aubert et al., 2003). Finally, IT outsourcing is vulnerable to moral hazard. Moral hazard is created by the client’s inability to observe what the supplier is doing. The supplier can therefore claim that it is exerting great efforts and that poor performance is due to external circumstances. It is very difficult for the client to challenge these claims successfully. Examples of behaviour falling under the moral hazard label include cheating, shirking, free-riding, cost padding, exploiting a partner or simple negligence. In order to curb moral hazard, clients have to rely on competition. Multi-sourcing is often used in those cases. If more than one supplier knows the client firm and systems, then any under-performing supplier can be replaced easily. It becomes easier to press suppliers for better prices and higher quality (Wiener and Saunders, 2014). Designing the contract is only a first step in ensuring a good outsourcing arrangement. Outsourcing relationships have to be managed throughout the duration of the contract.

Managing the contract When managing outsourcing contracts for Accounting Information Systems it is important to consider two aspects of contract management: formal and informal (Barthélemy, 2003). Both 126

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aspects are important to ensure a good working relationship with the supplier and a satisfactory outcome for the client. The formal part involves managing the activities associated with service level agreements and other elements directly associated with the contract. Every contract is expected to explicitly define the responsibilities of each party, how activities are measured, who owns each process, etc. (Goo et al., 2009). Agreements are expected to be as clear and complete as possible, dictating expected performance levels and associated penalties when these are not met (Kim et al., 2013). These elements have to be actively tracked to ensure that the outsourcing relationship is successful, notably that it generates the cost advantages expected by the client (Barthélemy, 2003). Good contract management serves as a foundation for a good relationship between the client and the supplier. Such a contractual foundation enables client and supplier to establish common norms and ultimately trust in the relationship (Goo et al., 2009). However, the formal elements associated with management of the contract are not the only elements of the outsourcing relationship that have to be managed. Relationship management is essential and includes all the mechanisms used to work with the supplier after the contract is signed (Qi and Chau, 2012). These relationship mechanisms traditionally enable client and supplier to exchange information about the needs of the client and the activities performed. For example, coordination will involve a series of committees bringing together personnel and executives from the client and the vendor. Escalation of any issue will follow a well-established path (Balaji and Brown, 2014). Relationship management can also include social events between the parties, informal meetings and other integration activities between the client and the vendor (Balaji and Brown, 2014). All these activities increase knowledge-sharing between the parties. The client will take advantage of these communication forums to share future growth plans with the vendor, who in turn can plan ahead, offer information about technological opportunities and support the client beyond the simple execution of the activities. The quality of the communication between the parties is very important for a successful outsourcing relationship (Qi and Chau, 2012). When performed consistently, relationship management enables the creation of shared values. These greatly facilitate interactions between the client and vendor (Goo et al., 2009). A combination of formal contractual management and informal relationship management has been found to be the most satisfying solution for outsourcing management. It enables the client to get the best combination of short-term gains from the vendor and long-term performance from the outsourcing strategy (Barthélemy, 2003).

Risk assessment Like any other business activity, the outsourcing of Accounting Information Systems entails some risk. These operational risks have to be adequately assessed, measured and reported. IT outsourcing risk has traditionally been measured in terms of risk exposure, which is defined as a combination of the probability that an undesirable outcome associated with the contract will occur and the magnitude of the loss associated with this outcome (Aubert et al., 2005). There are many potentially undesirable outcomes associated with outsourcing contracts. Unexpected costs are the most common and can come from high transition costs, lock-in situations, costly contractual amendments, escalation or hidden costs. The client can also experience reduced quality of service, lose organisational competencies and face litigation (Aubert et al., 2005; Earl, 1996). Finally, the client can face compliance issues if the outsourcing contract is not set up properly (Gandhi et al., 2012). The potential damage associated with each of these possible undesirable outcomes has to be weighed. 127

Benoit Aubert and Jean-Grégoire Bernard Table 10.1 Risk factors Associated with the client:

• • •

Client’s lack of experience or expertise with the outsourced activity Client’s lack of experience and expertise with contract management Client’s lack of experience with outsourcing

Associated with the vendor:

• • •

Supplier’s lack of experience and expertise with the activity outsourced Supplier’s size Supplier’s financial instability

Associated with the interaction or with the activities in the contract:

• • • • •

Poor cultural fit between client and supplier Size of the contract Interdependence between the activities in the contract and the activities kept inside the firm Measurement problems Task complexity

• •

Technological discontinuity Uncertainty about the legal environment

Environmental characteristics

The likelihood of each undesirable outcome can be determined by looking at a series of associated risk factors, which act as proxies for the probability of an undesirable outcome. This approach is common to many fields. For example, when assessing the risk of a heart attack, physicians will look at the associated risk factors: smoking, drinking, lack of physical exercise, poor dietary habits, genetic antecedents, etc. These, in turn, can lead directly to risk management strategies. For IT outsourcing, the most common risk factors are presented in Table 10.1 (Aubert et al., 2005). These factors are characteristics of the client, the vendor, the activities themselves or the environment in which the company operates. It is important to note that these characteristics will vary with each situation and the type of outsourcing chosen. For instance, evidence suggest that cloud vendors carry more debt (relative to assets) than non-cloud ones, compromising their financial stability (Alali and Yeh, 2012). Managing the risk of an outsourcing arrangement thus involves the following steps: (1) evaluating the impact of possible negative outcomes, (2) evaluating the various associated risk factors to determine the likelihood of each possible negative outcome and (3) devising corresponding risk mitigation mechanisms. While a full description of these mitigation mechanisms is beyond the scope of this chapter, one can easily see that risk factors offer a first source of mitigation mechanisms. Once the client knows what the “sources” of risk are, it can adjust its strategy to reduce the risk. For example, if the highest risk factors are associated with the vendor, the client may decide to select a better suited vendor. If the highest risk factors are associated with the client, it can decide to get additional expertise before entering an outsourcing relationship, or gain experience by first awarding a small and easily monitored contract. Similarly, activity-specific risk factors can be managed by modifying the portfolio of activities considered for outsourcing. Each category of risk factors listed in Table 10.1 has to be evaluated carefully before finalising the outsourcing decision. In most organisations risk has to be assessed and reported on a regular basis. Outsourcing is a significant endeavor for an organisation. It can provide significant benefits. However, it is not a risk-free solution. Adequate risk reporting and management is essential when considering outsourcing as a solution for information systems activity. 128

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Future managerial dilemmas in Accounting Information Systems outsourcing Accounting Information Systems are constantly evolving. In terms of technological platforms, a decade ago the main change was the integration of accounting systems into larger software packages (Sutton, 2006). More recently, the changes mostly consist of advances in cloud computing and service providers (Asatiani and Penttinen, 2015). These advances are not only changing how accounting information is produced and recorded in organisations, but also how the accounting function is organised. These changes pose new dilemmas for managers considering exploiting these technological advances.

Data accessibility and confidentiality When introducing a vendor, the company gives a third party access to its accounting systems. What are the consequences of this access? On one hand, it might reduce the security of the data since the systems can be accessed on public networks, shared technological infrastructure and by more people, some of whom are not even employees of the firm. On the other hand, it might increase security since the third party, not being part of the organisation, has no incentive to tamper with the data since it is not concerned with its operations. Insights can be gained from studies of auditing outsourcing. It has been observed that internal auditors are less objective than auditors from an external provider (use of outsourcing). The internal auditor, being an employee, is more vulnerable to internal pressures, which could affect its recommendations (Ahlawat and Lowe, 2004). This provides support for the idea that outsourcing Accounting Information Systems is more secure. The vendor’s employees have nothing to gain (and probably a lot to lose) by tampering with the data and are more isolated from pressures emanating from the client than internal employees would be. This means that the risk of data being altered is probably lower when the systems are outsourced. In addition, the use of an external provider for accounting activities can also lead to knowledge-sharing, which can benefit the company (Prawitt et al., 2012).

Data ownership and portability The proliferation of cloud computing in recent years has exposed a new challenge with Accounting Information System outsourcing. In some cases, client companies sign a contract directly with their cloud provider. In other cases, the contract is signed with an accounting firm offering the accounting service. This accounting firm, in turn, has a contract with a cloud provider to host the system (Asatiani and Penttinen, 2015). In those situations, who owns the client company’s data? Is it the client or is it the accounting firm? It turns out that this can be a muddy situation and in some cases the client firm might actually not control its own data (Macpherson, 2014). Depending on the type of contract signed, the client company could be locked-in with the accounting firm. Even cloud providers take the time to explain these differences and advise clients to have a clear discussion with their accounting firm before agreeing on an outsourcing contract (Ridd, 2014).

Data integrity and regulatory compliance Unlike other types of organisational information, the production of accounting information and financial statements is subject to regulation. Regardless of the form of the outsourcing arrangement 129

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considered, the ultimate responsibility for integrity of the information produced by accounting systems lies with the organisation, not with the cloud or service provider. If a cloud or service provider fails to meet reporting standards and requirements due to a technological or process breakdown, the organisation can incur a risk of penalties for non-compliance. However, if the organisation does not have the resources or competence to keep up-to-date with regulation, the cloud or service provider might be in the best position to do so on its behalf. While some cloud and service providers hire independent auditors to provide assurances that their practices follow regulatory standards, managers should also develop internal controls to assess their supplier’s compliance. The challenge in developing such controls lies in the fact that the accuracy and integrity of accounting information can be difficult to verify without independently reprocessing the transactions that produced the accounting information in the first instance. Also, the production of accounting information will tend to become fragmented across an ecosystem of cloud providers, which will complicate tracing the source of integrity and non-compliance problems.

Future research A significant body of knowledge on outsourcing has been developed as outsourcing became commonly used. However, the forms outsourcing can take are increasingly complex. This creates several opportunities for additional research on Accounting Information Systems outsourcing. For example, we now observe several layers of contracting. Contracts are nested. Vendors sub-contract some services to other providers, who in turn use the capabilities of external sources (Jansen, 2011). These layers of sub-contracts dilute the knowledge related to the activities performed and the data used and can limit the accountability of vendors. At the same time, they enable more complete services at very competitive prices. How should contract design and reporting mechanisms evolve to take into account the layering of contracts? Most of the literature has been looking at the relationship between a client and a vendor, or between a client and multiple vendors. What happens when the vendor is also a client in another relationship? Another area in which research is needed is the international dimension of outsourcing and offshoring. When activities are managed by vendors, they can be moved from one country to another to take advantage of different cost structures. The data can be stored in one country while the employees working with it are in other countries. This creates risks (Nassimbeni et al., 2012). Regulations differ from one country to another and understanding each country’s privacy laws, government surveillance strategies and intellectual property regulation is a challenge. In addition, these can change depending on political pressures. New risk management strategies will need to be developed to assess and manage those risks. The compliance challenge associated with the practices of suppliers and their sub-contractors also require additional research efforts. How managers organise accounting functions in response to such dilemmas is an interesting line of inquiry. Data must be traced through an increasingly complex maze of systems. Auditing cannot be an afterthought. Auditing strategies have to be developed in parallel with contract development to ensure their soundness. Even when auditing strategies are being developed (Wang et al., 2015), how to implement them into auditing standards and practices and how to embed them into contracts remains a challenge.

Conclusion The use of outsourcing is growing steadily. Companies use it to increase their flexibility and adapt to changes in regulations or in technological landscapes (Deloitte, 2014). For Accounting 130

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Information Systems, the outsourcing option has to be considered. Managers have to assess the extent to which their activities are predictable and measurable. The more they are, the better candidates they are for outsourcing. Managers must remain aware that rational motives may not be the only ones at play when considering outsourcing decisions. Political games may influence the decisions. Once the decision to outsource is taken, it is important to devise a contract that will motivate the vendor to exert maximal effort. The contract should also protect the client from any lock-in situation. This is the only way to ensure that the client can switch suppliers easily and thus benefit from the competition between them. The relationship between client and supplier has to be managed. Beyond the contract, the clients will keep communication channels alive to ensure their suppliers have the information required to provide good service. As with any major decision, managers have to conduct a thorough risk assessment of the outsourcing decision. Outsourcing can lead to some negative events and regular monitoring of risk factors is essential. The use of a third party, the vendor, for accounting activities brings potentially positive and negative consequences. Managers will have to recognise that good outsourcing management is a balancing act. The challenge is to get most of the advantages of each governance mode while avoiding the pitfalls.

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11 Accountants’ roles and accounting-related technologies João Oliveira

Introduction This chapter discusses how the roles of accountants in organisations have been and may continue to be affected by developments in information systems (IS), and accounting and integrated systems in particular. Our examination spans across both financial accountants’ and management accountants’/controllers’ roles (Gerdin et al., 2014), rebalancing the literature with greater attention to the latter. Many researchers characterised management accountants’ roles and their evolution and explanatory factors, with recent attention on the shift from being a “bean counter” to becoming a “business partner” (for example, Burns et al., 2014; Byrne and Pierce, 2007; Goretzki et al., 2013; Herbert and Seal, 2012; Järvenpää, 2001, 2007; Quinn, 2014), but considerably fewer have explicitly encompassed financial accounting related roles as well (Mouritsen, 1996). However, the influence of these technological developments for accounting and accountants’ roles is not deterministic. Accountants’ roles go beyond mere accounting practices (Goretzki et al., 2013) and reflect overall contributions by accountants within particular organisational contexts. Actually carrying out a particular role depends on its recognition and acceptance by at least some actors, such as the expected beneficiaries of the underlying practices (Lambert and Pezet, 2011); in addition, roles are in a permanently contestable domain, including by other professionals aiming to carry out similar roles to enhance their organisational position (Burns et al., 2014). Therefore, understanding accountants’ roles requires framing the influence of technologies within the particular, complex socio-technical settings where these techniques are deployed. This chapter is structured as follows. The first section briefly discusses several technologies, both traditional, contemporary or still only emerging, relevant for the accounting area. The second section briefly characterises the main roles taken by accountants and explores how each of these roles may be affected by the various technologies. The third section adopts a more nuanced perspective about accountants’ roles and the influence of technology, discussing how roles depend on multiple organisational factors, including other actors’ recognition, acceptance or competition, beyond strictly technological factors. A fourth section offers some concluding remarks. 133

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An overview over accounting-related technologies From stand-alone to pervasive: the widened scope of accounting technologies Traditionally, IT architectures were based on multiple, decoupled systems. In both financial accounting and management accounting, best-of-breed solutions (promising superior performance in a restricted area, such as bookkeeping, consolidation, treasury or budgeting; see Leahy, 2004) or generic purpose solutions (for example, spreadsheets, particularly popular among management accountants in order to autonomously develop flexible analytical models) had little or no integration with operational systems. Manual data entry by accountants was time-consuming and error-prone. However, stand-alone systems have increasingly developed more or less automatic interfaces to exchange information, i.e. becoming increasingly coupled, or have been replaced by integrated systems, in particular by Enterprise Resource Planning (ERP) systems. ERPs are now commonplace (Grabski et al., 2008) as organisation-wide, tightly coupled IS, not only among large organisations but increasingly also among smaller ones. Initially, ERPs had the ambition to be virtually the single system satisfying all IS needs, through their wide variety of functional modules. Data flows seamlessly within an organisation across its ERP’s multiple modules, promoting (and also requiring) single data entry and greater data quality. These effects expand when ERPs of business partners become connected to allow electronic transfers of transaction data between the ERPs. Since accounting activities typically occur at the end of business processes (Kanellou and Spathis, 2013), manual data entry in accounting becomes drastically reduced. Drilling down the data inside the ERP single database also enables analyses at different levels of aggregation. However, and although ERPs proved to be highly efficient transactional systems, their analytical capabilities were found relatively limited (Rom and Rhode, 2006; Scapens and Jazayeri, 2003). ERP vendors therefore gradually focused on making ERPs the major backbone of the IS architecture to which other systems can connect. One example concerns Customer Relationship Management (CRM) and Supplier Relationship Management (SRM) solutions, increasingly found either within the ERP or as best-of-breed solutions tightly attached to it. As another example, Data Warehouses and Data Marts storage systems and Business Intelligence (BI) and Strategic Enterprise Management (SEM) solutions are now already mature technologies to explore, flexibly and in-depth, the high volume of information stored in ERPs, as well as external information (Rom and Rhode, 2006). The recent developments of cloud computing, Big Data and the Internet of Things have also been found relevant for accounting. Cloud computing emerged as an alternative to have applications and hardware in premises, drawing them from the cloud whenever required. In general, cloud computing reduces up-front costs, changes fixed costs into variable costs and is highly scalable. It promotes agility and cooperation regarding deploying or updating a single system and accessing and sharing data stored in the cloud. In accounting, cloud-based accounting solutions and collaborative work based on consistent information and systems may lead to a more adaptive finance function (CIMA, 2010), in particular for small and medium enterprises (Kristandl et al., 2015). Big Data technology seeks valuable patterns in huge, unstructured data from multiple origins, such as transactions, financial markets, social media and even images or videos. Big Data is characterised by the three Vs of high volume, velocity and variety, requiring new analytical approaches with high value potential (Bhimani and Willcocks, 2014). The Internet of Things has linked networks of computers, machines, objects and even animals and people, with unique identifiers and transferred data without human intervention. Connecting billions of devices increased, and will drastically continue to increase, available data even further, enabling new high value areas to deploy real-time analytics. 134

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Finally, robotisation applied to services, including accounting, is still only an emerging technology, based on software programmable to execute human tasks. Even after ERP-based integration already eliminated many manual tasks, there are still repetitive, relatively low value-added tasks across different business applications requiring manual intervention. Based on Robotic Process Automation (RPA), software robots mimic human actions and automate those repetitive tasks via existing user interfaces; i.e. RPA “plugs-in” not-yet integrated processes. A process candidate for RPA has three characteristics: “The actions are consistent, with the same step being performed repeatedly; it is template driven, with data being entered into specific fields in a repetitive manner; it is rules-based, to allow decision flows to alter dynamically” (E&Y, 2016a, 4).1 E&Y (2016a) indicates benefits in productivity, cycle time and accuracy and compliance, reducing the need for employees to perform repetitive, high-volume and rule-based activities. According to proponents (E&Y, 2016b; SAP, 2016), applying RPA to finance enables radical cost efficiencies and increased control, and it allows skilled resources to focus on value creation. While a more detailed analysis with regard to specific accounting areas is carried out later in this chapter, some (still mostly only anticipated) effects cut across the various areas and are now analysed. Alongside expected further reductions in accountants performing low value-added tasks, accountants with deep knowledge of business processes are still needed for very high value-added activities: first, to software initial configuration and on-going maintenance; second, to apply subjective insights and assessment required to deal with exceptions in automated processes and to perform higher-value processes; third, to perform advanced analysis and interpretation, review and approve reported information, and make complex decisions – although in the future cognitive RPA may also start performing basic analyses for more advanced assessment by experts (E&Y, 2016b); fourth, to ensure that valuable finance and practical process knowledge is retained in the organisation. In the next section, more specific expected consequences of RPA for each role are discussed.

Accountants’ roles and influences from accounting-related technologies An overview and taxonomy of accountants’ roles The literature focusing specifically on financial accounting has largely neglected the topic of accountants’ role, in a stark contrast with the management accounting and control literature, which frequently even expands its scope to include all areas of accounting. Controllers’ roles have long attracted attention, as in Simon et al.’s (1954) classifications of score keeping, attention directing and problem-solving, or Hopper’s (1980) classification of management accountants’ roles as scorekeeping and customer service. Recently, significant attention has been devoted to the transition from the traditional (and stereotypical) “scorekeeper”/“bean counter” to the “business partner” supporting managers with highly relevant insights based on close business intimacy, and on the advantages, problems and tensions of this transition (Burns et al., 2014; Byrne and Pierce, 2007; Goretzki et al., 2013; Järvenpää, 2001, 2007; Quinn, 2014, to indicate just a few). We now draw upon four of Mouritsen’s (1996) categories of accountants’ roles of bookkeeping (including administrating), consulting, banking and controlling, a taxonomy chosen given Mouritsen’s clear characterisation of roles across both financial and management accounting. While some technologies are particularly relevant for only some accounting roles, other technologies potentially impact all roles. ERPs are the prime example of wider pervasiveness, due to their potential (or at least ambition) to fulfil all the needs of all users in an organisation (Dillard et al., 2005). The ensuing analysis will separately analyse the relevant technologies for each role (for a more technology-based approach, see Belfo and Trigo, 2013), although in practice these roles are likely to interact (Mouritsen, 1996) and even conflict (Burns et al., 2014; Hopper, 1980). 135

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The bookkeeping (and administrating) role The bookkeeping role concerns “maintaining financial recording systems focusing on recordkeeping, development of the general ledger, the financial accounting reports and administrative and auditory controls” (Mouritsen, 1996, p. 288). It refers mostly to financial accounting’s traditional activities, from record-keeping to reporting, including administrative and auditory controls, typically beyond and detached from core organisational activities (Mouritsen, 1996), merely focused on the resulting financial transactions. We include in this category the role of administrating, focused on “administering customers and suppliers as debtors and creditors” (Mouritsen, 1996, p. 288), given similarities in how these two roles are affected by the analysed technologies. The implementation of ERPs has had dramatic effects upon bookkeeping. Information flows seamlessly from the operational modules (for example, sales and distribution, production, human resources, etc.) to the financial accounting module, avoiding much low value-added work of bookkeeping manual data entry. Overall, ERPs’ efficiency in transaction processing, relevant for many high-volume, repetitive tasks of bookkeeping, was one of the ERPs first benefits to be recognised (for example, Booth et al., 2000). In fact, the two major benefits in accounting identified in Kanellou and Spathis’s (2013) survey are key to the bookkeeping area: efficiency in gathering data and generating results; less time to close accounts and issue financial statements. Seamless, automated and real-time information flows with an ERP are not without their issues. As Quattrone and Hopper (2005) point out, transactions (as well as errors) made by nonaccounting staff (such as a shop floor employee producing a shipping document or issuing a receipt) have immediate repercussions in financial accounting information. Spreading awareness and knowledge among non-accounting staff about their actions’ repercussions within the accounting area may be required. Indeed, the challenge increases in dimension and complexity when it becomes inter-organisational, as information about transactions between organisations starts being exchanged between their interconnected ERPs. Inter-organisational cooperation between the respective accounting (and other) departments becomes essential; for example, between key partners, a supplier may include a particular code in the documents it issues to assist the customer when recording the transaction. In addition, as not only accounting records but also controls increasingly become more real-time, ranging from management controls to internal and external auditing controls through continuous auditing, the challenges posed by this tight integration must be considered. Financial consolidation activities also benefit from the adoption of an ERP, or at least a single accounting system, across the consolidating entities. In addition, both ERP vendors and best-ofbreed vendors have developed consolidation solutions with interfaces capable of drawing and organising data from across most financial accounting solutions, eliminating much manual effort. Nevertheless, the complexity of consolidation still leaves many areas requiring intervention and judgement by skilled accountants involved in this role. RPA has already started being deployed in bookkeeping activities, such as “general accounting (allocations and adjustments, journal entry processing, reconciliations, intercompany transactions and close) [and] Financial and external reporting” (E&Y, 2016b, p. 6). Therefore, RPA may further reduce bookkeeping accountants performing high-volume, repetitive, rule-based tasks, particularly in Shared Services Centres (E&Y, 2016b; SAP, 2016), recommending their shift towards the remaining value-added areas highlighted in the previous section: software configuration and maintenance, deal with exceptions and processes requiring subjective insights and assessment and make advanced analyses, interpretation and decisions. 136

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As a conclusion, bookkeeping related IS, in particular ERPs and other connected IS, and RPA promote the evolution of the bookkeeping (and administrating) role in two opposite ways. First, towards reduction of low-value activities, in a process often depicted as the “death of the bean counter” within financial accounting, particularly in larger organisations. Second, the continuing need of high value-adding accountants for bookkeeping – albeit in smaller numbers – to configure and maintain software, solve exceptions in human/machine shared processes, and focus on providing business analysis and insights, in a greater engagement with the rest of the organisation and introducing a consulting role into the financial accounting domain.

The banking role The banking role was related by Mouritsen (1996) with a focus on cash and currency management, in particular at a complex level, and with finance; however, in this study the banking role is restricted to treasury issues, more commonly considered as falling within the realm of accounting. Treasury activities involve tracking and managing information on account balances, transaction details and cash positions, executing fund transfers and short-term investments, and making cash flow forecasts. Like the bookkeeping role, the banking role is traditionally beyond and detached from core business activities, which are merely “a context for its work” (Mouritsen, 1996, p. 297). ERP vendors have developed specific modules to deal with treasury and financial issues, and cash management in particular, seamlessly connected with the remaining ERP modules2, an increasingly popular alternative, versus interfacing best-of-breed solutions with the ERP. The ERPs’ efficiency in gathering data and generating results (Kanellou and Spathis, 2013) is also felt in the banking area, for example, reconciling operational and banking transactions. Linking the organisation’s banking solution with the banks’ IS is now a standard feature in many organisations, even smaller ones, and it has virtually eliminated manual entry of financial transactions data. A recent survey from the main ERP vendor, SAP (2015), highlights that, when multiple systems are involved, gathering data across multiple bank accounts, entities and geographies still requires significant manual efforts, making group-wide cash assessment and management still difficult and time-consuming. Only a minority of the surveyed companies could instantly aggregate cash balances from global bank accounts (12%) or instantly prepare a consolidated cash forecast (8%), by using interactive self-service interfaces linking with the banks’ IS (although 26% and 20%, respectively, could perform those tasks within minutes). Most respondents indicated that treasury had to become not only faster in preparing and delivering cash reports and forecasts, but also better in delivering to managers more in-depth, forwardlooking cash analyses – even using advanced forecasting to leverage Big Data in finance (for example, to manage exposure to markets’ financial risks). And, as already mentioned, treasury has been highlighted as an area where RPA can be most effectively deployed (E&Y, 2016b). As a conclusion, in the banking-related accounting area, IS developments promote reducing manual tasks, more effective group-wide cash management and faster, comprehensive and more accurate cash reporting, forecasting and managing. Therefore, while a decreasing number of manual and low value-added activities are to remain relatively isolated from the rest of the organisation, these IS developments promote transforming the banking role from a detached back-office role (Mouritsen, 1996) to a provider of value-added insights closely involved with the business.

The controlling role Mouritsen (1996) relates the controlling role with budgeting and budgetary control, recording the performance data and ensuring managers’ compliance with budgeted figures. These are the 137

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traditional activities within the management accounting and control area, and are typically (derogatorily) depicted in the literature as “scorekeeping” or “bean counting”. Some criticisms targeted at this role are related with the wider critique of the budgeting technique itself, in particular within the Beyond Budgeting approach (Hope and Fraser, 2003). However, keeping scores and monitoring performance is not specific to a particular management accounting and control technique, and spans from traditional cost accounting to contemporary techniques such as the Balanced Scorecard. Cost accounting, budgeting and variance analysis have traditionally been carried out through spreadsheets, whose stand-alone nature tended to require significant manual work in gathering and entering data from multiple sources. Interfaces between spreadsheets and other systems, both accounting and non-accounting ones, were valuable whenever available, but sometimes data had to be manually gathered from physical devices. Data manipulation, calculations and reporting in spreadsheets tended to be complex and error prone (for example, Goretzki et al., 2013). Moreover, when these activities were carried out at a local level, visibility and comparability at a higher, corporate level were limited (Oliveira and Clegg, 2015). Calculation and reporting delays limited their managerial usefulness. Indeed, scorekeeping to feed data to contemporary tools like the Balanced Scorecard have also experienced similar data gathering difficulties, limiting their usefulness (Ribeiro, forthcoming). Clearly, technological constraints affected the scorekeeper role, focused on information production rather than on its analysis, underpinning the derogatory caricature of the (cost) accountant as “knowing the costs of everything and the value of nothing”. Systems integration, and ERPs in particular, significantly changed cost accounting and budgeting procedures. The ERP costing module is able to directly retrieve the required diversified types of information from the ERP database; and, very recently, in the last couple of years, manual gathering data from physical devices (for example, consumption meters) has started being eliminated, as the Internet of Things connects and permanently gathers data from an increasing number of those devices. Improved data access within ERPs promotes measuring and controlling more than the cost and profitability of products (the traditional cost object). Detailed, integrated information about sales and distribution promotes more accurate and faster customer profitability analysis. For example, information about the products and customer locations involved in each sales transaction enables immediately estimating distribution costs, before the transportation invoice is received; and combining current period and longer-term historical data enables estimating sales quantity discounts (rappels), whose definitive value can only be calculated at the end of an agreed period. Integrated systems have also promoted less effort to gather data about actual performance for budgeting control, and tools including what-if models and scenario planning facilitated planning activities and hence budgets construction. Early research about ERP effects in management accounting and control practices revealed only moderate effects (for example, Booth et al., 2000; Hyvönen, 2003; Granlund and Malmi, 2002), and two potential explanations were offered. A first explanation was inherently temporary and referred to a potential “time lag” effect: at the time of the studies, not enough time had elapsed since the ERP implementation for effects to materialise. A second explanation was more structural, arguing that although ERPs improved transactions processing efficiency and data quality, they were limited in supporting higher-value, flexible analyses. This limitation was addressed by both ERP and best-of-breed solutions vendors, through several developments: Data Warehouses or Data Marts (subsets of a Data Warehouse) to extract and consolidate data to enable deeper analyses; Business Intelligence (BI) solutions building information “cubes” to allow analysis in multiple perspectives; and Strategic Enterprise Management (SEM) solutions for the implementation of multiple advanced analytical techniques. Recent studies show how ERPs have indeed affected controllers’ roles, in particular over time and when supplemented by 138

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analytical systems (Goretzki et al., 2013; Herbert and Seal, 2012). Goretzki et al. (2013) show that, in a manufacturing firm, the ERP implementation provided a crucial information base with important impacts in control, although in that immediate period there was only a moderate roles change. Only years later, after subsequent developments, including the adoption of Business Intelligence portals to develop reporting and planning processes (among many other organisational changes, as discussed later), did a more substantial shift in roles occur, towards “controllers as business partners”. Technological developments have also been changing the timing of the activities within the controlling role. Traditionally, controlling was done only periodically, with the peak of activities concentrated in a few days around the accounts closing date. With integrated systems automating the calculation of costs and variances, plant controllers may be able to control those figures on a more continuous basis (for example, through a daily analysis of produced batches) and greater depth. This enables them to provide managers with feedback which is not only more timely but also more insightful, given the enhanced capacity to explore data in the integrated system and to obtain still “fresh” operational explanations that enrich qualitative analyses of reported data (Oliveira, 2010). However, control is not only within accountants’ domain, and the literature has identified a “hybridisation” phenomenon in management accounting and control (Caglio, 2003). Given greater data availability across the organisation through ERPs and accounting knowledge becoming more dispersed, some line managers became increasingly able to monitor their budgets and access and monitor the automatically calculated variances, and autonomously carry out in-depth flexible analyses of their business unit based on BI systems designed by controllers but dispensing with the controllers’ intervention on a daily basis (Goretzki et al., 2013; but cf. Granlund and Malmi, 2002). Therefore, the controlling role may become more diffused in organisations, across the accounting area and line management. Dechow and Mouritsen (2005) depict how an ERP system made control become a “collective affair” in a particular organisation, rather than remain exclusively in the department of accounting struggling with limited integration, contributing to the marginalisation of controllers. These two examples, based on very different technological contexts, reveal that controllers may face competition from other organisational actors becoming “hybrid accountants”. However, “hybridisation” in the opposite direction has led to a second type of “hybrid accountant”. These are technology-empowered accountants, with in-depth business knowledge, able to provide high-value insights on explaining and identifying business-critical root causes of good or bad past performance and insights on how to improve performance. These hybrid accountants have been integrated as business team members, in terms of their daily activities and their physical location (Burns et al., 2014; Goretzki et al., 2013), expanding the controlling role into the realm of the consulting role, analysed next.

The consulting role In the consulting role, the accountant supports organisational activities through specialised, often ad hoc analyses, as an internal consultant, providing useful and relevant information to managers and involved in broad business matters (Mouritsen, 1996). Therefore, the consulting role is similar to the “business partner” role, amply discussed (and typically recommended) as an evolution from the bean counter stereotype in recent literature. While, as discussed above, consulting can also be based on financial accounting information analysis, the consulting role is most commonly associated with the management accounting area. The consulting role also benefited from the greater and faster data availability enabled by ERPs, but it has been less negatively affected regarding headcount reduction since it is not based 139

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on the high-volume transactional activities taken over by ERPs. This consulting role has been particularly influenced by ERPs “organisational accounting” benefits (flexibility in information generation, applications integration, timely and reliable information for decision-making and improved quality of reports) (Kanellou and Spathis, 2013), in particular when leveraged by the developments combining Data Warehouses/Data Marts, BI and Strategic Enterprise Management solutions described above, to support flexible analyses of internal and external information, with greater depth and breath, and hence generate greater value-added insights. Recent technological developments which distinctively affect this consulting role, more than any other role within accounting, concern Big Data and the Internet of Things. Exploring these new huge and increasing amounts of data opens up new possibilities to generate insights to assist managers, including from a financial perspective. For example, Bhimani and Willcocks (2014) highlight that consumers’ on-line track prior to purchases is extremely valuable information. Capturing that non-economic information, beyond the actual economic transactions that are the traditional realm of the accounting consulting role, “offer the potential of developing financial intelligence and shaping cost management as well as pricing and operational control decisions” (p. 475–476), boosting the potential to provide high-value insights. This section provided an overview of how developments in IS have influenced, and may influence in the future, accountants’ roles of bookkeeping, banking, controlling and consulting. However, this overview gave limited consideration to the multiple contingencies, limitations, complexities and tensions highlighted in the literature about accountants’ roles. This more nuanced appreciation of accountants’ roles is the focus of the next section.

Changes in accountants’ roles: beyond technological determinism Extensive literature has analysed financial and non-financial impacts of IS such as ERPs, CRM and SRM (for example, Hendricks et al., 2006; Nicolaou and Bhattacharya, 2006). For example, in the accounting area, Kanellou and Spathis’s (2013) survey identified a range of ERP benefits (“IT accounting benefits”, “Operational accounting benefits (time)” and “Organisational accounting benefits”), some of which were related in the previous section to particular accounting roles. However, this stream of “cause-and-effect” literature reveals that, in spite of overall positive effects (suggesting that the “time lag” effect proposed in initial studies may have already disappeared for many adopting organisations), there was substantial variation in obtained results; in addition, unsuccessful or incomplete implementations and lack of improvements were not infrequent. In fact, the literature on IS in general, and Accounting Information Systems (AIS) in particular, has long recognised that the effects of technology are not deterministic, including the multiple benefits of technology anticipated by vendors, consultants and implementing organisations (Oliveira and Ribeiro, forthcoming). To better understand these varied outcomes, processual studies, based on social and behavioural approaches (Granlund, 2011) provide rich details on why and how IS are introduced in particular organisational contexts and how repercussions emerge and unfold. Both “successes” and “failures” were associated with idiosyncratic, on-going repercussions. For example, in Hassan and Mouakket (2016) mismatches between ERPs built-in (best) practices and some organisational practices in routine bookkeeping led to increased manual work and required making workarounds within the ERP to achieve ERP functioning without affecting the previously existing practices (see also Beaubien, 2012). In Ribeiro (2003), an implementation of an ERP and its management accounting functionalities was confronted with technical issues and, in particular, opposing power strategies which allowed some actors to successfully resist any relevant impact upon management accounting and control practices. In Dechow and Mouritsen (2005), structural 140

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technological inflexibility and implementation idiosyncrasies led to limited integration and visibility in financial control, hence promoting the prevalence of an operational view of control. In Oliveira and Clegg’s (2015) case, repercussions in control practices and structures of power could only be explained by considering the joint effects, in the entire organisational actornetwork, of the introduction of an ERP and the creation of two organisational structures: a Shared Service Centre and a Corporate Centre. These four cases, across roles such as bookkeeping and controlling, recommend caution when predicting effects of innovations in ERPs and AIS upon actual accounting practices. And since not even effects upon accounting practices can be anticipated with certainty, then clearly even greater caution must be taken when discussing “effects” upon accounting roles, given the institutionalised elements underpinning roles as normative phenomena (Goretzki et al., 2013). Recent literature has been particularly attentive to the complex process of accountants’ role changes, in particular towards the business partner role (Goretzki et al., 2013). This shift has been shown to be complex and problematic. For example, Jack and Kholeif (2008) reported an only partially successful ERP implementation, which did not achieve full organisational integration and did not actually change the accounting systems and practices and the role of the management accountants. Among the multiple reasons for this outcome, the authors stress the importance of strategies of multiple stakeholders within the organisation (from different functional and hierarchical areas) and around the organisation (including funding agencies and the software vendor). There were different internal and external expectations over the roles of the management accountants, and instead of the more forward-looking and influential role ambitioned by some actors, the one which ultimately emerged “was returned to that of data custodian and information provider for others who controlled the organisation” (Jack and Kholeif, 2008, p. 43). Detailed processual studies highlight that “natural business partnering” (emphasis added), as an interviewee in Järvenpää (2007) put it, is nothing but “natural” and straightforward. Achieving this outcome actually required the complex organisational change of removing the routine tasks to a Shared Service Centre, while leaving other controllers in business units; and the complexity involved is visible in the variety of the eight intervention tools deployed to develop business orientation in management accounting: structural interventions regarding decentralisation; AIS; innovations in accounting; human resources management; directing of personal attention; role modelling; formally “managing” values; storytelling (Järvenpää, 2007). Recent micro-level studies have shed further light on how the outcomes of technical innovations depend on holistic strategies by organisational actors to have new or changed roles recognised and accepted by other organisational members. Lambert and Pezet (2011) highlight how the acceptance of a new consulting role depended on the perception and acceptance of the validity and relevance of the reported information, in their case organisation. The information produced was repeatedly cross-examined in monthly performance review meetings, in a process that only gradually established the recognition that the produced information was valid – and hence only gradually contributed towards establishing the management accountants’ role and the recognition of their central position within the organisation. In a similar vein, Goretzki et al. (2013) highlight how institutionalisation of a new business partner role in a case organisation required actors to conduct three interrelated kinds of purposive institutional work, through legitimising the new role, (re-)constructing role identities and leveraging upon broader level changes to support organisational-level desired changes. Overall, these micro-level studies show that while technical innovations are indispensable to achieve change in accountants’ roles, any actual role changes depend on the interrelated effects emerging from diverse actors’ strategies within particular organisational networks. Therefore, the effects of technologies upon roles are not deterministic, and the influences identified in the previous section between technologies and 141

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specific accountants’ roles should be recognised as merely high-level, general characterisations, rather than “cause-and-effect” relations.

Conclusion This chapter aims at contributing to bridge the AIS and the accountants’ roles literatures. It explores how four accounting roles have been, and may continue to be, affected by developments in IS, and accounting and integrated systems in particular, while highlighting that such technological influences upon accountants’ roles are not deterministic but depend on case specificities, including organisational actors’ strategies. The bookkeeping, the banking and the controlling roles seem to have been the most affected by the automation enabled by integration within ERPs and/or across systems, including interorganisational ones. The reduction of the need of book keeping accountants due to this integration has been recently reinforced by the emergence of Robotic Process Automation, although high value-adding activities in the area of book keeping are still available in niche areas. Banking and controlling, while also pressured towards headcount reductions, emerge as areas with more opportunities to provide value through enhanced analytical capabilities. Supported by new technologies, hybridisation of the controlling role poses both threats and opportunities: threats, when managers undertake traditional control tasks and dispense accountants’ intervention; and opportunities, when controllers become increasingly advisors, cross over to the consulting role and eventually become members of the business teams. With regard to the consulting role, its traditional emphasis on ad hoc analyses enables greater potential to leverage on a large array of technologies, not only the already established ERPs, Data Warehouses/Data Marts, BI and SEM solutions, but also the recent developments of Big Data and the Internet of Things. Overall, while the low value-added activities of the bookkeeping, banking and controlling roles are likely to remain detached from core organisational activities, the remaining and even expanding higher-value activities in those roles, as well as in the consulting role, are related with a greater engagement with the rest of the organisation. Indeed, this chapter suggests that even though the “business partner” term may be more immediately related with the consulting role as described by Mouritsen (1996), it may also be found within the bookkeeping, banking and controlling roles when accountants become successfully engaged with providing relevant analytical insights for the business. While highlighting drastic influences of these technologies in the various areas of accounting and accountants’ roles, this chapter also stresses that the previous broad brush conclusions are not deterministic, and that accountants’ roles evolve in highly contextually specific ways (Mouritsen, 1996). Changes in roles do not occur naturally and free from resistance; on the contrary, they are dependent of explicit strategies by organisational actors pursuing particular objectives, including through securing ambitioned key roles (Goretzki et al., 2013; Lambert and Pezet, 2011). Therefore, and while changes in roles are unlikely to be successful without suitable supporting technologies, accountants should have a clear perception of the intertwined technical, organisational and social challenges at stake in their particular settings; this wider awareness will be key to develop and deploy strategies to mobilise those technologies in order to successfully shape accountants’ roles.

Notes 1 An automated process in accounting is exemplified by Deloitte at https://youtu.be/lbJI3Ghe98c. A video from Ernst & Young (visualised by the author at the 3rd Meeting of Service Centres, Porto, 1 July 2016) depicts a software robot using and interfacing between different applications like a human actor.The robot first analysed a travel expense scan and identified and extracted the relevant data; then, it introduced the 142

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data in appropriate fields of an expense claims software; finally, it emailed a manager to request his/her approval. 2 As a detailed exemplification, see SAP’s cash management solution at https://youtu.be/TKjXI7I8hwo.

References Beaubien, L. (2012). Technology, change, and management control: a temporal perspective. Accounting, Auditing & Accountability Journal, 26(1), 48–74. Belfo, F. and Trigo, A. (2013). Accounting Information Systems: tradition and future directions, Procedia Technology, 9, 536–546. Bhimani, A. and Willcocks, L. (2014). Digitisation, Big Data and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490. Booth, P., Matolcsy, Z. and Wieder, B. (2000). The impacts of Enterprise Resource Planning systems on accounting practice – the Australian experience. Australian Accounting Review, 10(3), 4–18. Burns, J., Warren, L. and Oliveira, J. (2014). Business partnering: Is it all that good? Controlling & Management Review, 58(2), 36–41. Byrne, S. and Pierce, B. (2007). Towards a more comprehensive understanding of the roles of management accountants. European Accounting Review, 16(3), 469–498. Caglio, A. (2003). Enterprise Resource Planning systems and accountants: towards hybridization? European Accounting Review, 12(1), 123–153. CIMA (2010). Cloud computing could lead to a more adaptive finance function. CIMA Insight E-Zine. www.cimaglobal.com/Thought-leadership/Newsletters/Insight-e-magazine/Insight-2010/InsightDecember-2010/Cloud-computing-could-lead-to-more-adaptive-finance-function/. Dechow, N. and Mouritsen, J. (2005). Enterprise Resource Planning systems, management control and the quest for integration. Accounting, Organisations and Society, 30(7–8), 691–733. Dillard, J. F., Ruchala, L. and Yuthas, K. (2005). Enterprise Resource Planning systems: a physical manifestation of administrative evil. International Journal of Accounting Information Systems, 6(2), 107–127. Ernst & Young (2016a). Robotic process automation: automations next frontier. https://webforms.ey.com/ Publication/vwLUAssets/ey-robotic-process-automation/$FILE/ey-robotic-process-automation.pdf. Ernst & Young (2016b). Robotic process automation in the finance function of the future. https:// webforms.ey.com/Publication/vwLUAssets/EY-robotic-process-automation-in-the-finance-functionof-the-future/$FILE/EY-robotic-process-automation-in-the-finance-function-of-the-future.pdf. Gerdin, J., Messner, M. and Mouritsen, J. (2014). On the significance of accounting for managerial work. Scandinavian Journal of Management, 30(4), 389–394. Goretzki, L., Strauß, E. and Weber, J. (2013). An institutional perspective on the changes in management accountant’s professional role. Management Accounting Research, 23, 41–63. Grabski, S., Leech, S. A. and Sangster, A. (2008). Management accountants: a profession dramatically changed by ERP systems. CIMA Research executive summary series, 4(5), 1–9. Granlund, M. (2011). Extending AIS research to management accounting and control issues: a research note. International Journal of Accounting Information Systems, 12, 3–19. Granlund, M. and Malmi, T. (2002). Moderate impact of ERPS on management accounting: a lag or permanent outcome? Management Accounting Research, 13(3), 299–321. Hassan, M. K. and Mouakket, S. (2016). ERP and organisational change. International Journal of Organisational Analysis, 24(3), 487–515. Hendricks, K. B., Singhal, V. R. and Stratman, J. K. (2006). The impact of enterprise systems on corporate performance: a study of ERP, SCM, and CRM system implementations. Journal of Operations Management, 25(1), 65–82. Herbert, I. and Seal, W. (2012). Shared services as a new organisational form: some implications for management accounting. The British Accounting Review, 44, 83–97. Hope, J. and Fraser, R. (2003). Beyond Budgeting: How Managers Can Break Free From the Annual Performance Trap. Boston, MA: Harvard Business School Press. Hopper, T. (1980). Role conflicts of management accountants and their position within organisation structures. Accounting, Organisations and Society, 5(4), 401–411. Hyvönen, T. (2003). Management accounting and information systems: ERP versus BoB. European Accounting Review, 12(1), 155–173. Jack, L. and Kholeif, A. (2008). Enterprise Resource Planning and a contest to limit the role of management accountants: a strong structuration perspective. Accounting Forum, 32, 30–45. 143

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Järvenpää, M. (2001). Connecting management accountants, changing roles, competencies and personalities into the wider managerial discussion. Publications of the Turku School of Economics and Business Administration, LTA 4/01, 431–458. Järvenpää, M. (2007). Making business partners: a case study on how management accounting culture was changed. European Accounting Review, 16(1), 99–142. Kanellou, A. and Spathis, C. (2013). Accounting benefits and satisfaction in an ERP environment. International Journal of Accounting Information Systems, 14, 209–234. Kristandl, G., Quinn, M. and Strauß, E. (2015). Controlling und cloud computing – Wie die cloud den informationsfluss in KMU ändert. ZfKE – Zeitschrift für KMU und Entrepreneurship: 63, Controlling in und für KMU, 281–304. Lambert, C. and Pezet, E. (2011). The making of the management accountant – becoming the producer of truthful knowledge. Accounting, Organisations and Society, 35, 10–30. Leahy, T. (2004). Best-of-Breed Software. http://businessfinancemag.com/technology/best-breed-software. Mouritsen, J. (1996). Five aspects of accounting departments work. Management Accounting Research, 7, 283–303. Nicolaou, A. I. and Bhattacharya, S. (2006). Organisational performance effects of ERP systems usage: the impact of post-implementation changes. International Journal of Accounting Information Systems, 7(1), 18–35. Oliveira, J. (2010). Power and Organisational Change: A Case Study. PhD Thesis, Dundee: University of Dundee. Oliveira, J. and Clegg, S. (2015). Paradoxical puzzles of control and circuits of power. Qualitative Research in Accounting & Management, 12(4), 425–451. Oliveira, J. and Ribeiro, J. (forthcoming). Sistemas Enterprise Resource Planning. In M. Major and R. Vieira (eds.) Contabilidade e Controlo de Gestão: Teoria, Metodologia e Prática – 2ª Edição. Lisbon: Escolar Editora. Quattrone, P. and Hopper, T. (2005). A time–space odyssey: management control systems in two multinational organisations. Accounting, Organisations and Society, 30(7/8), 735–764. Quinn, M. (2014). The elusive business partner controller. Controlling & Management Review, 58(2), 22–27. Ribeiro, C. (forthcoming). The Uses (and Non-usage) of the Balanced Scorecard: The Case of EDP Produção. MSc dissertation, University of Porto School of Economics and Management, Porto, Portugal. Ribeiro, J. (2003). Institutionalism, Power and Resistance to Management Accounting – A Case Study. PhD Thesis, University of Manchester, Manchester. Rom, A. and Rhode, C. (2006). Enterprise Resource Planning systems, strategic enterprise management systems and management accounting – a Danish study. Journal of Enterprise Information Management, 19(1), 50–66. SAP (2015). Next Generation Needs for Cash Management. http://go.sap.com/documents/2015/07/966ef4a4357c-0010-82c7-eda71af511fa.html (upon registration). SAP (2016). Digital Finance – Transforming Finance for the Digital Economy. http://go.sap.com/ documents/2016/03/a4d16bd0-627c-0010-82c7-eda71af511fa.html (upon registration). Scapens, R.W. and Jazayeri, M. (2003). ERP systems and management accounting change: opportunities or impacts? A research note. European Accounting Review, 12(1), 201–233. Simon, H., Gwetzkow, H., Kozmetsky, G. and Tyndall, K. (1954). Centralization Versus Decentralization in Organising the Controllers Department. New York: The Controllship Foundation.

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12 Big Data and knowledge management with applications in accounting and auditing The case of Watson Daniel E. O’Leary

Watson analyzes unstructured data (and) 80% of all data today is unstructured. IBM, n.d.

Introduction In 2011, when IBM’s Watson competed on Jeopardy1 it used a cluster of 90 servers using a total of 2880 POWER 7 cores, with massively parallel processing capacity. The cluster had 16 terabytes of RAM, with 4 terabytes of disk (e.g. Deedrick, 2011). It was true parallel processing, analytics and big data. It also was true knowledge management of a wide range of generally text-based knowledge, at a level that had not been achieved in prior large-scale systems. At the conclusion of that contest, after Watson had defeated two of the most successful previous Jeopardy winners, one of the human contestants closed the show paying homage to “our new computer overlords.” At that time, Watson provided an alternative vision of knowledge management. Instead of knowledge compiled by people that would then be searched by people, Watson provided an intelligent alternative. Computers would read and capture knowledge themselves. People would play an important role in developing and training the system, but the system would be more autonomous than previous knowledge management efforts. Knowledge management would now literally use “big data” and tease knowledge from that big data, in real time.

Purpose of this chapter The purpose of this chapter is to investigate big data and knowledge management in the context of so-called cognitive systems, with a particular focus on IBM’s Watson as a case to illustrate both an emerging form of knowledge management and how firms are analyzing big data to generate knowledge from data. This analysis includes a number of applications that have been developed to address a number of accounting and auditing problems. In particular, this chapter analyzes how Watson has been brought into business settings and used or proposed for use in accounting, auditing and finance. 145

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Outline of this chapter The next section provides a brief overview of big data, knowledge discovery and knowledge management followed by some background on Watson, while the subsequent section focuses on issues of bringing Watson out of the lab and to business. Then the focus is on some of Watson’s current cognitive capabilities and the knowledge management functions that they accomplish. At that point in the chapter we drill down specifically on a number of reported audit uses by Big 4 audit firms. Additional uses of other Artificial Intelligence (AI) products by some Big 4 companies are then analyzed. In the subsequent section, we examine Watson Analytics, including how it ranks among other solutions developing analytics. We review a prototype analysis of accounts receivable data developed using Watson Analytics. Finally, the chapter summarizes the discussion, its contributions and some extensions.

Big data, knowledge management and knowledge from data The purpose of this section is to briefly review the notions of big data, knowledge management and knowledge from data.

Big data Big data has been described using five key V-words: volume, velocity, variety, veracity and value (Zikopoulos et al., 2012, 2013). Volume indicates that there are larger amounts of data than in other traditional settings. Velocity indicates that new data is generated more rapidly than data in more traditional settings. As an example, social media users generate and forward on (e.g. retweet) increasing amounts of information. Variety reflects the breadth of data being generated in non-traditional settings, including social media. Variety suggests that the data is more than classic numeric accounting data, but can include a range of other types of data, including text, images and video. Veracity refers to the truth or reliability of the data. Veracity suggests that users are interested in information about the quality or reliability of the data, and that tools used to analyze the data should include analytics about that veracity. Value refers to the contribution of the data and analysis of the data to the enterprise. In general, value is dependent on volume, velocity, variety and veracity. O’Leary (2013) and others have analyzed some of the uses of big data in accounting and auditing. Perhaps one of the most important approaches to creating value with big data is to generate knowledge from that big data, also known as knowledge discovery.

Knowledge from data Historically, much of the generation of knowledge from data has been developed from numeric or at least structured information. However, as noted above, much of the emerging big data is unstructured, typically text, audio or video files. As a result, some of the most important analysis of big data is done in the analysis of text, audio and video. Unfortunately, since by definition, unstructured data has no structure, unstructured data is difficult to use and make inferences (e.g. Simon, 1996). Accordingly, because the data is unstructured, different approaches must be initiated in order to try and generate structure from the data. In many settings, this structuring process is referred to as generating analytics from the data. Those analytics can take many forms, depending on the data that is available to be investigated. For example, there is substantial video available regarding sports world wide. As a result, recently it was noted that a National Basketball 146

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Association team, the Phoenix Suns, started tracking the number of “high fives” as a means of gathering knowledge about team camaraderie (Cunningham, 2016).

Knowledge management Knowledge management literally refers to managing knowledge resources. Such resources can include a wide range of information and knowledge. Apparently, there is substantial value to knowledge management as researchers have found that the stock market seems to react to news about knowledge management systems (e.g. DeFond et al., 2013). Knowledge management has long been of interest in accounting and auditing. For example, as noted by O’Leary (2002), the major accounting firms are known to use knowledge management for a number of issues internally: news, who knows who, industry intelligence, internal expertise, human resources, frequently asked questions, lessons learned, proposals and engagements and best practices knowledge bases. Historically (e.g. O’Leary, 1998), those knowledge bases are generated by people capturing information that allows individuals to connect to others that may have knowledge but also information that has been converted for their particular uses.

IBM’s Watson: background IBM’s Watson, named after Thomas Watson, former Chief Executive of IBM, apparently took 24 scientists four years to develop (Associated Press, 2011). The implementation for Jeopardy combined a number of Artificial Intelligence and computation tools together in a system specifically designed to compete against human players on Jeopardy. After the show, many wondered what Watson meant for IBM, knowledge management, business and humanity.

Implications of Watson Watson’s success has led to a change in the way that IBM does business. A review of IBM’s annual reports over the time frame 2013–2015 indicates that Watson has become a key component of IBM’s strategy moving forward at that time. Reportedly (Waters, 2016), Dr. John Kelly, Senior Vice President of Cognitive Solutions and IBM Research, has called Watson “the biggest most important thing I have seen in my career.” The Associated Press (2011) called Watson “the most significant breakthrough of this century.” Watson also was a triumph for knowledge management. Developers were able to capture, design and load into memory all of Watson’s Jeopardy knowledge (Deedrick, 2011). At one level, this suggests that designers were able to isolate all of the necessary knowledge for Watson on Jeopardy. As a result, one implication is that when using Watson as a design for knowledge management there is some cohesive body of knowledge that can be identified, captured and reused. It has been at least in part because of Watson that innovators in business and accounting (e.g. Agnew, 2016) have suggested that big data, data analytics, Artificial Intelligence and workflow analytics are changing how business is being done. Accounting and consulting firms such as Deloitte (2015) have indicated that systems such as IBM’s Watson will disrupt the ways that business is done through so-called cognitive computing. As a result, it is probably not surprising that recently a number of commentators have suggested that analytics and Artificial Intelligence will disrupt current white-collar jobs (e.g. McLellen, 2015). In particular, it has been suggested that at the large accounting firms, large numbers of auditors will become redundant 147

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(e.g. Agnew, 2016; Peterson, 2015). Accordingly, some commenters have suggested that audit firms will no longer need a large number of junior staff and that engagements will not need large numbers of auditors (Williams-Grut, 2016).

IBM’s current instantiation of Watson Currently, references to Watson are more general than the set of applications developed for competition on Jeopardy, since Watson’s game-based structure does not fit directly into many business settings. In particular, “IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data” (IBM, n.d.). Rather than a single system or even multiple systems, Watson is beginning to refer to a broad portfolio of tools and applications that can be used to generate potential solutions to a broad base of business problems.

Bringing Watson (cognitive) to business Although Watson was designed to rapidly process huge volumes of text as part of learning Jeopardy, it has not been straight forward to turn Watson into a business product, particularly in accounting, auditing and finance. The role of business is not to play Jeopardy. As a result, IBM has taken a number of steps to turn Watson into a system or systems designed to facilitate knowledge management of business problems. Those steps include integrating the portfolio of individual capabilities from Watson into the cloud, encouraging and providing developers with a number of Watson capabilities to build their own applications and building an ecosystem of partners to help build out potential applications. This has resulted in a number of functioning applications.

Integration of Watson into the cloud IBM has integrated many of the Watson-based capabilities into a cloud-based approach, often eliminating the need for on-premises computing and leveraging both the cloud and Watson capabilities. In addition, placing Watson’s capabilities into the cloud increases access to the capabilities to a broader group of potential user firms and allows users the ability to make use of parallel processing capabilities that users might not otherwise have access to. Finally, by building Watson applications into the cloud, there can be broad-based software-as-a-service use of the Watson applications and base capabilities. Although there are a number of clear advantages to placing these capabilities and applications in the cloud, some users see these capabilities and applications as requiring limited incremental work or effort by IBM, with some suggesting that the cost also should be only incremental. As a result, the cloud-based approach can raise issues regarding revenues and other concerns such as cost versus value pricing for Watson tools and applications apps.

Watson developer cloud IBM’s current strategy with Watson is to encourage partners to develop applications. Watson’s developer cloud (IBM, 2016a) provides developers a number of capabilities in the form of application program interfaces (APIs). As an example, the “Emotion Analysis” service analyzes text in order to detect anger, disgust, fear, joy and sadness in a sample of text (Figure 12.1). 148

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Output The Tone Analyzer Service analyzes text at the document level and the sentence level. Use the document level analysis to get a sense of the overall tone of the document, and use the sentence level analysis to identify specific areas of your content where tones are the strongest.

Document-level Emotion Anger Disgusl

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The Emotion graph represents the likelihood

The Language Style and Social Tendencies graphs represent the amount of a language or

that an emotion tone is present in the text.

social tone present in the text. Learn more.

Learn more.

Figure 12.1 Watson tone analysis of an email

A number of uses have been proposed for the tone analyzer. One potential use is as a tool to review communications before they are sent (IBM Watson Developer Cloud, n.d.). For example, an email can be reviewed for anger, disgust, etc. and alternative words or phrases can be used to tone down the message. For all intents and purposes, email messages can be reviewed and tweaked until the emotional measures fall within particular appropriate emotional parameters. Figure 12.1 provides the review of an email that likely could benefit from parameterization to affect the perceived emotion of the text to reflect less anger.

Watson ecosystem partners The notion of an “ecosystem” was probably first introduced by Tansley (1935), where it referred broadly to a community of living organisms and non-living environmental components, such as soil, air and water, all interacting together as a system. The notion was later expanded to digital communities. As noted by Fiorina (2000), in one of the first discussions of a “digital ecosystem”: If I had to distill all the trends and developments of the Digital Renaissance and instant communications down to one single concept, it would be this: It’s that we are in a single global ecosystem – wired, connected, overlapping and bumping into one another, benefiting from each other’s successes and suffering from each other’s failures. It may sound sappy and trite, but it’s true. As diverse as our languages, our cultures and our tastes may be, together we are all part of one ecosystem now. Over time, the notion of ecosystems appears to have become even more specialized to individual digital settings. Some of the earliest mentions of “Watson’s ecosystem” appear at the beginning of 2014 (Gillespie, 2016). Watson has its own ecosystem based on its cloud-based structure, the availabilities of multiple capabilities and applications and a number of potential developers of additional applications. 149

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Using Watson for knowledge management: an example using Alchemy Language In March of 2015, IBM acquired AlchemyAPI. With that acquisition, IBM acquired a number of capabilities that it has begun to implement as part of Watson. In addition, it acquired all of the Alchemy ecosystem and all of the Alchemy developers. Alchemy Language is a collection of APIs that provide natural language processing through text analysis (Watson, n.d.). This set of APIs provides the ability to put structure onto unstructured text. In particular, at the time of writing, it provides a number of different potential knowledge management capabilities, including the following: • • • • • • • • • •

Entity extraction (cloud computing, United States); Sentiment analysis (documents are given a score and a rating of positive or negative); Emotion analysis (documents are categorized for the amounts of different emotions); Keyword extraction (keywords are extracted); Concept tagging (different concepts are tagged and rated for relevance); Relation extraction (relationships between companies or companies and capabilities); Taxonomy classification (categorization); Author extraction (author information, if present, is extracted); Language detection (e.g. English); Text extraction.

Watson provides additional knowledge management capabilities. Rather than having a person go through and track who is the author of a document, what might be some keywords or how the particular knowledge contribution should be catalogued by some taxonomy, the Watson system provides those capabilities. In particular, Watson provides a number of capabilities designed to facilitate content management. Based on these capabilities, a number of classic knowledge management librarian capabilities appear to be automated. However, Watson also expands on those kinds of knowledge management librarian capabilities. As an example, the “emotion analysis” (as discussed above) service analyzes text in order to detect anger, disgust, fear, joy and sadness in a sample of text that can be used as above or for other purposes.

Drill down on Watson’s (cognitive) capabilities More broadly, Watson’s (cognitive) capabilities are divided into four basic categories: language, speech, vision and data insights (IBM, 2016) and there are a number of applications available in these areas that can be used as the basis of additional applications. In order to illustrate some of the capabilities, a subset of applications will be reviewed. There are more applications in language (twelve) than all the applications in the other three areas put together (six). In addition to Alchemy Language capabilities, there are services in the following other areas: concept expansion, concept insights, conversation, dialog, document conversion, language translation, natural language classifier, personality insights and relationship expansion. “Concept expansion” is used to develop a dictionary of contextually related words or find and organize text based on that dictionary. One of its intended uses is to build dictionaries for financial services. “Concept insights” understands the concepts in documents and provides recommendations of related documents, topics or people. “Conversation” allows a developer the ability to add conversation capabilities to their applications. It is designed for creating virtual agents and “chat bots.” “Document conversion” allows developers to change a document to 150

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a different format, e.g. text. “Language translation” allows development of the translation of a language to other languages in one of three different domains (conversation, news and patents). “Natural language classifier” allows developers to create natural language interfaces, interpreting the text and the intent of the text, providing a classification and a corresponding confidence level. In the case of weather, classifications would relate to temperature and weather conditions, such as rain or fog. “Personality insights” takes text of 3,500 words or more from one individual and categorizes the text in terms of personality characteristics (e.g. openness) and values (e.g. traditional). “Relationship expansion” uses machine learning to analyze news articles to find entities and relationships between them. The other three areas are speech, visual and data insights. “Speech” takes speech inputs and turns it into text or takes text inputs and turns it into speech. This set of applications can take as inputs and provide as outputs, different languages. There currently are two services under visual. “Visual insights” analyzes photos and videos in order to gather information about hobbies, life events and products. “Visual recognition” tries to answer the question, “What is in this image?” “Data insights” captures Alchemy Data News that indexes roughly 250,000 news articles and blogs every day. Search is available over the last 60 days. Different characteristics of the articles are captured including the sentiment of the article, any of roughly 20 taxonomies can be used (e.g. sports) and the entities range from companies to organizations to people. Such information can be used to monitor clients and a wide range of other activities (e.g. KPMG, 2014).

Watson as knowledge management An analysis of these capabilities matches many classic knowledge management capabilities. As examples, knowledge management staff might be charged with making documents available in multiple languages as part of multi-lingual knowledge management (e.g. O’Leary, 2008). As another capability, scanning news releases is a classic knowledge management function (Eccles and Gladstone, 1991). However, there are a number of capabilities that have received limited attention in knowledge management, including the notion of doing personality scans and queries. Thus, Watson also provides additional capabilities to knowledge management, further evolving the discipline. However, the Watson capabilities provide limited ability to investigate numeric problems – the focus is clearly on the 80% text or “dark data” in the world, likely consistent with the original Jeopardy application.

Uses of Watson (cognitive) in Big 4 auditing firms Recently, KPMG and Deloitte were reported as investigating the use of Watson in accounting and auditing issues. This is being done in part through corporate alliances between Deloitte and IBM and between KPMG and IBM (Deloitte, n.d.-a). In particular, IBM’s Watson is being used in accounting and auditing settings, including risk management, accounting lease standards, due-diligence and credit rating processes.

Risk management Recently, Deloitte (2015) indicated that they had planned on using a cloud-based approach to Watson for risk management, aimed at understanding the extent to which a firm’s framework is meeting constraints described in the appropriate regulations (Jackson, 2015). In particular, they were developing a system designed “. . . to disaggregate and classify regulations, paragraph by paragraph, helping clients to compare their control framework in direct relation to current and 151

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emerging regulatory mandates. It can help to provide a streamlined view across the business, helping to confirm regulatory control coverage, manage remediation and prioritize the efforts of future regulatory change programs” (Deloitte, 2015).

Leases A Watson-related system was developed to help Deloitte clients address new lease accounting standards released by the International Accounting Standards Board (IASB) and the Financial Accounting Standards Board (FASB). In particular, FASB Accounting Standards Codification (ASC) 842 aimed at “. . . improving lease accounting policies and to anticipate issues with managing a diverse portfolio of leases” (Deloitte, 2016). Broadly, leases are used for real estate and equipment, and companies can be either lessees or lessors. LeasePoint is integrated with another IBM offering for real estate, Tririga and IBM’s Watson for the Internet of Things (IOT). As noted by Deloitte (2016), “LeasePoint is designed to provide clients with the tools necessary to address the new regulations, sustain compliance and better manage lease portfolios using cognitive insights and analytics.” Apparently, LeasePoint will be available in a cloud-hosted software-as-a-service environment, where clients can use its capabilities to assess the effects of various leasing scenarios. The system will provide dashboards that help visualize business intelligence information through automated reports (Deloitte, n.d.-b). Accordingly, LeasePoint provides lease management and insights into leases through business intelligence generated through and around an existing application.

Regulatory compliance In a joint effort, IBM and Deloitte have announced that they will be developing a governance, risk and compliance (GRC) application based on Watson and OpenPages Risk analytics (Collins, 2015). That effort is discussed in more detail in IBM (2015b), including its integration with business intelligence capability, using Cognos. As a result, this is consistent with building around an existing application, while embedding business intelligence to help capture knowledge embedded in that application.

Due diligence platform Taft (2016) reported that KMPG has built a system, called Astrus, designed to facilitate due diligence by gathering large quantities of information about third parties in order to capture information about their integrity and reputation. Apparently, the system is built on top of some of the Watson technology and due-diligence software developed by Astrus. This type of application would automate many knowledge management content processes, such as searching out information about audit clients and potential audit clients and their management, in order to determine if an audit engagement with some client should be renewed or even initiated.

Credit rating process In addition, Taft (2016) reported that KPMG engaged IBM Watson in order to build a prototype to support auditing various credit rating processes at a financial institution. The system is designed to augment their auditor’s abilities to test credit risk controls. The system should allow the analysis of the credit judgments in order to identify the exceptions that require additional follow-up. 152

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Macaulay (2016) provides additional insight into the “Commercial Mortgage Loan Audit” prototype system. The system is designed to process an entire credit file for each loan. As part of the system design, key portions of the grade given to a loan by KPMG were identified and used by the system to determine the loan grade. Each loan was then evaluated based on a confidence level assessment, information extracted from the particular credit file and market sources. The basic approach is a five-part process summarized in Figure 12.2. First the system extracts the facts from the file. Second, the strength of critical dimensions (e.g. payment history, primary source of repayment, collateral and guarantor) is evaluated. Third, those strengths are then weighed and translated into a scale (e.g. AAA, AA, etc.). Finally, the system juxtapositions the KPMG system rating and the client’s rating of the loan in order to note exceptions.

Non-Big 4 “audit-like” uses Another firm, not a Big 4 audit firm, has built a “cognitive” system for audit-like purposes in risk management. The company, Domus Semo Sancus, has built a risk management system based on Watson technologies called SafetyNet (IBM, 2015c). Its system uses multiple language application program interfaces to provide analytics that capture whether some agent is engaging in “illegal practices.” Apparently, the system is in use by at least one government entity to vet potential investors, speeding the process from weeks to days.

Summary of uses in Big 4 auditing firms Although IBM Watson is being credited with these and other advances, Watson actually is a portfolio of different approaches and systems that provides the substrate on which these solutions are based. In addition, the Watson-like approaches apparently are being integrated into existing domain-specific product offerings, ultimately providing business intelligence to or on top of those systems. Further, Watson can be used to broadly analyze text, then determine anomalous text compared to standard and then point the analyst to the anomalous text. Finally, it appears that at least at this time the focus is on “wrapping” existing applications that have been developed with cognitive computing capabilities.

Additional uses of AI in Big 4 auditing Broadly, Watson has been categorized as a cognitive system, embedding Artificial Intelligence, natural language and additional approaches. This section briefly summarizes the use of general AI approaches as recently reported by the Big 4.

••

Loan Amount $10M

Payment History: Weak

Purpose: re-finance

PSOR: Strong

Collateral: A properties

Collateral: Strong

Appraised Value: $1 OOM

Guarantor: Weak

2 AA

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AAA

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Figure 12.2 Loan grading process 153

Daniel E. O’Leary

Although Deloitte has been reported as working with IBM on Watson projects, they also have been linked to another firm using AI. Kira Systems provides software that allows users to analyze contracts. While discussing Deloitte (Galang, 2016), “They have vast amounts of unstructured information that they can’t understand right now, and our software can help bring them understanding of what’s in these unstructured documents,” Kepes (2016) suggested that at Deloitte there were a reported 3,000 active users of Kira System’s document analysis system, analyzing uses for mergers and acquisitions, investigations and contract management. Reportedly, two of the leading audit firms, Price Waterhouse Cooper and Ernst & Young, are using Artificial Intelligence and analytics to change their auditing process (e.g. Brennan, 2016) but the limited evidence in the literature does not allow generation of specificity of those applications. As a signal of their interest of “breaking out of the audit box,” seemingly both PWC and EY have investigated using drones in stock count audits (e.g. Agnew, 2016).

Numeric data – analytics and knowledge from data: Watson Analytics The original Watson was designed for analysis of unstructured data, such as text, and not designed to analyze numeric data. Accordingly, business intelligence analytics would be necessary to tease out knowledge from the numerical data in conjunction with visualization capabilities. Statistics and Artificial Intelligence have long been used for knowledge discovery. As a result, it is not surprising for commenters to note the link between big data, analytics and knowledge management (e.g. Lamont, 2012). Much of contemporary knowledge discovery is couched in “analytics.” In the case of IBM, “Watson Analytics” provides a set of tools to help analyze numeric data. Watson Analytics provides the ability of users to have a system that will perform an initial and facilitate a subsequent deeper analytical analysis of data of concern to the user. Watson Analytics apparently does not assume that the user has much knowledge about statistics and analytics. Rather than statistical terms (e.g. correlation), the term “strength” is used to describe the relationship between variables. Instead of depending on the user’s statistical knowledge, the system apparently has some built-in “intelligence” about analytics and statistics. When presented with a data set, the system will generate a set of six questions referred to as “starting points.” Users can then generate additional questions if the system-generated questions do not provide appropriate insights. In addition, typically the results developed by the system can be presented in different visualization formats if the system choice is inappropriate. As a result, this type of system potentially is likely to be of greatest interest to managers and other users with limited statistics or analytics knowledge. On the other hand, users with detailed statistical knowledge are unlikely to be completely satisfied with the lack of model building control and access to classic statistical tests. Statistical concepts such as time series, correlation, statistical significance, etc. apparently are not explicitly accounted for in the system. The notions of “dependent” and “independent” variable are replaced by “target” and “input” field, respectively. However, Watson Analytics does provide an analysis of the data quality, indicating “. . . how ready the data is for analysis” (IBM, 2015a, p. 6). Computing the data quality for each field apparently considers a number of factors, including missing values, outliers, symmetry, skewness, imbalance and other issues. The overall data quality score is an average of the data quality score of every variable in the data set. Further, the existence of a data quality score emphasizes the importance of the quality of data to the user. An example is presented in Figure 12.3. This quality number allows the user to have direct information about the veracity of the data. 154

Big Data and knowledge management

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Figure 14.1 A typical spreadsheet It could be difficult for other users to read through the formulae and understand the underlying assumptions for the projection, particularly when there are special assumptions such as an absolute value plugged into the formula for a specific cell.

WACC =

15%

Project A

2

Time period: 0 Cash flow: -1000 Discounted cash flow: =dcf(Proj_A_CF,wacc,D5) Cumulative discounted cash flow: =dccf(Proj_A_CF,wacc,D5)

550

360

=dcf(Proj_A_CF,wacc,E5) =dccf( Proj_A_CF, wacc,E5)

=dcf(Proj_A_CF,wacc,F5) =dccf(Proj_A_CF,wacc,F5)

Discounted Payback: =dpayback(Proj A CF,wacc) Project B Time period: 0 Cash flow: -1000 Discounted cash flow: =dcf(Proj_B_CF,wacc,D13) Cumulative discounted cash flow: =dccf(Proj_B_CF,wacc,D13)

340

360

=dcf(Proj_B_CF,wacc,E13)

=dcf(Proj_B_CF,wacc,F13)

=dccf(Proj_B_CF,wacc,E13) =dccf(Proj_B_CF,wacc,F13)

Discounted Payback: =dpayback(Proj B CF,wacc)

Figure 14.2 A spreadsheet may have custom functions Managers can focus on interpretation of the appraisals and provide feedback on the financial analysis.

and they have to make subjective judgements. On the other hand, this may show that there are gaps between the awareness of such factors and the incapability of spreadsheets to capture or to represent them. Traditional financial methods can be narrowly focused on direct financial impact. For example, the SID appraisal process may ignore the financial impact (positive or negative) that can be cascaded through the value chain (Shank and Govindarajan, 1992). Departmental managers may overlook the synergies or frictions that can be generated through the value creation. Spreadsheets are usually close-ended. The outputs of a specific CID/SID spreadsheet are rarely linked to the 176

Accounting for capital investment appraisal

input of related CID spreadsheets of other departments. Aggregated benefits/impacts cannot be easily observed even by senior management. Traditional financial methods cannot capture non-financial benefits. The competitive advantages of companies are more and more determined by intellectual capital (IC) investment, namely human capital, organisational capital and relational capital. The measurement of IC involves the identification of the relationship between a company’s intangible resources and its activities, and the development of a list of predominantly non-financial indicators to represent the growth/decline of such resources as well as the efficiency and effectiveness of management activities (Chiucchi, 2013). Most of the measurements of IC are qualitative and narrative. A typical company-wide IC report (Chiucchi, 2013) may contain 30 to 200 IC indicators and tens of thousands of words in narrative description. For example, intangible benefits such as the “learning experience” that can be passed from one department to another through a new project cannot be captured in financial terms. Another example is the difficulty in determining the financial benefits and terminal value of a modern customer management database from a relational capital perspective. A spreadsheet is a numerical tool. It can capture some qualitative information but it does not enable or enhance extensive sharing of qualitative or textorientated information. Non-financial benefits are always treated as afterthoughts or just quick comments to some specific cells in the spreadsheet (see Figure 14.3). Financial and non-financial considerations in SID process are not well integrated in spreadsheets.

Limitation of spreadsheets in strategic analysis and risk assessment Spreadsheet analysis may encourage short-term biased SID. IRR and NPV methods are supposed to capture the effects of all future returns. Typical sensitivity analyses are focused on using different discount rates, different scenarios of investment outcomes and assigning probabilities for each scenario. While all three parameters are usually submitted by departmental managers, studies show that senior management are more inclined to use a higher discount rate as a buffer (e.g. Drury, 1990; Abdel-Kader and Dugdale, 1998; Kaplan and Atkinson, 2015). Another issue is that managers usually use the same discount rate throughout the lifetime of the projects, which is inappropriate as uncertainty of the project will be reduced after the initial periods. The reliance of using higher and flat hurdle rates as investment decision rules will inevitably cause the SID projects to be short-term driven. Spreadsheets for SIDs are typically project specific and are not WACC

=

15%

Time period: Colshflow: Discounted cash flow: Cumulative discounted cash flow:

0 (1,000) (1,000) (1,000)

(42)

vid had a discussion Susan : I think the number th me and he ggests adding two

Discounted Piiyback: 3.24 Project B Tim e period:

Cash flow: Discounted ct~~sh flow: Cumulative discounted cash flow:

0 (1,000) (1,000) (1,000)

340 296 (704)

360 272 (432)

450 296 (136)

530 303 167

Discounted Payback: 3.45

Figure 14.3 Spreadsheets can add basic comments but they do not encourage active information and opinion sharing 177

Elaine Harris, Thinh Hoang and George Ngan

linked with historical data (although they can be if the IT department can create custom data retrieval functions). If post-investment audit results cannot be retrieved on an ad hoc basis, senior managers may not be able to perform sensitivity analysis objectively (e.g. realised historical costs of capital or success rates of similar projects). Alkaraan and Northcott’s (2006) survey asked respondents to rank eight different techniques for assessing risks (adjusted payback period, adjusted IRR, adjusted discounted rate, adjusted cash flow, probability analysis, computer simulation, CAPM and scenario analysis). Their study found that scenario analysis, adjusted IRR, probability analysis and shortened payback period are the most commonly used risk assessment techniques. However, respondents only assigned more importance to adjusting payback period, IRR, discount rate and forecast cash flow for SIDs. There was no significant difference detected on using other more advanced risk assessment methods between SIDs and non-SIDs. It is not easy for end-users to use spreadsheets to incorporate advanced models for risks. Managers can have faulty assumptions about the status quo and this could become a serious issue for companies that are operating in highly competitive industries (Adler, 2000). If advanced methods are not used, and sensitivity analyses are conducted merely by adjusting parameters that are based on the status quo, cash flow projections may turn out to be too optimistic or may have ignored the opportunity cost of “not doing the project”. In fact, modern spreadsheets are capable of performing basic dynamic scenario analysis and optimisations, but managers (end-users) do not necessarily have the skills to incorporate the appropriate dynamic models into their financial analysis (see Figure 14.4 and Figure 14.5). Other users of spreadsheet also may not have the skills to interpret the more advanced models. While managers may be aware of the shortfalls of traditional financial appraisal techniques, the spreadsheet is not designed to fill those gaps. Both decision-makers and managers might be overwhelmed by the quantitative information presented by the numbers-focused spreadsheet. This situation may foster a vicious cycle of inflating projections. When spreadsheets cannot appropriately reflect and analyse the intangibles factors and/or are incapable of incorporating advanced risk assessment methods that can be easily understood and used by end-users, decisionmakers may simply make a conservative estimate of the financial parameters. If departmental managers perceive the primary decision criteria to be financial, represented by spreadsheet output, with investment risks being assessed simply by inflated discount rates, they may decide to inflate the cash flow (Carr and Tomkins, 1996). WACC =

•

15%

Project A

Time period:

0

2

Cash flow:

(1,000)

550

360

Discounted cash flow:

(1,000)

478

263

Cumulative discounted cash flow:

(1,000)

(522)

(259)

Solver Parameters

Set Objective:

To:

0 Max

1. 1

SDSlO 0

Min

0 Value Of:

0

Bv Changing Variable Cells:

1. 1

I SCS2

Discounted Payback: 3.24

Subject to the Conmaint s:

Project 8 Time period:

1

Cash flow:

(1,000)

340

360

Discounted cash flow:

(1,000)

296

272

Cumulative discounted cash flow:

(1,000)

(704)

(432)

Discounted Payback: 3.45

SDS6 = 400 ~SHS6