System Trust: Researching the Architecture of Trust in Systems [1st ed.] 978-3-658-25627-2, 978-3-658-25628-9

Table of contents :
Front Matter ....Pages I-XVI
Introduction (Patrick Sumpf)....Pages 1-13
Trust in Systems (Patrick Sumpf)....Pages 15-39
System References (Patrick Sumpf)....Pages 41-67
Conditions and Consequences of Trust in Systems (Patrick Sumpf)....Pages 69-95
Toward an ‘Architecture of Trust’ (Patrick Sumpf)....Pages 97-135
Case Study: Trust in the Energy System (Patrick Sumpf)....Pages 137-217
Conclusions (Patrick Sumpf)....Pages 219-235
Back Matter ....Pages 237-250

Citation preview

Patrick Sumpf

System Trust Researching the Architecture of Trust in Systems

System Trust

Patrick Sumpf

System Trust Researching the Architecture of Trust in Systems With a foreword by Prof. Todd R. La Porte

Patrick Sumpf Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany Dissertation University of Mannheim, Germany, 2017

ISBN 978-3-658-25627-2 ISBN 978-3-658-25628-9  (eBook) https://doi.org/10.1007/978-3-658-25628-9 Library of Congress Control Number: 2019933860 Springer VS © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer VS imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Foreword

One of the persistent concerns threaded through our everyday lives attaches to critical functions in services to citizens and consumers, such as energy provision or internet connectivity. Can those of us who depend on these services count predictably on their availability in times of need? Can we “trust the systems” that enable and constrain our everyday lives? In this work, Patrick Sumpf mounts a sustained examination of trust relationships at the impersonal macro-systemic level. It is the most comprehensive effort to date to explicate the conditions that underlie these relations and which vary the degree to which users (citizens and consumers) delegate the wide-ranging interactions shaping daily life to large scale technical and social systems. The study, prompted in part by changes in Germany as that nation carries out a major transformation of its energy production and distribution networks, goes beneath the physical modes of electricity production, coordination and service, to focus on the social groups and dynamics of those who produce and consume, and who are expected to “trust the system” enough to follow the received rules of engagement, use and cooperation. Sumpf offers an elaborated vocabulary of the factors, nuances and relational elements undergirding the perceptions and foundational interactions of citizen-system trust dynamics – an “Architecture of Trust”. This is complemented with a finely grained, empirically grounded examination of the perceptions of users regarding the grounds for trust and what might be the conditions of institutional trustworthiness. (One can see the lineaments for application across a number of domains other than energy.) Citizens and consumers are quite likely to confront continuing developments in impersonal configurations that extend the range and depth of dependence on trust-demanding system operations. As a consequence, the means to calibrate deficits or surpluses of trust between those who are dependent upon

VI

Foreword

large technical and social production systems and their corporate and public operators take on heightened importance. Sumpf’s work contributes markedly to conceptual frameworks that can aid in clarifying the challenges that confront technical designers, managers and public overseers. It adds to the grounds for imagining cautionary tales and analyses of how “system” designers or developers consider their work and advocacy. And it suggests refinements in understanding the social conditions that buttress the trustworthiness of system leaders and deployers, as well as “system overseers” (often public regulators). Finally, it amplifies the need to improve the bases for recognizing institutional or organizational shortfalls in understanding how “socio-technical systems” are experienced by those citizens and consumers who delegate their choices to impersonal system interaction with little accountability or potential for relief. This is not a facile work. It requires concentration and reflection. These will return considerable value.

August 2018

Todd R. La Porte, Professor of the Graduate School and Professor Emeritus of Political Science University of California, Berkeley

Acknowledgments

Writing this book was ultimately made possible by a research position at Karlsruhe Institute of Technology (KIT). I am grateful to those responsible, in particular Christian Büscher, Carsten Orwat and Armin Grunwald. I would like to wholeheartedly thank the supervisors of this dissertation, Matthias Kohring and Torsten Strulik, for their continuing and expedient advice, which decisively guided this work. I owe a special thanks to my internal supervisor at the Institute for Technology Assessment and Systems Analysis (ITAS) at KIT, Christian Büscher, whose diverse input has greatly benefitted this book. I received further valuable inspiration and counsel from Stefan Böschen, Reinhard Heil, René König, Bettina-Johanna Krings, Todd R. La Porte, Andreas Lösch, Jens Schippl, and Christoph Schneider. My gratitude is extended to all of them. I am also thankful for the work of Mira Klemm and Sylke Wintzer, who have supported me in the copyediting process and with formal requirements. In the same regard, special thanks are due to Julie Cook, who has raised the quality of this book through exceptional proofreading, and also Britta Göhrisch-Radmacher and Anita Wilke of Springer VS, for their patience and dedication in the editing process. Moreover, I would like to thank my many other colleagues at ITAS for providing a vibrant PhD research community. Neither this work nor the privilege of university education would have been possible without the support of my parents and grandparents, which is why I sincerely thank them for their support throughout the years. I dedicate this book to my grandfather Rudolf Sumpf, whose departure from our lives was much too early.

Contents

1  Introduction ................................................................................................ 1  1.1  Motivation and Research Objectives ................................................... 1  1.2  Theoretical and Empirical Approach ................................................... 6  1.2.1  Theory ......................................................................................... 6  1.2.2  Case Study ................................................................................... 8  1.3  Methodology ...................................................................................... 11  2  Trust in Systems ....................................................................................... 15  2.1  2.2  2.3  2.4  2.5 

The Dualism of System Trust ............................................................ 15  Decision-Making and Compulsion .................................................... 20  Intersections in Trust Research .......................................................... 25  An Architecture of Trust: First Sketch ............................................... 30  Reflexivity, Construction, Attribution ............................................... 34 

3  System References .................................................................................... 41  3.1  3.2  3.3  3.4 

Systems as Trustees ........................................................................... 42  Systems as Trustors ........................................................................... 46  Open and Closed Systems.................................................................. 52  System Identity .................................................................................. 57 

4  Conditions and Consequences of Trust in Systems ............................... 69  4.1  4.2  4.3  4.4 

Trust and Control ............................................................................... 70  Controlling Complexity ..................................................................... 75  Knowledge and Non-Knowledge ....................................................... 81  Trust and Risk .................................................................................... 87 

X

Contents

5  Toward an ‘Architecture of Trust’ ......................................................... 97  5.1  5.2  5.3  5.4  5.5 

Trust and Expectations....................................................................... 98  System Communications ................................................................. 106  Trust in Technology and Organizations ........................................... 112  An AoT for the Energy System ....................................................... 123  Collection of Hypotheses ................................................................. 132 

6  Case Study: Trust in the Energy System .............................................. 137  6.1  Energy System Transformation ....................................................... 137  6.1.1  Impact and Logic of Smart Grids ............................................ 138  6.1.2  Consumer Involvement and the Role of Trust ......................... 140  6.2  Case Study Approach: Overview ..................................................... 144  6.3  Design and Methodology ................................................................. 146  6.3.1  Operationalization ................................................................... 149  6.3.2  Interview Guide ....................................................................... 162  6.4  Results ............................................................................................. 163  6.4.1  Initial Remarks and Observations............................................ 163  6.4.2  Category I: System Identity ..................................................... 166  6.4.3  Category II: Expectation Nexus .............................................. 182  6.4.4  Category III: Reassurance Patterns.......................................... 198  7  Conclusions ............................................................................................. 219  7.1  7.2  7.3 

The Reality of System Trust ............................................................ 221  System Constitution and Attributions of Trust ................................ 224  Outlook: System Trust in Theory and Practice ................................ 229

References ..................................................................................................... 237

Figures

Figure 2-1.

Major Components of Trust and Intersections in Trust Research .................................................................................... 29 

Figure 3-1.

Social Systems: Trustee or Trustor? Based on Luhmann (1995) ........................................................................................ 50 

Figure 4-1.

Narratives of Trust and Distrust ................................................ 86 

Figure 6-1.

Distribution of Hypotheses to the Categories of System Identity, Expectation Nexus and Reassurance Patterns ........... 151 

Figure 6-2.

Total Numbers and Distribution of AoT Elements.................. 168 

Figure 6-3.

Total Classification of System Identity ................................... 175 

Figure 6-4.

Personal Expectation Differentiation....................................... 184 

Figure 6-5.

AoT Allocation of Personal Expectations ............................... 185 

Figure 6-6.

AoT Allocation of Societal Expectations ................................ 186 

Figure 6-7.

Direction of Expectations: AoT Allocation ............................. 188 

Figure 6-8.

Experienced or Hypothetical Problems in the Energy Sector .. 200 

Figure 6-9.

Four Dimensions of Trust in the Energy System..................... 210 

Tables

Table 2-1.

An Expectation Nexus. Based on Schutz (1967) and Luhmann (1995) ........................................................................ 33 

Table 3-1.

Possible System Characterizations for Trust Research.............. 65 

Table 4-1.

Current and Future (Potential) Risks in the Energy Sector........ 88 

Table 5-1.

Illustrative References of Expectations in the Energy System 102 

Table 5-2.

PROP TV’S – An Architecture of Trust for the Energy System ..................................................................................... 128 

Table 6-1.

Interview Questions about Category I: System Identity .......... 154 

Table 6-2.

Interview Questions about Category II: Expectation Nexus.... 158 

Table 6-3.

Interview Questions about Category III: Reassurance Patterns .................................................................................... 161 

Table 6-4.

Distribution of AoT: Most Frequently Mentioned Elements... 170 

Abbreviations

AoT

Architecture of Trust

BNetzA

Bundesnetzagentur (German Regulatory Agency)

BSI

Bundesamt für Sicherheit in der Informationstechnik (German Federal Office for Information Security)

CE

Certainty-Equivalent/Equivalent-Certainty

Chap.

Chapter (of this book)

DoS

Degree of Systemacy

DSM

Demand-Side Management

EN

Expectation Nexus

ES

Energy System

EVs

Electrical Vehicles

EW

Energiewende (energy transition)

ICT

Information and Communication Technology

LTS

Large Technical Systems

MLP

Multi-Level Perspective

NGOs

Non-Governmental Organizations

NIMBY

Not In My Backyard

NoE

Number of Elements

NoL

Number of Levels

OTS

Organizations, Technology and adjacent Systems

XVI

Abbreviations

PROP TV’S

Persons, Roles, Organizations, Programs, Technology, Values, Systems

PRPV

Persons, Roles, Programs, Values

PS

Patrick Sumpf

PV

Photovoltaic

R&D

Research and Development

RES

Renewable Energy Sources

RP

Reassurance Patterns

Sect.

Section (of this book)

SG

Smart (Electricity) Grid

SI

System Identity

SoS

Security of Supply

ST

System Trust

STS

Science and Technology Studies

TA

Technology Assessment

VPP

Virtual Power Plant

1 Introduction

1.1 Motivation and Research Objectives Modern societies are based on trust. Each time an action chain occurs that relies on others conforming their behavior to one’s own expectations, trust is in operation as an enabler for that action. Imagine the odds of a simple plane journey you plan to take: you book a flight trusting that the plane will be there three months from now, and that you will be able to board it, equipped only with a smartphone ticket. You trust that a transport opportunity will be available to take you to the airport on time on that specific day. In addition to trusting in countless other details that must run smoothly for your trip to be successful (food will be delivered to your plane, the captain will be well-trained to fly, the technology will be up-to-date, etc.), you may also trust that a refund system will be in place in case of delay or if the plane is overbooked. If we transfer this idea to the collective level, we can imagine how much more complex things become once larger groups of people, including organizations, are involved. The airline company, for instance, has to trust their suppliers to deliver all the necessary supplies, and the airport operator is trusted to control refueling and correct maintenance of the planes. In addition, both the airline company and airport operator trust their employees, in that they will report for work, and follow necessary protocols, for example. Finally, trust in the government is involved regarding matters of licensing and oversight, while, for their part, governments need to trust airlines to provide the technology for civilian or military transport. It is apparent that in order to achieve complex “high reliability organizations” (La Porte 1996) such as airports and their concomitant action chains, trust is indispensable on several different fronts and levels. But what exactly are these fronts and levels that are involved in collective trust? What specific references is trust directed at, and by whom? And what are the societal conditions and consequences of such trust? © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_1

2

Introduction

Due to the centrality of these questions, key social researchers such as Anthony Giddens or James Coleman have founded whole sociologies on the issue of trust. While Giddens primarily traced his ideas of social order back to trust in “expert systems” (see Giddens 1990, 83ff) and people’s reliance on scientific knowledge, Coleman stressed the importance of trust intermediaries that tie trustors and trustees together, such as advisors or guarantors in “systems of trust” (see Coleman 1990, 188ff). The system terminology for trust was used early on by Niklas Luhmann, through whom the complexities of modern society and their reduction through trust became another quest of social research (Luhmann 1979; Luhmann 1988). Trust research outside of sociology has often rather naively used the ‘systems’ term and avoided its concretization (Morgner 2013). In social science research that is focused on technology, the term ‘systems’ has become increasingly popular: we read reports about “great transformations” (WBGU 2011, 5; Magnuson 2013) of “socio-technical systems” (Geels 2004) such as energy, climate, water management, telecommunications, or traffic systems. Frequently enabled by modern ICT technology, these transformations indicate massive change on a grand scale, evoked by governments, industry, and NGOs alike. Perhaps never before has humanity intervened into the ‘systems of systems’ to a comparable degree in order to alter the setup of historically grown infrastructures and their social operation patterns. In this context, the question of trust in systems – as introduced by Luhmann, Coleman and Giddens – acquires new urgency, as yet uncommented upon in both narrower trust research and socio-technical systems debates. The transition toward low-carbon energy systems, for example, is creating new decentralized electricity suppliers: more and more households are installing photovoltaic panels on their rooftops, or cogeneration plants in their basements, thereby feeding electricity into the grid. These new, decentralized energy producers require intelligent digital control and management. For this purpose, a “smart grid” (Giordano et al. 2011; Ramchurn et al. 2012) is to be created. And yet, creating a smart grid entails many uncertainties. While today we take the permanent availability of electricity from power outlets for granted, future consumers will have to pay much more attention to ‘demand response’ issues, mainly in order to compensate for the supply fluctuations associated with renewable energies in the emerging system. As a result, electricity provision might depend on the (in)actions of consumers. But at which interfaces with the energy

Motivation and Research Objectives

3

system are consumer uncertainties formed, their trust or distrust shaped, or their actions swayed decisively? If the collective action chains mentioned above rely on trust in order to be executed, how are they secured against the background of major transformations and the potential for distrust? How does trust operate in systems such as energy, telecommunications, or traffic, and what social consequences are incurred when trust is altered, deficient, or even granted over-generously? How can we measure and evaluate such collective, systemic trust, especially in the context of transforming these systems? The success or failure of a system’s “great transformation” (Magnuson 2013) may hinge on individual consumers’ and collective actors’ readiness to give or withdraw trust. Hence, research into the role of trust in systems promises vital insights for both social science and policy. The crucial relevance of system trust is contradicted by its marginalization in the research community and an underdeveloped understanding of what the phenomenon entails, both theoretically and practically. Since the introduction of the basic idea by Simmel (cf. Möllering 2001) and use of the specific term and its more detailed conception by Luhmann (1979), beyond the contributions from Anthony Giddens and James Coleman (both 1990) not much has been published to further clarify the phenomenon. Christian Morgner (2013) has argued convincingly that the distinction between trust and system trust, although widely referred to in writings on trust, has experienced neither theoretical nor empirical advances over the last decades, despite an overall increase in trust research (Bachmann and Zaheer 2013). Reflecting this situation is the fact that an author outside the narrower trust research community (Schneier 2012) has rephrased the classic Luhmannian claim that modern society primarily requires analysis of system trust, as opposed to trust in persons, with particular reference to current digitalization challenges: “In today’s complex society, we often trust systems more than people. It’s not so much that I trusted the plumber at my door as that I trusted the systems that produced him and protect me. I trusted the recommendation from my insurance company, the legal system that would protect me if he did rob my house, […] and – most of all – the general societal systems that inform how we all treat each other in society. Similarly, I trusted the banking system, the corporate system, the system of police, the system of traffic laws, and the system of social norms that govern most behaviors. […] I’m not really concerned about how specific people come to trust other specific people.” (Schneier 2012, 6)

4

Introduction

The contradiction between this diagnosis of the increasing emergence of complex, entangled systems in the world surrounding us today and the seeming lack of interest, impact or relevance attributed by scholars to research on system trust is the motivation for this book. The major objective of this research has therefore been a more extensive elaboration of what exactly ‘system’ and ‘trust’ mean. In everyday language, we usually speak of a ‘system’ without investing much thought into what this actually means and if it is in fact justified to speak of ‘a system’ in one case, but maybe not in another. We thus apply an open, soft or loose systems idea: usually, we assume a system is something beyond the personal, something that intertwines several components. This leads us to discuss ‘systems’ that are in fact as different from one another as the energy system, the political system or the psychic system.1 Even in academia, this loose idea of a system dominates: in the case of smart energy grids, for instance, scholarly debates often circle around “socio-technical systems” (Geels 2004), indicating the grid’s linkages between social components (organization, regulation, consumer behavior) and technical components (solar and wind power, grid infrastructure). As a consequence, trust in systems as diverse as social, technical or psychic systems is theoretically conceivable, pointing to very basic questions about the possibilities and conditions of “trust as a social reality” (Lewis and Weigert 1985). This holds true for debates on system trust in particular. System trust is usually distinguished from other forms of trust (e.g., personal/organizational trust, confidence, reliance, etc.), due to its supposedly different nature: while trust in persons is usually concrete, specific and conscious, trust in systems (such as energy supply or the economy) is rather abstract and latent, for its reference is not always clear. Personal trust is markedly depicted as a symmetric relationship that allows the trusted objects (= persons) to react to the offer of trust, while systems cannot communicate and therefore constitute an asymmetric relationship between trustee and trustor – “one-way trust” (Hardin 2002). While concrete organizations are often equated to persons in such a relationship and are of

1

The term ‘psychic system’ is used in separation from a social system in the English translation of Luhmann’s book “Social Systems” (1995). Both types of system (social and psychic) process meaning, as opposed to technical or organic systems, for instance. Psychic systems are to be understood as a human consciousness or mental system, and consist of thoughts as their system elements (cf. Luhmann 1995, 59ff; 255ff). There is no supernatural meaning of any kind implied.

Motivation and Research Objectives

5

great interest to trust research (Lane and Bachmann 1998), trust in “abstract systems” (Giddens 1990) and collective phenomena such as democracy, markets or technology are viewed skeptically or denied. Indeed, questions of distinguishing trust from neighboring terms such as reliance or dependency dominate the debate (Warren 1999). In addition, the operationalization of abstract entities for potential research into system trust has rarely been attempted. Due to its latent character and supposed lack of decision-making, even the most prominent scholar in system trust – Niklas Luhmann (1988) – has replaced the term ‘system trust’ with ‘confidence’, alluding to the low degree of influence people have on the abstract systemic processes of society. The distinction between trust and confidence has remained popular ever since its introduction (e.g., Earle and Cvetkovich 1995; Seligman 1998; Hardin 2013) and has contributed to the cementation of the idea that ‘trust’ should be reserved for personal relationships, whereas ‘confidence’ is involved in collective trust phenomena: “Trust remains vital in interpersonal relations, but participation in functional systems like the economy or politics is no longer a matter of personal relations. It requires confidence, but not trust.” (Luhmann 1988, 102)

The fact that these contradictions and confusions – including Luhmann’s switching terms – have remained largely uncommented upon (Morgner 2013) is a strong indicator for some fundamental shortcomings in the way corporate or collective trust issues are approached beyond organizational research. It seems that three fundamental questions need answering in order to come to terms with what ‘system trust’ could mean, and to assess its usefulness for trust research: 1. 2. 3.

Is a concept of trust that includes trust in systems feasible? What is a ‘system’ and how can it be a recipient of trust? How can empirical research into system trust – based on theoretical advances – be approached?

In order to shed light on these questions, the analysis in this book will be embedded in the major debates surrounding the phenomenon of system trust. These debates include distinctions such as:   

system trust versus confidence, consciousness versus latency, decision-making versus compulsion,

6

Introduction

 symmetry versus asymmetry, and  mental versus social processes of trust. Recapitulating these issues will help to find intersections as well as flaws and gaps in the existing research. As a result, it may be possible to specify where conceptual improvement is necessary – and what pieces of the puzzle are already available to answer the questions above. 1.2 Theoretical and Empirical Approach 1.2.1 Theory Against the background of unanswered questions, flaws, and criticism of the concept of ‘system trust’, this book tries to make sense of it through newer developments of sociological systems (Luhmann 1995) and of trust theory (Kohring 2004; Strulik 2004; Möllering 2006a; Morgner 2013). But the book also relies on classic contributions to the field (Luhmann 1979; Coleman 1990; Giddens 1990) as these works still offer some rich, unexplored material. By considering the general possibility of trusting systems, the analysis is based on the exposure of a dualistic notion of system trust (Kohring 2004), which leads to a distinction between general and specific functioning of a system in question of trust (Chap. 2). A subsequent discussion about modern-day conditions of system trust results in the theoretical possibility of conscious trust in systems based on trustor decision-making – a research intersection that legitimates the usage and definition2 of trust (Sect. 2.3): “[…] as a reflexive process of building on reason, routine and reflexivity, suspending irreducible social vulnerability and uncertainty as if they were favourably resolved, and maintaining a state of favourable expectation towards the actions and intentions of more or less specific others.” (Möllering 2006b, 356)

Yet intentional system trust (and its research) needs to specify precisely what type of system it involves, which results in emergent trustee systems equipped with identity for trustors to relate to (Chap. 3). This analysis is accompanied by 2

Definitions in this book are provided in accordance with the guidelines by C. Wright Mills: “When we define a word, we are merely inviting others to use it as we would like it to be used; for the purpose of definition is to focus argument upon fact, and the proper result of a good definition is to transform argument over terms into disagreements about facts, and thus open arguments for further inquiry” (Mills 1959, 34).

Theoretical and Empirical Approach

7

clarification of the ‘systems’ term involved in trust research to date, eventually assessing the role of systems as both trustees and trustors, a gap in research so far (Sect. 3.2). To achieve this, empirical data is presented that allows for concretization of the abstractions of system trust in the context of the energy system, as well as digital services, finance and politics. In Chap. 4, this concretization leads to trust’s embedment in numerous ‘conditions and consequences of trust in systems’, such as control, complexity, (non-)knowledge, and risk. Finally, an ‘Architecture of Trust’ (AoT) is presented, as the major contribution of this book to the debate on system trust. The AoT is built upon service expectations toward persons, roles, programs and values as a first ordering of possible trustees (whom or what is trust placed in?), which is one of the central questions in system trust research (Chap. 5). More generally, the aim of this book is to comprehend the idea of system trust through developing working hypotheses and inferring possible consequences for operationalization and empirical analysis. The hypotheses merge into three analytical categories regarding system identity, an expectation nexus, and reassurance patterns. A first attempt at empirical operationalization and application based on consumer interviews is then conducted in Chap. 6. Lastly, there is a threefold conclusion (Chap 7). It will be shown that the distinction between personal and system trust/confidence (as introduced by Luhmann and reproduced by other trust scholars) is overestimated, for it obscures the potential of trust research related to some of the most pressing societal challenges. In fact, all trust can theoretically be considered system trust (Rühl 2005), which is one of the greatest challenges in system trust theory. There is evidence that the differences between what is considered to be ‘personal trust’ and ‘system trust’ are outweighed by their shared features. Specifically, trust as an expectation toward the future can be directed at persons, roles, programs, or values (Luhmann 1995) – all of which can both represent a system and simultaneously involve personal and corporate references of trust. Moreover, technology, organizations, and adjacent systems should be carefully considered for inclusion as potential trust references, too, merging into an emphasis of the constructivist nature of trust and its dependence on social processes of attribution (Sects. 2.5 and 5.3). In this way, research through the AoT can demonstrate how “trust as fiction” (Möllering 2006a, 112) is created through unraveling the “equivalent-certainties” (Luhmann 1979, 50)

8

Introduction

trustors attribute in order to build up, maintain, or dismiss trust in specific branches of society. But first, the vital differentiation between ‘system trust’, ‘confidence’, and ‘familiarity’ – the most widely known distinctions in this field – is clarified, with the help of classic theoretical foundations and current empirical developments (Chap. 2). Broken down to its essentials, the difference between trust, confidence, and familiarity lies in different degrees of trustors’ reflexivity. Hence, ultimately, trust research should complement the discussion on terminology with the empirical consequences unfolded by the suspensive character of trust when high proportions of accelerated future (non-)knowledge are ‘digested’, as will be illustrated by the finance and energy transition examples. By prioritizing these latter issues, one approaches an impact orientation of trust in social reality – looking at societal branches where trust matters most. 1.2.2 Case Study The current transition of electricity provision from fossil fuel-based generation toward renewable energy sources (RES) – particularly in Germany (BMWi and BMU 2011) – serves as an illustrative case study for the research on system trust. This transition entails other major shifts, such as moving from centralized toward decentralized power generation patterns and a smart electricity grid (SG) with strong elements of information and communication technology (ICT). These developments will be described in more detail in Chap. 6 insofar as they concern the issue of trust. The connections between this energy transition and trust were explored in the project “Systemic Risks in Energy Infrastructures”3, where researchers first asked how trust and distrust could affect the role of smart grid consumers in the governance of future energy systems. In the background, practitioners and researchers are calling for more consumer involvement in energy services (Giordano et al. 2011; European Commission 2015). Currently, the average energy user is largely unfamiliar with this situation, which poses crucial challenges both in the present and for the future (Büscher and Sumpf 2015). How will consumers and industrial actors cope with active patterns of electricity generation and consumption, given the fact that energy today seems to be part of a “naturalized background, as ordinary and unremarkable to us as 3

https://www.itas.kit.edu/english/projects_orwa11_sysrisk.php Accessed April 24th, 2017.

Theoretical and Empirical Approach

9

trees, daylight, and dirt” (Edwards 2004, 185)? How will consumers’ attitudes toward markets, technology, regulation, and organizations as elements of the energy system change in their new role as electricity vendors? It seems that in this connection, ‘system trust’ is a more promising conceptual tool than “social acceptance” (Kasperson and Ram 2013). But what exactly is the special relation between trust and the energy system, and what are the traits of this ‘system’?4 The most striking current technical project in Germany is the development toward renewable energy integration and smart grid technology – the German energy transition, referred to as ‘Energiewende’. Visions of the German future energy system comprise the transformation toward decentralized energy generation, involving innovative technical components such as sophisticated grid sensors or ‘intelligent’ ICTs, as well as innovative social arrangements such as novel market mechanisms, so as to improve efficiency, environmental protection, and affordability of electricity (BMWi and BMU 2011; B.A.U.M. Consult 2012). In order to secure the crucial 50 hertz frequency5 in the system that provides stability and security of supply, energy transitions rely on demand-side management (DSM) in order to cope with volatile supply through renewable energy sources like solar and wind. SGs are supposed to mitigate these volatilities by enhancing communication within the grid and between the suppliers and consumers of electricity, and are developing into the leading technology paradigm in transitioning energy systems. As a result, we are confronted with a historic change: no longer can we afford to have a critical mass of people not trusting the emerging energy system and its components, since its functioning is likely to be dependent on its participants’ trust. How can this assumption be substantiated? One distinct feature of SGs is the application of ‘smart meters’ that enable two-way communication between consumers and suppliers, thereby creating active consumers, often termed ‘prosumers’. Thus, to facilitate the future smart grid, formerly passive 4 5

Intriguingly, systems with a technical connotation are often explicitly named this way, even in everyday language. The background to this and whether it is appropriate or not will be explored in Chap. 3. Electricity systems depend on the stability of these frequencies – e.g., 50 hertz in Europe or 60 hertz in the US (Neidhöfer 2011) – in order to ensure grid functions and avoid bottlenecks and outages. These can occur through an imbalance in demand and supply, e.g., through a surplus of electricity generation. In the German grid, experts speak of the ‘50.2 hertz problem’ when the frequency increases to 50.2 hertz and parts of the supply that caused the increase (e.g., growing PV capacities) are shut down, causing potential power failures (Döring 2013).

10

Introduction

consumers are supposed to play a critical role by adapting their energy consumption behavior through smart meters and becoming engaged in energy trade, helping to maintain electricity availability, but also serving the purpose of ‘grid-supportive’ measures, which are required to ensure grid stability and the security of supply. Concrete activities of prosumers could, among others, entail the use or avoidance of certain electrical appliances (e.g., heat pumps, air-conditioners, washing machines) at given times depending on grid conditions, or the discharging of electric vehicle batteries to the grid, responding to (smart meter) signals with the aim of securing a critical demand-supply equilibrium due to the envisioned proportions of volatile RES electricity generation (Amin and Giacomoni 2012; Ramchurn et al. 2012; Sumpf et al. 2014). In the past, energy consumers could be allowed to distrust the energy system and/or its concomitant components (e.g., nuclear plants) without triggering overarching, systemic consequences. The future grid, due to its possible dependency on the ‘appropriate’ energy behavior of individual users, is likely to be vulnerable to the cumulative effects of massive simultaneous actions by electricity customers outside the expected behavioral patterns envisaged by SG proponents. With the broad-scale rejection of E10 biofuel, for instance, the German public has already observed the emergent power of collective consumer distrust (d’Arcy Hughes 2011). This is all the more relevant given consumers’ potential perceptions of risk, such as power outages, hacking, and loss of control, or economic disadvantages such as price increases or lack of benefits from future business models. As a consequence, trust in ‘the energy system’ seems a suitable object for detailed scrutiny, which can (a) provide results in its own right, and (b) serve as an example of system trust (ST) in a highly relevant and observed field that promises insights for the broader questions of trust in systems. And indeed, the original idea of ST (Luhmann 1979) includes a trustor who “assumes that a system is functioning and places his trust in that function, not in people” (ibid., 50). So anyone trusting the energy system, in the sense of supply security, invests trust in complex, nontransparent and often overwhelming structures beyond any concept of personal trust – an aspect which relates heavily to the transformation of a pre-modern (Giddens 1990) or stratified society (Luhmann 1979; 1995) toward modern societies. Crucially, this trust involves reliance on processes of

Methodology

11

which the user lacks any knowledge to evaluate. Instead, they must rely on “impersonal trust” (Shapiro 1987), i.e., on strangers with whom one is not familiar (Zucker 1986). This axis between the system and addressable units such as persons seems key to comprehending system trust: “Although everyone is aware that the real repository of trust is in the abstract system […] it is flesh-and-blood people (who are potentially fallible) who are its operators.” (Giddens 1990, 85)

This intertwining between theoretical issues of system trust and its empirical unfolding in a branch such as energy will receive special attention in Chaps. 5 and 6. Chap. 6 will then explore the current relationship between consumers and the energy system in detail, based on the theoretical framework developed in the preceding chapters. Sect. 1.3 explains how. 1.3 Methodology The methodology of this book relies heavily on theoretical works about system trust, including their enrichment and recombination. From the consideration of these ST contributions, this book progresses toward an empirical analysis in a perhaps untypical way. Accordingly, each relevant section will close with one or more hypotheses that are inferred from the theoretical elaborations conducted in the respective chapter (Hypotheses 1-15). In this way, the hypotheses serve as both summaries of the most important aspects, and condensations of striking points for further theoretical and empirical research. Finally, these hypotheses merge into a framework that inspires the exploratory study with 30 average electricity consumers in Chap. 6. Another source of influence on hypothesis building for this empirical system trust analysis are expert interviews that have been conducted by the author in collaboration with colleagues from Energy-Trans.6 Eight guideline-based 90-120 minute interviews were conducted with leading experts from various branches of the German energy sector. These included major technology companies, power supply companies, federal and local industry associations, and consumer and environmental associations. These expert interviews,

6

The interviews were conducted in 2013 within the project “Systemic Risks in Energy Infrastructures” of the Helmholtz-Alliance Energy-Trans. The interviewees are included in the list of references in anonymized form.

12

Introduction

as a pre-stage of empirical research, helped clarify much of the practical background knowledge on energy developments, and are also partly significant for hypothesis building. Wherever this is the case, it will be indicated by direct quotations or references in the text, mainly in Chaps. 4 and 5. The direct references concentrate on four sources, namely an industry association, an environmental association, a consumer protection association7, and a power company. These interview-based expert statements used in the book are direct quotations instead of being evaluated with sophisticated coding – the latter approach is reserved for the more extensive exploratory study in Chap. 6. In this main qualitative study, I conducted 30 guideline-based interviews with a sample of average electricity consumers, grounded on systematic inclusion of hypotheses developed throughout this book. The hypotheses derived – from theoretical argumentation and expert interviews – are integrated as the basis of the main qualitative study at the end of the book (Chap. 6). This process of examining hypotheses qualitatively without the claim of being able to completely verify/dismiss them is due to the lack of comparable work in the field. This is substantiated by an absence of empirical studies on ST and the relatively small number of publications on the topic. This overall situation required some unconventional ways of looking at trust in systems and translating them into a first empirical screening. This latter aspect describes the focus of the case study in Chap. 6, which provides details on the methodology and design in the respective sections. The method of using hypotheses and qualitative research is additionally justified by the fact that the empirical results were intended to demonstrate the usability (or otherwise) of the theoretical work in this book; only in a second step are they used for the purpose of scaling ‘trust in the energy system’. Therefore, the objective of the empirical research is narrowed down to exploring the possibilities of researching system trust. This is done by evaluating the operationalization performance of the hypotheses reflected by the case study results, and by drawing conclusions for the plausibility of the theoretical underpinnings. A brief glimpse into the research design of the case study can now be provided. The interview guide used for discussions with 30 average electricity consumers is grounded on systematic inclusion of hypotheses developed throughout 7

Such German ‘associations’ are often comparable to American non-profit/non-governmental organizations (NGOs).

Methodology

13

the book. Their application toward deducing concrete questions for energy consumers led to a threefold design of categories under scrutiny: (1) system identity, (2) an expectation nexus, and (3) reassurance patterns of the 30 interviewees. The first two parts aim at the system understanding of participants (1) and their expectations toward it (2). Analytical category 3 covers the final part of the interviews and tackles ways of building or dismissing trust among the respondents and their narratives of justification. The goal of this approach is to prepare the development of methods for empirical system trust research by examining the operationalization capacity of hypotheses 1-15. Accordingly, conclusions derived from their applicability in the study can provide first hints about empirical validation or dismissal of the theoretical assumptions and the consequences for further research (Chap. 7).

 

2 Trust in Systems

2.1 The Dualism of System Trust Much of the scholarly debate about system trust (Luhmann 1979; Coleman 1990; Giddens 1990) has referred to discussions on phenomena at the periphery of trust, such as confidence or familiarity. These considerations do justice to the idiosyncratic character of trust in collective entities such as the abstract economy, compared to trust in concrete persons, for instance. Is trust based on a calculated decision, or is it experienced implicitly, as a routine behavior that leaves little room for alternatives (‘compulsory’ trust)? In the discussion, it is widely acknowledged that all three modes of trust (system trust, confidence, and familiarity) share a peculiar form of latency. And yet, the concrete relationship between the aspects of consciousness and latency among these modes of trust is not clear: “Trust is different from ‘weak inductive knowledge’, but the faith it involves does not always presume a conscious act of commitment. In conditions of modernity, attitudes of trust towards abstract systems are usually routinely incorporated into the continuity of day-to-day activities and are to a large extent enforced by the intrinsic circumstances of daily life. Thus trust is much less of a ‘leap to commitment’ than a tacit acceptance of circumstances in which other alternatives are largely foreclosed. Still, it would be quite mistaken to see this situation as just a sort of passive dependence, reluctantly conceded […].” (Giddens 1990, 90)

Niklas Luhmann (1979) similarly struggled to describe the peculiar relation between conscious and latent forms of collective trust, between decision-making and compulsion in trusting systems such as the economy, politics, or science. Acknowledging that system trust has thus incorporated parts of familiarity within itself, Luhmann, in his classic study (ibid.), still attributes partial consciousness and precedence of decision-making to it:

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_2

16

Trust in Systems “All these considerations point to the conclusion that system trust has absorbed certain functions and attributes of familiarity […]. Although system trust is shown to be more or less absorbed, more or less latent, it is fundamentally different from the ‘naive’ experience of familiarity with the everyday world. In system trust one is continually conscious that everything that is accomplished is a product, that each action has been decided on after comparison with other possibilities. System trust counts on explicit processes for the reduction of complexity, i.e. on people, not nature.” (ibid., 58; emphasis in original)

Later, Luhmann (1988) – without explicit mentioning – replaced the term ‘system trust’ with ‘confidence’. According to him, confidence is the more basic mode of trust, without which we could not live at all. However, unlike a trusting person, a confident one attributes disappointments to the social environment and not to own decisions. According to Luhmann, confidence in systems such as politics occurs in the face of danger, a condition that is stripped of human influence; unlike trust, confidence neither implies risk-taking nor decision-making. Both risk and decisions have no place in dealing with societal systems since personal influence on their performance is low (ibid., 102f). The relationship between confidence and trust can be described as mutually enabling and reinforcing, creating a circle between viciousness and virtuosity, as a lack of confidence can lead to a lack of trust, and so on (Luhmann 1988, 104). Giddens (1990) also introduces the idea of different levels of trust in collective entities, but views the relationship between them much more moderately. He tries to link ideas of trust, confidence, faith, and hope into a comprehensive overall conception of trust8 – most importantly though, he never challenged the general idea of genuine “trust in abstract systems” (Giddens 1990). Luhmann, with his later distinction between trust and confidence (1988), reserved the term ‘trust’ for the study of personal relationships and ‘confidence’ for human involvement with collective social entities. Crucially, and unlike Luhmann and some other scholars (recently: Morgner 2013), this book distinguishes between system trust and confidence. This distinction permits consideration of an active, intentional trust relationship with social systems (Kohring 2004; Kohring and Matthes 2007). It is apparent that Luhmann is the only scholar among the major

8

“At this point we reach a definition of trust. Trust may be defined as confidence in the reliability of a person or system, regarding a given set of outcomes or events, where that confidence expresses a faith in the probity or love of another, or in the correctness of abstract principles (technical knowledge)” (Giddens 1990, 34).

The Dualism of System Trust

17

contributors to the idea of systemic trust who has abandoned its general possibility. Anthony Giddens (1990) – just like Coleman (1990), Hardin (2002), Möllering (2013a; 2013b) and others – never doubted that there is ‘genuine’ trust in systems under provision of the respective references of trust. Obviously, the conditions of the general possibility of system trust are attached to possibilities of active participation in systemic processes and their specification “regarding a given set of outcomes or events” (Giddens 1990, 34). Clarification is crucial, both of this active participation pattern of system trust, and its distinction regarding different types, such as confidence. Distinguishing between two types of trust in corporate phenomena is not a new idea for research into this form of trust. Drawing on some of Luhmann’s ambiguities concerning system trust, Anthony Giddens states that trust in abstract systems is not only mobilized in their general functioning as such (which is Luhmann’s main line of argument), but also in their proper functioning (Giddens 1990, 34) – considering not only that it works but also how it works. In this connection, Matthias Kohring (2004) alludes to a dualistic notion of system trust in the literature, initiated by Luhmann. According to Kohring (ibid., 102ff), system trust usually refers to (a) generalized media of communication such as money or power and their basic functioning – the latent expectation that money can buy goods and politics can sanction criminals. Instead, it should be directed at (b) the assessment of more specific service expectations of how these media perform from the subjective perspective of certain trustors: “System trust is to be conceptualized as mutually aware trust relation between concrete service receptors (e.g., clients) and concrete service providers.” (Kohring 2004, 131f; translation PS)

In this way, the two types of trust (a & b above) represent what was identified as system trust and confidence: The general stability of money (e.g., the functioning of markets) or power (e.g., the functioning of democratic elections) can be much more plausibly considered a latent, diffuse routine activity (type a/confidence) than the assessment of concrete expectations of individual or collective trustors toward the output of the political system or the economy (type b/system trust). These output expectations toward politics or the economy as a conscious trust relationship could entail legislative action by politicians or returns on investments from stock market activity. Unlike Giddens, who discusses trust in “symbolic tokens” (equivalent to communications media = type a) and their

18

Trust in Systems

“proper” functioning in a more objective way (Giddens 1990, 33f), Kohring (2004) alludes to the variability and contingency of “favourable trust expectations” (Möllering 2006b) that are not the same for every trustor. The associated process of decision-making, risk perception, and conscious evaluation of service expectations regarding fulfillment and/or the responsibility of trustees is vital in this modeling of system trust. The confusion between trust in the general functioning of a system and trust in the proper functioning of a system (Giddens 1990; Kohring 2004) is at the heart of what has prevented a sophisticated conception of system trust so far: “The dimensions of specific trust thus need to be derived from the respective functional expectations toward a social system.” (Kohring 2004, 124; translation PS)

Looking at a system such as energy supply, normative expectations of the specific functions and services that are assessed and decided upon on a subjective basis could include permanent, sustainable electricity supply at low cost with opportunities for personal entrepreneurship, for instance. These relatively routinized expectations (just moving the cord to the power outlet in the expectation that electricity will always flow) can also be narrowed down to more detailed frameworks of expectations regarding what price thresholds are acceptable and which energy mixes consumers are prepared to accept, for example. While today, electricity is a largely invisible, taken-for-granted resource (Shove and Warde 1998), future visions of active “energy entrepreneurs” and “prosumers” (Ramchurn et al. 2012) herald shifts in system-client relations. Consequently, the public’s current balance of differentiation between general and specific expectations might well be under transition. The idea of modeling energy system expectations will be further explored in Chap. 5. Meanwhile, Matthias Kohring regards ‘genuine’ system trust – with traits comparable to general social trust – as type b, which is “trust in the programs of social systems” (Kohring 2004, 110; translation and emphasis PS). A program is “a complex of conditions for the correctness (and thus the social acceptability) of behavior […] if the behavior of more than one person has to be regulated and made expectable” (Luhmann 1995, 317). In this way, programs such as the ‘austerity policy’ or the ‘energy transition’ regulate how systems such as politics, the economy or energy are expected to perform against the background of selectivity in the actions of trustees. The referencing of system trust as trust in

The Dualism of System Trust

19

programs is the first step toward comprehending trust in the energy system. Unlike type a, which is mainly reflexive trust – for example trusting that others have the same trust as oneself in communications media such as money – type b offers more potential for system trust as an active social process with normative service expectations toward the system’s performance. Trust in communications media resembles reliance on basic values such as stability, profitability, or justice as the most abstract expectations – confidence. Given the fact that the energy system can be regarded as an “expectation complex of services” (Büscher and Sumpf 2015, 3), it makes sense to distinguish: 

expectations directed at the general fulfillment of functions on a value level (e.g., security of supply), from



service expectations of a more specific nature, e.g., green electricity from particular sources at a reasonable price from a local provider.

Systems such as the political system, the economic system, or the energy system can thus be compared in terms of trust, despite their differences as either functional systems of society (politics, economy) or systematic complexes providing services such as electricity. More details on systems building and classification for the study of trust will be provided in Chap. 3. At the same time, the unraveling of the dualistic notion of system trust by Giddens and Kohring is helpful as it denotes (a) a more latent collective trust mode and (b) a more conscious one, without giving up the idea of actively trusting systems (as Niklas Luhmann did [1988]). As a conclusion, it can be stated that there are two modes of collective trust9 – a more general (abstract, latent) mode and a more specific (concrete, conscious) one. Here, trust can also be conceived as an active factor of participation in systems. This is underscored by distinguishing different levels of service expectations toward systems. Consequently, these findings do not eliminate the general distinction between two trust modes such as trust and confidence. Yet they do undermine the equalization of system trust and confidence (e.g., Luhmann 1988; Morgner 2013). As a first hypothesis, we can formulate: 9

This seems to be confirmed by most trust researchers on collective trust issues – even Luhmann, in his original study (1979), concedes that: “[…] we must differentiate between two different levels, or scales of the problem of trust” (FN 34, 60). On another occasion, he makes explicit references to multiple levels of system trust in politics, e.g., trust in communication opportunities and general system functioning (ibid., 56).

20

Trust in Systems

H1: System trust does not equal confidence as it can be an action based on trustor decision-making. In fact, trustors establish service expectations toward the general and specific functioning of a system, which are both phenomena of ‘genuine’ trust.

2.2 Decision-Making and Compulsion The central condition for system trust to make sense is its connection to risk and decision-making (Kohring 2004): in cases where no choices are involved, there can be no trust. Experiencing the contingencies of social systems as something to be influenced by decision-making, on the other hand, leaves open the possibility for trusting or distrusting them. A lack of alternatives, as described by previous scholars (see above), leads to compulsion and dependency and does not feature the characteristics of trust (Luhmann 1979; Giddens 1990). An analysis of system trust which considers trust in systems as a genuine trust phenomenon needs to solve these problems in order to come to terms with its theoretical foundations. Hence, observations that underscore the genuine system trust paradigm are a prerequisite for developing a deeper analysis. There is sufficient initial plausibility in the claim that current empirical developments provide alternative choices in many realms of society which did not exist decades ago when the theoretical foundations of system trust were laid out. These developments – largely driven by digitalization – increase both the pressure and probability of decision-making regarding both types (a & b, see Sect. 2.1) of trust in systems: Type a: Today, system participants have many more ‘opt-out’ options than twenty or even ten years ago. This refers to dealing with a system on a general, abstract level and deciding whether or not to take part in it at all. Neither Luhmann nor Giddens recognized this complete opt-out option in dealing with system trust, but instead underlined its ‘compulsory’ character. Giddens (1990) stated:

Decision-Making and Compulsion

21

“An individual who invests in certain shares on the advice of a stockbroker and who loses money might decide to hold money in a credit account instead. That person might even resolve to hold assets only in gold in the future. But once again, it would be very difficult to disengage from the monetary system completely, and this could only be done if the individual were to try to live in self-sufficient poverty.” (1990, 91)

While there is still some general validity to this, parts of the equation have changed: first, the number of alternatives, including for ‘entire systems’, has increased. A recent trend in energy provision, for instance, is autonomous selfsupply, even autarky, enabled by electricity generation in one’s own backyard as a means to achieve complete independence from external suppliers.10 Thus, today it is easier and more fashionable than ever to create one’s own private energy system that operates independently from the overall system to a maximum degree. German researchers working on experimental projects in smart grid development, for instance, speak of a “cellular” approach to energy that allows various (re-)combinations between local, state and federal electricity provision, respecting political dynamics between corporate business models and citizen cooperatives (B.A.U.M. Consult 2012). Moreover, Gidden’s statement regarding individuals not being able to escape the modern economy also needs to be adjusted: today, we are facing privately founded virtual currencies like ‘bitcoin’ aimed at achieving more independence from the classic monetary system – still at a fledgling stage, admittedly, but as the first serious alternative that has acquired a certain threshold status.11 While this cryptocurrency is still coupled to established currencies (dollar, euro, etc.) to determine its value, self-sufficiency is an explicit goal, possibly even on the basis of non-monetary transfers (Yardeni 2015). Moreover, as a current pattern of the modern economy, some companies have managed to provide ‘islands’ of alternative trade in areas such as organic food supply or ‘sharing economy’ platforms that exclusively work online and often do not require money to operate. Although these developments 10

11

In the expert interviews, one expert said that more and more companies might start to build their own power plants out of distrust in German energy policy, for instance. Another example is that of an interviewee in the consumer case study who had a solar panel on his private home, and who told me that his major reason for installing the panel was independence, not sustainability. http://www.forbes.com/sites/investopedia/2014/02/28/bitcoin-innovations-and-obstacles/#1dbaea03b3aa Accessed April 24th, 2017. What is apparent is that in media coverage on ‘bitcoin’, the issue of ‘trust’ in the new system is often explicitly mentioned.

22

Trust in Systems

are on the brink of fundamentally changing the economy, many of the subsystems of these economic pioneers are still partly connected to the ‘mainstream system’ and do not yet offer substantial alternatives for everyone to choose from. Still, these remarks suffice to challenge Giddens’ earlier skepticism about opting out of general systems and being able to live in alternative environments – because those are often more trusted while the classic ones are distrusted, which is a major reason for people’s changing preferences in these areas. A second aspect, moreover, is that attitudes have changed insofar as “to live in self-sufficient poverty” (Giddens 1990, 91) seems to be more respected than ever as an alternative lifestyle within particular communities (e.g., trading and finance through ecological banks, growing organic food through cooperatives and alternative supermarkets, online currencies or money-free sharing economies), which continue to grow and establish alternatives to what ‘mainstream systems’ offer.12 Digitalization does not change everything, but users can now choose to altogether opt-out of software which uses their data and to live an offline lifestyle as an accepted social pattern of “self-sufficient data poverty” (cf. Giddens, above). On the other hand, they can choose to become digitally competent citizens, giving ‘informed consent’ to participate in the services around them. However, while there is no compulsion to take part in, for example, LinkedIn, Facebook or Twitter, the social costs of not participating can be high and need equivalent degrees of self-confidence. Type b: This type refers to complexity increases within those general systems, and the particular choices to be made for participants in connection with trust or distrust. My observation is that internal system complexity is increasing and – most importantly – will have effects on average users which increase vulnerabilities in trust toward systems (cf. Sect. 4.2). A glimpse into current developments of smart electricity grids, for instance, shows what is expected of us

12

Giddens (1990, 90) notes: “An individual may choose to move to a different area, for example, rather than drink fluoridated water, or drink bottled water rather than use that from the tap. It would be an extreme attitude, however, to refuse to use piped water altogether.” Patterns of attribution change quickly, so that the “extreme attitude” of today can be common sense tomorrow or is delegated from being an external factor to a ‘should have known’ in advance issue. This substantiates the relevance of an ‘architecture of trust attribution’, which Chap. 5 will elaborate on.

Decision-Making and Compulsion

23

through visions of ‘prosumers’: Firstly, formerly passive consumers are supposed to increasingly develop an active role, adapting their energy consumption behavior by using electricity devices like smart meters, involving app-based control interfaces and consumption monitoring. Secondly, they are to actively participate, as electricity vendors with self-generated electricity, in “virtual power plants” (Amin and Giacomoni 2012) and “smart markets” (BNetzA 2011). According to the technical requirements in prevalent visions, this form of DSM will not only result in a more efficient use of energy, it will also involve ‘grid-supportive’ measures by system participants. These entail the use or not of certain electrical appliances (e.g., heat pumps, air-conditioning, and washing machines) at given times, depending on grid conditions, thereby helping maintain grid stability and security of supply.13 The likelihood of increased interactive features and decision-making in such an environment is striking (Shove and Warde 1998). Even though the exact degree of consumer involvement is not yet fully clear, probabilities of increasing reliance on consumers are high according to experts14, recent federal legislation (BMWi 2015), and international developments (Giordano et al. 2011). Another example relevant to ‘type b trust’ is participation in systems of ‘digital services’, such as app-based computers (smartphones, tablets) in countless modern domains which build their own systems (e.g., bitcoin), or attachments to adjunct systems as enabling ICT infrastructure. Here, choices and the self-responsibility of participants gradually become more significant (Eskens et al. 2016) as a consequence of higher probabilities of decision-making attribution by trustors. These pressures have reached a level that has practical implications when it comes to decisions about accepting terms of trade on the internet. For example, as early as 2008, McDonald and Cranor calculated that average internet users would need 40 minutes every day in order to read all necessary privacy policies (McDonald and Cranor 2008). Given the increase in mobile apps since 2008, more and more decisions are demanded of digitally ‘enlightened’ consumers in order to exploit the benefits of modern applications, while the risks are supposed to be clarified (‘understanding’ terms of use). All in all, digitalization is a multifold and powerful driver of 13 14

Based on the volatility risk of renewable energy sources such as wind and solar. In one of the expert interviews, an interviewee said: “And I can truly imagine how (and that is why I’m still, nonetheless, not completely cutting off communication), in the end, the private household will finally really be able to play a greater role through its marketing demand.” (environmental association/NGO 2013, 61-62; translation PS)

24

Trust in Systems

increased decision-making in general, starting with the basic platform choice (e.g., iOs, Android, Windows Mobile), which transforms traditional ways of life (shopping, watching movies, navigating, making music, ordering taxis, etc.) and allows individual customizations of device operations in details that increasingly push users into taking responsibility (Rathenau Institut 2016). Digitalization – and this is a particularly important aspect in this book – greatly mobilizes the changes in the electricity sector that are referred to as “smart grid” (BMWi 2015): this emerging environment also facilitates decision-making and responsibility patterns among trustors in contrast to compulsory experiences. The foregoing justifies a revaluation of the old15 idea of system trust (cf. Schneier 2012). Currently, we are witnessing more and more entanglement and intertwining in the systemic complexes around us that increasingly obstruct our attempts at control – as the opposite of trust. Trustors’ increasing need for orientation through decision-making demands along with their resulting vulnerabilities move trust (or distrust) in systems into a central position. Given the empirical developments described so far, it makes more sense than ever to speak of systems where active trust is perceived as risky and conducted by choice (Kohring 2004), or at least determined by one’s own decision, possibly in a context where trust has been previously disappointed. In a way, in their arguments for system trust compulsion and lack of alternatives, both Luhmann and Giddens fell victim to recent and current developments in digitalization, which creates new systems in a virtual world that offer perhaps unprecedented choice varieties and feedback loops on physical-world lifestyles and business models. At this point, the classic argument of ‘compulsory’ system trust without alternatives can be increasingly challenged. What follows are some closing remarks on the ‘revalued’ importance of system trust. It is always easy to talk about rising complexity, differentiation, decision-making, etc. in order to justify the increasing importance of a phenomenon such as trust in systems. That is why these assertions are more thoroughly analyzed in Chap. 4, which discovers that ‘absolute complexity’ – in terms of a logical increase in system elements – is not necessarily decisive for trust. Rather, it is the perception of complexity that unfolds as a key influence on trust. Significantly, this perception regulates the attribution of system participation as either a self-made decision (leading to system trust) or as an external, compulsory 15

The original study by Niklas Luhmann was published in German in 1968.

Intersections in Trust Research

25

force that seems unavoidable (classically leading to confidence). Empirical insights into this kind of shift will also be a central aspect of the case study in Chap. 6. Finally, the general distinction between choice and compulsion is more blurry than some authors suggest, and is, in the empirical contexts described above, merely relocated instead of truly suspended. Accordingly, it is not a contradiction to discover there is more choice in trusting/distrusting systems, but still experience compulsory trust at some point, because non-knowledge – as the point where trustors’ rational capacities for contemplating the reasons for trust are exhausted – is always involved in the act of trust (Möllering 2006b). Therefore, the concept of having a choice does not necessarily equal a trustor’s ‘free will’, oppressed by the compulsory systemic forces that leave the individual with potentially no influence, as suggested by the works of Luhmann (1979, 58) and Giddens (1990, 90f). After reviewing current empirical material on smart grids and online platforms (see above), it is more likely that shifts in the proportions between knowledge and non-knowledge affect decision-making processes around system trust. What knowledge do trustors reassure themselves with in questions about trusting a system? How much non-knowledge are they prepared to tolerate, yet still conduct the ‘leap of faith’ to trust (Simmel 1978; cf. Möllering 2001)? The references trustors make about the system in this spectrum of what they know and what they do not know are pivotal for relationships of trust or distrust, rather than – theoretically or objectively – whether or not they have a ‘real’ choice. This spectrum will be further explored in Chap. 4 on ‘Conditions and Consequences of Trust in Systems’. H2: Current empirical developments challenge the assumption that system trust is merely a compulsory experience and underscore its possible decision-making component.

2.3 Intersections in Trust Research The results from the Sections 2.1 and 2.2 are highly relevant for formulating possibilities of building trust in systems like the energy system. First, the fact

26

Trust in Systems

that system trust potentially shares the basic characteristics of trust – and not confidence – is a striking argument. While many scholars have emphasized the differences between general trust (e.g., in persons or organizations) and trust in systems, the findings so far allow us to challenge these assumptions. Precisely, they allow us to state that, from a conceptual point of view, trust in systems generally can be treated like trust in persons: it can be regarded as a positive expectation for the future, but directed at a systemic object instead of a person or an organization. As a result, this approach identifies and stresses the shared features of system trust and personal trust rather than their differences. On the basis of assuming that trusting systems is (partially) a decision-making process on behalf of involved trustors, and that service expectations toward that system are formed and evaluated at different levels of abstraction, it is plausible to equate a system with a person or an organization regarding its role as a trustee.16 More precisely, further elaboration should focus on the relationship and interplay between the two (Luhmann 1988; Morgner 2013). Following this path allows consideration of the fact that at least two trust types are crucial in collective trust arrangements and can be directed at multiple trustees at different levels of system abstraction. In view of these results, drawn from both traditional and current research, it makes sense to turn our attention toward recent elaborations on trust theory that consider trust mainly on the personal and organizational level. Indeed, since system trust is a phenomenon of trust, not of confidence, we can apply general trust definitions17 to the study of system trust and infer common characteristics that are also valid for trust in systems. For this purpose, one can rely on a trust definition from Guido Möllering, one of the “forceful” scholars in theorizing trust over the past two decades (Lewis and Weigert 2012, 25): “Hence I define trust as a reflexive process of building on reason, routine and reflexivity, suspending irreducible social vulnerability and uncertainty as if they were

16 17

This is not to contradict or deny their differences and interplay, which uncovers complex relations between them in the AoT that will be explored in Chap. 5. As first mentioned in the introduction, definitions in this book are provided in accordance with the guidelines by C. Wright Mills: “When we define a word, we are merely inviting others to use it as we would like it to be used; for the purpose of definition is to focus argument upon fact, and the proper result of a good definition is to transform argument over terms into disagreements about facts, and thus open arguments for further inquiry” (Mills 1959, 34).

Intersections in Trust Research

27

favourably resolved, and maintaining a state of favourable expectation towards the actions and intentions of more or less specific others.” (Möllering 2006b, 356)18

While countless aspects must be considered in order to reach a definition (cf. Möllering 2001; 2006a) and understand its full implications, three essential components of this definition deserve attention. These components are wellfounded aspects of trust research (Fivat and Pasquier 2015) and thus build a solid basis for studying system trust: 

Non-knowledge: This first component relates to the “reflexive process of building on reason, routine and reflexivity” (Möllering 2006b, 356; emphasis added) in the above definition. It pays tribute to the multi-referential nature of trust between mental and social antecedents. In particular, it refers to the fact that trust rests on both knowledge and non-knowledge (Simmel 1978; Möllering 2001), while the perception of specific non-knowledge is the key driver of trust (Strulik 2007): trusting instead of knowing, not trusting through knowing (Kohring 2001; Möllering 2013a). Non-knowledge is overcome by overdrawing available information, a process that renders the positive future expectation of trust an illusion (Luhmann 1979). The interplay between reason (rational calculation), routine (deeply familiar action that is rarely questioned), and reflexivity (risk perceptions and the contemplation of what could go wrong) among trustors form the core of studying trust.



Suspension: The act of suspension is a side effect of overcoming nonknowledge, as it transforms perceived risk into imagined certainty: “[…] suspending irreducible social vulnerability and uncertainty as if they were favourably resolved” (Möllering 2006b, 356; emphasis PS). Guido Möllering, tracing suspension back to Simmel as an “element of unaccountable faith” (ibid., 371), makes the term and concomitant concept of suspension a central piece of his trust research. At its heart, suspension touches upon the temporal dimension of social reality, as it requires the transformation of imagined futures into the present as a foundation for decisions on trust or distrust. In this way, it relates to the way trustors create “certainty-equivalents” (Luhmann 1979, 50) by suspending doubt and disbelief while reaching a state of trust.



Expectations: Möllering, intriguingly and unlike many other scholars, mentions the ‘irreducibility’ of uncertainty (see definition above). This irreducibility is an aspect which has been neglected in other domains of trust

18

First introduced in Sect. 1.2.1.

28

Trust in Systems

research, for instance in the context of energy transitions (Bellaby et al. 2010). The permanent social process of “uncertainty absorption” (Brunsson 1985) is thus key to “maintaining a state of favourable expectation towards the actions and intentions of more or less specific others” (Möllering 2006b, 356; emphasis added). Luhmann (1979) referred to this maintenance of action ability under conditions of not knowing how the future will unfold in his famous quote of “reducing complexity” through trust as a social mechanism. Concerning this expectation building “towards the actions and intentions of more or less specific others” (Möllering above), one might add the differentiation between reasons (or motives) and references (or dimensions) of trust (Kohring 2004, 120ff): the particular expected actions and intentions of trustees are reasons for trust or distrust, while the level of directing these expectations at – more or less specific others – are their reference. Analysing Möllering’s trust definition to discover the alignment of nonknowledge, suspension and expectations with the definition’s three parts, reveals that there is more to a comprehensive analysis of trust. A term mentioned frequently is risk. Consequently, an important addition to the prior enumeration can be made by stressing the special role of ‘risk’ as both a necessary condition for trust to be evoked (Luhmann 1988; Kohring 2004) and as a consequence of trust (Strulik 2011). Perceived risks that need to be bridged by trust thereby relate to the selectivity – the freedom of action – of potential trustees (Kohring and Matthes 2007). Moreover, the perception of risk (or its absence) needs to be processed by trustors, leading to questions of complexity in their social environment. Complexity, as Sect. 2.2 ‘Decision-Making and Compulsion’ has initially shown, is a dominant force in the empirical fields targeted in this book and therefore a constitutive force in building or diminishing trust. Finally, the process of ‘scanning’ complex social environments for indicators of trust or distrust, its (dis)continuity, justification or dismissal has been coined control of trust (Luhmann 1979; Shapiro 1987). This aspect completes the major elements of trust research and therefore requires attention in the study of system trust. As a conclusion, we can say that these six components (non-knowledge, suspension, expectations, risk, complexity, control) fully apply to the study of system trust and will thus be the center of attention in this book. Each of these six components (Figure 2-1) will be illuminated thoroughly in a dedicated section in Chaps. 4 and 5, with ‘suspension’ being integrated in the ‘Trust and Expectations’ Sect.

Intersections in Trust Research

29

5.1. This will reveal what system trust is framed by, before the ‘architecture of trust’ is unfolded, with special emphasis on the energy case (Chap. 5). Summarizing the six different elements of trust here shows that they heavily relate to and depend on one other. Separation into single terms or phenomena is an analytical necessity, not an empirical-practical reflection of how trust ‘really works’. As a result, I will describe the logic of the single trust elements by taking their mutual interdependencies into account.

Expectations

Complexity

Risk

Trust

Control

Non-knowledge

Suspension

Figure 2-1. Major Components of Trust and Intersections in Trust Research

Despite all the convincing research on trust which is available, theories about these mutual interdependencies suffer from a central omission: the underestimation of the necessity of a well-founded concept of ‘expectations’. The key insight underlying the proposed ‘architecture of trust’ (Chap. 5) is that expectations play a pivotal role in the trust process. While often given an important status in trust research, expectations have not yet been explored to their full potential, although this would be especially helpful for the study of trust in systems. Indeed, despite the term’s popular standing and frequent mention in trust research, the dimension of “maintaining a state of favourable expectation towards the actions and

30

Trust in Systems

intentions of more or less specific others” (see definition above) has received less attention than its neighboring categories (such as suspension or risk). In this way, Sect. 2.4 serves as a preview of the central theoretical part of this book. 2.4 An Architecture of Trust: First Sketch Researchers on trust have over and over again referred to trust as an expectation: “Trust is the expectation that arises within a community of regular, honest, and cooperative behavior, based on commonly shared norms, on the part of other members of that community.” (Fukuyama 1995, 26; emphasis PS)

Although Fukuyama seems to confuse references with reasons for trust, he emphasizes trust’s status as future expectation. Lewis and Weigert stress that: “Trust exists in a social system insofar as the members of that system act according to and are secure in the expected futures constituted by the presence of each other or their symbolic representations.” (Lewis and Weigert 1985, 968; emphasis PS)

We saw that Möllering defines trust as: “[…] maintaining a state of favourable expectation towards the actions and intentions of more or less specific others.” (Möllering 2006b, 356; emphasis PS)

In contrast to their prominent exposition in many definitions and elaborations on trust, differentiations of expectations have been somewhat underestimated as their meaning was more or less taken for granted. Yet, considering the results from Sect. 2.3, a closer look at expectation building seems to be crucial in order to understand processes of system trust in particular: expectations can be diffuse or concrete, long- or short term and target multiple yet not all social entities. As a consequence, the constitution of expectations can be of utmost significance for both empirical reality and the study of trust. Concerning expectations, Luhmann (1979) noted in his initial trust study: “Trust merges gradually into expectations of continuity, which are formed as the firm guidelines by which to conduct our everyday lives. But not all expectations of this nature involve trust; only those concerning conduct do so, and among the latter only those in the light of which one commits one’s own actions and which if unrealized will lead one to regret one’s behaviour.” (ibid., 25)

The differentiation of particular expectations can tell us a great deal about where trust is at work and where it is not. Referring to Luhmann’s quotation, it seems

An Architecture of Trust: First Sketch

31

obvious that expectations involving trust are concrete and particular as they trigger ‘commitment’ to specific individual actions. Expectations of a more diffuse and abstract nature, on the other hand, may be viewed as expectations of general system functioning. Beyond the understandings of conventional wisdom, ‘expectations’ are a deeply rooted concept in sociology and social psychology, where they are seen to build the basis of the social structure of society. Expectations, mutual expectations and expectation expectations form the basis of social life and determine the communication process (Goffman 1959; Parsons 1964; Watzlawick et al. 1967; Mead 1967; Luhmann 1995). In fact, expectations can be understood as the structures of social systems (Luhmann 1995, 293). Therefore, it is no coincidence that trust, as a particular future-oriented resource, appears to us in the shape of expectations. If expectations both constitute the structures of social systems and manifest the shape in which trust unfolds itself, then the decisive domain for trust research may be expectation building in social systems. In other words, the objects of trust (or references, or what trust can be directed at) are closely linked to what expectations can be directed at. Despite differences in their general interpretations of social order, several sociologists have described similar addressees of expectation building at different levels of abstraction: “Sociological theory has experimented here with different ideas, all of which assume that perspectives identifying nexuses of behavioral expectations must be ordered along a continuum from abstract to concrete.” (Luhmann 1995, 314f)

Alfred Schutz (1967, 186ff) has referred to “persons”, “roles”, and “types of action-patterns” as such orderings of expectations from concrete to abstract.19 Luhmann (1995) relied on several researchers in addition to Schutz20 to further differentiate “types of action-patterns”, and added a fourth level of abstraction. He speaks of “persons”, “roles”, “programs”, and “values” as such orderings of expectations (ibid., 315). Intriguingly, we have already come across programs as a reference of system trust (Kohring, 2004, 102ff). ‘Programs’ are inner differentiations of social systems that coordinate expectational nexuses beyond roles “if the behavior of more than one person has to be regulated and made 19

20

In his terminology, Schutz refers to personal “ideal-types”, also involving roles (Schutz 1967, 187) and “course-of-action types” (ibid., 188) which comprise role combinations and social rules embedded in an institutional context. Although Schutz mentions the generalization of expectations, he prefers to speak of “typifications” in this context (Schutz 1962, 19ff). See Luhmann 1995, FN 99, p. 576.

32

Trust in Systems

expectable” (Luhmann 1995, 317). This intersection between results from researching system trust (Kohring) and sociological insights into the generalization of expectations (Schutz, Luhmann) indicates that the relationship between the different levels of abstraction in the expectation nexus is promising for understanding the processes of trust in systems. Moreover, persons, roles, programs, and values can serve as more precise recipients of trust than the above “more or less specific others” (Möllering 2006b, 356). Torsten Strulik (2011) refers to an “architecture of trust” in the mediation of trust in the financial sector (ibid., 246). I want to refer to the expectation nexus – persons, roles, programs and values – as such a trust architecture.21 This can contribute to enhancing discussions on system trust as it responds to prior findings and shortcomings in the literature in several ways: 1.

It provides multiple addresses of trust beyond persons without excluding personal trust.

2.

It offers a scale of expectations from concrete to abstract which allows for localization of respective expectations.

3.

As a result, we can formulate that the more abstract expectations are generalized, the more likely it is that general system functioning is involved rather than specific system functioning. More precisely, general functioning can be translated as expectations toward values as the most abstract and diffuse level in the nexus.

4.

System trust can be translated as trust in programs, as suggested by Kohring (2004, 110).

5.

Trust in programs can still be conveyed by trust in persons and/or roles. Values can also be represented by persons, roles, and programs. Overall, the “expectation nexus” offers multiple ways of relating different levels of trust to each other in a more specific and reliable way. It can – once filled with empirical data – order the complexity of system trust in often opaque societal fields and allow us to study enabling or vicious circles between different levels of trust (Luhmann 1988) in a tangible way. A visualization (Table 2-1) sums up the most important findings so far.

21

Another source of the term is a book from an American architect who discusses “leadership by design” and literally refers to an “architecture of trust” that helps bring people together to pursue common projects based on his experiences in architecture projects (Swett and Thornton 2005, 12).

An Architecture of Trust: First Sketch

33

Table 2-1. An Expectation Nexus. Based on Schutz (1967) and Luhmann (1995) Level of expectation generalization Concrete Abstract

Possible reference of system trust

Specific functioning

Representative of programs and values

Persons

Roles

Programs

Classic system trust in communications media, general functioning, etc.

Values

General functioning

Personal trust; representative of other levels

As the final hypothesis from Sect. 2.4, we can suggest:

H3: The basis of all trust research is the study of expectations. Based on established sociological concepts, service expectations toward both the general and specific functioning of a system can be ordered on a scale from concrete to abstract, such as persons, roles, programs, and values. The differentiation of these service expectations and their evaluation through assessment of system outputs by trustors form the basis of system trust and its research.

34

Trust in Systems

2.5 Reflexivity, Construction, Attribution My observation is that trust scholars have used the distinctions between modes such as (system) trust, confidence, and familiarity to indicate degrees of reflexivity, attention, and awareness of the involved trustors: are contingency and risk reflected upon when forming a decision (trust), are they perceived as compulsory (confidence), or perhaps not reflected upon in ‘business as usual’ (familiarity)? In consuming electricity, for instance, people can reflect on their influence on the risk of power outages, perceive being exposed to power outages as a danger, or not think about it at all. While these are helpful categories to understand individual reflexivity, the important point here is what we make of these findings, and how this can instruct research on system trust. It can be argued that the discourse on collective trust issues has focused primarily on terminology and distinguishing it from personal trust so that some general conceptual shortcomings might have been underestimated. In particular, two aspects may obstruct a deeper understanding of system trust: firstly, some sort of objectivity and, secondly, the insinuation of individual motives for trusting or being confident. Trust researchers from social psychology (Earle and Cvetkovich 1995; Siegrist et al. 2005; Siegrist 2010), for instance, follow up on the Luhmannian (1988) introduction of the trust/confidence divide in that they use it as the basis of their empirical research on risk perceptions of new technologies: “The key distinctions between trust and confidence are these: Trust involves risk and vulnerability, it is important when familiarity is low. Confidence, on the other hand, is based on high levels of familiarity. The objects of trust are persons […], but confidence can be had in just about anything.” (Siegrist et al. 2005, 147)

This leaves the reader with questions about familiarity, which – in contrast to trust and confidence – in this concept seems to go without definition, but mainly about how trust and confidence are to be distinguished. This is done through seemingly open (trust) and closed (confidence) futures (ibid., 147), but also by denoting different possible objects of either trust (‘persons’) or confidence (‘just about anything’). Later, Siegrist, Gutscher and Earle (2005) specify general confidence as “positive expectations towards systems” (ibid., 151; emphasis PS), which is particularly relevant for the purposes of this book, because it indicates that trust in systems is something excluded from their “TCC (trust, confidence and co-operation) model” (Siegrist 2010, 1022).

Reflexivity, Construction, Attribution

35

Again here, in the TCC model, we see the range of objects with which trust or confidence might be associated being emphasized as the cornerstone of definitions of trust or confidence and, in addition, their empirical occurrence. In a comment on Hardin’s (2013) contribution to the debate on trust and confidence in government, Möllering (2013a, 56) reminds us that “[s]eeing how ‘trust rests on illusion’ […] it is not trivial that citizens may imagine direct relationships with the government, since government actions may affect them, so that their trust (or lack of it) can be genuine, and not just a matter of confidence.” This argumentation connects smoothly with insights from Sect. 2.1 about specific and general expectations toward a system, which Möllering refers to as “general expectations towards the government and those expectations that concern one’s own vulnerability” (ibid., 56). In conclusion, this underscores the idea of genuine system trust on several levels without the necessity of denoting it as confidence. Like Earle and Cvetkovich (1995) and Siegrist et al. (2005), Adam Seligman (1998) provides an example of how trust is distinguished from confidence by referring to objects of trust or confidence that pertain to only one of them. The theoretical categories Seligman uses touch upon the essential aspects of insinuation of individual motives and objectivity, which are both necessary to reexamine an approach to system trust, which I will propose in closing this section. Insinuation of motives: Seligman provides an example for definitions of trust and confidence by quoting the trust scholar Virginia Held and implying that it “is not quite correct” to say that “’she trusts the plumber to do a non-subversive job of plumbing’” (Seligman 1998, 392). His main argument is that the relationship between Held and the plumber is based on mutual expectation expectations of business, legal and sanctioning rules rather than being a personal relationship, so that (system) confidence is the more appropriate concept compared to (personal) trust. As convincing as this sounds at first22, here Seligman appears to have insinuated motives of trust or confidence: trustors like Held might think 22

Ironically, the plumber example was also referred to in Bruce Schneier’s (2012) quote in the introductory section 1.1 to justify a system trust approach compared to a personal trust approach. Yet in that case, Schneier still referred to trust in systems (instead of confidence) and wanted to make a general case of the importance of systems compared to individual people in modern society. While the plumber situation might indeed be a good example for this, we still have to be careful not to insinuate trust motives and be predisposed about references that trustors relate to.

36

Trust in Systems

that they trust the plumber, but in reality, that trust is directed toward something else, or is confidence in legal sanction mechanisms. But what if trustors do primarily rely on the personal touch with their plumber, who they have hired for 20 years? What if clients do not only expect general functioning of legal sanctioning, but very specific performances from their plumbers that qualify for system trust in the sense explicated in Sect. 2.1/H1? From Seligman’s perspective, these alternatives seem ruled out, because logically possible trustor-trustee relations in a given situation seem pre-determined, thereby potentially underestimating the fictional component of trust (cf. Möllering above) that allows for more empirically driven flexibility. While this view seemingly oversimplifies the co-existence, overlapping or intermediation (Coleman 1990) of different trust objects that may represent a system like the ‘economy’ or its sub-systems of construction and allied trades, it also implies that trust scholars might know what average trustors refer to in mobilizing trust, and what those trustors’ mental states in doing so could be.23 Yet “[i]n effect, it is the subjects and not the researcher who defines trust, because there is no clear meaning of the term” (Hardin 2013, 33). Objectivity: generally, insinuating reasons for trust, and declaring certain relationships as characteristic of trust and/or confidence, can be predisposed in that it attempts defining the objective nature24 of trust and confidence in social reality: ‘trust’ is (exclusively) this, ‘confidence’ is (exclusively) that, ‘faith’ and ‘hope’ relate to this social phenomenon, etc. I am skeptical as to what extent this is helpful in advancing research on systemic trust issues. This is because such an approach tends to (a) stress individual states of mind over social conditions 23

24

In another example, as a first person statement describing a doctor/patient situation, Seligman states that he does not ‘trust’ the doctor but has “confidence in her abilities, in the system that awarded her the degree on the wall […], as well as in the epistemological assumptions of American medicine.” (Seligman 1998, 392). Even though this seems to involve the author’s motives alone: if we transfer this idea to average trustors, my assumption is that most of us are far from comprehending any “epistemological assumptions of medicine”, which is exactly why trust replaces this lack of knowledge and often seems to evoke rather intimate trust relationships between doctors and patients (cf. Barber 1980, 60). This is not an argument against definitions or terminological differentiations (see FN on definitions in Sect. 2.3), but against seeking the objective nature of social phenomena through definitions. Rather, attributions determine the ‘nature’ of social phenomena (see further below), which can differ depending on the societal observer conducting the attribution (cf. Luhmann 1995, 301; 2013, 167ff).

Reflexivity, Construction, Attribution

37

and (b) is biased toward ex ante reasoning that implicitly deems trust to be a linear, chronological process that is insensitive to (re)constructive processes of attributing trust or confidence: “Trust that is personal or that is confidence (system trust) is often understood as a relationship between particular people or between particular people and particular things. This approach ignores the complex attribution problem which lies behind the construction of labelling references such as people and organisations (collective actors). It is vital that this point is acknowledged and addressed.” (Morgner 2013, 518)

Although I do not concur with Morgner on equating system trust and confidence, I am convinced that unless system trust is approached in this way, research risks missing the point by disregarding its constructive character based on contingent attributions. One need not say that the distinctions in different behavioral modes surrounding trust are obsolete – in fact, they can be quite useful in describing certain ‘phases’ of behavior which involve collective trust. Regarding the electricity sector, for instance, one could argue that members of the public evolve from being observers (passively consuming a permanent electricity supply) to being active participants in future smart grids (‘prosumers’, and ‘energy entrepreneurs’). This evolution can be described as a shift from familiarity (reflecting little or not at all on the electricity supply) and confidence (perceiving themselves to have no influence on the energy system whatsoever) to trust: choosing between new suppliers and providers, deciding on tariffs, deciding on technologies (smart meters, apps), and taking the blame if things go wrong (cf. Shove and Warde 1998). In this way, current dangers are likely to be increasingly perceived as risks by consumers25 and therefore attributed to personal decisionmaking instead of external forces. The pivotal point here is that the attribution between active decision-making or being exposed to the decisions of others, between risk or danger, between system or environment, is itself contingent and not objectively given; it can change depending on the reflexivity of a trustor, or their exposure to social pressure in a certain moment. In this way, a situation of trust can quickly turn into one of mere confidence, and vice versa, depending on 25

As a reminder: according to Luhmann (1988; 2005), a situation of danger is a condition that is stripped of human influence, because it is a mere experience of external circumstances; risk, on the other hand, is a concept tied to human decision-making and its consequences. In Luhmann’s constructivist theory, risk and danger rely on attribution and are not objectively given, depending on how actors evaluate a social situation.

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Trust in Systems

personal and societal rules of responsibility, naivety, or the sustainability of a social situation. Crucially, these attribution processes and their variation in social reality determine the course of action a trust biography can take, regardless of whether we call alignments with these attributions ‘trust’ or ‘confidence’. The approach of this book therefore focuses on the social fluidity and reconstructive character between the modes of trust, confidence, and familiarity identified above. This reconstruction is created by processes of attribution and ascription26 (Malle 1999). Attributions to situation or circumstance, and toward self or other (by trustors, trustees, or societal observers) can explain sudden changes in behavior such as the ‘should have known’ phenomenon: changing expectations between taking decisions (risk) and being exposed (danger) due to the neglect of information or because of external pressure to take responsibility. In a similar vein, with reference to empirical research, Michael Siegrist argues in a comment on the TCC model that: “The interpretation of a situation determines whether a behavior or a statement is viewed as confidence or as trust related. As a result, it is often difficult and sometimes even impossible to decide whether a given item measures trust or confidence. […] The focus on the two constructs trust and confidence must not hinder progress.” (Siegrist 2010, 1023)

This progress could lie in sophisticated development and application of an attribution concept to (system) trust research. An attribution perspective may help to uncover how trust is justified in the first place: what certainty equivalents (Luhmann 1979, 50) are ascribed when trust is established and maintained? Who or what is referred to in social systems for the creation of “trust as fiction” (Möllering 2006a, 112)? The inclusion of attribution concepts in collective trust research could lead to better consideration of (re-)constructive processes, social conditions, and a view on trust that stresses empirical manifestations of trust/confidence over objectivizing their traits in theoretical models. As a consequence, we arrive at the final hypothesis of this chapter:

26

The combination of trust and attribution research is not new; see, with other objectives and framings, Pillutla et al. (2003), Hatzakis (2009), or Jelinski (2014).

Reflexivity, Construction, Attribution

39

H4: System trust relies on attributions to different objects, self or other, for reassurance of trust and sanctions in case of disappointments. Attributions highlight the present-relatedness of trust because they can change over time.

Chap. 3 narrows the understanding of ‘systems’ in connection with trust research before the resulting conditions and consequences of trust in systems are discussed (Chap. 4). Finally, an ‘architecture of trust’ (Chap. 5) is presented, seeking to account for the attribution problem.

3 System References

In “Trust as a Social Reality”, Lewis and Weigert (1985) differentiate between cognitive, emotional, and behavioral bases of trust. Möllering (2013b) distinguishes between (mental) antecedents and the (social) consequences of trust. Luhmann (1979) and Giddens (1990) have both tried to disclose degrees of consciousness and latency among phenomena of system trust and confidence. Overall, the previous sections have illustrated the localization of corporate trust discussions in an environment of social psychology – somewhere between consciousness and communication. However, without neglecting the need for or relevance of mental trust antecedents and psychological research, one can emphasize the sociological impact of trust: “We see that the primary function of trust is sociological rather than psychological, since individuals would have no occasion or need to trust apart from social relationships. In addition, we would like to argue that, like its function, the bases on which trust rests are primarily social as well.” (Lewis and Weigert 1985, 969)

This statement is particularly valid for trust in systems. Being a “collective cognitive reality” (ibid., 970) with primary social functions, bases, and consequences, it is no coincidence that every author who has made a major contribution to system trust is a sociologist (Simmel 1978; Luhmann 1979; Barber 1983; Lewis and Weigert 1985; Coleman 1990; Giddens 1990). The complexities of modern societies make trust a fundamental requirement in structuring social order against the pressure of contingency and the selectivity conditioning our actions. As Morgner (2013) puts it: “The term ‘personal’ denotes psychological, mental, or emotional capability, while ‘system’ refers only to a very limited slice of social reality. This paper does not claim that trust has few mental consequences; the focus is on a societal framing of trust.” (Morgner 2013, 518)

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_3

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System References

This “societal framing” regarding the systems term and its meaning for trust is of special interest here. Both the trust literature and general social research suggest that this framing is multifold and concerns at the minimum, systems as trustees (Sect. 3.1), systems as trustors (Sect. 3.2), open and closed systems (Sect. 3.3), and system identity (Sect. 3.4). 3.1 Systems as Trustees Building on the preceding work of Georg Simmel (1978; see Möllering 2001 for a discussion), the initial idea of system trust was established by Niklas Luhmann (1979) with reference to the functioning of politics, economics, and science by trusting their communications media such as power, money, and truth. Anthony Giddens and James Coleman also used system terminology and referred to trust in “abstract systems” such as symbolic tokens or expert systems (Giddens 1990, 83ff) as well as intermediaries in “systems of trust” (Coleman 1990, 188ff). Trusting the energy system in this way seems like a perfect example of the classic Luhmannian idea of system trust: anyone who trusts in the security of the electricity supply “assumes that a system is functioning and places his trust in that function, not people” (Luhmann 1979, 50). Besides, according to Luhmann, system trust is trust in “the stability of the value of money” (1979, 50) and other “highly abstract system processes” (ibid., 56). These relate to a “long chain of information processing” (ibid., 53) that creates particular output in the economy (payments), the political system (collectively binding decisions), or academia (truthful research). Scholars have used these comprehensible, perhaps catchy elaborations to denote other systems of trust such as the financial system securing the international flow of money, loans, and credit trade (Zucker 1986; Shapiro 1987; Coleman 1990)27, or public trust in the political system (Warren 1999; Hardin 2013). However, Niklas Luhmann’s initial idea of system trust is far from clear – it often serves as a rather shimmering term that has achieved a certain overarching status for researching trust phenomena beyond persons and organizations.

27

What is apparent is that many of the systems under scrutiny in the context of system trust phenomena seem to be economic subsystems such as the financial system. Another system that might be examined is the “food chain” providing groceries for society (Sumpf 2013).

Systems as Trustees

43

By looking at Luhmann (1979) closely, one can find at least five different references of system trust that are more or less related to one another: 

Trust in symbolically generalized media of communication such as money, power, or truth (50ff, 55)28;



Trust in sanctioning and internal control mechanisms of systems to enforce their functioning (57f);



Trust in the general operability of a system (52, 56)29;



Reflexive trust, i.e., trust in the trust that others have toward a shared trustee (69f);



System trust as a “diffuse trust” whose exact reference cannot quite be located (53).

While many of the contradictions expressed through variations like this can be explained by the dualistic notion of system trust (Sect. 2.1) and the problem of trust references (Sect. 2.4), the major aspect of concern here is Luhmann’s use and specification of the term ‘system’. At this point in his work, a system that is to be trusted reminds the reader of a rather open, indeterminate system, a conglomerate of heterogeneous elements. This is indicated by variations in references to systems and trust throughout the book and elements with a socio-psychological character: we might ask who is the trustor in relations of system trust, an individual psyche, a “personal system” (Luhmann 1979, 22), or an organization? What is the actual object of trust, and how can we distinguish trust references? Intriguingly, Luhmann himself admits confusion and, with special emphasis on science and technology, mentions: “On the other hand the object of trust in the case of functional authority, as with money, is basically abstract and thus intangible. It is often the case that someone transmits authentically, but he is only the last link in a long chain of information processing. Does one trust the chemist, or his assistant, or the doctor, or is it medicine, science or technology?” (1979, 53)

Eventually, in Luhmann’s conclusion on the case of science, system trust:

28 29

“[…] in the form of trust in the specific medium”. Here, explicit references to several levels of system trust in politics, e.g., trust in communication opportunities and general system functioning, are made.

44

System References “[in] its typical form is trust in specialized and demonstrable abilities to process information, in functional authority and, ultimately, in the ability of science to function as a system of action.” (ibid., 52; emphasis PS)

This last point neatly reflects the status of systems research for Luhmann at the time, explaining much of the confusion: although Luhmann had a basic concept of system/environment, complexity reduction, border/element problems, etc. in his trust study, he used a fledgling idea of action systems (not yet clearly distinguishable from Parsons or Coleman) that would change considerably in his later works. Indeed, a more flexible interpretation of trusted ‘systems’ – a rather fashionable concept at the time – was prominent in classic research on trust (e.g., Barber 1983; Lewis and Weigert 1985) and was a very common term in general sociology involved with trust (Parsons 1964; Coleman 1990; Giddens 1990). Strikingly, this rather flexible notion of ‘open systems’ – heterogeneous elements linked to form an emergent entity generating a specific output30 – today still dominates discourses relating to “critical infrastructures” (Kröger 2008; Kröger and Zio 2011) or “socio-technical systems” (Geels 2004; Rohracher 2008) such as the energy system as a potential object of trust. As the case study in Chap. 6 will target trust in the energy system, an understanding of the most common concepts for describing systems that comprise social and technical processes is required here, as well as an assessment of their suitability as systemic trustees. It is easy and intuitive to hide the complexity of electricity supply in ‘a system’, but it is harder to ask about its concrete components and non-components and grasp what ‘the system’ actually is. In debates on technology assessment (TA) and the transition of large technology sectors such as energy supply, critical infrastructure and socio-technical system concepts are addressed to indicate linkages between social (organization, consumer behavior, decision-making) and technical (plants, devices, pipes, control rooms) components in large sectors of society like water management, electricity or traffic (Hughes 1987; Geels 2004; Rohracher 2008; Mayntz 2009). Backed by a long tradition of historical research (Hughes 1983), recent analysis has focused on the governance of “large technical (infrastructure) systems” (LTS/LTIS) (Mayntz 2009) and “sociotechnical infrastructure systems” (Edwards et al. 2007; Jackson et al. 2007). Overall, these scholars have analyzed

30

‘Open systems’ also entail the absence of sharp distinctions between system and environment.

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the emergent qualities of entities wherein social and technical elements interrelate to enable provision of services in the respective societal domains. Despite differences in philosophical schools, conceptual preferences, and (dis)aligning components inside or outside their ‘systems’, these approaches share an understanding of ‘open’ systems or networks of heterogeneous elements, held together by various coordination mechanisms (e.g., market, government) and social or technical norms (traffic rules, technical standards). Questions about system boundaries and elements belonging/not belonging to a specific system are related to an aggregate ‘purpose’ of the respective system: providing electricity, transportation, water, or ICT infrastructures. Socio-technical systems in this way: “[…] tend to be understood in terms of networks of actors and institutions clustered around the fulfilment of social and economic functions.” (Smith et al. 2005, 1507)31

A related strand of socio-technical systems research is transition research: “Transitions are commonly described as changes in socio-technical systems, which refer to shifts from one energy, transport, housing or agro-food system to another.” (Geels 2014, 1)

The entity in the focus of transition research is mostly conceptualized in terms of various social and technical elements like “material and social structures, such as policies, culture, technologies or markets” (Fuenfschilling and Truffer 2014, 1), as well as physical structures like machines and technical installations which are aligned with formal, legal, and economic structures (de Haan and Rotmans 2011, 92). There is little attempt to clarify the distinction between technical systems and social systems – both the technical and the social are taken as “functionally equivalent” with regard to an overarching system function such as energy supply, transportation, etc. (Büscher and Sumpf 2015, 5). Consequently, apart from the prominent position the term ‘system’ occupies in LTS/LTIS or transition research, little effort has been made to conceptualize a system beyond neighboring concepts such as regime, configuration, or 31

The historical origins of such a conception regarding systems as a ‘political entity’ can be found, e.g., in Chap. XXII of Hobbes’ Leviathan (Hobbes 2005, 167, reprint [1651]): “By systems I understand any numbers of men joined in one interest or one business.” Also, early definitions by J. H. Lambert, from the 18th century, refer to conditions which have to be met when one speaks of systems: identifiable components which are recognizably connected to one another by a purpose. This connection has to be temporally stable, as long as the purpose requires it (Strub 1998, 835ff).

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structure. The ‘soft’, rather network-like use of ‘system’ renders the terminology somewhat metaphorical and signals a preference toward the ‘open systems’ idea described above. The same basic pattern – although in more sophistication – is evident in Luhmann’s early conception of system trust, as the above discussion has shown. Here, too, several components of different natures are connected to form an overarching system that confronts trustors. Importantly, such systems are usually designed as trustees, i.e., systems that exist in the surrounding world as potential recipients of trust. A similar design is followed by the early Luhmann, as well as by the socio-technical systems scholars from LTS and transition research. Here too, systems are described and designed entities in the social world that we analyze and observe as researchers. Although many of these researchers explicitly use the concept of trust in their analyses, the systems which they describe – as open systems, networks, regimes, and so on – may nevertheless be considered as potential objects of trust. As trustees, they are real-world systems with identifiable traits of either a single (politics, economy, science) or multifold socio-technical nature (energy, traffic, water) – waiting for trustors to deal and interact with them. The concept of systems in the narrower discussions of systems theory is notably different to, if not the opposite of an ‘open system’ conception. It seems that there are a range of conceptual frictions between softer and harder system concepts that deserve some attention in the debate around system trust. In his early book on trust, Luhmann himself stated: “An adequate treatment of this subject would call for an outline of complete system theories for the personal and the social system. The material for such an undertaking could not be extracted from the literature without difficulties, to say nothing of its being fitted thereafter into the framework of this study.” (1979, 78)

Without claiming the ability to close this research gap at this point, it seems fruitful to ask: how would the later Luhmann – as a leading scholar of systems theory – have designed a system and what consequences does that have for researching trust? 3.2 Systems as Trustors Mirrored by the more recent breakthroughs in sociological systems theory (Luhmann 1995; Luhmann 2012; Luhmann 2013), the ‘open system’ concept seems

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rather untypical, since Luhmann tended to opt for strict separation of system references: organic, mechanic, psychic32, and social systems in general, as well as the separation of social systems into interactions, organizations, and societies (Luhmann 1995, 2). Modern societies, for that matter, are composed of functional subsystems like politics, law, or religion. These systems operate according to distinct codes that do not allow for direct intersection of systems elements – the systems are “operationally closed” (Luhmann 1995, 34; 444; Luhmann 2012, 49ff). Social systems are constituted by communications (not actions) as homogenous elements, and therefore not arbitrarily open (ibid.). In essence, Luhmann’s newer systems conceptions can be broken down into two major contributions that help to better comprehend system trust: the idea of self-referential systems and the scaling of social systems. Self-referential systems: The basic idea of social systems is simple, claiming that “there are systems”, thus emphasizing their ontological over their mere analytical status (Luhmann 1995, 12). This anchoring of systems in social reality supports his constructivist idea that systems are created through differentiating themselves from an environment, which is defined as everything but the system. The distinction of system/environment is fundamental to systems theory and represents the starting point for all further scrutiny. The mechanism behind systems building is their creation as a result of identity building, by self-separation from external processes and a reflection of the system/environment distinction within the system. In other words: they build an emergent identity. Once this happens, distinct systems such as politics and law, or an organization such as a company, emerge as they part from their environment. This self-reference (Luhmann 1995, 13ff; 437ff) of systems is one of Luhmann’s most influential ideas, and concerns the basis of social operations: politics are concerned with political communication, the economy with payments, and organizations with decisionmaking – morality and reason are only dealt with on a secondary level. If the social world is only accessible through self-referential systems, in modern Luhmann’s view, this must be reflected in system trust concepts, too: the key deduction to be inferred from this is that every self-referential system creates its 32

This is a reminder for readers that ‘psychic systems’ are to be understood as a human consciousness or mental system, and consist of thoughts as their system elements (cf. Luhmann 1995, 59ff; 255ff).

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own subjective reality regarding its environment – also in the case of trust in (a system in) the environment. The major question for system trust, therefore, is not primarily the exact constitution of the targeted system as trustee, but its perceived condition by the trustor system in question, e.g., an interaction system. In this way, the original open systems idea is basically a variable for telling us how complex and opaque a system that we want to trust can be, but it does not tell us how this complexity is reduced by trustors who are confronted with it. So it is self-referential systems as trustors (psychic systems and social systems) that create “trust as fiction” (Möllering 2006a, 112) according to their own rules of operation. In other words, trust is an outcome of a system/environment relationship that is created by a trusting system as the (internal) replacement reality for the (external) system reality. Not new, but heavily substantiated at this point, is the consequence that trust is related to “self-confidence” (Luhmann 1979, 27) as it is a product of a self-referential system such as a consciousness or an organization. Scaling of social systems: Among social systems, Luhmann differentiated between interactions, organizations, and societies (Luhmann 1995, 2). Importantly, one finds systems at all three levels33 of societal communication: as interaction systems among people who are (physically or virtually) present to one another, as organization systems constituted by decisions, and as societies comprised of functional systems such as politics, economy, law, science, etc. Societal events thereby constitute all of these domains simultaneously, depending on an observer’s position and reference: interaction in the hallways of a company might amount to current gossip, but perhaps it densifies into a formal decision, and in this way reproduces society. Luhmann’s system definition covers all three of these social aggregates. This definition has direct and yet unnoticed consequences for the theory of system trust: if an interaction involving at least two people is a social system, and if an organization is a system of decisions as a special type of communication (Luhmann 2013, 143), then, theoretically,

33

The three levels are not to be understood as a hierarchy or in terms of ‘big vs. small’, e.g., from micro (interaction) to macro (society) (cf. Esser 1993, 98ff). Rather, Luhmann designed them as different horizons of communication which can achieve higher (society) and lower (interaction) degrees of complexity.

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system trust encompasses trust in or through these systems, too. This is surprising insofar as the original and many succeeding conceptions of ‘system trust’ have portrayed it as trust in “abstract systems” (Giddens 1990) or in functional systems of society (referring back to Luhmann’s distinction). Yet, the idea of social systems not only relates to the notion of complex, black-boxed ‘systems’ as trustees that receive trust or distrust, it also relates to more downscale systems among people who are present to one another, such as social groups, who build ad hoc communication capacities, for instance (Luhmann 2013, 145). Moreover, trust in organizations or institutions, from this angle, is nothing else than trust in systems. A substantial part of trust research has also neglected multiple other connotations of social systems and trust. For instance, personal trust, too, can be conceptualized in terms of system trust: “System-trust is not only applicable to social systems but also to other people as personal systems.” (Luhmann 1979, 22)

While Luhmann seems to refer to the psychic system of an individual as systemic trust object in this case, Rühl (2005) stresses the relational aspect of personal trust: he claims that in the end, every act of trust is system trust as it involves at least two people in a reciprocal connection and therefore forms a communication system built upon this personal trust (ibid., 122f). Coleman (1990) argues in a similar direction when he describes self-reinforcing trust relationships between people and organizations forming a “system of trust” (ibid., 188ff). Guido Möllering, describing his process-oriented idea of trusting and talking about system trust in a side note, mentions that “the question is not so much if the system ‘itself’ can be trusted […] but if the system supports the social process of trusting” (Möllering 2013b, 4). It seems that both early (Luhmann 1979; Coleman 1990) and recent (Rühl 2005; Möllering 2013b) researchers on trust were on the brink of detecting a fundamental turnaround in system trust, yet never explicitly formulated it in its full radicalism: that the system involved with system trust is not necessarily the trustee (Figure 3-1). With neither the means nor the necessity to evaluate all of these aspects in this book, these rather compressed remarks already signal substantial conceptual omissions in system trust research. Most fundamentally, they blur the widespread distinction between personal and systemic trust that occupies many trust

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Society Functional systems Organizations

Interactions

Figure 3-1. Social Systems: Trustee or Trustor? Based on Luhmann (1995)

researchers as an unquestioned taken-for-granted concept. Yet, is it helpful to identify a ‘system’ everywhere, and on every level around us, in order to come to terms with system trust? Sects. 3.3 and 3.4 deal with this question and present conclusions. For now, as an inference from the discussion of self-referential systems, the scaling of social systems, and the doubts concerning person-system relations, one could argue that most importantly, the system reference in system trust should: relate to the trustor as much as to the trustee. The reason is that it involves systems on both sides of the trust relationship and/or relies on a conceptualization of trust as a resource that is always embedded in the logic of a social system of communication.

All this has more connotations for the relationship between consciousness and communication – mental and social antecedents of trust – than can be considered at this point. What seems striking for research on trust in, by, or through systems is that more attention needs to be directed at how systems generate expectations of trust, instead of looking at the specific traits of systems that these expectations target. Only in this way is it possible to identify how the external system (trustee)

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is perceived in a system reaching a state of trust (trustor). ‘The system’, in this case, is a self-referential social system (Luhmann 1995) at different levels of society: groups, organizations, functional systems. Psychic systems (individual consciousness) can fulfill the same function, but need to communicate their reasons and motives for trusting in a social context in order to come into effect. This means that individual psyches are a necessary but insufficient condition for the trust phenomenon: they are black boxes for their social environment and consist of thoughts, not communication, making them unobservable for society in their genuine world of thoughts (Luhmann 1995, 137ff). For researching system trust, this means that we have to be aware of the introductory statement of Lewis and Weigert (1985): “Like the Durkheimian collective representation, the sentiment of trust is manifest in the psyches of individual group members, but this must not lead us to the common but erroneous inference that trust is fundamentally an individual and behavioral phenomenon produced by rational machinations of autonomous, calculating individuals.” (Lewis and Weigert 1985, 976)

This citation leads back to Lewis and Weigert’s conviction that both the function and antecedents of trust are socially induced (ibid., 969). This means that in looking at the incitement of trust or its justification, we have to look at social processes as much as when looking at disappointments of trust. This can be illustrated by referring once more to the distinction between trust, confidence, and familiarity. Most strikingly, the social consequences of all three modes are the same: conforming action. This means that on a societal level, the effects of trust, confidence, or familiarity toward a certain trustee are hardly separable. Major differences can be primarily observed in the way disappointments or affirmations are attributed personally: If I know that I took a risk (i.e., out of trust), then I react differently to disappointment than if I feel I did not take a risk (i.e., out of confidence or familiarity). Research on mental conditions and the individual effects of system trust (or distrust) would then encompass issues such as depression, guilt, or doubt, resulting out of personal disappointments. Conversely, ‘competency traps’ of trustors repeatedly attributing perceived confirmations of trust to their own behavioral influence might lead to a feeling of overconfidence. These effects matter for individual psyches – not for society (Morgner 2013).

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This idea is furthered by the fact that the attribution rules of trust disappointment and the ascription of references to justify trust are also socially constructed – even if they rely on mental information processing as a necessary condition. The dominant pattern is one of “retrospective sense-making” (Weick 1995); hindsight implies an ex post justification of motives and reasons for trust. However, the justification strategies which we thought about before may have been different (Luhmann 1995, 137ff). Consequently, trust needs to be subjected to these construction processes in social systems, primarily since they determine the rules by which trust is motivated in the first place and then attributed to system or environment, self or other, and perceived as risk or danger when disappointed: “Although there are individual differences relevant to the trust factor, the cognitive content of trust is a collective cognitive reality that transcends the realm of individual psychology.” (Lewis and Weigert 1985, 970)

The ‘architecture of trust’ – as Chap. 5 will unravel – is a contribution toward more precisely describing that collective cognitive reality. In order to get there, this section closes with a hypothesis following from the prior elaborations: H5: System trust references can also be systemic trustors – not only trustees as ‘abstract systems’. Trust in systems is actively constructed within low-scale systems as trustors (e.g., interactions, organizations) independent of scholarly descriptions of the ‘real’ system reference.

3.3 Open and Closed Systems Discussions surrounding the ‘nature’ of systems can be further sophisticated by distinguishing them from competing notions such as networks, webs, or structures. As Paul Edwards et al. (2007, 12) emphasize: “In general, […] infrastructures are not systems. Instead, they are networks or webs that enable locally controlled and maintained systems to interoperate more or less seamlessly.”

Looking from local to global levels of interdependent socio-technical processes, the authors differentiate systems, webs, and networks mainly by their control capacities:

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“Thus we define a spectrum running from systems (centrally organized and controlled) to networks (linked systems, with control partially or wholly distributed among the nodes) to webs (networks of networks based primarily on coordination rather than control).” (Edwards et al. 2007, 12)

They end up characterizing systems equipped with “closed, stable boundaries”, while networks and webs remain “open and reconfigurable” in their boundaries (ibid., 12). Through their elaborations, the authors thus refer to a weaker yet similar version of what was introduced as “operational closure” of systems (Luhmann 1995, 9) in Sect. 3.1: a distinct mode of operation that differentiates a system from its environment and gives it unique coding, distinct boundaries, and an identifiable functional unit in society. This alludes to the fact that as an antagonist concept to open systems, there are closed systems (ibid., 9). Consequently, it seems that closure and openness are key elements of a system description, which deserve some more attention. What is the benefit of these distinctions and what can be gained for the study of system trust? Notably, in contrast to the socio-technical systems introduced by scholars in Sect. 3.1, Edwards et al. allude to the closure of both technology and social processes against each other. While most transition (Geels 2004) and LTS (Mayntz 2009) researchers presume the “functional equivalency” (Büscher and Sumpf 2015, 5) of technical and social elements, they rather neglect their fundamental differences: “The technical network of energy flow is completely neutral to communication; in other words, information is produced outside the network […]. Causal relations between technological physics and communicated information are freed of overlap and take the form of structural coupling.” (Luhmann 2012, 180)

As a result, the distinct operational modes of technology can be broken down to “functioning simplification in the medium of causality” (Luhmann 2005, 87), leading to determinacy in its working logic: closed technological systems, as machines, generate the output which designers programmed them to generate. If a machine does not work, it is broken and should be fixed. Social systems inherently contain the element of surprise, which is built in through double contingency34 (Parsons 1964; Luhmann 1995) and thus makes the social world inescapably indeterminate – control of society remains an illusion. The relation 34

“Something is contingent insofar as it is neither necessary nor impossible; it is just what it is (or was or will be), though it could also be otherwise” (Luhmann 1995, 106).

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between social and technical elements, therefore, is described by Luhmann (2012, 180, 312ff) as structural coupling rather than functional equivalency. If one relies on such a stricter notion of ‘closed systems’ of homogeneous character (e.g., social systems like organizations consisting of communication versus technical systems consisting of closed causal simplification) as Luhmann (2012) suggests, one ends up with a much more careful and modest definition of what a socio-technical system such as energy supply looks like and what follows from here. In their version of open and closed system typology, for instance, Edwards et al. (2007) specifically address organizations such as “Local electric power company” and “Enterprise computing (e.g., banks, insurance companies)” (ibid., 12) as primary examples of closed systems. Accordingly, applied to organizations as closed social systems as part of a larger socio-technical conglomerate, one encounters their multi-purpose design (Luhmann 2013, 150): referring to the reproduction, i.e., the maintenance of operation, boundaries, or identity of such a system, one quickly discovers that organizations like regulatory agencies, utilities, ministries or banks operate in several loose systems and networks such as electricity, health, telecommunications, water management, etc. simultaneously. This can be illustrated by private enterprises involved to some extent in electricity provision while, at the same time, having to maintain business and compete on markets (i.e., in different contexts than electricity) – no matter where revenue comes from, it has to be secured. Consequently, it is not far-fetched to state that the reproduction of a social system such as a private company is primarily a self-serving purpose and not necessarily aligned to an energy system’s overall purpose of providing electricity for society. At this early point, we can already claim that an idea of socio-technical systems as closed, seamless entities of heterogeneous elements has flaws insofar as it lacks the capability to capture the notion of organizations contributing to these systems besides other societal domains they serve. Elsewhere, Christian Büscher and I (2015) have presented a definition35 of socio-technical systems that tries to do more justice to the notion of systemic closure. Accordingly, socio-technical systems can be described as:

35

This is a reminder for readers that I share the understanding that “the purpose of definition is to focus argument upon fact, and the proper result of a good definition is to transform argument over terms into disagreements about facts, and thus open arguments for further inquiry” (Mills 1959, 34).

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“[…] expectation complexes of services, to which technical and social systems contribute, to dissolve from moment-to-moment socio-technical problems.” (Büscher and Sumpf 2015, 3)

This definition has the advantage of capturing the constitution of the overall system as both trust’s source (expectation complex) and its target (services to which technical and social systems contribute), as well as offering a realistic view of how this comes about (by dissolving from moment-to-moment sociotechnical problems). Besides, it neatly connects to the act of trust by portraying the energy system as an expectation complex (Sect. 2.4). Yet, at the center of this definition, there is the temporal issue of ‘dissolving socio-technical problems from moment-to-moment’. Behind this issue is the paradigm of presentrelated system operations that sustain and reproduce systems such as the energy system. This paradigm does justice to the notion of “operational closure” (Luhmann 2012, 49ff) as a “radically constructivist concept” (ibid., 12). As Dirk Baecker (2011) highlights: “Networks do not have boundaries, systems do. That is why ‘network’ is a structural notion, whereas ‘system’ is an operational notion.” (ibid., 22)

Besides the distinction between open and closed systems, this process component of how openness and closure come about seems apt, i.e., how a system reproduces itself and maintains stability through the process of operating. This operational notion can be illustrated with a helpful example from the energy sector. The operability of a system is concerned with sustaining social reality. In the energy system, for instance, grid operators have to take unforeseeable decisions to secure the crucial 50 hertz frequency36 for system stability in real-time (Roe and Schulman 2016) – this is what makes up the dynamic of the system and poses decisive challenges to control room managers for system reproduction: “We return then to the key point that large sociotechnical systems cannot be designed to be ‘damned foolproof’ and that highly reliable systems are managed to be reliable beyond design. If they were operated only according to design, they wouldn’t be managed with the resilience needed for reliability. That means designers, be they engineers, policy makers, or senior executives, must trust and facilitate the skills of control room operators to add the necessary resilience to the engineered 36

See FN 5.

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System References foundations of high reliability. [...] this is in no way assured when top-level officials introduce major technological innovations into the real-time operations of infrastructure control rooms.” (Roe and Schulman 2016, 156; emphasis PS)

Apart from the intriguing notion of trusting control room operators to do their job and maintain system stability, the real-time operability of a system managed in this way is pivotal. As Roe and Schulman point out, the system is managed by control room operators through real-time decision-making, largely independent of the technical design standards of the system or imagined formalities and rule sets that were given to them in advance. The system is what happens now, in the control room, in its social system operations. In this way, the operational paradigm is also substantiated by the empirical idea that systems reproduce themselves through communicative acts – as elements of a social system – from moment to moment all the time: the system ‘operates’ by connecting communications together in real-time, creating a basal yet crucial present-relatedness of social reality (cf. Luhmann 2012, 49ff). For trustors, be they organizational (i.e., governments, operating agencies) or individual (i.e., citizens, electricity clients), this has far-reaching consequences for the suspension of trust (Sect. 5.1). In conclusion, the emphasis of concepts such as operational closure offers sharp instruments of observation for systems scholars. Closed systems approaches are popular in technical disciplines in the sense of closed causal relations of technology (Ropohl 2009), and also in parts of “radically constructivist” approaches such as Luhmann’s (2012, 12) idea of self-referential social systems that operate exclusively via communication. On the basis of operational closure, systems can uphold a unique analytical status and be more easily parted from neighboring concepts such as network or web through stricter definitions of boundaries, functional logic, and unity. Conversely, the term ‘open system’ alludes to the absence of sharp distinctions between a system and its environment and accommodates heterogeneous elements within its borders, such as technology and social action. In this sense, an open system could be understood as conceptually equivalent to a network, web, or complex (mentioned in the above definitions). It seems that the emphasis on either an open or closed system depends largely on research focus, scale, and suitability for the analytical question targeted. Using an open systems idea, as largely conducted in LTS and STS literature, one can see that technology and social actors work together closely and span great distances and resources in order to provide a societal purpose. Using

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a closed systems idea, one realizes that they work in very particular, non-arbitrary ways. From a basic research angle, the closed systems idea might be the more ‘realistic’ perspective, since it includes the idiosyncrasies of, firstly, technology and, secondly, social systems: technology can be deemed controllable/predictable when operating as constructed without failures. As for social systems (e.g., organizations and functional systems that operate autonomously), they provide a huge potential for contingency, yet in accordance with their respective codes of operation (Luhmann 1995). For the purposes of system trust research, a closed systems approach unfolds its potential mainly in the realm of trustors, not that of trustees. In this way, by analyzing closed systems interacting with one another in a network, for instance, one could reintroduce the relationship between system and environment and highlight their mutual adaptations. This facet connects smoothly to the idea of system-internal trust-building from Sect. 3.2 by stressing the social psychological nature of trust. Consequently, closed systems approaches can stress that the interplay between technical and social systems in ‘socio-technical systems’ will always be strongly impacted (although not determined) by each closed system’s particular properties. In other words, depicting technology and social processes on equal grounds does justice to neither of them.37 As a consequence, Niklas Luhmann concluded: “The (subsequently classical) distinction between ‘closed’ and ‘open’ systems is replaced by the question of how self-referential closure can create openness.” (ibid. 1995, 9)

Our endeavor though, is finding a systems model that can embody an object of trust. In this case, the answer relies not so much on the notion of ‘self-reference’ as on that of ‘emergence’. 3.4 System Identity The previous sections have presented – in a non-exhaustive way – a range of system concepts and neighboring terms concentrated around aspects of closed vs. open systems and their possible interplay. Apart from the advantages and 37

An exception is the distinct phenomenon of insinuated human agency of technology, which is treated in Sect. 5.3. It leads to the contested question of whether technology can be an object of trust, since trust is – as was stated frequently – a social phenomenon.

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disadvantages of those concepts and the trade-offs that seem associated with them, there may well be sufficient common ground to lead to a substantive conclusion for the study of system trust. Crucially, a unifying idea in conceptualizing systems – whether using open or closed systems approaches – is the idea of system emergence, i.e., the quality of a system brought about through a multitude of events, but which cannot be explained by the properties of these single events alone. Scholars around open systems concepts – in the sense of stable systemic entities tailored toward a frequent service output – use the concept of emergence as a common, yet sometimes implicit reference: “Functioning eventually is caused by the web of interactions of actors in their social structures and physical world, as functioning has an autonomy with respect to them.” (de Haan and Rotmans 2011, 92; emphasis PS)

In transition research of socio-technical systems such as energy, water, or telecommunications (Geels 2004), references to the micro-macro model of James Coleman (1986; 1990) can be found. Accordingly, Frank Geels (2004) also refers to transition research in terms of the “multi-level perspective” (MLP). This perspective is, in part, based on the micro-macro scheme from Coleman, as Geels introduces three levels of socio-technical research on different social scales that he calls niche, regime, and landscape, from concrete to abstract (Geels 2004, 910ff). Behind this is a concept of autonomy or self-dynamic of the general system as an effect of the emergent interplay between the three levels. Coleman himself has described the idea of emergence as follows: “The action, or behavior, of the system composed of actors is an emergent consequence of the interdependent actions of the actors who make up the system.” (Coleman 1986, 1312)

In this way, Coleman identified one of the most important qualities of a system as its ability to act or even ‘behave’ as a distinct unit apart from its single components as well as other potential systems in the environment. Closed system scholars like Luhmann referred to the phenomenon of emergence mainly as ‘outdifferentiation’ of systems: “If a social system emerges in this manner, I speak of it differentiating out […] against what this process then makes into the environment” (Luhmann 2013, 2; emphasis PS). Nevertheless, Luhmann stressed that social systems, through their process of emergent outdifferentiation, build their own code of operation out of a unique functional logic determining their dynamic. He explicitly emphasized their identity building and

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self-reference in distinction to their environment (Luhmann 1995, 16ff). Thus, no matter if we speak of open or closed systems, their unifying character seems to be that the system under analysis – be it a socio-technical system, a functional system of society, or an organization – has its own identity, logic, and irreducible traits that make it distinct and identifiable through emergent qualities. This special status of a system that distinguishes it from (systems in) its environment and establishes an emergent level of sociality is an important intersection point in both ‘softer’ and ‘harder’ systems theory models. As a conclusion, emphasizing some terms over others based on distinctions between them (e.g., system, network, web, structure) is not necessarily more useful than emphasizing certain characteristics of these entities in order to get an idea of their identity, which results out of emergent qualities at the system level. By stressing the possibly shared characteristics of open and closed systems, the road is clear to “transform argument over terms into disagreements about facts, and thus open arguments for further inquiry” (Mills 1959, 34). This statement sharply describes the use and scope of defining terms: they serve as working definitions that guide the way into a deeper understanding of the facts and mechanisms behind the issues they describe, but are by no means the be-all and end-all. In our case, for example, it is more important to recognize ‘systemacy’38 and identity among the possibly emergent socio-technical entities under scrutiny than to decide whether we should call them a system, network, or structure. Thus a social practice or rule derived out of empirical plausibility should always claim primacy over (perhaps established) terms. As a consequence, I would like to state my understanding of a system (related to systemic trustees) as emergent entity developing an identity that unfolds a systematic interplay between homogeneous or heterogeneous elements in order to generate a superior purpose. What further arguments underscore this pragmatic, problem-oriented system design, and what follows from this for studying trust in systems? The analysis so far has shown that “emergence” (Coleman 1986; 1990) is a common factor in describing systems of different natures, and embodies a major reason for the analytical success and practical relevance of the term ‘system’. Emergence represents the common ground for all systems building, be it sociotechnical systems in LTS and transition research (Mayntz 2009; Geels 2004; de 38

“Differentiation provides the system with systematicity” (Luhmann 1995, 18; emphasis by PS).

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Haan and Rotmans 2011), or functional systems of society such as politics, science, or the economy (Luhmann 1979; 1995). In each case, an emergent system develops an identity that unfolds a systematic interplay between (homogeneous or heterogeneous) elements of the system, which are otherwise separated, and in order to generate a superior purpose. These purposes are diverse and can be the provision of a service like electricity, the internet or public administration, or an indispensable function in society like collectively binding decisions (politics), the pursuit of truth (science) or distribution of goods (economy). In each case, an emergent entitiy and subsequent identity is created that provides more than the system’s single elements could provide alone. This identity, which is stable over time (Strub 1998, 835ff), leads to ‘real-world’ systems building (Luhmann 1995, 2) and establishment as a reference in the empirical world. In this way, even public communication processes allow references to societal domains such as ‘the energy system’, ‘the financial system’, or the ‘political system’, literally and explicitly as a system. It seems that the degree of systemacy and density of elements in these areas is so high that even the public audience perceives their systematic identity and stable interplay. Accordingly, as a means to characterize such systems, we could ask: how dense is the interaction between the heterogeneous elements of a socio-technical system? What is the degree of systemacy in order to achieve a certain purpose such as providing electricity? Is the purpose clearly stated and shared among system actors as well as the means to achieve it? These questions can help approach the notion of ‘identity’ in different types of societal systems, because identity is decisive for the potential of trust in those systems. In answering these questions, one discovers a multi-faceted picture that reveals differences in the way systems are perceived and addressed: while ‘the energy system’, ‘the financial system’, or ‘the political system’ are widely disseminated terms used by many people without much reflection, it would be misplaced to talk about ‘the system of digital services’ or ‘the IT system’ beyond a small-scale technical unit, for instance. It can be assumed that the degree of systemacy, purpose, and responsible actors in this field around digital services, big data, and online platforms is not (yet) as clear as it seemingly is in the prior examples. Dealing with cyber infrastructures and ICT, for that matter, we see a dispersed and patchy sector that is partly established but still in the making and does not provide clear addressees and services that are undoubtedly identifiable

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by each and everyone. With app-based tablets, smartphones, desktop computers, and countless additional offers in the increasing ‘Internet of Things’, there is a complex arrangement around consumers who are permanently requested to agree to the “biggest lie on the web” (Greiner 2012) – having read and agreed to the terms and conditions of use.39 At the same time, there is little visibility of who is in charge or responsible for what happens – which is a characteristic of a “Beta Society” (König and Nentwich 2014) and leaves much of the internet in a permanent preliminary state. On the other hand, systems like the energy system provide visible addressees, such as utility companies or regulatory agencies, and have a network of more or less identifiable actors on a local-to-national scale. ‘The system of digital services’, as a possible conglomerate of everything related to the internet, lacks such clarity, even if this might be an evolutionary status rather than someone’s intent. Nonetheless, Google represents many activities on the web and is decisive for an abstract, unadressable system (Vaidhyanathan 2011), for it owns a monopolist market share (especially in Europe) which epitomizes the primary gateway to the internet (Hakim 2014). Still, perceptions of responsibility remain diffuse and users rather uninformed (König and Nentwich 2014), even if online platforms’ business models are often founded on the idea that the user is an active part of a constantly changing system rather than merely an external component observing the system from the outside (Raymond 2000; Bruns 2008). Due to such diffuse perceptions, we do not (yet) speak of a ‘system’ when we think of digital services. This case serves as an illustration to discuss perceptions of the variety of societal domains around us and learn about ‘system identity’. It seems that ICT breakthroughs such as smart technology, online platforms, and related hardware – even though they are on the brink of becoming omnipresent – still primarily serve as a complementary infrastructure enabling other services such as smart grids or smart homes. In this way, they tend to improve existing things, rather than necessarily constituting the innovation itself. Their function, with respect to the service they provide, relates to nothing relevant to society in itself (with the notable exception of social media applications); indeed, they merely lift established sectors (energy grids, homes, harbors, airports, taxis, etc.) onto a new, enhanced level. In a similar fashion, for example, what we would call ‘the traffic system’ can be considered a rather 39

See http://www.biggestlie.com/ Accessed April 24th, 2017.

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vague system indicating a diffuse function (enabling and regulating physical traffic) executed through diverse, rather loosely coupled elements (infrastructure maintenance through multi-level governance, signaling technology, police enforcement, etc.). Since energy, data, and traffic systems can be associated with high shares of technology, their inclusion as a reference in the environment of systems terminology is more probable because the systems term is very popular among technical disciplines (Ropohl 2012). Therefore, the reference can be plausibly assumed to permeate from professional into public use through the technical infrastructure in respective domains (energy, water, traffic, etc.) and their association with engineers, technicians, natural scientists, and so forth as system designers and operators. On the other hand, the cases of politics, the economy, or finance40 prove that ‘systemacy’ can also be achieved without substantial physical technology being involved.41 Apparently, there is no way around recognizing the systematic interdependence of financial markets in a global knowledge society (Willke 2007), for example, creating what we call and perceive as ‘the financial system’. Similarly, the systematic operation of politics was coined in the 17th century42 (Hobbes 2005) and has established itself in political science (Easton 1953) and in public use as ‘the political system’. Moreover, this embodies the semantic advance of systems terminology. Concluding, this initial analysis shows that systems of various backgrounds and domains (especially socio-technical systems and functional systems of society) can be compared by looking at traits such as systematic element relations and density, clarity of functions and services, and actor responsibility. After all, a well-established concept could help to classify systems according to their density of element relations and interdependencies: the distinction between tight

40

41 42

It seems that another connection between system and public use could be the insinuated malicious intent or manipulative element of actors involved with these systems, such as bankers, politicians, or managers (just think of cases involving systematic doping, fraud, conspiracy, etc.). Accordingly, I cannot claim full historic or linguistic validity of my assumptions regarding public uses of systems terminology. What it shows is that a more systematic (!) engagement with system comparisons of this kind along more complex criteria with more rigorous empirical and multi-disciplinary backup would be fruitful. ‘Algorithms’ as bases for decision-making on stock markets are not a completely new phenomenon (Hickman 2013), but can hardly be associated with widely known technology in the financial sector. For sure, they do not count as large, visible technology. I cannot preclude at this point that earlier mentions of systematic political operation took place throughout history.

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and loose couplings (Weick 1976; Perrow 1984; La Porte 2015). Yet, the identification of system properties such as tight or loose element couplings that help determine a degree of systemacy does not serve a self-fulfilling purpose: as this section looks for ‘system identity’, loose or tight element relations result in strong or weak identities that have consequences for the system condition and for potential trustors. The underlying thesis is simple: the higher the density, the more systemacy we discover in societal systems of various natures, and the stronger their identity – and vice versa. Identity, then, means visibility and the ensuing potential of (dis-)trusted systems to be related to by average trustors. In the light of the very first hypothesis formulated in this book (H1, Sect. 2.1), it is mandatory for a system to be an identifiable unit in the social environment of trustors in order to qualify as an object of trust. If trustors really form decisions on the basis of trusting systems, then they must perceive that system as such, and not merely its single components. These considerations can – apart from more accurately describing a societal system – help to deduce service expectations toward these systems that form the basis for system trust (Sect. 2): in fact, it makes a difference if I expect some kind of ‘data privacy’ from numerous services with little clarity of responsibility (system of digital services), or if I concentrate my expectations precisely on certain price thresholds, security of supply, and sustainability (energy system). The same difference exists between broad expectations of technical functionality of street lights and fixing of road bumps (traffic system), for instance, against more exactly expecting a fair tax rate, rent control in cities up to a certain amount, and a contingent-based immigration law that does not discriminate among sender nations (output from political system). Thus, the stronger the identity of a system, with a clear purpose, actors, and components, the higher the probability of finer-grained expectations toward it as a basis for trust in that system. In other words, the emergence or autonomy of the respective system – its identity – crucially triggers probabilities in systemic trust or distrust. The degree of identity also regulates inscription of the system’s features in social reality and determines the way it is perceived and attributed by trustors, both for the justification of trust in the first place, and for responsibility in cases of expectation disappointments. Hence, a high degree of identity would make a system very visible for trustors and increase the probabilities of ‘genuine’ system trust, while its

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absence would indicate trust relationships with single components instead of with the system as such. The issue of responsibility is key in this context and is specifically linked to trust in that it presumes liability and/or sanctioning addressees when expectations of a system are disappointed (e.g., power outages, data mining, stock decline, car accidents, etc.). Yet, no matter if responsibility for consumers’ disappointments is actually accepted or not, the symbolization of risk associated with these potential disappointments is significant for the creation of trust or distrust toward a system. In other words, where no risks are perceived or where risk cannot be symbolized as a harmful threat, trust will prevail (see Sect. 4.4 for details). One reason for the absence of distrust in big data applications, for instance, is the fact that ‘real-life consequences’ are barely known or reported, beyond sensational stories such as predictive customer analytics by American shopping giant ‘Target’.43 The absence of real-life scenarios of what could go wrong in using 100 digital apps with the highest possible data intensity44 is still driving trust in online platforms in the form of increasing transactions and gullible user traffic. There is simply no apparent reason for distrust. ‘Symbolization of risk’ is therefore the decisive category representing a system’s responsibility structure in connection with trust or distrust. In summary and conclusion, five criteria for characterizing systems (Table 3-1) can be stressed: tight and loose element coupling, functions and services (concrete/diffuse), symbolization of risk, semantic advance, and possible representatives. These criteria have a special relation to the incitement or absence of trust in a system in that they manifest the system’s degree of identity.

43

44

A teen-aged, female Target customer has allegedly received coupons for baby clothes and nursery furniture due to her prior consumption patterns with Target. Target relied on a novel marketing activity based on a ‘pregnancy prediction score’. Reportedly, the girl was indeed pregnant without knowing about it before the Target advertisements (Duhigg 2012). A nuclear power plant can blow up and radiate a whole community for centuries – a very vivid risk.

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Table 3-1. Possible System Characterizations for Trust Research Type of system Identity criteria

Energy

Digital services

Traffic

Finance

Politics

Tight/loose coupling

Tight

Loose

Medium

Tight

Tight

Symbolization of risk

High

Low

Medium

Medium

Low

Functions and services

Concrete

Diffuse

Diffuse

Concrete

Concrete

Semantic advance

High

Low

Medium

High

Medium

Google

Local administration, police

Banks

Politicians, parties

Possible representatives

Utilities

This is a crucial supplement to the earlier system understanding presented above, as it concretely directs the study of system suitability for the purposes of trust in systems. Research on system trust can benefit from categorizing supposed ‘systems’ according to the characteristics presented here. They primarily serve as exemplary purposes of possible system traits that could be relevant to the trust problem, and are by no means final or exclusive. The contents of the table are educated guesses to trigger other scholars’ thoughts on how to go about and define system characteristics for a specific group of trustors. To be clear, the value of system descriptions greatly depends on the observer targeted: average consumers, business experts, and scientists do not necessarily share perceptions about the same system. Let us look at energy, for instance. In this case, many consumers concentrate on their utilities, certain technologies, and price comparisons. Traders are interested in stock markets, energy generation reports, and inside information on merging and acquisitions. Scientists and researchers, on the other hand, want to know how the system really works – they are interested in truth from a scholarly perspective. These different logics and interests are crucial in relation to trust-building since trust reacts to self-constructed system perceptions

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– be they scholarly correct or not. In the field of consumers (studied in the empirical case of Chap. 6), one can test awareness of certain societal branches as ‘systems’ in order to identify trustees who appear repeatedly, and allow for building experiences and a history with respect to that system. This identity – stable over a certain period of time – is the basic requirement for a system to become a suitable object of system trust as a visible entity (the system) and/or the concomitant actors representing it (e.g., my local utility company, an oversight agency such as the Bundesnetzagentur, or Google for that matter). Importantly, it makes a difference if trust in the representing actors of a system is built up as trust in those actors individually, or merely as mediators or access points to that system, so that trust in the actor is equated with trust in the system (cf. Sect. 5.2). As a consequence, such systems appear as a reference frame for communication and therefore trust in empirical reality (Garfinkel 1963; Luhmann 1995), primarily as background reality for attributing trust to trustees. Subsequent questions comprise matters of the ‘communication capacity’ of systems, i.e., their ability to be a communication address in the ‘architecture of trust’ (cf. Chap. 5). Luhmann’s later concept of a self-referential system is incorporated in this context through the idea that every act of trust is a trustbuilding system’s internal construction of its environment, which holds other emergent systems as potential objects of trust. The first conclusions for a working definition45 of ‘system trust’ are as follows: System trust is trust by a system into a system. Systems are created by self-reference, emergence, and identity building. Trust in a(n) (external) system is always an internal construction of a system depending on its ‘subjective’ perception. More precisely, it is system trust into its environment.

As a final, comprehensive hypothesis from this chapter, we can formulate that:

45

As mentioned earlier above, definitions serve as a pragmatic means of understanding in this book and not as the ultimate goal of social science endeavors – this is reserved to hypothesis generation here. This working definition could serve as a template for scholars with particular interest in further developing fine-grained definitions.

System Identity

H6: For a system to be an object of trust, it needs to build a stable identity. An emergent system like this is available to trustors as commonly shared background reality.

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4 Conditions and Consequences of Trust in Systems

This chapter provides a more detailed description of the state of the art in trust research and its special connection to trust in systems. Sect. 2.3 discovered control, complexity, non-knowledge, risk, expectations and suspension to be the decisive components of trust analysis. In this chapter, four of these components will be discussed in their own section, while expectations and suspension will be analyzed more closely in Sect. 5.1. The six components of trust are introduced in order to achieve a holistic view of the conditions and consequences of trusting systems. Let us remember that the initial analysis in Chap. 2 has shown that system trust must not be treated differently from other forms of trust, if it is to advance in the discussion and reflect the empirical reality of contemporary societies. Moreover, Chap. 3 has demonstrated that ‘system identity’ is pivotal for trustors to perceive a collective entity such as politics, finance or the energy system as an object of trust. Against this background, and without neglecting existing differences between various objects of trust, it is important to see that trust in systems may follow the same basic rules as trust in other entities such as persons or organizations. This is the reason why trust’s general framing conditions apply in the case of system trust too and need to be elaborated at this point. The six components of trust are presented in a circular manner, i.e., in no specific order (e.g., hierarchical or linear), reflecting their relation in empirical reality (cf. Figure 2-1). Chap. 4 will uncover turns with particular significance to studying system trust, including a distinct sociological lens on system trust as a “collective cognitive reality” (Lewis and Weigert 1985, 970). Through study of how trustors control (4.1) environmental complexity (4.2) and process (non-)knowledge (4.3) and risk (4.4), the crucial basis is laid out for analysis of trustors’ ‘reassurance patterns’ in trust or distrust (Chap. 6).

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_4

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4.1 Trust and Control Many of the prior issues discussed have touched upon the information processing capacities of trustors toward trustees, and the role of knowledge in the process of trust creation. This practice of scrutinizing a certain object of trust for indications of trustworthiness has been termed control of trust (Shapiro 1987; Luhmann 1979, 28f). In trust theory, trust and control may be regarded as opposites: Relying on trust signifies a renunciation of control (however difficult this ‘control’ of a trust object might be), and pursuing ultimate control represents a scheme where no more trust is necessary, because all relevant factors are or seem to be known. Consequently, it is the control of trust – and not control of the world (or system) that we are concerned with in everday life. The reason is simple and concerns an abundance of complexity and a scarcity of resources: as we are unable to control every social process around us, we have to draw the line somewhere and rely on trust instead of actual control in the sense of supervision, scrutiny or observation of people, organizations or systems. The question of trust and control, then, always depends on degrees of toleration and accessibility of non-knowledge by trustors: how much information am I ready to leave undiscovered? To what degree do I actively search for, or become passively influenced by trust-related attitudes toward my social environment? As a consequence, Luhmann (1979, 29) refers to the control of trust as a process of “symbolic control”, which acknowledges that the information we are seeking in order to confirm or dismiss trust cannot factually guarantee its justification; instead, this information serves as a symbolic signifier. If my friend reassures me that he will not scratch my precious vinyl record the next time he borrows it, I can reaffirm my trust based on his word – without knowing if he will live up to the promise. Controlling symbols of trust with regard to organizations or abstract systems is even more delicate: here, people have to rely on information they read in the news or their experiences with individual people belonging to, yet not fully representing an organization, or even a whole branch of society such as politics or finance. Drawing on his three trust constituents ‘internalization of external complexity’, ‘learning’ and ‘symbolic control’, Luhmann (1979, 26ff) emphasized that:

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“by changing personal trust into system trust, the process of learning is rendered easier but control is made more difficult.” (ibid., 50)

Depending on the type of trust object, the possibilities of control vary: looking for suspicious behavior among my friends is hard, but it is still easier than finding out if the government is trustworthy. However, the latter is still manageable, compared to trusting in the stability of global financial markets or the electricity supply. In these domains, for a small number of experts it is vaguely yet theoretically possible – at diminishing degrees – to assess the trustworthiness of these systems on a factual and causal basis. But the vast majority of the population has to rely on signaling indicators: “But to retain mastery over such events places very great demands on attention and time, the acquisition of knowledge and intelligence, so only a few people manage it.” (Luhmann 1979, 50)

It is important to notice here that control of system trust is, to a certain extent, theoretically possible – thus making this an issue about choice rather than compulsion – although practically this is rather inconceivable: “In practical terms, control over trust can only be exercised as someone’s main occupation. Everybody else must rely on the specialist involved in such control, and thus is forced to remain on the periphery of events.” (ibid., 57)

Within the array of symbolic indicators accessible to the lay public, there is more and less attention people can and want to pay toward collecting information on complex and time-intense socio-technical patterns around them. A dramatic example is the use of digital applications on smartphones and desktop computers: Social media apps (e.g., Facebook or WhatsApp) and cloud computing services (e.g., Dropbox) are used heavily by millions of private consumers and business users. At the same time, control of data by mainstream users in the sense of process comprehension of what these services actually do ‘in the system’ is constrained due to the fact that the benefits of use are extraordinarily high (who operates without cloud computing or some social media app nowadays?) while the cost – in terms of time, knowledge and risk awareness – of controlling terms of use and ‘looking behind the curtains’ to figure out the ‘real’ data streams and connections are even higher, although theoretically not impossible to execute. This is why disappointments in trusting social media platforms like Facebook or cloud computing services with sensitive data (party pictures, confidential client data) are likely to be attributed today as a risk taken by

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the trustor, while the early period of digitalization (e.g., early 2000s) still saw some mercy toward the naïveté of broad mass users. What do I know and what don’t I know concerning the use of my data on these platforms? And what should I have known in case something goes wrong? Those are the decisive questions regarding trust in systems and its conditions and consequences. While the app economy still sees a trust advance by many users – a massive decline in use of services or a shift toward alternative providers has not yet happened on a broad-scale (Kolany-Raiser et al. 2018) – other collective fields such as processed grocery supply, nuclear energy technology or established political parties have experienced setbacks in public support or shifts of activity toward sustainable foods, renewable energy or alternative political movements. While all these examples deserve in-depth analyses in their own right to do justice to their idiosyncrasies, this comparison shall solely sensitize for the various degrees of attention, awareness and reflexivity46 we dedicate toward abstract systems around us. At the same time, the mere knowledge about a risk involved in using Facebook, buying a processed egg or believing what politicians say does not lead us to alter trust into distrust on a causal basis, for instance. On the contrary, we often trust idiosyncratically despite contrary information that, on a rational basis, should lead us not to trust – or even not to distrust, as in the EHEC O104:H4 case (Sumpf 2013).47 The question is: why do we care less about certain pieces of information while other incidents – even in the face of complete irrationality – cause a substantial disruption of trust? This aspect can be initially elaborated by drawing on the sociology of knowledge, which takes us back to the distinction between system and environment: how does a system (e.g., a psychic system or an organization) receive and process information to control its environment for indicators of trust and distrust? To begin with, it is helpful to distinguish between data, information and knowledge (Willke 2007). In order to achieve the status of ‘knowledge’, which makes a difference for the system and is embedded in an effective rule set programming its behavior, raw data must be classified by a system into a set of internal relevance in order to evolve into information and ultimately knowledge. This distinction helps to understand that information is not neutral and system46 47

Cf. Sect. 2.5. Scientific certificates proving vegetables to be non-poisonous did not reduce the level of distrust in consuming sprouts, tomatoes and cucumbers among European consumers in 2011.

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specific in its effects on trust-building or destruction, as not every set of data or information makes a difference for the system. Further, it clarifies that information processing always occurs internally in a system and is a construction of the system in question (cf. Willke 2007). A process of knowledge management by trustors toward the system in question is decisive for the understanding of system trust. In other words, to understand the logic of system trust means to understand what information is circulated, how, and why in order to impact on system trust attitudes. Trust researchers tend to conceptualize the act of trust as a relationship (e.g., Zucker 1986, 3; Blau 1964, 64) between a subject (the trustor) and an object of trust (the trustee), whose trustworthiness is perceived through the collection of information on that particular object: “Trust is an evidentiary phenomenon: That is, it changes with evidence in favour of, or against, further trust.” (Lewicki and Brinsfield 2012, 35)

Given the momentum of irrationality in every act of trust and the particular robustness of many system trust situations, this statement overestimates the calculative, ‘evidence-oriented’ role of information altering systemic trust attitudes. Trust scholars like Matthias Kohring and Guido Möllering, however, have provided convincing analyses that the rather calculative, rationalistic approaches to trust miss the heart of its working logic (Kohring 2004, 112ff; Möllering 2006b, 356). Comparing his own approach with game theory models of trust and rational-choice concepts, Möllering (ibid., 356) explains that “the need to find alternative explanations is evident, because rationalistic explanations regularly face the paradox that they are either explaining trust away or explaining everything but trust.” Additionally, we could inquire if rational-choice theory and the evidence-foundation of trust were true, how would that explain the many trusting activities people perform even where risks are well-known and ‘evidentiary’ information is widely available? Instead of claiming that a certain piece of information causes trust to be given or withdrawn48, system trust can be explored more fruitfully by referring to the concept of “trust thresholds” (Luhmann 1979, 73ff):

48

Such a claim tends to serve the insinuation of motives and sense-making in hindsight, as shown in Sect. 2.5.

74

Conditions and Consequences of Trust in Systems “For the distribution over time of the various attitudes (familiarity, trust, and distrust) the existence of thresholds is important. The concept of thresholds […] denotes an artificial discontinuity which levels out the area of experience before and after the threshold, and thus makes for simplification. A whole range of possible differences is thus drawn together under a single crude distinction and the rest are repressed into a sub-threshold latency. In an area of experience which is ordered by thresholds one can assume that the foundations of behavior remain constant, or at least that one can remain indifferent about any distinctions until one crosses the threshold; then a small step brings great changes.” (ibid., 73)

It has long been observed that trust is given as a “risky advance” (ibid., 24), so that “we trust when we have no reasons against it” (Hartmann 2011, 30; translation PS) – despite arguments that people try to control trust by actively searching for rational arguments that support their trust-based actions. On the basis of the threshold concept, the contrary situation in environments of dominating distrust is also conceivable: until positive information is available that potentially relieves negative experiences with systems such as politics, the electricity supply or administration efficiency, distrust will prevail. Still, it seems that the atmosphere of distrust, once the threshold is crossed, is harder to turn back to trust than vice versa (Luhmann 1979, 73f). This is particularly relevant for processes of system trust that allow for fewer opportunities of (prior, justifiable) control, as pointed out above. In conclusion, the build-up, fluidity and withdrawal of trust seem more regulated by the logic of thresholds than by “evidence” (Lewicki and Brinsfield 2012, 35): thresholds can explain irrational robustness, the granting of trust in advance, path dependencies and sudden changes in distrust, insofar as the threshold is crossed. In this way, the threshold determines how ‘evidentiary’ information actually is, thus altering our trust-associated behavior. In other words, a threshold discriminates between the influence of and indifference to information on trust. If thresholds are the empirical reference of a trusting (robust, immune against disappointment) or distrusting (being suspicious, looking for reliable facts) general attitude toward a system, then it makes sense to distinguish robust from sensitive trust thresholds. H7: Trust thresholds determine the subjectivity of information processing toward societal systems. They can be robust or sensitive, favoring a trusting (robust) or distrusting (sensitive) general attitude toward a system.

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4.2 Controlling Complexity By controlling trust in our social environment through symbolic indicators, we encounter complexity – multiple options, contradictory sources of information and abundant opportunities and choices to select from. Complexity as a social condition surrounding us has had an astonishing career in both technical and social sciences, and numerous theories and concepts have been built on it (Ropohl 2009). Niklas Luhmann has made the complexity gap between system and environment – where the world always holds more meaning and opportunities than we can realize or comprehend – the central research problem of sociological systems theory (Luhmann 1995). Trust, in his famous quote, then unfolds as “a mechanism of complexity reduction” (Luhmann 1979) – culminating in the decisive question of how that reduction is executed (Möllering 2013b, 12), not merely its functional necessity. Early on, Luhmann alluded to the accelerating role of technology in exacerbating issues of trust: “So it is not to be expected that scientific and technological development will bring events under control, substituting mastery over things for trust as a social mechanism and thus making it unnecessary. Instead, one should expect trust to be increasingly in demand as a means of enduring the complexity of the future which technology will generate.” (ibid., 15f)

Luhmann experienced the leading technology paradigm of today – information technology – only in a fledgling condition.49 However, Lee and See (2004) more recently stated that: “Because automation and computer technology are growing increasingly complex, the importance of affect and trust is likely to grow.” (Lee and See 2004, 76)

The diagnosis of growing complexity, sometimes formulated as a complaint, is a widespread statement that concerns socio-technical change in various domains such as global finance (algorithms and human expectations), energy (prosumers and SG), big data (‘internet of things’ and data-based services), or the sharing economy (shared apps and mobility patterns). In general, it is about a growing number of options and elements in the associated systems and a decreasing level of transparency. This results out of technological specialization (e.g., coding) or a mere increase of actors and business models in a certain market, especially in 49

Niklas Luhmann died in 1997.

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cases of large transformations such as energy transitions or the app-based services sector. Knowledge shortages with specific respect to the incumbent actors and the requirements of re-orientation (e.g., strategies of the German ‘big four’ power companies; classic taxi companies against Uber or Lyft) lead to a situation of feeling overwhelmed by empirical developments and – in some cases – political reform agendas, as in the German energy transition. In an interview with a business expert on SG and regulation from a leading German industry association, we50 were told: “Yes, well complexity is probably the big issue. Complexity is increasing, as is thereby the danger of mastering complexity, partly because of the increasing number of actors and [partly] because of the increasing number of – I’ll call it – technical disturbances and their causes. Well, which examples exist? The problem of the 50.2 Hertz51 is – I believe – a good example. Where these simultaneous swarm effects occur, you also see (gas) effects that can completely derail a system.” (industry association 2013, 90-91; translation PS)

So what does it mean to talk about “increasing complexity” and how does trust react to complexity? Problems of complexity can generally be approached in three ways: (1) to allude to increasing complexity or the opacity of certain technologies or businesses is either trivial, or fulfills a function of obscuring the truth or distracting from the real problems – ‘I can’t grasp what’s going on and that’s why I cannot act upon it, but I can justify my paralysis and attribute blame somewhere else’; (2) Complexity is encountered by actors who are seriously involved with finding solutions to problems that are hyper-complex; (3) Some scholars and critics argue that complexity has always been a problem and uncertainty is already at a high level today; and considering the reception limits of external influences on the human brain, the discussions about ‘complexity increase’ are overstated. I shall comment on these three arguments in the next paragraphs. First (1), the issue of ‘complexity’ is far from trivial. Analysis of the German energy transition strongly indicates that the complexity problem – at least 50

51

Interviews were conducted in the project “Systemic Risks in Energy Infrastructures” from the Helmholtz-Alliance Energy-Trans. We asked eight leading German experts from diverse branches about their understanding of the energy system and future changes. Please see Sect. 1.3 for further information. The 50.2 hertz problem (cf. FN 5) leads to a disrupted state of equilibrium between demand and supply in the grid to secure technical operability which is increasingly delegated toward new commercial and private actors in the field. These are supposed to conduct grid-supportive load management to ease the dangers of demand-supply imbalances through ‘demand-side management’, particularly under RES volatility conditions.

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from the perception of the involved actors – is ‘real’ and that more and more uncertainty is bestowed upon politics, business, academia and the general public that can only be ignored at the cost of crisis or system breakdown. The authors of an influential study from the German Academy of Technical Sciences (Appelrath et al. 2012) speak of a “complexity trap”: feeling overwhelmed by the challenges to implement the Energiewende and therefore getting stuck in the status quo, running the risk of not achieving the RES aims of 2050 set by the German government (BMWi and BMU 2011). This condition was confirmed and underscored in the aforementioned expert interviews, and can also be found in various publications (B.A.U.M. Consult 2012; ZfK 2015; acatech 2016). Moreover, during energy summits and conferences, you can feel an “atmosphere of awakening” (Khala et al. 2014), as designers of the new system try to transform complexity challenges into business opportunities. Certainly, some incumbent players could use the existence of complexity as an excuse for keeping the old system, but this is a problem relating to public relations and lobbying, rather than to the question of trust. The second aspect (2) concerns complex operational problems that the constructors of a large-scale project like the Energiewende are faced with. One big technology paradigm characterizing the German energy transition (besides installation of RES capacities such as wind and solar) is the digitalization of energy systems. Wi-Fi based communication, smart meters, app-controlled devices etc. in households and the industrial world are the envisioned future and partial present in the electricity domain (B.A.U.M. Consult 2012; BMWi 2015). With respect to this issue of ‘infrastructure convergence’ between physical and digital networks in the energy realm in order to realize a SG, a representative from a German power supply company said: “From my perspective, I believe one must set up systems in such a manner as to, at least, ensure that the principal system, i.e. the technical energy system, can function even when the subsidiary system does not happen to work. Thus an internet [connection] failure should not lead to a failure of the energy system – one must in any case ensure this separation. I don’t know how one can do this; I believe that in this case, too, we haven’t found the right solutions.” (power company 2013, 79-80; translation PS)

While there is considerable literature underscoring the apparent risks of these entangled ‘systems of systems’ and their complexity management (Kröger and Zio 2011; La Porte 2015; Roe and Schulman 2016), current empirical projects

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such as the German Energy Transition suggest that they involve new domains of hybrid knowledge that is created in the process of innovation itself. Germany experiences a lively situation of energy start-ups, radically changing the idea of how electricity supply is organized (B.A.U.M. Consult 2012; acatech 2016). Incumbent actors in the German energy system, like the power company representative quoted above, are supposed to develop new business models and technologies for SGs which integrate knowledge from engineering, computer science, economics and, increasingly, social sciences.52 The latter is important as the change induced by the new technologies mainly concerns altered behavioral patterns among consumers and corporate actors, due to new business models, tariff structures, online platforms, energy entrepreneurship among decentralized generators etc. In order to cope with the cybersecurity issues and data privacy of the increasing integration of ICT in the energy realm, the German agency BSI53 – a federal agency for security in information technology – is a responsible actor providing operative solutions. For smart electricity meters in private homes and industrial complexes, they recently released the “BSI protection profile” that is the foundation for the scheduled smart meter rollout in Germany beginning in 2017 (BMWi 2015). Regarding the BSI and its work, one expert said: “But even that is incredibly complex; I mean who can still understand, what the BSI is doing there.” (industry association 2013, 93-94; translation PS)

Concerning the third and final aspect (3), increasing complexity does indeed depend on a perception of that increase. From a constructivist perspective, complexity is not increasing if we are not confronted with it in terms of our attention, increased options or time capacities. In a study on terms of use with internet customers54, McDonald and Cranor (2008) found that as early as 2008, when the smartphone was barely introduced, an average user would need 40 minutes each day to read the online privacy statements of the services they used. This means that even in cases where “absolute complexity” in terms of increasing numbers 52

53 54

The current profile for recruiters in the energy sector is to look for candidates between engineering, business and computer science – the “energy economics computer scientist” as a German start up representative at a conference in Karlsruhe coined it (Khala et al. 2014). From a more systemic view, social sciences gain relevance as behavior beyond the power outlet has stronger repercussion loops on the future energy system. ‘Bundesamt für Sicherheit in der Informationstechnik’ First introduced in Sect. 2.2.

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of system elements and possible relations between them occurs (La Porte 2015), its actual perception shapes our trust-associated actions toward it. Concerning the example of energy transition that has been mainly used as an illustration so far, it also makes a difference if average users are targeted as trustors faced with complexity, or industrial actors with responsibility in the grid. The latter often cannot afford to absorb too much complexity but rather deal with it productively (see Sect. 4.3), while average customers are frequently held back from experiencing too much complexity (Khala et al. 2014). If the industry association and power company experts quoted above are correct, complexity in the energy domain is rising in absolute terms, both in respect of the mere number of elements in the system (actors, technologies, data, infrastructures etc.), and in respect of possible new relations and dependencies (as the ICT integration demonstrates). This logical, numerical increase in system elements (quantitative) and their possibilities of relating to each other (qualitative) leads to more possible operations within the system. Such a development is overwhelmingly documented in many publications and visions of future energy – making it a probable reality for corporate and increasingly private actors in the present and future energy economy. Still, the nature of complexity is not sufficiently grasped only by an approximative logic, but rather by means of amplification and acceleration, which can lead to increasing perceptions of complexity. Todd R. La Porte describes the current condition of SG development in Germany as a state of “amplified complexity” (La Porte 2018) since actors are forced to follow external policy programs that enable the energy transition in the first place. Acceleration of system processes can be illustrated by desired “realtime control” (Amin and Giacomoni 2012) of the future system through ICT in control rooms, for instance. This expected acceleration of interaction between grid actors through Wi-Fi based communications, Smart Meter Gateways, grid sensors, etc. is at the heart of what the energy system transformation is about and what trust relates to most sensitively. If system complexity is amplified and its interactions accelerated through integration with other systems such as ICT, questions of knowledge and accelerated decision-making arise. With respect to this problem, Roe and Schulman have asked: “What happens when knowledge requirements of an infrastructure's domain of competence are so intensive and demanding that its control operators can't realistically know the other infrastructures they are connected to with the same depth as they know their own systems? Does this mean that there must be a control room of

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Toward the end of their book (ibid., 156), they reach the conclusion: “That means designers, be they engineers, policy makers, or senior executives, must trust and facilitate the skills of control room operators to add the necessary resilience to the engineered foundations of high reliability. [...] this is in no way assured when top-level officials introduce major technological innovations into the realtime operations of infrastructure control rooms.” (emphasis PS)

While this statement primarily relates to organizational trust within the energy grid, consumer trust is affected by this development, as Chap. 6 will show. From a trust perspective, these expected developments toward amplification, acceleration and related knowledge diversification lead to an increase of the system’s reliance on trust instead of control. This is a functional consequence of applying the trust concept, since trusting means a renunciation of control – whether chosen or forced. This might be a condition of the transition phase of the Energiewende and the means of controlling and monitoring the new, more complex system are yet to be developed. Still, it is safe to say that in the displayed environment of complexity absorption, the probability of trust as a more sensitive currency in future energy systems is likely. Overall, even an absolute increase in complexity only has consequences for trust insofar as it is perceived and referred to by trustors. In the energy example, different levels of trustors – individuals and organizations – have different system knowledge and access. While businesses exploit trust as a driving force in the transition phase, individual consumers still have time to prepare for their role as ‘prosumers’. Empirical investigation would have to figure out the degree of complexity awareness among trustors (Chap. 6). Sect. 4.3 tries to locate that awareness more precisely. Meanwhile, the simple hypothesis we can deduce from this complex section is:

H8: Complexity and its role for trust depend on trustors’ perceptions.

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4.3 Knowledge and Non-Knowledge Sect. 4.2 has demonstrated the linkage between complexity and knowledge: high knowledge requirements in system operation can amplify complexity, accelerate interactions and thus increase the relevance of trust to keep up action capacity in such an environment. Moreover, this also shows us the complexity of trust itself. Trust is a multi-faceted phenomenon: It rests on both knowledge and nonknowledge (Simmel 1978; Luhmann 1979; Möllering 2001). If I find out that my energy provider raised prices contrary to previous promises, then I might cancel the contract, thereby tolerating the ignorance of not knowing if my new supplier will live up to my expectations either. Accordingly – as was argued in Sect. 4.1 – trust partially “is an evidentiary phenomenon: that is, it changes with evidence in favour of, or against, further trust” (Lewicki and Brinsfield 2012, 35). The second requirement to complete an act of trust is the suspension of nonknowledge (Möllering 2006a). This rather mysterious component and “missing element” of trust (ibid., 105ff) has attracted much attention and has been declared decisive for its idiosyncratic logic by many influential researchers in different semantic variations (e.g., Simmel 1978; Luhmann 1979; Giddens 1990; Lewis and Weigert 1985). Without this reference to non-knowledge (risk, uncertainty) and its absorption through a ‘leap of faith’ (Simmel 1978; cf. Möllering 2001), trust would not be the dynamic societal resource that has triggered so much scholarly interest. At the heart of trust lies this relationship between trust and non-knowledge, i.e., the process beyond the weak inductive knowledge that it partly rests on. As a consequence, the concept of trust should be centered around this relationship. First and foremost, the relationship reveals (a) the working logic of trust and (b) as a result trust’s consequences may be emphasized, i.e., its domains of major impact. At the same time, doing justice to prior sections, this emphasis gives way to a sociological lens on trusting systems in particular. Torsten Strulik (2004; 2007; 2011) alluded to the sensitive reaction of trust toward non-knowledge and ignorance55 in global finance: the constantly changing, highly reflexive levels of ignorance involved in coping with the permanent 55

Non-knowledge and ignorance are used interchangeably here. The connotation of ignorance is rather negative – in the sense of knowledge potentially knowable but yet denied – so it should be noted that the concept presented here rests on the idea of irreducible non-knowledge as a permanent factor of social life.

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prediction of the future – stock market knowledge ages fast – accelerates the catalysis of trust as an action-enabling mechanism in this environment. The halflife of knowledge in this sector is presumably lower than the weather forecast: knowledge about the value of companies, the emergence and decline of technologies and the expectations shaping this process are under constant review. Crucially, financial system knowledge evolved from an internally flowing resource that was picked up as a side effect of working in that field, into an explicit trading currency that is reversible, contested and highly decisive for spheres of influence in the sector (Strulik 2004; Willke 2007). Apart from these influential effects of (however volatile or fluctuating) knowledge in terms of business development, investment flows, regulatory conditions and so forth, Strulik shows that ultimately non-knowledge caused trust to become a driving force in the financial sector (ibid., 2004). High proportions of non-knowledge – in the sense of reference to an open future holding surprises for the actors involved – in Strulik’s view led to ignorance being “exploited productively” in global finance (Strulik 2004, 179; translation PS). Under the accelerated conditions of rapidly changing expectations in spite of high pressure to act, trust can be understood as a mechanism that helps individuals and organizations to gain action capacity against the background of (irreducible) knowledge shortages56, but exploits these same uncertainties productively, by transforming them into permanent drivers of decision-making (ibid., 73ff). In this way, trust can be seen as a major creative force in enabling societal transitions and concomitant business models such as credit default swaps or e-commerce: “Trust lets ignorance become productive” (Strulik 2011, 244; translation PS). Another field with a similar environment for trust dynamics is the rapidly growing market of big data applications (Kolany-Raiser et al. 2018). It is striking that the development toward a more complex state of the German energy system (Sect. 4.2) is comparable to this productive “exploitation of ignorance” (Strulik 2007, 244; Strulik 2004, 65). In the precarious and highly innovative field of German SG development, actors are uncertain about twofold market structures (prosumers both buy and sell electricity), interactive technologies (smart meters, control applications) and fledgling supervisory systems. In 56

See Möllering’s definition (2006b, 356) for a similar depiction of the irreducibility of ignorance as a trigger for trust.

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this environment of permanently changing knowledge, in which science, politics, businesses and consumers are implicated, the only current constant is change. Trust is developing a similar role as an enabling mechanism for increased decision-making dynamics, countering the actors’ ignorance of highly uncertain yet positively valued futures in terms of realizing economic, political and greater societal opportunities. Under the urgent premise to act, trust helps exploit the rationalities of the system in terms of business models, technology invention and realization of a transformation project like the Energiewende. For more precision about the role of non-knowledge in evoking trust and its possible contributions to trust research, it is helpful to look at some established findings of the sociology of non-knowledge (e.g., Japp 2000). According to this field57, both the share and type of non-knowledge involved in decisionmaking generate differences in the requirements of trust and its consequences. The share of non-knowledge relates to the balance between knowledge and nonknowledge: it makes a difference if I primarily rely on (supposedly) certain knowledge and feel like little stands between myself and complete certainty (so that no trust would be necessary). Conversely, as in the examples of finance, energy and big data mentioned above, the involvement of trust is intensified if references to non-knowledge dominate the process. However, instead of causing insecurity or paralysis for individuals, Strulik argued that the domination of ignorance leads instead to a productive utilization that is enabled by trust (ibid., 2004, 58ff; 73ff). The second differentiation concerns the type of non-knowledge. On the basis of classic research in the field, Japp (2000) distinguishes references to either specific or unspecific non-knowledge by decision-makers: knowing what we do not know, or not knowing what we do not know.58 Moreover, he has linked this 57

58

It is acknowledged that there are multiple schools and scholars in the sociology of (non-) knowledge which do not necessarily constitute a homogenous field. Japp’s contribution draws, among others, on Funtowicz and Ravetz (1992), Merton (1987), and Luhmann (e.g., 2005), eventually stressing a systems theory view on (non-) knowledge. Others might emphasize phenomenological (e.g., Schutz 1967), or risk society (e.g., Beck 2009) approaches, for instance. Donald Rumsfeld, at a news briefing of the US Department of Defense in 2002, famously said: “As we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don’t know we don’t know.” http://www.theatlantic.com/politics/archive/2014/03/rumsfelds-knowns-and-unknowns-theintellectual-history-of-a-quip/359719/ Accessed April 25th, 2017.

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distinction to perceptions of risk and catastrophe (Rescher 1983): if reference to known unknowns is predominant, then risk calculation is executed, i.e., the establishment of trust is possible. In this way, known unknowns are also part of what actors consider ‘knowledge’ for the incitement of trust. Yet if unknown unknowns dominate reasoning in decision-making – through addressing possible catastrophes59, for instance – refusal of risk-taking is likely (Japp 2000), i.e., there is a turn toward distrust (Sumpf 2013). Quite intriguingly, these patterns demonstrate linkages between the different forms of (non-) knowledge and trust, and help to specify the classic Simmelian diagnosis that Möllering recovered: trust is a phenomenon that lies between knowledge and non-knowledge (Möllering 2001) – how exactly this works can be narrowed down with the help of the aforementioned categories. In this connection, the reason why specific non-knowledge is decisive for the incitement of trust is precisely because it is in the middle of knowledge and non-knowledge (Simmel 1978; Luhmann 1979; Giddens 1990; Möllering 2001): ‘knowing’ what I do not know, or knowing what can go wrong. Not coincidentally, this kind of specific non-knowledge can be described as the risk component in trust: a perception of specific unknowns in the present which triggers contemplation about their possible materialization in the future. For energy customers, these risks can be power outages or price increases of electricity, for instance. One knows what could happen, yet does not know if, how, or when. Accordingly, trustors in the field of finance know that they run the risk of decreasing stock value and loss of money – just not its exact materialization. In order to come to trust and still invest, they have the choice of either bridging the risk through expectations of favorable outcomes (trust), or referencing the unknown, the unspecific non-knowledge involved. And that can lead to a refusal of risk-taking, turning into distrust – sorry, I can’t invest. Consequently, it is not surprising that the correlation between trust and specified ignorance is delicate, as specified non-knowledge represents what we consider to be perceived risk in the incitement or refusal of trust. The differentiation and emphasis of non-knowledge as a key driver of trust and distrust is 59

Nicholas Rescher (1983, 67), in this connection, talked about the “Unacceptable Risk Principle”: “A disparity of risk exists when the maximum possible loss associated with one of the choice-alternatives is massively, nay, ‘incomparably’ greater than that associated with others. In such cases we regard these comparatively catastrophic alternatives as automatically ineligible […].”

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additionally supported by the fact that trust has been described a mechanism of complexity reduction (Luhmann 1979): it shortens chains of information and is more prone to ignorance than to processes of mere knowledge accumulation. In this way, the study of non-knowledge embodies what major scholars have said about trust: trusting instead of knowing, not through knowing (Kohring 2001; Möllering 2013a). The situation of reaching a state of trust through specifying the unknown (i.e., perceiving risk) yet holds shares of both knowledge and nonknowledge, as the original Simmelian idea (Simmel 1978; cf. Möllering 2001) insinuates. While trustors who still commit a ‘leap of faith’ (ibid.) against known unknowns thus overdraw the available information (Luhmann 1979, 32) in reaching a state of trust, those who distrust can be expected to emphasize unknowns over available information or reject that information altogether. Accordingly, we can say that the continuum of trust and distrust is heavily influenced by the type and share of (non-)knowledge trustors refer to: if reference to knowledge is predominant, then probabilities are high that certainty will prevail against the dominance of risk perceptions. If the latter are referenced frequently and doubts articulated, the chances are that risks are either known and absorbed (to be interpreted as trust) or known but rejected (to be interpreted as distrust). This leaves us with three general narratives of knowledge treatment for trustbuilding, or refusal, where references to either knowns or unknowns prevail: (1) certainty (dominance of knowledge, low share or absence of non-knowledge); (2) risk calculation (specific non-knowledge involved but bridged); (3) refusal of risk-taking (dominance of specific or unspecific non-knowledge). These types could then be plausibly interpreted as different forms of trust (1 and 2) and distrust (3), as shown in Figure 4-1. It is apparent that these are ideal categories that do not necessarily appear in such clarity empirically. Their major purpose is to enrich the debate with more precision on the knowledge/non-knowledge mixes that are decisive for trust/distrust-building, yet have not been systematically analyzed so far (Lewicki and Brinsfield 2012). Under recognition of the special role of non-knowledge, Chap. 6 will try to find first clues on what patterns of trust attitudes and forms of knowledge can be uncovered in the energy sector. For example, it is conceivable that there are also several facets of distrust: distrust could be provoked by attributing dangers, i.e., circumstances trustors perceive to be outside their sphere of influence (Luhmann 2005).

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Possible Units of Measurement

Outcome

Three Forms of Referencing Knowledge

Trust Certainty

Low/High Weak/Strong There/Not there

Risk Calculation Distrust

Refusal of Risk-Taking

Figure 4-1. Narratives of Trust and Distrust

Additionally, Figure 4-1 places ‘risk calculation’ between trust and distrust, alluding to the fact that both trust and distrust could be possible at the same time in cases where trustors accept certain risks (trust) but reject others (distrust). Accordingly, Lewicki and his co-authors (Lewicki et al. 1998; Lewicki and Brinsfield 2012) argue that trust has usually been researched as a rather onedimensional concept (trust or distrust), whereas multiple simultaneous attitudes could be possible, such as trusting someone in certain respects while distrusting them in others. Hence, they suggest: “There is no argument that ‘high trust’ exists at one end of the continuum, but it is not clear what is at the other end. Is it low trust? No trust? Or even distrust? It is not clear what it is, but it is not necessarily the ‘opposite’ of high trust” (Lewicki and Brinsfield 2012, 33f). Overall, the connections of knowledge references in coming to attitudes of (stronger or weaker) forms of trust and distrust is still a puzzle that trust research has to come to terms with (Guo et al. 2015), and this accounts for the ‘possible units of measurement’ column in Figure 4-1. The framework developed here is an attempt to achieve some hints on the empirical narratives to be detected in the case study on trust in the energy system. In line with the theoretical elaborations of other trust researchers, the result might encompass mixed forms of trust and distrust that are not yet foreseeable, rather than merely the three ideal types presented above.

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Concluding from this section, the analysis of trust is intrinsically an analysis of knowledge and non-knowledge. However, as a consequence of the constructivist nature of trust, it cannot be about real knowledge or lack thereof, but about references to the different types of knowledge or ignorance presented above. Referencing indicates the contingency in relying on either knowledge or non-knowledge in the actual justification of decision-making, and says nothing about whether referenced risks or certainties of trustors are true or false (Japp 2000). Setting a marker in this way alludes to strategies for absorbing uncertainty which make sense primarily to trustors, and within the system, but not necessarily to external observers. As a consequence, it seems that for trust-building, environmental complexity is translated in terms of either predominantly referring to knowledge (certainty), specific non-knowledge (risk calculation), or unspecific non-knowledge (refusal of risk-taking). This means that for empirical scrutiny of trust, there are three major narratives to expect that may lead to different behavioral modes and consequences, such as weaker and stronger forms of trust and distrust. While this allows us to further complete the hypothesis below, Sect. 4.4 will elaborate in more detail on the decisive risk component for trust.

H9: Complexity and its role for trust depend on perception. Perception, in turn, can vary according to narratives of knowledge or specific and unspecific non-knowledge. The dominance or mix of those narratives can be typically linked to forms of trust and distrust among trustors.

4.4 Trust and Risk The coupling of trust and risk has been most prominently and thoroughly described by Niklas Luhmann (1979) and can be declared a “central thought” in the study of trust (Kohring 2004, 89; translation PS). In his trust sociology, Luhmann describes trust as a “risky investment” (ibid., 24), indicating its foundation on at least partial ignorance and therefore the ever threatening potential for disappointment. In a subsequent article (1988), he specified the combination of both phenomena as being linked by decision-making: trust involves a decision

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between known alternatives that can be attributed to the trustor. Therefore trust provokes taking a risk, because the decision operates on a contingent basis and can be regretted ex post (ibid.). Hence wherever trust is involved, it is preceded by the perception of risk (Kohring 2004, 89ff). Sect. 4.3 has demonstrated that risk is a materialization of specified non-knowledge – knowing what I do not know. As such, perceived risk needs to be bridged and absorbed in order to equip trustors with action capacity and lets them experience the illusion of trust: perceiving a risk, i.e., something specific that might go wrong, yet acting upon the expectation that it is not going to materialize. To equip this idea with some empirical underpinnings, energy consumers’ current and future risks (ordered by severity) are shown in Table 4-1. Table 4-1. Current and Future (Potential) Risks in the Energy Sector (Current) ‘Dumb’ Grid

(Future) Smart Grid

1. Price increases

1. RES-induced: power outages/bottlenecks

2. Lack of sustainability

2. Insufficient return on investment (prosumer)

3. Power outages

3. ICT-induced: hacking, data abuse, espionage, loss of control

4. Terrorist attacks

4. Sustainability paradoxes (flat rate versus efficiency)60

5. Nuclear Power

5. (Cyber) terrorist attacks

These common risks have been extracted from expert interviews, academic literature and conference attendances, in accordance with the publications mentioned in Sect. 4.2. Table 4-1 displays the most probable risks looming in the social environment of energy customers currently and – with limited but mostly justified certainty – in the future. If the idea of system trust is supposed to make sense, then it needs to react to the perception of consumer risks – if we target this group for an analysis of trust in the energy system, for instance. 60

Experts in the energy field are partly worried that potential future business models such as ‘electricity flatrates’ could torpedo the sustainability goals of improved energy efficiency that go along with energy transitions.

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In order to come to trust the energy system, trustors would refer to specific unknowns, like the fear of power outages or price increases, but yet provide reasons why they would view their materialization as unlikely – they bridge the risk through referring to an “equivalent-certainty” (Luhmann 1979, 50) such as engineering skills (to prevent power outages), or market competition (to prevent price increases). Consumers who would not be able to bridge or absorb that risk could for example buy a home generator, to circumvent power outage situations or become more independent from markets, controlling their own electricity generation to a greater degree as well as making it cheaper. These behavioral consequences of not being able to bridge a perceived risk could be described as distrust. To uncover these sorts of ‘narratives’ related to the categories of knowledge and non-knowledge presented in Sect. 4.3 is a major task for empirical research, and will guide the case study in Chap. 6. Overall, it is apparent that the perception and respective treatment of risk associated with a trust object is a decisive regulator of different trust attitudes, which need to be taken into sufficient account. There is still however another risk component involved in issues of trust. On the basis of Luhmann’s initial idea of trust as a “risky investment” (Luhmann 1979, 24), Strulik (2007; 2011) has demonstrated that trust is not only a risk for the involved trustor, but also systematically generates risk for the environment the trustor is involved with, and ultimately for society. This is counter-intuitive, insofar as the vast majority of trust researchers declare trust to be a positively valued resource. According to Fukuyama, for instance, trust is: “the expectation that arises within a community of regular, honest, and cooperative behavior, based on commonly shared norms, on the part of other members of that community.” (Fukuyama 1995, 26; emphasis PS)61

In an extensive review of trust research, Barbara Misztal (1996, Chap. 3) described the main functions of trust to circle around “integrative” and “lubrifying” functions in support of social cohesion. At different levels of society, according to Misztal, trust leads to more predictability of social interaction, generates a sense of community and increases the probability of human cooperation (ibid.). Bruce Schneier (2012) transfers this idea to the societal level in his book,

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Definition first referred to in Sect. 2.4.

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“Liars and Outliers: Enabling the Trust that Society Needs to Thrive”, and concludes that: “trust in society works like oxygen in the atmosphere. The more customers trust merchants, the easier commerce is. The more drivers trust other drivers, the smoother traffic flows. […] The more trust is in the air, the healthier society is and the more it can thrive. Conversely, the less trust is in the air, the sicker society is and the more it has to contract. And if the amount of trust gets too low, society withers and dies.” (ibid., 6)

Emphasizing the positive functions of trust, Schneier thus adheres to ‘deficit models’ of trust (Strulik 2011), which typically result in academic or public urges to overcome states of a lack of trust as deficits of trust are frequently deemed undesirable by researchers and/or policymakers. Misztal, for example, refers to possible crises of democracy based on a trust deficit (Misztal 2001). These authors, in representation of many trust researchers (cf. Strulik 2011), stress trust’s role as the ‘grease’ of society: an inherently positive societal phenomenon whose absence may have disastrous consequences, and whose dissemination should be supported in any way possible (ibid., 239). Apart from this cohesive aspect of trust, which undoubtedly fulfills crucial societal functions, its “dark side” (Skinner et al. 2013) has been rather neglected. Having trust is usually considered as better than a lack of trust, or distrust (cf. Lewicki et al. 1998) – crises of trust are, in Strulik’s words, declared to be “diseases that need to be cured by all means” (Strulik 2011, 239; translation PS). Still, it seems that scholars are picking up the trace that trust may be a “poisoned chalice” (Skinner et al. 2013) as well as a beneficial social resource. Torsten Strulik (2011) exemplifies his approach of growing societal risk generation through trust by referring to the global financial system: here, the often onesided demand for trust in opaque financial derivates and trust in intermediaries like rating agencies has contributed to a dysfunctional state of overdrawn trust in the financial system. This situation has muted critical voices and led to insufficient (partial) distrust, meaning that distrust could not remedy the situation (ibid.). In this way, the latest global financial crises have been caused partly by inflated trust investment through overconfidence (Willke et al. 2013, 68), i.e., associated market actors who overstretched the exploitation of future market ignorance and drove the high demand for trust as an ‘action enhancer’. In this way, trust can evolve into a facilitator of risk, and into blind trust that raises risk dynamics through unreflecting demand for and operation of trust actions (Strulik

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2011). This results in an unquestionning way of dealing with trust conditions and consequences, in order to give way to the unfolding of economic innovation potential. By linking trust to risk, accelerated decision-making, and ignorance in a knowledge-intensive branch such as finance, Strulik alludes to the unique functions and consequences of their interplay. The systematic attachment between trust and risk – in contrast to common trust research which highlights trust’s integrative effects – raises awareness both for viewing trust as a source of risk, and for the beneficial function of distrust in social systems (cf. Luhmann 1979, 71ff). Accordingly, distrust can also serve an important function of learning about and remaining mindful toward one’s environment and sudden societal changes. This is a crucial prerequisite for a system’s reproduction, particularly in times of transformation or crisis. Sociologically, distrust is not the opposite of trust but its functional equivalent, reducing complexity into a narrow action corridor by making a few actions probable and certain others highly unlikely (Luhmann 1979, 71ff). In other words, distrust provokes counterstrategies to circumvent the distrusted situation or object by means of a search for alternatives, boycott, aspiration for autonomy, etc. In comparison to a lack of trust, which can prevent people from executing any actions, a distrusting attitude typically results in a mobilization of action potential, just like trust. In this way, distrust can work to counter developments of trust/distrust equilibria that tip toward the trust side and create ‘blind trust’, by promoting affirmative decisionmaking in areas with a massive need of “ignorance exploitation” (Strulik 2007, 244). In other words, the positive functions of trust – cooperation, fluidity, action capacity – are created through disguising what could go wrong, but this potential risk is not mitigated but rather downplayed, and still looms in the background. Accordingly, the higher the risk, i.e., the specific non-knowledge bridged by trust, the higher the potentials for disappointment can become, with the resulting societal effects. This has far-reaching consequences for the conceptualization and research of system trust. If risk is inevitably and irreducibly incorporated into every act of trust – not only preceding but systematically generating it – then this alludes to the very logic of trust that should be at the heart of every definition, analysis and description of the phenomenon, especially from a sociological perspective. It seems that the impact of trust in terms of risk generation

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is what is most significant for society, in addition to its antecedents as preceding yet mostly individual phenomena. Strulik goes as far as claiming that the degree of trust that is given in knowledge-intensive, innovative branches such as finance determines its vulnerability for crises and system breakdowns (Strulik 2011, 247). Two major implications follow from this: 

Considering the high-risk setting, the innovativeness and dynamics of the rapidly developing SG environment, and changing knowledge, trust takes a very special form in the current and future German energy sector, as opposed to more general (e.g., basic trust in fellow citizens), or more specific forms of trust (e.g., trust in friends or family members). This ‘special form’ is characterized by an accelerated demand for, as well as consequences of trust, and this should be reflected in an impact-driven definition incorporating this logic. Accordingly, on the basis of Strulik’s work and the increasing awareness in the trust research community (Lewicki and Brinsfield 2012; Skinner et al. 2013), one should focus on trust processes in transformational sectors like energy, finance, digital services, mass media and other systems undergoing radical change. It seems that in these sectors, issues of trust unfold a peculiar dynamic that is most congruent with its inner working logic: the creation of societal risk through accelerated demand and the operation of trust as a driver of the envisioned societal changes. At the same time, this serves a particular view of the issue of trust in systems, as it stresses the societal consequences of trust in the systemic contexts of society: looking at branches where trust matters most.



To capture these abstract conditions of trust and translate them into a research framework, it makes sense to refer back to the relationship of trust and distrust. While trust generates risk by disguising its potential materialization, distrust counters this development by rejecting risk-taking, and directs action onto alternative paths. From a societal perspective, it seems the dosage of trust and distrust is key toward keeping an equilibrium between both resources in order to secure the functionality of social systems (Luhmann 1979, 71ff). To tackle this challenge, Lee and See (2004, 55), for instance, suggest attempting a state of “calibrated trust” that would mediate between “overtrust” and distrust, in order to secure an appropriate level of trust in automated systems like algorithms and software agents. It is clear that there can never be an ‘appropriate level’ of trust in an objective way, since trust is by its nature always unjustified (Luhmann 1979, 78f). On the other hand, it is equally true that both permanent trust as well as permanent distrust are dysfunctional for the operation of social systems (ibid., 71ff).

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This functional argument can never claim substantial, prospective knowledge about the justification of trust or distrust – but what it can do is give general advice about monitoring the balance between trust and distrust in order to improve the chances of collective operability and functionality. In other words, keeping track of the balance might help us to mitigate the risks of both ‘overtrust’ (blind trust, overconfidence62 etc.), and skepticism or the insinuation of harmful motives (negative outcomes of distrust), with a special focus on trust in systemic societal contexts. To break this down on an empirical micro-level and give advice on how to monitor trust and distrust is, following everything that has been said so far, a highly delicate undertaking that needs to be executed with exactness, modesty and patience. Scholars in the trust community have begun to enhance the trust concept with complementary issues on distrust and their mutual relationship. In a recent review, for example, Fabrice Lumineau (2014, 6) has captured the positive and negative outcomes of both trust and distrust in a comprehensive way. Furthermore, Guo, Lumineau and Lewicki (2015) have presented three models of possible trust/distrust relations: model one views trust and distrust as “two ends of the same conceptual spectrum with overlapping range” (ibid., 65). In other words, a state of trust can – but does not necessarily have to – go along with a state of distrust toward the same trustee. An example would be a differentiated trust assessment by trustors involving trust in one facet of the trustee (e.g., parliament as one facet of the political system), but distrust in another (like an individual politician). A second model considers an “in-between range” (ibid., 65) in that same spectrum: trust and distrust can outbalance each other through higher and lower proportions (e.g., high trust in parliament only allows for lower amounts of distrust in related facets of the political system), yet remain substitutable as functional equivalents for the same social problems. Finally, a third model is concerned with trust and distrust being “separate concepts on different dimensions” (ibid., 65). While this differentiation shows that not all questions concerning the relation of trust and distrust have been answered yet (ibid., 62), it is an important 62

Willke et al. (2013, 68ff) speak of “overconfidence” in the governance capabilities of financial system regulation, for example. My impression is that overtrust, blind trust and overconfidence all allude to the same basic mechanism, which tends to underestimate risks, establishes favorable expectations and reacts to non-knowledge productively rather than cautiously.

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step toward tackling the peculiar interplay between trust and distrust, besides researching the numerous phenomena other than trust, or merely concluding high or low trust in society at a certain point in time (see for a critical review: Möllering 2013b). However, the heuristic presented by Guo, Lumineau and Lewicki (2015) is tailored toward trust and distrust in personal relationships. Typically, this takes place within teams and departments in organizations (ibid.), but differs from researching trust in systems regarding interaction density and possible levels of trust attribution. In this light, it is not clear right away which of the models best suits an analysis of trust in systems. Considering the prior analysis of trust and distrust, it seems apt to follow model two for several reasons: 

First, it captures the logic of trust and distrust as functional equivalents, as described by Luhmann and Strulik in this section. For the same reason, model three can be discarded.



Second, it connects neatly to the idea of robust or sensitive trust thresholds (Sect. 4.2), which is helpful in identifying general attitudes of trust or distrust toward a system.



Third, however promising, it would overstretch the framework of this book to look for simultaneous attitudes of trust and distrust toward differentiated traits of the same system (model one). An approach like this would require pre-existing work in a field such as energy in order to have prior assumptions, possible contradictions and differentiations. Still, the final conclusions after the empirical analysis in Chap. 6 will consider possible interplays if they are uncovered.

At this point, the only relevant deduction to be made from this recent trust/distrust research is that there is a continuum, which provides both positive and negative possible outcomes of both trust and distrust. An analysis of system trust therefore, must not only be interested in how that trust or distrust is established or dismissed, but also what the possible outcomes of such social patterns are on a broader scale. This is particularly valid considering the risk component inherent in all trust, and which makes potential ‘blind trust’ or ‘overconfidence’ in systems a serious societal threat, as the financial system breakdown in 2008 has shown (Strulik 2011; Willke et al. 2013). In other words, it is urgent to supply a strategy which helps interpret results concerning trust and distrust in systems in

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a meaningful way, instead of merely describing how trust is achieved or abandoned. In this book, therefore, the equilibrium idea as a functional state of trust and distrust in a societal system (cf. model two above) serves as such a framing. It serves as a first guide on how to interpret results from system trust research when we consider (weak or strong) trust and distrust in the energy system, for instance. What does it mean when people trust the energy system? Is it a good thing when the public follows the Energiewende in Germany and we achieve broad “public acceptance” (Kasperson and Ram 2013)? Are distrusting citizens the ‘bad guys’ who prevent progress, or do they help detect flaws in the emerging system? More research on the relationship between trust and distrust, as well as reliable deductions for empirical research, are necessary (Guo et al. 2015, 62), but the analysis presented here highlights the relationship’s importance for system trust research in particular. A final hypothesis on Sect. 4.4 can be formulated as follows:

H10: An equilibrium of trust/distrust is an appropriate interpretation scheme for studying system trust. The normativity of systemic trust research is therefore the collective functionality of systems rather than justified trust or distrust.

Chap. 4, in summary, has shown how trustors’ system-internal reasoning is programmed by control of environmental complexity related to risk and nonknowledge. In the end, these factors lead to the establishment of expectations that represent materialized trust or distrust. These expectations are now the center of attention in Chap. 5.

5 Toward an ‘Architecture of Trust’

The prior general elaborations influence ideas of how system trust can and should be approached theoretically and researched empirically. Firstly, the previous sections have revealed several insights into systems building and research: 

Systems as target of trust are usually depicted as open systems involving heterogeneous elements such as socio-technical systems.



Trust is constructed inside self-referential systems from a modern systems theory view.



Ergo both subject and object of trust can be regarded as a system.

As a consequence, the question of the external constitution of a system is transformed into one of how a trustor as psychic or social system (e.g., person, organization) portrays that system internally. In other words, “how do actors create the fiction that enables them to trust?” (Möllering 2006a, 112). Secondly, as a consequence of the underlying requirements of trust attribution and systems conceptions, a trust perspective inspired by constructivist notions helps focus on the fluidity of trust: during the supposed ‘decision situation’ of trust before an action, or facing disappointment in the light of having to constantly (re-) construct one’s view. Trust’s fluidity highlights the importance of (possibly changing) attributions of trust and distrust toward the multiple objects available to address – in every other moment in time. This occurs to justify trust in the first place (and to possibly reinforce it), and again after trust has been disappointed, e.g., in trust crises. The ‘Architecture of Trust’ (AoT) contains both objects of reference for system trust (persons, roles, programs, values) as well as rules of attribution following disappointments and renewals/erosions of trust or redirections toward distrust. It provides a background reality of what environmental addressees trust and distrust can be and are likely to be directed at. It is a framework broad © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_5

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enough to capture the complexities of trusting systems, yet specific enough to account for concrete empirical references in a field like energy supply, which serves as the main example in this section. In this way, the ‘Architecture of Trust’ relates to two major elements of a trust relationship that involve processes of attribution and mutually reinforce each other: 

Reassurance of trust or distrust through attributing references in the AoT as ‘equivalent-certainties’.



Sanctioning of disappointed trust and rules of attributing responsibility.

5.1 Trust and Expectations Much of the sense-making in system trust stands and falls with the reference of trust in a system. Besides its basic condition as a risky act of trust in a system based on decision-making (see Sect. 2), the question of what reference trust is directed at is key to understanding and analyzing such trust. The unraveling of the electricity supply with its concrete components and non-components makes it easier to grasp what ‘the system’ is associated with and where trust could be placed. Staying with the energy example, what exactly are available references – the grid, pylons, plants, markets, supervision agencies – and how are they established/dismissed? Are trust expectations directed at ‘the system’ as a whole, the interplay of its elements, or single components of the system like engineers or certain technologies? What about communication capacity, addressability and representations of the system? The analysis of expectations as a distinct category of studying (system) trust is this book’s major contribution to the wider debate and will be unfolded below. Guido Möllering has described the process of suspension as the “missing element” (Möllering 2006a, 105ff) in trust research that is conducive for trust’s analysis and its central working logic (cf. Sect. 2.3). Suspension transforms perceived risk into imagined certainty: “[…] suspending irreducible social vulnerability and uncertainty as if they were favourably resolved” (Möllering 2006b, 356; emphasis PS). While this remains true for system trust as well, it seems that another one of these “missing elements” requires a closer look at trust expectations. Möllering (2006b, 371) has alluded to the fact that Gianfranco Poggi, in

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his introductory remarks to “Trust and Power” (Luhmann 1979), mentions the Hegelian notion of ‘Aufhebung’ (German for suspension) as a corresponding idea of Luhmann’s famous concept of ‘complexity reduction’. Luhmann himself (ibid., 79), however, refers to suspension in describing trust in combination with expectation building: “It is precisely in the realm of the stabilization of expectations, which is where the problem of trust belongs […]. […] Trust is then nothing other than a type of systeminternal ‘suspension’ (Aufhebung) of this kind of contradiction in expectation.” (Luhmann 1979, 79)

Contradictory expectations, in Luhmann’s understanding, concern the simultaneous expectations of both affirmation and disappointment that accompany the state of trust. These rather insecure, volatile expectations going along with trust are at the heart of what trustors have to cope with: they expect both continuity (things are going to ‘turn out well’), while remembering the risky terrain they are entering (things can go wrong). The application of “system-internal” (i.e., psychic or organizational systems) strategies to cope with this contradiction is what Luhmann (1979) seems to consider as Aufhebung, or suspension: “The real problem, however, is trust which is unjustified and which yet justifies itself and so becomes creative.” (ibid., 79)

This creativity, then, is what is needed and unavoidable to make the “fiction of trust” (Möllering 2006a, 112f) a reality for the involved actors. This happens through a “stabilization of expectations” (Luhmann 1979, 79). The stabilization of (trust) expectations is the result of socially controlling risk, non-knowledge and complexity (cf. Chap. 4) by trustors, as the materialization of suspension. Stabilized expectations are thus the (permanent, repeated) outcome of suspension and build the social bases for trust (maintenance of favorable expectations) and distrust (maintenance of unfavorable expectations). Overall, expectations can serve as the center of system trust research in different facets:63 63

An additional facet would be the distinction between cognitive and normative expectations. Cognitive expectations relate to those that are adapted after disappointment, normative ones are kept even against disappointment (cf. Luhmann 1995, 320f). Cognitive expectations often relate to market-based activities, where learning is apt to unfold innovation potential. Normative expectations usually relate to the public sector and legislation, where societal norms are set through law (but also informally) and upheld even when citizens violate them. The connection of this distinction with the insights on expectation generalization in this book must be left to future research.

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

The material dimension of expectations: what do trustors expect?



Differentiation of expectations: number, concreteness and temporal scope of expectations.

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Direction of expectations: who or what is the target of expectations?

Analyses of expectations can tell us a great deal about trust in empirical reality: who expects what of whom, or what? How precise are those expectations and how many are there? Taking on these questions promises to reveal the heart of what trusting a system is about. The key dimensions for researching expectations toward systems are the possible references of trust that Sect. 2.4 has initially illustrated: objects of trust (or references, or what trust can be directed at) are linked to what generalized expectations can be directed at. Despite differences in their general interpretations of social order, several sociologists have described similar addressees of expectation, building on different levels of generalization such as “persons”, “roles”, “programs” and “values” (cf. Table 2-1. An Expectation Nexus. Based on Schutz (1967) and Luhmann (1995), Sect. 2.4). The next step in comprehending system trust is bringing specific expectations of trustors (what do trustors expect from whom or what?) in a societal field like energy supply into line with the available references to direct them at. An additional issue is the relationship between the expectational nexus of persons (e.g., a minister, a particular public expert or a friend), roles (e.g., engineers, managers, politicians), programs (e.g., the energy turn, an economic subsidies program, local provider policy), and values (e.g., security of supply, profitability, sustainability), and ‘the system’ they are attached to – seeking the ‘real’ reference of trust and finding out about their possible interplay. Prior to this, let me clarify the use of the four levels in accordance with the definitions provided by Luhmann: 

Persons: In Luhmann’s understanding, persons are not equivalent to their psychic and organic systems, but rather serve as attribution units for expectations and actions: “Instead, a person is constituted for the sake of ordering behavioral expectations that can be fulfilled by her and her alone. One can be a person for oneself and for others. Being a person requires that one draws and binds expectations to oneself with the help of one’s psychic system and body, including expectations about oneself with regard to others.

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The more expectations and the more different types of them that are individualized in this way, the more complex the person” (Luhmann 1995, 315). 

Roles: Roles are a widespread and well-known concept in sociology, which reacts to the differentiation between personal and role-bound expectations: “Roles can, as distinguished from individual persons, then serve as abstracter perspectives for the identification of expectational nexes. To be sure, a role is tailored to what an individual human being can perform, but with respect to any individual person it is both more specific and more general. On the one hand, only a portion of a human being’s behavior is expected in the form of a role; on the other, the role is a unity that can be performed by many different human beings: the role of a patient, a teacher, an opera singer, a mother, a first-aid worker, and so forth (Luhmann 1995, 315f; italics original). Distinguishing between ‘person’ and ‘role’ in empirical research is not always easy, and will be discussed in detail whenever relevant. In addition, it should be noted that roles can also be filled by organizations.



Programs: Because roles, in Luhmann’s view, should not be overestimated in their expectation-binding performance, he alludes to ‘programs’ as a significant level of expectation generalization in society: “Role-bound expectational identifications do not exhaust the possibilities for abstraction, however. A person can go beyond this by not restricting himself to the behavioral possibilities open to an individual person. We call the order of expectations that results from this programs. […] The level of programs becomes independent of the level of roles to arrive at this abstractness if the behavior of more than one person has to be regulated and made expectable. Thus a surgical operation is not only a role performance but a program. […] There are one-time programs, but also programs for ongoing and repeated use” (Luhmann 1995, 317). In organizations, “programs are expectations that hold for more than one decision” (Luhmann 2013, 150).



Values: “On the highest attainable level of establishing expectations, […] values are general, individually symbolized perspectives which allow one to prefer certain states and events. Even action can be assessed in this way – for example, as promoting peace, as just, as polluting the environment, as an expression of solidarity, […] and so forth. Because all actions can be valued positively and negatively, one can tell nothing about the correctness of an action from its valuation” (Luhmann 1995, 317f). “Values contain no

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rules for dealing with conflict between values. There is no transitive or hierarchical order of values” (Luhmann 2013, 123). As a result, “there can be no absolute values that take precedence in every situation” (ibid., 123), so that the main function of values, according to Luhmann, is to guide the conduct of programs: “If they are to perform their specific task in the best possible way, programs often must be formulated as highly complex, variable and unstable with regard to details. Value consensus then alleviates communication about the program’s contingency […] and build on the expectation that everyone must agree on at least these values” (Luhmann 1995, 318). The following Table 5-1 depicts an initial collection of the four levels – persons, roles, programs and values (PRPV) – in the energy field that emerged out of the expert interviews (cf. Sect. 1.3). Table 5-1. Illustrative References of Expectations in the Energy System

64

Persons64

Roles

Programs

Values

Neighbor

Scientist

Demand-Side Management

Security of Supply

Co-Worker

Expert

Carbon Footprint

Profitability

Friend

Engineer

Smart Metering

Efficiency

Family Member

Market Leader

Unbundling

Privacy

Chancellor

Sanctioning

Roadmap

Sustainability

Finance Minister

Supervision

Energy Turn

Transparency

The descriptions of persons remain rather abstract at this point, but will be differentiated in the consumer case study (Chap. 6). The frictions between expectations toward ‘individuals’ or ‘persons’, i.e., persons as ‘expectation complex’ or as a social address, are hardly dissolvable in this book, just like distinguishing these from role expectations. In the expert interviews, interviewees referred to the personal element of ‘the finance minister’ or ‘the neighbor’, for example. More detail will be provided in Chap. 6.

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These interviews comprised 90-120 minutes of guideline-based discussions conducted with eight leading experts from various branches of the German energy sector in 2013. They included major technology companies, power supply companies, federal and local industry associations, and consumer and environmental associations.65 The data were extracted from the expert interviews based on manually applied rules that included the sociological definitions of persons, roles, programs and values according to Luhmann (1995). The data were selected qualitatively rather than according to e.g., the number of times they were mentioned by the interviewees and thus are more illustrative at this point than representative. The contexts of the expectations implicitly or explicitly mentioned by the interviewees relate to either their own or third party expectations (e.g., stakeholders, business, politics, general public etc.) in relation to developments in the energy sector they talked about. The collection serves as a framework to start from in identifying the relevant addressees, particularly for average consumers, which will be further narrowed down in the next sections. To be an ‘address’ or a reference of trust does not necessarily imply communication possibilities with that reference.66 The first step is to clarify the level of generalization at which trustors may attribute expectations, without describing the actual content, or materiality, of that expectation. The expectational nexus – representing ‘the energy system’ for involved actors – serves as a background frame (just like ‘society’ or the ‘mass media’)67 that supplies potential addressees whom people refer to, in order to (1) mobilize trust, or (2) attribute blame to when disappointed. In a more fine-grained analysis, the four types of generalized expectations provide a pre-defined setting for attributions of trust 65 66

67

Such German ‘associations’ are often comparable to American non-profit/non-governmental organizations (NGOs). A note on the usage of ‘addressable’, ‘reference’ and ‘communication capacity’ at this point: while some scholars differentiate between address and reference of trust (e.g., Kohring 2004), I treat these terms in less strict separation. I regard both address and reference as possible targets of expectations, and resolve the issue of reciprocity, i.e., trustee feedback, by referring to communication capacity (Luhmann 1995) of trustees, such as persons and organizations. That is why terms such as ‘addressee’ and ‘reference’ of trust are used in a congruent manner, knowing that there are trust references that are addressable and non-addressable, as stated in H15. The decisive distinction of reassurance and sanctioning of trust follows from this (Sect. 5.1; 5.3), as well as possible representation and intermediation of AoT components (Sect. 5.2). Niklas Luhmann has described this for both general society (Luhmann 2013, 131ff) and mass media in particular (Luhmann 2000, 1ff) as the background reality we take for granted in interaction systems among people who are present to one another.

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and distrust that is conditioned yet not determined. Within this pre-defined setting, called herein the ‘architecture of trust’ (cf. Strulik 2011, 246), attributions of trust and distrust take place as (social) expectations toward persons, roles, programs and values. It is an attributional network connecting subjects and objects of trust under certain guiding rules that condition its ascription in two major domains: 

Reassurance of trust and distrust: trust has been described as a “risky advance” (Luhmann 1979, 24); as an illusionary process involving reference to “equivalent-certainties” (ibid., 50) that replace the lack of knowledge encountered in situations of perceived risk. In a field like energy supply, those possible equivalents of certainty are not random – the trust architecture provides references that trustors can attribute their favorable expectations to, thereby enabling their ‘leap of faith’ and the creation of actionability – a state of trusting (Möllering 2013b). Analysis of trust in this way enables research to look at the relevant points of reference for trustors in a certain societal field. This might allow us to more precisely answer questions about how people come to trust (ibid.) in systemic contexts by absorbing uncertainty through reference toward very particular addressees. The ultimate objective would be to discover the relevant quantity (how many references?) and quality (dominance, rules of attribution?) of gateways to the system.



Sanctioning of disappointed trust: Niklas Luhmann (1988) has separated the logics of trust and confidence not only by their supposedly different expectational directions, but also by their attributional causality in cases of disappointment: if my trust is betrayed, I am likely to regret my own decisions; if confidence cannot be confirmed, the blame is put somewhere else (ibid., 102f). This ‘somewhere else’, regarding the social environment of the trusting system, could be narrowed down to prominent objects of trust that the architecture makes available: an address to blame for the disappointment of an expectation, and possibly to sanction. This concerns rules of attribution that determine the direction of the causality (self/other, risk/danger, system/environment etc.), and social rules about how the trust relationship is continued from here: placing responsibility for the disappointment at a certain reference and inferring (or neglecting) consequences for further action in favor of trust or distrust.

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Both of these categories are crucial for analysis of system trust and serve as guiding aspects in the case study in Chap. 6. In line with what Chap. 4 has illustrated, mobilization of trust or its dismissal can empirically be observed through narratives of specific and unspecific non-knowledge (Sect. 4.3). In this process of referencing certain knowledge (trust) or supposed uncertainties (distrust), trustors are expected to address components of the AoT and use them as either reassurances of trust or distrust (first category above) or addressees of responsibility in cases of disappointed trust (the second category). These aspects require more elaboration on the communication capacities of AoT components: a person is able to communicate, while roles, programs and values as such cannot interact with trustors. This pivotal issue is tackled in Sect. 5.2. Another key element of AoT is the relationship among the different levels of abstraction (PRPV) and their positioning with regard to the general ‘energy system’. Are persons, roles, programs and values merely representing the system and are thus the real references of trust, since the system as such is unapproachable? Drawing back on lessons from Chap. 3, we can say that for ‘genuine’ system trust to make sense, ‘system identity’ (Sect. 3.4) must be visible as an emergent reality apart from the single components of the system. Still, in line with what Luhmann (2000, 1ff; 2013, 131ff) described for society or mass media (see above), the background reality of the AoT for the energy system becomes effective in concrete situations of attributing expectations toward its components without ever touching or seeing ‘the system’ itself. Yet, AoT is shared among actors in the field as a common reference frame in talking about energy and acting inside its structures, thus drawing the system boundaries in the very moments where peoples’ trust or distrust makes a difference. As Coleman (1986; 1990) has suggested for his micro-meso-macro model, there is no contradiction in assuming this sort of side-by-side existence of abstract and concrete entities in the social world: a person who is a good friend of mine could operate solar panels on his house. Since he is an electrical engineer, for me he takes the role of an expert. In alignment with many other societal roles and programs (scientists, politicians, managers, subsidies policy, energy turn), he represents a positive carbon footprint and therefore embodies sustainability. Playing through PRPV architectures in this way quickly shows that abstract expectations need concrete representations, without the one thing being more real than the other.

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Sect. 5.2 sheds a more detailed light on this relationship, while this section closes with the following hypothesis:

H11: The empirical reality of an abstract system such as energy is conveyed to trustors through an architecture of trust references. This expectation nexus of ‘persons’, ‘roles’, ‘programs’ and ‘values’ is a predefined setting that holds concrete references for reassurances and disappointments of trust or distrust.

5.2 System Communications Much of the discussion on trusting systems, as mentioned initially (Chaps. 1 and 2), has revolved around distinctions such as abstract/concrete, symmetric/asymmetric and personal/systemic. The ‘architecture of trust’ challenges their underlying assumptions and offers alternatives for researching system trust. Concerning the basic parameters of social action, the four levels of expectation generalization clearly differ in abstractness: the radius of options that individual persons can fulfill is lower than in a role they possess, not to speak of a program coordinating several roles on a collective level. Values are the most abstract level in this “expectational nexus” (Luhmann 1995, 315) which offer great potential for variations and conflicting norms: following certain values simultaneously, such as sustainability and affordability of electricity, can lead to behavioral paradoxes as a single action might promote one of them while curtailing the other (ibid., 318). Still, with reference to the trust factor, the distinction between concrete and abstract addressees of trust should not be overestimated: “However, to describe personal trust as concrete and confidence as abstract would be misleading. We know that relationships with people can be very abstract and that relationships with social concepts, such as love, can be very concrete. This always leads to a formulation of ‘more or less,’ which is imprecise and variable. […] The attribution theory concept of concrete versus abstract can be replaced by a more insightful and helpful one: addressable versus non-addressable.” (Morgner 2013, 519)

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And indeed, the question of whether ‘the system’ is addressable in order to respond to the communicative offers by trustors has been of crucial importance in the debate so far. In fact, it has led most researchers to the assumption that system trust is something fundamentally different from personal trust, since the condition of ‘reciprocity’ is rarely fulfilled – the mutual communication between trustor and trustee that leads to the built-up of trust on the basis of observable indicators accessible to trust control. Most importantly, this includes the possibilities of assessing expectations directed at a specific object of trust through direct contact (persons) or mediated experience (systems). As a result, system trust has often been labeled ‘asymmetric’, compared to the symmetry in communication opportunities between individual people (discussions in: Warren 1999; Hardin 2002; Benseler et al. 2003; Möllering 2013a). In this way, system trust’s special role as ‘confidence’ was mainly justified, with the consequence that it is not seen as ‘genuine’ trust (e.g., Siegrist et al. 2005).68 With reference to an architecture of trust as embedded in the conceptual remarks throughout the book, these assumptions may be reexamined. If trust is an expectation toward the future and expectations, in turn, can be directed at persons, roles, programs and values (cf. Sect. 2.4), then ‘genuine’ trust appears in the shape of trust in these addressees. Trust in persons and roles, as primarily involving individuals, is as real and concrete as trust in programs and values and may be disappointed and fulfilled, altered and adapted. By sticking to this sociological modeling of trust as an expectation based on AoT, one can help resolve normative arguments over possible trust objects beyond persons from a sociopsychological (e.g., discussions in Benseler et al. 2003) or an ethical point of view (e.g., Baier 1986). The same applies to conceptions of trust as a somewhat irrational, barely explicable feeling toward other humans, for instance (e.g., Frevert 2013). Trust is an internal system construction that relates to expectations toward its environment (containing other systems) which involve both personal and collective targets of trust – every level that expectations can be generalized at in the AoT, as Sects. 2.4 and 5.1 have shown. As a consequence, the 68

Picking up the discussion from Sec. 2.1, it should be stressed that most of the authors involved in the narrower discussions on trusting systems have rarely doubted its genuineness: “At this point we reach a definition of trust. Trust may be defined as confidence in the reliability of a person or system, regarding a given set of outcomes or events, where that confidence expresses a faith in the probity or love of another, or in the correctness of abstract principles (technical knowledge)” (Giddens 1990, 34; emphasis PS).

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means of information gathering about the trustee – be it a single person or a political program such as the energy transition – is essentially the same, as it happens through system internal inference of external conditions. Thus, the reference to the symmetry of personal trust relationships and the asymmetry of collective trust relationships seems overstated: information about a person or a program, for instance, can be easy or difficult to attain or be assessed in a reliable way – cloudy judgments are not reserved to evaluations of systems’ performances. Moreover, both personal and collective trust can be either directly experienced or mediated – think of trust in publicly known individuals conveyed through mass media, or a very direct interaction with an administrative organization. This leads back to the above mentioned problem of system addressability and the communication capacities of trust references. Reflecting the prior elaborations, the answer to this seems twofold: due to the internal construction processes of trustors, the reassurance of trust (finding certainty-equivalents in AoT to mobilize trust) requires expectation building toward the environment but not necessarily communicative addressability of trust references; disappointment of trust and possible sanctions (suing my electricity provider, asking for compensation, calling a hotline etc.), on the other hand, require communication efforts and therefore personal or organizational addressability. As a consequence, instead of playing out personal and collective trust against each other, both theoretical reflection (further discussions in: Benseler et al. 2003) and empirical cases such as the Eastern European revolutions of 1989 (Morgner 2013) suggest the analysis of their interplay to be more helpful. To gain new insights on system trust based on the aforementioned conceptual AoT framework, one can look at the case of energy system transformation in Germany. Experts from the German energy sector have contributed to the formulation of two crucial hypotheses concerning the working logic of public trust in the energy system. One of them said, around discussions of technology acceptance: “But it doesn’t work without trust, because people – well, let’s put it like this: there are a few specialists who are preoccupied with the very last luster terminal somewhere in the field and they must know this stuff; they do know this stuff and they can fully comprehend it. But other people basically don’t really care. Rather, these other people believe; they are influenced by their sense of intuition and then they trust those people who do understand the last luster terminal and who can credibly

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say that all is okay. I don’t know where to draw the line. But I do have the impression that this is how matters stand and, moreover, when my neighbor does this, well then, it must be alright. […] All that is required is knowing someone who trusts.” (environmental association/NGO 2013, 112-113; translation and emphasis PS)

This interviewee is an expert on renewable energies and consumer behavior and has been involved in German public debate on energy, accompanying various citizens’ dialogues. The interviewee’s experience with issues of public trust seems to reflect an old idea of trust research: because the energy system itself is too complex and multifold for most laypersons, a neighbor, friend, or public figure serves as a representative whose assurances mediate trust toward the opaque system. Consequently, for the energy system, the hypothesis can be formulated that high numbers of consumers place their trust in these intermediaries (persons, organizations) that represent the system for them. This can be substantiated by another statement from a comparably involved consumer expert who was asked about trust-building mechanisms in future smart grid systems: “But such regional organizations, I believe the local transport sector is always quite a fitting example. There is a local service provider […] which does accomplish excellent work. But beyond the provider, there is a transport association which organizes this work. Trust is actually generated by the transport association, with its participants. It’s the transport association which says: there are ticket issuing machines everywhere and they must work; the price structure is in principle fair everywhere and if someone doesn’t follow the rules in a tram, well then he gets a wallop; or when the tram does something [wrong], then one must pay later on. That’s the kind of organization which I’m talking about and it’s better located at a regional level. Such an organization may have certain monitoring functions and it may also undertake certain organizational tasks – thus the organization could even amount to being the responsible system supervisor which must, well, basically be neutral and control the system… but it’s still a long way to get there.” (consumer protection association/NGO 2013, 98-99; translation PS)

Trust research has already alluded to mutual connections between different levels of trust such as persons, organizations and systems within an AoT. Endress (2003) for example, mentions the instructive example that trust in politicians or political parties can be found to be low while trust in democracy still prevails – or vice versa (ibid., 347 [4]). This general idea can be complemented through AoT that specifies levels of trust according to the sociological conception of expectational generalizations. Meanwhile, the basis for researching trust interactions like this has already been provided by researchers of system trust, yet without being applied systematically, and without being connected to ideas

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of trust attributions toward specific references. I refer to the concept of “access points of abstract systems [that] are the meeting ground of facework and faceless commitments” (Giddens 1990, 83). Anthony Giddens further described this idea as follows: “Access points are points of connection between lay individuals or collectivities and the representatives of abstract systems. They are places of vulnerability for abstract systems, but also junctions at which trust can be maintained or built up” (Giddens 1990, 88). “Although everyone is aware that the real repository of trust is in the abstract system, rather than the individuals who in specific contexts ‘represent’ it, access points carry a reminder that it is flesh-and-blood people (who are potentially fallible) who are its operators.” (ibid., 85; emphasis PS)

Apparently, Giddens alludes to the generalizations that people conduct by converting an encounter with a “face” representing an abstract system into actual experience of that very system. In this way, the personal interaction with a system representative literally operates as an interface of trust between the individual and the abstract system. System trust, according to Giddens, is trust that is mediated by certain access points which represent the system in question without giving up the notion that ultimately, it is the system trust is placed in. In a similar basic fashion, James Coleman (1990, 180ff) discussed “intermediaries of trust”. He specified three forms of trust intermediation – advisors, guarantors and entrepreneurs – that take a role of intermediary between trustor and trustee regarding trust in judgment (J) and the performance capacity (P) of trustees (ibid., 180ff). Although his three forms of trust intermediation mainly relate to trust-based transactions between people or organizations, his main idea is a similar notion to Giddens’, since he emphasizes pivotal processes in between trustor and trustee. These interfaces mediate trust or distrust instead of enabling direct interaction with the actual trustee – be it a person, an organization or a system. In line with this argumentation, for example, Coleman states that: “Increasingly, it appears, the mass media constitute the intermediary in whose judgment persons place trust” (ibid., 194).

From Coleman’s elaborations, we can infer that his conception of trust intermediation also touched upon systemic trust, even if he preferred to talk about selfreinforcing “systems of trust” rather than ‘system trust’ in Giddens’ or Luhmann’s sense (Coleman 1990, 188ff). Concluding, the simple yet far-reaching conceptual message here is that the actual reference of trust in collective units

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is ultimately ‘the system’ as trustee, yet might still be represented by certain associated components interacting with trustors. Unlike what Giddens and Coleman appear to claim though, these components are not necessarily limited to addressable humans or organizations (as will be discussed in more detail in Sect. 5.3). Through these interfaces of the system, the idea of trusting systems through ascribing references in expectational nexes (persons, roles, programs, values), and attributing blame toward actors with communication capacity (e.g., individual persons) in cases of disappointment becomes empirically feasible. What has been neglected so far however, is a thorough analysis of the ‘trust architecture’ in certain fields of system trust that would allow judgments about the concrete actors of intermediation and their degree and intensity of availability and/or dominance. For the energy case, the quoted expert statements suggest that both single persons (cf. environmental association expert) – perhaps as “opinion leaders” (Katz and Lazarsfeld 1955) – and regional organizations (cf. consumer protection expert) play a decisive role for the mediation of system trust. This mediation might then be both abstract and concrete, personal and systemic, symmetric and asymmetric, and shows that we may need to recalibrate some of the categories we use to describe and compare different types of trust. Finally, as the expectational nexus (PRPV) does not include organizations as recipients of trust, we have to consider their analytical appropriateness separately. Moreover, trust in technology is a frequent aspect with regards to sociotechnical systems like energy, which is why Sect. 5.3 elaborates on Trust in Technology and Organizations. The hypotheses inferred from this section are:

H12: While systems such as energy are reflected by AoT, its elements are merely mediators of (dis-)trusting the system. Although it might be accessed through its representative elements, the ultimate reference of trust is the system itself rather than its elements.

H13: Persons (family, friends, electricians) and regional organizations (e.g., utilities) play a major role in reassuring trust or reinforcing distrust in the energy system.

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5.3 Trust in Technology and Organizations Sect. 5.2 has concluded that system trust is conveyed to trustors through intermediation at the system’s access points. Before considering this ‘how’ feature of system trust in the particular case of the energy system, one needs to determine what system trust can be directed at. This involves the much debated question regarding to what extent technology, among other non-social references, can be an object of trust in the first place. A second field of scrutiny is trust in organizations, which has not been dealt with specifically so far but emerged out of hypothesis H13. One can start from the general assumption that trust relations are theoretically possible in multiple directions and involve various objects, whether individual or collective, person or system.69 Sects. 2.5 and 5.2 have revealed shortcomings in classic system trust differentiations, so that an attitude of conceptual openness seems appropriate and promising, yet in line with the established findings of trust research. Möllering’s useful definition of trust says that it is “a state of favourable expectation towards the actions and intentions of more or less specific others” (Möllering, 2006b, 356). While he thus reserves trust for social, human-based relationships (actions and intentions can hardly be attributed to machines, for example), he hints at one of the greatest weaknesses of trust research so far by mentioning “more or less specific others” (ibid.). The fact that he uses ‘others’ instead of a more concrete entity and declares them to be “more or less specific” shows how he struggled with identifying convincing rules for entities that are legitimate recipients of trust. Although this weakness can be countered by introducing an AoT in the shape of PRPV (persons, roles, programs, values) as legitimate references of trust, non-socially based entities like technology do not occur in that schema. So is trust in technology possible, and can it be part of AoT? Trust is an inherently social phenomenon as a basis of establishing social order in society (Luhmann 1979; Lewis and Weigert 1985; Kohring 2004). Sociologically, it is of secondary importance that trust relations are psychologically sound or stable with regard to the individual psychic system. But a trustor – e.g., 69

Compare once again Giddens’ (1990, 34) definition of trust: “At this point we reach a definition of trust. Trust may be defined as confidence in the reliability of a person or system, regarding a given set of outcomes or events, where that confidence expresses a faith in the probity or love of another, or in the correctness of abstract principles (technical knowledge)”.

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a person or an organization – needs to be potentially able to enter into a communicative interaction with the trustee, because trust relies on communication as basal elements of social systems (Luhmann 1995). Therefore, the possibility of trust in technology – just like any trust relationship – seems to hinge on the possibility of communication with technology. Since trust has to be conveyed in communication-based social systems in the shape of expectations (cf. Sect. 3.2), trust appears where communication appears. Dirk Baecker (2011), for instance, argues that even though communication is intrinsically social and occurs in human interaction, it is not necessarily bound to ‘persons’ and can theoretically take place among different kinds of entities, i.e.: “Any entity able to read constraints into degrees of freedom without necessarily feeling obliged to attribute a degree of freedom or constraint to itself, to the other, or to the situation, but which allows both features, negating and implying themselves, to float freely in exploring and exploiting a situation qualifies for communication.” (Baecker 2011, 24)

In this way, Baecker links qualification for communication to the ability to distinguish between information and utterance among communication participants (Luhmann 1995) – which creates the element of surprise in chains of communication – as well as references to both knowledge and non-knowledge by entities participating in communication. Further, he adds the criteria of enablement and constraint of self-set degrees of freedom, i.e., self-created and restricted selectivity: “In conclusion, we emphasize that our scrutiny of selectivity, recursivity, and closure seeks to clear the ground for reformulating a notion of communication that is not bound to either humanist or modern prejudices but examines what makes a relationship social. [...] [W]e focus on the communicative event itself, not on the participants, examining its eigen-dynamics, which have no need of ‘real’ persons or the ‘presence’ of both actors to develop and thrive in the hands of self-recruited editors, programmers, audiences, and users.” (Baecker 2011, 23)

Relating these arguments back to possibilities of communication with technology, there is initial evidence that we are experiencing a threshold-crossing to artificial intelligence (AI) with insinuated human qualities.70 Even though current technologies might not be fulfilling these criteria, and possibilities of deeper 70

Cf. James Barrat: Our Final Invention: Artificial Intelligence and the End of the Human Era. http://www.ipg-journal.de/kommentar/artikel/rage-against-the-machine-901/ Accessed April 15th, 2017.

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examination are limited in this book, the case of AI currently employed in many corporate technologies and user devices indicates that we might be on the brink of a paradigm shift in this domain: international AI thinkers, including leading Silicon Valley entrepreneurs and scientists, have repeatedly published open letters about technology breakthroughs and societal future scenarios involving intelligent technology and its chances and risks.71 While current developments might still entail primitive AI, one of many applied examples is software agents that are used for client communication by businesses. A personal experience from my own work-place may resonate with the reader. A co-worker told me that he had wanted to return an item which he had bought online. So he had written an email to the customer service about his issues with the article and asked about the return policy. He received a reply stating acceptance of his complaint and the required information for the return. After he had written another email asking a more detailed question, he was called by an employee about the open questions and discovered that he had been writing to and from a virtual, non-human agent. This example struck me, because my co-worker told me about his disappointment when he learned that he would have never known that he had communicated with a machine if the staff member had not told him about it. This is a powerful example of how the insinuation of social capacity toward technology can make communication with it seem real – and therefore be real in its consequences72 – for the involved human trustor. In this way, Baecker’s criteria for participation in communication are fulfilled in that the software agent ‘implied itself’ and “float[ed] freely in exploring and exploiting a situation” (Baecker 2011, 24). The agent led the human actor to believe that it distinguished between information and utterance, and created a contingent communicative understanding that seemingly qualified for a social encounter. In conclusion, one could say that wherever communication according to Baecker’s criteria seems possible for trustors, expectations of contingency (i.e., results can turn out one way, or another way) can be directed at a participant in a communication system, and thus trust is theoretically possible, however fictitious or unsustainable it might be.

71 72

Cf. https://futureoflife.org/ai-open-letter Accessed April 15th, 2017. Compare the well-known ‘Thomas theorem’: “If men define situations as real, they are real in their consequences” (Thomas and Thomas 1928, 572).

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In effect, this means that trustors can imagine a relationship with technology – especially as long as things work out for them and trust is affirmed through meeting their expectations. Guido Möllering (2006a, 112f) has repeatedly referred to trust as “fiction”, where fictional trust is defined as the standard case of trust, and not some trust form deviating from reality. We may have to allow ourselves to believe that some trustors form relationships with technology and in this way uphold their trusting fiction. One may take the example of driving a car: drivers have been reported to drive their cars onto the runways of airports (Cole 2013), or into remote forest reserves (NewsComAu 2010) by stubbornly following their navigation software. The soothing voice they might have heard that told them – due to a problem in the map – to drive on and on, while they were allegedly passing several ‘do not enter signs’ guarding e.g., the runway, must have clouded their common sense judgment over what they should have noticed with their bare eyes. In the latter case, the drivers reportedly moved away rocks blocking the road and unlocked gates, in order to reach the spot the maps were leading them to in the forest (NewsComAu 2010). These situations might be interpreted as simple, short-term forms of trust in technology: if we take the results from Chap. 3 seriously, trust is formed internally in psychic, interaction or organization systems as trustors. This means that the external reference of trust (e.g., the inventor of a technology such as apple maps, the company selling it, or the guy from the store) is not necessarily directly involved in forming trust in concrete situations of reassurance. This further means that through its ‘socialization’ by trustors, technology may generally qualify as a pseudo-social object for trustors, which potentially leads to directing expectations of contingency at technical devices – in this case perhaps through personalization of a specific navigation technology equipped with a human voice. In contrast to the interaction with the software agent mentioned above, the trustor’s favorable expectation possibly directed at a quasi-social item like a navigation system cannot be returned directly, due to lack of technology sophistication, which is where addressability comes into play: since navigation technology is usually unable to qualify for communication in the sense of Baecker above, ‘trust’ in interactions with it could only be ‘one-way’ trust by means of personalization. As a result, short-term expectations and performance assessments could be possible, but no addressability in terms of a serious interaction involving responsibility, blame, reliable information etc. This might be different in

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cases where technology is insinuated with (full) communication capacity by trustors, thus including possible addressability options when social mimique by technology is a given - we need someone to address in case things go wrong. In conclusion, the distinction between addressability and communication capacity (cf. Sect. 5.2) seems once more to be a cornerstone in researching system trust that deserves more attention, especially in the face of current technology developments in the ‘next society’ (Baecker 2011). In general, it is not fully clear who or what trustors refer to when being reassured of trust in the moments of contemplation or social action described above, as initially noted in Sect. 2.5. A mix of expectations of the quasi-social object and the background addressees representing it (e.g., the inventor of a technology, the company selling it, etc.) is as conceivable as either of them in isolation, but ultimately, this is an issue reserved for empirical analysis. Yet there is no logical reason to assume that it is exclusively or primarily the background addressees of trust that are valid for trust mobilization and/or reassurance, and exclude relationships that are quasi-social. This is because the quasisocial trust objects might function as a transmitter (cf. Coleman’s intermediary) between trustors and social background addressees. Whether this means that trust is placed in the transmitter itself or in the background address, or in both, is a complicated question that also refers back to the attribution problem of trust. In line with H12 (cf. Sect. 5.2), my hypothesis is that technology might operate as a mediator of trust in the energy system rather than embodying trust in the technology as such. Like other AoT elements, technology as a system representative could be a conduit for system trust, where trust in the single element and ultimately in the system reach a state of congruence. While initial conclusions on these complex queries are discussed in Chaps. 6 and 7, one may additionally refer to Kohring’s assumptions on socio-technical relations in system trust: “Technologies can fall short on public acceptance through the very inability of social actors to address trust, for example, because trust in technologies is always trust in the social actors who represent these technologies. These social actors can be persons, organizations or the entirety of the action context, which is referred to as expert system by Giddens.” (Kohring 2001, 93; translation PS)

The socio-technical relations in this ‘entirety of the action context’ such as the energy system are to be carefully explored in the consumer case study (Chap. 6)

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based on the hitherto developed assumptions. Moreover, they serve to inspire further research and thought in the theoretical trust community about how to link the inherent social heritage of trust with modern technology developments and the implications for trust theory and practice. Helpful for this could be the strengthening of an underresearched aspect introduced in Sect. 5.1: the distinction between reassurance, and sanctioning of (disappointed) trust. The necessity of addressability arises in cases of sanctioning broken trust, in that we need a social address who can take care of our complaints. In a majority of cases though, functioning trust in socio-technical relations in terms of successful interaction can be assumed the standard situation (Wagner 1994; Kohring 2001), which does not necessarily involve addressable sanctioning entities, even if (insinuated) communication capacity with that entity might be available. Based on this, it becomes clearer why non-social entities like technology might be a means of reassurance for mobilizing trust, while – through their usual lack of addressability – they cannot be referenced as a sanctioning address after trust has been disappointed: you cannot complain about poor service to your navigation system or software agent (yet). Keeping apart these two phases does not always appear in all clarity empirically, but separating them analytically is helpful in analyzing system trust. To be sure once again, this does not imply that the mobilization of trust is possible without directing any sort of expectation at a (pseudo-)social object, but that this object does not necessarily need to be able to provide immediate feedback. These are cursory remarks about a real-time change which we are observing currently; this change is much contested and will continue to be so over the next decades. At this point, one cannot equate trust in technology with trust in social systems. However, radical denials of ‘genuine’ trust in technology, as put forward by conservative scholars, are even less convincing given the above findings. Indeed, the paradigm of insinuating social qualities to technology is another example of how digitalization might change the conditions and consequences of trust in systems (Schneier 2012). This does not imply that trust relationships are possible at will with each and every technology or other non-social objects – but it does embody a call for sensitivity toward the insinuation and fiction potential of trustors with regard to whom or what they trust. As a conclusion, for the consumer case study to come (Chap. 6), and others that could be inferred from this research, one should carefully start working with a notion of

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technology as a possible “equivalent-certainty” (Luhmann 1979, 50) in order to reassure trust. On the other hand, sanctioning of trust always requires addressees with undoubted communication capacity. Ironically, these could also be technologies, when we take the described AI developments seriously. For the AoT, this means that technology as a possible recipient of trust for the energy case should be initially included. We have to settle for the fact that there might be trustors who create their fiction of trust in the energy system through efficiency issues of renewable energies, or their distrust through catastrophe scenarios of nuclear power, for example. One important constraint is that current energy technologies are analogous for the most part, so that the insinuation of social qualities might be restricted. Still, as mentioned before, the empirical analysis will bring to light technology relations and a modest evaluation of what this could mean for system trust. Trust in Organizations In contrast to trust in technology, the existence and operation of trust in organizations is far less disputed and has almost become a taken-for-granted domain in trust research (Kramer and Tyler 1996; Lane and Bachmann 1998; Kramer and Cook 2004; Gillespie 2012). This is partly due to the fact that we are dealing with a social address of trust in this case that can even appear as a trustor, not only trustee – “Trust within and between organizations” (Lane and Bachmann 1998, book title, emphasis PS). Surprisingly, the descriptions of generalizing expectations delineated above do not include organizations: (P)ersons, (R)oles, (P)rograms, (V)alues. While persons as an addressable reference of trust do appear, organizations are left out of this schema. Still, when one talks about a ‘system’ or about ‘impersonal’ trust, trust in organizations plays a major role in the debate (e.g., Zucker 1986; Shapiro 1987; Benseler et al. 2003; Strulik 2007). In the case of trust in the energy system, for instance, organizations such as regulatory agencies, the government, facility operators or utilities companies are frequently mentioned (Bellaby et al. 2010; Greenberg 2014). Trust relations between the public and organizations such as the ‘US Department of Energy’ (DOE), nuclear plants, supervisory institutions etc. are implied (cf. SEAB 1993; La Porte and Metlay 1996). As a result, it makes sense to incorporate organizations as another reference for trust expectations in the architecture of trust. This

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is inevitable insofar as organizations embody the only addressable system components besides persons, as they have communication capacity (Luhmann 2012, 145) – an important function for the sanctioning of trust. In theoretical terms, organizations are at the intersection of roles and programs: they are more than roles but less than programs from a societal perspective, albeit that their inner differentiation is constituted by just these structural elements.73 Accordingly, organizations comprise a whole range of professional roles such as senior managers, assistants or specialists. Their mutual connections in formal organigrams can also merge into programs, whether conditional or purpose-bound (Luhmann 2012, 150ff). Organizations thus follow programs as internal differentiations that coordinate the different roles it contains in order to synchronize decision-making rules, hierarchies, communications channels and the like. In a way, one could say that organizations are solidified roles and programs constrained by membership and constituted by decisions as their communicative elements (Luhmann 2012, 142f). The decisive difference between roles, programs and organizations is that the latter have the capacity of “communicating with systems in their environment” (ibid., 145). Therefore, they are available as an address of communication, and able to react to trustors sanctioning claims following disappointed trust incidents. Organizations also serve as addressees of trust expectations. In contrast to these rather organization-specific roles and programs, the RP components (Roles and Programs out of PRPV) of the AoT are tailored toward societal roles and programs, especially when public trust in systems is targeted. Thus, broader roles such as engineers and politicians, or programs such as CO₂ reduction strategies and Smart Metering are in the foreground of attention. The latter type of programs, particularly in the energy example, often concern coordination efforts, not only “if the behavior of more than one person has to be regulated and made expectable” (Luhmann, 1995, 317), but also when several organizations across functional systems of society are intended to be synchronized. As a result, one can conclude that generally, programs are located on a 73

One could possibly extend the inner differentiation of organizations to the full range of persons, roles, programs, and values, and research the perception of these differentiations for cases of organizational trust. Theoretically, as mentioned throughout Sects. 5.2 and 5.3, a possible intermediation role of organizations for system trust compared to their role as actual trustee are of particular interest. In practical terms, Chaps. 6 and 7 comment on the role of organizations based on the consumer case study.

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more abstract level of generalized expectations as their complexity can outperform the differentiations of single organizations. The Energiewende as the major (political) program involved in the case study, for example, tries to coordinate the behavior of hundreds of various organizations in Germany and beyond to reach its goal of 80% RES electricity by 2050 (BMWi and BMU 2011). Although this does not imply any hierarchy or succeeding control in the relationship between politics and target organizations (Luhmann 2012, 151), it alludes to the differences in scope, self-description and the mere number of involved decisions a program can attempt to coordinate. As a first conclusion, this reasoning would allow us to place organizations as an address of expectations between roles and programs, so that the AoT could be enriched by an “O” between R and P, so that we arrive at ‘PROPV’. On the other hand, it seems theoretically infeasible to precisely determine the different stages of abstraction that the AoT could take beyond the given PRPV paradigm. As a result, the relationship between organizations and the PRPV components of AoT is subject to further discussion and will be evaluated through the empirical study. One major issue following from this is the linkage between organizations and roles at a societal level of system trust. Prominently, Luhmann (1979) remarked in his classic trust study that: “trust in the ability of systems to function includes trust in the ability of their internal controls to function.” (ibid., 57f)

This noteworthy statement alludes to the hypothesis that the public relates with and forms expectations toward ‘control and sanction agencies’ or ‘experts and scientists’ as role references to reassure trust, rather than concrete organizations such as the German supervisory agency Bundesnetzagentur. This is why hypothesis 14 is formulated as:

H14: In the AoT, organizations with the function of supervising and system control are less specifically addressed than roles associated with that function. The fact that these roles and functions are fulfilled in general is more important than which specific organization conducts it.

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In close proximity to roles such as scientists or experts are more generalized attributions by trustors, such as trust in ‘science’ or ‘politics’ when it comes to supervising and system control of energy supply. A majority of the interviewed experts mentioned several systemic trustees such as science, politics or the economy in the neighborhood of the energy system, so it makes sense to include another dimension of possible trust references, to be called hereafter ‘(adjacent) systems’. This category shall be abbreviated with an (S) for (S)ystems: not to be confused with the overall energy system, for (S) can include functional systems such as politics and the economy or other collective entities which the public might refer to when matters of electricity provision are addressed. The integration of the grouping ‘adjacent systems’ (S) into the PRPV schema is more challenging than aligning it with organizations. The question whether systems are more or less abstract than values74, for instance, could be answered in different directions: the fact that even society as the most complex social system (Luhmann 2012) could be considered as an ‘adjacent system’ in the AoT illustrates this quite clearly. Even more questions arise if we ask about the alignment of technology (T) into the AoT as the initial paragraphs of this section have argued. Technology has been discovered as a plausible reference of trust reassurance in cases where trustors create a fiction that relies on pseudocommunicative technology. This is why technology, in sectors such as energy where it dominates, should be considered a possible element of AoT. Thinking of mostly energy-related technology such as nuclear power plants, solar panels or smart meters, technology can be expected to form a mostly abstract experience for consumers in this realm. But is technology more or less abstract than systems or values? One difference between the three possible new references (O)rganizations, (T)echnology and adjacent (S)ystems (OTS) introduced in this section, and the established references PRPV is that they reside on potentially differing scales: the expectational nexus of PRPV displays different levels of generalizing expectations that constitute the social structure of society. Society, then, is composed of interactions, organizations and functional systems (Luhmann 1995), while it 74

Another challenge could be, in analogy to organizations, the inner differentiations of adjacent systems as functional systems of society, which possibly amount to persons, roles, programs, and values associated with these adjacent systems. Due to complexity reasons, this additional research vector is left out here, noting that future studies interested in deeper resolution of this category should follow up.

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is the most abstract social system in itself. Accordingly, one could also argue that the OTS components do not align directly with PRPV so as to put them in the same enumeration (such as ‘PROPTVS’, for instance), but rather serve as sub-categories of PRPV: organizations (O) could be interpreted as addressable, permanent manifestations of particular roles and programs, for example. Systems (S) and Technology (T) could be represented by certain values that people expect them to embody and deliver. These could be associated with particular systems or technology such as honesty in politics or the sustainability of renewable energy sources. Overall, it seems the relationships between these different elements still pose more research questions than can be answered by available theories. Without doubt, the closed ordering of PRPV is an established finding of some of the most renowned sociologists (Schutz 1967, Luhmann 1995) and can thus be seen as a highly convincing setup of plausible expectation targets. More than four levels of such generalization would then be in need of substantial explanation and would need to take sophisticated elaborations from general societal theory into account. However, these are highly complex demands that cannot be fully treated as a side issue in this book. As a result, it seems convincing and sufficient at this point to regard organizations (O) as a tied category of roles and programs leading to ‘PROP’ (see above), and technology (T) and adjacent systems (S) as possibly tied to values at a more abstract level of expectation building, just to mark their general scaling (leading to ‘TVS’). My pragmatic conclusion thus results in a seven-elementschema (PROPTVS) that should be treated as a four plus three (PRPV + OTS) for the energy case in order to account for the unanswered differences in scaling between the various elements. For matters of readability, however, ‘PROP TV’S’ makes a more usable tool. The consumer case study presented in Chap. 6 will provide more useful insights into this question beyond the theoretical elaborations, helping to gain further clues about the ordering of AoT and relationships among its components.

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H15: The original AoT of persons, roles, programs and values (PRPV) can be enhanced by organizations, technology and adjacent systems (OTS) for the study of energy. This ‘architecture of trust’ holds addressable (persons, organizations) and non-addressable (roles, programs, values, technology, adjacent systems) references of trust. All of them can serve as reassurances of trust, but addressable references serve exclusively in cases of disappointed trust that is sanctioned because they can be communicated with.

5.4 An AoT for the Energy System A combined AoT that contains organizations, technology and adjacent systems on top of the original PRPV – PRVP + OTS – results in: Persons, Roles, Organizations, Programs, Technology, Values and Systems. This assemblance of possible trust references will hereafter be referred to as ‘PROP TV’S’75, which will now be filled with empirical data in its seven dimensions. Once this step is taken, PROP TV’S will reflect the AoT for the energy system and serve as a basis for the case study. In the following, the dimensions of the AoT (which were presented theoretically and justified throughout the previous sections) will be fleshed out with data from the German energy transition. This data concerns the concrete question of what persons, what roles, what organizations etc. are actually part of the AoT, and why. As mentioned before, I will concentrate on the expectations of average electricity consumers toward elements of the AoT. The question of consumer expectations toward the energy system is, initially, less about what these expectations exactly are. In a first step, it is useful to analyze how concrete or diffuse they are on the scale between addressing persons or values. This will tell us about potentials between general and specific system functioning, between passive and active stances toward the system and potential attribution directions of disappointments. Also, it tells us about the localization of responsibility in the system: who is responsible for the fulfillment of my expectations? These 75

Note that this particular scaling and order of elements must not be taken for granted and is subject to further scrutiny. Cf. Sect. 5.3.

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considerations lead to answering the more decisive question of how expectations are established, justified and confirmed/dismissed, and where trustors look in doing so. This lens can help in figuring out the decisive interfaces of the system for trustors, and outweighs the mere content or ranking of expectations that trustors formulate in other studies. As Schubert, Meyer and Möst (2015) have determined for the German case, the stability and origins of expectations toward the system, as expressed by trustors, are highly volatile. If we understand better what the sources for expectation building are (and are not), we have a higher probability of understanding the conditions, reasons and fluctuations of trust in a system like energy supply. As the antecedents for trust must be primarily inferred from the social world (Lewis and Weigert 1985), it makes sense to look at the typical references of trust that are involved in the energy debate and be as precise as possible about them. These probable references of trust can be derived from academic studies and literature surrounding the energy case. They will complement the initial references from expert interviews that were presented at the beginning of this chapter (Table 5-1. Illustrative References of Expectations in the Energy System). This concrete collection of references in the PROP TV’S format filling up the AoT serves as an example of how system trust can be made empirically feasible in a field like energy. Moreover, following the elaborations in Sect. 3.4, AoT is theoretically applicable to systems such as politics, finance, digital services or traffic. In this book, it also serves as a foundation for a first empirical examination of plausibility in Chap. 6. Much of the research involving social aspects of energy systems has centered on “public acceptance of new energy technologies” (Kasperson and Ram 2013). Issues of trust (along with credibility, fairness, justice and the like) are often mentioned in this connection but rarely systematically conceptualized (Kohring 2001; Todt 2011; Greenberg 2014). More sophisticated trust research, on the other hand, has rarely addressed energy issues, so that pre-existing work helpful for progress is scarce (Greenberg 2014). It is apparent that in both acceptance and trust papers addressing energy issues, the question of precise references and domains of either acceptance or trust – i.e., where are expectations toward the energy system directed? Who trusts whom in the energy realm? – has largely occupied researchers, yet has only partially provided satisfying answers.

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In order to infer the information necessary to complete the AoT in the energy field, we need to look at both acceptance and trust studies, under the consideration of the prior theoretical elaborations on system trust. While some studies on acceptance of energy technology, siting decisions or policy programs do not attempt to conceptualize ‘acceptance’ apart from as an everyday intuition, others try to specify relevant domains. Wüstenhagen, Wolsink and Bürer (2007, 2684), for instance, differentiate between socio-political acceptance (of technologies and policies; by the public; by key stakeholders; by policy makers), community acceptance (procedural justice; distributional justice; trust) and market acceptance (consumers; investors; intra-firm). Within their framework, one can see the tension between different relations, levels and references involved: while the separation between the three domains of sociopolitical, community and market is already somewhat random (control and sanction agencies? Experts and science?), trustors (consumers, investors, public, stakeholders) are mixed up with trustees (technologies, policies, procedural and distributional justice). Trust is subordinated in one domain as a companion of procedural justice in community conflicts. Overall, it seems that a comprehensive framework that would collect and organize all the expectations between trustors and trustees to bring some justified order to the assemblage presented here is missing. One of the substantive contributions to trust and energy is a special issue on “the role of trust in managing uncertainties in the transition to a sustainable energy economy” (Bellaby et al. 2010). Several authors contributed to questions of trust in the energy system concerning different levels and themes (ibid., 2614): 

“[…] Trust between citizens and authority – expert or political – with respect to benefits and risks of any new technology required.



[…] Investigation of trust between producers, distributors and regulators of the energy system.



[…] Trust around change in consumption practices involving energy.



[…] The high level issue of trust between states that must be party to agreements involving global energy futures.”

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Undoubtedly, the trust relations mentioned in this classification are important. In particular, the mentioning of organizational actors as trustors (producers, distributors, regulators, states) is an innovative approach. Still, the origin, impact and choice criteria, particularly of relevant trustees in this framework, remain somewhat blurry and do not exhaustively relate to prior trust research. In fact, all trustees mentioned in both studies (Wüstenhagen et al. 2007; Bellaby et al. 2010) can be ordered within the sociological expectation nexus concerning roles (e.g., authority), programs (e.g., policies) and values (e.g., procedural and distributional justice). A very recent study by Schubert, Meyer and Möst (Schubert et al. 2015) on “The transformation of the German energy system from the perspective of the population”, identifies twelve “acceptance factors” that are ordered along the continuum of profitability, security of supply and sustainability – the three major goals of the German transition referred to as the “energy policy triangle” (ibid., 51; translations by PS). The acceptance factors entail: 

Profitability: employment; costs affecting consumers; costs affecting the economy; distributional justice.



Security of supply: domestic generation; geopolitical risk; resource availability; stable grid condition.



Environmental Sustainability: land use; global emissions impact; local emissions impact; risk potential (ibid., 51; translation PS).

Most importantly for this book is the conversion of these factors into the AoT. Intriguingly, most of the dimensions addressed by the authors can be linked to the level of programs: employment, costs, domestic generation, land use, emissions impact, risk potential. Apart from that, they apparently serve to pursue values: profitability, security of supply and environmental sustainability. Some – like distributional justice or stable grid condition – are on the border between programs and values. Overall, Schubert, Meyer and Möst (2015) imply that the public mainly relates to programs representing certain values as key domains of acceptance among energy-related expectations. An additional aspect is their connection of the acceptance factors to questions of the subject and object of acceptance: in their understanding, the “complete system” (object) of the German electricity supply is targeted by “the German population” (subject) (Schubert et al. 2015, 51; translation PS).

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Finally, in Table 5-2, I have sketched an AoT (trustors and trustees) of Germany’s energy system transformation according to the prior studies and the expert interviews introduced in Sect. 5.1. For reasons of completeness and research promotion, I have also included all possible trustors that were mentioned throughout the studies on the German case of energy system transformation. While this book concentrates on broader public stances toward the energy system, the category of possible trustors beyond persons can be fruitful for additional analyses of trust between organizations, or even of trans-systemic nature (Strulik 2011). Finally, I have indicated rules of intermediation, reference and addressability as they were pointed out throughout Sects. 5.1 to 5.3. I want to consider the inclusion of these three aspects – (1) trustors and trustees, (2) single elements of level generalizations, and (3) rules of attribution – a proper prerequisite for a serious understanding of what an AoT embodies in a collective realm of society. For reasons of simplification, not every single element mentioned above is included in the AoT table below.

Groups

Individual

Citizens

Those affected

Stakeholders

Investors

Consumers

(P)ersons

Collective

Comments

Family member

The Public/ Population

Neighbor

Intrafirm

Co-worker

Friend

Regulators

States

Chancellor

Producers/ Distributors

Finance Minister

Reference of Personal Trust; Possible Intermediary

POSSIBLE TRUSTORS

Climate alliance

Experts Engineers

Power company

Local provider

Bundesamt für Sicherheit in der Informationstechnik (BSI)

Bundesnetzagentur (BNetzA)

(O)rganizations

Government

Scientists

Sanctioning

Supervision

Political Authority

Expert Authority

(R)oles

Possible Densification of Reference of Roles and Programs; System Possible IntermediSupervision ary and Control

Employment

Costs (affecting consumers/economy)

Emissions impact (global and local)

Smart Metering

Land use

DSM

(P)rograms

Reference of System Trust

POSSIBLE TRUSTEES

Table 5-2. PROP TV’S – An Architecture of Trust for the Energy System

Technologies

Solar panel

Smart meter

Electric vehicle

Nuclear plant

Smartphone

The Grid

ICT Gateways

(T)echnology

Procedural/ Distributional Justice

Transparency

Sustainability

Privacy

Efficiency

Profitability

Security of supply

(V)alues

Legal proceedings

Science

Mass media

Economy

Politics

Complete system (of German electricity supply)

(S)ystems

Emergent Background Possibly Reference of Reality as Insinuated with General System Open or Social Quality Functioning Closed (Adjacent) System

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Research on trust through AoT is not the answer to every problem of trust. However, it can provide strategies to analyze trust at different levels of abstraction in cases where multiple references appear as possible trust objects. Importantly, it can indicate relations between different levels such as persons representing certain roles and values and as such embody the system for trustors. Concretely, AoT presents a predefined background setting of non-arbitrary trust references in collective societal sectors that make attribution of trust toward them more probable than toward references not included in AoT. Hence, I have presented an exemplary in-depth scrutiny of the energy sector with the most typical references appearing in studies, literature, and expert interviews. These are to be examined in the case study in Chap. 6. Meanwhile, AoT is applicable to other societal domains besides energy in the same basic way as the expectational nexus of PRPV is universally valid. Also, ‘system identity’ is a characteristic presumably occurring in many other fields besides energy, such as politics, finance, mass media, the medical system and others (cf. Sect. 3.4). In this way, ‘genuine’ system trust can be researched by considering the right addressees in the latter sectors and treating them as either an emergent system or a fragmented complex that relies on trust in its single components rather than the whole unit. A key difference between different sectors of society with regards to researching system trust through AoT is the deduction of relevant categories on top of PRPV: while the energy case could plausibly be associated with holding references of trust in technology (T), other societal systems such as politics, the economy or mass media might not see this emphasis. For our purposes, this category can be regarded as a variable for special trust references of importance in certain sectors but not in all, so that individual arrangements of AoT are possible. Instead of technology, for instance, trust in the economy could include trust in brands (B) (Hellmann 2010) as a pivotal indicator of absorbing uncertainty for the creation of trust or distrust. On the other hand, there is no reason to dismiss either organizations (O) or adjacent systems (S) from the AoT since both references seem universally valid in all of society’s central domains: no functional area exists without organizational trustees, and no societal system receiving public or professional trust can be addressed without neighboring systems on the radar of trustors. For clarity in these entanglements, complexity in interdependencies between societal systems such as politics, finance, the economy, law, energy or mass media is simply too high.

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As a result, the enumeration of PROP TV’S in some cases might be changed to ‘PROP B(rands)V’S’ or ‘PROP M(iscellaneous) V’S’. Moreover, a reduction to the basic parameters of PRPV is always conceivable, perhaps with an additional M(iscellaneous) category for other collected references of interest. A final issue is the scope and radius of this kind of systemic trust research: what can we see from the AoT angle and where are its blind spots? Firstly, a functional view is deployed, looking for trust references in the two major situations of trust attribution: reassurance and sanctioning. By targeting, in the AoT, the “equivalent-certainties” (Luhmann 1979, 50) that trustors use to create the fiction of trust, one gets a step closer to comprehending how the process of suspension is actually executed, and what expectations are directed at what point. The second, related question concerns attributions in cases of (potential) disappointed trust: decision-maker or environment, risk or danger, myself or others – who takes the blame? The AoT holds addressable references such as persons and organizations in a societal field like energy that are likely to be ascribed in cases where people do not hold themselves responsible. These attributions, to be clear once again, do not necessarily concern the individual psychological status of trustors, but the societal prerequisites and consequences of their respectively altered actions (Lewis and Weigert 1985; Malle 1999). This perspective, which was introduced as a distinct sociological lens on system trust in Chap. 4, is reflected by AoT research. There is a high theoretical probability the AoT of a collective societal domain holds the decisive elements trustors use to reassure trust: the expertise of a politician or their neighbor, the skills of German engineering, the wisdom of Energiewende policy or the local agency executing all of this. After a certain amount of calibration and remodeling, the AoT might uncover the decisive intersections between the audience and the system in question. It will hopefully show us the access points (Giddens 1990) and intermediairies (Coleman 1990) that really count for trustors and are dominant in conveying system trust. While the framework in its current condition is still far from redeeming this, it represents a fresh start and a first impression on empirical research possibilities. While the AoT can be quite complex in itself, it helps to order some of the entanglements and complexities in opaque societal fields that sometimes seem to hold endless possible recipients of (public) trust. With the addition of corporate trustors in Table 5-2, it can be extended toward issues of organizational trust if the research focus requires it. With AoT,

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we now have a nexus of trust references that trustors are likely to address – one way or the other. What the AoT cannot tell us is whether trust in one of its objects, or placement on its levels of generalization is justified or not. The analysis so far has demonstrated that trust is and always remains an illusion at the heart of its working logic. Its self-deceptive character – incorporated through the act of suspension – leads to the ultimate conviction that trust can never be justified. As Luhmann (1979) noted: “It is precisely in regard to this problem that the ethical view of trust proves inadequate. It looks for an answer to the question, under what circumstances one ought to trust, and arrives at the conclusion that while in human society trust is an ethical command, one should not place trust blindly but only where it is earned. Thus the problem of trust is transformed into a cognitive problem, despite the fact that it has its roots precisely in inadequate cognitive capacity. This ‘solution’, therefore, could be formulated thus: give trust when there is no need for it. The real problem, however, is trust which is unjustified and which yet justifies itself and so becomes creative.” (ibid., 78f)

AoT is a contribution toward uncovering some of that creativitiy by trustors in justifying unjustifiable trust. While the normativity of trust research, as a result of this, cannot lie in knowing about the correctness of justifying trust in advance, it can be re-introduced on another level. Trust is inherently normative and everywhere you mention it, aspects such as disappointment, betrayal and their effects on trustors and trustees raise their heads and concern the attentive audience. With the help of Strulik’s (2011) argument on systematic risk generation through trust, a more collective normativity on a societal level could be the overall objective of trust research, in particular when the focus is on system trust (cf. Sect. 4.4). Accordingly, AoT research should be focused on patterns of trust and distrust in “great transformation” realms of society (WBGU 2011) that digest high shares of uncertainty – politics, mass media, digital services, energy – and are likely triggered by imperatives of planned societal change. Under these circumstances, trust unfolds its peculiar dynamic as both enabler and risk generator simultaneously, cementing its idiosyncratic nature. To catch this dualism and counter it with partial distrust through a systemic lens of equilibrium could be a normative stance of AoT research in order to secure “proper” functioning of systems (Giddens 1990, 34) and help avert crises and system breakdowns (Strulik 2011, 247).

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5.5 Collection of Hypotheses The previous chapters have presented hypotheses as a result of the theoretical propositions developed in earlier sections. Their function was threefold: firstly, to provide distillations of knowledge into one or two sharp points, secondly, to deliver the theoretical basis necessary to engage in the study of system trust in multiple fields of society, and lastly, to prepare a case study of the energy system. Here is a collection of the hypotheses in chronological order: Chap. 2: Trust in Systems 

H1: System trust does not equal confidence as it can be an action based on trustor decision-making. In fact, trustors establish service expectations toward the general and specific functioning of a system, which are both phenomena of ‘genuine’ trust.



H2: Current empirical developments challenge the assumption that system trust is merely a compulsory experience and underscore its possible decision-making component.



H3: The basis of all trust research is the study of expectations. Based on established sociological concepts, service expectations toward both the general and specific functioning of a system can be ordered on a scale from concrete to abstract, such as persons, roles, programs, and values. The differentiation of these service expectations and their evaluation through assessment of system outputs by trustors form the basis of system trust and its research.



H4: System trust relies on attribution to different objects, self or other, for reassurance of trust and sanctioning due to disappointments. Attributions highlight the present-relatedness of trust because they can change over time.

Chap. 3: System References 

H5: System trust references can also be systemic trustors – not only trustees as ‘abstract systems’. Trust in systems is actively constructed within lowscale systems as trustors (e.g., interactions, organizations) independent of scholarly descriptions of the ‘real’ system reference.

Collection of Hypotheses



133

H6: For a system to be object of trust, it needs to build a stable identity. An emergent system like this is available to trustors as commonly shared background reality.

Chap. 4: Conditions and Consequences of Trust in Systems 

H7: Trust thresholds determine the subjectivity of information processing toward societal systems. They can be robust or sensitive, favoring a trusting (robust) or distrusting (sensitive) general attitude toward a system.



H8: Complexity and its role for trust depend on trustors’ perceptions.



H9: Complexity and its role for trust depend on trustors’ perceptions. Perception, in turn, can vary according to narratives of knowledge or specific and unspecific non-knowledge. The dominance or mix of those narratives can be typically linked to forms of trust and distrust among trustors.



H10: An equilibrium of trust/distrust is an appropriate interpretation scheme of studying system trust. The normativity of systemic trust research is therefore collective functionality of systems rather than justified trust or distrust.

Chap. 5: Toward an ‘Architecture of Trust’ 

H11: The empirical reality of an abstract system such as energy is conveyed to trustors through an architecture of trust references. This expectation nexus of ‘persons’, ‘roles’, ‘programs’ and ‘values’ is a predefined setting that holds concrete references for reassurances and disappointments of trust or distrust.



H12: While systems such as energy are reflected by AoT, its elements are merely mediators of (dis-)trusting the system. Although it might be accessed through its representative elements, the ultimate reference of trust is the system itself rather than its elements.



H13: Persons (family, friends, electricians) and regional organizations (e.g., utilities) play a major role in reassuring trust or reinforcing distrust in the energy system.

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

H14: In the AoT, organizations with the function of supervising and system control are less specifically addressed than roles associated with that function. The fact that these roles and functions are fulfilled in general is more important than which specific organization conducts it.



H15: The original AoT of persons, roles, programs and values (PRPV) can be enhanced by organizations, technology and adjacent systems (OTS) for the study of energy. This ‘architecture of trust’ holds addressable (persons, organizations) and non-addressable (roles, programs, values, technology, adjacent systems) references of trust. All of them can serve as reassurances of trust, but addressable references serve exclusively in cases of disappointed trust that is sanctioned because they can be communicated with.

Overall, these 15 hypotheses can serve as results in their own right. They condense insights about system trust on the basis of literature from trust research, systems theories and general social research. They may be of great value to researchers interested in a systemic-societal approach to trust. Going through them one by one, it is apparent that they cover diverse issues and do not always synchronize on the same conceptual level. This is why some framing information is provided at this point. Chap. 2 provides the most basal hypotheses (H1 – H4) that need to be proven right in order for the notion of system trust to make sense. They concern decision-making, expectation building and attributions around trust and should underline every serious attempt at researching collective trust phenomena. Chap. 3 encompasses a scrutiny of systems terminology and concepts around issues of trust and distrust. The two hypotheses (H5 and H6) concern capacities of systems as both trustor and trustee, alluding to another set of fundamental prerequisites for the study of system trust. In any case, researchers of trust should consider relating to either one of these concepts and try to incorporate them in both theoretical and empirical approaches. Chap. 4 offers hypotheses at different levels of analysis (H7 – H10). While H8 and H9 are abstract guides for empirical research in need of further operationalization, H7 and H10 are recommended interpretation schemes for the outcomes of trust analyses. Accordingly, H7 and H10 are to be applied in the aftermath of studying trust in order to put results in the rightful place. Chap. 5 offers five hypotheses (H11 – H15) that deepen understanding of some of the prior hypotheses and also introduce new ideas. H11 and H15 take the idea of AoT to finer resolution and advise explicit dimensions for empirical

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reseach to take into account. While H12 is an addition to improve systems understanding, H13 and H14 are more concrete hypotheses targeting practical rules of trust mediation. Chap. 5 drew heavily on the energy case to make its points, but the general framework of AoT and the inferred hypotheses are applicable to all collective societal fields which are under potential analysis regarding trust. Their degree of abstraction and the foundation in social theory is an advantage here, and references to the energy case serve demonstrative status rather than exclusive applicability. The hypotheses serve as a patchwork to come to terms with an underresearched phenomenon. The practical tool underlying all of the hypotheses remains the AoT, as a scheme to observe system trust in empirical reality. But different allocations, orderings and hierarchies of the hypotheses presented here are both possible and desirable, and readers can extract the material they need to form their own framework for systematic empirical studies drawing on them. One suggestion is the case study presented in Chap. 6, which attempts an ordering into three categories and examines the general plausibility of the hypotheses. At the end, a first effort is made to apply some of the ideas presented in this book and give meaningful answers to some questions about system trust.

6 Case Study: Trust in the Energy System

This empirical chapter will operationalize and apply some of the hypotheses generated so far. In order to do this, the case of energy system transformation – particularly in Germany – serves as an illustrative example. Sect. 6.1 introduces the case and its current dynamic as underlying background material. It stresses in particular, why the case of energy system transformation is a potentially suitable example of studying trust in systems. The socio-technical patterns of current ‘energy turns’ are presented to culminate in their interrelations with the phenomenon of trust. In this way, the consumer case study serves both as a trigger for deducing general insights for system trust theory, as well as supplying insights into the actual case of what role trust plays in the energy transition in Germany and beyond. The remainder of Chap. 6 tackles an overview of the case study approach, its analytical categories and methodology. In these sections, information is provided that links the theoretical results and hypotheses so far with their application to the empirical material. In Sects. 6.2 and 6.3, this is deepened into the operationalization of the hypotheses into questions for an interview guide, which is presented in Sect. 6.3.2. Finally, after consideration of the study conduct and lessons learned, results from each of the three categories under scrutiny are presented and interpreted (6.4). 6.1 Energy System Transformation We live in an age of “great transformations” (WBGU 2011, 5; Magnuson 2013). One of the most remarkable and widespread transformations of our time is transitioning energy systems toward renewable, low-carbon energy sources that contribute to more efficient use of resources and sustainability (ibid.). Further © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_6

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objectives of energy system transformation are affordability and profitability of electricity for both consumers and industry, as well as security of supply under the changing conditions (BMWi and BMU 2011; Giordano et al. 2011; European Commission 2015; acatech 2016). One major component of global developments of this kind are ‘smart grids’ (SG) – energy systems that are equipped with intelligent automation technology and internet connectivity which allow its components to communicate with one another in order to achieve the ambitious targets of the proclaimed ‘energy turns’ (Giordano et al. 2011; Amin and Giacomoni 2012; Ramchurn et al. 2012). In Germany the SG is supposed to enable an 80% RES (renewable energy sources) integration of the system by 2050 (BMWi 2015), while simultaneously creating new markets around ‘energy computer science’76 (Khala et al. 2014). In this way, the SG could allow for both technical as well as economic progress, according to the self-set targets (BNetzA 2011). While in the German case many components of smart energy systems are still under experimental scrutiny (B.A.U.M. Consult 2012; Lösch and Schneider 2016), some other countries have already had their first waves of technology penetration. Notably, the US market is a leading example where smart electricity meters have been implemented throughout the country’s households (Depuru et al. 2011; Barringer 2011; Galbraith 2012), but without comparable shares of renewable energy in the system as in Germany. Similar developments are visible in European countries such as the Netherlands, Italy and the UK (Giordano et al. 2011; Asendorpf 2012). Consequently, the SG is a means for efficiency increases of energy systems in general, without necessarily involving high shares of renewable energy integration as is the case in Germany, a country that has decided to conduct a nuclear phase-out (BMWi and BMU 2011). 6.1.1 Impact and Logic of Smart Grids Smart grid definitions vary greatly in detail but can be narrowed down to one general distinction: a technical systems-focused notion and a generalist notion. While the first one relates primarily to sensor and actuator systems and their communication in the technical grid, the latter often includes social elements such as end-user involvement (Amin and Wollenberg 2005). In this way, the 76

The current profile for recruiters in the energy sector is to look for candidates between engineering, business and computer science – the ‘energy economics computer scientist’ as a German start up representative at a SG conference in Karlsruhe coined it.

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inclusion of smart electricity meters, for instance, can be seen as such a socially oriented SG component. Smart meters are a major means of enabling two-way communication between and among consumers and suppliers. As such, a major element of current energy scenarios is the widespread interlinking of households, commerce and industry into an intelligent system of energy management (B.A.U.M. Consult 2012). This occurs mainly via smart meter communication so as to improve efficiency, environmental protection and the affordability of electricity (Giordano et al. 2011; Amin and Giacomoni 2012; Ramchurn et al. 2012). Consequently, by including smart meters in a conception of SG, I follow the rather generalist view on SG energy systems. From the generalist perspective, smart grids not only emphasize a technology shift toward ICT-driven energy systems based on ubiquitous computing, but also toward ‘socio-technical’ systems that heavily include and depend on commercial and private consumers and their repercussions for the system. With reference to Ramchurn et al. (2012, 89ff), five components become primarily relevant for a social science research focus on smart grids: Demand-Side Management (DSM), Electric Vehicle Management (EVs), Virtual Power Plants (VPPs), the emergence of energy prosumers and ‘self-healing’ networks. These five SG components largely react to the ‘volatility problem’ of RES and the ensuing system repercussions: the future system and transition phase rely more and more on RES depending severely on favorable environmental conditions such as permanent wind or sunshine. As a consequence, the need for balancing mechanisms such as DSM or VPPs emerges. Both are end-user involving activities that aim at controlling the demand side of electricity and enabling new business opportunities through ICT (Ramchurn et al. 2012). Consumers – be they commercial or private – are heavily implicated in this conception, as the overall complex of energy supply including generation, transport and distribution of electricity has to be aligned with a crucial equilibrium of demand and supply to secure the 50 hertz frequency.77 This is chiefly coordinated through ICT signaling mechanisms in the envisaged SG (Pearson 2011; Ramchurn et al. 2012; Sumpf et al. 2014). It is in this way that the integration of volatile RES is enabled, as consumers should not request too much electricity in times of low RES output, for instance. Conversely, at times of high shares in RES generation, customers are supposed to concentrate their use of electricity 77

See FN 5.

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in order to shift so-called ‘peak loads’. According to technical prerequisites in dominating visions, this not only results in higher energy efficiencies, but also serves the purpose of ‘grid-supportive’ measures such as DSM, required to secure grid stability and security of supply (Amin and Wollenberg 2005; Amin and Giacomoni 2012; B.A.U.M. Consult 2012). Accompanying this vision is the widespread idea of creating ‘prosumers’ (active consumers who produce and consume energy) who adapt their energy consumption behavior through smart meters and become engaged in energy trade with self-generated electricity. In this way, they could help to maintain electricity availability and contribute to the overall grid performance if necessary. In doing so, formerly passive consumers are supposed to increasingly develop an active future role in dealing with electricity devices, as well as being able to act as electricity vendors in “Virtual Power Plants”78 and “Smart Markets” (BNetzA 2011; Giordano et al. 2011; Amin and Giacomoni 2012; B.A.U.M. Consult 2012; Ramchurn et al. 2012). 6.1.2 Consumer Involvement and the Role of Trust The consequences of energy system transformation driven by SG developments are multifold. The most basic insight is that from a scholarly point of view, intelligent automation technology, control engineering, ubiquitous computing and internet connectivity need to be identified as the central qualitative change in the electricity sector from this point onwards.79 Energy transitions are not about changes in the dominating energy technology alone, e.g., from nuclear, coal and gas toward wind, solar and biomass. The integration of these technologies into the system and their effectuation to make them work profitably, efficiently and sustainably is crucially enabled by ICT. In the German case, this is of special importance insofar as it mitigates the volatilities of RES. A system relying on 80% RES provision in 2050 will need sophisticated demand-supply balancing 78

79

VPPs, as current Siemens research illustrates (Jopp 2014), can be imagined as a web portal of cloud computing networks between local providers and various electricity generators who are willing to combine their electricity loads with those of other actors, even across national borders. The means to achieve this are ‘virtual’, the actors being ‘plugged’ together through exposition of available load information (kWh, gigawatt) in the cloud. The objectives of VPPs include load management for grid stability and novel business opportunities. In fact, it goes way beyond that, as energy sectors such as gas or heating are also included in plans for an overarching, long-term smart grid (BMWi 2015).

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to secure the 50 hertz equilibrium80 (Döring 2013). This is only achievable by controlling the demand side of electricity through smart grids, in combination with energy storage solutions. This argumentation leads to some far-reaching insights about the restructuring of energy systems: first, the SG is likely to take over energy systems in one way or another, due to its broad-scale applicability beyond merely balancing RES volatilities (cf. US case mentioned above, where efficiency gains are in the foreground); Second, inclusion of the average consumer in energy services is a likely development, possibly accompanied by rising needs for attention, reflexivity and awareness among consumers. This may occur even if today their role is still negligible in many cases, and the degree of consumer involvement in SG is contested (Darby 2010; Givens 2014; Schiemenz 2014; Ward 2014; ZfK 2015).81 A representative of a leading German environmental association said in an expert interview: “And I can truly imagine how (and that is why I’m still, nonetheless, not completely cutting off communication) in the end, the private household will finally really be able to play a greater role through its marketing demand.” (environmental association/NGO 2013, 61-62; translation PS)

Even though business models are targeting easy and convenient solutions (Khala et al. 2014), the scenario described on the foregoing pages hardly implies business as usual for electricity clients. This is particularly the case considering their status of alienation from the topic: “The fact is that mature technological systems reside in a naturalized background, as ordinary and unremarkable to us as trees, daylight, and dirt. Our civilizations fundamentally depend on them, yet we notice them mainly when they fail, which they rarely do.” (Edwards 2004, 185)

Now, all of a sudden, the energy system reaches beyond the power outlet and involves the customer as an acting part with causal effects on system performance. At this point, the intersections with the topic of trust emerge: if people perceive causal effects of their own behavior on the output of energy services for them, their attribution is more likely to be risk-based than danger-oriented,

80 81

See FN 5. For a discussion on smart meter distrust prior to a mass rollout in the Netherlands, see von Schomberg (2013).

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because they relate their actions with corresponding consequences. Such an increase in attention, reflexivity and awareness includes decision-making and possible regrets as well as an ‘active’ prosumer culture, leading to relations of trust/distrust rather than familiarity. Considering the results from Chap. 2, the SG scenario sounds like a breeding ground for a system trust that is: “an action based on trustor decision-making […] and not merely a compulsory experience. In fact, trustors establish service expectations toward general and specific functioning of a system […] through assessing system outputs.” (H1-H3)

The key deduction for emerging system-client relations is that in the SG electricity clients are likely to become components of a system they once barely noticed, and they therefore have to include themselves in a determinant of system performance. With reference to H1 through H3, one can say that with high probability, trust judgments in terms of system output evaluation in smart grids will rely not only on external processes but on self-assessment of consumer behavior. As a consequence, we can predict that “[b]ecause automation and computer technology are growing increasingly complex, the importance of affect and trust is likely to grow” (Lee and See 2004, 76). To say that the ‘importance’ of trust grows is not a normative statement that favors trust over distrust (cf. Chap. 4). What it means is to stress that relations of trust are going to have a much more decisive function in future energy systems than they do today, since trust can be the enabler of SG business models – just as distrust could be its obstacle (Büscher and Sumpf 2015). For consumers, ‘waking up’ from familiarity and turning to attitudes of trust or distrust, would be a central qualitative change from a conceptual point of view, if things turn out as the described visions indicate.82 As Sect. 4.2 initially noted, the German energy transition builds up a lot of complexity in different dimensions – this is valid for politics, academia and businesses as much as for consumers (See ZfK 2015, 4). Consumer treatment and reduction of this complexity – their perception of complexity (H8) – and the resulting references of knowledge and non-knowledge are at the center of trust analysis. As a result, the study of trust in systems based on the energy example seems promising considering the current empirical dynamics.

82

Shove and Warde (1998) formulate a comparable paradigm of change in the electricity sector without using the terminology and conceptual premises and consequences sketched out here.

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The attentive reader has perhaps discovered a flaw in the argumentation so far: complexity, interactivity, ICT control – all these attributes of smart grids mentioned in this section and throughout the book are mainly future-oriented and barely concern current electricity customers. Some of the components one finds in SG visions, for instance, are depicted in a world far away from what we know today – the final state of the transformed German energy system is projected by the federal government around 2050 (BMWi and BMU 2011). This leads practitioners in the field to discuss some of its envisioned components, such as virtual power plants, as “phantasies” (industry association 2013, 73-74). Still, the present is the only empirical reality that I have access to, not the future, nor the past – and if we can be said to access the past, then this is only through the present. As a consequence, the vision and current innovation dynamics of SG in the energy sector serve as a springboard to address the relationship of the 2016 audience with the energy system. Even though the degree of interactivity is considerably lower than that of the expected future system, there are sufficient intersections available, since developments such as RES generation have long been known by the population in Germany. Moreover, the current popularity of the Energiewende in German media and business might contribute to a favorable environment for interviewing people about their thoughts on energy. In the end, the decisive argument is that there is no way of conducting empirical research other than approaching electricity customers of the current system. After all, this provides the unique opportunity to compare the results of this analysis on system trust with the clients of the present against what is expected of them in the future. In this way, the following consumer case study serves a twofold function: firstly, it provides general answers to the questions on system trust incorporated in the 15 hypotheses developed in the theoretical observations. Secondly, it supplies feedback for comparing these results with the future scenario unfolded here, and provides a short conclusion about the ‘SG preparedness’ of consumers and what is at stake for the overall energy system, given the odds of trust (Chap. 7). As a basis for discussion and deduction of the arguments provided in this section, one may close with the following hypothesis about the state of the current electricity client audience:

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H16: A historically grown robust trust threshold in the energy sector has created tendencies of familiarity, low risk perceptions and an abstract relationship with the energy system among trustors. Although system awareness and basic patterns of decision-making exist, trustors feel exposed to the system rather than considering their behavior as the potential cause of outputs.

6.2 Case Study Approach: Overview On the basis of the prior theoretical considerations, the consumer case study has the objective to explore the possibilities of empirical research on system trust. This objective is quite modest as the existing methodological landscape offers little support, and the theoretical assumptions developed in the previous chapters need some general plausibilization before more detailed empirical scrutiny is possible. While the general possibility of researching system trust according to the hypotheses developed in this work are in the foreground of attention, the case-specific trust issues on energy seem promising in offering some details at this point. In this way, the example of trust in the energy system serves as (a) a means to exemplify system trust in general so as to infer general rules from the specific case, and (b) offers insight into concrete developments in one field of systemic trust that is under increasing public observation: global energy transitions. The German energy turn – as one of the role model transformations in this domain – suffers some idiosyncrasies as a national example, but also represents general patterns of upcoming changes (especially consumer patterns) that allow for interpretations on a more universal level. Still, the overarching goal is to make sense of researching system trust in general and help with directing future studies. The categories of the case study are threefold. In alignment with the hypotheses (H1-H15) that were developed throughout Chaps. 2-5, the following three aspects will be scrutinized: firstly, system identity; secondly, the expectation nexus and, thirdly, the reassurance patterns of the interviewees. This is in order to evaluate the operationalization performance of the hypotheses and infer conclusions for adjustment. In this way, the study can provide first hints about

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the empirical validation or dismissal of the theoretical assumptions, as well as methodological support on empirical system trust research. While the exact operationalization of the hypotheses leading to the three categories will be provided in Sec. 6.3.1, an introductory preview will be given at this point: 

System Identity (SI): Chaps. 2 and 3 demonstrated the importance of system awareness and identity among trustors in order to make the idea of system trust plausible. Part of my assumption is that today, decision-making in the energy realm is rather low. Still, the perception of the energy sector as a pseudo-closed, emergent system prevails, even if its ‘genuine’ trust relationship with the audience is not yet well-developed (cf. H16). In combination with the system turn toward the subject of trust – i.e., trustors’ psyche or the interaction system creating the expectation of trust – this category tries to identify the interviewees’ understanding of the energy system as an object of trust in the context of the social system that is producing it: the interview situation.



Expectation Nexus (EN): A central outcome of the discussions around system trust is its foundation on expectations. As trust is a favorable expectation of the future, two basic questions arise: what are these expectations toward system output (e.g., electricity provision, sustainability, CO₂ reductions, risk aversion, low costs) and on what level of specification can they be located? Who or what is addressed for the fulfillment of these expectations: persons, roles, organizations, programs, technology or (adjacent) systems? First answers to these questions can help to get a grip on the energy system representation for the audience, i.e., what reference of the nexus (or something else) is mentioned most and builds an access point to the non-addressable system? It also reveals much about the current information base of trustors.



Reassurance Patterns (RP): Expectations are formed on the basis of knowledge and non-knowledge: what specific knowledge (e.g., supposed certainty) or non-knowledge (e.g., supposed risk) is narrated by the interviewees that determines their expectations toward the energy system? With reference to H9 in particular, strategies of reassuring and/or dismissing trust in the energy system shall be uncovered. The goal in this category is twofold: First, it is supposed to reveal the risk perceptions of trustors concerning the energy field; second, on the basis of that, narratives of dealing with these risks are targeted to uncover references to the various types of knowledge and ignorance involved in trust/distrust creation. The goal in this section is to find hints of original patterns of reassurance that can be

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linked to trust and distrust, as well as attribution patterns regarding sanctioning of trust disappointments. 6.3 Design and Methodology The consumer case study was conducted between February and March 2016 in seven German cities and villages, mainly in and around Hannover and Karlsruhe, in the German language. I interviewed N=30 people as a sample of average electricity consumers. Selection was made based on loose knowledge (‘friends of friends’) and a resulting ‘snowball system’ that allowed me to meet further people the interviewees recommended. In the end, I managed to obtain an acceptable balance of average age, education, residence, and gender so that despite its small absolute size it is still a ‘quota sample’. The study entailed a problemoriented, guideline-based interview with 30 participants of an average 25 minutes duration. It was conducted on the basis of the following interview guide, visible only to the interviewer. Interview Guide » For interviewer: System Identity 1.

What comes to your mind when I use the term ‘electricity supply‘?

2.

Have you ever thought about all the things required for delivering electrical energy to your household? Which requirements can you think of?

3.

From your personal perspective: who or what is involved in permanently maintaining electricity generation and upholding security of supply?

» For interviewer: Expectation Nexus 4.

What does a properly functioning energy supply accomplish for you personally?

5.

What does a properly functioning energy supply accomplish for society?

6.

From your personal perspective: who or what takes care of implementing your conceptions?

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» For interviewer: Reassurance Patterns 7. 8.

Do you already have any experience regarding problems in the energy sector? Where would you pinpoint the responsibility or cause of these problems?

» For interviewer: Wrap-up 9.

Do you believe that the German energy transition will change any of the statements you have made so far?

The interviews were transcribed verbatim with simple standards (only long breaks ˃5 seconds or evident loud laughter were included to indicate special situations). Non-verbal utterances or linguistic features that interpret uttered language into a canon of second order meaning have been excluded from consideration because this study relies on explicit statements. As a result, the transcripts are available in the original wording in written form with line numbering and serve as a source of direct, explicit language for the coding of units within the three categories. The only schemes of interpretation are the underlying hypotheses on system trust research developed in the book. Methodologically, the basis of this empirical research is qualitative content analysis (Mayring 2000; Krippendorff 2003; Hsieh and Shannon 2005; Kuckartz 2012). To be exact, I am following a paradigm of content structuration, rather than type-building or evaluative content analysis (Kuckartz 2012, 72ff). This approach is particularly recommended under circumstances where first empirical hints are sought in order to affirm or dismiss general catgeories and subcategories. Evaluative and type-building qualitative analysis usually require previous work to rely on in order to make sense (ibid., 75ff). Nevertheless, introductory evaluative parts will be included as far as possible. Practically, structurative content analysis is frequently of mixed character as it includes a deductive-inductive category-building strategy. In this case, a strategy of primarily deductive category-building on the basis of my theoretical work and expert interviews is pursued, a tactic Hsieh and Shannon (2005, 1281f) call “directed content analysis”. Inductive category-building from the actual material is in the background due to the extensive theoretical preludes that were necessary. Two rounds of coding are used. In the first round, this applies the deductive categories

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and simultaneously alters them inductively based on the material. In a second round this altered category system is applied again to complete the coding. A category can mean many different things. In this case, one can understand it as an analytical category (and not a factual, thematic, natural or formal category) that is based on several hypotheses concluded throughout the book. Each of the three categories therefore concentrates one major domain of empirical trust research ([S]ystem [I]dentity, [E]xpectation [N]exus, [R]eassurance [P]atterns) as a merging of the various theoretical underpinnings discussed earlier. In this way, the term ‘category’ for each of these blocks of hypotheses is to be understood in a loose fashion – concepts or dimensions could be substitute terms. Once again, this is less about wording than about the arguments and strategies behind it. Consequently, the three categories (SI, EN, RP) will be used as both a structural and an interpretational scheme. Structurally, the interview guidelines were ordered along the three categories in terms of block I, II and III. The questions in these blocks were exclusively aligned with the respective category. For purposes of interpretation after the data has been retrieved, the categories simultaneously serve as a more or less closed unit of analysis. While through the transcription of interviews the sampling and recording unit are congruent (Krippendorff 2003), the unit left to identify is the content or coding unit (ibid., 99ff). These units – the actual text segments, words, sentences etc. to be coded – are primarily located within the respective categories to make sense in their interpretation for these very categories. While the final evaluation of the results encompasses comparative aspects between the categories along the overall research question of corroborating system trust, detailed linkages between them in terms of ‘people who said this in category SI tended to say this in RP’ are neither desirable nor practically feasible at this point and are therefore omitted. The actual operationalization of the three categories is conducted in Sect. 6.3.1. The deductive category-building described so far will be broken down to coding patterns that are based on the AoT developed in Chap. 5. Accordingly, for instance, the system identity (SI) of interviewees in category I will be explored through codes that align with PROP TV’S and will collect units in the text that align with organizations, technology, persons, programs etc. A second analytical scheme will collect explicit statements of systemacy in the text that put the AoT components in relation – or not. In case of apparent frictions, the

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analysis shall also be enhanced with inductive coding during the qualitative research process. This feedback loop will leave the analysis with a certain openness for surprises and blind spots that might result from the relatively strict application of the categories and hypotheses identified to be characteristic of system trust. The operationalization, coding and interpretation approach of each of the single categories SI, EN and RP can be studied in detail in Sect. 6.3.1. I try to present an utmost degree of transparency in order to clarify my approach and treatment of the material. According to Kuckartz (2012), this way of creating comprehensibility in qualitative studies is what makes them more accountable, understandable and precise when it comes to assessing qualitative data. A perception of arbitrariness or random interpretation of data can be mitigated in this way. My elaborations so far and those to come are an attempt to pay tribute to this philosophy. 6.3.1 Operationalization The previous five chapters have presented many theoretical premises and foundations about (system) trust. Given the amount of conceptual material underlying the study of trust in systems and their underresearched nature, it seems impossible to examine all of the hypotheses83 developed here in empirical detail right away. Rather, they need to be combined into larger blocks in order to find out about their general plausibility and fine tuning can be carried out as a result of the study. As Sect. 5.5 has initially noted, the hypotheses are partly on different levels of abstraction and range; they do not yet allow for absolute specificity in terms of category and coding rule inferral. This is due to the complexity of the topic and the lack of particular empirical approaches on the issue. These circumstances led me to dealing with the theoretical aspects of system trust in a rather circular order – mere logic or simple chronology seemed inappropriate. As a result, the hypotheses in their current shape are a consequence of the way in which the chapters were aligned. Many of the neighboring hypotheses within one chapter make sufficient sense in relating to one another but some are in need of further re-alignment. This will be done by calibrating them under the umbrella of three analytical categories that embody the major dimensions of interest in 83

To recall the complete list of hypotheses, please refer to Sect. 5.5 ‘Collection of Hypotheses’.

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this book: System Identity, Expectation Nexus and Reassurance Patterns. These three fields of scrutiny build the distilled basis of analytical concepts that system trust needs to be associated with. 

System Identity: One package of hypotheses (H5, H6, H12) deals with the constitution of the system in questions of trust. It has been deemed an essential task of this book as early as the introductory sections that ‘what system’ as the reference of trust needs to be studied empirically in order to acquire meaningful material. This material needs to indicate three things in accordance with the three hypotheses: the interview situation as a system of communication contributing to the construction of trust in that moment (H5); the identification of a system such as the energy system as an emergent entity (H6), or a patchwork of single components as objects of trust (H12). In short: system description, understanding and systemacy are at stake.



Expectation Nexus: A second decisive category of scrutiny throughout the book has been ‘expectations’. My analysis culminated in the idea that expectations are at the center of trust research and form the basis of the AoT (Chap. 5). H3, H11 and H15 clearly have the analysis of expectations at their heart which is why they build the foundation for this category. More precisely, the general task in this category will be to affirm or dismiss the general plausibility of expectations in trust research and more deeply to investigate the relevance of PROP TV’S sub-categories for the energy case.



Reassurance Patterns: A third aspect of analytical attention in this book was dedicated to the concept of trust/distrust incitement through suspending or upholding doubt and attributing certain narrative patterns to these actions. As a consequence, it makes sense to collect H8, H9, H13 and H14 in this category as all of these hypotheses concern processes of potentially reassuring, dismissing or modifying trust in the first place, or in the context of disappointments. H8 and H9 are at the exclusive center of attention in the interviews, while H13 and H14, through their concreteness, must be delegated to future research. This creates space for the delicate analysis of the knowledge/non-knowledge narratives (H8, H9) that will easily fill this dimension in the interviews. Closely connected to the latter hypotheses will be the scrutiny of trustors’ risk perceptions.

The prior alignment to the three categories left out some hypotheses: H1, H2 and H4 are so fundamental that no single category can capture them in isolation from the others. Therefore, they will be addressed in the final conclusions based

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on results in all three categories. H7 and H10 are hypotheses that concern the way trust is interpreted and can hardly be examined in interviews. Therefore, they will also be reflected in the final conclusions, in terms of first application to the results from the single categories. Below, Figure 6-1 shows how the hypotheses from throughout the chapters merge into the respective analytical categories (SI, EN, RP) for data analysis. H5 H6

System Identity

Interview Guide Category I

H12

H3 H11

Expectation Nexus

Interview Guide Category II

H15

H8 H9

Reassurance Patterns

Interview Guide Category III

H13, H14 Figure 6-1. Distribution of Hypotheses to the Categories of System Identity, Expectation Nexus and Reassurance Patterns

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6.3.1.1 System Identity The three hypotheses underlying this category are: 

H5: System trust references can also be systemic trustors – not only trustees as ‘abstract systems’. Trust in systems is actively constructed within lowscale systems as trustors (e.g., interactions, organizations) independent of scholarly descriptions of the ‘real’ system reference.



H6: For a system to be object of trust, it needs to build a stable identity. An emergent system like this is available to trustors as commonly shared background reality.



H12: While systems such as energy are reflected by AoT, its elements are merely mediators of (dis-)trusting the system. Although it might be accessed through its representative elements, the ultimate reference of trust is the system itself rather than its elements.

To comprehend the ‘existence’ of a system has always been a delicate endeavor that requires much modesty and patience to undertake and evaluate (Luhmann 1995, 2ff). In line with the argumentation in Chap. 3, I am looking for a system perception among trustors that allows for comprehension of the energy system as a pseudo-closed, emergent system with a stable identity (H6). This identity makes a system – be it ‘really’ that way or not – visible to the audience as a background reality to their experience of the outputs such a system generates for them. From a constructivist point of view, such a system perception is a prerequisite for talking about ‘trusting a system’ and justifies that it is the system as such – not its components or representatives – that is “the real repository of trust” (Giddens 1990, 85). The concept of system trust stands and falls by this kind of system comprehension (H12). To explore the degree of system awareness among the interviewees, I pursued an active and a passive strategy: Actively, I started by asking questions related to spontaneous associations of trustors with the energy sector. Without mentioning the term ‘system’ directly and rather alluding to ‘energy or electricity supply’, the interviewees were expected to provide the genuine connections that pop up in their heads. Depending on whether their answers remained rather abstract (e.g., techno-political complex) or emphasized concrete actors or components (e.g., utilities, technologies), I would react accordingly and ask for some

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further details to provoke contemplation. Interviewees who remain on a systemic level could be challenged to think about small-scale actors, while people stressing the latter in the first place would get a response triggering the complexity of interactions in the energy system that actually lead to electricity provision (see commentary on interview guide below). All in all, a questioning technique that flexibly reacts to the trustors’ initial responses and moves from abstract to concrete and back serves to localize peoples’ system understandings and the stability of their attitudes against challenging feedback questions. Finally, answers outside the expected realm of energy system associations are possible and explicitly desired in this qualitative design to assure for misdesigned assumptions on my side. Passively, I follow an ethnomethodological strategy (Garfinkel 1967) in comprehending system understanding. That is, I create the reality of the energy system together with the interviewee and assume that, if no explicit renunciation occurs, we both agree on this as underlying reality. In this way, we make ourselves “accountable” (ibid., 1) for creation of everyday practices, instituting expectation expectations that are ‘inter-subjectively’ valid and remain unquestioned. In other words, I introduced the terms of “electricity supply” or “provision with electrical energy” – avoiding the systems term – and used it without explanations in a taken-for-granted manner. If no one questionned me, I have to assume that we are both talking about the same thing, no matter what images people might have in their heads. This strategy needs to be interwoven with the primary approach of asking some explicit questions about system understanding. In a similar fashion, Garfinkel and others have conducted a number of social experiments, also involving creation and distortion of trust (Garfinkel 1963; Sacks and Jefferson 2006). Moreover, the application of ethnomethodology helps to combine an approach of treating both object (the energy system) and subject (the interaction system of the interview) as a system and therefore fully recognizes the social dynamics of the construction of trust on the side of the trustor (H5). Table 6-1 displays the underlying hypotheses, inferred question(s) and the strategy behind it as initially noted above.

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Table 6-1. Interview Questions about Category I: System Identity

Questions

Strategy

Background

1. What comes to your mind when I use the term ‘electricity supply‘?

Spontaneous associations; reflexivity and awareness; single components vs. system

H6

2. Have you ever thought about all the things required for delivering electrical energy to your household? Which requirements can you think of?

Reflexivity support; system understanding and elements; systemacy of electricity generation

H6, H12

3. From your personal perspective: who or what is involved in permanently maintaining electricity generation and upholding security of supply?

Sorting out and relating elements; emphasizing certain elements before others; single components vs. system

H6, H12 Whole category I (implicit): H5

Finally, a comment on the coding and evaluation efforts of this category. In general, all categories applied in this study rely on explicit statements rather than implicit signs or indicators (as described in the ‘Design and Methodology’ Sect.). This makes it both easier and harder to find concrete answers, but is a situation that I have to accept at this point. Accordingly, interviewees’ explicit words, statements and sentences relating to my coding scheme will be significant in this study. My coding scheme, as the strategies laid out in Table 6-1 suggest, relate to system elements (type, number, dominance) and their degree of systemacy. For the first aspect (system elements), I will use the AoT framework developed in Chap. 5 to make sense of the interviewees’ answers. Thus, I will sort their answers with the PROP TV’S scheme of the AoT according to the definition of its elements given throughout the book. Secondly, I will try to infer information about systemacy of element interrelation through the explicit statements of interviewees. As questions two and three in Table 6-1 indicate, energy system relationships are targeted without explicitly mentioning either system or certain element types. In the course of these two questions and its sub-inquiries, technique, patience and sensitive ‘testing

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through’ was of utmost importance. In this way, questioning techniques of repeating what interviewees enumerated in the prior question, having them contemplate out loud about relations or dominances of components are applied. The major objective is to find a common understanding of what was talked about and guide them to answer the complex and very general questions to a satisfying degree. All of this is done without raising the impression of suggestive questions, leaving enough room for participants to unfold their own, possibly deviating narratives. Once again, much care is needed to constantly avoid using any of the terms or concepts sought in this category such as system, systemacy, an overall emergent complex, identity, collective action etc. As a conclusion, one can apply the following sub-categorization to guide coding, as described above: System Identity 

System elements (type, number, dominance)



Degree of systemacy

A final note concerns H5: this hypothesis is of such fundamental character that it cannot be analyzed in isolation or as a direct consequence of one single question tailored toward its rationale. Rather, it needs to be taken as a general prerequisite, for this category in particular, but even for the whole interview. As a consequence, it serves as a reflection hypothesis, which (hopefully) leads the interviewer to the sensitive, cautious attitude necessary to reach the desired objectives.84 6.3.1.2 Expectation Nexus To recall the hypotheses in this category: 

H3: The basis of all trust research is the study of expectations. Based on established sociological concepts, service expectations toward both the general and specific functioning of a system can be ordered on a scale from

84

A more sophisticated operationalization of H5 is both challenging and tempting but cannot be delivered in the scope of this study. Its formulation – and consideration – alone is quite a farreaching effort.

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concrete to abstract, such as persons, roles, programs, and values. The differentiation of these service expectations and their evaluation through assessment of system outputs by trustors form the basis of system trust and its research. 

H11: The empirical reality of an abstract system such as energy is conveyed to trustors through an architecture of trust references. This expectation nexus of ‘persons’, ‘roles’, ‘programs’ and ‘values’ is a predefined setting that holds concrete references for reassurances and disappointments of trust or distrust.



H15: The original AoT of persons, roles, programs and values (PRPV) can be enhanced by organizations, technology and adjacent systems (OTS) for the study of energy. This ‘architecture of trust’ holds addressable (persons, organizations) and non-addressable (roles, programs, values, technology, adjacent systems) references of trust. All of them can serve as reassurances of trust, but addressable references serve exclusively in cases of disappointed trust that is sanctioned because they can be communicated with.

The nexus of expectations, as Chap. 5 has shown, deals primarily with two things. First, it describes the generalization level of expectations, i.e., their abstractness: whether the majority of people simply expect ‘security of supply’ from the energy system, or are expectations finer grained, perhaps tuned towards green electricity from certain sources at a certain price range? The level of expectation generalization which people address tells us a great deal about their system experience: the more specific their expectations are, the more probable it is that there is an active, conscious stance toward the system that includes perceptions of decision-making and personal disappointment and/orregret. For example more abstract or diffuse answers (e.g., ‘I just want electricity all the time’) probably signal little prior reflection and a rather passive stance toward electricity provision. Answers to these questions can provide hints about the level of generalization at which interviewees locate their expectations in a field like energy supply, implicitly revealing their dominant access points to the system. The question of ‘system identity’ can also be sharpened with ongoing discussion. In sum, this flexible sharpening of thoughts throughout the interview is important, because I try to recognize and further utilize the interviewees’ answers along the way.

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Another aspect is the material dimension of expectations: besides their specification and level localization, I wanted to find out what people actually expect of the energy system: political control, sustainability, CO₂ reduction programs, technology changes? I am interested in discovering what people care about in the energy field. This is in line with how specific or diffuse this attitude is, what level of generalization it concerns and who or what is potentially addressed to take care of it. On the basis of the initial energy system AoT presented in Sect. 5.4, it is of particular importance whether the interviewees refer to values (implying general system functioning), for instance, or perhaps emphasize small-scale actors and their roles and programs when articulating their expectations. This is vital for an evaluation scheme in this section of the study. All in all, this leads to consideration of the following dimensions of expectations (cf. Sect. 5.1): differentiation (number, generalization level), materiality (what is it?) and direction (who or what takes care of it?) of expectations. Expectation Nexus 

Differentiation



Materiality



Direction

All three sub-categories of the ‘Expectation Nexus’ can be explored with the PROP TV’S scheme of the AoT. This will be applied as the major coding rule that aligns interview text with the elements of AoT. Table 6-2 outlines the individual interview questions with their strategies and background. The basic strategy behind the questions is similar to that of the prior analytical category on ‘system identity’: avoiding explicit terms (e.g., ‘expectations’) yet trying to get interviewees to tailor their answers to the question as far as possible. To still allow room for deviations and thus examine the general plausibility of the hypotheses, the questions remain at an abstract level rather than giving hints as to what answers might be expected.

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Table 6-2. Interview Questions about Category II: Expectation Nexus

Questions

Strategy

Background

4. What does a properly functioning energy supply accomplish for you personally?

Personal specification of normative service expectations toward the system

H3

5. What does a properly functioning energy supply accomplish for society?

Sorting out of expectations on societal level

H3, H11

6. From your personal perspective: who or what takes care of implementing your conceptions?

Fulfilment of these expectations: who or what is responsible?

H15

This was intended to be done only in cases where interviewees did not know what to say or did not understand what is requested of them. Collectively, the questions serve as a basis for evaluation of the appropriateness of the underlying hypotheses rather than of single hypotheses leading to one specific question. This is due to the fact that the hypotheses themselves are considerably abstract. As a consequence, the aim of the ‘Expectation Nexus’ category is to find affirmation or dismissal of the general applicability of the AoT in the way theorized in Chaps. 2 and 5. Such affirmation/dismissal can provide first hints on the validity of AoT as “predefined setting” (H11), and expectation evaluations by trustors as “the center of system trust” (H3). H15 is of special relevance when the additional OTS elements are targeted, in particular for trustees’ execution of expectations. Finally, this category can tell us a lot about decision-making potentials (H1, H2) in system trust. Implicitly, through analysis of differentiation of trustors’ expectations, it is possible to get an idea about the complexity of system-client relations for them. As a consequence, the more sophisticated expectations are, the more probable an active, decisionist stance toward electricity consumption can be plausibly assumed.

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6.3.1.3 Reassurance Patterns The four hypotheses underlying this category are: 

H8: Complexity and its role for trust depend on trustors’ perceptions.



H9: Complexity and its role for trust depend on trustors’ perceptions. Perception, in turn, can vary according to narratives of knowledge or specific and unspecific non-knowledge. The dominance or mix of those narratives can be typically linked to forms of trust and distrust among trustors.



H13: Persons (family, friends, electricians) and regional organizations (e.g., utilities) play a major role in reassuring trust or reinforcing distrust in the energy system.



H14: In the AoT, organizations with the function of supervising and system control are less specifically addressed than roles associated with that function. The fact that these roles and functions are fulfilled in general is more important than which specific organization conducts it.

The operationalization and evaluation of ‘reassurance patterns’ concentrates on hypotheses 8 and 9, because H13/14 are too specific for analysis in this guideline-based interview and must be left for future research. The two prior sections (6.3.1.1 and 6.3.1.2) merge gradually into these ‘reassurance patterns’: this Sect. 6.3.1.3 seeks to reveal hints about the relationship between different levels in the expectation nexus with perceptions of knowledge and non-knowledge, i.e., certainty or risk. Categories I and II (‘system identity’ and ‘expectation nexus’) have elaborated on the system understanding and expectation building of interviewees. Where people have not referred to problems they perceive in relation to the energy provision so far, they are introduced here. This is done in accordance with Table 4-1. ‘Current and Future (Potential) Risks in the Energy Sector‘ introduced in Sect. 4.4. Perception and bridging of at least partial risk is vital to detect a relationship of trust. Alternatives include not bridging perceived risk, which communicate suspicion, possibly resulting in distrust. Besides, situations of familiarity might be detected in cases where no risk is associated, and instead the certainty of failure-free electricity provision is communicated without contemplation. As there are many nuances between certainty and insecurity, high and low risk, knowledge and non-knowledge, the main goal in this category III is to favor an exploratory approach to evaluating the communicative pattern building of these reassurances:

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

As stated in H16 and extensively discussed in Chap. 4, my own expectations in this field are to find more or less distinct types of reassurance: a majority of familiar ones, and a minority of trusting and distrusting ones. In other words, I expect the consumer to communicate either no worries at all (familiarity), a lot of worries at the same time (distrust), or minor insecurities that are not taken too seriously (trust). As a consequence, a sophisticated heuristic (e.g., linguistic rules, pre-defined variables of risk allocation etc.) in order to classify risk and knowledge can be spared at this point since detection of the mentioned types does not require sensitive measuring frameworks. It would be a task of future research on the basis of this exploratory field intervention to take this to finer dissolution once first order has been generated with this study.



Secondly, while it is true that my own expectations are brought into the interviews by applying some pre-defined categories (laid out in the moment), I want to make sure I remain receptive to surprises. If I create a specific scaling or measurement for risk or knowledge, I run the risk of trying to find what I am looking for. Therefore I will apply this H9 as loosely as possible for the sake of qualitative neutrality and in tune with the preliminary character of the theoretical findings so far. Another issue is the complicated definition of sub-categories such as specific and unspecific non-knowledge, for these render exact pre-definition almost impossible. Rather, a general assessment of interviewees’ treatment of knowledge and risk is sought, in order to find possible types that relate to trust, distrust or familiarity and get some first affirmation or dismissal of the general plausibility of H9.

As a closing question, a situation of crisis and change was introduced to the interviewee with reference to potential disruptions of expectations to find out about their current attribution of responsibility in cases of disappointment: who or what would they blame if their expectations were disappointed today? This question flexibly reacts to the prior responses of interviewees, i.e., they can talk about disappointment in insufficient security of supply (if this is the major direction of argument, for instance), or disappointment of more complex expectations if these were mentioned throughout the interview. Attributions of disappointment move in two basic causal directions (see Sect. 5.1 and 5.2): toward oneself or toward the social environment. Where the latter is addressed, interviewees would be asked about a more precise sanctioning location.

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In conclusion, questions in this category should target narratives of knowledge and (specific/unspecific) non-knowledge. As laid out in Sect. 4.3, specific non-knowledge can be linked to what we call ‘risk’ and therefore builds the basis of analysis. As risk itself is a controversial term evoking multiple associations for participants – especially in the energy case – it is substituted in the interview by ‘problems’, ‘things that go wrong’, or ‘worries’. From there, narratives of bridging risk (trust), not perceiving it at all (familiarity), or rejection of taking risks (distrust) are triggered. Within a(n) (artificial) situation of risk and crisis (e.g., power outage), interviewees attribute responsibility for disappointed expectations (cf. H4). In a wrap-up question, particpants are asked to mirror their prior statements against the dawning situation of change in the face of Energiewende. This last question rather serves general interest motives and plot completion than specific hypothesis examination. Table 6-3 displays the last three questions of the interview which are dedicated to the category ‘Reassurance Patterns’. Table 6-3. Interview Questions about Category III: Reassurance Patterns

Questions

Strategy

Background

7. Do you already have any experience regarding problems in the energy sector?

Personal specification of risk-related experiences and handling

H9

8. Where would you pinpoint the responsibility or cause of these problems?

Attribution of responsibility toward self/other

H9, (H4)

9. Do you believe that the German energy transition will change any of the statements you have made so far?

Wrap-up question

Consequently, the category “Reassurance Patterns” tackles three sub-categories:

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Reassurance Patterns 

Knowledge



Non-Knowledge (specific, unspecific)



Attribution Causality (self/other)

6.3.2 Interview Guide System Identity85 1.

What comes to your mind when I use the term ‘electricity supply’? alternatively: ‘energy sector’ 2. Have you ever thought about all the things required for delivering electrical energy to your household? Which requirements can you think of?  From your personal perspective: can you order these requirements by relevance (possibly think out loud)? 3. From your personal perspective: who or what is involved in permanently maintaining electricity generation and upholding security of supply? 

Expectation Nexus 4.

What does a properly functioning energy supply accomplish for you personally? 5. What does a properly functioning energy supply accomplish for society? 6. From your personal perspective: who or what takes care of implementing your conceptions?  Do you actively pursue this implementation?

85

The interview guide was translated by the author from German to English.

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Reassurance Patterns 7.

Do you already have any experience regarding problems in the energy sector?  How did you deal with these problems?  Have you any ideas regarding the problems that may arise during the supply of electricity? What do you think could hypothetically go wrong? 8. Where would you pinpoint the responsibility or cause of these problems?  Whom/what would you consider responsible when something goes wrong, i.e., if the electricity supply were interrupted? Who/what would undertake the re-establishment of energy delivery? Wrap-up 9.

Do you believe that the German energy transition will change any of the statements you have made so far?

6.4 Results 6.4.1 Initial Remarks and Observations Prior to presenting the results of the study, I will briefly discuss the following issues: (1) Sample Information, (2) Interview Impressions, and (3) Coding Rules. Firstly, Sample Information (1) concerns the basic social structure of the interviewed persons. It builds the foundation for all knowledge derived from them. Fortunately, I was able to acquire a fairly balanced set of interviewees regarding the basic socio-demographic information (sex, age, education, residence) gathered at the end of the interviews. The study was conducted with N=30 participants with an average age of 39 years (15 men/15 women) in seven German cities, mainly in and around the cities of Hannover and Karlsruhe. Larger cities starting at 100 000 inhabitants were considered urban, others rural (15 urban residence/15 rural residence). The education level was aligned as

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‘basic’ (8), ‘intermediate’ (6), and ‘advanced’ (16) in accordance with the German education system, with the addition that a college or university degree was necessary to qualify for the ‘advanced’ share. ‘Basic education’ can be compared to successfully graduating from high school, ‘intermediate’ requires some additional schooling before college or university education. None of these basic sample data was put in a causal relationship with other answers given by participants, in the form of ‘male tenants from urban areas have very abstract expectations of electricity supply’, for instance. It merely helps demonstrate the sample’s balance with respect to its socio-demographics, thus increasing the study’s credibility. The second introductory aspect concerns Interview Impressions (2). In the course of conducting the interviews, it was fascinating to observe how the theoretical underpinnings transformed into empirical condensation. Particularly noteworthy is the fact that the questions developed in the operationalization Sect. 6.3.1 did not cause irritation for the interviewees apart for some minor exceptions. This means that overall, every question made sense to the participants and was not challenged in its meaning, intention or adequacy. This is a first important step to achieving plausibility with regard to some of the implications of system trust, since it shows that the general categories used – represented through the respective questions – appear in the ‘life-worlds’ of participants, one way or the other. Naturally, every interview requires clarifications and explanations in some way. Problems occurred with the notion of ‘energy sector’, where many interviewees had trouble grasping its meaning, although most people supplied associations for it, but fewer than for ‘electricity supply’. A field of slight renunciation of questions was in the second block of the interview (‘Expectation Nexus’), which starts by asking about a ‘properly-functioning electricity supply’. Here, some participants repeated the question to themselves and partly asked for clarification, before giving tremendously detailed answers and assessments about their expectations of the field. The same was true for the subsequent question about ‘proper functioning on the societal level’, where many participants provided rich and well-founded answers. Overall, this interview category II (Sect. 6.4.3) demonstrated a smooth working of the interview guide in the vast majority of cases.

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In the ‘Reassurance Patterns’ Section 6.4.4 (category III), everything also went smoothly apart from one interviewee who was not able to suggest anything that might go wrong in the energy sector apart from power outages. Even after several attempts of rephrasing ‘what could hypothetically go wrong in the domain of electricity supply’, we could not reach an understanding of what could possibly be meant by that beyond power ouatges. This has implications for both risk perception and system understanding, considering that 29 other people named several issues, and some supplied coherent and complex story lines. In line with the argumentation in Sect. 6.3 related to Coding Rules (3), I have enhanced and specified the first round of coding from operationalization inductively. This means that the material helped in deciding how exactly some of the theoretical prerequisites from the three analytical categories should be aligned with text segments and evaluation. The details are presented in each category below, but I want to provide some reflective comments here that concern the whole study. First, I was surprised how well the sub-categories of the three main categories worked out intuitively. I had no problems aligning text units with the coding structure set up in this way. For the first two major categories (System Identity and Expectation Nexus), no changes seemed neccessary, even after going through the material for the second time. This means that both ‘System elements’ (type, number, dominance) and ‘Degree of systemacy’ (SI) as well as ‘Differentiation, Materiality and Direction’ (EN) are the final variables in the respective categories. The third category ‘Reassurance Patterns’ (RP) received the most alteration, in that one variable was added generally (‘certainty-equivalents’ = CEs) and the variable ‘risk index’ was added on top of the already established ones (knowledge/non-knowledge/attribution causality). This is due to the expected exploratory character of this category, which proved to be rich in insights yet required some minor modifications of variables. By and large, this sort of enrichment of the category RP also plays out regarding the evaluation scheme, which is genuinely qualitative-interpretative. This means that analytical category III offers material that will be aligned with the variables only in the shape of its written text and interpreted to plausibilize them in any way possible. This is done against the background of some surprises concerning the interviewees’ strategies of aligning knowledge and non-knowledge, and them possibly mentioning CEs to overcome perceived risk. A far more complex picture than

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originally painted in H16 is the result, which has intriguing consequences for modeling and research of system trust. In evaluating the first two categories, I was able to partly find numerical support for the qualitative assessment. In the first category (SI), this concerned the amount of system elements coded with regards to the AoT as well as their differentiation as reflected by the number of different levels addressed. Finally, I counted the number of statements uttered that would qualify as indicators of systemacy relations and put those in connection with the prior numbers to establish a qualification for the ‘degree of system identity’ of the interviewee. This procedure proved especially useful given the application of AoT, which helped to quickly align the statements of interviewees. Consequently, I have also applied AoT broadly throughout analytical category II. This category displays expectation differentiation (of single interviewees and for society) on different AoT scales, as well as expectation materiality and direction toward addressees of implementation. Overall, the AoT scheme was used thrice in this category, while expectation differentiation was ‘measured’ mainly by the number of personal expectations and – once again – its scale diversity regarding the AoT. This approach proved useful in obtaining a transparent, well-founded impression of the diversity of expectations in the energy field, while remaining embedded in an overall qualitative assessment. In sum, I have used the AoT scheme of PROP TV’S as a coding tool for sub-categories five times. More details on the Coding Rules of the respective categories will be provided in the next three sections, along with the results. 6.4.2 Category I: System Identity Let us recall the questions aligned with this category: System Identity 1.

What comes to your mind when I use the term ‘electricity supply’?  alternatively: ‘energy sector’ 2. Have you ever thought about all the things required for delivering electrical energy to your household? Which requirements can you think of?

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

From your personal perspective: can you order these requirements by relevance (possibly think out loud)? 3. From your personal perspective: who or what is involved in permanently maintaining electricity generation and upholding security of supply? Following up on the previous section, I want to explicate some more coding rules prior to presenting the results of this category. This is crucial not only as a formal requirement but also to understand the content of the results and how I got there. As mentioned above, system identity is to be determined as a combination of system elements (type, number, dominance) and their degree of systemacy. The first aspect is accomplished through application of AoT in the shape of PROP TV’S. More precisely, this means that the interview transcript is worked through while attributing text segments to the respective levels of AoT, according to its definition in Chap. 5. I took single words or phrases to align with all categories except for programs, which require combinations of several roles to coordinate some kind of overarching effort in action chains. This alignment was comparatively unproblematic, apart from minor problems in distinguishing roles from organizations, for instance: is “electricity supplier” a role or a specific organization? After going through all the interviews, I re-directed some of the alignments made there to achieve overall coherence. Answers like “nuclear energy plant” were coded both as “technology” and “organizations”, since it was not always clear which was meant, and double attributions account for this. More than one attribution to a code was prevented in situations where an interviewee mentioned the same thing with different wording, i.e., “renewable energy” while later saying “alternative sources”. Here, I coded only once. In order to establish the AoT reflecting interviewees’ associations with “electricity supply”, I only used the first part of the transcript that actually encompassed the questions in this category. Figure 6-2 displays the distribution of the AoT levels as total numbers from all interviews. Persons did not appear often, while technology and programs were dominant. Values were foregrounded, while adjacent systems rather remained in the background. Roles were also a decisive element that often appeared in this part of the interviews. All in all, AoT provided a helpful scheme

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that intuitively allowed for text unit allocation on all levels. This was more the case for some than for others, as technology represents the vast majority of AoT allocations, while single, specific persons represent an almost negligible margin (Figure 6-2). Overall, many participants displayed quite vivid associations with both “electricity supply” and “energy sector” (question 1) and were able to mention diverse components on several AoT levels. Strikingly, technical elements played a large part in many narratives86, often alluding to energy generation technologies (e.g., nuclear, solar), technical infrastructures (e.g., transformers, cables) and consumer devices (e.g., smartphones, white goods). On the program Persons 300 250 Systems

200

Roles

150 100 50 0 Organizations

Values

Technology

Programs

Figure 6-2. Total Numbers and Distribution of AoT Elements

level, people most frequently mentioned coordinative efforts around generation, transport and distribution of electricity, as well as economic (R&D, load management, monopolies), and political programs (energy turn, NIMBY, subsidies). Organizations often encompassed power generation plants and local utilities 86

It should be mentioned that in this category, interviewees have not been further asked about their concrete technology comprehensions, e.g., whether technology as such or in relation with social elements is referred to. Category I counts on intuitive, open answers as sketched out in Sect. 6.3.1. However, the role of socio-technical trust objects in line with the findings from Sect. 5.3 will be discussed further below.

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while roles concentrated on consumers and citizens, respectively (Table 6-4). Besides systems, both persons and roles were the least mentioned AoT scales in this part of the study. It seems as if the more lifeworld-oriented elements such as neighbors, friends, family (persons) or electricians and service staff (roles) play a smaller part in direct, open questions about electricity supply. Instead, the first, unfiltered associations and narratives people come up with move in the direction of rather abstract technology (power generation technologies and technical infrastructure) and programs (organizational patterns of electricity generation, transport and distribution, load management, subsidies policy). Organizations, in this section, must be placed between abstract and concrete, as about half of these were addressable (such as certain local suppliers or federal power companies with respective names), while the other half was mentioned as a category (“big companies” or generation plants). I included “cities” and “countries” as well as “households” as frequent references. While the former can be aligned as “organizations” through their administrative aspects of organizational operation of both city administrations and nation-states, the latter is a compromise I made in order to include this important item. Households are a crucial element of reference in both energy debates and among my interviewees, so that their inclusion seems apt, while acknowledging that from a strict theoretical point of view their qualification as organizations might be disputable. Table 6-4 collects the most important references and their frequencies throughout the interviews. As a consequence of these results, we can draw some first conclusions. It is remarkable that the AoT levels participants mostly talked about were located at the rather abstract, non-addressable scalings of technology, programs and organizations, with the latter often being unspecified (Table 6-4).

Co-Workers (1)

Friends (1)

Consumers, end users, clients (12)

Mr. Homann [head of German Regulatory Agency BNetzA] (1)

Engineers, Experts (4)

Agents, intermediaries (6)

Workers, service staff (7)

Suppliers, producers (11)

Citizens, single/ private people (11)

(R)oles

(P)ersons

Internet portals (2)

Public institutions, regulators (10)

Companies, corporations (14)

Households (16)

Cities, countries (18)

Local utilities, energy suppliers (19)

Power generation plants (21)

(O)rganizations

Costs (13)

Private programs (17)

Political programs [energy turn, NIMBY, subsidies] (27)

Economic programs [R&D, load management, monopolies] (28)

Generation, transport and distribution of electricity (35)

(P)rograms

Table 6-4. Distribution of AoT: Most Frequently Mentioned Elements

Consumer devices, appliances [power outlets, EVs, white goods] (56)

Technical infrastructure [grid, transformers, cables, etc.] (61)

Energy generation technologies [nuclear, wind, solar, etc.] (131)

(T)echnology

Innovation, R&D (4)

Affordability, profitability (12)

Accomplishment of civilization (14)

SoS, availability, failure-free operability (14)

Sustainability (17)

(V)alues

Media, Medicine, Grocery supply (3)

Technical systems (3)

Energy system (4)

Politics (4)

The population, society (7)

Economy, markets (7)

(S)ystems

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If we leave technology out of consideration, we can see that programs, values (e.g., sustainability, SoS, accomplishment of civilization) and organizations were all considerably higher attributed than roles or persons. So even on the scales of merely social elements, the abstract has an overweight against the concrete. In the technology column (Table 6-4), a distinction between the first two (more abstract) items (power generation technologies and technical infrastructure) and the third concerning “consumer devices and appliances” (power outlets, EVs, white goods) is apparent. Here, as elaborated in Sect. 5.3, it is not always clear whether consumer relations with technology are genuinely concrete or abstract, but the mentioning of everyday electricity-consuming devices such as smartphones, washing machines or power outlets indicates a different degree of abstraction from mentioning high-voltage cables, storage technologies or transformers. These observations lead to some further conclusions about the participants’ system descriptions: no interviewee mentioned AoT elements on only one or two of its levels, for instance, so that a certain diversity of elements can be attributed to each single participant, making a strong case for a ‘socio-technical’ perception of electricity supply. Still, tendencies of ‘biased’ narratives in terms of favoring one or more levels and/or content of the AoT are detectable. While this is not the place for a truly ‘type-building’ content analysis87, I want to allude to the detection of at least four different characterizations of energy systems that crystallized in a first, unsystematic scrutiny of the material: technology-driven, market-economic, political-environmental and pragmatic-indifferent. While these types are not set in stone, they signify that among system descriptions the ‘socio-technical’ can deviate in favor of (abstract) technology, economic programs and organizations, political programs and values, or lifeworld-oriented technology, organizations, roles and values (pragmatic-indifferent). The question of whether these ‘types’ would withstand more rigorous empirical scrutiny and have an influence on the overall constitution of system trust must be left to future research. The second part of the analysis in this category concerns the sub-category degree of systemacy. My treatment of the interview material changed slightly in comparison to the system elements (type, number, dominance) presented above. When going through the interviews, I realized while answering questions in the second block on the ‘expectation nexus’, many participants seemed to reflect on 87

As described in Sect. 6.3.

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their answers given in the first block (‘system identity’). Therefore, in this category, insofar as statements indicating element systemacy occurred, I referred to all parts of the interviews to extract them for interpretation. Overall, most statements relating to degree of systemacy still remained in the first block or early in the second block of the interviews. Below are some prime examples of statements that indicate systemacy of system elements, i.e., ‘emergent action’.88 One interviewee, answering who or what secures execution of their expectations (question 6), said: “For a start our instructions on how to build come from above, from the politicians. Then it descends to the federal states, who really try to implement all these national instructions. Then it descends again, to the companies, who then basically produce the electricity. The companies pass it on to their workers, who deal with production in the factory, who are responsible for operating the machines; and then it goes on to the electrician who deals with electricity provision deficits in households and basically fixes everything on site, getting things right again; moreover, the municipal energy provider must check exactly that […]. The whole thing only works when each small cog works.” (LS10014089, 64)

Answering the question of who or what guarantees the reliability of energy supply in Germany (question 3), another interviewee said: “I wouldn’t point at a particular institution. I mean, I’m not an absolute expert in the field, but it’s really a cooperation between various things; on one hand, the technologies enable the coordination, or the constant supply; on the other hand, there are diverse agents. Well, earlier I mentioned the Bundesnetzagentur [German federal agency for electricity, gas, telecommunications, post and railway]; another example are companies that maintain the network stability according to demand, providing it with electricity. It’s somehow a variable, flexible system that is intervowen and it must then provide a constant supply. I couldn’t really say which agent exactly, or which technology exactly is crucial; rather, it’s an interplay.” (LS100142, 21)

These are clear and outstanding examples that were not replicated in other cases. Sometimes people merely referred to “the whole thing” (e.g., LS100147, 33) or 88 89

Cf. Sect. 3.4 to recapitulate the theoretical bases. This referencing system is a reproduction of the file naming of consumer interviews in transcribed format, and the letters and numbers used (‘LS1001’) have no distinct meaning other than serving the purpose of anonymity. One exception is the last two digits, ranging from ‘12’ to ‘47’, which represent the number of interviews. Some interviews were recorded in several audio and corresponding transcription files, so that the number of 47 subtracted by 12 is slightly higher than the total number of 30 interviews. The number of ‘64’ refers to the line numbering in the transcription file.

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“different interacting levels”90 (e.g., LS100141, 70) as indicators of emergent systemacy. One interviewee answered the first question of the interview by stating: “Electricity supply is an elementary important […] system” (LS100115, 4), a term which was mentioned quite often by interviewees. People also used synonymous terms such as “domain” or “realm”. All in all, I tried to pinpoint explicit utterances of element relations and systemacy rather than single terms to evoke the coding, as elaborated in the operationalization section. Further examples of affirmative statements of systemacy and element relations include: (a)

“In the end, a series of institutions and societal forces must work together.” (LS100115, 84)

(b) “Indeed, the electricity must be generated; the electricity must somehow be brought there, be transported, must logistically be freighted. I do not know what to call it now [Interviewee laughs]. There must somehow be an electricity network.” (LS100117+18, 38) (c)

“I think one can’t determine a specific thing; instead, it must work as a whole”. (LS100145, 23)

(d) “A distribution station, yes. I need a producer of power outlets, exactly [Interviewee laughs]. Yes, I believe that this is difficult, because in fact I also might need… I need the people, who produce these things; and then I need things which boost energy sources in a manner in which such a thing must nowadays be done. I need people, I don’t know, who produce solar cells. A lot more connected stuff. But yes, [laughs], that’s the basic outline, exactly.” (LS100146, 23). I also tried to account for notions of rejecting systemacy, especially as an element of the ‘ethnomethodological approach’ presented above.91 This means that each time an interviewee mentioned doubt or skepticism regarding a notion that tries to relate elements or puts them in systematic interplay, I noted that as a negative, just as I noted affirmative statements. For instance, some interviewees rejected the notion of an “energy sector” (question 1) which led to subtractions in determining their degree of systemacy. In order to come to a comprehensive

90 91

All citations of this kind and quotations of interview passages are translations conducted by the author from the original German transcripts. Cf. Sect. 6.3.

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and transparent way to display this information and infer consequences for system identity (SI), a simple formula was used: 𝑺𝑰

𝑁𝑜𝐸

𝑁𝑜𝐿

𝐷𝑜𝑆

Where: NoE = Number of Elements, NoL = Number of AoT Levels and DoS = Degree of Systemacy NoE and NoL refer to the introductory paragraphs of this section: NoE encompasses the total number of elements mentioned by single interviewees according to the AoT (see Figure 6-2), while NoL refers to the diversity of scaling on the AoT levels PROP TV’S. In other words: how many elements did interviewees come up with and how diverse were they? This means that the more codings that were possible on the different levels of PROP TV’S, the higher the number given for NoL. In this case, it theoretically ranges from zero to seven, since PROP TV’S counts seven levels. In practice, all values ranged between three and seven, because no participant only mentioned programs or roles, for instance. DoS refers to the total sum (including negations) of statements on systemacy (as disussed above), and resulted in numbers between -1 and 5. Altogether, this led to a classification of each interviewee in the shape of NoE/NoL/DoS, e.g., 18/4/1. As a rule of interpretation, I chose a simple threefold scale of low-medium-high to describe the overall degree of system identity (SI) in this category. After having gone through a considerable amount of interviews, I was able to set a reasonable minimum standard for a medium SI at NoE ≥ 10, NoL ≥ 2 and DoS ≥ 1. A high SI would be attributed in cases where NoE reaches 20, NoL reaches 4 and DoS reaches 2. Importantly, this means that DoS crucially influences the overall alignment, as someone with DoS = 1 can never be tagged with a high SI, while a DoS equaling 0 or below automatically leads to a low SI classification, no matter how high NoE or NoL might be. This unavoidably sets the standard for my own limited sample of N = 30 and needs to be validated in follow-up studies to account for coincidental biases among the participants.

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As a consequence, I was able to attribute a low, medium or high system identity to each single interviewee and add them up to a total sample share (Figure 6-3). 14 12 10 8 6 4 2 0 Low

Medium

High

System Identity

Figure 6-3. Total Classification of System Identity

What does this mean? First of all, what it does not imply is any kind of quantitative logic that would result in percentages or any kind of generalized inferences. The numbers used here are internal attributions of items that reflect a first tendency within this relatively small sample. Still, what Figure 6-3 tells us is that two thirds (21) of the interviewees have a system identity understanding that is medium or high. That was not to be expected, given the formulation of H16 (cf. Sect. 6.1.2). Still, 9 participants were not able to go beyond basic associations or form conncetions between the things they put forward (low SI in Figure 6-3). A common statement relating to this group was ‘I never really dealt with this topic’, or ‘That is something you don’t want to have anything to do with’, indicating low degrees of reflexive capacities on energy issues. In order to get to a more systematic interpretation of the results, I want to mirror them with the three hypotheses underlying this category. From there, we can draw conclusions, starting with H5:

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H5: System trust references can also be systemic trustors – not only trustees as ‘abstract systems’. Trust in systems is actively constructed within low-scale systems as trustors (e.g., interactions, organizations) independent of scholarly descriptions of the ‘real’ system reference.

This most abstract hypothesis, as mentioned in the operationalization Sect. 6.3.1, can never be fully proven, but rather needs plausible context. In the interviews, this context was twofold: (a) the interaction system between interviewer and interviewee and (b) the assumption that a common reality between these two exists unless one party rejects the ideas of the other. (a)

Reflecting on the interaction situation with interviewees and considering the data presented above, it is apparent that the issue of self-referentiality is underestimated in trust research so far. The system descriptions of the participants were so multifold, even among only 30 people, that an idea of a ‘real-life, objective energy system’ outside the building the interview was held in becomes more than blurry. In self-referential closure, interviewees created systems through interacting with me and my questions that, as introduced above, can be related to at least four different types: technologydriven, market-economic, political-environmental and pragmatic-indifferent. These playful and creative internal constructions are much more important for trust/distrust-building among the interviewees than any kind of external system reference that would tell them ‘the truth’ about energy engineering or economics, for instance. The phantasy of participants clearly outsmarts the techno-economic properties of electricity supply and renders them one of many realities: if people want to think that electricity comes from the power outlet92, then that system understanding is perfectly valid. If someone is convinced RES are not profit-able93, this perception merges into the system description and unfolds consequences. This is additionally substantiated by the fact that a majority of participants remained more or less consistent in their stories, in that their system understanding also related to certain expectations (category II) and risk perceptions (category

92

“I don’t deal with that. If there is something I hate, then it is such… really. [Laughs.] Isn’t it enough that there is a power outlet, or that the oven can be put on? [Laughs.] Well, that is zero physics.” (LS100128, 33) “Why do we pay an RES levy, why do we pay it? It cannot be explained with normal profitability reasons… no-one would do that, it’s huge nonsense.” (LS100126, 21)

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III). In other words: if consumers trust a system, it is their own system and not necessarily something we know as outsiders or academic analysts. And this system identity has consequences for trust in that it sets the framework for possible expectations and risk reflections. (b) The second aspect of H5 concerns the ethnomethodological idea that everyday, micro-scale reality is constructed through unacknowledged sharing of mutual insinuations of what is ‘the case’ at a certain moment in time (Garfinkel 1967). In the interviews, I tried to loosely apply this idea through assuming that people shared a more systemic view on the topic of electricity by going along with the terms, ideas and concepts built into the questions. While I actively avoided leading questions, I still asked about abstract issues that were tailored toward the concept of system trust developed here. Under the circumstances, suprisingly few interviewees questioned my wording. As noted above, several interviewees communicated problems with the term ‘energy sector’ that I used as a follow-up to ‘electricity supply’ (question 1) for those participants who had trouble coming up with quick answers. These troubles merged into my attribution of DoS statements, which means they are thoroughly considered in the evaluation of SI. Other instances of rejection occured mainly with ‘proper functioning of electricity supply’ in the second block (question 4), where a very small number of people needed clarificatory advice in order to answer the question. Additionally, one person did not contribute anything apart from power outages to answer question 7. Overall, this shows that implicitly, interviewees shared the insinuated realities accompanying the questions in all three analytical categories. It is proof that the interview guideline’s setup according to the theory of system trust made sense to participants, since the overwhelming majority of participants were able to answer right away without any kind of doubt about what was expected of them. It is a first sign toward a plausible idea of system trust based on system identity, an expectation nexus and reassurance patterns. The second hypothesis in this category and one of the central insights in this book is H6: H6: For a system to be object of trust, it needs to build a stable identity. An emergent system like this is available to trustors as commonly shared background reality.

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A good point at which to start evaluating H6 is Figure 6-3: through a threefold procedure (NoE, NoL, DoS), I was able to assign two thirds of the sample with a ‘medium’ or ‘high’ system identity factor. Starting with the basics, it seems surprising that so many participants had diverse and vivid associations with the topic of electricity. Given the historical evolution of the system and its current state, where electricity is a taken-for-granted resource, it must be unexpected that people came up with an average of 23 system elements (NoE), five AoT levels (NoL) and many with direct references to an emergent entity they are dealing with (DoS). While only the eight persons equipped with a high SI (Figure 6-3) can be seriously awarded a differentiated system perception potentially leading to relations of system trust, the 13 medium scored participants seem to be on the threshold between easy answers and a tendency toward allowing for more complexity. Statements in the medium group included: “Well, that’s the way the calculations are; in the end, it’s about the peak output and… well, I’m not that well versed with this […] It’s a bit like a stock exchange, too” (LS 100141, 23).

This alludes to the fact that these are people with an initial interest in energy who try to connect the dots, but often cannot precisely point out possible relations between elements. Some participants uttered contradictions in that they came up with statements of systemacy (DoS), but simultaneously revoked their classification as ‘high SI’ through rejective behavior toward the notion of an ‘energy sector’, for instance. In sum, it is fair to say that half of the medium SI group can be classified as prone to a genuine system perception of energy, while the other half rather resembles a group of single component observers without much overarching sense of emergent processes, as the following statement exemplifies: “Well, what must be guaranteed is that the energy provider, who basically provides energy to our household, is itself supplied with energy, either from… water or with a wind turbine. I don’t know how that works at the EnBW [one of the big German power companies], where the electricity comes from. And I don’t know how the technology works, how it’s done, how energy is produced and then flows to us. I don’t know that either. In any case, there must be cables that flow into each household.” (LS100112, 87)

A little later in the same interview the participant explains that the only representative aspect of electricity supply is “the companies that sell electricity”

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(ibid., 126), like the local supplier. These sorts of interviewees in the medium group combine an impression of pragmatic indifference, i.e., self-centered use of electricity, with some attempt of looking behind the power outlet while sticking to easily identifiable, single components instead of relationship narratives. An intriguing question – tackled in the final conclusions in Sect. 7 – is how far horizontal mobility between these (low, medium and high SI) groups is conceivable, given the goals of energy transition policies at targeting increased consumer involvement. Moreover, the low SI group containing 9 participants (Figure 6-3) needs to be taken into consideration. This group is definitely not able to link an identity with a possibly emergent energy system, and must be seriously questioned in terms of trust-building capabilities toward a system. This becomes clear by looking at some statements from this group. One participant, answering the add-on question 3 about the most relevant aspects of electricity supply, said: “Well, I probably can’t do much in my daily life without electricity. I’d be very limited, in that respect. No light. [Laughs.] I wouldn’t be able to plug in my hair straightener. [Laughs.] Well okay, but I also wouldn’t be able to use all the household appliances: dishwasher, washing machine. That would be pretty hard, such a life without electricity.” (LS100132, 35)

This statement substantiates the priorly mentioned bias of some people toward mainly associating electricity supply with their everyday routines and daily household appliances. Most of the low SI group members can be located in this consumer role, who care about using electricity for their own good, while often admitting that not much else is on their radar: “Electricity cables, power outlets. […] In the end, that’s the most important stuff for me. The SVO [German local utilities] or the electricity provider, whatever its name, it needn’t be called SVO. In the end, I don’t care how this provider brings electricity here.” (LS100133, 22)

This interviewee, responding to the second question on what is required to supply electricity into a home, mirrors much of what other participants in this group uttered. Some extended their associations into price comparisons of suppliers, SoS and on request a rudimentary mentioning of some generation technologies. Overall, this group remains weak in possible detections of overarching system relations. Another share of this group mentioned more elements (NoE) and AoT levels (NoL) in total, but lacked any kind of systemacy statements (DoS) and

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expressed their associations in a disparate manner rather than in comprehensive relational terms. Under these circumstances, what constitutes a “commonly shared background reality” and a “stable identity” (H6) for trustors should finally be elaborated. Given the fact that diverse descriptions and multiple system realities occurred among the interviewees, it is clear that the commonly shared background reality of the energy system is not one specific reality. On the contrary, it encompasses several different interpretations by trustors and finds its culminating point in the fact that all interviewees displayed a socio-technical description of electricity supply that involves several different components. These different components can be further broken down to different overall types of people (e.g., technical, economic, political, pragmatic) or varying degrees of SI (low, medium, high). As a consequence, the commonly shared reality among trustors relates to a socio-technical core of basic system components that include at least 23 elements on five AoT levels, representing the average numbers of responses. Since this mutual set of components remains the same for every participant, we can conclude that the reality of electricity supply for the sample is not the same among them, but is at least similar in its basic shape. Personal preferences in the direction of the technical, economic, political or pragmatic aspects of energy are differentiated expressions within that core set of components, which unfold their effects in the overall deduction of conclusions among the three categories (Chap. 7). Given the different SI ratings of low, medium and high, participants also differed sharply in their degree of system identity perception. This makes it plausible to assume that by ‘stable identity’, we have to understand that identity, in the eyes of this sample, relates more to there being a system at all than what exactly that system consists of. As noted above, roughly half of the sample can be plausibly assumed to notice some kind of system identity of electricity supply (eight high SI + half of the medium SI group). The other half of the sample are rather single component observant, not really making much of the possible interconnections between the elements they mentioned. This discussion on emergent system perception versus single component perception takes us toward H12 as the final hypothesis of this category:

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H12: While systems such as energy are reflected by AoT, its elements are merely mediators of (dis-)trusting the system. Although it might be accessed through its representative elements, the ultimate reference of trust is the system itself rather than its elements.

To begin with, it seems reasonable to say that the theoretical composition of AoT captured the reflection of the energy system. 694 codings on seven levels (cf. Figure 6-2) support the idea that this instrument was able to absorb and translate interviewees’ associations into a complex, yet structured AoT. It was rare that important issues were left out of AoT which could not be captured in this category, except for items that were coded by other means than AoT as indicated. Overall, this first application can be called successful according to the prior expectations formulated in Chap. 5. Further judgment about AoT applications will be provided in the following sections. Let us remind ourselves that H12 lays out one of the foundations for ‘genuine’ system trust through putting the system and its parts into a relationship relevant to trust attitudes. Considering this, it seems that the sample must be split in two halves on this issue: one with a rather element-oriented view, and one with a (in part potential) systemic view. In the latter case, elements such as local utilities, electricians or subsidy policies work as a transmitter for something greater that interviewees are aware of – the system. People in the former category, on the contrary, rather view these as single subject-object relationships between them and their utilities company, them and their electrician, or them and an administration carrying out a political program. Consequently, a theoretical, ‘genuine’ relationship of trust with a system can only be assigned to about half of the sample. Further insights on possible element-system relations among the interviewees and their consequences for trust, distrust and familiarity will be provided in the final conclusions (Chap. 7), after all three empirical categories have been reviewed. In Chap. 7, I will also comment on the overall system identity of electricity supply in the light of the interviews, taking into account the five criteria developed in Sect. 3.4: tight and loose coupling, symbolization of risk, functions and services, semantic advance and possible representatives.

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6.4.3 Category II: Expectation Nexus Let us recall the questions asked in this category: Expectation Nexus 4. What does a properly functioning energy supply accomplish for you personally? 5. What does a properly functioning energy supply accomplish for society? 6. From your personal perspective: who or what takes care of implementing your conceptions?  Do you actively pursue this implementation? As before, I want to start with some coding and evaluation rules. I applied a separate AoT frame to all three questions asked in this category. This means that for all three sub-categories of expectations, i.e., differentiation (number, generalization level), materiality (what is it?) and direction (who or what takes care of it?), I coded the respective text material according to AoT. The coding usually worked straightforwardly with attributions toward AoT components that were intutitive to make, as the subsequent examples and figures in this section will demonstrate. However, minor controversies regarding certain pieces of AoT attribution cannot be ruled out completely, e.g., whether a “minister” is a person or a political role, or whether “decentralization” (in the sense of self-owned energy communities) is a program or a value. The threshold between persons and roles, on the one hand, and programs and values on the other is sometimes blurry. Another controversy concerns the borderline between organizations and systems: although “the state” could also be considered a system, I referred to it as an ‘organization’ whenever the context of the federal government was indicated. In this way, in these rare cases of coding uncertainty, I tried to judge from the context of the interviews what level of generalization the interviewee was most likely referring to in the respective text segment. A final issue is technology: here, as will be explicated below, I also found interview extracts where the

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probability of reference to the technology itself – rather than a program coordinating it, for instance – overweighed and led to (relatively rare) codings of technology. However, apart from minor deviations, conducting these codings in another way would not decisively change the basic patterns of the AoT constitution. In the following, all the empirical data will be presented first, before turning to the evaluation that involves the underlying hypotheses of this category only. The first sub-category in question concerns the differentiation of expectations that I have inferred solely through the number and generalization level of personal expectations (question 4) of interviewees. After reviewing the material for the first time, this seemed the most reliable and transparent way of assessing the differentiation level of expectations, which in turn can tell us about probabilities of system trust (cf. H3). An alternative could have been to involve issues of materiality (personal and/or societal content of expectations) and direction (where are expectations directed) as well. To avoid suggestive interpretation and possibly overstate interviewee expressions, I treat these sub-categories separately and conclude further below with an overall picture taking all three subcategories into account. My analysis concerning mere differentiation, however, concentrates on how many different issues interviewees brought up when responding to the question 4, “What does a properly functioning energy supply accomplish for you personally?”, and on how many levels of generalization these can be located. This includes counting utterances such as “security of supply”, “affordability” or “sophisticated mechanisms for public infrastructure supply”. While the first two are considered to be values, the latter was mentioned as a societal program securing role coordinations to achieve infrastructure functioning. For each participant, I then counted the number of mentions (subsequently treated as ‘expectations’) in response to question number 4. The range of responses people mentioned was between one and six items. Scaling-wise, generalization levels reached from one to three, i.e., some interviewees stated merely one value, such as “security of supply”, while others perhaps added some more values and a program, and/or a role such as provision of “service staff”. In order to group the differentiation levels gained in this way, I used the same basic pattern as in Sect. 6.4.2: low, medium and high (Figure 6-4). To qualify for a high degree of differentiation, participants needed a minimum of three expectations on two AoT levels of generalization. This rule seemed most appropriate after

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having gone through all interviews for the first time. In addition, it revealed that a medium differentiation degree – reflected against all interviewees – made sense starting at three expectations where the generalization level stayed the same (e.g., three values). 14 12 10 8 6 4 2 0 Low

Medium

High

Expectation Differentiation

Figure 6-4. Personal Expectation Differentiation

It was reduced to two expectations (in two cases) whenever these two were located on different generalization levels (e.g., a value and a role). Overall, the medium group entailed mostly participants with a larger number in expectations, usually three, but all on the same level of abstraction. In this context, another evaluation rule was that no participant with only one level of generalization could be assigned a ‘high’ differentiation degree, no matter how many expectations they uttered. Therefore, one case even encompassed a 4/1 ratio (number of expectations/number of levels), meaning four expectations on the same level that were still coded medium. People in the ‘low’ group were assigned with 2/1 and 1/1, the latter counting six and the former five times, adding up to a total of 11 in this group (Figure 6-4). High differentiation degrees, in eight cases, encompassed a 3/2 ratio, while the rest splits up into 4/2, 3/3, 5/3 and 6/2, making

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up a total of 12 (ibid.). This illustrates quite forcefully that division lines are sharp, clear and consistent, especially between low and high. But what exactly were the expectations and levels people referred to most commonly? What was their expectational materiality?

Systems

Persons 60 50 40 30 20 10 0

Roles

Values

Organizations

Technology

Programs

Figure 6-5. AoT Allocation of Personal Expectations

Figure 6-5 displays the level location frequency of the personal expectations uttered by interviewees in respone to question 4. It is striking that values such as “security of supply”94 (31), “affordability”95 (12), “sustainability”96 (8) or “easy handling/little attention” (3) comprised the vast majority of interviewees’ answers to this question. Other values mentioned include transparency, regionality, maintenance of prosperity or societal availability of electricity, all of which were stated once. Second, but far behind, is the mentioning of programs. Here, the leads were “sophisticated mechanisms for public infrastructure supply” (4) and R&D programs such as “electromobility” or “energy storage” (2). Other programs such as “nuclear waste management”, “consumer choices 94 95 96

Equivalents mentioned were: availability, reliability, demand-oriented supply, permanent supply, interruption-free supply. Equivalents mentioned were: cost efficiency, price stability. Equivalent mentioned was: environmental cost/benefit.

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through competitive markets” or “self-owned energy communities” were each mentioned once. Overall, the ratio between codings of values against programs is 60/13 (Figure 6-5). Roles finally received 4 codings, being “service staff” (3) and “energy-saving consumer” (1). All of this data was extracted from answers to question 4 to build the analytical basis for the Personal Expectation Differentiation (Figure 6-4). To complete the picture, the next part will display the results from questions 5 and 6, before turning to the overall evaluation of the results. Question 5 of the interview guide dealt with the societal materiality of expectations (Figure 6-6) by asking: “What does a properly functioning energy supply accomplish for society?” Persons 50 40 Systems

Roles

30 20 10 0

Values

Technology

Organizations

Programs

Figure 6-6. AoT Allocation of Societal Expectations

At first sight, the results (ibid.) are intriguingly identical with those of the prior question on personal expectations of electricity supply accomplishments (Figure 6-5): In both cases, values dominate over programs. And yet, Figure 6-6 holds a much more balanced relation between values and programs than Figure 6-5. It seems that in the domain of expectations directed at the societal level, interviewees referred more to programs such as R&D (e.g., mobility, storage technology) or economics (e.g., demand/supply, load management, market competition) and sustainability programs (e.g., nuclear waste management, environmental cost/

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benefit analysis). Overall, 25 programs were mentioned compared to 43 values (see Figure 6-6). Values in this sub-category comprised similar materiality as in the case of personal expectations, but involved a twist of societal care: security of supply in terms of “universal access” to electricity (16), universal affordability (12) including “fair prices” or “abolishment of energy poverty”, and sustainability (11) relating to “reason”, “clean energy” or “environmental impact”. The values mentioned under question 4 were similar, yet not expressed so much in connotation with societal justice as the ones under question 5. Furthermore, values were mentioned in lower numbers in response to question 5, while this subtraction seems to have caused a surplus in the program realm, where numbers rose from 13 (question 4) to 25 (question 5). Values dropped from 60 to 43. Overall, however, both Figure 6-5 and Figure 6-6 display some kind of ‘clockshape’ with values and programs ‘pointers’ that are supplemented with minor mentions on other levels such as technology (both one), roles (four in Figure 6-5) and organizations (one in Figure 6-6). A somewhat different picture results when question 6 and thus the direction of expectations comes into play. “From your personal perspective: who or what takes care of implementing your conceptions?” was asked at this point. As laid out in the operationalization Sect. 6.3.1, this question tried to connect to the previous ones on differentiation and materiality. It sought to locate the direction of implementation and responsibility for the expectations people mentioned personally (question 4) and for society (question 5). What emerged from this could be described by a ‘rhombus-shape’ of the AoT (Figure 6-7): it includes several codings at every level of generalization except for values. By far the most commonly mentioned were organizations, followed by roles, programs and (adjacent) systems. Moreover, individual persons and technology were partly attributed to take care of interviewees’ conceptions. In total, the most frequent counts were power companies, suppliers and local utilities (26), and “politics” (counted as a functional system of society) (15). Other popular addressees were more concrete political actors at federal (10) and state (6) levels, or programs such as “regulation”, “laws” or “legislation” (9). Roles such as “workers”, “consumers” and “citizens” (8), or “entrepreneurs” and “engineers” (both 5) add to the picture.

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Systems

Persons 60 50 40 30 20 10 0

Roles

Values

Organizations

Technology

Programs

Figure 6-7. Direction of Expectations: AoT Allocation

Phrases alluding to concrete persons such as “single people” and “everyone for themselves” (4) were mentioned, as well as “ministers” (2) or even “Ms. Merkel” (1). How can we explain the different AoT shapes (clock versus rhombus) and what do they tell us for the purposes of assessing hypotheses H3, H11 and H15? How does the extracted degree of ‘personal expectation differentiation’ (Figure 6-4) influence their setup? In order to get started, let us first recall H3 to evaluate its significance compared to the material presented so far:

H3: The basis of all trust research is the study of expectations. Based on established sociological concepts, service expectations toward both the general and specific functioning of a system can be ordered on a scale from concrete to abstract, such as persons, roles, programs, and values. The differentiation of these service expectations and their evaluation through assessment of system outputs by trustors form the basis of system trust and its research.

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Concerning H3, we can see that the basic concept of expectations as defined in this book is placed in central position. Overall, it seems clear that the utterances of participants responding to questions 4-6 allow for qualification as expectations. It was easily possible to code their answers into the AoT levels without leaving out any important text segments. Crucially, from differentiation through materiality and direction, the AoT made up of PROP TV’S proved to be a helpful guide in ordering the complexities of electricity supply. Every level was attributed in the overall coding process, with more and less emphasis on certain components, depending on the issue under scrutiny. The next step is to enhance the evaluation onto “service expectations toward both the general and specific functioning of a system [that] can be ordered on a scale from concrete to abstract” (H3). After reviewing the material, it is apparent that consumers view their involvement with the electricity supply as an activity that goes beyond mere technical functioning. In fact, while a majority emphasized the importance of a permanent, interruption-free supply as their main concern with electricity, others referred to issues such as service staff provision, nuclear waste management, economic and political R&D programs or societal acceptance as their personal expectations. This distinction vividly demonstrates how general and specific functioning are reflected empirically, in that technical functionality through the value of supply security (general) is mentioned alongside social, economic and political aspects, as well as details that condition SoS (specific). In this respect, one participant (responding to question 4) said: “So, a properly functioning provision of energy is for me, as a consumer, naturally; I say, first I have… sure, it’s very simple… adapted to my needs; basically, that electricity is there. […] Sure, it’s also important that one already agrees that it’s adapted to need, but I don’t want to… I don’t really want electricity or energy at this point wasted for provision. So the provision should work, but it’s not okay to say that it doesn’t matter how many resources one somehow wastes for this provision, provided that it works.” (LS100115, 59)

The average number of expectations among all interviewees is 2.6, often including “security of supply”, “affordability”, “sustainability” or “public infrastructure supply”. These values, especially SoS and affordability, can be counted as major components of general system functioning as a result of their frequent, sometimes exceptional mentions by interviewees. However, the average number of expectations reaches 3.3 when only medium and high differentiation groups (Figure 6-4) are taken into consideration. While low differentiation participants

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usually mentioned one or two expectations, high ones talked about up to six. This is where an assessment of service expectations targeting specific system functioning draws particular plausibility. Remaining solely at the level of personal expectations (question 4), interviewees came up with a breadth of topics that give meaning to this specificity of functioning and the respective expectations. These include values such as “easy handling/little attention”, “transparency”, “regionality”, “technical security”, “maintaining prosperity”, “brightness” (of lights) or “societal availability”. Moreover, programs were popular among participants who alluded to aspects such as “consumer choices through competitive markets”, “self-owned energy communities”, “continuous information policy” or “possibilities of usage of energy sources”. One participant requested “public organization in favor of societal acceptance” and provided several details on how to achieve this aim. Finally, roles such as “service staff” or “energy-saving consumer” and “emergency electricity technology” as a presumed technology item were part of interviewees’ responses. The variety of these expectations is a strong indicator of the fact that general functioning of the system (the provision of electricity) is very much linked with proper functioning embedded in a number of neighboring conditions. Thinking about general and specific functioning in terms of center and periphery, we might say that the central expectation toward electricity supply – its permanent availability – is supplemented by a range of peripheral aspects that merely touch upon general functioning of the system. Rather, a whole set of frequently normative demands justifies Giddens’ discussion of “proper system functioning” (Giddens 1990, 34) that is more than fulfillment of a (technical) purpose. It touches upon the performance of a whole system (Kohring 2004, 110), accounting for both that and how its purpose is achieved. This is reflected in this study through participants’ affirmation of supply security, yet embedded in affordable prices, in sustainable sources, in regional structures and on competitive markets, for instance. Admittedly, this mostly concerns the high differentiation group and parts of the medium one (Figure 6-4) – but it still empricially demonstrates something that previously has only been an educated assumption in the trust literature. The idea of specificity in expectations and “differentiation of these service expectations and their evaluation through assessment of system outputs by trustors” (H3) can be further accentuated with reference to the societal domain of

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expectations. Interviewees’ answers to question 5 provide another source of plausibility for a differentiated set of demands that people associate with electricity supply beyond general functioning. While emphasis on values and programs remains, their specification and creativity is remarkable. Asked “What does a properly functioning energy supply accomplish for society?”, 16 interviewees stressed the universal access character of electricity supply for society. Some mentioned its civilisatory achievement role and alluded to universal affordability and fair prices, and even demanded the abolition of energy poverty (12). Other values mentioned besides “sustainability”97 (11) were “dependence” (1), “temporary electricity limitations” (1), “decision autonomy for usage of energy sources” (1) or “importance for public order” (1). This enumeration is an indicator of sensitivity among participants who said things like: “There are really lots of people who don’t have the money to pay for this every month, so that their electricity is simply cut off, because they have missed one [monthly] installment. This I believe to be unbearable, since there should be some kind of fund, like a common ‘pot’ that can guarantee that such people get electricity or warm water, even in bad times, like in winter, for example. Now there is a family, one can see it often enough in the newspaper, now there are families with two children. And the father lost his job through some circumstances, no matter which; in any case, there is no more money to pay the installment for this month. Why is their electricity cut off?” (LS100137, 112)

Not only does this participant mention the issue of energy poverty as something undesirable for a properly functioning energy supply in society, but also suggests a ‘fund’ for those affected and conveys his message in a passionate way. Similar observations can be made by looking at programs mentioned as societal expectations. Here, it was striking that among the many programs mentioned in realms such as R&D, the economy or sustainability, a considerable number of participants had quite detailed and committed expectations. These entailed arguments for public infrastructure supply in order to provide hospitals, public lighting or emergency plans with the necessary resources (5). Two of these interviewees demanded a policy initiative that would research into prevention possibilities of blackouts: “Besides, there is frequently the fear of this blackout; one should consider this in timely fashion, what happens… so, [one should] on one hand act preemptively, in

97

Equivalents mentioned were: reason, clean energy, environmental awareness/impact.

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Case Study: Trust in the Energy System order to prevent such a blackout and invest, invest also in the grid and similar stuff, but on the other hand also consider what to do if there is a blackout, where most of the problems arise and how to prioritize action […] Such a risk management basically, or catastrophe management, one might call it.” (LS100145, 47)

Another participant favored the removal of some of the electricity infrastructure like wind turbines or cables/pylons for esthetic reasons. One said a properly functioning electricity supply should accomplish “no lobbyism”, while two others stressed the importance of “energy savings behavior”. These examples illustrate the empirical creativity and partly detailed occupation with the issues by some of the participants. While a large share (11 out of 30) remains on the simpler side of energy consumption (cf. the ‘low’ bar in Figure 6-4), the majority can be said to be at least prone to differentiated sets of expectations toward electricity supply (19). In addition, one should consider that the differentiation factor inferred above (ibid.) does not formally include the prior elaborations on societal expectations, but merely personal ones, and therefore is a rather modest categorization of people’s differentiation degrees. Still, eleven participants are classified as ‘low’ in terms of expectation differentiation. This leaves us with another split in the analysis of the sample: when we align the seven members of the medium group (7) with the other groups (three with low and four with high, Figure 6-4), we end up with a sample split in half (15+15) regarding potentials of differentiation. Just like ‘system identity’ (discussed in the previous Sect. 6.4.2), the ‘expectation nexus’ offers a divided result concerning the level of complexity with which interviewees deal with electricity supply. A final aspect relevant to H3 additionally substantiates this, which is “[expectation] evaluation through assessment of system outputs by trustors” (ibid.). I asked about 25% of participants the add-on to question 6: do you actively pursue this implementation?98 This interviewee’s response was typical: “Naw, I don’t really follow that further. On the other hand, of course one notices when the costs rise, or when electricity no longer comes out of the power outlet; one does notice that and follow up on it, but it isn’t so acute that I have to continually keep up-to-date, or the like. It isn’t like that.” (LS100131, 72)

The overall attitude of interviewees was a rejection of an active lookout pattern for implementation of their conceptions. Instead, they stressed activities such as 98

See interview guide at the beginning of this section or in Sect. 6.3.2.

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reading in news media, talking to friends and co-workers about it or realizing change in the natural and social environment. Attention to mass media was an aspect every participant who responded to the question mentioned: “Exactly. And really whatever gets through the media about the issue generally. But it isn’t like I now consciously sit down and type into Google [laughs] what exactly is the current situation. That isn’t the case.” (LS100147, 106)

In sum, these statements allude to a rather passive pattern when it comes to verifying or dismissing established expectations. The material issues people expect from electricity supply are more likely to be challenged by a news show they watch accidentally, a talk with a friend on shared experiences or an advertisement in the streets than by organized research. Consequently, we have to be clear that the assessment of system outputs by trustors happens more on a random basis than systematically, even if the low turnout in responses (25%) does not allow for more than these indicative inferences. After all, it seems probable that the higher shares of differentiation and specificity concern interviewee’s expectation-building capacities rather than their evaluation of system outputs. A closing aspect related to H3 is its connection to AoT as a “scale from concrete to abstract” (ibid.). The prior results can help to shed some empirical light on this theoretical assumption. Important to understand in this connection is that ‘specific functioning’ or ‘specific service expectations’ do not necessarily relate to concrete conceptions of electricity supply, i.e., reference to addressees like persons or organizations. On the contrary, as the clock-shaped AoTs on personal and societal level allocations (Figure 6-5 and Figure 6-6) show, consumers’ expectations are never located on the most concrete levels of AoT in the first place. Only when it comes to attributing responsibility, i.e., determining direction, do addressees come into play more frequently (Figure 6-7). Capacities of addressability are an issue in H15:

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H15: The original AoT of persons, roles, programs and values (PRPV) can be enhanced by organizations, technology and adjacent systems (OTS) for the study of energy. This ‘architecture of trust’ holds addressable (persons, organizations) and non-addressable (roles, programs, values, technology, adjacent systems) references of trust. All of them can serve as reassurances of trust, but addressable references serve exclusively in cases of disappointed trust that is sanctioned because they can be communicated with.

Mirrored by the empirical results presented so far, the enhancement of PRPV by OTS can be generally affirmed as an appropriate choice. All three items in OTS play an important role in different AoT constellations that would have been missed if they had not been available as coding elements. In particular, ‘organizations’ build a major block of peoples’ expectations when it comes to the attribution of responsibility (Figure 6-7). ‘Technology’ plays a subordinate role in all AoTs throughout this section, yet appears in each of them, mostly in the direction scheme (ibid.). ‘Adjacent systems’ are negligible in the clock-shaped AoTs but hold an important position in the rhombus AoT with a total of 21 mentions. Among these is the second most frequent direction of responsibility: “Politics” (15). Apart from this, adjacent systems, organizations and technology were coded heavily in the system element AoTs from Sect. 6.4.2 (Figure 6-2 and Table 6-4). A second issue concerns the hypothesis’ claim on addressability which leads back to the distinction between concrete/abstract. With the help of the previous elaborations, their relation can be more precisely described. At first sight, it seems that the split between addressable (persons, organizations) and nonaddressable references of trust (roles, programs, values, technology, adjacent systems) is reflected by the clock AoTs (Figure 6-5 and Figure 6-6) versus the rhombus AoT (Figure 6-7): while the former almost exclusively prioritizes programs and values, the latter includes persons and especially organizations to a large degree. This makes sense insofar as the rhombus AoT, to a certain extent, requires addressability, communication capacity and actionability for implementation of peoples’ conceptions. Yet what it also holds is a considerable share of roles and programs, both of which are not directly addressable. Especially

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roles must be highlighted here as they display the second largest AoT level in Figure 6-7 (22 counts) while both clock-shape AOTs together add up to merely four. These observations lead to the conclusion that roles, just like persons and organizations, do not primarily embody personal or societal expectations, but rather dominate discourses on expectational implementation and responsibility attribution. Surprisingly, this does not concern expert roles as a possible system supervision element alone, but also roles such as “entrepreneurs”, “workers”, “consumers” or “the legislative”. Much of the participants’ conceptions of ‘caretaking’ (question 6, Figure 6-7) are situated within the array of these roles. Moreover, the focus on roles triggers some further conclusions. Roles are the second most concrete level of generalization in the AoT. Still, they are not addressable as such, unless persons or organizations are attached to them as representatives. This alludes to two different supplementary aspects: (a)

One pattern of interpretation moves in the direction of insinuated communication capacity and actionability by trustors. What Sect. 5.3 has assumed for technology in particular could even be extended to roles, programs and systems. Looking at Figure 6-7, we can see that all AoT levels except values were mentioned in responses to “Who or what takes care of implementing your conceptions?”. If interviewees appear to be convinced that “entrepreneurs” (roles), “regulation” (programs), “wind turbines” (technology) and “politics” (adjacent system) are all able to take care of implementing their expectations, then an insinuation of actionability is not farfetched. Regarding roles, it seems clear that we can interpret people’s understanding of entrepreneurs, engineers or citizens as individual persons or groups that are (undoubtedly) actionable. Concerning regulation or politics, however, it seems less obvious to say that a program or a functional system of society is actionable in and of itself. Judging from the empirical material, this conclusion gains momentum and yet again stresses the relevance of “emergent action” (Coleman 1986, 1312). While persons, organizations and groups might be the only social entities fully equipped with communication capacity (cf. Sect. 3.2), it seems that attributions of ‘caretaking’ (question 6) merely rely on insinuated collective actionability instead of personal or organizational addressability. From this angle, addressability as a process seems to be much more flexible in empirical reality than simply targeting those AoT units that can be directly communicated with.

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(b) A second vector connects to the former in that it relates AoT elements of different abstraction degrees with one another. Even though strict addressability rules in terms of communication capacity are practically not feasible, the dominance of organizations in Figure 6-7 is striking. This alludes to the conclusion that although addressable organizations are not necessarily the only entity for people to direct their expectations at, they were coded in almost every interview and thus unfold particular significance. Persons and roles, as further levels of concreteness, complete the picture in dominating Figure 6-7, compared to Figure 6-5 and Figure 6-6. Just as roles can be plausibly assumed to be a melting category for communicating persons and groups (see above), programs could be regarded as a related category for organizations. Hence, programs such as “regulation”, “norms and standards”, or “German RES law”, are embodied by organizations such as “Bundesnetzagentur”, “the environmental ministry” or “cartel authorities”. To further exemplify, other notable cross-level intersections throughout the AoTs entail: Ms. Merkel (P) – state representative (R) – federal government (O) – legislation (P) – politics (S); single people/everyone for themselves (P) – citizens (R) – NGOs (O) – consumer protection (P) – wind turbines (T) – societal forces (S) [based on Figure 6-7 results]; power companies’ backup responsibilities (O) – public infrastructure supply (P) – renewable energy sources (T) – universal access to electricity (V) [based on Figure 6-6 results]; service staff (R) – research and development (P) – emergency electricity technology (T) – technical system security (V) [based on Figure 6-5 results]. While there is no complete consistency with regards to these observations that could provide detailed deductions of level relationships, these examples illustrate possibilities of mututal representation and symbolization among AoT levels. In the rhombus-shaped AoT on expectational directions, for instance, there is a heavy similarity in related mentions across levels within the ‘PROP’ components. Thus, persons, roles, organizations and programs frequently share the same content adapted to the various generalization levels as in the above examples. Here, it seems that any level could represent the other, although usually the more concrete ones stand for more abstract ones. In the clock-shaped AoTs, on the other hand, a similarity exists among the material content of programs and values, alluding to the conclusion that consumers, in formulating expectations, primarily mention abstract values as system traits (e.g., sustainability), yet alongside more concrete programs that embody these values (e.g., subsidies programs for RES).

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This conclusion is also plausible in the light of values as the only level of generalization that does not appear in the rhombus AoT, yet dominates both clock AoTs. From this angle, it seems that the more concrete interviewees had to be, the less likely it was that values were mentioned, and the more likely PROP components. In these cases a downward logic from abstract to concrete regarding expectation building (Figure 6-5 and Figure 6-6) toward implementation (Figure 6-7) can thus be concluded. Finally, a short comment on H11, the only hypothesis left to investigate:

H11: The empirical reality of an abstract system such as energy is conveyed to trustors through an architecture of trust references. This expectation nexus of ‘persons’, ‘roles’, ‘programs’, and ‘values’ is a predefined setting that holds concrete references for reassurances and disappointments of trust or distrust.

Against the background of having applied AoT in both categories I and II so far, a first statement can be made regarding its usefulness. The smooth way of coding text segments with AoT is an indicator of its reflection of an “abstract energy system” (H11). While AoT holds more concrete and abstract references within itself, it generally displays a more concrete continuum than ‘the energy system’ as such. In this way, the idea of a “predefined setting that holds concrete references” (H11) did not encounter much contrary evidence in the current and previous sections. The fact that nearly every significant aspect mentioned in the interviews could be coded with the help of different AoTs is, initially, a strong argument for this sort of pre-definition. The empirical results and conclusions so far that were largely derived from AoT interpretation is another. Hence, the fact that PROP TV’S largely matches the predefined field of electricity supply, in the way applied here, may be interpreted as a first indicator for the general plausibility of AoT as a template in other fields of systemic trust. Finally, comments on the distinction between reassurances and disappointments/sanctionings, which H11 also refers to, will be discussed in Sect. 6.4.4 on Reassurance Patterns.

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6.4.4 Category III: Reassurance Patterns Let us recall the interview questions relevant for this category: Reassurance Patterns 7. Do you already have any experience regarding problems in the energy sector? 

How did you deal with these problems?



Have you any ideas regarding the problems that may arise during the supply of electricity? What do you think could hypothetically go wrong?

8. Where would you pinpoint the responsibility or cause of these problems? 

Whom/what would you consider responsible when something goes wrong, i.e., if the electricity supply were interrupted? Who/what would undertake the re-establishment of energy delivery?

Drawing conclusions on ‘reassurance patterns’ is challenging, because its underlying sub-categories were – inevitably – less pre-structured than in categories I and II. Indeed, the matrix between knowledge, specific and unspecific nonknowledge and the three varieties of dealing with it (familiarity, trust, distrust) are probably the most experimental part of this evaluation. In this sense, the empirical material has proven this cautious approach to be correct, in that it is rich in surprises and offers turns that are perhaps unexpected. One major insight is that identifying certain statements that clearly indicate knowledge, or specific/unspecific non-knowledge, is a difficult endeavor to begin with. Once you turn to their dominance (H9), as opposed to their absolute clarity, however, things become clearer and pave the way toward analyzing linkages to trust and distrust. Depending on the various risks involved in electricity supply, trust and distrust can even occur simultaneously, an observation to be unfolded below.

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Due to the gravity of this undertaking, the results presented here are of illustrative character and for the majority of conclusions relate to several selected cases rather than to all 30 interviews. The main reason for this is that the risks that were mentioned by the participants – reacting to question 7 – were so multifold that no complete scrutiny of their knowledge/non-knowledge narratives regarding each single risk could be conducted. Simple practical reasons (lack of time) as well as comparison problems (not every participant mentioned the same [number of] risk[s]) stood in the way. In addition, the approach is further justified by the fact that this part of the interview was planned, from the start, as the most exploratory. The coding rules therefore primarily entail qualitative assessment of text segments, as H9 is the center of attention in the text analysis to follow. The precise sub-categories for category III specified in the operationalization section were knowledge, non-knowledge (specific/unspecific) and attribution causality (self/other). Firstly, I will deal with knowledge/non-knowledge narratives in order to begin to understand how suspending doubt and establishing (un-)favorable expectations unfold empirically. In a second step, I will turn to attribution causalities which will encompass interviewees’ reactions to a crisis scenario based on question 8. As laid out in Sect. 4.3, at least three typical forms of marking knowledge and ignorance are likely to occur: certainty, risk calculation and refusal of risktaking. Thus, the first issue in need of attention is the detection of perceived risk involved with electricity supply so that its calculation and/or refusal might be pursued. This has been approached through question 7 of the interview guide. Figure 6-8 below displays the things that people said did go wrong for them personally in electricity supply, or hypothetically could go wrong in that domain. The numbers (0-30) in the left hand column reflect the number of people who mentioned that risk. In the graph, it is apparent that “power outages” are the only risk mentioned by almost every participant (27). Three interviewees did not mention them directly, but reacted to this problem once I asked them about it. Regarding power outages, participants not only vividly described experiences of actual outage situations, but also mentioned the dependency of themselves and society on uninterrupted supply. Accordingly, the risk of power outage or bottlenecks, to a large extent, is associated with the consequences of not being able to use electric devices and the vulnerability of society in branches like medicare or security of data management. The second most frequent problem people

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alluded to was related to “nuclear energy” concerns, sometimes also coal. 14 participants (see Figure 6-8) were afraid of exploding nuclear plants or incidents similar to Chernobyl and Fukushima that would cause harm to themselves and society. Moreover, issues like “cost/benefit analysis of energy generation sources” – also in the light of societal costs – as well as “nuclear waste management” were among the aspects mentioned in this context. Further dominant problems encompassed issues such as “resource scarcity” and related “dependency” (8), as well as “administration problems with utilities companies” (6). While the former entailed fears such as “rationalization of electricity”, the latter alluded to problems people encountered in the management and accounting of their personal suppliers, or stories they heard about. 30 25 20 15 10 5 0

Figure 6-8. Experienced or Hypothetical Problems in the Energy Sector

A last field of risk perception that interviewees were prone to is related to the potentially harmful consequences of energy technology toward humans, the environment, or animals: one issue concerns “diseases” like cancer or multiple sclerosis possibly triggered through infrastructure such as pylons, cables or PV

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panels (6). A second aspect pertains to “negative RES influences” like birds flying into wind turbines, for instance, or turbines causing esthetic distress or noise harassment for the population (6). Other problems mentioned on a comparable scale were “opaqueness of markets and politics” (6), “RES volatility problems” (4) or “energy poverty” (4). Less frequently mentioned were risks such as “electric shocks” (3), “electricity theft” (2), “terrorist attacks” (2), “hacking” (2) or “rising prices” (1). During the interviews, I did not give any hints and coded only what was said by participants on their own behalf. This is why I want to consider the two most frequent mentions in Figure 6-8 – power outages and nuclear energy concerns – as most substantial for the analysis of knowledge/non-knowledge narratives. Not only is their significance the highest among all uttered risk perceptions, an additional argument for power outages as an exemplary risk to study is a very practical one: it is the only risk where there is data from every interview, as power outages, if they were not mentioned naturally (27 cases), were brought up in interview question 8. Here, responsibility attributions in a crisis situation such as a power failure were requested from participants so that each interview contains references around that problem perception. Furthermore, references to nuclear energy concerns can be found throughout almost half of the interviews, so that this risk perception builds another suitable source in search of ‘reassurance patterns’. Just how interviewees discussed the self-declared problem situations of power outages and nuclear energy will be illustrated in the following paragraphs. In order to do so, I will quote decisive passages that show how knowns and unknowns are dealt with by the respective interviewees and what conclusions they respectively arrive at. At the same time, the connection to modes such as certainty, risk calculation or risk refusal, and ultimately trust or distrust will be revealed. A first lesson to learn about risk articulations is that one cannot directly conclude the severity of the problem from its mere statement. In other words, the utterance of a risk is not equivalent with how high or probable that risk is in the perception of the interviewee. In this case, even though almost every participant brought up the problem of power outages, this does not in itself say much about people’s assessment of the materialization probability of that risk. And since risks and their treatment were dealt with in a qualitative way, people’s risk assessment had to be determined through precise text analysis. One concomitant

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insight is that even though problem perceptions in a certain respect (e.g., nuclear catastrophe) might be uttered, certainty in bridging them can still be high. As the example of power outages will demonstrate, it is conceivable that people argue in patterns of familiarity against a risk brought up by themselves. This is counter-intuitive, insofar as familiarity usually neglects risk perceptions in the first place and relates to past experience, while trust involves and bridges risk through certainty-equivalents (cf. Kohring 2004, 95ff). Still, references to past experience and diffuse, unspecific knowledge occurred frequently throughout the interviews and led to reaffirm a proposition from the end of Sect. 4.4: that familiarity is a plausible reassurance pattern separate from system trust with distinct narratives that treat uncertainty differently than in trust patterns. In this respect, we can upgrade the idea H9 introduced in that different “forms of trust and distrust” (H9) also include familiarity as such a form. Precisely, this is a form that creates certainty through past-related, superficial knowledge (usually experience), and is characterized by a pragmatic and sometimes indifferent way of dealing with risk. Familiarity, in this way, indeed seems to be the ‘lost companion’ of system trust that confidence could not redeem (Kohring 2004, 112) – at least on the basis of these sample evaluations. Before we go into the data analysis in more detail, let us recall the different types of knowledge involved in presumably evoking modes like familiarity, trust and distrust (see Sect. 4.3): 

Communication of knowledge (certainty, experience)



Communication of specific non-knowledge (risk calculation)



Communication of unspecific non-knowledge (refusal of risk-taking)

The results of this study illustrate that empirical reality is far more complex than merely expressing three ideal types of knowledge and non-knowledge. This insight is particularly valid regarding the first sub-category of ‘knowledge’, which can take various shapes as reflected in the interviews. In response to question 7, one interviewee said: “An overload isn’t possible. [Laughs.] Not having electricity isn’t possible. The networks [are such] that temporary failures are unlikely. There are always emergency options which can be used, to restore electricity… Of course, one can always watch a movie about catastrophes like power outages, but I do not believe in them.

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[Laughs.] And I believe, what could happen? Is a nuclear plant supposed to explode? Then we still have other options for drawing energy. So we’ll always have energy available.” (LS100143, 95-99)

This statement embodies the most certain way of reassuring oneself against possible risks and problems across all the interviews. It is by no means representative of many interviewees, yet it reflects one way of creating absolute certainty, which I will call ‘specific knowledge’. This participant is simply one hundred percent sure about what they are saying at this moment with reference to the risks involved. There is no doubt left whatsoever, and that makes this statement a case of neither trust (since non-knowledge is absent), nor familiarity (as pure knowledge is brought up rather than experience), nor distrust. Another participant argues in a similar, yet slightly weaker manner, when discussing responsibility for power failures (question 8): “The municipal utilities companies, [...] the energy suppliers, they have all kinds of emergency teams which are sent off immediately and they can check on their own, with their switchboards… plainly spoken, they know exactly what is in the earth, they know if they can’t reach it because it’s underground. As usually the cables are on the surface. Nobody else can be responsible. Here in our house nothing can go wrong, since the electricity meters and the main power lines are always checked, almost yearly. The meters are replaced every two or three years; I mean the inner parts of these switching things.” (LS100125, 100)

Here, while the degree of certainty that “nothing can go wrong” (ibid.) is also high, the interviewee primarily relates to certainty-equivalents such as emergency teams, cable plans, maintenance and replacement of meters etc. in order to reassure themself. This means that instead of explaining the reasons for this certainty through knowing exactly why there are no technical possibilities that allow for that risk, as in the previous case, the references are mainly to external reassurances that technicians and the regional supplier provide. This type of reasoning occurred in 12 of the interviews, with more or less reliance on technical or social certainty-equivalents that bridge a perceived risk such as power outages and make it appear insignificant. In other words, people trust, after having gone through a process of risk calculation. Further examples of creating certainty include the reliance on personal experience, an indicator of familiarity:

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a)

“Yet other problems concerning energy provision… well, I mean, our electricity provision is relatively stable, meaning there is always electricity coming out of the power outlet at my house.” (LS100141, 82)

b)

“As I said, my experience that such a thing [outage] has not yet happened to me in 33 years.” (LS100144, 222)

These statements illustrate that consumers arguing in this familiar way relate to their everyday experiences with power outlets and electricity consumption rather than to technical and/or social certainty-equivalents to justify their favorable expectations. Reference to experience, in this sense, is typically characterized by a continuation of favorable expectations through linear reproduction of the present out of the past. Trust-related expectations, however, relate to the future, not the past (Luhmann 1979, 19f). Another aspect that repeatedly occurred among interviewees arguing in this way was a reference to what I want to call unspecific knowledge, as opposed to the specific knowledge in the introductory quote. The same interviewee who dismissed the idea of power failures through non-experience in 33 years, at a later stage of the interview said, when responding to potential RES volatility problems: “Ya, I don’t have any such concerns actually. Electricity will be provided somehow” (LS100144, 270). This idea of “will be provided somehow” (ibid.) can be interpreted as an indicator of unspecific knowledge, in that it portrays certainty, yet in a completely generalized and therefore unspecific way. It is almost tautological as it could be translated as ‘it is what it is’. This way of diffusely signaling certainty even though no substantial knowledge, certainty-equivalents or even bridged doubts are articulated is a pattern represented among a total of 15 interviewees, who can be characterized as ‘familiar’ with regard to the risk of power outages. It would be convenient if these different narratives of knowledge and certainty-creation occurred as a leading paradigm in all interviews so that clear characterizations in each case would be possible. Yet, the empirical material offers a multi-faceted picture where even supposedly clear examples are rich in contradictory aspects. The most important one is that the prior mentions of certainty and knowledge are themselves frequently embedded in framings of nonknowledge, so that one conclusion is that knowledge and non-knowledge always go together, even though one side usually dominates. The following case illustrates how entangled the different forms of knowledge and their referencing in empirical reality are, as the participant responds to question 7:

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“Power lines can be sabotaged; power plants can explode and countries can refuse to exchange electricity, for example, when the wind isn’t blowing. Private households and the industry would then really suffer devastating consequences, since planned processes could simply not be carried out, or production halts, refrigerators fail, or even highly poisonous material, which must be contained somehow, cannot be secured any longer. […] I do believe that this is possible, but I consider it unlikely to happen to us. Based on this experience. […] Apart from Chernobyl und Fukushima, of course, I can’t think of anything now. And sure, I do not know the reliability of provision redundancies, should something fail, what problems would really come up. However, due to our dense coverage, I do not consider a failure of electricity to be a big problem, except for some kind of radiation or some other other stuff like that, of course. (LS100139, 71f; emphasis PS)”

Proportions of experience, specific non-knowledge and unspecific non-knowledge frequently cross paths as in this interviewee’s elaborations, making clear ascriptions to one of the three ideal types very difficult. One result from studying the interview data is that even if, as in this case, many different ways of reassuring and reasoning occur that might even be contradictory, it is often the final reference people make that reveals their probable attitude. In this case, after some dialogue about certainties and uncertainties in the case of power outages, the participant finally says that: “Yes, I do suppose it’s like that; that must be behind it. But, as I said, I don’t really know how big these redundancies truly are.” (LS100139, 71f)

In the end, it is clear that the risk of outages (as well as some other risks, including nuclear catastrophes) cannot be ruled out and are expected simultaneously: “Yes, possibly… well sure, everything is possible” (ibid.). Whenever people stress that ‘everything is possible’, we have to assume that they are not able to specifiy their ignorance about an involved risk, but rather reckon with the unspecifics of what could happen, including the worst case. In other words: a nonmaterialization option of risk is refused. These conclusions are especially fitting against the background of comparative narratives that provided absolute or relative certainty in reassuring themselves against the risk of power outage as introduced above. In those cases, interviewees left no doubt concerning their belief in a safe power supply, be it through certainty-equivalents (trust) after calculating the risk or emphasis of experience (familiarity). In the latter case, however, suspension of doubt does not work all the way; it seems that the participant uncovers their own flaws of reasoning while trying to explain their position. Even under provision of several

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CEs and the personal experience that power failures are extremely unlikely, a stable state of favorable expectation of safe power supply cannot be detected. Rather, the participant has specific (the unknown “redundancies” of the system) and unspecific non-knowledge (“everything is possible”) which dominate their reasoning (LS100139, 71f). To conclude from this emphasis of non-knowledge that a person is generally distrustful of supply security would be overstated, yet trust as a clear pattern of reassuring oneself can also be excluded from consideration. It rather seems like a narrative that emancipates itself from familiarity as the participant mentions their positive experiences in the above quote, yet realizes the self-deceptive character of this attitude at the same time. Hence, these short remarks already signify the fluidity of the attitudes of familiarity, trust and distrust that allows for quick mobility between them (cf. Sect. 2.5). A clear example of unspecific ignorance domination is documented in the following case: asked explicitly about their personal reassurance whether the electricity supply is safe and will not be cut off, one interviewee responded: “Well, in these turbulent times which we are now living in, all kinds of things are possible. So one can keep in mind that something like that may of course happen. Imagine we are at war and a bomb hits something here. Of course the electricity then vanishes.” (LS100137, 182)

While this sort of complete negation of knowledge or specific non-knowledge regarding the probability of power outages was rare (three times total), it exemplifies another narrative of relating to unspecific unknowns: everything can happen, the consequences could be disastrous and no specific (non-)knowledge could ease this attitude (cf. Japp 2000). The borderline between unspecific and specific unknowns, in this connection, is often more blurry than expected, especially because lay assessments of risk rely on much less scientific rigor than political, public, or academic debates on the same issues. Still, one important criterion Japp (2000, 231) introduced for the exposure of unspecific ignorance is a negation of specific (non-) knowledge claims – a refusal of risk-taking – that would possibly try to rationalize a perception of catastrophe or disaster. In other words, unfavorable consequences of high severity are expected to materialize from a risk that cannot be contained by possibly moderating counter arguments, no matter how rational they might be (cf. Rescher 1983). The following quote embodies this narrative with reference to nuclear energy risks:

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“Yes, and indeed that is always so old-fashioned, but for the last five years, as I read again just now… [Laughs.] But yes, Fukushima was such a moment, since of course one… and then again one compares, how does Japan differ from Germany? Okay, so they have earthquakes and the danger of tsunamis, but equally important, they have a reputation for being extremely at the cutting edge of technology… and one always has the feeling, oh my god, then it can also happen to the French, and to us, too… and then one hears about stuff like, oh, at the Dutch border there were very many incidents, even so long ago already, so […] [But technological advances or the like], that doesn’t convince me anymore.” (LS100146, 162)

In this case, it is apparent that the participant rejects notions of technical superiority as a possible CE for bridging the risk of nuclear energy problems. Rather, they relate to specific unknowns such as technological (a)symmetries between different nations or the supposed nuclear malfunction incidents along the DutchGerman border. Through the way anxieties about nuclear energy are presented, one realizes a narrative strategy of dealing with risk that can be explained as follows: countering specific non-knowledge (are German nuclear plants safe from malfunction incidents?) with specific knowledge (security standards are high and probabilities of materialization are low), in this case, is succeeded by contrasting it with the unspecifics of an actual materialization of the risk: a catastrophe situation (Rescher 1983; Japp 2000). This is why former certainties of German engineering standards or ease with borderline plant incidents turned into worries that cannot be bridged any more, possibly resulting in distrust. A similar strategy is used by the following interviewee, when discussing the problem of RES volatility: “Yes, I do think, particularly with regard to renewable energies that are drawn from wind, sun and water, who can promise that the sun will shine everyday, that we’ll always have enough wind? And hydroelectric power stations: who can promise that the river will flow in torrents every day, so that the barrage can power the turbines, that the turbines will be powered? So it always also depends on the environment. […] I claim that it cannot be guaranteed to 100%. Well, we can probably maintain a large part for a certain period. Energy storage is really being tried, but how efficient is storage and how effective would the energy gain be if we really worked only with alternative energies – without nuclear reactors, without coal-fired power stations? Well, I am concerned that it wouldn’t reach 100%.” (LS100140, 99-101)

A comparable notion, as previously detected, is visible in this statement: the participant asks informed questions that can be qualified as specific nonknowledge, since they know that they do not know if the wind will blow suffi-

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ciently or energy storage technology will be available in the future. The interviewee is able to specify their ignorance. Nonetheless, they are apparently unable to bridge that ignorance into a favorable expectation about the turnout of RES technology to secure supply in the German electricity system. Rather, they acknowledge that they are not convinced this state will be reached and implicitly allude to another catastrophe with yet unspecific consequences: putting the German security of supply principle at risk, possibly resulting in bottlenecks and interruptions of energy deliveries. German consumers have rarely been confronted with such a situation which carries unforeseeable difficulties. In the end, the conclusion that the interviewee is “concerned that it wouldn’t reach 100%” (ibid.) is a clear indicator of an unfavorable future expectation which can be translated as distrust. Concluding, we can say that distrust may result both from refusal of risk calculation through referencing unspecific non-knowledge (‘everything is possible!’) as well as conducting risk calculation through referencing specific ignorance (‘does the sun shine constantly?’), but ending up with a failure of bridging it favorably. In comparative perspective, the different interviewee statements, narratives and attitudes described so far allow for some broader conclusions. Regarding the latter case, the participant’s attitude of distrust is reserved to the risk of RES volatility – the very same interviewee has no worries about power outages in the present or nuclear safety issues, for instance. In both cases, the interviewee supplies various CEs and communicates certainty in the face of these potential risks, while articulating the severe doubts over reliance on solely RES in the future system. Another interviewee (LS100145) has a similar yet slightly different mix of perceptions: outages are seen as a high risk and this interviewee does not come up with CEs to bridge it (and also uses private batteries to protect against it), while nuclear plant safety is substantiated with several CEs and accidents are regarded as highly unlikely. Yet another interviewee perceives the exact opposite (LS100146): power outages are dealt with through referencing familiar experience, while nuclear energy problems are seen, due to refusal of CEs, as a high risk, indicating a strong sense of distrust. The pattern of these comparisons leads to a captivating insight: it seems that the existence of multiple simultaneous attitudes of trust, familiarity and distrust toward related risks is conceivable. In other words, a person trusting the safety of power supply does not necessarily

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trust that of nuclear plants nor the mitigation of RES volatilities during the transition process. Someone who is skeptical of uninterrupted supply could still see no risk in using nuclear power for it, and yet trust that the government will do everything to secure the necessary resource deals. If we assume that all of the risks mentioned by participants belong to the same overall topic – electricity supply – we can conclude that their separate assessment merely seems to be one interface of a larger systemic complex. Considering that risk perceptions touch upon the domains of contingency and selectivity of trustees (Kohring 2004) in that they unravel where and how trustors could be betrayed, we can say that the collection of risks associated with a certain trust object can be referred to as its dimensions (ibid., 170ff). These dimensions, as a consequence, allow for several neighboring attitudes of trust, distrust and familiarity that can be overlapping, contradictory, re-inforcing etc. On the basis of the affirmative system identity results in Sec 6.4.2, and Kohring’s theoretical foundations above, we can claim that the different attitudes of trust, distrust and familiarity are directed toward the respective dimensions of the same overall system: the energy system. These dimensions of the energy system simultaneously serve as dimensions of trust in the system and can be deduced from the diverse risk mentions that were collected through the interviews. One dimension, in this sense, is a result of aggregated risks under an umbrella of related risks that concern similar problem perceptions by trustors. Under slight enhancement of previously less covered risk utterances, Figure 6-9 collects and structures the risks mentioned at the beginning of this section (Figure 6-8) into four respective dimensions of trust in the energy system: technical security and operability, physical safety and environment, market efficiency and transparency, and public order and justice.

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Physical Safety & Environment

Technical Security & Operability outages, RES volatility failure of EW hacking nuclear operability electricity meter operation

rising prices monopolies opacity varying international standards administration issues utilities

landscape & animals diseases (e.g.,, cancer, MS) nuclear waste terrorist attacks/ war

electricity greed/ theft resource scarcity/ dependency rationalization of electricity energy poverty predatory behavior

Market Efficiency & Transparency

Public Order & Justice

Figure 6-9. Four Dimensions of Trust in the Energy System

After analysis of the knowledge/non-knowledge circumstances in trustors’ risk assessments, we can now turn to the interpretation of (upgraded) H9 on the basis of the empirical material: H9: Complexity and its role for trust depend on trustors’ perceptions. Perception, in turn, can vary according to narratives of knowledge or specific and unspecific non-knowledge. The dominance or mix of those narratives can be typically linked to trust, distrust, or familiarity among trustors.

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The complexities of possible problems associated with electricity supply have been translated by trustors into a diverse array of risk perceptions as Figure 6-9 demonstrates. These perceptions did indeed vary according to emphases of knowledge/non-knowledge varieties that resulted in differentiated attitudes of trust, distrust and familiarity. These attitudes are directed toward four dimensions of the energy system that can be described as technical security and operability, physical safety and environment, market efficiency and transparency, and public order and justice. Their occurrence is regulated by dominances of either knowledge or non-knowledge, leading to multiple narratives of dealing with uncertainties in the electricity domain. These narratives and their linkages to the trust phenomenon result in partly new distinctions that I want to recapitulate in the following. To begin with, I detected references to both specific and unspecific knowledge. These modes of reasoning relate to people who communicate either knowledge that replaces ignorance and creates absolute certainty in their argumentation (specific knowledge), or those who rely on often diffuse, experience-based familiarity (unspecific knowledge). While the distinction between specific/unspecific has usually been applied to non-knowledge, it is important to notice that the study of trust also requires sensitivity in distinguishing different forms of knowledge, since they lead to unique reassurance patterns. A second distinction is that of knowledge/non-knowledge mixes that can tip to either side of the equation: a situation that involves both knowledge and ignorance in the same line of reasoning can either bridge that uttered ignorance (i.e., perceived risk) and finally stress the knowledge share (trust), or tip to the ignorance side and fail to conduct that leap (distrust). Both scenarios involve a mix of knowns and unknowns in a process of risk calculation whose outcome is open to either conclusion. Still, both settings are vulnerable to shares of specific knowledge and specific non-knowledge, which cause the decisive tippings through either stressing known knowns (social or technical CEs replacing nonknowledge) or known unknowns (will energy storage technology be available in the future?) that in turn determine the establishment of trust or distrust. A final relevant setting is that of unspecific non-knowledge and concomitant distrust: here, specific knowledge or non-knowledge claims are refused altogether and relegated into the realm of unknown unknowns. This attitude mainly occurred with reference to nuclear energy issues or power outages as a result of catastrophe situations such as terrorism or war. For the potentially affected interviewees

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in the sample, these situations not only meant a refusal of risk-taking and thus distrust, but rather embodied a perception of danger that partly exposed helplessness toward external conditions (Luhmann 2005). Beyond this, the results allow for some further differentiations and precisions. In particular, the treatment of power outages by participants reveals some patterns that mainly concern crossovers between the respective co-existence of trust, distrust and familiarity. The following statement reflects the general demeanor of a number of participants: “Well, I must say, I don’t get particularly upset when there is a power failure. It happens occasionally… a few years ago, I was writing an assignment and then the electricity broke down. I can only say, thanks to Microsoft-Word for automatically saving the work, so that I only lost one page. But it was unpleasant. And yet, in private circumstances, it’s rather… infrequent; it only happens very, very rarely.” (LS100120, 41)

Several interviewees deal with the risk of power outage in an analogous way to this participant, namely through simultaneous expectation of favorable and unfavorable future developments, in that they know that it can happen, but usually no severe consequences emerge and things get fixed quickly. Others stressed that we are very dependent on electricity as a society, and would be in great trouble if power outages happened on a larger scale for a longer time. Yet this was portrayed as being very improbable, for a number of reasons, and participants were hardly concerned. This narrative leads to a variety of interpretive patterns for trust research since it accentuates once more that familiarity, trust and distrust are closer to each other than perhaps expected. In the above quotation, we can see that the interviewee has taken on a pragmatic stance toward power outages (“I don’t get particularly upset”), and expects their materialization, yet with only low probability (“very, very rarely”) and with little severe consequences for them through technological certainty-equivalents: “thanks to Microsoft-Word for automatically saving” (LS100120, 41). Above all, this example reflects an old idea introduced by trust research: the incorporation of familiarity within system trust. In his early study, Luhmann (1979) conceded that “system trust has absorbed certain functions and attributes of familiarity” (ibid., 58), a statement which receives empirical underpinning in this study. While at the time, Luhmann alluded to the compulsory character of system trust, the results here indicate that some people ‘trust familiarly’ because they do not perceive any severe consequences

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from outage risks, and not because they lack choices. These insights match with another of Luhmann’s early findings, namely that trust, as a general rule, relates more to those risks that potentially cause greater harm than if only little potential damage is perceived (ibid., 24). Thus, ‘familiar trusting’ or simply ‘pragmatism’ that is theoretically aware of risks yet does not actively calculate them – with or without using CEs – might be a fitting description for this specific pattern of reassurance. Moreover, attitudes that resemble familiarity yet included a nuance of indifference occurred among a small amount of the sample. One participant, for instance, told me that their partner had a lot of knowledge on the electricity topic “and is interested in that. Unfortunately, I am not.” (LS100122, 244). Responding to one of the first questions of the interview, another said: “Wooh, I’ve never really thought about that. [Laughs.] Naw. Seriously, that’s the kind of subject which I have no clue about. [Laughs.] Electricity is there, great, any more information…” (LS100116, 30)

In response to the question about constant delivery of electricity (question 3), one interviewee said: “I have no idea; who does guarantee that? I can’t think of anything. Really, nothing. […] Perhaps one should think about this.” (LS100128, 40)

In these cases, people not only refer to past experience (usually indicating familiarity) to reassure their expectations toward power supply, but also display blunt non-knowledge. This ignorance (“no interest”, “never really thought about that”, “I can’t think of anything”) remains unspecified and thus leads to the conclusion that if people refer to unknown unknowns but do not use them to support distrusting attitudes, as shown earlier, then they can serve as another means of occupation relief and merely support underlying familiarity. Ultimately, this means that unspecific non-knowledge – just like specific non-knowledge – has a dualistic basis as cause for either distrust or familiarity. In light of its indifferent nature and primary reliance on ignorance instead of experience, however, this narrative stance seems to resemble what Aaron Wildavsky called ‘fatalism’ as one facet of cultural risk theory (Wildavsky 1988). As a neighboring nuance of familiarity, fatalists would include people who do not really care that they do not care, and live with that situation with no expectations whatsoever, just functionally using the services of electricity supply. From a theoretical point of view,

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familiarity and fatalism could be the unreflective siblings of trust (familiarity) and distrust (fatalism) respectively, as distrust usually evokes a reflexive stance of counter-action toward the distrusted object or situation that fatalists do not provide. A conclusive aspect is the close proximity and fluidity between modes as different as trust, familiarity and distrust. As the latter example demonstrates, even unspecific ignorance can induce an attitude that resembles familiarity (‘fatalism’) and not necessarily distrust as in the earlier cases of this section. This tells us that the different forms of referencing knowledge and non-knowledge react very sensitively to small changes in setup and that the core narratives presented in this section are easily enhanced by neighboring nuances. Depending on the risk involved (e.g., power outages, nuclear energy problems, RES volatility etc.) and their perceived materialization probability, modes of trust, distrust and familiarity can more or less merge together into an expectational framework that integrates contradicting stances toward one risk (like power outages) respectively with several risks as in the four dimensions introduced in Figure 6-9. Whether attitudes of trust, distrust or familiarity unfold their practical impacts as separate social modes is then regulated by respective attributions in cases of disappointment. In other words, even though people claimed in the interviews – directly or indirectly – that certain patterns of reassurance apply to their reasoning, only a situation of actual guilt attribution and possible behavioral consequences will uncover those patterns in crisis situations and break up the fluidity between them. 6.4.4.1 Attribution Causalities The following final results section will illustrate some of the attribution causalities that the interviews brought to light. These involve attributions of responsibility in the case of (regretting) power outages through interview elaborations based on question 8. Conceptually, this takes us back to the distinction between reassurance and sanctioning of trust and distrust (cf. Sect. 5.1). To begin with, question 8 in the interviews asked: “Whom/what would you consider responsible when something goes wrong, i.e., if the electricity supply were interrupted?” Interviewees mentioned such a broad range of problems and risks when responding to the preceding question 7, that I made sure everyone evaluated responsibility attributions regarding the risk of power outages so that comparability

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could be provided. This is also coherent with the prior risk assessment of power outages by all 30 interviewees through question 7. Interviewees provided relatively unambiguous answers to this question. First of all, they distinguished between the possible cause and the need for reinstallment of electricity and/or potential reimbursement for damage caused through an outage. This is also a result of how question 8 was framed: it was posed as a scenario of longer outages (e.g., a couple of days), and the hypothetical condition of harm done to personal belongings reliant on electricity, like servers and databases, for instance. The causes for power outages on such a scale, according to interviewees, were primarily located in “higher forces” and weather conditions such as storms, lightning or flooding. The second most common response was that construction machinery like excavators ripped apart power lines through working on underground projects unrelated to the electricity grid. Lastly, on a lower scale, technical defects and “human failure” were mentioned. Altogether, these answers are particularly attention-grabbing against the background of future blackout risks that are primarily related to threats such as RES volatility or hacking, for instance (cf. Table 4-1. Current and Future (Potential) Risks in the Energy Sector). Concluding, it seems that consumers today are not yet prepared to take more complex technical or human-caused problems into greater account, given that “higher forces” and extreme weather conditions dominated their line of reasoning. Regarding the attribution of responsibility for taking care of the power outage scenario and potentially supplying technical and/or financial relief, three major strategies were observable among the participants: self-attribution, attribution toward external references, and attributions toward a ‘diffuse system’. The most common attribution was one that involved the responsibility of people’s own energy provider, illustrated by the following quote: “Now I can’t really imagine such long power outages - when there is kinda bad weather, or a power pylon burns down, or I dunno. I’d blame my supplier, or just inquire there. Who else could be to blame? One couldn’t blame the weather really or anything else for the situation.” (LS100138, 97)

This explanation was delivered by a total of 17 interviewees in different degrees of clarity. To them, it was more or less clear that the only option to attribute responsibility to was their personal supplier of electricity. Even if interviewees conceded that there might be more actors involved in either repairing and/or

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having caused the outage, it seemed only practicable to contact their personal utilities company. Consequently, people who have attributed in that way can be said to delegate responsibility for materialization of a risk (power outage) to an external address, i.e., to ‘others’ and not themselves. A small share of these 17 interviewees also delegated blame onto an external reference, yet were not concrete enough to attribute a particular organization. As one participant put it: “There have indeed been these cases where people said that, in the end, there were chain reactions. Somewhere, somehow, something went wrong and then somehow a kinda wave went through the network, and then it hit the whole thing, or greater parts of the country. I don’t know, didn’t that happen in France once, or something [...] where there were these developments. Whether such a thing could happen here, I don’t know... perhaps their network was older than ours is; I don’t know, I can’t say. In any case, these are highly complex systems, too, and, to that extent, I would also have difficulties in instantly saying: ‘Yes, sure, one immediately knows who the actor is, and what the cause is’.” (LS100115, 139f)

This sort of ‘diffuse system’ attribution reflects the perception of a small percentage of interviewees who have the impression that a complex arrangement of actors is involved in electricity provision and repair processes and give in to that complexity in that they deny hasty ascriptions to their own supplier, for instance. Yet another, greater share of the overall sample (13) gives in to the complexitites of electricity supply in a perhaps unexpected way: through potentially attributing responsibility for personal damage through power outages to themselves. Intriguingly, almost half the sample can thus be seen as prone to a self-delegation of responsibility in the following or a similar manner: “I think I’d be the kind of person who’d say: ‘Okay, it happened. I can’t change anything now.’ Well, I wouldn’t know what could be so serious. Sure, the freezer might break down, so the stuff would all go bad, but otherwise...” (LS100128, 96)

Among the 13 people who argued in this way, the majority mentioned both attributions to themselves or an external reference as a possible reaction to a severe power outage. In a total of two cases, participants were certain that they would only blame themselves for it in that they should have had emergency electricity generators, for example. This narrative of (potential) self-attribution is striking because it implies that a considerable amount of people would, according to their own elaborations, see themselves as responsible for possible damage that power outages could do to them or their personal belongings. This result is a robust backup for the idea of ‘genuine’ system trust, as it reinforces

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the decision-making component in trusting the system to supply electricity and then personally regretting this decision if something goes wrong, as Luhmann (1988) initially noted. Applying to about half the sample, it is also consistent with the prior splits between participants regarding system identity (category I) and expectation differentiation (category II) so that the assumption of partial system perception and concomitant decision-making becomes increasingly solidified. On the other hand, it is important to bear in mind that no causal relationship exists between the utterances of peoples’ potential attribution directions and their actual attributions in real-life situations where a power outage really happens and requires their attention. This gap remains a weak spot in all empirical social research relying on questioning its objects of interest, yet is unbridgeable (Adorno 1972, 200).

7 Conclusions

Our journey of studying system trust has come to an end. Numerous classic and current theoretical concepts and ideas were required on this voyage, in order to reach a satisfying basis for an empirical glimpse into trusting systems. Along the way, a dualistic notion of system trust was uncovered that ties ‘genuine’ trust in systems to characteristics of decision-making. An increased likelihood of trustors’ deliberate actions with respect to systems, instead of compulsory experiences, was part of this diagnosis, underscored by current empirical developments. A trustor’s alternate, unreflective scheme of dealing with collective uncertainty remains (system) familiarity, but not confidence. The classification of collective uncertainty absorption as confidence (e.g., Luhmann 1988; Earle and Cvetkovich 1995; Seligman 1998; Morgner 2013) marked an attempt to emphasize its supposed non-trust-like character. The present analysis however, shows that trust in systems is comparable to trust in persons and organizations, and thus follows a similar working logic. This conclusion is possible because of the foundation of trust on expectations, which can be directed at both personal and collective entities, such as persons, roles, programs and values (Schutz 1967; Luhmann 1995). Through closer study of such expectational generalizations (ibid.), it was shown that these references can be recipients of ‘genuine’ trust, including systems as a social entity. Apart from this theoretical possibility of systems as trust recipients, a remaining constraint is that the system under scrutiny – energy, for instance – displays a visible identity for trustors to connect with, in order to achieve full functionality as an object of trust. This ‘system identity’ is particularly relevant for systems as trustees, such as the energy system, mass media, finance or politics. Nevertheless, systems also form trustors such as a psychic system, an interaction system or an organization. System references are described in the literature as bearing various traits and characteristics, be they open or closed, social or technical, psychic or organic, or even socio-technical in nature. In the end, what unites system descriptions © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019 P. Sumpf, System Trust, https://doi.org/10.1007/978-3-658-25628-9_7

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that are more than just a metaphor is emergence, for it is an idea that portrays systems with collective powers greater than the sum of its single elements (Coleman 1986). This system emergence has real-world effects which equip systems with unique social qualities that competing concepts fail to offer. Since emergent systems in this way qualify as ‘genuine’ recipients of trust (and not confidence or familiarity), the conditions and consequences of system trust are based on five aspects relevant to the study of all trust: control, complexity, nonknowledge, risk and suspension. Only through thorough examination of these aspects, which represent crucial intersections in trust research, did a framework emerge that allows for empirical observation of how trustors go about trusting or distrusting a system. This involves symbolic control of a complex social environment through knowledge management that determines whether risks are suspended or upheld and thus turned into trust or distrust. Robust or sensitive trust thresholds are an influential force that directs trustors’ knowledge management. Moreover, analysis of these five trust components emphasized the proposed purpose of trust research in that it rephrased trust’s inherent normativity as collective functionality of systems through a trust/distrust equilibrium, rather than focusing on justifiable trust. A sixth and deeply neglected aspect of trust examined here was expectations. As trust is a favorable expectation of the future, it is ultimately bound to the recipients of these expectations, who form the addressees in possible relationships of trust. According to Alfred Schutz (1967) and Niklas Luhmann (1995), expectations can be directed at persons, roles, programs and values (PRPV). By adding organizations, technology and adjacent systems (OTS), this ‘Architecture of Trust’ (AoT) was enhanced to form a comprehensive framework of possible trust attributions. These attributions are necessary social operations for trustors insofar as direct communication with ‘the system’ is impossible. As a result, trustors need addressable objects (persons, organizations) and non-addressable objects (roles, programs, values, technology, adjacent systems) in the AoT as either reassurances of trust, or addressees for sanctioning. The Architecture of Trust, for the first time in system trust research, allows for a concrete background reality of attributions in an empirical field to be examined against trustors’ preferences. This undertaking has been conducted between 2013 and 2017 in a time of disruptive change and dynamic re-organization of a societal branch under transformation. The energy transitions in Germany and

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many other countries around the world build a meaningful basis for the study of system trust under the conceptual umbrella presented here. The transition narratives of planned systemic transformation and grand-scale change of both technology and social patterns offer valuable insight into empirical manifestations of trust and distrust in systems. The following paragraphs will illuminate the results of studying trust in the energy sector more precisely, and tie them to the aforementioned theoretical premises, merging into the analytical categories of system identity, expectation nexus and reassurance patterns. 7.1 The Reality of System Trust This book began with the promise of inquiring into three distinct questions: 

Is a concept of trust that includes trust in systems feasible?



What is a ‘system’ and how can it be a recipient of trust?



How can empirical research into system trust – based on theoretical advances – be approached?

I want to fulfill this promise and answer these questions one by one throughout the conclusions. The first concerns my conception of trust and its feasibility for inclusion of systems. My trust concept has been modeled in proximity to Möllering’s (2006b) definition which entails the components of control, complexity, non-knowledge, risk and suspension (Figure 2-1). These components, for the most part, have been fed into the analytical category of reassurance patterns (category III), which concerns the empirical reasoning of suspending or upholding doubt and disbelief. One component of trust that received special attention was expectation, and this led to the establishment of AoT references. These references are portrayed on a spectrum ranging from persons, roles, programs, and organizations to technology, values and adjacent systems (Table 5-2). Their study in multiple constellations of the expectation nexus (category II) was a second major source of assessing the feasibility of trust in systems. The overall system of electricity supply, which is reflected by the AoT, was analyzed with regard to its identity for average consumers. This system identity (category I), as a final analytical category, is the most critical aspect of trusting a system, for it is prerequisite for trustors to relate to an overarching entity like the ‘energy system’ rather than its single components.

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The consumer case study of energy provides significant indications that the theoretical concept of trust unfolded in this way offers feasibility for inclusion of trust in the energy system. After evaluation of the ‘system identity’ section, one may plausibly assume that roughly half of the interviewees perceive some kind of systematic complex of electricity supply which entails both technical and social elements. These interviewees could form structured connections between the single elements they mentioned, and some of them explicitly referred to system emergence, or to collective action. The other half of the sample must be qualified as ‘single component observant’, since possible interconnections between the elements they brought up were largely omitted. For the case of energy, this means that people trust their supplier, an engineer or a program, rather than ‘the system’. Nonetheless, for the former half, single elements associated with the system (supplier, engineer, program) are access points to the system, while clarity about the existence of an emergent system holding these elements together prevails. As a result, the study allows for the conclusion that at least half of the sample is inclined to form trust relationships with the energy system as a collective entity. In other words, trust in systems is real – for parts of the sample. Further evidence in favor of this situation is provided by results from the ‘expectation nexus’ section. I was able to determine participants’ differentiation of expectations which led to another split in the sample: approximately half of participants had higher, and half lower expectation differentiations toward the system. The fact that half the sample has a high differentiation degree toward electricity supply is strong general evidence in support of active system trust instead of a compulsory system experience – people would not expect much from something that they do not have any influence over. The average number of expectations among all interviewees is 2.6, often including values like “security of supply”, “affordability” and “sustainability”, or programs concerning public infrastructure supply. Members of the high differentiation group discussed up to six expectations toward the energy system, among them roles like “service staff”, “R&D programs” or values like “easy handling”, “regionality” or “technical safety”. Moreover, societal values and programs like universal access and affordability, nuclear waste management, or the abolishment of energy poverty were mentioned. Due to the large numbers and level varieties of expectations involved, the idea of concrete service expectations toward the proper functioning of a system (Giddens 1990; Kohring 2004) is plausible. The study

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unraveled notable detail in parts of consumers’ expectations that include issues such as prevention of micro interruptions and voltage fluctuations, the establishment of people-owned energy communities, or market competitiveness. While the assessment of expectation outcomes – the system performance – is presumably controlled rather passively by trustors, attributions of ‘caretaking’ of their expectations frequently rely on insinuated collective actionability instead of personal or organizational addressability. In other words, the system takes care of their expectations. A final aspect that can be inferred from the empirical categories to answer the feasibility question of system trust is a possible decision-making component in trusting the energy system, underscoring its ‘genuine’ trust character and distinguishing it from confidence. First, probabilities in decision-making can generally be attached to differentiations in expectation: the more people expect, the more likely are attributions of personal decision-making in cases of disappointment. In light of the high expectation differentiation density throughout half of the sample, this general idea is a reasonable place to start. One illustration is the risk of power outages which leads to results from the ‘reassurance patterns’ section: more than one third of the sample was qualified as fully or partly prone to self-attribution of blame in case of a power outage in their home or business. In other words, a considerable share of people may blame themselves should they have to live without electricity for some time. This can be explained by reference to preventive measures (like emergency generators or battery packs) that interviewees either owned or would regret not owning in case of electricity bottlenecks. This is a conclusive sign of personal decision-making as opposed to mere system exposure, with concrete effects of risk-taking and personal guilt attribution. However, the circumstances around power outages are not applicable to every dimension of trust or distrust in the energy system. Other domains of risk perception such as nuclear energy problems, resource scarcity or terrorist attacks do not necessarily imply patterns of self-attribution in case of risk materialization. People who are suspicious of nuclear plant security, for instance, felt exposed to an external threat. This indicates a link between distrust and perceptions of danger, instead of risk (Luhmann 2005). Overall however, these frictions between the attributions to one’s self or another, risk or danger, and trust or distrust in different system dimensions are substantial arguments in favor of

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the general concept of system trust: if the individual contradicts themself in evaluating the numerous risks associated with electricity supply, this validates the multi-layered reasoning process between non-knowledge, suspension, risk, complexity and control among consumers of energy. It tells us that an active paradigm of dealing with various system dimensions in a differentiated manner and assessing their outputs carefully actually happens among average citizens. Concluding, this might be a first empirical indication that it makes more sense to speak of ‘system trust’ than of ‘confidence’. This is additionally supported by the ‘opt-out’ choices (to become independent of the system through self-generated electricity) discussed by interviewees. Thus, a concept of general participation in, or a dismissal of societal systems, appears as reasonable as system-internal decision-making that regulates attitudes toward the different system dimensions. All in all, a concept of trusting or distrusting a whole system achieves empirical plausibility at this point. 7.2 System Constitution and Attributions of Trust The second question raised in the introduction was ‘What is a system and how can it be a recipient of trust’? The theory chapter on ‘system references’ (Chap. 3) concluded that for the sake of trust objects, system design largely lies in the eye of the beholder. This is reflected vividly in the results of this study, which revealed almost 700 elements on seven scales of abstraction as immediate associations with electricity supply (Figure 6-2 and Table 6-4). A socio-technical system emerged in this way, largely involving technology but also programs, organizations, roles and values. With reference to the five criteria of system identity introduced in Sect. 3.4 (tight and loose coupling, symbolization of risk, functions and services, semantic advance and possible representatives), half of the interviewees appeared to consider electricity supply as a tightly coupled system with numerous risk potentials and diverse functions and services to be expected and evaluated. Semantic use of the term ‘system’ is prevalent in that group, as is a diversified set of system representatives reaching from technology (basic associations), to values (expectations), and organizations (sanctioning). The other half can be described as perceiving a loosely coupled, if not dispersed, complex of electricity supply that does not necessarily involve risk-taking but rather familiar behavior. Functions and services of energy supply are reduced to

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supply security and affordability, semantic references are absent, and representatives often reserved to utilities companies. Still, the diverse system comprehensions by trustors find their culminating point in the fact that all interviewees provided a socio-technical description of electricity supply. This makes it plausible to assume that ‘system identity’, through the eyes of the sample, signifies perceiving a system, generally, as opposed to determining specifically what that system consists of. Another significant condition for systems building and operation is the detection of four dimensions of system trust (Figure 6-9) that determine very precisely what the system is with regard to matters of trusting or distrusting it. Based on interviewees’ problem descriptions in the ‘reassurance patterns’ section, a list of relevant dimensions of trust in the energy system were extracted: technical security and operability, physical and environmental safety, market efficiency and transparency, as well as public order and justice. Interviewees displayed various simultaneous attitudes of trust, distrust or familiarity toward these four aggregations of risk perceptions, which justifies Guo, Lumineau and Lewicki’s idea of parallel trust-related attitudes against the same overall object (Guo et al. 2015). Detailed analysis of the four dimensions must be left to future studies due to a lack of data here. The only risk fully covered in the interviews concerned power outages, where interviewees associated with attitudes of familiarity (15), trust (12) and distrust (3) against that risk resulted in a ratio of 15/12/3. For all other risks raised in the study (such as hacking, electricity theft, administration issues with utilities, or diseases caused through energy infrastructure), additional empirical research is necessary to achieve a full picture of system trust in the energy sector. Strikingly, all of the aggregated dimension headings contain values (security, safety, transparency, justice), which emphasizes values’ special relation to system trust. Moreover, we can assume that the four separate trust dimensions reflect integration with the overall energy system, at least for those parts of the sample who are prone to recognizing ‘system identity’. The next part of the second question is ‘how can a system be a recipient of trust’? Firstly, a crucial turning point in the theoretical Chap. 3 was the recognition that the system is not necessarily a recipient (i.e., trustee); it can also be the trustor in a relationship of trust (Figure 3-1). This insight goes back to the idea of self-referential systems (Luhmann 1995) and marks a change in terminology, compared to the classic conceptions of system trust. In the case study, I was able

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to incorporate this feature as a self-reflexive tool regarding the construction of trust or distrust in the interaction situation of the interview – a system construed through the eyes of a system. Within this interaction system, interviewer and interviewee mutually created an external system reference of energy to potentially serve as an object of trust. In other words, the (external) energy system was created inside the interaction system, i.e., internally. The simple consequence of this abstract idea is to take the creativity of trustors in establishing system descriptions more seriously. This is particularly relevant against the backdrop of objectified, often technical and economic views of the energy system that claim existence of the ‘real’ system with its scientifically discoverable characteristics. The interviewees did not follow such objective assumptions, by constructing diverse system narratives that deviate from expert descriptions, and yet unfold comparatively ‘real’ consequences, especially for the incitement and decline of trust. So how exactly did these narratives enable the energy system to appear as a trust recipient? First of all, narratives relate to different stages of the trust process, most importantly to the stages of reassurance and of sanctioning. Grounded on this central differentiation, trust is to be understood as a circular, present-based reflexivity over risk-related behavior. Regarding ‘reassurance patterns’ (category III) of trust or distrust, this means that moments of doubt over a certain risk lead to an evaluation of certainty-equivalents in order to (repeatedly) execute a leap of faith – or fail to do so in the case of distrust, which still requires alternate affirmation. If trust was established earlier through such a leap, but is now perceived as having been betrayed through opposing indicators, sanctioning comes into play. Sanctioning follows different rules of attribution to reassurance, since sanctioning requires addressability of trustees in order to communicate complaint or recompense. Only self-sanctioning is exempt from addressability. Embedded in these key stages of reassurance and sanctioning, the energy system appears as a trust recipient through attributions of multiple kinds: in AoT language, technology (T), organizations (O) and roles (R) can be considered as central reassurance levels. This means that through these “access points” (Giddens 1990, 83) to the system, people can reassure themselves in situations of doubt, with reference to their personal supplier or an electrician, for example. Noteworthy in this connection is the prominent positioning of technology such as “additional cables”, “emergency power” or “redundancies” as reassurance

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items for trustors in the interview answers, indicating that the operability of technical devices exerts ample influence on trust in the associated system. Trustors are also reassured by programs that are associated with technology, such as “emergency (power) plans” and “maintenance of machinery”, but also “security standards” or “education of staff”. This reinforces the assumption from Sect. 5.3 that technology might not be trusted as such, but in connection with social elements (like programs coordinating technology through human interference), and ultimately embodies a conduit to the system rather than an isolated trust object. Overall, the function of technology or electricity suppliers as “intermediaries of trust” (Coleman 1990, 180ff) can be explained as follows: an element of the AoT – technology, organizations, roles – is deemed an equivalent of certainty under uncertain conditions and its properties symbolize system properties as element and system reflect each other. In other words: in the moment of trust evaluation, the intermediary embodies the system, as representation lets them unite for a moment of direct trustor-system experience. These assumptions are only possible on the basis of half the interviewees’ perceptions of a robust system identity. As a result, it is evident that these trustors project their experience with system elements (such as technology or suppliers) on the emergent energy system as the ultimate reference of trust or distrust. Turning to the sanctioning stage of trust, we encounter the significance of organizations again: organizations, through their communication capacity, are much more common as addressees of sanctioning than as objects of reassurance, alluding to the special status of electricity suppliers such as utilities companies in this process. On the other hand, mentioning adjacent systems such as “politics” or “markets” was popular in the interview answers. More specific sanctioning attributions took place in hypothetical scenarios of power outages: here, as mentioned above, more than a third of study participants claimed that they would ascribe responsibility for consequences to themselves, while the majority said that they would blame their electricity supplier. Among the latter group, a small number acknowledged that a ‘diffuse system’ might be to blame for an outage situation, rather than concrete persons (e.g., themselves), or organizations (e.g., ‘others’, such as their personal suppliers). As a consequence, we can say that even though the significance of persons and organizations as addressable AoT units increases dramatically when sanctioning is approached, results indicate that interviewees frequently avoid concrete actors and rather refer to

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collective actionability of adjacent systems such as politics and markets or other arrangements between roles, organizations, programs and adjacent systems. This scheme is shown consistently in the ‘Expectation Nexus’ Sect. 6.4.3, as well as ‘Reassurance Patterns’ Sect. 6.4.4. As a conclusion, we can say that systemic emergence prevails in both expectation formations toward a collective entity (like the energy system), and in sanctioning unfavorable outcomes of that system by addressing collective actionability in cases of disappointment. Finally, reassurance patterns entail even more complexity than is incurred by relating to different AoT levels as certainty equivalents, in that elaborate patterns of communication are prevalent that reassure trust (as opposed to its disruption). The occurrence of such communication patterns is regulated by dominances of either knowledge or non-knowledge, leading to multiple narratives of dealing with uncertainties in the electricity domain. These narratives and their linkages to the trust phenomenon result in partly new distinctions that are the central outcome of the ‘Reassurance Patterns’ Sect. 6.4.4. To begin with, references to both specific and unspecific knowledge were detected. These modes of reasoning relate either to people who communicate knowledge that replaces ignorance and creates absolute certainty in their argumentation (specific knowledge), or to those who rely on often diffuse, experience-based familiarity (unspecific knowledge). While the distinction between specific and unspecific has usually been applied to non-knowledge, it is important to notice that the study of trust moreover requires precision in distinguishing different forms of knowledge, since they lead to unique reassurance patterns. A second distinction is that of knowledge, as opposed to non-knowledge. These can be mixed in various proportions, and a situation can tip to either side of the equation: a situation that involves both knowledge and ignorance in the same line of reasoning can either bridge that uttered ignorance and finally stress the knowledge share (trust), or tip to the ignorance side and fail to conduct that leap (distrust). Either case involves a mix of knowns and unknowns in a process of risk calculation whose outcome is open to either conclusion. Still, both settings are vulnerable to shares of both specific knowledge and specific nonknowledge that cause the decisive tipping through stressing either known knowns (social or technical CEs replacing non-knowledge), or known unknowns (will energy storage technology be available in the future?) that deter-

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mine the establishment of trust or distrust. A final relevant setting is that of unspecific non-knowledge and concomitant distrust: here, specific knowledge or non-knowledge claims are refused altogether and relegated into the realm of unknown unknowns. This attitude mainly occurred with reference to nuclear energy issues and power outages as a result of catastrophe situations such as terrorism or war. Crucially, one notices a concurrence between knowledge and non-knowledge in most of the above constellations, so that clear alignments with trust, distrust or familiarity are not always possible, especially across risk perceptions. This alludes to the fluidity between the three modes (waking up from familiarity, or swinging from trust to distrust). Furthermore, it clarifies their simultaneous, possibly contradictory occurrence within different risk perceptions of power outages, nuclear energy problems or RES volatility. Trustors portrayed familiarity with one risk, distrust to another and yet trust concerning a third. 7.3 Outlook: System Trust in Theory and Practice The final research question introduced at the beginning of the book was “How can empirical research into system trust – based on theoretical advances – be approached?” I would like to start answering this question by evaluating my own empirical efforts, before indicating omissions and unfolding an ensuing outlook. For most underlying features of the empirical case study, more indications to justify plausibility than rejection have been found. This applies to the hypotheses of categories I and II in particular, while category III accomplished a generous exploration of ‘reassurance patterns’. All three categories need broader and more systematic application to reinforce their initial findings. Overall, aspects such as system awareness, expectation differentiation, separation of attribution stages (reassurance/sanctioning) and the AoT experienced a first demonstration, confirming the introductory assumption of this book that system trust research is a fruitful undertaking for social scientists, both in theory and practice. System trust research relates to the energy system as much as to other societal systems like politics, mass media, digital services, traffic or finance. Through the concept of ‘system identity’ and its resulting five criteria developed in section 3.4 (tight and loose coupling, symbolization of risk, functions and services, semantic advance, and possible representatives), systems across society can be classified for trust purposes. The same applies to the ‘expectation nexus’

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and ‘reassurance patterns’. Taken together, these three analytical categories allow for the study of systems as recipients of trust. System trust, in its genuine form, then, manifests in cases of high system identity, differentiated expectations and diverse risk perceptions that are reassured by complex narratives of knowledge and non-knowledge. These conditions have to be studied carefully in order to classify a societal system prone to system trust, especially with regard to possibilities of decisionmaking. For example, it makes a big difference if a system is trusted as a whole, or if single system components are trusted, or merely familiarity or even distrust apply. When a system is trusted as a whole, the service provision relation between trustor and trustee comprises sophisticated expectations directed at the trustee (Kohring 2004, 131f), expanding the spheres of trustor decision-making. Trusting single actors or technologies of the system alone, without greater system awareness by trustors, leads to less complex patterns of interaction. On the other hand, genuine system trust results in an increase of trustors’ likelihood to attribute disappointments with system interaction to themselves, reinforcing the system-client relation. In addition, both expectation sophistication and the personal sanctioning direction of system trust have different consequences for decision-making than an attitude of distrust or familiarity: distrust appears to contract the decision-making spectrum, and a state of familiarity relies on mere experience without forming differentiated expectations toward the system. Societal systems, once they are trusted, can be plausibly assumed to form various dimensions of trust that trustors connect to (Kohring 2004, 170ff). In the case of the energy system, for instance, four dimensions of system trust were detected that embody generalized risk perceptions related to the system. One of the main achievements of this book’s case study is the unraveling of the relevant risks for trustors in the energy field and the alignment of those risks with four corresponding dimensions (as displayed in Figure 6-9). Further research should assess trustors’ multifold risk perceptions according to the parameters introduced in category III. Such an assessment would allow researchers to categorize risk treatment by trustors with respect to trust, distrust and familiarity. In this regard, the only risk for which I extracted reliable results concerned power outages, which is merely one risk among several in the ‘technical security and operability’ dimension of the energy system (Figure 6-9). In the case of outages, based on the knowledge and non-knowledge patterns presented in Sect. 7.2 of

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the Conclusions, 15 out of 30 people trusted that power outages do not occur, 12/30 were familiar with them not occurring, while 3/30 were not able to bridge their uncertainties and can therefore be expected to distrust. The continuation of this line of research seems promising, for it would complete the trust/distrust/familiarity configuration with the remaining risk perceptions of energy, and ultimately with the system trust dimensions. On the basis of such an endeavor, it would be possible to not only show the general plausibility of system trust empirically, but also its precise composition and differentiation according to the numerous risks involved. In conducting this future research, trust scholars should be wary of the empirical research conditions discovered in this study. In particular, they should consider the unit of analysis for observing ‘trustors’: are trustors single or in a relationship? Do they live in shared apartments or generate their own electricity supply? These living conditions can have a great impact on decision-making patterns and influence trust judgments decisively. In the case study on energy, I discovered relationship dependencies where one partner took all electricity related decisions, or members of shared apartments delegated all decision-making to a central tenant. Similar patterns apply to more expert based decision-making among owners of energy generation technology, who are dependent on “opinion leaders” (Katz and Lazarsfeld 1955) in their social environment to guide their actions. More generally, the embeddedness of individual decision-making in a social context of external influences should be more systematically included in future system trust studies. This would also contribute to clarifying how social systems generate trust as trustors, apart from limiting their application to trustee analyses. Moreover, future empirical research can benefit from carefully considering various methods of collecting data. While the reliance on interviews or questionnaires in the initial context of seeking general plausibility for system trust is appropriate, more detailed follow-up studies might need to enhance the methodological portfolio. This can include methods that are capable of detecting concrete actions and attributions of trustors concerning the relevant field of system trust. While interviews and questionnaires rely on the explicit statements of trustors about their (in-)actions and attributions, trust is determined by these concrete actions’ manifestations or absence, so that methodological innovation for elaborate system trust analysis seems mandatory. These methods might then

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well be “digital methods” (Rogers 2013), not least because more and more trust relevant communication takes place online. To look for trust/distrust indicators in chat rooms, online forums or comment sections of news and social media, with a linguistically informed approach, could be a direction to follow. Such an approach could also take a more standardized, quantitative turn, as many of the variables used in the consumer case study have initially proven themselves and been refined throughout the Results and Conclusions sections. Finally, research conducted with methodological innovations would be well-advised to particularly emphasize patterns of interpretation for the results of system trust analyses. While the categories of identity, expectation and reassurance developed in this book provide the basic instruments for discovering system trust relationships, the assessment of consequences for trustor, trustee and society leaves numerous aspects to study. In this regard, the initial hypotheses posed in Chap. 4 concern thresholds and equilibria of trust and distrust. Thresholds offer a means of qualifying trust related information as decisive or negligible for trust judgments, and they thus provide hints for discovering ‘tipping points’ between trust and distrust. In the energy case, a robust threshold concerning power outages prevents trustors from swinging to distrust even when experiencing occasional power outages. This is documented by the vast majority of trust and familiarity qualifications (27 out of 30 interviews). Regarding other risk perceptions such as nuclear energy problems or the volatility of renewable energy sources, more sensitive thresholds of trust seem to be operating, since more distrusting attitudes were discovered in comparison to power outages. Consequently, after inclusion in comprehensive follow-up studies, trust thresholds could tell us more precisely what it means that people trust, distrust or are familiar with a certain system dimension. Threshold research could help uncover how solid or shaky trust and distrust are, and help understand probabilities and conditions of turnovers between trust, distrust and familiarity. A similar function is fulfilled by the concept of a trust/distrust equilibrium. While from an individual perspective, the logic of trust inhibits us from favoring trust over distrust (or vice versa), a systemic view interested in avoiding system dysfunctionality (Luhmann 1979, 71ff) must carefully consider the respective shares of trust and distrust. A balanced share between trust and distrust in the system, as a consequence, ensures that the overall system rationality is safeguarded, even if individual trusting or distrusting actions cannot (and should

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not) be deemed right or wrong. For the rationality of the German energy system transition, for example, we may ask whether the discovery of strong trust in, and familiarity with, the system regarding security of supply is too passive: given the technical RES volatility problems and the social volatility of integrating ‘prosumers’ into a system that possibly depends on their behavior, partial distrust might stimulate the more active behavioral patterns needed to realize a ‘smart grid’. How many potential future disappointments with energy delivery would consumers accept before their attitudes changed? How much distrust – in the sense of critical attention and reflexivity – do we need to successfully execute a massive energy transformation? These are the questions, from a more practical point of view, that concern a re-formulated normativity of trust research at the collective level, which might require some form of institutionalized ‘trust management’ to generate meaningful answers. The notion of such an equilibrium ultimately leads to asking about the ‘volume’ or amount of trust in systems, and thus to a concluding theoretical trajectory. Since we cannot count trust (in the sense of one trust, two trusts, etc.), our ability to quantify it is limited. And yet we frequently discuss ‘high’ or ‘low’ levels of trust toward certain trustees. But what is the measurement of system trust and how, if at all, does it scale? Is it just there or absent, or rather weak and strong? Does strong trust enable more risky action than weak trust? These questions are all the more important regarding possible simultaneous attitudes of trust, distrust and familiarity against different facets of the system as discovered earlier. Conceptual suggestions in the debate concern trust and distrust as a) “two ends of the same conceptual spectrum with overlapping range”, b) allowing for an “in-between range”, or c) constituting “separate concepts on different dimensions” (Guo et al. 2015, 65). These exploratory models about proportions, range and scaling of trust and distrust have neither led to satisfying conclusions so far (ibid.) nor been considered for particular requirements of system trust. For a start, one could trace back to threshold ideas of trust and distrust: only if a certain threshold of trust in the system is reached can its functionality be guaranteed and the necessary volume established. If trust falls short of reaching the threshold, dysfunctionalities are probable. Surpassing the threshold, on the other hand, also causes dysfunctional states, due to the inherent risk-generation logic of trust for the system (Strulik 2011). As a consequence, discussions regarding equilibria and thresholds of trust, i.e., the appropriate proportions for system

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functionality, will always pertain to an inflation or deflation of system trust (cf. Luhmann 2012, 230f; Morgner 2013, 524). While this study has provided support for the general reasonability and setup of system trust, more theoretical work on the societal role of trust is necessary. First and foremost, such theories should concentrate on the ‘volume’ question of system trust, and thus on the very foundations of its operation, location and impact. A first glimpse into possible answers can be inspired by the rarely addressed second part of Luhmann’s study about “Trust and Power” (Luhmann 1979, 107ff). Here, Luhmann notes that a ‘power vacuum’, as often claimed in conventional wisdom, cannot exist sociologically. Rather, some form of power is always impactful, according to the “constant total amounts” principle (ibid., 140f). In this connection, the volume question of power is resolved by explaining that power is a potentiality in the social system that is always looming in the background, yet only applied in cases where it is actually challenged (ibid., 123f). In other words: power is most effective in cases where power-subjects voluntarily act as the power-holder expects – if sanctioning is threatened, power has already been broken. This pattern can be brought into analogy with trust: trust, just like power, is always operative to a certain degree (and thus ‘voluminous’) in that it enables communication, cooperation, action – no modern society can exist without trust reaching a certain threshold. In fulfilling this positive function as a lubricant of social interaction, trust is rarely noticed by the actors relying on it – things just work out fine. But when trust is withdrawn, a crisis situation interrupts smooth operation and alters its volume, changing the concomitant social structures. One of many questions left to answer is whether we are dealing with a ‘constant total amount’ of trust in the system, i.e., withdrawal of trust at one point means gain at another and so on, and what that means for system rationality. Intriguingly, another similarity between trust and power could be their status as communications media in social systems. Originally, Luhmann (1979, 48ff) considered system trust to be a favorable expectation toward smooth operation of communications media like power, money or truth. And yet the close theoretical proximity of trust and communications media has never led to consideration of trust as such a medium itself, despite the fact that “Trust and Power” (ibid.) were published under a common heading. The theory of communications media has always stressed an idea of systemic emergence in that love,

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power, money or truth were considered as system capital rather than personal attributes (Parsons 1964; Luhmann 1979, 116). Comparably, it makes sense to understand trust as a system resource that individuals connect to, and exploit or dismiss for their (in)actions. The concept of communications media could also help explain why trust should not be regarded as a property of people that is carried by single trustors, but as a fluid condition for systems, perhaps even as a catalyst for communication (Luhmann 1979, 114). In this way, trust could be regarded as a success medium of communication (Luhmann 2012, 121), enhancing the probability of communicative acceptance (i.e., of ‘consensus’) through its application. In contrast to power, money or truth, however, trust is not bound to single functional systems like politics, the economy or science, but unfolds impact across all societal systems. This research branch of trust as a possible communications medium would require careful assessments against Luhmann’s initial take on trust as a “social mechanism” (Hedström and Swedberg 1996). In this connection, complementary perspectives about system trust’s societal role regarding “collective behavior” (Granovetter 1978) or “transsystemic trust” (Strulik 2011; Schweer and Siebertz-Reckzeh 2014) would be beneficial to its advance in the trust discourse.

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