The Business Case for Employee Privacy: Empirical Analysis of the Effects of Employee Privacy on Empowerment, Creativity, and Job Satisfaction [1 ed.] 9783428538263, 9783428138265

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Betriebswirtschaftliche Forschungsergebnisse Band 140

The Business Case for Employee Privacy Empirical Analysis of the Effects of Employee Privacy on Empowerment, Creativity, and Job Satisfaction

By

Andreas Ostermaier

Duncker & Humblot · Berlin

ANDREAS OSTERMAIER

The Business Case for Employee Privacy

B e t r i e b s w i r t s chaf t l i ch e Fors chu ng s e rgebn i s s e Begründet von

Professor Dr. Dr. h. c. mult. Erich Kosiol (1899 – 1990) Fortgeführt von dessen Schülerkreis

Herausgegeben von

Professor Dr. Ernst Troßmann Universität Hohenheim

in Gemeinschaft mit

Professor Dr. Oskar Grün Wirtschaftsuniversität Wien

Professor Dr. Wilfried Krüger Justus-Liebig-Universität Gießen

Professor Dr. Hans-Ulrich Küpper Ludwig-Maximilians-Universität München

Professor Dr. Gerhard Schewe Westfälische Wilhelms-Universität Münster

Professor Dr. Axel von Werder Technische Universität Berlin

Band 140

The Business Case for Employee Privacy Empirical Analysis of the Effects of Employee Privacy on Empowerment, Creativity, and Job Satisfaction

By

Andreas Ostermaier

Duncker & Humblot · Berlin

Die Fakultät für Betriebswirtschaft der Ludwig-Maximilians-Universität München hat diese Arbeit im Jahre 2011 als Dissertation angenommen.

Bibliografische Information der Deutschen Nationalbibliothek Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.d-nb.de abrufbar.

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© 2013 Duncker & Humblot GmbH, Berlin

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Foreword There has been much debate about privacy recently. Privacy does not only pertain to the relationship between citizens and the government, but also to that between organizations, such as firms, and their members. As a contribution to this ongoing debate, Andreas Ostermaier examines empirical effects of privacy to see whether these effects are such that they provide an argument for firms to protect their employees’ privacy. It is crucial for firms that their employees are committed to their goals and join their efforts to achieve them. Firms establish all kinds of control mechanisms to this end. However, control has its drawbacks. It may result in distrust, crowd out intrinsic motivation, and cause reactance. Firms must therefore balance control and privacy. As technological progress creates new control mechanisms, the balance must be re-established every day. This is both an economic and ethical challenge. It is ethical because privacy is a value cherished by many; it is economic as the empirical effects of privacy matter to firms. The author finds evidence that privacy is associated with job satisfaction and empowerment and, though in a more complex relationship, creativity. The message of this thesis for firms is to carefully consider their employees’ concerns about privacy. From a conceptual viewpoint, this work is worth reading for two reasons at least. First, it employs a fresh analytical approach to business ethics, which examines how values relate to one another logically and empirically. Second, it addresses issues both in the realm of business ethics and the management of firms. It offers highly interesting insights in both regards, which have scientific as well as practical implications. I therefore hope that it finds a broad audience and inspires further research. Munich, July 2013

Prof. Dr. Dr. h. c. Hans-Ulrich Küpper

Preface Privacy and control are constitutive elements of relations between people. Markets and firms are no exception to this rule. Whenever people interact, they influence, and mostly want to influence, each other’s actions. In the business context, many seem to be convinced that more control is better than less. Debates about privacy suggest that organizations, private or public, strive to reduce the privacy, or freedom from control, of the people they deal with. As technological advances improve the chances to control people, the question arises how much privacy people should have. Privacy is not only something people value, but it may also be beneficial for me to respect the privacy of others. Privacy gives people freedom, and they may well use this freedom to do things which I appreciate. The purpose of this study, which was accepted by the Munich School of Management of the Ludwig-Maximilians-Universität München as a doctoral thesis in 2011, is to explore this argument in the business context. Irrespective of whether employees have a right to privacy, does privacy produce effects which make it worth protecting from a business viewpoint? Privacy is a yet unexplored field, and my attempt to answer this question was an exciting endeavor which would have been impossible without the support of many. I am particularly grateful to Professor Hans-Ulrich Küpper, who gave me all the freedom to pursue my research interests, challenged my work where this was necessary, and always encouraged me to go forward. He struck the right balance between privacy and control in supervising my research. Likewise, I am indebted to Professor Dieter Frey, who readily discussed my research with me and offered invaluable comments. Both influenced and shaped me as a researcher and individual. Over the years, I have benefited from the advice, criticism, and encouragement of more colleagues and friends than I can name. I owe especially much to Philipp Beltz, Markus Brunner, Benedikt Hofmann, Andreas Schempp, and Dominik van Aaken. They and all my colleagues contributed largely to my work. Furthermore, their company made this journey an enjoyable one. We started as colleagues and turned into friends. I am also indebted to researchers from other universities as well as many people whom I do

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Preface

not know personally but who answered my survey and thus provided me with the data for my investigation. Finally and most importantly, I thank my parents and family. They shared all the ups and downs of my research with unwavering patience. My parents believed in me when others had doubts. Their unremitting support gave me the chance to write this thesis. Munich, July 2013

Andreas Ostermaier

Contents

1 The 1.1 1.2 1.3

Business Case for Employee Privacy . . . . . The Ethical Challenge of Employee Privacy . . . . An Analytical Approach to the Privacy Challenge Making the Business Case for Employee Privacy .

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1 1 2 4

2 Conceptualization of Employee Privacy . . . . . . . 2.1 The Relationship between Privacy and Control . . . 2.2 The Balance between Achieved and Desired Privacy 2.3 Perceived vs. Objective Privacy . . . . . . . . . . . . 2.4 Privacy Regulation Behaviors . . . . . . . . . . . . . 2.4.1 Control over the Environment . . . . . . . . . 2.4.2 Control over Communication . . . . . . . . . 2.4.3 Control over Personal Information . . . . . . 2.4.4 Control over the Work–Life Boundaries . . .

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3 Development of a Measure of Employee Privacy . . . . . 3.1 The Development of Measures . . . . . . . . . . . . . . . 3.2 Development of the Initial Items . . . . . . . . . . . . . 3.2.1 Items for Control over the Environment . . . . . 3.2.2 Items for Control over Communication . . . . . . 3.2.3 Items for Control over Personal Information . . . 3.2.4 Items for Control over the Work–Life Boundaries 3.3 Validation of the Measure . . . . . . . . . . . . . . . . . 3.4 Final Measure of Employee Privacy . . . . . . . . . . . .

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23 23 25 25 26 28 29 31 33

4 The Effects of Privacy on Creativity and Job Satisfaction 4.1 Privacy and Creativity . . . . . . . . . . . . . . . . . . . . . 4.1.1 Creativity as an Objective of Companies . . . . . . . 4.1.2 The Effect of Privacy on Creativity . . . . . . . . . . 4.2 Privacy and Job Satisfaction . . . . . . . . . . . . . . . . . 4.2.1 Job Satisfaction as an Objective of Companies . . . 4.2.2 The Effect of Privacy on Job Satisfaction . . . . . . 4.3 The Mediating Effects of Empowerment . . . . . . . . . . .

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4.4

4.3.1 The Concept of Empowerment . . . . . . . . . . 4.3.2 The Effect of Privacy on Empowerment . . . . . 4.3.3 The Effect of Empowerment on Creativity . . . . 4.3.4 The Effect of Empowerment on Job Satisfaction The Control Variables and their Effects . . . . . . . . . 4.4.1 Choice of the Control Variables . . . . . . . . . . 4.4.2 Person-Related Effects . . . . . . . . . . . . . . . 4.4.3 Job-Related Effects . . . . . . . . . . . . . . . . . 4.4.4 Context-Related Effects . . . . . . . . . . . . . .

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44 45 47 49 51 51 52 54 55

5 Empirical Test of the Effects of Privacy . . . . . . . . 5.1 Research Setting, Participants, and Procedures . . . . 5.1.1 Research Setting . . . . . . . . . . . . . . . . . 5.1.2 Participants . . . . . . . . . . . . . . . . . . . . 5.1.3 Procedures . . . . . . . . . . . . . . . . . . . . 5.2 The Measures . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 The Measure of Creativity . . . . . . . . . . . . 5.2.2 The Measure of Job Satisfaction . . . . . . . . 5.2.3 The Measure of Empowerment . . . . . . . . . 5.2.4 The Measure of Creative Potential . . . . . . . 5.2.5 The Measure of Motivating Potential . . . . . . 5.2.6 The Measure of Climate for Creativity . . . . . 5.2.7 The Measures of the Manifest Variables . . . . 5.3 Statistical Procedures . . . . . . . . . . . . . . . . . . 5.3.1 Choice of the PLS Approach . . . . . . . . . . 5.3.2 Evaluation of Reflective Measurement Models . 5.3.3 Evaluation of Formative Measurement Models . 5.3.4 Evaluation of the Path Model . . . . . . . . . . 5.4 Results of the Empirical Analysis . . . . . . . . . . . . 5.4.1 Evaluation of the Measurement Models . . . . 5.4.2 Evaluation of the Structural Model . . . . . . . 5.4.3 Analysis of Moderating Effects . . . . . . . . . 5.4.4 Test for Common Method Variance . . . . . . .

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. 61 . 61 . 61 . 62 . 66 . 68 . 68 . 71 . 73 . 74 . 76 . 79 . 80 . 83 . 83 . 85 . 86 . 88 . 90 . 90 . 96 . 102 . 109

6 Discussion of the Results . . . . . . . . . . 6.1 Summary of the Results . . . . . . . . . . 6.2 Limitations and Implications for Research 6.3 Managerial Implications . . . . . . . . . .

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111 111 113 116

Preface

A Appendix: Statistics and Tests . . . A.1 Test of psa and csv for Significance A.2 The Sobel Test for Mediation . . . A.3 Test for Moderation . . . . . . . .

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119 119 119 120

B Appendix: Tables . . . . . . . . . . . . . . . . . . . . . . . . . 121 B.1 Correlations between the Privacy Items . . . . . . . . . . . 121 B.2 Indirect and Total Effects . . . . . . . . . . . . . . . . . . . 121 C Appendix: German Questionnaire

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D Appendix: English Questionnaire . . . . . . . . . . . . . . . 133 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

List of Figures and Tables Figure Figure Figure Figure Figure Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table

2.1: 2.2: 4.1: 5.1: 5.2:

3.1: 5.1: 5.2: 5.3: 5.4: 5.5: 5.6: 5.7: 5.8: 5.9: 5.10: 5.11: 5.12: 5.13: A.1: B.1: B.2:

The Privacy Regulation Process . . . . . Job Satisfaction as a Function of Privacy Hypothesized Model . . . . . . . . . . . . Test of the Hypothesized Model . . . . . . Test for Common Method Variance . . . .

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Validation of the Privacy Scale . . . . . . . . . . . Populations, Samples, and Response Rates . . . . Gender, Age, Job Experience, and Leadership . . . Level of Education . . . . . . . . . . . . . . . . . . Field of Education . . . . . . . . . . . . . . . . . . Evaluation of the Privacy Scale . . . . . . . . . . . Evaluation of the Creativity Index . . . . . . . . . Evaluation of the Job Satisfaction Scale . . . . . . Evaluation of the Empowerment Scale . . . . . . . Evaluation of the Climate for Creativity Index . . Relationships between the Latent Variables . . . . Evaluation of the Path Model . . . . . . . . . . . . Type of Organization as a Moderator . . . . . . . Gender, Education, and Leadership as Moderators Critical Values for psa and csv . . . . . . . . . . . . Further Indirect and Total Effects . . . . . . . . . Correlations between the Privacy Items . . . . . .

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33 62 64 65 66 91 93 94 95 96 97 98 104 107 119 121 122

List of Abbreviations and Symbols ABB AVE CI Clim CP S Crea csv EduL Emp EmpComp EmpImpt EmpM ean EmpSDet f2 JDI JDS JobExp JS MPS MSQ OCB OLS PLS P riv P rivCom P rivEnv P rivInf P rivW lb psa

Arbeitsbeschreibungsbogen Average variance extracted Condition Index Climate for creativity Creative Personality Score Creativity Coefficient of substantive validity Level of education Empowerment Competence Impact Meaning Self-determination Effect size Job Descriptive Index Job Diagnostic Survey Job experience Job satisfaction Motivating Potential Score Minnesota Satisfaction Questionnaire Organizational Citizenship Behavior Ordinary least squares Partial least squares Privacy Control over communication Control over the environment Control over personal information Control over the boundaries between work life and private life Proportion of substantive agreement

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List of Abbreviations and Symbols

Q2 R2 ρc SD TCI VIF

Stone–Geisser Q2 (Measure of predictive relevance) Coefficient of determination Composite reliability Standard deviation Team Climate Inventory Variance Inflation Factor

Chapter 1 The Business Case for Employee Privacy 1.1

The Ethical Challenge of Employee Privacy

Privacy is an issue that receives considerable attention from both the general public and the scientific community. Notable economist George Joseph Stigler, who in 1980 found that “this interest in privacy is paradoxical, for the average citizen has more privacy [. . . ] than ever” (p. 623), probably would have been even more surprised to learn that rather than fade, this puzzling interest has kept increasing over the last three decades. It seems that never has as much been written about privacy as today. The only threats to privacy that concerned Stigler (1980) were those posed by governments. Unlike governments, he argued, companies have limited resources and for that reason, the cost of collecting private data, sending unsolicited mail, or monitoring employees limits the invasion of privacy. Since then, however, technological advances have reduced the cost of such practices considerably. For instance, it is now possible for a communication company to track every call its clients make, every website they visit, and in some cases, every physical trip they take. Few would have imagined in the 1980s that this would one day be feasible, let alone affordable. Some of the critical practices that human resources departments use, such as background checks (Lam and Harcourt, 2003), drug testing, genetic screening (Kupfer, 1993), and workplace surveillance (Moore, 2000) are also an object of current debates. Privacy is often associated with the protection of private data and personal information. The dot-com companies in particular are constantly scrutinized by the media; by contrast, privacy at the workplace attracts comparatively sporadic attention. This is unsurprising since, especially in the western world, many people are consumers of the products and services of dot-com companies and members of virtual social networks, so that the privacy issues arising in these contexts concern almost everybody. The privacy of employees, on the other hand, poses more particular problems that are of much lesser general interest, even though anyone working for a company does face work-related privacy issues. Moreover, work naturally

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1 The Business Case for Employee Privacy

implies restrictions of privacy, whereas the “big” issues, such as genetic screening, still concern only few employees. Nevertheless, employee privacy is highly relevant both as an economic and ethical issue since most people spend a considerable part of their lifetime working. Work is therefore a chance for self-fulfillment and an essential part of human life. This makes the work life a matter of ethics and ethics a matter of economic research (Küpper, 2005b; Küpper, 2006b, p. 29). The relation between employee and employing company is usually more complex than that between client and company because, far from being limited to the mere exchange of goods and services, it is closer, lasts longer, and is multifaceted. The complexity entails a variety of interesting privacy issues (Persson and Hansson, 2003; Hansson and Palm, 2005). New technologies like those mentioned above create opportunities but at the same time pose ethical challenges. They make restrictions of privacy possible but do not imply them. It is fallacious to conclude that people should do what they can do. While it is far from obvious that people deserve as much privacy as they used to have despite those new opportunities, it is no more clear that they should have as little privacy as is technically possible. Technological advances both allow and oblige us to reconsider the question of how much privacy people should have and revisit the ethical foundations of privacy.

1.2

An Analytical Approach to the Privacy Challenge

In his analytical approach to business ethics, Küpper (2005a, 2006b) suggests that research should examine the foundation and impacts of the underlying values and norms of ethical issues, as well as how these relate to other values and norms. Thus, whether a company respects employees’ privacy or not, empirical effects will result, some positive, some negative, some neutral from the company’s viewpoint. Taking as a criterion the value of economic success, which translates into a profit-making norm, the value of privacy depends on how the effects of (not) respecting privacy relate to profit-making. It might even be argued that privacy, rather than derive its value from economic success, is intrinsically valuable. However, while most people agree that, for example, freedom has intrinsic value (e.g., Küpper, 2006a; Küpper, 2006b, pp. 86–7), privacy remains controversial. There is evidence that privacy is widely recognized as a value, which some even call fundamental. For example, the right to privacy is acknowledged by both national law and international treaties and can be regarded as a fundamental employee right,

1.2 An Analytical Approach to the Privacy Challenge

3

which companies must accept (Küpper, 2006b, p. 235–6). Taking Ulrich’s (2008) integrative approach to economic ethics, one might argue that the right to privacy is what stakeholders in a rational discourse would agree upon. Much of the literature in business ethics seems to take for granted that privacy is either a fundamental value or logically derives from a fundamental value. In this vein, Moore (2000) states that privacy is linked to more fundamental (liberal) values, such as autonomy and respect for a person, without, however, elaborating on this link. Likewise, Persson and Hansson (2003) postulate that employees have a prima facie right to privacy (which, of course, can be overridden by competing moral principles), but fail to make clear where this right comes from. A notable exception is Schoeman (1992), who argues that privacy is a special form of freedom (namely freedom limited to self-related behavior) and, as such, by implication a fundamental value like freedom. By contrast, many economists, including Chicago School champions Stigler (1980) and Posner (e.g., 1981, 1983, 1998), have taken a critical stance on privacy. Assuming that people behave according to the Homo economicus model and that social welfare should be maximized, they suggested that privacy has undesirable empirical effects. For instance, Posner (1978) put forward that people abuse the privacy of information, or “privacy as secrecy,” to conceal disreputable facts and deceive everyone else about their value as “transaction partners” (e.g., as an employee, client, spouse, etc.). Privacy, he argued, prevents efficient transactions and results in a loss of welfare. Elsewhere, Posner (1979) theorized that other forms of privacy, including “privacy as seclusion,” likewise decrease welfare. Later economic research has produced a more balanced account but stuck to Posner’s assumptions (e.g., Hermalin and Katz, 2006; for a review, see Hui and Png, 2006). This research suffers from a number of limitations, including both a lack of empirical evidence and overly pessimistic assumptions about human behavior. Conclusions drawn from these assumptions should therefore be interpreted with caution. As will be argued below, privacy sets people free from constraining control and allows for self-determined or “discretionary” behaviors. The literature on organizational citizenship offers rich evidence that employees actually use this freedom to do more than they are required to, engaging in so-called extra-role behaviors, rather than just capitalize on it by concealing personal failure or incompetence, minimizing their effort, or defrauding their company (e.g., Organ and Ryan, 1995). Nevertheless, recent reports on invasions of employee privacy by major companies suggest that employers tend to readily adopt the pessimistic stance and expect the worst from their employees.

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1 The Business Case for Employee Privacy

Of course, there are legitimate reasons for restricting employee privacy, such as avoiding deviant behavior, preventing company resources from being wasted, ensuring client safety, and so on (Martin and Freeman, 2003). In other words, employee privacy may clash with other company norms and values, which, in such cases at any rate, will override it (Persson and Hansson, 2003). However, companies should take into account that limiting employee privacy may entail undesired effects or block desirable effects, such as the discretionary behaviors mentioned above.

1.3

Making the Business Case for Employee Privacy

As the previous section indicates, there are arguments both for and against respecting employee privacy. While the arguments in favor of privacy are mainly based on the relationship of privacy to more fundamental values or norms, the arguments against it derive, or at least seem to derive, from the primacy of the company’s interests. This “mismatch” is unfortunate as it makes the arguments incommensurable, in the sense that trading off rights against benefits is like comparing apples and oranges. Possibly even worse, companies may perceive employee rights to privacy as a result of legal paternalism that conflicts with their interests, although it is far from clear that the economic case against privacy holds, and not at all evident that the employee right to privacy necessarily impairs business. Still, it might seem that there is a dilemma between ethical (i.e. protecting privacy) and economic norms (e.g., making profit). A particularly elegant solution to both problems—the incommensurability and the dilemma—is the “business case.” Making the business case for an action which at first glance seems to conflict with company goals, means demonstrating that in reality this actions supports them.1 The business case involves translating ethical categories into economic categories and solves the dilemma by showing that in fact it does not exist. Homann and Blome-Drees (1992) describe the same idea in terms of economic and ethical goals (see also Küpper, 2006b, pp. 194–200; Küpper, 2007). Apart from the special case of goal independence, four cases remain: actions that are desirable from a business but not ethical viewpoint; actions that are desirable from an ethical but not business viewpoint; actions that are undesirable both from a business and ethical viewpoint; and, lastly, actions that are desirable from both a business and ethical viewpoint. In the context of these 1 It may sometimes be sufficient to show that a presumably neutral action benefits the company, or that a measure which seems to be negative is neutral, although the latter would hardly be called a business case.

1.3 Making the Business Case for Employee Privacy

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categories, there is a business case for an action which seems to be desirable from an ethical but not business viewpoint if it is actually desirable from both viewpoints. Making the business case for employee privacy is an ambitious endeavor. It is not sufficient to show that respecting employee privacy does have desirable effects for the company. The possible negative effects of privacy make it necessary to prove that the positive effects outweigh the negative ones, i.e. that from the company’s viewpoint, the benefits of respecting employee privacy outweigh any potential damage. Existing research is marked by a lack of empirical data on the effects of privacy, both desirable and undesirable. In light of this, any attempt to weigh the positive and negative effects would be like trying to run before one can walk. A more modest but still challenging endeavor is to identify the positive impact of employee privacy in the first place, because, as Küpper (2006b) put it, ethically principled decisions in human resources management have empirical consequences. If these are considered to be positive by the decision-maker, i.e. the company, as well as the people concerned thereof [. . . ], highlighting the relevant empirical relationships will strengthen the case of the underlying principles (p. 232, translation by the author). The purpose of this study is to empirically analyze whether respecting employee privacy has positive effects from the company viewpoint. It has been argued that employees are more empowered, creative and satisfied with their job if it allows them to have as much privacy as they desire. It seems reasonable to assume that companies want their employees to be empowered, creative, and satisfied, either as an intrinsic or instrumental goal of human resources management. In order to test whether privacy produces the arguable effects, a survey was conducted among several hundreds of employees from different organizations in both the private and public sectors. The preliminary work and the results of the empirical analysis will be presented at length in the rest of this study. First, however, it is necessary to put forward a concept of privacy that can be translated into an empirical measure. To this end, it will be shown that privacy is closely linked to control and, more specifically, that it can be conceived as a function of control over a number of behaviors that individuals use to regulate the amount of privacy they have (Chapter 2). These behaviors relate to control over communication, the environment, personal information, and the boundaries between work life and private life. For each of these behaviors, a measure was derived, discussed with other researchers, and modified according to their feedback (Chapter 3).

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1 The Business Case for Employee Privacy

The starting hypothesis is that privacy has positive effects on empowerment, creativity and job satisfaction, where empowerment serves as a mediator between privacy, on the one hand, and creativity and job satisfaction, on the other hand. To assess the effects of privacy, a detailed model will be presented, which includes even more than these four “variables” (Chapter 4). Subsequently, the collection of the data, the measures of the variables other than privacy, and the results of the analysis will be described (Chapter 5). These results and their implications for further research and for everyday business practices will be discussed in conclusion (Chapter 6).

Chapter 2 Conceptualization of Employee Privacy 2.1

The Relationship between Privacy and Control

The word “privacy” is frequently used, which might suggest that the underlying concept is relatively uncomplicated. However, this impression is illusive: although a lot has been written about privacy, there is still no generally accepted definition. Nevertheless, most definitions and concepts share the idea of control as an element of privacy (Kruse-Graumann, 1980, p. 113). Indeed, Johnson (1975) went so far as to claim that “any adequate conceptualization of privacy must involve personal control” (p. 83), where control is understood as the chance to alter the probability that something happens (Schaffer, 2001) or, more specifically, to alter a person’s behavior (Frey and Jonas, 2002; Kelvin, 1973). An individual can obviously control or determine his or her own behavior to the extent to which it is not controlled by someone else. Control can take many forms, ranging from coercion to mere expectations.1 Although most conceptions of privacy involve control, they differ in how they relate privacy to control. Some theorists conceive privacy as “having control,” others as “not being controlled.” For instance, Westin (1967, p. 7) defines privacy as control over personal information, Altman (1975), as “selective control of access to the self or to one’s group” (p. 18); contrariwise, according to Jourard (1966), “privacy is experienced [. . . ] as freedom from interference” (p. 318), and in Kelvin’s (1973) view, an individual “enjoys privacy [. . . ] to the extent to which the probabilities of his behavior are not causally affected by others” (p. 251). Both approaches can be seen as complementary in that an individual’s behavior is necessarily controlled either by himself or herself or by others: people are free to determine their own behavior to the extent that they are free from the control of others. In contrast to freedom, privacy is limited to control over self-related behavior 1 For instance, people’s behavior can be affected by the mere awareness that their actions (or the outcomes thereof) will be compared to a benchmark (Küpper, 2008, p. 213). Similarly, the presence of others represents already a constraint typically considered a violation of privacy (Kelvin, 1973).

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2 Conceptualization of Employee Privacy

(Schoeman, 1992). On the basis of the above, privacy can be defined as the extent to which an individual is free from the control of others and thus free to control his or her self-related behavior. While it is clear that a person who has privacy has control, it is not clear whether privacy generates control or vice versa. In the literature on privacy, control or concepts akin to control (e.g., freedom or autonomy) appear among both causes and effects (or “functions”) of privacy. Altman (1975) conceptualizes privacy as the result of a boundary regulation process, while, at the same time, “a major function of privacy is regulation of interaction” (p. 28). According to Westin (1967), privacy is control over personal information, and personal autonomy is one of the functions of privacy (p. 33); by the same token, having control over personal information could be seen as an essential element of autonomy. Again, these approaches are compatible. On the one hand, if privacy is freedom from the control of others, it necessarily depends on the control others have, in the sense that not being controlled is the opposite of being controlled. On the other hand, privacy implies having control: people are free to determine their own behavior to the extent to which it is not controlled by others. In a nutshell, privacy can be said to result both from and in control. Besides depending on the control exerted by others, privacy also depends indirectly on one’s own control, which in turn can regulate the control exerted by others. For instance, imagine a person who can choose whether to disclose his or her religious affiliation. The decision to do so may lead people to comment on it, try to dissuade that person from his or her faith, encourage him or her to practice it more firmly, judge the person’s deeds by his or her creed, and so on, all of which are forms of exerting control. Withholding this information means reducing the control of others and, as a result, increasing his or her own control. Control over personal information (e.g., the choice whether to disclose it or not) indirectly determines control over one’s own behavior. Similarly, consider a person who wishes to have a confidential conversation and for that reason would rather withdraw and hold it in private. If he or she cannot choose to withdraw, either it will be impossible to hold that communication or it will be necessary to find different ways of communicating. The choice whether to withdraw indirectly enhances the person’s control over his or her own behavior. These examples illustrate that privacy results from “behaviors which enhance and maintain one’s control over outcomes indirectly by controlling interactions with others” (Johnson, 1975, p. 90). The causal relationship between control and privacy is crucial for measuring privacy. Privacy cannot directly be observed and must therefore be

2.2 The Balance between Achieved and Desired Privacy

9

measured indirectly, using either the causes or effects of privacy as proxies. However, the effects would be a bad choice in the particular case of privacy. While the number of behaviors which can be used to regulate privacy is limited, as will be argued below (Section 2.4), there are no definite limits to the actions and behaviors that people can engage in because they have privacy and that they are therefore free to engage in.

2.2

The Balance between Achieved and Desired Privacy

The term privacy can be used to describe a state which a person finds ideal. A person would then either have privacy or not, and it would not be possible for him or her to have too much or too little privacy. The technical use of the term is different, however. Thus, Kelvin (1973) stated that “privacy is a matter of degree, not all-or-none” (p. 251), and Altman (1975) referred to a lack of privacy as “crowding,” to excessive privacy, as “social isolation.” The right amount of privacy depends then both on the person and the situation, considering that some people are rather sociable, others more solitary, and that certain tasks require seclusion, others teamwork. The amount of privacy a person actually has (the achieved level of privacy) must always be related to the amount that he or she wishes to have (the desired level of privacy). Rather than maximize or minimize their achieved privacy, people minimize the difference between their achieved and desired levels of privacy, the latter of which is a function of both personal needs or preferences and a specific situation. If there is a difference between the achieved and desired levels of privacy, there are three possible reactions: adjust either the achieved or the desired level of privacy or, if neither of these options works, resign oneself to the imbalance. Taken together, these three options form a privacy regulation process, which is depicted in Figure 2.1. This model is an application of Frey and Jonas’s (2002) theory of perceived control to privacy.2 In terms of this theory, an imbalance between achieved and desired privacy reflects a lack or loss of control. As long as a person has control over the behaviors intended to regulate his or her privacy, he or she can easily adjust privacy levels. Any imbalance causes reactance, which motivates a person to reestablish control. The three possible reactions—problem-focused coping, emotion-focused coping, and learned helplessness—are equivalent to the 2 The theory was developed by Kumpf et al. (1978) and has been refined by RostSchaude et al. (1979), Osnabrügge et al. (1985), and Frey and Jonas (2002). For a more recent overview, see Fritsche et al. (2006).

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2 Conceptualization of Employee Privacy

aforementioned reactions to an imbalance between achieved and desired privacy. Adjustment of Achieved Privacy

Achieved Privacy = Desired Privacy

Learned Helplessness

Achieved Privacy = Desired Privacy

Adjustment of Desired Privacy

Figure 2.1: The Privacy Regulation Process (adapted from Frey and Jonas, 2002)

Problem-focused coping corresponds to regaining objective control, i.e. to adjust the achieved level of privacy or achieve more or less privacy. This involves behaviors which can be directly observed, such as physical withdrawal or lowering one’s voice to communicate something confidentially. Dickenberger et al. (2001) pointed out that such behaviors may also be non-compliant or destructive. Marx (2003) described several “moves” people may make to thwart surveillance, for some of which Spitzmüller and Stanton (2006) found empirical support. However, behaviors to regulate privacy are not necessarily destructive, as the examples of physical withdrawal and lowering one’s voice illustrate. These non-destructive behaviors can be observed and will serve as the basis for deriving a measure of privacy (see below, Section 2.4). Emotion-focused coping refers to emotional reactions that mitigate the consequences of a loss of control, but not to behaviors that help reestablish objective control. For instance, a loss of control is experienced as less dramatic if the events that bring it about can be foreseen or explained ex post (Frey and Jonas, 2002). Companies can and do enhance emotionfocused coping when they adopt practices that invade their employees’ privacy. They involve employees when implementing such practices (Alge, 2001), give advance notice (Alder et al., 2006; Hovorka-Mead et al., 2002; Raciot and Williams, 1993; Stone and Kotch, 1989), or justify their usage (Alder et al., 2006; Stanton, 2000). In turn, employees react differently to monitoring, depending on whether it is presented as being in the company’s or in their own interest (Chalykoff and Kochan, 1989; Griffith, 1993), and

2.2 The Balance between Achieved and Desired Privacy

11

accept it more readily when the task monitored is vital to the company (Alge, 2001; Fusilier and Hoyer, 1980; Raciot and Williams, 1993).3 Learned helplessness, like emotion-focused coping, is not directly reflected in a specific behavior. Unlike emotion-focused coping, it refers to the inability to cope with the loss of control, which results in negative psychological, physiologic, and physical effects, such as stress, loss of motivation, and depression (Frey and Jonas, 2002; Greenberger and Strasser, 1986; Greitmeyer et al., 2006). These effects have been investigated carefully with regard to crowding, i.e. too little privacy, to a lesser extent for social isolation, i.e. too much privacy (Schweizer-Ries and Fuhrer, 2006). Although learned helplessness does not imply (deviant or destructive) behavioral reactions, its effects can impair the company indirectly, as it may result in employees taking sick leave, absenteeism, or employee turnover. Hence, it is not only in the employees’ but also in the company’s own interest to avoid learned helplessness. Work, as has been noted in the previous chapter, is an important part of people’s life, given that most people spend much of their lifetime working and their work shapes their lives (e.g., Küpper, 2005a). Consequently, employees’ privacy depends much on their company, which influences their privacy regulation process in many respects, to begin with the proportion of leisure to working time. In all these respects, the company determines the level of achievable privacy. Whether the privacy regulation succeeds or fails depends on whether employees can achieve their desired level of privacy. If after adjusting their expectations they can still not achieve their desired level of privacy, they must resign themselves to the imbalance, which, however, may lead to the negative effects typical of learned helplessness. Employees’ reactions do not depend on their achieved privacy, but on the difference between their achieved and desired privacy. For example, assuming that job satisfaction is a function of privacy, it can be intuitively supposed that the better employees can balance the levels of achieved and desired privacy, the more satisfied they are with their job. Any deviation from the balance will reduce their satisfaction, no matter whether the job does not allow them to achieve a reasonable level of privacy or they fail to adjust their desired level of privacy to the realities of their job. Conversely, if the privacy they achieve equals the privacy they desire—whatever their desired level of privacy—, they should also achieve the highest level of

3

Interestingly, employees even adopt the justifications given by their company (Allen et al., 2007).

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2 Conceptualization of Employee Privacy

job satisfaction with regard to the issue of privacy. Figure 2.2 plots job satisfaction as a function of both achieved and desired privacy. Job satisfaction

Crowding

Social Isolation

Achieved privacy

Desired privacy

Figure 2.2: Job Satisfaction as a Function of Privacy

It should be noted that this control-based concept of privacy does not postulate that more privacy is better than less (as many empirical studies implicitly do). If, for a given level of desired privacy, the level of privacy that is achieved exceeds the level that is desired, job satisfaction will decrease again. In Figure 2.2, this would correspond to crossing the “ridge” of the function. This is true also for the reverse: although having too little privacy is presumably the more common problem, one can well imagine situations where an employee would prefer having less privacy or more interaction with other people. For instance, a lighthouse keeper is more likely to suffer from an excess rather than a lack of privacy, even though communicative people will rather not choose to become lighthouse keepers.

2.3

Perceived vs. Objective Privacy

As suggested above, rather than maximize or minimize their achieved privacy, employees minimize the difference between their achieved and desired privacy. The company interferes with this process in that it determines the employees’ achievable privacy in the context of the relationship between employer and employee. Different people react differently to the same situation with regard to privacy. This is because the level of desired privacy depends both on the person and the situation, and so does, by implication, the difference between achieved and desired privacy. A similar distinction can be made between perceived and objective privacy.

2.3 Perceived vs. Objective Privacy

13

Kelvin (1973) stated that “[p]sychologically, privacy is perceived privacy, just as freedom is perceived freedom” (p. 252).4 Nevertheless, “objective” privacy has often been examined in empirical research. For instance, Sundstrom et al. (1980) measured “architectural” privacy (as opposed to “psychological” privacy) as a function of the number of partitions surrounding a work space (see also DuVall-Early and Benedict, 1992). Likewise, Maher and von Hippel (2005) distinguished between “objective” and “perceived privacy,” while other authors used similar measures under different labels (Crouch and Nimran, 1989b; Oldham and Fried, 1987; Oldham et al., 1991; Oldham and Rotchford, 1983; O’Neill, 1994; Sundstrom et al., 1982b). Finally, some studies have surveyed perceived in addition to or instead of objective privacy (Crouch and Nimran, 1989b; Oldham, 1988; Oldham and Rotchford, 1983; O’Neill, 1994). There are two problems with measures of objective privacy, which Zalesny and Farace (1987) described as follows: “First, individuals occupying the same physical setting often perceive it quite differently [. . . ] Second, the perceived situation often has a greater effect on individuals than does the objective situation” (p. 246). Maher and von Hippel (2005) echoed Zalesny and Farace’s second point, reasoning that “employees’ perceptions of the impact of the physical characteristics of the work environment may be more important in influencing reactions to the workplace than the characteristics themselves” (p. 224, emphasis added). As an example, one may imagine two employees working in an openplan office, who desire different levels of privacy. In this environment, the employee who generally desires a higher level of privacy may experience a lack of privacy, whereas his or her colleague who desires less privacy probably will not mind the type of setting. Both face the same situation but perceive it differently and, as a result, react differently. For instance, the former employee may try to arrange his or her workplace so as to increase his or her achievable privacy. In terms of the effect of privacy on job satisfaction, the same environment will decrease the former employee’s but will not affect the latter’s job satisfaction. The process of privacy regulation outlined above is based on the theory of perceived control. Moreover, the purpose of this study is not to examine the effects of working conditions (such as workplace design) or company practices (such as information policy), but the effects of privacy, which, 4 Other researchers are less specific in this respect, as can be seen from the definitions cited at the beginning of this chapter (Section 2.1). In fact, the same problem occurs in research on control (Skinner, 1996). Frey and Jonas (2002), however, clearly refer to perceived control.

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according to the point made by both Zalesny and Farace (1987) and Maher and von Hippel (2005), depend on perceptions rather than objective features. It is therefore reasonable to adopt Kelvin’s (1973) position and consider perceived rather than objective privacy. To summarize, privacy is the outcome of a regulation process, which involves both observable and unobservable behaviors, i.e. behaviors aimed at adjusting the achieved and desired levels of privacy. For the purpose of developing a measure of privacy, the relevant behaviors are those that can be observed. In the next section, four types of privacy regulation behaviors are distinguished: control over the environment (Section 2.4.1), communication at work (Section 2.4.2), personal information (Section 2.4.3), and the boundaries between work life and private life (Section 2.4.4).

2.4

Privacy Regulation Behaviors

2.4.1

Control over the Environment

Although privacy is a social rather than spatial phenomenon, it is nevertheless related to space. Even in everyday language, phrases indicating distances are often used figuratively to describe relationships between people (e.g., “approach,” “be close to,” “distance oneself from,” or “keep away from” a person). According to Altman (1975), people use personal space and “territory” to adjust their achieved privacy.5 The concept of personal space is ethological and was popularized in the social sciences by Hall (1959, 1966). Sommer (1969) compared personal space to an invisible bubble surrounding each individual. Personal space and territory are closely related. Little (1965, p. 237) characterized personal space as “a form of territory,” and one of the types of territory distinguished by Lyman and Scott (1967) corresponds to personal space. Hirshleifer (1980) used both terms interchangeably. The main difference consists in how people usually react to invasions of personal space and territory. If an individual’s personal space is invaded, he or she will withdraw rather than drive the intruder away in order to reestablish physical distance, whereas an invasion of his or her territory will most likely produce the opposite reaction. In any case, regulating physical distance is difficult when there are too many people in too small a space without physical boundaries. Personal space at work has often been measured as the objective distance between workplaces (Sundstrom et al., 1980, 1982a; Oldham and Rotchford, 1983; Oldham and Fried, 1987; Oldham et al., 1991; DuVall-Early and 5

Territory is a technical term, adopted from environmental psychology.

2.4 Privacy Regulation Behaviors

15

Benedict, 1992) or by social density, which represents the average area per head and is easier to determine (Oldham, 1988; Oldham and Rotchford, 1983; Oldham and Fried, 1987; Oldham et al., 1991; May et al., 2005; O’Neill, 1994; Maher and von Hippel, 2005).6 Both distance and social density have been found to correlate significantly with perceived privacy (for a review of the literature, see De Croon et al., 2005). Of course, to estimate privacy as conceptualized in this study, it will be necessary to measure perceived control over personal space rather than objective distance. Territory can be defined as “areas controlled on the basis of ownership and exclusiveness of use” (Goffman, 1963), which “are personalized or marked in some way” (Sommer, 1969). Ways of demarcating territory include personal “arrangements of objects in the area” (Pastalan, 1970). The demarcation of one’s territory prevents conflicts. For example, usually people do not barge in a room occupied by someone else without permission; otherwise, they risk being sent out by the occupant. So, closing one’s door will normally be enough to mark a room as one’s territory. While an office may be a person’s territory, he or she can at best have personal space on the hallway, which belongs to no one. Thus, there are specific behaviors to regulate privacy linked and limited to territory. Territory takes many forms (Lyman and Scott, 1967), and so do behaviors that are intended to mark or defend it. For instance, an employee may regard his or her office, locker, or even personal computer as his or her territory (Altman, 1975, p. 108), and walls or partitions as boundaries. Offices can be marked with name plates and personalized with pictures, plants, etc. (Altman, 1975, p. 112; Becker, 1973; Vinsel et al., 1980; Oldham and Rotchford, 1983). To mark one’s computer, one may protect it with a password or personalize the settings. While some of the researchers cited in the previous section measured the objective properties of the workplace, such as the number of partitions, privacy depends on perceived control over territorial behaviors, which can encompass those objective properties. 2.4.2

Control over Communication

Communication offers many ways of regulating the “mental distance” from others (Westin, 1967, p. 32). It is also among the privacy behaviors considered by Altman (1975), who further distinguished between verbal and nonverbal communication. Verbal communication refers to spoken or written words, nonverbal communication to body language, i.e. gesture, 6

Social density is not the same as crowding (Section 2.2), which is a psychological concept (Stokols, 1972). It was used as a proxy in these studies, though.

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2 Conceptualization of Employee Privacy

posture, facial expression, but also things such as clothing and hairstyle. Nonverbal communication includes paralanguage, i.e. features such as tone, pitch, and pacing of the voice in spoken language or handwriting in written language (Argyle, 1975). Verbal communication can be used to regulate privacy directly, as people can simply tell others whether they want to communicate with them or not (Altman, 1975, p. 33). However, it also affords more subtle means of regulating mental distance. For instance, there are several forms of addressing people, including the use of titles (e.g., “Reverend” or “Professor”), the first, last, or nickname, which may be perceived as polite or impolite, depending on the context (Mehrabian, 1981, p. 63). Thus, while it is common for employees to address each other informally in some countries (e.g., in Northern Europe), there are others where business contexts are supposed to be less casual and the polite form of address is preferred. The formality of communication also depends on things such as vocabulary, pronunciation, and speech style (Altman and Taylor, 1973; Mehrabian, 1981). For instance, people use language varieties (e.g., dialects, sociolects, jargons, etc.) or possibly foreign languages, depending on whom they talk to, and thus exclude others from their conversation. By these means, they regulate distance and achieve privacy (Davis and Oleson, 1971; Herman, 1961). Conversely, privacy can be invaded by the imposition of corporate language, language taboos, rules that prohibit the use of dialect, etc. Such invasions may result from social control among employees, as well as from corporate policy. Among the means of nonverbal communication, an employee’s appearance, including clothing, adornment, hairstyle (Kruse-Graumann, 1980, p. 149), is most likely to be controlled at work. This form of control need not be explicit; social control among employees may be even more important (Altman, 1975, pp. 36–42). For instance, though explicit dress codes for managers are not very common, they still wear suits just as police agents wear uniforms. As clothing is an important means of impression management and even self-expression (Stone, 1962; Goffman, 1979), dress codes, both implicit and explicit, restrict a person’s control over behaviors that concern the regulation of their privacy. While employees generally adapt their expectations in this regard, the restriction becomes obvious when they complain about such regulations as “going too far.” The choice of the “channel” of communication determines the amount of information transmitted (Shannon, 1948; Weaver and Shannon, 1963) and is therefore an important, albeit less obvious, means of regulating privacy. For instance, people interact more intensively when talking face-to-face

2.4 Privacy Regulation Behaviors

17

than on the phone, where they cannot see each other.7 The more channels individuals may choose from, the better they can regulate their privacy. However, companies often restrict access to some channels in order to avoid the waste of resources (e.g., working time). For instance, many US companies monitor their employees’ use of the phone, e-mail, voice mail, etc. to prevent misuse (Allen et al., 2007; Brown, 1996, 2000; Wen and Gershuny, 2005). Studies on open-plan offices suggest that the chance to communicate confidentially is crucial for privacy (Oldham, 1988; Carlopio and Gardner, 1992). 2.4.3

Control over Personal Information

Companies influence their employees’ privacy in two ways. On the one hand, they determine the working conditions and thus indirectly the privacy that employees can achieve. For instance, whether employees can achieve more or less privacy depends on whether they have their own individual offices. On the other hand, companies directly interact with their employees with regard to organizational matters, such as contracts, which are established between a natural person—the employee—and a legal person—the company. Interactions with an organization are different from those with other human beings and therefore have to be considered and measured separately. How a company acts toward its employees depends on the information it has about them. Thus, it seems reasonable to use employees’ perceived control over their personal information as a measure of their achievable privacy. The information a company has about its employees enables it to reward or “punish” them for their behavior. In turn, employees anticipate the company’s (re)actions and act accordingly. The behaviors that result from this “calculus of behavior” (Laufer et al., 1974; Laufer and Wolfe, 1977) are not self-determined but at least to some extent controlled, or motivated, by the (expected) rewards or punishments dispensed by others. Of course, people, just like organizations, use information to control one another. However, controlling the environment, communication, and the boundaries between work life and private life, which will be discussed below, imply control over personal information. For instance, when employees close the door of their office, they do not only stop others from communicating 7 Thus even today, where video conferences could technically replace meetings, businesspeople often prefer to travel around the globe to meet each other face-to-face. The purpose of face-to-face meetings is to reduce privacy and intensify the interaction between people.

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2 Conceptualization of Employee Privacy

with them, but also prevent others from being informed about what they are doing. Likewise, the choice of one’s communication channel influences the quantity of information transmitted. Thus, it would be redundant to measure control over personal information with respect to other individuals in addition to control over communication, but it must still be measured with respect to the company as an organization. Although selective disclosure is the principal means to control information, it is to some extent possible to control information already disclosed. For instance, an employee’s authorization may be required for the company to disclose received information to third parties, use it for purposes different from those it was originally collected for, and so on. Several measures of, or related to, control over personal information have been developed, which differ in the aspects they cover and in how detailed they are. All such measures, however, share certain elements. Thus, Woodman et al. (1982) distinguished between collection, use, and disclosure of personal information (i.e. to third parties). Stone et al. (1983) added storage of information to this list in order to develop a new measure, which was later adopted by Eddy et al. (1999). Drawing on Stone et al. (1983), Mossholder et al. (1991) listed collection, storage, and disclosure, but excluded use of information. In a carefully developed measure of concerns about organizational information privacy practices, Smith et al. (1996) identified concerns about the collection of information, correction of errors, improper access to, and unauthorized secondary use of information. This measure has been used in subsequent research both in its original form (Earp and Payton, 2006; Korzaan and Boswell, 2008) and variously modified (Alge, 2001; Alge et al., 2006; McNall and Roch, 2007). It is neither possible nor desirable to consider all aspects accounted for in the literature when forming a parsimonious measure of control over personal information. However, this brief review of the literature shows that there are at least a number of issues that have to be taken into account; namely, the collection and use (purpose) of personal information, as well as access to it both by people within and by third parties outside the company. 2.4.4

Control over the Work–Life Boundaries

Most research on privacy in the context of business relationships has tended to focus on physical privacy and privacy of information, both of which are elements of privacy “at work.” By contrast, there is little research on the relation between work life and private life, although they are interdependent and cannot be separated. As it was argued in the first chapter, work is both a part of a person’s life and also shapes his or her life. The overlap

2.4 Privacy Regulation Behaviors

19

between private life and work life is twofold: first, work can spill over into private life and, second, it may restrict those private activities which are not work-related. Work can be said to spill over into private life when people cannot properly define the temporal and spatial boundaries within which they work. Typical examples include working overtime, taking work home, or being on call. What these examples have in common is that the employee cannot control how long he or she works within reasonable limits. Likewise, uncommon working hours, which may include night shifts, involve work rhythms that impose serious restrictions on people’s social activities. By contrast, flexible working hours enhance employees’ control over their time, provided that flexibility is offered as an option rather than imposed to them. Technically, many people are no longer tied to a certain place to perform their work. Many companies provide their employees with notebooks, cell phones, and other mobile devices. Hard-copy documents are often substituted with data files, and electronic messages, unlike letters, can be read and answered outside the office. In addition, some employees arrange with their company to work occasionally from home, thus becoming parttime telecommuters. These advances may increase the control employees have over where and when they work, but may also reduce it if they are provided with company devices only to make them available everywhere and at any time for the company. In the long run, control over where and when an employee works is probably less important than the interdependence between the choices of place of employment and residence. Even though it is possible to commute long distances and maybe substitute telecommunication for face-to-face communication, most people need to live near where they work. Moreover, jobs differ in how much mobility they demand from employees. In many companies, mobility is a requirement. Yet every relocation entails a change of social environment, which affects both employees and their families. This may be perceived as a chance by some but as a serious restriction by others. In summary, flexibility and mobility (both in the long and the short run) may offer employees the chance to gain more control over the boundaries between work life and private life, but may also blur those boundaries. The level of achievable privacy therefore depends on how much control employees perceive to have over the spatial and temporal boundaries of their work-related activities. Their job influences people’s lives also indirectly. While it provides them with resources, defines their social status, etc., it imposes at the same time explicit or implicit restrictions, obliging them to do certain things and

20

2 Conceptualization of Employee Privacy

hindering them from doing others. The employment contract defines and limits the employer’s and employee’s mutual rights and duties. Explicit regulations reaching beyond work-related matters are presumably rare and usually hard to justify (Persson and Hansson, 2003; Küpper, 2005b). For example, in 2005 Wal-Mart had to revoke parts of its code of conduct in Germany, because its prohibiting employees from engaging in romantic relationships if these inferred with their work was considered an invasion of privacy by German courts. On the contrary, implicit overreach seems to be a more common problem. For instance, male bank cashiers are expected not to have long hair, beards, body-piercing, or tattoos. This would be perceived as a breach of—written or unwritten—rules. Clearly, a man cannot take off his beard and tattoos before and put them on again after work, so such rules prevent him from growing a beard or having tattoos at all. As another example, criminal behavior, even if unrelated to work as such, may have consequences on the employee. A bank cashier may be fired for shoplifting, which has nothing to do with his or her job but can be argued to call into question the integrity required for it. Most employees will probably not even be aware of such restrictions because people choose a job that suits them and adapt their expectations accordingly (i.e. their desired privacy). For instance, bank cashiers are not usually the kind of people who want to wear tattoos. Hence, the question is once again whether employees perceive their private life to be restricted by their work life or not. To summarize the concept of privacy developed in this chapter, privacy is the result of a process which aims at balancing achieved and desired privacy under the restriction of achievable privacy (Section 2.2). While adjustments of desired privacy are mental processes and cannot be observed, adjustments of achieved privacy can be observed, because they are reflected in a number of behaviors that relate to controlling the environment, communication, personal information, and the boundaries between work life and private life (Section 2.4). Each of these behaviors is used to adjust privacy in a certain respect. The level of achieved privacy depends on the control that individuals perceive themselves to exert over each of these behaviors (Sections 2.1 and 2.3). This concept can easily be translated into a measure of privacy. In order to estimate how well individuals can balance their achieved and desired privacy, it is necessary to measure their control over each of the behaviors that determine their achieved privacy. Developing such a measure requires that individuals are asked about their perceived control because their answers

2.4 Privacy Regulation Behaviors

21

will then account for prior adjustments of their desired privacy, which cannot be observed. The development of a measure which corresponds to these criteria will be the object of the next chapter, which also reports how the measure was validated.8

8 The measure is intended to capture the (im)balance between achieved and desired privacy and does not measure privacy as such (i.e. achieved privacy). Nevertheless, it will be referred to as a measure of privacy for convenience throughout the remainder of this work.

Chapter 3 Development of a Measure of Employee Privacy 3.1

The Development of Measures

While privacy has been measured in a number of studies, none of the existing measures fits the purpose of the present one. To investigate the effects of privacy as a whole, all its components—such as control over communication, the environment, and so on—must be taken into account. However, earlier studies on privacy have covered only single aspects. More precisely, from the literature cited in the previous chapter (Section 2.4) it is clear that prior empirical research has put emphasis on privacy as control over the environment and personal information, whereas control over communication and the boundaries between work life and private life have received less attention. Unlike age or gender, privacy cannot be observed directly. It is a latent variable as opposed to manifest variables. The measurement of latent variables is a standard problem in social sciences. Its solution consists basically in translating the latent variable into manifest variables that are causally related to it, and thus measuring it indirectly. Both latent and manifest variables form a construct. The manifest variables will be interchangeably referred to as items or indicators in this study. Depending on whether the items are the causes or effects of the latent variable, the measure is called a scale or index respectively, and the measurement model is accordingly described as formative or reflective (e.g., Diamantopoulos and Winklhofer, 2001; MacKenzie et al., 2005; Rossiter, 2002). It has been argued that privacy results from control over the environment, communication, personal information, and the work–life boundaries, which are latent variables like privacy. Privacy is therefore a second-order construct, with control over the environment, communication, etc. as first-order constructs. Control over the environment, for instance, depends, among other things, on whether one can mark one’s territory and avoid being seen or heard by others. As the items to be developed capture these observable causes of control over the environment, the measurement model of control over the environment is formative, and this is true for the measurement

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3 Development of a Measure of Employee Privacy

models of the other three first-order constructs as well. The four first-order constructs are again causal to privacy. Hence, privacy is measured on a scale.1 The distinction between formative and reflective measurement models is not merely a question of terminology. Depending on the measurement model, different criteria apply to ensure the quality of measurement, namely its reliability (absence of random error) and validity (absence of systematic error).2 Reflective measurements can be tested both for reliability and validity after the data have been collected. On the contrary, formative measurements can be tested only for validity, and this must be done, at least in part, before data collection. The criteria differ depending on the approach to scale developement chosen (Diamantopoulos and Winklhofer, 2001; Rossiter, 2002). A measure is said to be valid if it measures what it purports to measure. In formative measurement models, the indicators must cover the most important causes of the latent variable; that is, on the one hand, they must correspond to causes rather than effects; on the other hand, they must not omit any important cause, or the measurement will be incomplete (Diamantopoulos and Winklhofer, 2001). Rossiter (2002) made a strong case for seeking advice from experts when developing a measure. Presumably, they are in the best position to decide which is cause and which effect, and whether the items capture all important causes. Agreement among the experts consulted about causality and items can be taken as an indicator of validity. In the present study, the measure of employee privacy was developed in three steps that will be described in detail in the following sections. First, items were proposed on the basis of, and in line with, the conceptualization of privacy (Section 3.2). Then, both the concept and the initial items were discussed with experts whose agreement was sought in order to validate the measure (Section 3.3). Finally, the measure was modified to meet their suggestions, and hypotheses were formulated to be tested after the collection of the data (Section 3.4).

1 The causality both between the second- and first-order constructs and the first-order constructs and items can run in either direction, resulting in four types of measurement models (Jarvis et al., 2003). The measure of privacy is formative on both levels. 2 The objectivity of the measurement is not a concern in this study, since a selfadministered questionnaire was used to collect the data. Thus, no third person was involved in the measurement.

3.2 Development of the Initial Items

3.2 3.2.1

25

Development of the Initial Items Items for Control over the Environment

Control over the environment involves control over personal space and territory, as argued in the previous chapter. The concept of personal space appeared most often in experiments that analyzed, for instance, a subject’s reaction to an invasion of his or her personal space. This approach is inappropriate for a field study that investigates the effects of perceived control, rather than reactions to an actual loss of control. Instead, three items were designed to ask respondents to what extent they can control physical distance to others and withdraw from others’ sight and earshot (P rivEnv1 , P rivEnv2 , and P rivEnv3 ). Territory has often been measured in terms of physical enclosure (e.g., the number and height of walls surrounding an employee’s work space). However, because reactions depend on perceived control, different individuals react differently to the same situation. In the context of this study, the item P rivEnv6 concerns control rather than physical enclosure. As territory can take many forms and include objects such as a craftsman’s toolbox or a clerk’s personal computer, item P rivEnv4 extends to working equipment. Moreover, personal objects, ranging from name plates to pictures, plants, and furniture, can be used as “markers.” Item P rivEnv5 was designed to capture whether respondents can personalize their workplace. The items that were presented to experts and participants are listed below. P rivEnv1

I can control the physical distance between me and other people as I desire.

P rivEnv2

I can avoid being seen by other people when I don’t want to be seen at work.

P rivEnv3

I can avoid being heard by other people when I don’t want to be heard at work (e.g., for confidential communications).

P rivEnv6

I can control whether other people find themselves right in my work space as I desire.3

P rivEnv4

I can control whether other people have access to my workplace and working equipment as I desire.

3 For convenience, the labels are as assigned to the retained items. For instance, P rivEnv6 was later eliminated and thus reassigned the number 6 ex post, while it holds its “logical” position in the list.

26

P rivEnv5

3 Development of a Measure of Employee Privacy

I can personalize my workplace as I desire (e.g., bring along personal objects, choose my office furniture, etc.).

Both the experts involved in the validation of the measure and, later, respondents in the survey were told that “other people” referred to “people related to [the respondent’s] job, such as your colleagues, supervisor, clients, etc.” This explanation appeared initially in each item so that the items could be considered independently by the experts. In the final questionnaire, however, the explanations were summarized in a preliminary remark to keep the items short, and the items read as listed here. Respondents were required to answer using a seven-point Likert scale, ranging from (1) “Strongly disagree” to (7) “Strongly agree.” The statements were deemed sufficiently general to allow all respondents to choose an answer, irrespective of the exact appearance of their workplace, so the questionnaire did not include a “Don’t know” option. According to the above conceptualization of privacy, every item must measure whether an employee perceives that she can apply the privacy behavior described in that item to the extent he or she desires. The privacy behaviors examined here are: control over physical distance (P rivEnv1 ), withdrawal from company (which corresponds to control in this context) in order to be out of sight and earshot (P rivEnv2 and P rivEnv3 ), and control over territorial behaviors, including personal markers (P rivEnv6 , P rivEnv4 , and P rivEnv5 ). All items are statements on perceptions and the word “desire” (or “want to”) ensures that the respondents rate their achievable as compared to their desired privacy. 3.2.2

Items for Control over Communication

Communication has been identified as a crucial mechanism of controlling privacy, but is particularly hard to seize. However, the three aspects of communication identified above (namely verbal communication, nonverbal communication, and the communication channel) offer a basis for developing a set of items. It is plausible to distinguish furthermore whether or not communication is related to work. As more communication means less privacy, it follows that privacy depends on whether individuals can control how much they communicate with others. This is reflected in P rivCom1 and P rivCom5 , which concern control over communication with other people related and unrelated to the job respectively. There is a slight difference between items P rivCom5 and P rivCom1 in that too much communication with people unrelated to the job while being at work seems to be a rather unusual problem.

3.2 Development of the Initial Items

27

More subtle mechanisms to determine mental distance refer to how people communicate, i.e. the style of communication. For instance, if a person has no choice over the degree of formality in the vocabulary and forms of address used to communicate with others, and is obliged to use a particular accent or even language (perhaps other than his or her usual accent or mother tongue), that person is deprived of an essential means of controlling communication and thus privacy. Items P rivCom2 and P rivCom3 are intended to capture this aspect. Among the aspects of nonverbal communication, a person’s appearance or outfit is most likely to be regulated at work (e.g., there may be explicit or implicit dress codes). This is what P rivCom4 attempts to capture. P rivCom4 , like P rivCom2 and P rivCom3 , refers to communication exclusively related to work. “Indirect” effects are measured by the items that correspond to the boundaries between work life and private life (e.g., one cannot have entirely different hairstyles in work and private life, as in the example discussed in the previous chapter, Section 2.4.4). Finally, P rivCom7 and P rivCom6 refer to the choice of communication channel in work-related and non work-related communication, respectively. P rivCom1

I can communicate with other people at work as much/little as I desire.

P rivCom2

I can control how I’m addressed by other people at work and address other people at work as I desire (e.g., with title, first, last, or nickname, etc.).

P rivCom3

I can talk to other people at work how I desire. (“How” refers to features such as accent, jargon, language style, etc.).

P rivCom4

I have as much control as I desire over my appearance at work (e.g., clothes, hair, etc.).

P rivCom7

I have as much control as I desire over which channel (e.g., phone, e-mail, chat, etc.) I use to communicate with other people at work.

P rivCom5

I can communicate with people who are not related to my job as much as I desire at work.

P rivCom6

I have as much control as I desire over which channel (e.g., phone, e-mail, chat, etc.) I use to communicate with people who are not related to my job at work.

28

3 Development of a Measure of Employee Privacy

It was explained to respondents that communication included any form of communication, such as face-to-face, phone, e-mail, chat, etc. The meaning of “other people at work” was illustrated as in the previous list of items, whereas “other people who are not related to my job” might include, for example, the respondent’s family, friends, doctor, or hairdresser, etc. The items are listed here as they appeared in the questionnaire. The answer format was identical to that in the environment items. The design of the communication items follows the same criteria that applied to the environment items above. While the word “control” does not appear in the items P rivCom1 , P rivCom3 , and P rivCom5 , the idea of control is inherent in the word “can.” Again, the phrase “as I desire” invites respondents to compare their achievable and desired privacy, and the statements refer to perceptions. 3.2.3

Items for Control over Personal Information

Control over information, or the privacy of data, is what now most people think of first when they hear about privacy. It is therefore unsurprising that control over information is the object of many empirical studies and several measures, as the brief review of the literature in the last chapter has shown (Section 2.4.3). While none of these measures meet the requirements of this study, Smith et al.’s (1996) measure of concerns about privacy offers a basis for deriving items in line with the above conceptualization. Smith et al. cover all important aspects of control over information, namely the collection, unauthorized internal secondary use (i.e. for purposes other than for which the information was collected), and unauthorized external secondary use (i.e. disclosure to third parties), as well as correction of (deliberate and accidental) errors. However, their measure surveys employees’ attitudes to the data privacy practices of their company rather than their (achievable) privacy. Moreover, it is too detailed for a study that takes a broader view of privacy. For the purposes of the present study, four items were formulated to cover the aspects of collection (P rivInf1 ) and secondary use of information (P rivInf2 ), as well as internal (P rivInf3 ) and external access (P rivInf4 ) to information, which—implicitly or explicitly— can be found in most of the measures mentioned above. P rivInf1

My company gives me as much control as I desire over what personal information they collect about me.

P rivInf2

My company gives me as much control as I desire over what purposes they use my personal information for.

3.2 Development of the Initial Items

29

P rivInf3

My company gives me as much control as I desire over who within the company has access to my personal information.

P rivInf4

My company gives me as much control as I desire over whom outside the company they share my personal information with.

In the questionnaire, “personal information” was described as any information the respondent deemed to be personal (which corresponds to the idea of desired privacy) and which might include data on age, residence, pay, and the like. The answer format was the same seven-point Likert scale as for the other the environment and communication items. The design of the items follows that of the items on control over the environment and communication: first, all items refer to control over some aspect of information, i.e. collection, use, and internal as well as external disclosure. Second, the phrase “as I desire” prompts respondents to compare their achievable to their desired privacy in all cases. Third, all statements obviously refer to perceptions. 3.2.4

Items for Control over the Work–Life Boundaries

The fourth set of privacy regulation behaviors refers to control over the boundaries between work life and private life. Since this is the first measure which has been developed to account for this aspect of privacy, these items do not draw on the literature and are therefore rather tentative. In the previous chapter, two aspects of control over the work–life boundaries were distinguished, namely the spill-over of work into private life and restrictions on private activities imposed by the job, which have nothing to do with work in the first place (Section 2.4.4). To control whether work spills over into their private lives (which they may wish to avoid, although this is not necessarily the case), employees must be able to set spatial and temporal limits to their work. Temporally, they must then be able to schedule their work as they desire; that is, to decide—within reasonable boundaries—how long and when they work. This is what P rivW lb01 and P rivW lb001 seek to measure. P rivW lb2 surveys the respondents’ control over where they accomplish their work. P rivW lb5 , like P rivW lb2 , refers to space. However, while P rivW lb5 measures whether, for example, employees can do their work from home, P rivW lb2 refers to whether their job allows them to determine where their homes are located. This set of items is designed like those already described. The items measure perceived control over privacy, when achievable and desired privacy are compared. (Again, the idea of control is implicit in P rivW lb2 , even though the word does not appear as such.)

30

3 Development of a Measure of Employee Privacy

Apart from the spill-over just discussed, work may influence employees’ private lives in more subtle ways. On the one hand, it may well hinder them from doing things they would like to do; on the other hand, it may well oblige them to do things she would not do otherwise. For instance, a company may prohibit its employees from doing certain sports which are likely to cause accidents, or an employee of one car manufacturer may not feel free to drive a vehicle produced by a different car manufacturer. Equally, an employee may feel committed to take courses or read books in order to improve his or her skills for the job. P rivW lb3 and P rivW lb4 are intended to capture such restrictions and obligations. The design differs from the previous items, as it makes little sense to ask whether employees feel that they can control what they perceive as a restriction in the first place. (If they have control, there is no perceivable restriction, and if there is a restriction, they necessarily lack control.) P rivW lb01

I have as much control as I desire over how long I work. (Count work-related activities such as personal training, business trips, on-call duty, preparation of presentations, etc. as working time.)

P rivW lb001

I have as much control as I desire over when I work. (Count work-related activities, such as personal training, business trips, on-call duty, preparation of presentations, etc. as working time.)

P rivW lb5

I have as much control as I desire over where I work (e.g., in your office, at home, on business trips). (Count work-related activities, such as personal training, business trips, on-call duty, preparation of presentations, etc. as working time.)

P rivW lb2

My work allows me sufficient freedom to choose where I live. (For example, job-related changes of residence or stays abroad might restrict this freedom.)

P rivW lb3

My work hinders me from doing things in my private life I actually would like to do (e.g., certain sports, hobbies, etc.).

P rivW lb4

My work obliges me to do things in my private life I actually don’t like to do (e.g., read certain literature, etc.).

As in the case of the other privacy items, the statements had to be rated on a seven-point Likert scale, ranging from (1) “Strongly disagree” to

3.3 Validation of the Measure

31

(7) “Strongly agree.”4 In contrast to the other sets of items, however, the explanations and examples were attached to each item, so that no further information had to be given in the introduction to these items.

3.3

Validation of the Measure

For the measure of privacy to be valid, the items developed in the previous section must capture the essential drivers of privacy. More precisely, first, each item must represent a driver of privacy rather than of any other construct and, second, the set of essential drivers of privacy to which these items correspond must be comprehensive. Moreover, the validation must account for the fact that privacy, as conceived in this study, is a second-order construct. This implies that the items do not refer to privacy directly, but to the four first-order constructs of control over the environment, communication, information, and the work–life boundaries that are regarded as antecedents of privacy. Rossiter (2002) recommended interviewing experts and modifying the measure to meet their suggestions until a reasonable level of agreement is reached. Krafft et al. (2005) suggested that an item-sort task developed by Anderson and Gerbing (1991) should be used in these interviews. According to this technique, the interviewees are invited to assign to the appropriate construct items that pertain to different constructs. Furthermore, Anderson and Gerbing proposed that two indicators should be used to measure agreement among the interviewees for each item and that only the items which pass the test should be retained; the rest should be dropped.5 The proportion of substantive agreement indicates the degree to which the interviewees correctly assign an item to its posited construct. It is defined as psa =

nc , N

where nc is the number of interviewees who assign the item correctly and N the total number of interviewees. In turn, the coefficient of substantive 4 Strong agreement to the statements in P rivW lb and P rivW lb means that the 3 4 respondent cannot achieve as much privacy as he or she desires in that regard. The items were therefore reverse-scored. 5 Anderson and Gerbing developed their test for indexes rather than scales. While indexes are usually validated after data collection, they showed that their item-sort task reliably forecasts which items will be eliminated anyway, and can thus be dropped immediately so as to improve efficiency.

32

3 Development of a Measure of Employee Privacy

validity measures the degree to which an item is assigned to the intended rather than any other construct. It is defined as csv =

nc − no , N

where nc and N are defined as before, while no represents the number of interviewees who assigned the item to a construct other than the intended one. The values of psa and csv range from 0 to 1 and −1 to 1, respectively, and larger values indicate greater substantive agreement and validity. Under the null hypothesis that respondents assign an item randomly either to the correct or any other construct, a binomial test yields the critical values p¯sa and c¯sv for any given level of significance (see Appendix A.1). To validate the privacy measure, a heterogeneous group of 21 persons was consulted, including non-academic university staff, students, and doctoral students. For the item-sort task, autonomy was chosen as the second construct. The concepts of autonomy and privacy are closely related, which makes the test challenging. The autonomy items were taken from the Work Design Questionnaire (WDQ), which was developed by Morgeson and Humphrey (2006). The WDQ is a standardized and validated measure, which is also available in German (Stegmann et al., 2010). The WDQ measure of autonomy consists of nine items, as opposed to 23 privacy items. Privacy items would therefore easily be misassigned to autonomy. Each interview proceeded in three steps. First, interviewees were provided with definitions of privacy and autonomy in everyday language and asked to assign the items either to privacy or autonomy. Second, they were provided with definitions of the four first-order constructs of privacy and asked to assign each privacy item to one of those constructs. (This applied to all privacy items, even those that interviewees had sorted out in the first step.) To ensure objectivity, each interviewee was left alone while sorting the items. Third, the concept was discussed with the interviewees, who were asked individually whether they found that any items or even components (i.e. constructs, such as control over personal information or the environment) were missing from the set. According to the feedback from the third step of the interviews, the wording of the items was slightly modified and some items were supplemented with further explanations and examples. One item was removed because the interviewees found that it was redundant. By contrast, none of the interviewees identified any omissions regarding the items and the components of privacy. The results of the interviews are displayed in Table 3.1.

3.4 Final Measure of Employee Privacy

33

Table 3.1 Validation of the Privacy Scale Step 1 psa

Step 2 csv

??

psa

.71 .86??? .81??? .76?? .81??? .81??? .29

.43 .71??? .62??? .52?? .62??? .62??? −.43

1.00 .90??? .90??? .43 .52 .71?? 1.00???

1.00??? .81??? .81??? −.14 .05 .43?? 1.00???

P rivEnv1 P rivEnv2 P rivEnv3 P rivEnv4 P rivEnv5 P rivEnv6

.95??? 1.00??? 1.00??? .67? .71?? .95???

.90??? 1.00??? 1.00??? .33? .43?? .90???

.90??? 1.00??? .81??? .86??? .95??? 1.00???

.81??? 1.00??? .62??? .71??? .90??? 1.00???

P rivInf1 P rivInf2 P rivInf3 P rivInf4

1.00??? 1.00??? 1.00??? .95???

1.00??? 1.00??? 1.00??? .90???

.95??? 1.00??? .95??? 1.00???

.90??? 1.00??? .90??? 1.00???

P rivW lb01 P rivW lb001 P rivW lb2 P rivW lb3 P rivW lb4 P rivW lb5

.57 .33 .81?? .81?? .86?? .52

.14 −.33 .62??? .62??? .71??? .05

.95??? .86??? .86??? .90??? .95??? .57

.90??? .71??? .71??? .81??? .90??? .14

3.4

??

p < .05.

???

???

csv

P rivCom1 P rivCom2 P rivCom3 P rivCom4 P rivCom5 P rivCom6 P rivCom7

Note. N = 21. ? p < .1.

??

p < .01.

Final Measure of Employee Privacy

In the interviews, most items were assigned correctly to both privacy and the first-order concept of privacy they corresponded to, as can be seen from Table 3.1. The items that did not pass either or both steps were P rivCom4 , P rivCom5 , P rivCom7 , P rivW lb01 , P rivW lb001 , and P rivW lb5 . More specifically, many interviewees judged that P rivCom7 , P rivW lb01 , and P rivW lb001 measured autonomy rather than privacy, but once it was clear that these items pertained to privacy, they were correctly assigned to their first-order concept. On the contrary, P rivCom4 and P rivCom5 were

34

3 Development of a Measure of Employee Privacy

recognized as privacy items, but assigned to the wrong first-order concept. Finally, P rivW lb5 failed both tests. On the one hand, it is reassuring that more than 80% of the items passed both tests, and most of them did so easily. On the other hand, since all items have been carefully derived and substantiated, those that failed should not be hastily dismissed. Obviously, if an item is not even assigned to privacy, there is a strong case for discarding it. This is why P rivW lb5 (which failed both tests) and P rivCom7 were eliminated from the scale immediately. In turn, neither P rivCom4 nor P rivCom5 was dismissed. Both were consistently assigned either to control over communication or the work–life boundaries. Even though these items were not designed to correspond to control over the work–life boundaries, this outcome is plausible. In the case of P rivCom4 , the interviewees’ feedback suggested that “appearance” had not made them think of “communication” in the first place (indeed, the association is not obvious) and that they primarily thought of “indirect” effects (e.g., the tattoo from the example in the previous chapter, Section 2.4.4). That both P rivW lb01 and P rivW lb001 failed the first test was surprising, as they are crucial from the theoretical point of view. Moreover, they comfortably passed the second test. They were therefore not dismissed entirely, but replaced with the following item: P rivW lb1

I can determine the proportion of working time to spare time as I desire. (Include business trips, on-call duty, job-related trainings, etc. as working time.)

P rivW lb1 is a tentative item that mitigates the modification of the concept caused by the elimination of P rivW lb01 and P rivW lb001 and makes it possible to examine the effect of control over working time. This has the added advantage that it leaves only one item that, strictly speaking, lacks validity, and thus can be answered quickly. P rivEnv6 passed both tests but was still dropped. This was a major modification due to the feedback that the interviewees gave in addition to the item-sort task. While they had no trouble assigning the item both to privacy rather than autonomy, and to control over the environment rather than any other first-order concept, many interviewees found that P rivEnv6 was quite similar to P rivEnv1 , although both items were supposed to measure different things. (P rivEnv1 refers to the distance between work spaces, P rivEnv6 , only to the respondent’s own work space.) As those interviewees found that P rivEnv6 was redundant, the item was removed. Thus, the measure would be more parsimonious and, more importantly, would avoid giving double weight to a single aspect.

3.4 Final Measure of Employee Privacy

35

For the questionnaire, explanations and examples included in the single items were moved to introductions that preceded each set of items in order to keep the items short. In particular, the respondents were reminded that the items on control over personal information were about perceptions and that they should answer intuitively if they were unsure. This reminder was added in response to feedback from the pretest. There, some respondents had had trouble answering as they did not know their company’s information policies. Nevertheless, the “Don’t know” option was not offered for two reasons: first, respondents might too readily evade the “difficult” items by checking “Don’t know,” which would hamper comparisons with other studies. Second, as stressed earlier, the items were about perceived control, which means that everyone should be able to give an answer. The items on control over communication refer partly to communication related to work (P rivCom1 through P rivCom4 as well as P rivCom7 , which was dismissed, though) and unrelated to work (P rivCom5 and P rivCom6 ). To avoid any confusion among respondents and make this difference as clear as possible, the two subsets of the communication items were separated, and a short introduction was attached to each. Finally, the fact that privacy is a second-order construct has two further implications, one technical, the other conceptual. Technically, one further item had to be developed to measure overall privacy directly; that is, an item which reflects the second-order construct rather than the first-order constructs:6 P rivOall

Overall, I have as much privacy as I desire as an employee of my company.

This item should not be considered a part of the scale that has been presented in this chapter. Instead, it should be understood as a single-item measure of privacy that complements the scale. The answer format was the same as for the other privacy items. Conceptually, it was argued that each first-order construct is causal to the second-order construct. More precisely, the variables “control over communication,” “control over the environment,” “control over personal information,” and “control over the work–life boundaries” are assumed to have positive effects on the variable “privacy.” Formally, these assumptions are hypotheses, stated below, and can be tested empirically. 6 The method used to estimate the model to be developed requires each latent variable, including second-order constructs, to be directly measured. Both the method and its requirements and limitations will be described in more detail in the chapter on the empirical analysis below (Section 5.3).

36

3 Development of a Measure of Employee Privacy

Hypothesis 1a. The more control employees have over communication, the better they can achieve the level of privacy they desire. Hypothesis 1b. The more control employees have over their environment, the better they can achieve the level of privacy they desire. Hypothesis 1c. The more control employees have over their personal information, the better they can achieve the level of privacy they desire. Hypothesis 1d. The more control employees have over the boundaries between their work life and private life, the better they can achieve the level of privacy they desire. To summarize, this chapter records how a measure of employee privacy was developed and validated. While this was itself a major endeavor as well as contribution of this work to research, it is technically but a condition for investigating the effects of privacy. The next task is to justify the assumption that privacy has positive effects on both creativity and job satisfaction, which are mediated by empowerment. To this end, in the following chapter a number of hypotheses will be formed both on the direct and indirect effects of privacy as well as effects of other factors (i.e. variables).

Chapter 4 The Effects of Privacy on Creativity and Job Satisfaction 4.1 4.1.1

Privacy and Creativity

Creativity as an Objective of Companies

Creativity is generally appreciated and it is quite obvious that companies have an interest in their employees being creative. Creative employees tend to suggest novel ideas, which the company can translate into new products or procedures. Hence, creativity is a necessary condition for innovation, because “[o]rganizational innovation is the successful implementation of creative ideas within an organization” (Amabile 1988, p. 126; similarly, Maier et al. 2007). Innovation is a critical factor of success as it can help companies respond to challenges and achieve competitive advantages (Amabile, 1988; Frey et al., 2008; Oldham and Cummings, 1996).1 Creativity has long been considered a personal attribute. During the last decades, however, attention has shifted toward factors other than personality that determine an individual’s creativity, so-called “social” or “context factors” (for a review see Shalley and Gilson, 2004; Shalley et al., 2004). While personality is undoubtedly a crucial factor—some people are more creative, others less—, it has been established that context factors such as the qualities of supervisors and co-workers, the nature of the job, as well as aspects related to leadership style, organizational support for creativity, etc. also play an important role. For the company that wishes to foster creativity, these factors are the easiest starting points (Gaier, 2011). In the relevant literature, creativity refers to the production of ideas that are both novel and useful (George, 2007). For an idea to be creative, it must have the potential to create value for the company. The requirement that an idea ought to be useful implies that the generation of novel ideas is not an end in itself. Apart from this restriction, the above definition is otherwise fairly 1

It is clear that creativity does not necessarily entail these advantages and may even produce undesired outcomes. This point has recently been made by Shalley et al. (2004).

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4 The Effects of Privacy on Creativity and Job Satisfaction

broad. In terms of content, creative ideas can relate to any aspect of work, such as work procedures, products, services, and organizational structures. In terms of impact, creativity ranges from incremental to revolutionary ideas (George, 2007). According to this definition, creativity is not the privilege of a specific group of people (say, the employees working in the R&D department of a company). On the contrary, every employee can display creativity regardless of position, even though some positions may allow or require more creativity than others (Shalley and Gilson, 2004). Creativity is a broad term also in the sense that it may refer to persons, things, or processes. For example, artists or scientists are supposed to be creative persons; the results of their activities (such as a painting or invention) are creative; and the activities or processes which produce these results are also described as creative. In the literature on creativity, different terms have been introduced to describe these concepts more accurately, such as “creative personality” (Gough, 1979), “creative process engagement” (e.g., Gaier, 2011; Gilson and Shalley, 2004; Zhang and Bartol, 2010), or “creative performance” (e.g., George and Zhou, 2001; Tierney et al., 1999). In this study, three concepts related to creativity will be used: creative potential characterizes a person who is naturally creative; climate for creativity refers to a company climate that encourages employees to engage in creative processes and is favorable to creative effort; finally, creative performance describes the result of such effort (e.g., new ideas for products or services). For convenience, creative performance will be referred to as “creativity,” while the more accurate terms “creative potential” and “climate for creativity” will be used to refer to persons and the company. 4.1.2

The Effect of Privacy on Creativity

The link between privacy and creativity has rarely been examined explicitly. There is little research on this topic or at least privacy does not usually appear as a concept in studies on creativity. However, the idea that they are linked is quite common. A number of studies offer evidence that people associate privacy with creativity (e.g., Edney and Buda, 1976; Newell, 1994; Pedersen, 1997, 1999). According to these studies, creativity is widely considered to be a “function” of privacy in the sense that people tend to seek privacy in order to accomplish creative tasks. In fact, creative people like scientists or artists are commonly perceived as solitary (at least in the context of pursuing their creative endeavors). Nevertheless, research on creativity emphasizes the importance of context factors, including exchanges with colleagues and supervisors (Cummings and Oldham, 1997; Shalley et al., 2004). For instance, one might think of

4.1 Privacy and Creativity

39

a scientist working in his laboratory, absorbed by his work, who does not, especially not in this moment, care about other people and what they think (Jourard, 1966). Even though this is rather a caricature than a description of creativity, it can be argued that privacy and creativity are linked for several reasons. The link is most obvious where control over the environment is concerned. Employees who have sufficient control over their environment can achieve the seclusion often associated with privacy; that is, withdraw from the company of others when they desire. Seclusion minimizes the stimuli one is exposed to and, consequently, the distraction from the creative task one wants to concentrate on. “Social” distractions, such as overhearing phone conversations or people talking have been found to be particularly bothersome (Wineman, 1982), probably because “the actions and intentions of people are intrinsically more meaningful for us” (Goodrich, 1979, p. 5). Having privacy often refers to blocking such social stimuli, even though it minimizes other distractions as well. However, the essence of privacy is not the reduction of disturbances and seclusion is only one possible manifestation of privacy among many. More importantly, privacy implies freedom from the scrutiny or, more generally, the control of others. Technically speaking, privacy frees people from social control (Schoeman, 1992; Westin, 1967, p. 31, Lyman and Scott, 1967) and thus enables them to “act out of role” (Sundstrom et al., 1980, p. 114) by offering them the opportunity to be unconventional and test new approaches to problems. These may fail, of course, but may also turn out better than routine solutions. Creativity can involve those extra-role behaviors which privacy affords (Alge et al., 2006; Sundstrom et al., 1980; Maier et al., 2007). Privacy creates “criticism-free zones,” “reduces evaluation apprehension and allows individuals [. . . ] to experiment and to be creative” (Alge et al., 2006, p. 224).2 Usually, scientist and artists do not display failed experiments or sketches, and musicians performing in public present the product of their efforts rather than the efforts which produced it. People do not like to be seen failing but succeeding, and this is why we prefer to experiment and practice, i.e. try out new things, “in private.” Of course, social control is not intrinsically bad. It is necessary to keep an organization running and both occurs naturally and is generated 2 Privacy also allows people to repose and not concentrate on the issue at hand. Recent research suggests that the unconscious thought paid to a problem in these “periods of incubation” helps individuals come up with more creative solutions when they return to their task (Dijksterhuis and Meurs, 2006; George, 2007).

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intentionally (Schoeman, 1992). In a company, it helps align employees with the company’s aims and maintain proven patterns of thought and behavior. However, it tends to favor conservative attitudes in that it gives established routines the benefit of the doubt and prevents unconventional behavior (Warren, 2003). In particular, it may inhibit creativity, which often involves unconventional and even deviant behavior (Mainemelis, 2010). Interestingly, because the members of an organization anticipate control, it determines their behavior ex ante (Martin and Freeman, 2003; Wen and Gershuny, 2005). In that sense, social control affects the way people think and may stop employees from rethinking routines and voicing valuable criticism and feedback (Moore, 2000). These unintended consequences are hard to measure, though they may be important. While it is possible to observe the lack of innovativeness that results, it is not possible to observe ideas that are never developed or proposed in the first place. Even though the effect of privacy on creativity has never been a major topic of research, there is some empirical evidence that it exists. In an early study by Edney and Buda (1976), when the authors presented people with a number of activities and asked them to assign each to the setting where they would most prefer to conduct it, for intellectually demanding tasks, such as studying, reflecting about something, or creative writing, the participants chose mostly settings that afforded them privacy. Pedersen (1997, 1999), who used a similar research design, confirmed these results. Edney and Buda (1976) also found that the participants in a related experiment solved a creative task better when they were unobserved than when they were told that they would be observed. Alge et al. (2006) conducted a field study in an occupational context and found that privacy of information has a positive indirect effect on creativity, mediated by empowerment. The negative effect of close monitoring on creativity that has been reported in other studies (George and Zhou, 2001; Zhou, 2003) offers indirect support for this argument (Alge et al., 2006). Finally, items on creativity have been included in some measures of job performance (Lee and Brand, 2005) and job satisfaction (Carlopio and Gardner, 1992; DuVall-Early and Benedict, 1992), which were found to correlate with privacy or similar constructs. Thus, DuVall-Early and Benedict, who tested the impacts of privacy on single components of their measure, reported a significant effect. In light of these findings, it seems reasonable to expect that privacy has a positive effect on creativity. Hypothesis 2. The better employees can achieve the level of privacy they desire, the more creative they are.

4.2 Privacy and Job Satisfaction

4.2 4.2.1

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Privacy and Job Satisfaction

Job Satisfaction as an Objective of Companies

Whether employees are satisfied with their jobs matters to companies for several reasons. First, they have an ethical responsibility to care about their employees’ well-being because work forms an essential part of human life. Companies cannot decline this responsibility on the pretext that employees, according to the Homo economicus model, care for themselves and maximize their utility, including job satisfaction, anyway (Küpper, 2006b, pp. 232, 241; Neuberger and Allerbeck, 1978, p. 16). In fact, the interest for job satisfaction arouse with the Human Relations Movement as early as the 1930s, which recognized that this model is deficient (Brief and Weiss, 2002). Job satisfaction is an indicator of work humanization. Like other goals, including profit, employees’ well-being and, in particular, job satisfaction as a measure of their well-being, is a goal in its own right. Apart from the intrinsic value job satisfaction has for many companies, it has been argued that it is instrumental in achieving other goals, including economic goals. For instance, many studies examine whether job satisfaction relates to performance, absenteeism, employee turnover, health, and the like (Neuberger and Allerbeck, 1978, p. 19, Rosenstiel, 2007, pp. 440–444). In particular, there is a large body of research on the relation between job satisfaction and job performance (see Judge et al., 2001, for a recent overview). While correlations have been found (e.g., the correlation is about .3 for job satisfaction and job performance according to Judge et al.), it should be noted that the question of causality remains unsolved (Rosenstiel, 1975; Schmidt, 2006). In spite of the positive correlation between job satisfaction and job performance, it is not clear whether the former causes the latter or vice versa, whether both of them interact, or whether a third variable causes both of them, etc. (Judge et al., 2001). The focus on job performance does not mean that job satisfaction, apart from being an end in itself, is not instrumental in achieving further ends. For example, Bateman and Organ (1983) proposed organizational citizenship behavior (OCB) as an interesting outcome. OCB catches “contributions as volunteering for extra-job activities, helping others, and upholding workplace rules and procedures regardless of personal inconvenience” (Organ and Ryan, 1995, p. 776), which are desirable or even necessary for a company to work. OCB is much broader than measures of job performance, can clearly be argued to be a function of job satisfaction (Smith et al., 1983), and strongly correlates with it (e.g., Organ and Ryan, 1995). Thus, there is a number of reasons for companies to value job satisfaction.

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4.2.2

The Effect of Privacy on Job Satisfaction

The link between privacy and job satisfaction becomes intuitive in light of the process of privacy regulation conceived above (Section 2.2), and which is based on Frey and Jonas’s (2002) theory of perceived control. According to the process of privacy regulation, individuals seek to balance their achieved and desired privacy by adjusting either the former or the latter; if adjustment is not possible, the resulting imbalance causes them to experience learned helplessness. On the basis of the idea that individuals seek this balance, it is clear that any imbalance causes discomfort and, depending on the intensity and duration of the imbalance (which is permanent in the case of learned helplessness), dissatisfaction. The idea of balance is also the basis of many theories of job satisfaction. According to these theories, employees experience unsatisfied (physiological) needs or a discrepancy between their (cognitive) plans and reality as an imbalance that causes them to be dissatisfied. Satisfaction is reached as the balance is restored (Rosenstiel, 2007, pp. 428–34). In terms of control, an imbalance between achieved and desired privacy corresponds to a lack or loss of perceived control. This is also true for an excess of privacy; however, imbalances typically result from too little rather than too much privacy at work. For instance, if employees would like to make confidential phone calls but do not have the opportunity or do not feel free to do so, this may reflect a lack of control over their environment (i.e. they cannot avoid being heard), communication (they are not free to choose the telephone as a channel of communication), personal information (they are afraid that phone calls are recorded), or the boundaries between work life and private life (they cannot make the phone call at home because of unfavorable working hours). In each case, they are likely to blame their dissatisfaction on the pertinent facet of her job: the working conditions (the design of the work space which does not allow them to make phone calls out of earshot), their supervisor or colleagues (who do not respect the privacy of other staff sufficiently), or the management and the organization as a whole (who are responsible for those working conditions). Considerable empirical support has been found that perceived control has an effect on job satisfaction (e.g., Spector, 1986; Greenberger et al., 1989). A number of empirical studies examined specifically the effect of the physical environment on job satisfaction. Sundstrom et al. (1980) identified a set of workplace features which correlate with job satisfaction and which they termed “architectural privacy.” Later studies, where similar measures were used, echoed this idea (Oldham and Fried, 1987; Oldham et al., 1991; DuVall-Early and Benedict, 1992; Carlopio and Gardner, 1992). Similar

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results were obtained in studies where employees were interviewed before and after moving to an open-plan office (Oldham and Brass, 1979; Oldham, 1988; Zalesny and Farace, 1987) or where privacy served as a variable that mediated or moderated the effect of the environment on behavior (Oldham and Rotchford, 1983; Maher and von Hippel, 2005). The effect of control over the environment on job satisfaction is also supported by empirical research (Lee and Brand, 2005; O’Neill, 1994). The only study that investigates the effect of a non-environmental component of privacy on job satisfaction is that by Mossholder et al. (1991), who found that invasions of privacy of information affect job satisfaction. These studies have limitations, though. First, they do not catch the effect of privacy, but only of one of its (four) antecedents. Second, some of them do not account for the problem that the same environment is perceived differently by different people (see Section 2.3). For instance, individuals’ perception of their environment may be influenced by their ability to cope with social inputs (Oldham, 1988; Oldham et al., 1991; Maher and von Hippel, 2005), the complexity of their task (Sundstrom et al., 1980; Block and Stokes, 1989; Oldham et al., 1991; Carlopio and Gardner, 1992), their rank in the organization (Sundstrom et al., 1982b; Carlopio and Gardner, 1992), and so on.3 Third, single facets of job satisfaction rather than job satisfaction in general were measured (e.g., satisfaction with work space) or, with the notable exceptions of DuVall-Early and Benedict (1992) and Carlopio and Gardner (1992), ad hoc instead of standardized measures were used. In conclusion, it is reasonable to assume that privacy positively influences job satisfaction, as past research indicates. At the same time, it is worthwhile to test this effect once again with the above measure of privacy and a standardized measure of job satisfaction. Hypothesis 3. The better employees can achieve the level of privacy they desire, the more satisfied they are with their job.

3 Zalesny and Farace (1987), rather than control for these effects, used “measures of perceived physical setting [. . . ] instead of actual measures of physical elements of the work setting” (p. 246). Likewise, in this study respondents were asked about their perceptions.

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4.3

The Mediating Effects of Empowerment 4.3.1

The Concept of Empowerment

If privacy has positive effects on both creativity and job satisfaction, as argued above, these effects are probably complex. The concept of privacy is closely related to control. Consequently, control is crucial for the argument developed to justify Hypotheses 2 and 3. Freedom from social control is necessary for an individual to be creative, and a sense of personal control enhances job satisfaction. Privacy acts on both creativity and job satisfaction through control. For instance, while it is intuitive that people need privacy to be (more) creative, it is far from obvious that privacy causes or motivates them to be so. In other words, privacy may be a necessary but not sufficient condition for creativity. Still, the additional control granted to employees together with privacy may increase their intrinsic motivation and thus creativity. Likewise, an enhanced sense of control may increase their job satisfaction, supplementing the direct effect hypothesized above. The pivotal role of control in this argument suggests that control should be considered explicitly. An appropriate concept to catch these effects is empowerment, which has been defined as “intrinsic task motivation” (Thomas and Velthouse, 1990). Conger and Kanungo (1988) were the first to argue that empowerment—an ambiguous term up to that point—should be understood as a motivational rather than relational construct. Power, in a motivational sense, is more than formal authority. It can be better described as a self-determination (Deci and Ryan, 1985) or self-efficacy belief (Bandura, 1977, 1986). Accordingly, empowerment is not the transfer of authority (through delegation, participation, or resource sharing), but the enhancement of a “sense of personal mastery or a ‘can do’ attitude” (Conger and Kanungo, 1988, p. 476). People will not necessarily do something, such as perform a task, merely because they are authorized to do it. If they are confident that they have the ability to perform it and find it worthwhile (which they can be assumed to do as empowerment derives from the task), they are more likely to engage in that task in the first place. They are also more likely to carry it out pertinaciously because they perceive its execution itself as rewarding, so their motivation, up to a point, reinforces itself (Thomas and Velthouse, 1990). “Task motivation,” as Amabile (1988) aptly put it, “makes the difference between what an individual can do and what one will do” (p. 133). Refining Conger and Kanungo’s concept, Thomas and Velthouse (1990) distinguished four drivers of intrinsic motivation, namely impact, competence, meaningfulness, and choice. According to their approach, individuals

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feel empowered and thus motivated to accomplish a task if it makes sense to them (meaning), if they are able to do it (competence), if they feel that it “makes a difference” (i.e. has impact), and if they can decide how to do it (choice), and thus take causal responsibility. In terms of motivation theory (Lawler, 1973), “impact represents a performance–outcome expectancy, competence an effort–performance expectancy, and meaningfulness an anticipated outcome valence (for intrinsic motivation), whereas choice represents the perceived opportunity for a decision based on these variables” (Thomas and Velthouse, 1990, p. 672). Moreover, Thomas and Velthouse emphasized that empowerment depends on perceptions (see Section 2.3). For instance, employees’ motivation to accomplish a task depends on whether they believe that they are competent to perform it rather than on whether they actually are competent to perform it. Spreitzer (1995) translated Thomas and Velthouse’s concept of empowerment into a measure of psychological empowerment. She re-named the four components as impact, competence, meaning, and self-determination, where • meaning is the value of an individual’s work, as judged by that individual; • competence, an individual’s confidence in his or her capability to do the work; • self-determination, a sense of having a choice in doing his or her work; • impact, his or her influence on the outcomes of the work. Unlike Thomas and Velthouse, Spreitzer conceives empowerment as a general rather than task-related state. For instance, an individual’s perceived or expected impact does not depend on a single task, but refers to his or her work or job in general. (Consequently, Spreitzer’s items relate to the respondent’s work or job, not a particular task.) 4.3.2

The Effect of Privacy on Empowerment

Empowerment, according to Thomas and Velthouse (1990), stands for a new paradigm of motivation. In contrast to the classical/bureaucratic paradigm of strict controls combined with contingent rewards and punishments [. . . ] the newer paradigm involves relaxed (or broad) controls and an emphasis on internalized commitment to the task itself (p. 667).

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Privacy is intimately linked to control, being both a function of and a condition for control (see Section 2.1). On the one hand, privacy reflects the control employees have over their environment, communication, etc. On the other hand, it gives them control over their own actions, in that it reduces the control exerted by others. A company that grants its employees privacy increases their personal control both directly and indirectly. It is clear, therefore, that a company that aims to empower its employees is likely to grant them privacy. Put differently, privacy can serve as a means of empowering employees. While granting privacy empowers employees, it is not tantamount to granting them formal power. Unlike autonomy, which has been defined as “the amount of freedom and independence an individual has in terms of carrying out his or her work assignment” (Morgeson and Humphrey, 2006, p. 1323; similarly, Hackman and Oldham, 1975), privacy does not relate to the working tasks. For instance, discretion in scheduling the work or choosing the methods relates directly to the tasks at hand and would be called autonomy rather than privacy. Whether employees have the opportunity to withdraw, decide how to communicate, which personal information to disclose, or when and where to do their work relates to personal control, but not to the tasks as such.4 Autonomy is without a doubt a major driver of intrinsic motivation (Hackman and Oldham, 1975; Deci and Ryan, 1985) or empowerment (Conger and Kanungo, 1988; Thomas and Velthouse, 1990; Spreitzer, 1995). Moreover, it can be easily observed, because it involves, but is not limited to, the formal transfer of power. The effect of privacy, in turn, is not so obvious at first glance, but becomes perfectly plausible if empowerment is understood in the motivational rather than formal sense. To understand the effect of privacy on empowerment, it is helpful to think of violations, infringements, or restrictions of privacy. In fact, it is easier to see the negative effect of restricting than the positive effect of enhancing privacy on empowerment. (It seems that people become more aware of the benefits of having privacy when they lack or lose it.) For instance, while it may not be evident that employees feel empowered because they enjoy privacy at work, it is quite evident that employees who are permanently monitored and thus have very little privacy can feel hardly, if at all, empowered. Permanent monitoring may imply (depending on 4 In the last example, privacy and autonomy overlap, which became apparent in the validation of the privacy measure described in the previous chapter. Even though autonomy and privacy probably often go hand in hand, one can easily imagine jobs which provide much privacy but little autonomy and vice versa.

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the form it takes), that they cannot withdraw, withhold information, or communicate as they please, or that they face restrictions even in their “private” lives (i.e. outside the context of work). It may also imply that others gain control over their actions, which in this case would cease to be self-determined. Thus, monitoring can reduce personal control both directly and indirectly. Privacy protects people from being determined by others. It can be considered a necessary condition of self-determination and, as a result, intrinsic motivation, whereof self-determination is a proximal cause (e.g., Deci and Ryan, 1985; Hackman and Oldham, 1975). While this argument appears to be in line with prior conceptual work, there is little empirical evidence to support it. The effect of privacy on empowerment has only been tested once. More precisely, Alge et al. (2006) found that privacy of information has a positive effect on empowerment. This result calls for further tests. Hypothesis 4. The better employees can achieve the level of privacy they desire, the more empowered do they feel. 4.3.3

The Effect of Empowerment on Creativity

Creativity has been linked to empowerment from the very beginning of the development of this concept. Thomas and Velthouse (1990) postulated that “perceived choice (self-determination) produces greater flexibility, creativity, initiative, resiliency, and self-regulation” (p. 673). When Spreitzer (1995) developed her measure of psychological empowerment, she used “innovative behavior” as a criterion variable to test for nomological validity.5 She argued that, because empowered individuals believe they are autonomous and have an impact, they are likely to be creative; they feel less constrained than others by technical or rule-bound aspects of work. Furthermore, because empowered individuals feel selfefficacious, they are likely to be innovative in their work and to expect success (p. 1449). More precisely, each component of empowerment can be linked to creativity. If employees consider their job “meaningful, valuable, and worthwhile” (Hackman and Oldham, 1975, p. 162), they are likely to spend greater effort on understanding and solving problems and thus to come up with 5

That is, she took for granted that empowerment, if properly measured, must relate to innovative behaviors, and the empirical test actually confirmed her conjecture.

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less obvious and more creative solutions. Being empowered means that employees value their job and at the same time feel competent to find complex solutions and confident that their suggestions will be heard, i.e. that their efforts will have impact. In fact, there is considerable empirical support that self-efficacy (which corresponds to competence) influences creativity (Tierney and Farmer, 2002, 2004). Individuals who identify and take up challenges and develop new ideas to respond to those challenges rather than apply available solutions need to be independent, responsible, and proactive, that is, to show self-determination (Zhang and Bartol, 2010). Empowered employees will engage more readily and persistently in creative processes than those who perceives themselves as “powerless” (Zhang and Bartol, 2010). The affinity between empowerment and intrinsic motivation supports the argument that empowerment has a positive effect on creativity. Thomas and Velthouse (1990) conceived empowerment as intrinsic task motivation and theorized that impact, competence, meaningfulness, and choice are the most important proximal causes of empowerment and, consequently, of intrinsic motivation. Hence, empowerment may be equated to, or at least taken as a close proxy of, intrinsic motivation.6 The importance of intrinsic motivation for creativity, in turn, is generally acknowledged among researchers. For instance, it is central to both Amabile’s (1988) componential and Woodman et al.’s (1993) interactionist models of creativity. Woodman et al. argued that intrinsic motivation keeps an individual’s attention directed to the task. According to Amabile, “task motivation is responsible for initiating and sustaining the [creative] process” (p. 138). It intervenes at the beginning and in the middle of the process, when the problem is set and possible solutions are generated. Several pieces of empirical evidence support this argument. Spreitzer (1995), as mentioned above, found empowerment to be related to selfreported innovative behaviors. Alge et al. (2006) and Zhang and Bartol (2010) showed that empowerment mediates the effects of privacy and empowering leadership on creativity, respectively. Both used Spreitzer’s (1995) measure of psychological empowerment, but relied on supervisor ratings of creativity. More particularly, it was found that creative self-efficacy influences creativity (Axtell et al., 2000; Tierney and Farmer, 2002, 2004; Gunkel et al., 2007). Additional support comes from a number of studies that tested the effects of constructs of intrinsic motivation rather than 6 Zhang and Bartol (2010) considered empowerment and intrinsic motivation to be different constructs and measured them both. Unsurprisingly, empowerment had a strong effect on intrinsic motivation.

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empowerment (Andrews and Smith, 1996; Ohly and Fritz, 2007; Shin and Zhou, 2003; Shalley et al., 2009; Tierney et al., 1999). Hypothesis 5. The more employees feel empowered, the higher their creative performance. In light of the positive effect of privacy on empowerment hypothesized before (Hypothesis 3), the hypothesis on the effect of privacy on creativity (Hypothesis 2) needs refining. More precisely, privacy must be assumed to act on creativity both (a) directly and (b) indirectly through empowerment. Hypothesis 2a. Privacy has a positive direct effect on creativity. Hypothesis 2b. Empowerment mediates a positive indirect effect of privacy on creativity. 4.3.4

The Effect of Empowerment on Job Satisfaction

Empowerment has been argued to be closely related to intrinsic motivation, which, in turn, is usually assumed to occur together with job satisfaction. For example, both job satisfaction and intrinsic motivation are criterion variables in Hackman and Oldham’s (1975) job characteristics model. Accordingly, Thomas and Velthouse meant to identify those task assessments (i.e. impact, competence, meaningfulness, and choice) which are “the proximal cause of intrinsic motivation and satisfaction” (p. 668, emphasis added). The effect of empowerment on job satisfaction can be best understood when each of those four components is considered. Employees experience their job as meaningful if it fulfills or allows them to fulfill what they value (Hackman and Oldham, 1975; Thomas and Velthouse, 1990). As everyone can be supposed to derive satisfaction from doing what he or she values and finds worth doing, meaningfulness was early recognized as an important or indeed necessary condition of job satisfaction (Herzberg et al., 1959), although employees may also be motivated and satisfied to do a job which they do not find meaningful. In the latter case, however, both motivation and satisfaction will be extrinsic. For instance, if employees are paid well, they will work for the money and be satisfied with the payment rather than the job as such. Competence corresponds roughly to Bandura’s (1977; 1986) concept of self-efficacy, which can be described as having confidence in one’s own ability to perform a task. The link between competence and satisfaction becomes particularly clear if one thinks of a job one does not feel competent to do;

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indeed, it is hard to imagine someone who enjoys doing an over-demanding job. The literature on self-efficacy offers further support for this argument (Liden et al., 2000). Self-determination, in contrast to competence, reflects beliefs about a choice rather than mastery of behavior. It is akin to, albeit more general than, the concept of autonomy (Deci and Ryan, 1985). Both autonomy (e.g., Hackman and Oldham, 1975) and self-determination (Deci and Ryan, 1985) are considered drivers of intrinsic motivation. It has even been argued that people have a (psychological) need for self-determination, so that a sense of self-determination logically entails (job) satisfaction (Conger and Kanungo, 1988; Greenberger et al., 1989). Impact is the extent of personal control over organizational outcomes or the belief that one can “make a difference” (Thomas and Velthouse, 1990) at work. It includes involvement in the outcomes of work, as well as in decision-making processes (Liden et al., 2000). If employees feel that their efforts to make some impact are doomed to fail, their response will be reactance, learned helplessness, and finally work alienation (Ashforth, 1989; Spreitzer et al., 1997). A lack of impact is obviously frustrating. Some of these arguments are supported by empirical evidence, which is inconclusive, though. Spreitzer et al. (1997) found that meaning consistently predicts job satisfaction, while impact is unrelated to it. Their results for self-determination and competence were mixed. Barroso Castro et al. (2008), Kirkman and Rosen (1999), and Liden et al. (2000) investigated the mediating effects of empowerment. According to Liden et al., meaning and competence are related to job satisfaction, while self-determination and impact are not. Barroso Castro et al. found empowerment to be related to job satisfaction, but used a different measure, and so did Kirkman and Rosen, who investigated the effects on the team level. Wang and Lee (2009) attributed the inconsistent result to interactions between the dimensions. Although the results of studies that examined the effects of single dimensions of empowerment are inconclusive, Seibert et al. (2004) found that, overall, empowerment has a positive effect on job satisfaction. A metaanalysis conducted by Seibert et al. (2011) also supports the previous finding on this effect, which, according to the above arguments, has conceptual appeal anyway. Hypothesis 6. The more employees feel empowered, the more satisfied they are with their job. As was hypothesized further up (Hypothesis 4), privacy has a positive effect on empowerment. In light of the above, the hypothesis on the effect of

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privacy on job satisfaction (Hypothesis 3) needs refining as well: privacy can now be said to act on job satisfaction both (a) directly and (b) indirectly through empowerment.

Hypothesis 3a. Privacy has a positive direct effect on job satisfaction. Hypothesis 3b. Empowerment mediates a positive indirect effect of privacy on job satisfaction.

4.4

The Control Variables and their Effects 4.4.1

Choice of the Control Variables

All hypotheses presented so far refer to the direct and indirect effects of privacy on creativity and job satisfaction. Of course, creativity, job satisfaction, and empowerment have multiple causes and privacy is at best one of them. On the one hand, it is impossible to control for all these effects. On the other hand, if no control variables are taken into account, the effects of privacy may be overestimated. To achieve a balanced solution, a few select broad concepts were included to control for the most essential effects. The choice of broad concepts is reasonable because the purpose was to account for a variety of influences rather than examine a single one in particular. The control variables were chosen to cover three groups of factors, namely factors related to the person, the task, and the context. The person-related factors comprise personal creative potential, as well as education, training, and job experience. It will be argued that they influence creativity and empowerment. The motivating potential of the job is a task-related factor. It controls for influences other than privacy on empowerment. Finally, the climate for creativity accounts for context-related factors. Although the climate for creativity is related to creativity in the first place, it will be set forth that it also affects empowerment and job satisfaction. In addition, manifest data on gender, age, job type, and managerial responsibilities or leadership were collected. These data are necessary to describe the sample and can also be used as further control variables as illustrated in the next chapter (Section 5.4.3). However, no hypotheses on their effects will be developed in this chapter.

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4.4.2

Person-Related Effects

Creativity or, more precisely, creative performance, depends not only on the context, but also on the person. Personality, cognitive style and ability, knowledge, and intrinsic motivation are important person-related antecedents of creative performance (e.g., Amabile, 1988; Woodman et al., 1993). The interest of researchers has recently shifted to within-individual cognitive and affective processes and states, such as unconscious thought (Dijksterhuis and Meurs, 2006), moods (George and Zhou, 2002, 2007), or role identity (Zhou and Shalley, 2003; for an overview, see George, 2007). As the person-related determinants of creativity are manifold, it was impossible to consider all of them, so this study focuses on personality as well as cognitive factors and knowledge, while intrinsic motivation is captured by empowerment. Creative potential. Earlier research has found that some people are generally more creative than others, i.e. that they possess a higher creative potential (in the same way that people differ in intelligence). For instance, individuals who are unconventional, have broad interests, and feel confident of themselves are usually more creative than those who are conservative, have specific but narrow interests, and lack self-confidence (Gough, 1979). While it is common practice to attribute creative achievements to their author’s personality, Woodman et al. (1993) have expressed doubts that further research in this field could be fruitful. Nevertheless, “theorists must [. . . ] retain an appreciation for the creative person as a partial explanation for creativity in complex social settings” (p. 298). Personality traits such as the creative potential have been included in a number of recent studies at least as a control variable (e.g., Axtell et al., 2000; George and Zhou, 2001; Madjar et al., 2002; Shalley et al., 2009; Oldham and Cummings, 1996; Zhou, 2003). The results of these studies suggest that a person’s creative potential has indeed a positive effect on creative performance. In keeping with Woodman et al.’s recommendation and following the example of these studies, the concept of creative personality was included in the present model. Hypothesis 7. The more creative potential employees have, the higher their creative performance. Cognitive factors and knowledge. According to popular belief, “too much” knowledge or expertise inhibits rather than enhances creativity. Experts, so the argument goes, are too fixed on proven approaches to try

4.4 The Control Variables and their Effects

53

out new ones, and keep to trodden paths instead of attempting to break new ground (Woodman et al., 1993). Even though there may be some truth in this belief, more expertise is, on balance, better than less. For instance, expertise provides people with ideas or concepts which they can draw on to approach an unprecedented problem, combine to form new concepts, or use for benchmarking entirely new ideas or concepts (Amabile, 1988). Expertise corresponds to what Amabile (1988) has called “domainrelevant skills,” as opposed to “creativity-relevant skills.” The latter include traits like open-mindedness, risk orientation, persistence, and a cognitive style that enables an individual to explore new perspectives and pathways, or suspend judgment. While creativity-relevant skills may be personality traits (in which case they are covered by the concept of creative potential, see Woodman et al.), domain-relevant skills are acquired through education, training, and experience (Shalley and Gilson, 2004). Education, training, and experience provide people with techniques for solving problems, expose them to a variety of perspectives and experiences, and familiarize them with the use of experimentation (Amabile, 1988; Shalley and Gilson, 2004); in other words, they help people acquire creativity-relevant skills. Moreover, the level of education and training reflects cognitive abilities. It is therefore reasonable to assume that education, training, and experience have positive effects on creativity; the assumption is supported by plenty of empirical evidence.7 Hypothesis 8. The higher employees’ level of education, the higher their creative performance. Hypothesis 9. The more experienced employees are in their job, the higher their creative performance. Moreover, since the drivers of empowerment include competence, education, training, and experience will as well have positive effects on empowerment, as they will enhance a person’s feeling of competence and probably self-determination. Having said that, competence is conceived as a perception of the task, and employees who are more competent must not perceive themselves to be so, or may be assigned to more demanding tasks, so that 7 On the effects of education and training, see Farmer et al. (2003), Ohly et al. (2006), Scott and Bruce (1994), Shalley et al. (2000), Tierney et al. (1999), Tierney and Farmer (2002, 2004), and Zhou and George (2001), for example. On the effects of experience, see Gilson and Shalley (2004), Ohly et al. (2006), Scott and Bruce (1994), Shalley et al. (2000), Tierney and Farmer (2002, 2004), Tierney et al. (1999), and Zhou and George (2001).

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4 The Effects of Privacy on Creativity and Job Satisfaction

they are no more competent as related to their tasks. However, perceptions depend on facts, and therefore the assumption that education, training, and experience predict empowerment seems justified. Both Spreitzer (1996) and Seibert et al. (2011) provide empirical support for this view. Hypothesis 10. The higher employees’ level of education, the more empowered they are. Hypothesis 11. The more experienced employees are in their job, the more empowered they are. 4.4.3

Job-Related Effects

A person’s intrinsic motivation to perform a task or job depends both on that person and the task or job. (In contrast, an individual’s creative potential, as a personality trait, pertains only to that individual and does not depend on the job.) Hackman and Oldham (1975) proposed a complete model of how characteristics of the job act on intrinsic motivation and other outcomes (including job satisfaction). They referred to the contribution of the job to an individual’s intrinsic motivation as its “motivating potential.” According to their job characteristics model, a job has a high motivating potential if it is rich in variety, thus requiring multiple skills (skill variety), involves the completion of a “whole” piece of work (task identity), has impact on others (task significance), provides freedom in schedule and choice of methods (autonomy), and provides information on the individual’s performance (feedback). The effect of these five characteristics is mediated by critical psychological states, namely experiencing work as meaningful, feeling responsibility for the outcome, and being aware of the actual results of that work. These states correspond to the meaning, self-determination, and impact assessments, which are causal for empowerment (Thomas and Velthouse, 1990). It can therefore be argued that motivating potential has a positive effect on empowerment. The effect of job characteristics has been considered in studies both on empowerment and creativity.8 While some studies examined the effect of job characteristics (along with other factors) on empowerment (e.g., Jha and Nair, 2008), empowerment was mostly treated as a mediator, which is in line with the job characteristics model. For example, empowerment was found to mediate the effect of job characteristics on job satisfaction 8 There are numerous studies on the effect of job characteristics on job satisfaction, which, evidently, cannot be considered here in any detail. See Fried and Ferris (1987) for an early, confirmatory meta-analysis.

4.4 The Control Variables and their Effects

55

(Liden et al., 2000) and on job performance (Chen and Klimoski, 2003). Some studies focused on certain aspects of the motivating potential, such as autonomy, feedback, or job enrichment (e.g., Kirkman and Rosen, 1999). In their meta-analysis, Seibert et al. (2011) included further “work design characteristics,” which, as they found, also have strong effects. Until recently, most literature on creativity tended to examine the direct effect of job characteristics on creativity,9 rather than account for intrinsic motivation (or empowerment) as a mediator. Some studies investigated the effect of a job’s motivating potential (Farmer et al., 2003; Hatcher et al., 1989; Oldham and Cummings, 1996; Shalley et al., 2009); others focused on specific job characteristics that may be more closely related to creativity, such as job complexity (Frese et al., 1999; Ohly et al., 2006), autonomy (Axtell et al., 2000; Frese et al., 1999; Ohly et al., 2006; Shalley et al., 2000), variety (Axtell et al., 2000), creative requirement (Axtell et al., 2000; George and Zhou, 2001; Gilson and Shalley, 2004; Unsworth et al., 2005), or combinations of these. The job characteristics model plausibly assumes that the effect of motivating potential on both job performance (including creative performance) and job satisfaction is mediated by psychological states. As empowerment adequately covers these mediating effects, no further mediators have been included and the only effect that remains to be considered is that of motivating potential on empowerment. Hypothesis 12. The higher the motivating potential of employees’ job, the more empowered they are. 4.4.4

Context-Related Effects

Psychological climate can most generally be defined as individuals’ perception and interpretation of their organizational environment (James and James, 1989). Thus, while “climate” refers to the organization, it is an attribute of the individual rather than the organization (Carless, 2004). As this general concept of climate is very elusive, Anderson and West (1998) pointed out that it requires a particular referent (e.g., climate for innovation, climate for change). Likewise, Parker et al. (2003) argued that “individuals interested in predicting a specific outcome [. . . ] are best served by focusing on measuring perceptions of a specific climate” (p. 605; similarly, Witt and Beorkrem, 1989). The use of creative performance as a criterion variable 9

For exceptions, see the studies cited above in support of the effect of empowerment on creativity (Section 4.3.3).

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4 The Effects of Privacy on Creativity and Job Satisfaction

suggests that creativity should serve as the referent. Hence, climate for creativity was used as a control variable in this study. Siegel and Kaemmerer (1978) were among the first to identify characteristics of a climate which fosters employee creativity. Their characteristics include leadership support for the initiation and development of new ideas; participation in decision-making and “ownership” of ideas, which, they argue, gives people an incentive to develop new ideas; openness to new approaches to solving problems and tolerance for diversity; and ongoing change, which may be reflected in continuous questioning of the organization’s tenets and experimentation with alternative solutions to problems. Additionally, Amabile (1988) lists the provision of resources, including time. Anderson and West (1998), in turn, focus on support of supervisors, coworkers, and the organization in general for creativity, but also consider other features of an innovative climate. Creativity, according to recent reviews of the literature, depends on personal and contextual factors (Shalley et al., 2004; Shalley and Gilson, 2004; Zhou and Shalley, 2003). The contextual factors which are usually taken into account are: the job per se, supervisors, coworkers, the management or company, and a number of aspects which are hard to classify (such as goals and deadlines; evaluation, feedback, and rewards; or the physical work environment). Supervisor support has received particular attention, but studies on support from coworkers and management or the company also abound.10 While some constructs refer to support in general, collating the support of supervisors and coworkers (Baer and Oldham, 2006; Binnewies et al., 2008; Madjar et al., 2002), others are more specific, focusing on feedback (George and Zhou, 2007; Zhou and George, 2001), transformational leadership (Bass and Avolio, 1995; Shin and Zhou, 2003; Carless et al., 2000), or monitoring (George and Zhou, 2001; Zhou, 2003; Shalley et al., 2000). The most general construct is the climate for creativity, which encompasses the support of supervisors, coworkers, and management for creativity, but also reflects factors that transcend these categories, such as role expectations or identity (Farmer et al., 2003; Scott and Bruce, 1994; Tierney and Farmer, 2004), 10 For supervisor support, see Axtell et al. (2000), Bass and Avolio (1995), George and Zhou (2001, 2007), Ohly et al. (2006), Oldham and Cummings (1996), Scott and Bruce (1994), Tierney and Farmer (2002, 2004), Tierney et al. (1999), Van Dyne et al. (2002), Shin and Zhou (2003), and Zhou (2003); for coworker support, Farmer et al. (2003), George and Zhou (2001), Scott and Bruce (1994), and Zhou and George (2001); for management support, Amabile et al. (1996), Axtell et al. (2000), Farmer et al. (2003), Pirola-Merlo and Mann (2004), Shalley et al. (2000), Shin and Zhou (2003), Zhou and George (2001), and Zhou et al. (2008).

4.4 The Control Variables and their Effects

57

participation (Axtell et al., 2000), or rewards (George and Zhou, 2002). Indeed, some of the more specific measures were derived from climate measures, while, conversely, Scott and Bruce (1994) found climate for innovation to be related to more specific measures, such as supervisor and coworker support as well as the supervisor’s creativity expectations. All these studies tend to confirm the effect of climate for creativity on creativity. This is particularly true for earlier studies which specifically examined this general effect (e.g., Axtell et al., 2000; Gilson and Shalley, 2004; Pirola-Merlo and Mann, 2004; Scott and Bruce, 1994; Witt and Beorkrem, 1989). Hypothesis 13. The more supportive of creativity the climate in the company is, the more creative employees are. Thomas and Velthouse (1990) conceived meaning, competence, selfdetermination, and impact, which were referred to above as the drivers of empowerment, as those “task assessments” or, more technically, task-related cognitions (i.e. perceptions and interpretations) that produce intrinsic motivation. The climate for creativity can quite similarly be described as employees’ cognitions of their environment with regard to creativity. The climate for creativity corresponds to the aforementioned “newer” management paradigm (see the opening of Section 4.3.2), which is characterized by supportive rather than controlling leadership (e.g., Spreitzer, 1996; Zhang and Bartol, 2010), participation rather than top-down command (Spreitzer, 1996), and supports employees “in functioning independently in the pursuit of new ideas” (Scott and Bruce, 1994, p. 583). Thus, a creative environment enhances the employees’ self-determination and allows them to have some impact through experimentation and new ideas. The effect of climate, including innovative climate, on empowerment has been examined in several studies (e.g., Carless, 2004; Choi, 2007; Seibert et al., 2004; Spreitzer, 1996). Sometimes slightly different, but related concepts have been used, such as sociopolitical support (Kirkman and Rosen, 1999; Spreitzer, 1996). However, most research so far has focused on the impact of leadership (Avolio et al., 2004; Barroso Castro et al., 2008; Kirkman and Rosen, 1999) and, more specifically, supportive (Choi, 2007), non-controlling (Spreitzer, 1996) and empowering leadership (Zhang and Bartol, 2010). In that context, some studies have investigated the relationship between employees and superiors (Jha and Nair, 2008; Liden et al., 2000), whereas others looked at the relationship of employees with coworkers (Liden et al., 2000). On the one hand, these studies offer direct and indirect support that climate for creativity has a positive effect on empowerment. On the other

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4 The Effects of Privacy on Creativity and Job Satisfaction

hand, they suggest that climate constructs considerably overlap with other constructs, including leadership. This is also evident in the fact that the various climate concepts are often similar irrespective of their referent. Hence, while for example a measure of leadership is certainly best suited to measuring the effects of some style of leadership, the climate measure may also lend itself to controlling for at least a part of the effect that leadership has on empowerment. Hypothesis 14. The more supportive of creativity the climate in the company, the more empowered employees are. If a climate for creativity increases empowerment, as stated above, it can be expected to have a positive effect on job satisfaction, since empowerment (or intrinsic motivation) causes employees to be more satisfied with their job (see Section 4.3.4). Notwithstanding this indirect effect, the climate for creativity may directly act on job satisfaction. Employees who perceive their colleagues, supervisors, and the management or company to be tolerant of their singularities, open to and even supportive of their ideas, and to give them a say are likely to be satisfied. This support may not be limited to theoretical encouragement, but include practical resources, so that satisfaction can refer to the “social” facets of the job (such as colleagues and supervisors), but also extend to working conditions. From early on, one of the focal aspects of research on psychological and organization climates was their influence on job-related attitudes such as job satisfaction. The positive effect of climate on job satisfaction was established some time ago (for a recent review, see Parker et al., 2003), and since then researchers’ interest has shifted to a more sophisticated analysis of the topic (e.g., Baltes et al., 2002; Carr et al., 2003; Schulte et al., 2006).11 By contrast, there is less empirical support for a positive effect of the climate for creativity, which, it seems, has not been examined explicitly yet, although some studies offer indirect support (Hunter et al., 2007; Shalley et al., 2000; Tierney, 1997).12 Nevertheless, the above argument and the literature on 11 In fact, some suggested in early stages of research that the concept of climate was redundant as the two constructs overlapped. However, while climate denotes individuals’ perceptions of their environment, job satisfaction involves an evaluation of these perceptions (Schneider and Snyder, 1975). 12 Hunter et al. found that job satisfaction moderated the effect of climate for creativity on creative performance. Tierney examined the effect on job satisfaction of the gap between climate for creativity and cognitive style, while Shalley et al. (2000) studied how job satisfaction is affected by the fit between the creative requirements of a job and support for creativity.

4.4 The Control Variables and their Effects

59

climate in general justify the assumption that climate for creativity and job satisfaction are related. Hypothesis 15. The more supportive of creativity the climate in the company, the more satisfied the employees are with their job. Taken together, the hypotheses proposed in this chapter combine to form a complex path model, depicted in Figure 4.1. Each bubble corresponds to a variable, and each arrow represents a (positive) effect of one variable on another. The numbers of the arrows refer to the above hypotheses, the codes in the bubbles to the constructs or variables. Thus, P riv stands for privacy; P rivCom, P rivEnv, P rivInf , and P rivW lb, for control over communication, the environment, personal information, and the work–life boundaries; Emp, for empowerment; Crea, for creativity; JS, for job satisfaction; JobExp, for job experience; EduL, for level of education; CP S and M P S, for Creative Personality Score and Motivating Potential Score, which are measures of the person’s creative potential and the motivating potential of the job to be introduced in the next chapter (Sections 5.2.4 and 5.2.5); and Clim, for climate for creativity. Arrows 1a–1d refer to the privacy construct. According to the concept of privacy developed above (Chapter 2), perceived control over communication, the environment, personal information, and the work–life boundaries determine to what extent individuals feel that they can balance their achieved and desired privacy. Arrows 2–6 (heavy lines) depict the core model, which includes the effects of privacy on creativity, empowerment, and job satisfaction. Finally, Arrows 7–15 represent the effects on creativity, empowerment, and job satisfaction which were accounted for in order to accurately measure the effects of interest.

PrivWlb

PrivInf

PrivEnv

PrivCom

1d

1c

1b

1a

Priv

JobExp

12

10

Emp

2a

Figure 4.1: Hypothesized Model

MPS

4

11

EduL

14

9

Clim

3a

CPS

8

6

5

15

7

13

JS

Crea

60 4 The Effects of Privacy on Creativity and Job Satisfaction

Chapter 5 Empirical Test of the Effects of Privacy 5.1

Research Setting, Participants, and Procedures 5.1.1

Research Setting

Privacy is a matter of general interest, as there is hardly an employee or member of an organization whom it does not concern. Consequently, the survey that was conducted in order to test the model developed in the previous chapter addressed employees of both companies and non-profit organizations in the private as well as the public sector. More precisely, the organizations surveyed included two companies, four research institutes, and the business school of a German university. For convenience, these organizations are referred to as follows. Company A is a major producer of utility vehicles headquartered in Germany. The company employs 50,000 staff worldwide. The survey was conducted in one production site in Germany, where Company A employs about 7,000. Company B is a manufacturer specializing in fabric, headquartered in the US. The company has about 9,000 staff worldwide. The survey was conducted in one of its business units that is located in Germany. University is a major German public university with more than 5,000 faculty staff and over 20,000 students. The survey was distributed to the staff of the university’s business school. Research Institute A is a non-profit company in Germany, the activities of which include research on higher education and advisory services. The company employs about 300 staff, but the survey was limited to one department. Research Institute B, like Research Institute A, specializes in research on higher education and offers advisory services. The institute is located in Germany and has about 50 employees. Research Institute C also conducts research on higher education. Unlike Research Institutes A and B, it is a public institution. It is located in Germany and has about 15 staff.

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5 Empirical Test of the Effects of Privacy

Research Institute D is an umbrella organization that comprises several smaller institutes which study the German handicraft industry and are otherwise similar to Research Institutes A, B, and C. The institutes subsumed under Research Institute D resemble one another and closely cooperate. Their combined staff is about 70.1 5.1.2

Participants

A total of 404 individuals participated in the survey. The rates of response per organization are displayed in Table 5.1, where “population” denotes the number of potential participants. Table 5.1 Populations, Samples, and Response Rates

Company A Company B University Research Inst. Research Inst. Research Inst. Research Inst.

A B C D

Population

Sample

Datasets

Resp. rate (in %)

7, 000 40 200 80 50 15 70

218 34 45 48 24 13 22

214 34 43 47 24 11 22

3.1 85.0 22.5 60.0 28.0 86.7 31.4

As can be seen from the table, the response rate varies considerably, ranging from about 3% to 85%. The reason for this variance is that the data were collected in different ways. More specifically, an on-line survey was used to collect data from all organizations except Company A; to ensure confidentiality, hard-copies of the questionnaire were distributed instead, and a file was uploaded to the Intranet, which employees could print out, fill in, and submit individually. Thus, although the questionnaire was available to everyone and answering it was made as convenient as possible, it was still more cumbersome than the on-line survey. In Company B and Research Institute C, reminders were sent to the staff, which explains the high response rates compared to the other organizations. The median response rate is 31.4%. (The mean response rate heavily depends on the low response rate in Company A.) 1

These institutes were not examined individually and no separate data were collected. Individual institutes employ too few people to allow more detailed analyses.

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63

Almost all of the 404 questionnaires received were included in the database. Questionnaires were excluded if the respondent’s feedback suggested that his or her answers were not valid or if too many questions had not been answered. For instance, a couple of participants wrote in the feedback section that, contrary to the instructions, their answers did not refer to their current job, as a result of which their answers were discarded. Incomplete datasets were not categorically dismissed, but none with more than 5% of the values missing was retained. Overall, few values were missing, as almost half of the respondents submitted their answers via the on-line survey, which required them to answer most questions. The average proportion of complete to received questionnaires, excluding those from Company A, is 89%. For Company A, it is 75.2%. Overall, in 84.1% of the questionnaires, all questions were answered. Of the questionnaires eventually used, only 2.3% were incomplete by more than 1%.2 The analysis of the data reveals that male respondents account for 63.5% of the sample. The age range spans 20–64 years, with a mean of 39.7 and a standard deviation of 9.9 years. The majority of respondents (69.6%) are university graduates, while respondents who have completed a vocational training are the second largest group (23.8%). Few respondents have no high school degree (.5%) or have not finished any vocational or academic training (6.1%). The most frequent fields of education are engineering or technical training (38.8%), business administration (37.1%), and social sciences (12.1%).3 Job experience ranges from less than 1 to 40 years, with a mean of 11.6 and a standard deviation of 9.7 years. 28.1% of the respondents have managerial responsibilities. In the companies, almost all respondents describe their tasks as “business administration” (90.3%), only few as “technical” (6.5%) or “research” (3.2%). In the other organizations, respondents allocate their working time to research (38.2%), teaching and consulting (21.6%), administration (22.7%), and other activities (17.5%). The following three tables offer a more detailed account of the sample. Table 5.2 displays the statistics for gender, age, job experience, and managerial responsibilities (“leader”). University respondents are younger and have less job experience than those from the other organizations. The survey reflects the fact that the bulk of employees in German business schools are

2 For missing values, the sample means were imputed. If in the paper-and-pencil questionnaires single items of a multi-item measure were not answered, missing values were replaced with the mean of the values of the other items. 3 These figures are based on 350 cases. The field of education was the variable with the most missing values, probably because the question is both personal and required respondents to write an answer rather than just tick an option.

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5 Empirical Test of the Effects of Privacy

doctoral students who work as teaching assistants. The respondents from both companies are similar in age and job experience, though Company A’s staff seems to be more heterogeneous (both the standard deviations of age and job experience are higher than in Company B). The proportion of female respondents is much lower in Company A than in any other organization, including Company B, which is typical of manufacturing companies. The research institutes, again, show similar statistics. The variance of age and job experience in Research Institutes C and D is probably so high because they are closely related to universities and their staff includes doctoral students. Research Institutes A and B, in turn, are more akin to the companies in that they offer advisory services rather than limit their activities to research. Table 5.2 Gender, Age, Job Experience, and Leadership Male (in %) Company A Company B University Research Inst. Research Inst. Research Inst. Research Inst.

A B C D

73.8 50.0 51.2 61.7 33.3 45.5 54.5

Age

Job exp.

Mean

SD

Mean

SD

41.7 42.1 30.5 37.3 39.5 37.2 41.8

9.7 5.8 5.5 9.2 9.8 12.3 12.6

14.2 14.0 3.9 6.5 9.7 8.9 11.5

10.4 7.4 4.3 4.1 7.2 13.2 10.8

Leader (in %) 21.0 38.2 44.2 34.0 37.5 18.2 31.8

Note. N = 395.

Table 5.2 also shows that the proportion of respondents with managerial responsibilities is high overall, particularly in the University sample. It accounts for more than a third of respondents in all samples except that of Company A and Research Institute C. While the sample of Company B comprises high-ranking employees, the figures for the University and the three research institutes indicate that their staff includes student assistants who are supervised by doctoral students. Research Institute C is quite small and thus has a flat hierarchy so few employees hold leadership positions. On the whole, the respondents have a high level of education, as the statistics depicted in Table 5.3 show. Both universities and research institutes need academic staff, and unsurprisingly most respondents from these organizations hold a university degree. Respondents with a lower level of education may be student assistants or clerical staff. While most clerks have professional degrees, some may be university graduates, and others may have been trained on the job. Student assistants, working both at

5.1 Research Setting, Participants, and Procedures

65

Table 5.3 Level of Education

Company A Company B University Research Inst. Research Inst. Research Inst. Research Inst.

A B C D

University (in %)

Professional (in %)

High school (in %)

None (in %)

58.9 58.8 95.3 97.9 70.8 81.8 72.7

36.0 26.5 — 2.1 16.7 9.1 9.1

4.7 14.7 2.3 — 12.5 9.1 18.2

.5 — 2.3 — — — —

Note. N = 395.

the University and the research institutes, have not completed their degree and thus identify themselves as high school graduates. At closer inspection, the University figures suggest that most respondents are academic staff (in particular, teaching assistants), while student assistants did not participate.4 In the company samples, the proportion of university graduates is high as well. In the case of Company B, where the sample consists of highranking employees, this is not surprising. In Company A, the proportion of nearly 60% is likely to over-represent white-collar as compared to blue-collar workers. White-collar workers in general tend to be university graduates and also have access to computers. Thus, they received a personal invitation and could retrieve the questionnaire from the Intranet. In turn, blue-collar workers may have read public announcements and seen the questionnaires distributed in the firm only. As the white-collar workers had a better chance to learn about the survey and could participate more conveniently, they were more likely to answer. The fields of education indicated by most respondents across all organizations are language and culture, business administration and economics, social sciences, and engineering.5 Table 5.4 shows the distribution of respondents across these four fields. In both companies, almost all respondents had studied or had been trained in either business administration or engineering. As Company A manufactures vehicles, it is evident why the 4 The invitation was snowballed among the staff of the University. Student assistants are not usually included in universities’ mailing lists and therefore did not receive the invitation. 5 Note that these fields are independent of the level of education. For example, “business administration” refers to both vocational training or university education; likewise, “engineering” includes technical training programs.

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5 Empirical Test of the Effects of Privacy Table 5.4 Field of Education

Company A Company B University Research Inst. Research Inst. Research Inst. Research Inst.

A B C D

Lang. & culture (in %)

Business & economics (in %)

Social sciences (in %)

Engineering (in %)

.6 — — 8.7 20.0 — 9.5

29.9 54.8 76.2 10.9 35.0 36.4 52.4

— — — 65.2 30.0 45.4 4.8

67.2 32.3 2.4 2.2 — — 19.0

Note. N = 395.

proportion of engineers and technicians is particularly high. Likewise, the proportion of business-school graduates in the University sample comes as no surprise; on the contrary, educational backgrounds even turn out to be quite heterogeneous (20% trained in other fields, which are not reported in the table). This is also true for the research institutes. It is obvious that their fields of research are reflected in the education of their employees, many of whom are social scientists (Research Institutes A, B, and C) or engineers and technicians (Research Institute D). 5.1.3

Procedures

To launch the survey, each organization was contacted individually. Contact people were identified and sent personalized letters, where the purpose of the study and the design of the survey were briefly laid out. Moreover, a personal presentation of the research project was offered. However, this offer was accepted only by the companies. The contact person ensured the distribution of the questionnaire to the members of his or her organization. While the organizations accomplished the survey on their own, all details were arranged with the contact person and he or she was provided with additional material as needed (e.g., templates for invitations and reminders, posters, leaflets, etc.). The surveys were conducted between May and December 2010. According to the preferences of each organization, they lasted two to three weeks. While both an on-line and a paper-and-pencil questionnaire were available, all organizations except Company A opted for the on-line survey. In Company A, both hard copies of the questionnaire and boxes in which the

5.1 Research Setting, Participants, and Procedures

67

completed questionnaires should be collected were provided at frequented spots, such as the entrances and the cafeteria. In addition, the questionnaire was uploaded to the Intranet so that employees could print it out, fill it in, and drop it into one of the boxes. (However, the questionnaire should not be submitted on-line.) The questionnaire consisted of a cover letter, the measures, and a lottery. The cover letter identified the authors of the survey, outlined the purpose of the research project, and gave instructions on answering the questions. It also pointed out that the survey was anonymous and the data were collected for scientific purposes only. The lottery should motivate people to participate and prevent them from dropping out. To ensure full confidentiality, in the on-line survey the personal data collected for the lottery (i.e. personal e-mail addresses) were saved in a different database, so that it was technically impossible to track the answers. (There was no lottery for participants from Company A because the works council declined it.) The part comprising the measures was divided into sections, each of which corresponded to one construct (i.e. privacy, empowerment, job satisfaction, etc.). Thus, instructions relating to a certain construct could be given at the beginning of the respective section. The items in each section were randomized, while the order of the sections remained the same. The subsections containing the first-order scales of privacy (i.e. control over the environment, communication, etc.) were again randomized. To avoid missing values, the respondents were required to complete each section before proceeding to the next one (except for the sections on personal data). The structure of the questionnaire was as follows: 1. Motivating potential; 2. Privacy;

6. Job satisfaction; 7. Creative potential;

3. Empowerment; 4. Climate for creativity; 5. Creativity;

8. Personal data; 9. Feedback.

The order of the questions followed basically two criteria: more important preceded less important, and less sensitive preceded more sensitive questions. Privacy, empowerment, creativity, and job satisfaction are more important than the control variables, i.e. motivating potential, climate for creativity, creative potential, and the personal data. Conversely, questions on personal data, job satisfaction, and creative potential are more sensitive than those on privacy, and so on. The questionnaire started with questions on motivating

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5 Empirical Test of the Effects of Privacy

potential rather than privacy or creativity, though, to accustom participants to the question and answer formats. Another reason for placing questions on sensitive personal data at the end was that these items, unlike the more important ones, do not require much thought. The request for feedback had two purposes. On the one hand, feedback should help improve the questionnaire. As the survey was conducted in the different organizations successively, minor revisions were possible from one survey to the next. On the other hand, most of the questions were closed, i.e. the respondents could only choose one among several options. The feedback field afforded them a chance to give additional information, which might be important for assessing the answers. For instance, such information led several datasets to be dismissed, as mentioned in the previous subsection. Apart from minor differences, the questionnaire was the same for all participants. It mainly varied in the question on the working tasks, which had to be adapted both to the type of organization (company, university or research institute) and to the individual organization (e.g., Company A, Company B). The full on-line questionnaire both in German and English is reprinted below (see Appendices C for the German and D for the English version).

5.2 5.2.1

The Measures

The Measure of Creativity

Creativity can be measured in several ways, depending on the type of the study and the available data (Shalley et al., 2004; Zhou and Shalley, 2003). While laboratory studies often rely on expert judges (e.g., Shalley, 1995; Zhou, 1998), it is common practice in field studies, most of which have been conducted in the business context, to have an employee’s supervisors or coworkers evaluate his or her creativity (e.g., George and Zhou, 2001; Tierney and Farmer, 2002). Alternatively, objective data, such as patent disclosures, technical reports, or suggestions for improvement, can be used as proxies (e.g., Frese et al., 1999; Oldham and Cummings, 1996; Tierney et al., 1999). A more recent trend in research involves asking employees to rate their creativity themselves (e.g., Ohly et al., 2006; Shalley et al., 2009; Zhou et al., 2008). The reason why expert assessments in particular and third-party assessments in general (i.e. by supervisors or coworkers) have been preferred over self-reports is obvious. Experts are believed to be best at assessing data and respondents anyway, and the judgments of supervisors and coworkers are considered to be, if not more accurate, at least more objective than those

5.2 The Measures

69

of the respondents themselves. Data such as the number of suggestions are objective and measure what matters most to the company: creative ideas which are likely to make an impact. Nevertheless, there are several reasons for asking respondents to rate their creativity themselves, some related to measuring creativity as such, others related to this study. While an expert’s evaluation of a person’s creative performance on some task is certainly reliable, assessments by others are not necessarily better than self-assessments. For instance, a supervisor may just think of one employee as more creative than his colleague, though he is not, and thus overrate him. Moreover, creativity cannot always be observed. If an employee has, and perhaps proposes many insightful ideas, but they are not realized, that employee will not be perceived as creative though he is. (This is even more true for objective measures, of course.) Results from studies which relied on multiple sources to measure creativity, however, show that judgments tend to converge anyway, as the data were fairly correlated (Axtell et al., 2000, 2006; Ohly et al., 2006; Shalley et al., 2009). The reason for choosing self-reports for the purpose of this study has to do with privacy concerns. Data from multiple sources need to be matched, and however confidentially this is done, this prospect may discourage employees from participating. As this is especially true for people who are sensitive about privacy issues, the use of multiple sources may cause rather than prevent bias. At the same time, using multiple sources might have reduced the sample size because fewer employees in a particular company and fewer companies in general would have participated. German companies have become very sensitive about privacy issues, and works councils, whose agreement is necessary for any employee survey, even more so.6 The aforementioned self-report measures were all derived from otherreport measures. For instance, Ohly et al. (2006) had employees assess their creativity on an index designed by Tierney et al. (1999) to collect supervisor ratings. Where Tierney et al. asked supervisors how strongly they agreed with the statement that “[this employee] demonstrated originality in his/her work,” Ohly et al. asked employees how strongly they agreed with the statement “I demonstrate originality in my work.” Likewise, Shalley et al. (2009) adopted items from Oldham and Cummings (1996), Zhou et al. (2008), from Zhou and George (2001). Taking the same approach, this study drew on the most common other-report measures (see Maier et al., 2007, for an overview) in order to develop a self-report measure of creativity. 6 The works council of one company even declined the lottery at the end of the questionnaire, because it required participants to fill in contact data, although it was ensured that it was technically impossible to identify participants by their e-mail address.

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The index used here was mainly based on those of Zhou and George (2001) and Tierney et al. (1999), which together seem to cover the most important manifestations of creative performance. Redundant items were merged or dropped to keep the measure short. Abstract words, such as “originality,” were replaced with more precise terms. Likewise, “creativity” and its derivatives, frequently used by Zhou and George in their items, were substituted in order not to rely on the very concepts to be measured. The measure was pretested on a heterogeneous group of a dozen people, including secretaries, students, and teaching assistants from university as well as people working in companies, and modified on the basis of their feedback. The final index comprised the following ten items: Crea1

I find new ways to achieve goals or objectives.

Crea2

I find new ways to improve existing processes or products.

Crea3

I search out new processes or product ideas.

Crea4

I find new uses for existing processes, equipments, or products.

Crea5

I generate novel but operable work-related ideas.

Crea6

I’m ready to take risks when developing new ideas.

Crea7

I champion new ideas and promote them to others.

Crea8

I’m inventive at work when given the opportunity.

Crea9

I develop adequate plans and schedules for the implementation of new ideas.

Crea10

I approach problems with unconventional solutions.

To conform with the answer scales of the other measures and on the basis of the feedback of the participants in the pretest, respondents had to rate the statements on a seven-point Likert scale, ranging from (1) “Very inaccurate” to (7) “Very accurate.”7 The measurement model is reflective. Thus, if someone finds new ways to achieve goals or is ready to take risks, this is because he or she is creative, and not the other way round. This assumption underlies both Tierney et al.’s and Zhou and George’s measures. 7 Tierney et al. used a five-point Likert scale, ranging from “Never” to “Very often,” Zhou and George a six-point Likert scale, ranging from “Not at all characteristic” to “Very characteristic.”

5.2 The Measures

5.2.2

71

The Measure of Job Satisfaction

The amount of literature on job satisfaction is overwhelming and several standardized measures exist. The most popular ones are the Job Descriptive Index (JDI), devised by Smith et al. (1969), and the Minnesota Satisfaction Questionnaire (MSQ) by Weiss et al. (1967).8 The German counterparts of these questionnaires are the Arbeitsbeschreibungsbogen (ABB) which was developed by Neuberger and Allerbeck (1978) and draws largely on the JDI, and the Skala zur Messung der Arbeitszufriedenheit (SAZ) by Fischer and Lück (1972; see also Rosenstiel, 2007, p. 439). Obviously enough, it is reasonable to benefit from prior research and rely on one of these standardized measures rather than develop a new one. Job satisfaction has been basically conceptualized in two different ways. On the one hand, it can be taken to reflect an employee’s attitude toward his or her job as a whole. On the other hand, job satisfaction can be seen as the result of satisfaction with different facets of the job, such as coworkers, supervision, or pay (e.g., Ironson et al., 1989). Technically speaking, job satisfaction can be specified either as a first-order construct or as a second-order construct with each of these facets being a first-order construct. While the aforementioned standardized measures (except for the SAZ) are multifaceted, it is not uncommon to treat job satisfaction as a global construct, or even to use a single item to capture it (Wanous et al., 1997). Moreover, some surveys focus on satisfaction with a specific facet of the job rather than the job in general. Researchers who examined the effect of privacy on job satisfaction have clearly preferred measures of the job as a whole to multifaceted ones. More precisely, many combined items from the Job Diagnostic Survey (JDS), which was developed by Hackman and Oldham (1975), to form an index of global job satisfaction (Maher and von Hippel, 2005; Oldham, 1988; Oldham and Brass, 1979; Oldham and Fried, 1987; Oldham and Rotchford, 1983; Oldham et al., 1991).9 Other measures, some developed ad hoc, have been used as well, both in research on privacy (Zalesny and Farace, 1987; Lee and Brand, 2005) and empowerment (Barroso Castro et al., 2008; Kirkman and Rosen, 1999; Spreitzer et al., 1997). 8 Although developed decades ago, both questionnaires are still in use and have been updated. For instance, Ironson et al. proposed in 1989 their Job-In-General scale to supplement the JDI. 9 Note that Oldham, who was involved in most of these studies, unsurprisingly used his measure. The JDS translates into a measure the job characteristics model by Hackman and Oldham (1975), which was outlined in the previous chapter to hypothesize the positive effect of motivating potential on empowerment (Section 4.4.3).

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On the contrary, Carlopio and Gardner (1992) and DuVall-Early and Benedict (1992) administered the MSQ—the latter even the 100-item full version—, to analyze the effect of privacy (understood as control over the environment), on job satisfaction more carefully. Likewise, items from the JDI have frequently been used in the empowerment literature (Carless, 2004; Liden et al., 2000; Spreitzer et al., 1997). While Carlopio and Gardner used the appropriate MSQ sub-scale, other researchers created ad hoc specific measures of satisfaction with the environment in order to study the effect of physical privacy on satisfaction (Lee and Brand, 2005; May et al., 2005; Oldham, 1988; O’Neill, 1994; Sundstrom et al., 1980). Even though prior studies favored global measures, the multifaceted, seven-item short version of the ABB was preferred for the present study for several reasons. First, the ABB was designed for German respondents and is a standard measure, unlike whatever combination of translated JDS items.10 Second, being multifaceted, the ABB permits a more detailed analysis. For instance, privacy may increase satisfaction with the working conditions, but not with pay. Nevertheless, it may be still worthwhile to test whether there is an effect on satisfaction with the working conditions, even if there is no effect on global job satisfaction (see, e.g., DuVall-Early and Benedict, 1992). Third, the seven-item ABB is not much longer than typical three-item measures of general job satisfaction. The ABB assumes that job satisfaction is determined by seven facets, each of which is measured on a single item. These facets are satisfaction with the colleagues, supervisors, work tasks, working conditions, organization and management, career perspectives, and pay. The facets are represented by the following items (translation by the author):11 JS1

How satisfied are you with your colleagues?

JS2

How satisfied are you with your supervisors?

JS3

How satisfied are you with your work tasks?

JS4

How satisfied are you with your working conditions?

JS5

How satisfied are you with the organization and management?

10 Language is not an argument in the first place because the JDS is available in German as well. However, a measure developed for German respondents can still be assumed to be more appropriate than the translation of an English measure. 11 There was one more item on overall job satisfaction. Though these data were not used, they were collected anyway. The omitted item read “How satisfied are you overall with your job?” The answer format was the same as for the other items.

5.2 The Measures

JS6

How satisfied are you with your career perspectives?

JS7

How satisfied are you with your level of payment?

73

Each question was to be answered on a seven-point Likert scale, ranging from (1) “Very dissatisfied” to (7) “Very satisfied.” The items of the job satisfaction scale differ both in the stem (which is a question rather than a statement) and the leaf (which refers to satisfaction instead of agreement) from most other items. The measurement model is formative. 5.2.3

The Measure of Empowerment

Empowerment was conceptualized by Conger and Kanungo (1988) and Thomas and Velthouse (1990). Thomas and Velthouse, who refined Conger and Kanungo’s work, argued that an employee’s sense of empowerment was driven by her perceptions of meaning, competence, self-determination, and impact. Spreitzer (1995) translated Thomas and Velthouse’s concept into a measure of psychological empowerment. While other measures of empowerment do exist (e.g., Menon, 1999, 2001), Spreitzer’s clearly prevails and has even been adapted to other purposes, including the measurement of group empowerment (Kirkman and Rosen, 1999). Spreitzer specified empowerment as a second-order construct, with meaning, competence, self-determination, and impact as first-order constructs. She proposed three items to reflect each first-order construct. According to her concept, the measurement model of empowerment ought to be mixed, as the causality runs from the first-order constructs both to the secondorder construct and to the indicators, which reflect rather than cause the first-order constructs (Jarvis et al., 2003). Nevertheless, Spreitzer obviously inverts the cause–effect relationship between the first-order and second-order constructs when validating her measure. This specification has been upheld by Spreitzer herself (Spreitzer, 1996; Spreitzer et al., 1997) and other researchers who adopted her measure and used structural equation modeling (e.g., Carless, 2004; Zhang and Bartol, 2010), and has lately been echoed by Williams et al. (2009). Nevertheless, this specification contradicts the conceptual work by both Thomas and Velthouse and Spreitzer herself and is not very intuitive. Thomas and Velthouse proposed that the four task assessments (which correspond to the first-order constructs) were the proximal causes of empowerment; Spreitzer herself stated that “[t]he four dimensions are argued to combine additively to create an overall construct of psychological empowerment” (p. 1444, emphasis added). An example of Spreitzer’s specification would be the case of employees who perceive themselves as competent to

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fulfill their tasks or consider that they have some impact on what happens around them because they feel empowered. It is far more plausible, however, that they feel empowered because they realize that they are competent or have some impact. In keeping with this argument and unlike in earlier studies, the four first-order constructs were treated as causes rather than effects of the secondorder construct (see below, Section 5.4.1). Notwithstanding this adjustment, the items developed by Spreitzer (1995) were used. Spreitzer proposed twelve items, of which three correspond to each of the four first-order constructs. To keep the questionnaire short, one item per construct was eliminated. The final measure consisted of the following eight items:12 EmpM ean1 The work I do is very important to me. EmpM ean2 The work I do is meaningful to me. EmpComp1 I am self-assured about my capabilities to perform my work activities. EmpComp2 I have mastered the skills necessary for my job. EmpSDet1 I have significant autonomy in determining how I do my job. EmpSDet2 I can decide on my own how to go about doing my work. EmpImpt1 I have a great deal of control over what happens in my department. EmpImpt2 I have significant influence over what happens in my department. Each statement was to be rated on a seven-point Likert scale, ranging from (1) “Strongly disagree” to (7) “Strongly agree.” 5.2.4

The Measure of Creative Potential

An individual’s creative potential is probably the most important determinant of his or her creative performance. Field studies can hardly do justice to the importance of personality. Their strength lies in the analysis of contextual factors, and personality, if at all, has rather appeared as a 12 The questionnaire contained another item to directly reflect the second-order construct. Like for job satisfaction (Footnote 11), these data were eventually not used. The item read “Overall, my job makes me feel empowered.” The answer format was the same as for the other empowerment items.

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75

control variable. There are basically two measures that have been used to account for creative potential, both of which pertain to standardized personality tests (Shalley et al., 2004; Zhou and Shalley, 2003): Gough’s (1979) creative personality scale and the measure of the “Big Five” factor openness to experience. The creative personality scale was developed by Gough to calculate a creative personality score (CP S) which reflects a person’s creative potential. The scale forms part of the self-report California Psychological Inventory (Gough and Bradley, 1996). It is a list of thirty adjectives, each of which respondents are asked to tick if they feel that it aptly describes them. Eighteen of those adjectives characterize individuals who are creative, twelve, those who are not. The CP S has recently been used by Oldham and Cummings (1996), Madjar et al. (2002), Zhou (2003), and Shalley et al. (2009). (Shalley et al., who conducted a telephone survey, selected only ten of the thirty adjectives and asked respondents to rate them using the now more common Likert answer format.) Along with neuroticism, extraversion, agreeableness, and conscientiousness, openness to experience forms one of the five factors which, according to the Five Factor Model, describe human personality. The NEO Five Factor Inventory (NEO-FFI) translates this model into a measure and is therefore usually applied to survey these factors (Costa and McCrae, 1992; Borkenau and Ostendorf, 2008; for an exception in a study on creativity, see Baer, 2010). The short version of the NEO-FFI consists of five indexes made up of twelve items each, which, unlike the creative personality scale have the more familiar Likert format. Both Baer and Oldham (2006) and George and Zhou (2001) employed the NEO-FFI to measure openness to experience. The latter also surveyed conscientiousness. For the present study, the creative personality scale was preferred to the index of openness to experience for several reasons. First, while openness to experience is considered to be related to creative potential and has been found to predict creativity (George and Zhou, 2001), the creative personality scale still seems to capture the creative potential better; unlike the openness-to-experience index, it was designed to this end. Second, a review of the recent creativity literature suggests that Gough’s scale is more widely used. Third, since most items in the questionnaire developed for this study follow a similar format (they are formulated as statements to be rated on a seven-point Likert scale), the creative personality scale introduced variety. To keep the questionnaire short, of the thirty adjectives only twelve “positive” (CP Si+ ) and eight “negative” adjectives (CP Sj− ) were selected.

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The proportion of positive to negative adjectives remains thus the same as in Gough and Bradley’s (1996) complete list.13 CP S1+

clever;

+ CP S11

self-confident;

CP S2+

confident;

+ CP S12

unconventional;

CP S3+

egoistical;

CP S1−

cautious;

CP S4+

humorous;

CP S2−

commonplace;

CP S5+

individualistic;

CP S3−

conservative;

CP S6+

informal;

CP S4−

honest;

CP S7+

interests wide;

CP S5−

mannerly;

CP S8+

inventive;

CP S6−

sincere;

CP S9+

reflective;

CP S7−

submissive;

+ CP S10

resourceful;

CP S8−

suspicious.

The single adjectives are combined to form the CP S by adding 1 for each “positive” adjective checked and subtracting 1 for each “negative” one, that is CP S =

12 X i=1

CP Si+ −

8 X

CP Sj− ,

j=1

where CP Si+ and CP Si− take the values 0 and 1, depending on whether they have been checked or not. With twelve positive and eight negative adjectives, the creative personality score assumes values between −8 and 12. 5.2.5

The Measure of Motivating Potential

Work is an essential determinant of employees’ empowerment, creativity, and satisfaction. Its impact can be measured in terms of objective char13 The positive adjectives that were excluded are “capable,” “insightful,” “intelligent,” “original,” “sexy,” and “snobbish”; the negative ones, “affected,” “conventional,” “dissatisfied,” and “interests narrow.” The idea was to exclude those adjectives which were redundant (e.g., “clever” was kept, whereas “intelligent” was dropped) or which probably no one (e.g., “affected”) or everyone would check (e.g., “intelligent”).

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77

acteristics or individual perceptions.14 The collection of data on objective characteristics is complicated, however, and requires data from multiple sources to be matched. Attempting to collect such data would have probably led to fewer responses and possibly discouraged respondents sensitive about privacy issues. Moreover, different people react differently to the same job. Even though the motivating potential is an attribute of the job, it naturally depends on the person who attributes it. How employees perceive their job has been captured by means of a variety of measures, depending on the job characteristic of interest. For example, job complexity (Frese et al., 1999; Ohly et al., 2006), demanding work (Shalley et al., 2000), problem-solving demand (Axtell et al., 2000), heuristic task (George and Zhou, 2001), creativity required by the job (Unsworth et al., 2005; Gilson and Shalley, 2004), job control (Frese et al., 1999; Ohly et al., 2006; Axtell et al., 2000; Ohly et al., 2006), routinization (Ohly et al., 2006), and autonomy (Shalley et al., 2000) have been considered in the creativity literature. The empowerment literature, in turn, has focused more on the effects of feedback, role ambiguity, role overload, and the like (Seibert et al., 2011). A common measure in both the creativity (e.g., Farmer et al., 2003; Hatcher et al., 1989; Oldham and Cummings, 1996; Shalley et al., 2009) and the empowerment literature (e.g., Chen and Klimoski, 2003; Jha and Nair, 2008; Kirkman and Rosen, 1999; Liden et al., 2000) is the Motivating Potential Score (M P S). The M P S is a popular catch-all measure of the motivating potential of a job.15 It consists of fifteen items taken from Hackman and Oldham’s (1975) JDS and directly translates their concept of motivating potential (as a part of the job characteristics model) into a measure. It covers the five job characteristics of skill variety, task identity, task significance, autonomy, and feedback from the job, each of which is reflected in three items. Most of the more specific characteristics mentioned before can be subsumed under one of these five. The M P S was chosen for several reasons. First, it is broad, covering many aspects of the job rather than focusing on a single one, and is thus ideal to control for the job in general; second, it is parsimonious; third, it is standardized and has already been used in similar research designs. Even though the M P S is short, one item of each of the five indexes was

14 While the effect of objective job characteristics on creativity has been investigated (Shalley et al., 2000; Tierney and Farmer, 2002, 2004; Shalley et al., 2009), they have not been of major interest in the empowerment literature (Seibert et al., 2011). 15 More specifically, the M P S, just like the CP S, denotes the value (“score”) which the latent variable of motivating potential takes, not the measure. Following the common practice the measure itself is referred to as M P S here.

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dropped and the wording of the items was slightly adapted. Thus, each construct (i.e. characteristic) was reflected in two items, one positive, one negative: SV +

My job requires me to use a number of complex or high-level skills.

SV −

My job is quite simple and repetitive.

T ID+

My job provides me the chance to completely finish the pieces of work I begin.

T ID−

My job is arranged so that I do not have the chance to do an entire piece of work from beginning to end.

T S+

My job is one where a lot of other people can be affected by how well the work gets done.

T S−

My job itself is not very significant or important in the broader scheme of things.

AU T O+

My job gives me considerable opportunity for independence and freedom in how I do the work.

AU T O−

My job denies me any chance to use my personal initiative or judgment in carrying out the work.

F J+

Just doing the work required by my job provides many chances for me to figure out how well I am doing.

F J−

My job itself provides very few clues about whether or not I am performing well.

Each statement had to be rated on a seven-point Likert scale ranging from (1) “Very inaccurate” to (7) “Very accurate.” The item scores are combined both additively and multiplicatively to form the M P S, because according to the job characteristics model the characteristics interact. For instance, if the job does not offer any autonomy, its motivating potential is zero, however high the score on task significance. Following the formula proposed by Hackman and Oldham the M P S was calculated as follows: MPS =

SV + T ID + T S · AU T O · F J, 3

where SV is the average of SV + and (the reverse scored) SV − , and the like for T ID, T S, AU T O, and F J. As a measurement model, the M P S is

5.2 The Measures

79

both an index and a scale. The items reflect the first-order constructs, such as skill variety or autonomy, which, in turn, form the motivating potential. 5.2.6

The Measure of Climate for Creativity

The tradition of research on the climate for creativity is fairly long, although not as long as that on organizational and psychological climate in general. Since Siegel and Kaemmerer’s (1978) initial study, a number of measures have been developed that either survey only the climate for creativity or are part of comprehensive measures of climate (for a review, see Mathisen and Einarsen, 2004). These measures are adequate for the purposes of this study, eliminating the need to develop another measure. Siegel and Kaemmerer’s index of support for innovation consisted of about 60 items, covering three dimensions. Scott and Bruce (1994) adopted some of these items and added others to form an index of climate for innovation, which included two dimensions and consisted of 22 items. This index found its way into a number of studies; for instance, Shalley et al. (2009), Zhou and George (2001), and Zhou et al. (2008) combined seven, four, and five of Scott and Bruce’s items, respectively. Even when Anderson and West (1996, 1998) developed their 38-item Team Climate Inventory (TCI), they included some of Scott and Bruce’s items in the sub-index on support for innovation, which was used (sometimes together with other sub-indexes16 ) in several studies on creativity (e.g., Axtell et al., 2000, 2006; Pirola-Merlo and Mann, 2004). Other notable measures are based on Amabile’s (1988) theory of creativity, including Amabile and Gryskiewicz’s (1989) 130-item Work Environment Inventory, Witt and Beorkrem’s (1989) 39-item measure of climate for creative productivity, which was also used by Gilson and Shalley, 2004), and Amabile et al.’s (1996) KEYS, 66 items of which reflect six creativity stimulants and two creativity obstacles. This overview shows that there are many measures of the climate for creativity, which vary in length, breadth, and structure. The length ranges from four to over 100 items, the breadth from one to a dozen dimensions, and the same aspect is classified differently in different measures. According to Mathisen and Einarsen (2004), KEYS and Anderson and West’s TCI have the best psychometric quality. For this study, it was necessary to find a proven, parsimonious, and fairly specific measure, broad enough to cover influences otherwise not accounted for, but at the same time narrow enough to not overlap with those accounted 16

In addition to support for innovation, the TCI surveys vision, participative safety, and task orientation.

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for. Moreover, it should be short, its purpose being merely to control for the influence of the climate, rather than examine it at length. The sub-index on support for innovation of Anderson and West’s team climate inventory met these criteria best: the TCI is proven (e.g., Mathisen and Einarsen, 2004), the sub-index consists of eight items only (four on “articulated” and four on “enacted” support) and does not cover, for example, job-related drivers of creativity. Finally, a validated version of the TCI is available in German (Brodbeck et al., 2000), which is not the case for any other of the aforementioned measures. To further reduce the number of items, three of the four items for both articulated and enacted support were selected, resulting in the following items: Clim1

Assistance in developing new ideas is readily available for my colleagues and me.

Clim2

My colleagues and I are open and responsive to change.

Clim3

My colleagues and I are always searching for new ways to solve problems.

Clim4

My colleagues and I take the time needed to develop new ideas.

Clim5

My colleagues and I cooperate in order to develop new ideas.

Clim6

My colleagues and I provide each other with practical support for new ideas.

Respondents were asked to rate each of these six statements on a sevenpoint Likert scale, ranging from (1) “Strongly disagree” to (7) “Strongly agree.” Climate for creativity is a reflective measure; that is, the item scores are taken to result from the underlying latent variable. This corresponds to Anderson and West’s design of the measure and is the approach generally taken when measuring the climate. The reflective specification is, of course, necessary for items to be dropped (e.g., Rossiter, 2002). 5.2.7

The Measures of the Manifest Variables

Education. Respondents were expected to have heterogeneous educational backgrounds, since the survey addressed employees who had received vocational training or graduated from university in whatever field of education. Questions that asked respondents to supply too many details about their educational background were avoided for several reasons. First, the study did not require such information; second, such questions might have

5.2 The Measures

81

discouraged employees from answering, because they are cumbersome and, more importantly, may be perceived as a threat to the respondents’ privacy. Participants were asked about both their level and field of education or training. To establish the level, they were expected to choose the highest level of education or training they had attained from the following list: • Did not graduate; • Graduated from high school; • Completed vocational education/apprenticeship; • Graduated from college/university. To establish the field of education, they were asked to indicate their highest degree as exactly as possible, including the field (e.g., “B.Sc. in Physics”). Some would not answer because they did not bother or were concerned about their privacy. However, the question was near the end of the questionnaire and not mandatory, so the risk of someone dropping out was limited. Each answer was assigned to one of the following categories, which were sufficiently rough to apply to both vocational training and university education: • Arts; • Business and economics; • Social sciences;17 • Engineering. Job experience. Job experience is usually measured as the time an employee has spent on the job in question, that is job tenure (e.g., Scott and Bruce, 1994; Zhou and George, 2001; Tierney and Farmer, 2002, 2004; Ohly et al., 2006). In some surveys, respondents were asked in addition or instead how long they had been employees of their current organization, that is organizational tenure (e.g., Scott and Bruce, 1994; Tierney et al., 1999; Shalley et al., 2000; Zhou and George, 2001; Gilson and Shalley, 2004). Of course, job and organizational tenure may differ, as an employee may have held very similar jobs with different employers, but may have also 17

The category for social sciences was included because several research institutes specializing in social research were surveyed.

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changed jobs while working for the same employer. The questionnaire for this survey included an open question about job tenure, which seems to better approximate experience and expertise. Tasks and responsibilities. While employees’ behavior depends more on the perceived than the objective characteristics of their job (see above, Section 5.2.5), some surveys—notably on creativity—included questions about the respondent’s tasks. Some researchers even used taxonomies such as the U.S. Department of Labor’s Dictionary of Occupational Titles (DOT) (e.g., Shalley et al., 2000, 2009; Tierney and Farmer, 2002, 2004); others were content to distinguish job types (e.g., Oldham and Cummings, 1996; Scott and Bruce, 1994).18 Again, too detailed information was not necessary and might have reduced the number of respondents. Moreover, the M P S was used to account for the effects of the job on motivation. The question on the tasks was therefore rough and depended on the organization. Respondents from the University and research institutes were asked to indicate how much of their time they spent on research, teaching/consulting, administration, and other tasks. The answers of respondents from the companies were recoded into the categories “business administration,” “technical tasks,” and “research/design.” Several studies both on privacy (e.g., Carlopio and Gardner, 1992, 1995; Crouch and Nimran, 1989a; Zalesny et al., 1985) and creativity (e.g., Tierney et al., 1999; Tierney and Farmer, 2004; Ohly et al., 2006) controlled for (or even focused on) the effect of the hierarchical position. In this study, respondents were asked to indicate whether they held managerial responsibilities, i.e. led other employees. More detailed questions were avoided because of privacy concerns and because hierarchies can hardly be compared across organizations. Gender and age. To collect the usual demographic data, the respondents were asked to indicate their gender and year of birth in a closed and open question, respectively.

18 The DOT had not been replaced with the Occupational Information Network (O*Net) until when the above studies were conducted. The German equivalent of the DOT or O*Net would be the forms for job analysis provided by the REFA.

5.3 Statistical Procedures

5.3 5.3.1

83

Statistical Procedures Choice of the PLS Approach

The hypotheses proposed in the previous chapter combine to form a complex model which involves multiple variables and effects between them (Figure 4.1). Structural equation modeling is an appropriate method for estimating these effects and testing the hypotheses.19 There are two different approaches to structural equation modeling, which differ in their assumptions and application. The covariance-based approach, which is often identified with the popular LISREL and AMOS software packages, has long prevailed in social sciences. Recently, however, the variance-based or partial-least-squares (PLS) approach has become more widespread. A software package which implements the PLS algorithm and was used in this study is Ringle et al.’s (2005) SmartPLS. Technically, the covariance-based method consists in comparing the empirical to the theoretical (i.e. hypothesized) covariance matrix and minimizing the difference between them. According to the variance-based method, in turn, dependent latent variables are regressed alternately on their manifest indicator variables and other latent variables, and the effects are determined so as to minimize the residual variance of the dependent variables. When the covariance-based method is used, the fit between the covariance matrices serves as an indicator for the global quality of the model. The variance-based method, in turn, fits the model to the data, calculating latent variable scores. The strength of the former is therefore confirmation; of the latter, prediction. Still, the variance-based method can be used to test models all the same, as it allows for tests of significance (Chin, 1998). Neither approach to structural equation modeling is “better” than the other. The two methods are complementary, each having its strengths and weaknesses (Scholderer and Balderjahn, 2005). In particular, the variance-based method works with small samples, weak assumptions about the distribution of the data, and complex models, while the covariancebased method yields more accurate estimates, which can be generalized to the population (provided that the data are independent and normally distributed). Of course, many of the weaknesses of both approaches can be avoided with proper data and advanced statistical techniques. For instance, the consistency of variance-based estimates increases in the number of cases

19

A viable alternative is hierarchical regression modeling. However, structural equation modeling allows estimating the effects simultaneously.

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and indicator variables and they converge with covariance-based estimates (“consistency at large”) (Chin and Newsted, 1999). There are mainly three reasons for choosing the variance-based rather than covariance-based approach: small sample sizes, non-normally distributed data, and the use of formative measurement models. The present sample is quite large, comprising nearly 400 cases. The rule of thumb saying that the covariance-based method requires at least 200 cases is therefore easily met. For the variance-based method the necessary number of cases is ten times the number of regressors in the “largest” regression, among both regressions on latent and manifest variables (Chin, 1998); that is, 70 for job satisfaction, which is regressed on seven indicator variables. While the sample size is sufficient for both methods, the variance-based method allows small subsamples to be formed for more detailed analyses, including tests for moderating effects (see Section 5.4.3 below). It has been pointed out that despite a popular myth, covariance-based software can deal with formative measurement models, although they are less common (e.g., Raithel, 2009; Scholderer and Balderjahn, 2005). However, the covariance-based modeling of formative measures is subject to a number of restrictions (e.g., Jarvis et al., 2003; Williams et al., 2009). More precisely, each formative measurement model must be identified either through structural or measurement relations, which limits the flexibility in modeling or demands additional measures.20 Conversely, for a variance-based model to be identified it is enough that each latent variable is measured at all, irrespective of the method used. Hence, while the restrictions of the covariance-based method are not prohibitive, they make it at least impracticable for the complex model conceived above.21 Neither the sample size nor the use of formative measurement models make the variance-based approach mandatory, although it offers advantages over covariance-based modeling. The distribution of the data, however, is a more cogent argument. The Kolmogorov–Smirnov test for normality reveals 20 Each latent variable with a formative measurement model must be modeled to either influence at least two dependent variables with reflective measurement models or have an additional reflective measurement model (which implies that reflective measurement is possible and makes formative measurement redundant). 21 It should be noted, though, that the PLS approach also has restrictions. If each latent variable must be measured, this is true for second-order constructs as well. For example, privacy, which was measured indirectly through the P rivCom, P rivEnv, P rivInf , and P rivW lb first-order constructs, had also to be measured directly. To this end, a reflective single-item measure was used, P rivOall (see above, Section 3.4). To avoid this workaround for empowerment, the measures of the first-order constructs (EmpComp, EmpImpt, EmpM ean, and EmpSDet) were turned into measures of the second-order construct (Emp) as described below (Section 5.4.1).

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that the distribution of the data is not normal. The normal distribution of the data is a necessary condition for a number of covariance-based procedures, though, including common (parametric) tests of significance. In turn, the variance-based method works well with non-normally distributed data. In conclusion, the PLS method was used because of the additional analyses with subsamples, the more convenient use of formative measures and the complexity of the model, as well as the non-normal distribution of the data. In contrast to covariance-based models, there is no global measure of fit for variance-based models. Chin (1998) proposed a number of local tests for assessing the model. These criteria have been echoed in several methodological studies on the PLS approach (for recent examples, see Ebert and Raithel, 2009; Henseler et al., 2009; Schloderer et al., 2009). In keeping with the assumptions and algorithm of the PLS approach, these local measures are prediction-oriented and nonparametric. The following three subsections show how reflective measurement models (Section 5.3.2), formative measurement models (Section 5.3.3), and the path model are evaluated (Section 5.3.4). 5.3.2

Evaluation of Reflective Measurement Models

Both variance-based and covariance-based analyses proceed in two steps, namely a confirmatory factor analysis and a path analysis. While they differ in the second step, they are identical in the first step. The factor analysis reveals whether the indicators that are intended to reflect a certain construct load on the same factor, which corresponds to that construct. The loadings of the indicator variables on their construct or factor are the basis for the evaluation of the reflective measurement models, to which four criteria apply. SmartPLS conducts the confirmatory factor analysis and outputs the loadings, as well as the indicators corresponding to these four criteria (see Schloderer et al., 2009, in particular pp. 580–581, for the criteria). Indicator reliability. An indicator variable is assumed to reflect the underlying latent variable reliably if the latter explains at least half of the variance of the former. This is the case if the loading λij of indicator i on construct j is at least .7, because in that case the variance of this indicator variable, λ2ij , is at least .5. Construct reliability. All indicator variables that reflect a latent variable taken together measure it reliably if they are homogeneous or consistent. The composite reliability, ρc , is a measure of internal consistency, similar to

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5 Empirical Test of the Effects of Privacy

Cronbach’s α, which can be used to assess construct reliability. It should equal at least .6 and is calculated according to the formula P 2 ( i λij ) ρc = P , P 2 ( i λij ) + i var (εij ) where var (εij ) := 1 − λ2ij is the variance of indicator variable i that is not explained by latent variable j. Average variance extracted. The average variance extracted (AVE) relates the variance of the indicator variables that is explained by the latent variable they reflect to their overall variance. It should at least equal .5 and is calculated according to the formula P 2 i λij P P . AVEi = 2 + λ i ij i var (εij ) Discriminant validity. The AVE can also be used to check for discriminant validity. An index has discriminant validity if its AVE is at least equal to the squared correlations between the latent variable it reflects and any other latent variable with a reflective measurement model, that is 2 AVEi ≥ rik , i 6= k,

where rik is the coefficient of the correlation between latent variables i and k, both of which have reflective measurement models. This criterion is also known as Fornell–Larcker criterion. 5.3.3

Evaluation of Formative Measurement Models

While a formative measurement model cannot be tested for reliability, there are criteria for validity. However, unlike reflective measures, formative ones can only be validated ex ante. Notwithstanding this validation, there are statistical considerations which can justify the elimination of single items from a scale even ex post. There are at least three criteria that apply to formative measurement models, which refer to expert agreement, the weights of the indicator variables, and multicollinearity. While SmartPLS provides the figures on the sign, size, and significance of the indicator weights, the data for expert agreement and collinearity tests have to be be obtained separately (see Schloderer et al., 2009, in particular pp. 582–584, for the criteria).

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87

Expert agreement. The (ex ante) criterion of validity for formative measures is expert agreement, which is obviously hard to measure. Still, the proportion of substantive agreement (psa ) and the coefficient of substantive validity (csv ) have been proposed as indicators of expert agreement. The measure of privacy was the only to be developed for this study and was therefore validated as described above (see Chapter 3). The other measures— in particular, the formative measures—, were adopted from prior research and did not have to be validated again.

Indicator weights. Scale development implies hypotheses about the effect (“weight”) of each indicator variable on the latent variable it pertains to. More precisely, the former is supposed to be causally related to the latter either in a positive or negative sense. These hypotheses can be tested: the weight of each indicator variable should be significant and its sign should be as expected on conceptual grounds. As the latent variable is regressed on the manifest variable(s), the weights can be interpreted like the β coefficients in an OLS regression. However, if a weight is not significant, this does not necessarily mean that the indicator variable is dispensable and should be eliminated.

Multicollinearity. Unlike non-significance, multicollinearity is a more serious problem and may even justify the elimination of indicator variables. While multicollinearity does not affect the predictive power of the set of items (i.e. the whole scale), it flaws the estimates of the single weights and makes them change erratically. Thus, while reflective indicator variables should covary, formative indicator variables should not covary too much (Ebert and Raithel, 2009). The line is hard to draw, though, since this is an empirical rather than conceptual requirement. Conceptually, one can easily imagine that multiple causes of a construct covary. Measures of multicollinearity include the Variance Inflation Factor (VIF) and the Condition Index (CI), both of which are determined for each indicator variable. For a given indicator variable i, the VIF is VIFi =

1 , 1 − Ri2

where Ri2 is the coefficient of determination of that variable when regressed on the other indicator variables of the same latent variable. The VIF should never exceed 10 or, according to a more rigorous standard, 5. The CI is

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5 Empirical Test of the Effects of Privacy

s CIi =

eigenvaluemax , eigenvaluei

where eigenvaluei is the eigenvalue of indicator i and eigenvaluemax , the maximum eigenvalue of all manifest variables pertaining to the same latent variable. The CI should not exceed 30 or 15. Enumeration. Enumeration is quite a different approach to measuring latent variables and refers to both scales and indexes. (It is listed here because it is often discussed together with formative measurement models.) Enumeration means to calculate rather than estimate the score (i.e. value) of the latent variable from the item scores. Rossiter (2002) distinguished several types of measures and proposed a rule for each how to calculate the score as a part of his “C-OAR-SE” procedure of measure development. While enumeration consists basically in averaging and totaling the item scores according to the C-OAR-SE procedure, the developer of a measure sometimes demands other operations. Hence, while the CP S totals the item scores (Section 5.2.4), the M P S combines them additively and multiplicatively (Section 5.2.5). There are no empirical tests of reliability and validity other than validation by experts for enumerative measures. In this study, both reflective and formative measurement models were used. While most of the latent variables are estimated, the CP S and M P S are enumerated. In the case of the estimated measures, the type of measurement model was determined when the measure was introduced.22 Hence, the measurement models of privacy, empowerment, and job satisfaction are formative, whereas those of creativity and the climate for creativity are reflective. For these measures, the results of the empirical tests presented in the last and present subsections will be reported below. No such tests were carried out for the measures of creative and motivating potential. Both are regarded as valid, however, because they are widely recognized and have often been used. 5.3.4

Evaluation of the Path Model

As mentioned above, the variance-based and covariance-based methods are identical in the first step but differ in how they estimate the model in 22

See Chapter 3 with regard to privacy and the previous section with regard to the other measures.

5.3 Statistical Procedures

89

the second step. The variance-based algorithm regresses latent variables alternately on indicator variables and the latent variables they depend on. This iterative process stops when the changes of the estimates fall below a specified threshold from one iteration to the next. As the estimates thus obtained are local, there is no global measure of fit, in contrast to covariance-based estimates of models. Chin (1998), however, proposed a number of criteria that fit the variance-based method. These criteria include nonparametric tests of significance and measures of predictiveness. All figures which are necessary to evaluate the model are included in the SmartPLS output. Determination or variance explained. The coefficient of determination, R2 , indicates for each dependent latent variable how much of its variance is explained (and how well it is thus predicted) by the latent variables it depends on. The determination is substantial, moderate, and weak, if R2 is at least .67, .33, and .19, respectively. Predictive relevance. Predictive relevance refers to how well the model predicts the values of the indicator variables of dependent variables with reflective measurement models. To estimate the indicator scores, SmartPLS uses a re-sampling technique called blindfolding. A measure of predictive relevance is the Stone–Geisser Q2 . The model is said to have predictive relevance if Q2 is positive. This criterion is also known as the Stone–Geisser criterion. Effect size. For each path, the size of the effect of the independent on the dependent variable can be determined. A measure of the effect size is f 2 , which compares the R2 values of the dependent variable when the independent variable is included (according to the hypothesized model) and excluded. More precisely, f2 =

2 2 − Rexcluded Rincluded . 2 1 − Rincluded

The effect is large, medium, and small if f 2 of at least .35, .15, and .02, respectively. Significance. The PLS path coefficients are equivalent to standardized β coefficients in linear OLS regressions. They must significantly differ from zero and have the hypothesized sign. Because of the weak assumptions about the data, re-sampling techniques such as jackknifing or bootstrapping

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are used to determine pseudo t-values, which can then be interpreted like common t-values (Chin, 1998). More specifically, SmartPLS bootstraps the data.

5.4

Results of the Empirical Analysis

5.4.1

Evaluation of the Measurement Models

Privacy. Privacy was specified as a second-order construct with control over communication (P rivCom), the environment (P rivEnv), personal information (P rivInf ), and the boundaries between work life and private life (P rivW lb) as first-order constructs. The items do not refer to the second-order construct (P riv), but to the four first-order constructs, all of which have formative measurement models. (The causality runs from the items to the first-order constructs and from those to the second-order construct.) Hence, each first-order construct has to be subjected to the tests for formative measurement models.23 The privacy measure was developed especially for this study. Since the measurement model is formative, it had to be validated ex ante, as described above (Chapter 3). The formative measurement models of other latent variables (namely job satisfaction and empowerment) were adopted from earlier research and can be assumed to be valid. (The reflective measurement models can and must be validated ex post anyway.) The tests to be conducted in addition to the validation refer to the weights of the items, which should be positive and significant. Moreover, multicollinearity among the items might be a reason to exclude some of them, so that the weights of the others can be estimated more accurately. Table 5.5 depicts for each item the weight and level of significance, as determined by SmartPLS, as well as the VIF and CI. The VIF and CI values do not suggest that multicollinearity among the indicator variables is a serious problem for any of the four first-order constructs. Most VIF and all CI values fall short of the strict thresholds of 5 and 15, and even the VIF for P rivInf2 is far from 10. The signs of all weights other than P rivEnv2 are as expected. For every second-order construct other than P rivInf , at least half of the weights are significant. While the weight of P rivEnv2 surprisingly turns out to be negative, it is 23 P rivOall, which directly reflects the second-order construct, is necessary for identifying the model. It does not belong to the formative measurement model, though, but is a reflective single-item measure of privacy in itself. However, the common tests for reflective measures do not apply to single-item measures. For example, a single item cannot be tested for convergent validity.

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Table 5.5 Evaluation of the Privacy Scale Weight

VIF

CI

P rivCom1 P rivCom2 P rivCom3 P rivCom4 P rivCom5 P rivCom6

.08 .01 .11? .17??? .21??? .18???

1.35 1.22 1.62 1.30 1.83 1.80

5.42 8.82 8.97 9.87 11.21 12.52

P rivEnv1 P rivEnv2 P rivEnv3 P rivEnv4 P rivEnv5

.13?? −.04 .16??? .19??? .10??

3.21 3.85 3.25 1.73 1.87

4.18 6.54 7.11 8.17 8.91

P rivInf1 P rivInf2 P rivInf3 P rivInf4

.06 .17 .07 .28???

3.50 5.21 3.84 3.22

4.81 7.60 9.04 11.14

P rivW lb1 P rivW lb2 P rivW lb3 P rivW lb4

.36??? .18??? .11 .27???

1.18 1.08 1.13 1.02

4.39 6.22 7.15 10.52

Note. N = 395. ? p < .1.

??

p < .05.

???

p < .01.

not significant.24 These results call for a discussion of the non-significant weights, the negative, though non-significant weight of P rivEnv2 , and the measurement of control over information. That some of the weights are not significant is neither surprising nor alarming. As Cenfetelli and Bassellier (2009) pointed out, the probability that all weights will be significant naturally decreases as the number of items increases. Even in the absence of multicollinearity, all items together cannot explain more than the total variance of the latent variable. There are different strategies to reduce the number of items per construct. Namely, “redundant” items can be eliminated and conceptually related indicators can be merged (e.g., by averaging their scores) or regrouped into more specific constructs, according to conceptual considerations. This may be an avenue for future research. 24

P rivW lb3 and P rivW lb4 were negatively worded, but reverse-scored, so their weights should be positive.

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5 Empirical Test of the Effects of Privacy

The negative weight of P rivEnv2 can be explained empirically rather than conceptually. The item is positively correlated with all other indicator variables of P rivEnv, as well as with P rivEnv (see Table B.2 in Appendix B for the bivariate correlation coefficients). The weight of P rivEnv2 changes its sign in the presence of other indicator variables because of a suppression effect (Cenfetelli and Bassellier, 2009). While P rivEnv2 does not have a direct effect on its construct when interacting with the other indicator variables (its weight is not significant), it still increases the weights of P rivEnv1 and P rivEnv3 , although only slightly,25 and thus indirectly contributes to explain P rivEnv. The item was therefore retained. Unlike the other first-order constructs, P rivInf has more items with non-significant than with significant weights. This finding can be explained and dealt with in different ways. On the one hand, both the conceptual overlap and the empirical correlations of the items are considerable (see Table B.2). For instance, although it is possible that an employee has control over what information her company collects, but not, who has access to it, both types of control will normally occur together. Therefore, an additional item does not contribute much to explaining the latent variable. (If single items are eliminated, the weights of the remaining items increase, which precludes a suppression effect like in the case of P rivEnv2 .) One might conclude that it is possible to eliminate items without impairing the measure. On the other hand, even the items with non-significant weight contain valuable information, as they reflect the relative importance of the forms of control. Thus, having control over information depends more on having control over who shares personal information outside the company and what purposes such information is used for (P rivInf4 and P rivInf2 ) than over who has access to such information within the company and what information is collected (P rivInf3 and P rivInf1 ).26 This is not to say that having control over the collection of information does not matter; however, it is “absorbed” by other forms of control. In conclusion, while there are reasons to eliminate or merge items, in this case all were retained, as lack of significance does not invalidate a valid item. It is left to future 25 Their weights are without P rivEnv .12 and .15, respectively. The weights of 2 P rivEnv4 and P rivEnv5 even change less. 26 This pattern roughly corresponds to that of concerns about the privacy of information discovered by Smith et al. (1996). Smith et al. observed “that the highest levels of concern were associated with improper access and unauthorized secondary use. Lower levels of concern were associated with Collection and Errors” (p. 188). Unauthorized secondary use corresponds to P rivInf4 and P rivInf2 , improper access, to P rivInf3 , and collection, to P rivInf1 . (Correction of errors was not part of this measure.)

5.4 Results of the Empirical Analysis

93

research to disprove or confirm these findings and modify the P rivInf scale accordingly.27 P rivW lb1 deserves special attention, as this item was kept in the P rivW lb scale tentatively, even though it was not validated by the experts (and therefore could not be taken to be valid). Interestingly, it turns out to have the strongest effect of all P rivW lb items. Its impact is actually high enough to slightly increase the effect of the first-order construct P rivW lb on the second-order construct P riv (Hypothesis 1d). In view of this empirical evidence, the item was retained. Thus, despite the lack of expert agreement at the outset, this observation suggests that the item should not be prematurely dismissed until further expert interviews have been conducted. Creativity. The measure of creativity is reflective and meets all criteria introduced above. Table 5.6 lists the loadings of the indicators. All loadings are above .7 and thus reliable. Table 5.6 Evaluation of the Creativity Index Loading Crea1 Crea2 Crea3 Crea4 Crea5

.83 .83 .80 .75 .83

Loading Crea6 Crea7 Crea8 Crea9 Crea10

.76 .81 .80 .83 .82

Note. N = 395.

The composite reliability is .95, and the AVE, .65, both exceeding the thresholds of .6 and .5. Finally, Table 5.10 below shows that the measure satisfies the Fornell–Larcker criterion, because the square rooted AVE of Crea (.81) exceeds its correlation with any other latent variable. (The square rooted AVE is on the main diagonal, the relevant correlations in the same row and column at the left and below.) Job satisfaction. The measure of job satisfaction is formative. It can be assumed to be valid, because it was adopted from earlier research. Moreover, 27

Incidentally, the choice of different sets of P rivInf items did not alter the estimates for the rest of the model.

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it meets all criteria which apply ex post. Table 5.7 lists for each item the weight (including significance) as well as the VIF and CI values. Table 5.7 Evaluation of the Job Satisfaction Scale

JS1 JS2 JS3 JS4 JS5 JS6 JS7

Weight

VIF

CI

???

1.44 2.32 1.73 2.04 2.57 2.33 1.39

7.95 9.34 10.82 13.17 13.88 15.70 17.78

.18 .02 .29??? .16??? .07? .11??? .00

Note. N = 395. ? p < .1.

???

p < .01.

It is noteworthy that, while the VIF values are low, the CI values suggest that there is some multicollinearity among the indicator variables. In fact, the CI values for JS6 and JS7 slightly exceed the strict threshold of 15, though they are far off 30. Thus, multicollinearity does at least not appear to be so serious a problem as to justify the elimination of any indicator variables. All weights are positive, as they were expected to be, and most of them significant. Only those of satisfaction with supervisors (JS2 ) and pay (JS7 ) are not significant. The interpretation of these results is not so much that supervision and pay do not matter, but that satisfaction with these facets of the job contributed least to overall job satisfaction.

Empowerment. Empowerment is a second-order construct. Its firstorder constructs are meaning (EmpM ean), competence (EmpComp), selfdetermination (EmpSDet), and impact (EmpImpt). The measurement models of these first-order constructs have been said to be reflective. When the construct of empowerment was introduced above (Section 5.2.3), it was mentioned that the second-order construct (Emp) used to be modeled as causal to the first-order constructs. However, this approach conflicts with the conceptual research on empowerment, which suggests that, quite the contrary, the causality runs from the first-order constructs to the secondorder construct. The measurement model was therefore re-specified in this study in thise sense, and the effects estimated under the assumption that the causality is reverse.

5.4 Results of the Empirical Analysis

95

Notwithstanding this re-specification, the measurement models of the first-order constructs remain reflective. (For example, EmpM ean is causal to both the items—EmpM ean1 and EmpM ean2 —and the second-order construct; the same goes for EmpComp, EmpImpt, and EmpSDet.) However, a second modification was made to avoid the single-item measure of Emp (EmpOall), which would be necessary for the model to be identified. The indicator scores of the items that reflect the same first-order construct were averaged so as to simulate four single items which directly cause Emp. Empowerment could thus be specified as a first-order construct measured on a four-item scale without losing any information, because all indicator scores were taken into account.28 The formative 4-item measure of empowerment thus obtained satisfies the criteria stated above. Table 5.8 lists the weights, levels of significance, and VIF and CI values for each “item.” For convenience, EmpComp denotes the average of EmpComp1 and EmpComp2 , and so on. The weights of the four indicators are positive, as expected, and highly significant. The VIF values are low, whereas the CI values are high except for that of EmpM ean, but acceptable. The CI values of EmpComp and EmpImpt come close to 15, that of EmpSDet even to 30. Interestingly, the weight of EmpComp is markedly lower than the other three weights. Table 5.8 Evaluation of the Empowerment Scale Weight ???

EmpComp EmpImpt EmpM ean EmpSDet Note. N = 395.

.14 .27??? .30??? .31??? ???

VIF

CI

1.28 1.36 1.62 1.41

14.60 14.86 6.08 27.10

p < .01.

Both observations parallel prior research, even though the present specification of empowerment inverts the causality. Like the weight of EmpComp on empowerment, the loading of empowerment on EmpComp has been found to be lower than the others (e.g., Spreitzer, 1995, 1996; Zhang and Bartol, 2010). The multicollinearity is reminiscent of the problems Spreitzer 28 In fact, averaging reflective items corresponds technically to the enumeration rule Rossiter (2002) proposed for reflective measurement models in his C-OAR-SE procedure mentioned above (see Section 5.3.3). Butts et al. (2009) chose the same approach for empowerment, but continued to model the causality as before.

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(1995) faced when she tried to discriminate the four dimensions. Although conceptually distinct, she found them to be empirically (cor)related. Climate for creativity. The measure of climate for creativity, like that of creativity, is reflective and, after a slight modification, easily meets all criteria. After removing Clim2 , which had a loading of .67 only, the loadings of the remaining five indicator variables exceed .7, as displayed in Table 5.9. Table 5.9 Evaluation of the Climate for Creativity Index Loading Clim1 Clim2 Clim3

.79 — .83

Item Clim4 Clim5 Clim6

Loading .84 .88 .86

Note. N = 395. Loadings after removing Clim2 .

The composite reliability and AVE are .92 and .71, clearly above the critical values of .6 and .5. Table 5.10 below, at the end of this section, shows that the measure meets the Fornell–Larcker criterion as well. 5.4.2

Evaluation of the Structural Model

The main purpose of this study was to test the hypotheses on the effects of employee privacy developed in the previous chapter. Most generally, it was hypothesized that privacy has positive effects on both creativity and job satisfaction and that these effects are mediated by empowerment. To avoid omitted variable bias, several personal, job-related, and contextual influences were considered. These variables and the effects which link them form the path model depicted in Figure 4.1 above. For the reasons discussed earlier, Ringle et al.’s (2005) SmartPLS software package, which implements the variance-based PLS approach to structural equation modeling, was chosen to estimate the latent variables and the effects between them (Section 5.3.1). SmartPLS determines the weights and loadings for the formative and reflective indicator variables, which were used in the previous subsection to evaluate the measurement models, as well as the direct and total effects between the variables. (The total effect accounts for both the direct and indirect effects.) It also provides the figures necessary to determine the level of significance and effect size. The most important output can be found in Table 5.10, Figure 5.1 and Table 5.11. Table 5.10 lists for each variable the mean and standard

CP S Clim Crea EduL Emp JS JobExp MP S P riv P rivCom P rivEnv P rivInf P rivW lb

3.27 4.74 4.92 3.63 4.48 5.05 11.58 100.00 4.39 4.51 3.55 2.97 4.29

3.02 1.32 1.23 .62 .89 1.18 9.73 71.19 1.85 1.28 1.79 1.70 1.10

SD

— .15 .46 .14 .19 .04 −.07 .20 .05 .09 .11 .11 .01

1 3 4

(.81) .32 (.81) — .06 .26 .09 .60 .48 .00 .67 .12 −.01 −.07 −.33 .11 .21 .17 .02 .32 .07 .07 .27 .10 .12 .28 .09 .06 .25 .11 .19 −.04 −.10

2

— .63 .06 .32 .36 .32 .35 .24 .20

5 6

— −.07 .25 .49 .28 .36 .30 .28

Note. Square rooted AVE values appear in parentheses along the diagonal.

1 2 3 4 5 6 7 8 9 10 11 12 13

Mean 7

— −.09 −.09 −.11 −.17 −.15 −.03

Table 5.10 Relationships between the Latent Variables

— .35 .38 .55 .38 .27

8

— .53 .59 .39 .37

9

— .49 .44 .40

10

— .46 .31

11

— .25

12

5.4 Results of the Empirical Analysis 97

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5 Empirical Test of the Effects of Privacy

deviation as well as the correlations between the variables. Figure 5.1 depicts the path model, including both the variables and the path coefficients (i.e. the values of the direct effects). (Compared to Figure 4.1, the numbers of the hypotheses are replaced with the path coefficients.) Table 5.11 lists, in addition to these estimates, the hypothesized total and indirect effects (Hypotheses 2 and 2a as well as 3 and 3a) and the sizes of all effects (f 2 ).29 For the sake of completeness, the coefficients and levels of significance for indirect effects that were not hypothesized and total effects which differ from the direct effects are depicted in Table B.1 in Appendix B. Table 5.11 Evaluation of the Path Model Hypothesis/path

Coeff.

f2

1a 1b 1c 1d

P rivCom–P riv P rivEnv–P riv P rivInf –P riv P rivW lb–P riv

.26??? .40??? .06 .12???

.08 .20 .01 .02

2 2a 2b 3 3a 3b 4 5 6

P riv–Crea P riv–Crea P riv–Crea P riv–JS P riv–JS P riv–JS P riv–Emp Emp–Crea Emp–JS

−.06 −.12??? .06??? .30??? .26??? .04??? .14??? .41??? .29???

.02 .02 .16 .08 .08 .09 .03 .16 .09

CP S–Crea EduL–Crea JobExp–Crea EduL–Emp JobExp–Emp M P S–Emp Clim–Crea Clim–Emp Clim–JS

.35??? .17??? −.02 .07? .12??? .15??? .06 .52??? .42???

.19 .04 .00 .01 .02 .04 .00 .40 .20

7 8 9 10 11 12 13 14 15

Note. N = 395. ? p < .1.

???

p < .01.

29 SmartPLS determines the t-values for the direct and total effects only. See Appendix A.2 for the Sobel test, which was conducted to test the indirect effects for significance.

PrivWlb

PrivInf

PrivEnv

PrivCom

.12



.06

.40

.26

Priv

MPS

.14

.12



.15

.07

.26

Clim

.52

Emp

−.12

−.02

Figure 5.1: Test of the Hypothesized Model

JobExp

EduL

CPS



.29

.17



.06

.42

.41

.35

JS

Crea

5.4 Results of the Empirical Analysis 99

100

5 Empirical Test of the Effects of Privacy

From Table 5.11 and Figure 5.1 it is immediately clear that the estimates provide support for most hypotheses, whereas others have to be rejected. The first set of hypotheses (1a–1d) refers to privacy. The first-order constructs of control over communication, the environment, personal information, and the boundaries between work life and private life (P rivCom, P rivEnv, P rivInf , and P rivW lb) have been hypothesized to relate positively to the second-order construct (P riv). According to the estimates, this is true for P rivCom, P rivEnv, and P rivW lb, whereas the effect of P rivInf turns out to be non-significant. Hence, the data support Hypotheses 1a, 1b, and 1d, but contradict Hypothesis 1c. This result is unexpected and will be discussed in more detail in the last chapter. Empowerment (Emp), which was hypothesized to mediate the effect of privacy on creativity (Crea) and job satisfaction (JS), acts perfectly as expected. The direct effects of privacy on empowerment as well as of empowerment on creativity and job satisfaction are positive. These results support Hypotheses 4, 5, and 6. Likewise, the effect of privacy on job satisfaction is as hypothesized. The positive direct and indirect effects of privacy on job satisfaction provide support for Hypotheses 3a and 3b. In what is a typical example of partial mediation, the two effects combine to produce a positive total effect of privacy on job satisfaction, which corresponds to Hypothesis 3. The second surprising result is the negative direct effect of privacy on creativity, which refutes Hypothesis 2a. Privacy was hypothesized to have both a positive direct and indirect effect on creativity, but instead of being positive, the direct effect is negative. On the contrary, the indirect effect mediated by empowerment is significant, in keeping with Hypothesis 2b. The opposite direct and indirect effects offset one another, so that there is no significant total effect. As a result, Hypothesis 2, like Hypothesis 2a, has to be rejected. Technically, the effects of privacy on creativity are an example of mediation with suppression. Hypothesis 2 needs to be discussed at length in the final chapter as well. Most of the control variables act as they were supposed to. Creative potential (CP S) has a positive effect on creativity (Hypothesis 7), and so has the level of education (EduL) on creativity and empowerment (8 and 10). Job experience has a positive effect on empowerment (11), as does the motivating potential of the job (M P S) (12). The climate for creativity (Clim) has positive effects both on empowerment and job satisfaction (14 and 15). Contrary to Hypotheses 9 and 10, neither job experience nor the climate for creativity influence creativity directly. However, the total effect of the climate for creativity on creativity is significant, thanks to the strong

5.4 Results of the Empirical Analysis

101

indirect effect (see Table B.1 in Appendix B). Thus, the climate does have an effect on creativity, albeit indirect. The predictiveness of the model is fair. The coefficients of determination (R ), which can be calculated for the dependent variables, are .45 for privacy, .42 for empowerment, .40 for creativity, and .59 for job satisfaction. They thus fall in the range that Chin (1998) labels moderate (.33–.67). They might seem low at first because control variables account for both essential personal and contextual factors. However, the control variables are broad in scope and neither closely related to empowerment nor to job satisfaction. (On the contrary, they are closely related to creativity and therefore one might have expected the R2 value for creativity to be higher.) Still, the Stone–Geisser Q2 is positive for creativity, indicating predictive relevance. (Q2 applies to no other variable, because only creativity is a dependent variable and has a reflective measurement model.) 2

Table 5.11 shows that, as the values of R2 are moderate, this is true for the effect sizes (f 2 ) too. The effects of the first-order constructs of privacy on the second-order construct are medium (P rivEnv) or small (P rivCom, P rivW lb). Specifically, the effect of control over personal information on privacy is non-significant, and its size is negligible. Privacy is a predictor—albeit weak—of empowerment, creativity, and job satisfaction. (The indirect effect of privacy on creativity even reaches medium size.) Likewise, empowerment weakly predicts job satisfaction, whereas its effect on creativity is of medium size. Except for the climate for creativity, the control variables produce at best medium-sized effects. The effect of creative potential on creativity is medium. While the effects of the level of education and job experience on creativity are weak and negligible, the inverse applies to their effects on empowerment. Motivating potential also has just a small effect on empowerment. Only the effect of climate for creativity on empowerment qualifies as large. The climate for creativity weakly predicts job satisfaction, but does not predict creativity. This is unsurprising given that the former has already been found to act on the latter indirectly, while the direct effect is non-significant. The effect sizes are on the whole intuitive. While it was reasonable to expect that privacy would have effects on empowerment, job satisfaction, and creativity (though the latter expectation was not fulfilled), it was clear that these effects would not be large. The criterion variables depend on many factors; the control variables were especially included so that the effects of privacy, and consequently their sizes, would not be overestimated. By contrast, the effects of some control variables are surprisingly small.

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5 Empirical Test of the Effects of Privacy

Apart from the effect of the climate for creativity, which has already been discussed, one might have expected that motivating potential would predict empowerment better. While this is not the case, its effect exceeds that of privacy, and at any rate the job should be the more important driver of empowerment. The effects of education and job experience on creativity and empowerment are arguably ambiguous, which also explains the small effect sizes. While experienced employees can be more creative on account of their greater deposit of knowledge, they may also perceive their work to be more routine. At the same time, job experience correlates with seniority, so the effect on empowerment is unambiguous, because senior employees usually have more formal and informal rights than others. With regard to education, well-educated employees are normally given more challenging tasks, which require them to be creative; nevertheless, as both the level of education and the difficulty of the tasks rise, they do not necessarily perceive themselves to be more competent, let alone more self-determined and capable of having more impact. In conclusion, suppport for the hypothesized model is by and large satisfactory. Generally, the hypothesized effects were either corroborated or not corroborated, but in any case the results were not contrary to what was expected. The notable exception is the negative direct effect of privacy on creativity, which, however, was suppressed by the positive indirect effect. The effect of privacy on creativity and of control over information on privacy are the most interesting findings and will be further discussed in the last chapter. 5.4.3

Analysis of Moderating Effects

The description of the research setting and the participants shows that the people who were surveyed for this study are quite heterogeneous (Section 5.1). The sample includes male and female respondents of different ages, hierarchical levels, educational backgrounds, working either with a company or with a research institute. No hypotheses were developed on the effects of these factors related to the person, job, and context. Such hypotheses might have postulated that the job satisfaction of women depends more than that of men on privacy or that empowerment is more important for engineers than managers to be creative. Even though no hypothesizes were developed, it is necessary to account for such effects. If any of the aforementioned factors does make a difference, the estimates for the whole sample will conceal it and may even fail to reveal certain effects. More specifically, there may be opposite effects in

5.4 Results of the Empirical Analysis

103

different groups which cancel each other out, so that there seems to be no effect overall though it exists in individual groups. In practice, differences in the sizes and signs of the effects may call for different treatments of the groups; for instance, it might make sense for a company’s management to treat male and female employees differently with regard to particular issues. Hence, the investigation of differences between groups may help understand the results of the study and provide insights that are relevant for both further research and practical purposes. The considerable effect of the climate for creativity suggests that the context is a particularly influential factor. Of course, the context varies between and even within organizations and the climate for creativity actually reflects the context as it is perceived by the individual. Still, there might be a difference between the working conditions in the companies and the other organizations (i.e. the University and the research institutes), although many tasks of the employees of both types of organizations are similar. Thus, while work at universities or research institutes involves research, the survey did not even address the R&D departments of the companies. Moreover, neither universities nor the research institutes have to make profit, whereas the companies do.30 The type of organization may influence both the variables and the effects between them. The effects are particularly interesting, because, depending on whether there is a (strong) effect in a given organization type, the independent variable is a (strong) driver of the dependent variable. Technically speaking, the type of organization moderates the effects if they differ between employees of the companies and the other organizations. To test for such differences, the sample was divided into two subsamples and the path coefficients were estimated separately for each. Table 5.12 lists the path coefficients for both subsamples and, for comparison, those for the whole sample from Table 5.11. The last column displays the difference for each pair of coefficients and indicates whether it is significant (see Appendix A.3 for the test). According to the results displayed in Table 5.12, the type of organization influences the effects of the control variables rather than those this study focuses on (i.e. Hypotheses 1a–6). More particularly, the only significant difference in the effects of privacy is that empowerment turns out to be far more important for creativity in the research than in the business context. 30 Research Institutes A and B pursue both commercial and non-commercial goals. However, the commercial departments of Research Institute A did not participate in the survey and are poorly represented in the sample of Research Institute B. Moreover, profit-making informs the context of these institutes much less than that of companies.

104

5 Empirical Test of the Effects of Privacy Table 5.12 Type of Organization as a Moderator Hypothesis/path

Organization type

Total Bus.

Res.

???

???

???

∆

1a 1b 1c 1d

P rivCom–P riv P rivEnv–P riv P rivInf –P riv P rivW lb–P riv

.26 .40??? .06 .12???

.21 .38??? .02 .23???

.31 .41??? .19??? .09

.10 .03 .17??? .14??

2 2a 2b 3 3a 3b 4 5 6

P riv–Crea P riv–Crea P riv–Crea P riv–JS P riv–JS P riv–JS P riv–Emp Emp–Crea Emp–JS

−.06 −.12??? .06??? .30??? .26??? .04?? .14??? .41??? .29???

−.03 −.08 .05?? .30??? .24??? .06?? .19??? .25??? .32???

.05 −.09 .14??? .21?? .12 .10?? .22??? .66??? .44??

.08 .01 .09?? .09 .12 .04 .03 .41??? .12

CP S–Crea EduL–Crea JobExp–Crea EduL–Emp JobExp–Emp M P S–Emp Clim–Crea Clim–Emp Clim–JS

.35??? .17??? −.02 .07? .12??? .15??? .06 .52??? .42???

.35??? .27??? −.03 .01 .04 .07? .14? .54??? .41???

.29??? −.03 −.13?? .17??? .06 .42??? −.09 .36??? .36??

.06 .30??? .10? .16??? .02 .35??? .23??? .18??? .05

7 8 9 10 11 12 13 14 15

Note. Bus. stands for business (n = 284), Res., for research (n = 147). ? p < .1. p < .05. ??? p < .01.

??

Moreover, it is noteworthy that the direct effect of privacy on job satisfaction is not significant in the research subsample while it is highly significant both in the business subsample and the overall sample. Still, the strong indirect effect of privacy on job satisfaction (3b) is sufficient to make the total effect significant even in the research subsample (3). Control over both communication and the environment are drivers of privacy irrespective of the context; however, the subsamples differ in the effects of control over personal information and the work–life boundaries. It seems that the extent to which employees of the companies can set boundaries to their work life is relevant to their privacy while how their company deals with personal information is of lower interest for them. For

5.4 Results of the Empirical Analysis

105

employees of the University and the research institutes, the reverse is true. The effect of control over personal information is particularly important since it is not significant for the whole sample (Hypothesis 1c). More specifically, this finding implies that privacy of information, although its case may be overstated, remains an important driver of privacy at least in certain organizations. While this difference was not expected, is not implausible either. On the one hand, concerns about information privacy have become a matter of public interest only recently with the rise of social networks, and are probably prevalent especially among the young and well-educated. The respondents from the University and the research institutes are both well-educated and younger than those of the whole sample as the above description of the data showed (Section 5.1, in particular Tables 5.2 and 5.3); it is therefore unsurprising that they are more sensitive about issues related to privacy of information. Likewise, the balance between work life and private life may be more important to people when they grow older and have a family. It probably matters generally less to researchers, who are often supposed to be driven by intrinsic rather than extrinsic motivation, that is, draw their motivation from their work.31 The differences between the effects of the control variables refer to the level of education (Hypotheses 8 and 10), job experience (9), motivating potential (12) and climate for creativity (13 and 14). The last three effects are, again, quite plausible. It seems that in the research context empowerment depends much more on the job (12) and less on the climate (14) than in the business context. This is perfectly in line with the idea that for researchers their work is the essential source of (intrinsic) motivation. Unlike in the research sample, the climate does directly influence creativity in the companies (13), which makes it an important lever from the managerial viewpoint. This effect is noteworthy as it is suppressed in the overall sample. The negative effect of job experience on creativity in the research sample, which not only does not support but is actually contrary to Hypothesis 9,

31 Accordingly, Age should moderate the effect of P rivW lb on P riv, and both Age and EduL, the effect of P rivInf on P riv. The data support the first two conjectures: the interaction term of P rivW lb × Age has a positive effect of .37, and that of P rivInf × Age, a negative effect of −.33, on P riv (both coefficients are significant with p < .1). Thus, the effect of P rivW lb on P riv increases, while that of P rivInf decreases in Age. Conversely, the effect of P rivInf × EduL on P riv is not significant. It should be noted, however, that university graduates are over-represented, accounting for about 70% of the sample, which may affect the estimation of the interaction effect. Moreover, the effects of continuous moderators are generally hard to pinpoint with formative measurement models (Schloderer et al., 2009).

106

5 Empirical Test of the Effects of Privacy

appears surprising at first. (It should be noted, however, that in the whole sample, there is no significant effect either.) However, it is likely that high-ranking researchers (e.g., tenured professors at university) are underrepresented in the sample so that respondents with more job experience are, in fact, clerical staff, whose tasks are not more demanding despite their long experience. Finally, employees of the University and the research institutes seem to perceive their education as empowering rather than fostering their creativity, while the opposite is true for employees of the companies (Hypotheses 8 and 10). In the overall sample, both effects are significant. With regard to gender, the effects of the first-order constructs of privacy show a pattern similar to that of the type of organization, as can be seen from Table 5.13. It seems that women are more concerned about having control over personal information (Hypothesis 1c), whereas for men it is more important to control the boundaries between their work life and private life (1d). This does not mean, however, that work–life balance does not matter to women but that it is less important for their privacy whether they can control the work–life boundaries. Of course, the data do not reveal the reasons. For example, women might adjust their expectations of exerting control over these boundaries and thus balance achieved and desired privacy more easily. While the main effects do not significantly differ (Hypotheses 2–6), some effects are significant just in the male subsample (namely 2b, 3, 3b, and 4). Generally the direct effects are larger in the female subsample (2b and 3b), and privacy has no significant effect on empowerment (4). The effects of the level of education show the reverse pattern. While it enhances men’s creativity, it fosters women’s empowerment (8 and 10). Job experience increases both men’s and women’s empowerment, but reduces women’s creativity, the explanation for which is probably similar to that given above for the research subsample. Finally, the climate for creativity seems to be generally more important for men than women, although the difference is significant only for its effect on empowerment. The field of education may be a moderator for a number of reasons. It is both an indicator and cause of differences between respondents, as people tend to opt for the field which best fits them personally, and in each field, specific skills are trained. It is even a proxy for the tasks, as, for instance, blue-collar workers have typically attended a technical school, white-collar workers, a business school. In fact, there are some differences

−.11 −.15?? .04 .09 .33??? .02 .09 .42??? .25?? .33??? .06 −.15?? .19?? .09? .21??? .03 .40??? .39???

.40??? .35??? .17??? .20??? .02 −.02 .07? −.03 .12??? .13??? .15??? .14??? .10? .06 .57??? .52??? ??? .42 .46???

CP S–Crea EduL–Crea JobExp–Crea EduL–Emp JobExp–Emp M P S–Emp Clim–Crea Clim–Emp Clim–JS

∆

.03 −.01 .04 .12? .14? .03 .12? .35??? .25??

−.14?? −.21??? .07? .17?? .36??? .06 .17?? .43??? .34??? .36??? .30??? .03?? .06 −.01 .14??? .05 .56??? .54???

.19??? .36??? .02 .24???

Eng.

.21??? .50??? .16?? .05

Bus.

.33??? .07 .14?? .15?? .17?? −.15?? .22??? .05 .08 .04 .14?? .07 .07 .06 .17?? .54??? .31??? .07

.11 .08 .02 .10 .12 .03 .10 .08 .05

.02 .00 .18??? .09

Field of education ∆

.03 .15? .18 .01 .09 .00 .01 .02 .21?

.17? .20?? .03 .05 .22? .03 .05 .08 .09

.01 .14? .14? .19??

.41??? .10? −.08 .08 .11? .08? .21?? .55??? .31???

−.01 −.08 .06 .17?? .31??? .06 .17?? .36??? .33???

.44??? .17?? .17?? .26???

Yes

.32??? .18??? −.02 .03 .05 .14??? .03 .51??? .48???

−.08 −.14??? .05 .14??? .25??? .04 .14??? .38??? .26???

.20??? .47??? .02 .13???

No

∆

.09 .08 .06 .05 .06 .06 .18?? .04 .17?

.07? .06 .01 .03 .06 .02 .03 .02 .07

.24?? .30??? .15?? .13?

Leadership position

Note. The sample sizes are 251 and 144 (men and women); 129 and 135 (business and engineering); 111 and 284 (leader and no leader). ? p < .1. ?? p < .05. ??? p < .01.

7 8 9 10 11 12 13 14 15

2 2a 2b 3 3a 3b 4 5 6

.00 −.06 −.12??? −.07? .06??? .06??? ??? .30 .19??? .26??? .21??? .04?? .06?? .14??? .19??? .41??? .34??? .29??? .30???

Women

P riv–Crea P riv–Crea P riv–Crea P riv–JS P riv–JS P riv–JS P riv–Emp Emp–Crea Emp–JS

Men

Gender .28??? .41??? .19??? .06

P rivCom–P riv P rivEnv–P riv P rivInf –P riv P rivW lb–P riv

1a 1b 1c 1d

.26??? .40??? .06 .12???

Total

.26??? .40??? .02 .15???

Hypothesis/path

Table 5.13 Gender, Education, and Leadership as Moderators

5.4 Results of the Empirical Analysis 107

108

5 Empirical Test of the Effects of Privacy

linked to the field of education, as Table 5.13 illustrates.32 Thus, control over personal information matters more to graduates from business than those from technical schools (Hypothesis 1c) and the opposite is true for control over the work–life boundaries (1d). In addition, control over the environment matters more to business-school graduates (1b). In the case of graduates from technical schools and who perform technical tasks, the environment depends usually on the task, and therefore it may be natural for them to adapt their expectations. The effects differ only slightly depeding on the field of education. The main effects are more pronounced for graduates from business than those from technical schools. Thus, the effects of privacy on creativity (Hypotheses 2–2b) as well as job satisfaction (3 and 3a) are significant for the former, whereas for the latter only that on job satisfaction is significant, and still smaller. In both groups, creativity grows in the level of education (8), and job satisfaction, in the climate for creativity (15); however, the effects are stronger for employees with a technical background. Hence, the level of education (e.g., whether they have received vocational training or studied at university) seems to be more important for how creative they are. Interestingly, creativity increases in job experience for graduates from technical schools, but decreases for business-school graduates (9). Table 5.13 finally contrasts the effects depending on whether respondents hold a leadership position and thus have managerial responsibilities. Again, the differences are small. Control over communication, personal information, and the work–life boundaries (Hypotheses 1a, c, and d) appear to be significantly more important to employees with managerial responsibilities (who are arguably higher in hierarchy than those without such responsibilities), whereas control over the environment is less so (1b). In particular, both the effects of control over personal information and the work–life boundaries are significant in this group (1a and d). Only for employees with managerial responsibilities does creativity directly depend on the climate (13); by contrast, their job satisfaction does not so much depend on climate like that of other employees (15). In conclusion, the type of organization, gender, field of education, and leadership position do not make much difference. As far as differences occur, 32 The sample for the field of education is limited to 264 datasets, with 129 respondents having an educational background in business, and 135, in engineering. The sample is smaller because it includes only two fields of education and because certain values were missing, as mentioned above (see Footnote 3 in Section 5.1.2). For the other fields of education, the subsample sizes are too small to estimate the effects. If the fields are lumped together into the categories “Arts” (198 cases) and “Sciences” (150), that is, a total of 348 datasets, similar results are obtained.

5.4 Results of the Empirical Analysis

109

they concentrate on the effects of the first-order constructs of privacy and to the control variables, the former being especially relevant for this study. Specifically, for all moderators, control over personal information has a significant effect in one group, though it has not in the overall sample. By contrast, the main effects are stable across groups. In particular, privacy does not have a positive total effect on creativity in any group. Both results complement the theoretical and practical implications of this work, which will be discussed in the last chapter. 5.4.4

Test for Common Method Variance

As mentioned above, the data for this study were collected only from employees and not from multiple sources for several reasons (Section 5.2). Most importantly, matching the data would have reduced the sample size and probably biased the results, as it would have discouraged from participating especially those employees who are sensitive about privacy issues. However, relying on a single source may also cause biases (Podsakoff et al., 2003). Therefore, a number of precautions were taken to prevent so-called “commonmethod biases.” According to Podsakoff et al., the items are themselves one possible source of biases, as they may suggest certain answers to the respondents. To avoid item-related biases, the items were worded neutrally and unambiguously. Apart from the items on job satisfaction, there are hardly any items which are likely to invite respondents to give “socially desirable” answers. The items also varied in format, even though most of them were statements to be rated on seven-point Likert scales. In the case of motivating potential, positive and negative items were combined. In the case of job satisfaction, the item stems were questions and the anchors of the answer scale differed from the other measures. Creative potential was even measured with a checklist. A second source of biases is the context of the items. To prevent biases related to position or order, all items were randomized, and in the case of privacy, even the entire scales that correspond to the four first-order constructs were randomized. Items that capture different constructs were not intermixed to avoid artificial inter-construct covariance. Of course, respondents may have had their own “implicit theories” about how privacy, creativity, and job satisfaction are related; however, the effects looked for were never mentioned, and the length of the questionnaire and the manner in which the measures of the three main variables—privacy, creativity, and job satisfaction—were separated (see Section 5.1.3) made answering according to those theories difficult.

eigenvalue

110

5 Empirical Test of the Effects of Privacy

15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 0

5

10

15

20

25

30 35 Factor #

40

45

50

55

60

65

Figure 5.2: Test for Common Method Variance

To test for common-method biases, Harman’s single-factor test was conducted (Podsakoff et al., 2003). This test assumes that common method variance is reflected in a single—or at least one salient—factor, which explains most of the covariance among the measures. To test for such a factor, an exploratory factor analysis was conducted for all items pertaining to latent variables (including the CP S 33 ). The unrotated factor analysis solution yields, depending on the criterion, three to twelve factors. Figure 5.2 depicts the scree plot, where the forth factor marks the “elbow.” This suggests that there are three factors according to the scree test. The figure also shows that twelve factors have an eigenvalue of at least 1. The most variance accounted for by a single factor is 21.9%; the three most important factors account together for 42.2%. These results suggest that common-method variance is not a major problem in this study, because there is neither a single nor one particularly salient factor that accounts for most of the variance.

33 For a factor analysis, items must be scaled at least at interval level, strictly speaking. As the single CP S items are categorical, the score was used instead, which comes technically closer to this requirement.

Chapter 6 Discussion of the Results 6.1

Summary of the Results

Most people cherish privacy and are outraged when it is violated. From a business viewpoint, however, it is not so obvious why privacy deserves protection. Its “opponents” readily provide arguments which fit the particular problem at hand, whereas its “proponents” are content to declare it a fundamental right or point to diffuse general psychological or social needs for privacy. In the business context, fundamental rights and advantages for society as a whole are not a strong case in the face of immediate gains (or reduced losses) for the individual or company. In a way, the pros and cons of privacy are incommensurable, and the cons are more intuitive. The argument that there is a right to privacy, whether because privacy is intrinsically valuable or closely linked with what is recognized as a fundamental value (such as freedom or human dignity) is meritorious. That individual persons and society as a whole need privacy is suggestive. Nevertheless, it is worth adopting the logic of business and examine possible benefits of respecting employees’ privacy. If the benefits of protecting privacy outweigh those of restricting it, there is a strong case—a business case, indeed—for employee privacy. The purpose of this study was to examine whether privacy is beneficial from a business viewpoint. More precisely, it was tested whether employees who are able to balance their achieved and desired privacy feel more empowered and, as a result, are more satisfied with their job and more creative. Both job satisfaction and creativity are probably beneficial; that is, it can be assumed that companies want their employees to be empowered, satisfied, and creative. At least job satisfaction and creativity are generally acknowledged to be intrinsic or instrumental goals for companies. If privacy contributes to these goals, it is clear that companies which share these goals—and arguably many do so—should consider granting their employees privacy. To test whether privacy does influence empowerment, creativity, and job satisfaction, a survey was conducted among the employees of seven organizations in the private as well as the public sector. The data collected

112

6 Discussion of the Results

from these employees support the hypotheses partially. While privacy was found to influence both empowerment and job satisfaction, overall it has no effect on creativity. These findings are in line with results of past research (e.g., Alge et al., 2006; Maher and von Hippel, 2005). At the same time, this work contributes to research by extending these earlier findings in at least four respects. First, a concept of privacy was developed which integrates the literature on privacy. The different approaches that link privacy and control—that is, privacy as having control as opposed to not being controlled by others—were shown to be actually compatible in that not being controlled by others implies having control over oneself; hence, privacy can be said to result both from and in control. It was argued that control over certain behaviors determines privacy and can therefore be used as a measure of privacy; also, that people do not maximize but optimize their privacy; finally, that behavior depends more on perceived rather than desired privacy. Second, this concept was translated into a measure of overall privacy. To this end, four sets of items were developed and validated, each of which corresponds to one of the four types of control that are causal to privacy. This measure is entirely new and is intended to eliminate several flaws encountered in measures used in earlier research. Those measures focused on single aspects of privacy (notably privacy of information or physical privacy), were founded on a “the more the better” assumption, and measured objective privacy, without, however, controlling always for individual differences in perception. Third, a novel model was conceived and tested, which to some extent reproduced past models and allowed the results obtained here to be related to prior research, but at the same time included new hypotheses. In particular, most important factors other than privacy—personal, contextual, and jobrelated—were controlled for in order to avoid overestimating the effects of privacy. It is noteworthy that the effects of privacy on empowerment and job satisfaction (and of empowerment on creativity and job satisfaction) can still be detected when accounting for these other factors. Fourth, variance-based structural equation modeling was used to estimate the effects, whereas earlier research either relied on hierarchical regressions or covariance-based structural equation modeling. While the variance-based and covariance-based approaches are complementary, the former aims rather at prediction, whereas the latter aims at theory testing. Therefore, the results of this study can be taken to identify the drivers of employee reactions (such as being empowered, creative, or satisfied) to their company’s privacy

6.2 Limitations and Implications for Research

113

policy. This is particularly interesting for companies, which wish to influence and motivate employee behavior.

6.2

Limitations and Implications for Research

This study analyzed the impact of employee privacy in the business context, focusing on the kind of impact that might be desirable from the company viewpoint. Overall, it answered some of the questions it set out to consider, left others open, and even raised new ones. It has several limitations and points to a need for further research both on the conceptual and technical level. On the conceptual level, further research is desirable in at least three respects. First, the study corroborates results of earlier research, including that privacy does not have a positive effect on creativity. Instead, a positive indirect effect was found to suppress a negative direct effect. Nevertheless, the positive effect of privacy on empowerment suggests that privacy has other indirect effects that are of interest to companies.1 Empowerment is known to have a number of positive consequences other than creativity and job satisfaction, such as commitment, performance, or OCB (Seibert et al., 2011). The privacy–empowerment link is therefore a promising starting point both for further theoretical and empirical research on the impact of privacy in the business context. Second, although privacy does have some positive effects, the critical point is whether these positive effects of privacy outweigh the negative effects it may have.2 However, analyzing the negative impact and, a fortiori, the comparison of positive and negative impact were beyond the scope of this study and were therefore intentionally excluded from it. While this seems legitimate at this stage of research, both the positive and negative effects should be considered and possibly compared in future research. Interestingly, there is little empirical work on the negative effects of privacy either. Third, this study focused on some effects of privacy but did not examine its relationship with other values and norms, even though the analysis

1 It should be noted that the positive effect of privacy on empowerment holds in the presence of control variables and is quite stable across organization types, hierarchy levels, genders, and educational backgrounds (see Section 5.4.3). 2 Some economists cited in the first chapter emphasize the negative effects (Section 1.2). Note, for example, the negative direct effect on creativity, which is stable when moderators are taken into account.

114

6 Discussion of the Results

involved testing a few relationships and assuming others.3 Yet relationships and conflicts between values and norms are crucial elements of an analytical approach to business ethics (e.g., Küpper, 2006b, 2005a). A company may have multiple values or goals, and there may be interactions between these that need to be considered. Thus, the effects of privacy in an atmosphere of trust may be quite different from its effects where the employer–employee relationship is based on the exchange of performance for pay. On the conceptual level, this research should therefore be extended to include other positive effects of privacy, compare its positive and negative effects, and examine relationships and conflicts between privacy and other values and norms. At the same time, the two highly unexpected and in a way remarkable results call for a refinement rather than extension of the present analysis on the technical level. First, it is surprising that control over information does not seem to be related to privacy, given that privacy is often almost identified with the protection of private data. Second, it is puzzling to see this study refute that privacy is—on balance—related to creativity. The non-significance of the link between control over information and privacy can be interpreted in two ways. One possible conclusion is that the respondents have known too little about their organization’s privacy policy to give meaningful answers. Since there was no “Don’t know” option, there may be a central tendency error. Early feedback suggested that this problem might arise and, as a result, the guide to the items on privacy of information that was included in the questionnaire was modified (see Section 3.4). However, the descriptive data do not support this interpretation, since the mean and standard deviation of control over personal information are similar to those of the other first-order constructs (see Table 5.10). Another conclusion is that people are not as concerned about control over information as it is commonly thought. The other three first-order constructs—control over communication, the environment, and the work–life boundaries—act in a way as control variables that prevent overestimating the effect of control over personal information on privacy. In other words, one may conclude that the case of control over personal information is rather overstated. This interpretation is provocative but not implausible. For instance, many people disclose their data quite readily in e-commerce and social networks, which suggests that they do not worry so much about

3 It was tested whether privacy is related to empowerment, creativity, and job satisfaction. At the same time, it was assumed that companies value empowerment, creativity, and job satisfaction.

6.2 Limitations and Implications for Research

115

privacy of information as they are supposed to or pretend to do (e.g., Berendt et al., 2005; Hui et al., 2007). This observation notwithstanding, it should be noted that control over personal information has a significant effect for particular groups of respondents, as the analysis of moderating effects revealed (Section 5.4.3). Employees of the university and the research institutes, female employees, business-school graduates, and employees with managerial responsibilities are concerned about the privacy of information; employees of the companies, male employees, graduates from technical schools and probably performing technical tasks, and employees without managerial responsibilities are rather not. There is some evidence that the age moderates this effect (see Footnote 31 in Section 5.4.3). Even though the effect is not reflected in the estimate for the whole sample, control over personal information seems to matter to many employees. These results have at least two implications. First, future research should explore how privacy of information in particular relates to privacy in general. This question has yet to be tackled, as most research has so far tended to examine either physical privacy or privacy of information, but not how these interrelate, let alone how they relate to privacy as a whole. Second, the “disaggregation” of privacy in more specific constructs has probably concealed its true relevance, because the whole might produce more impact than the parts. It would be worth abandoning the piecemeal approach and prioritizing research into the effects of privacy as an overarching concept over research into isolated types of privacy, such as privacy of information or architectural privacy. That privacy does not have a positive effect on creativity is less surprising, as it is consistent with earlier research (in particular, Alge et al., 2006). In contrast to control over personal information, this result is stable even when moderators such as type of organization, gender, field of education, or managerial responsibilities are taken into account; in none of those cases was there a positive total effect (see Section 5.4.3). Besides confirming earlier research, this study adds a new finding, namely that the negative direct effect is suppressed by a positive indirect effect. Moreover, the specific design of the privacy measure points to the need, as well as to opportunities, for further research. The scale used in this study measured how well employees can balance their achieved and desired privacy. The interpretation is thus that in order to be most creative, employees should have either less or more privacy than they desire rather than be able to perfectly balance achieved and desired privacy. This finding seems implausible at first. However, studies in related

116

6 Discussion of the Results

fields have arrived at similar conclusions. For instance, Zhou and George (2001) and George and Zhou (2002) found that dissatisfaction and bad mood can enhance creativity, even though one might suppose that the opposite is true. Of course, this does not mean that employees are most creative when they are least satisfied, but that the maximum satisfaction and creativity do not coincide. While optimum privacy does not produce the maximum creativity, it is not clear how the two are actually related. The easy answer is that employees are most creative when they have either too much or too little privacy. However, the true answer is probably more complex. First, while employees are not most creative when they can perfectly balance achieved and desired privacy, it is unlikely that they are most creative, when the imbalance is too pronounced. Second, creativity may interact with personal or contextual factors or both. It is possible that certain people need a thorn in their flesh to be creative, whereas others need to be left alone. Certain tasks require teamwork, others do not. The same individual may need encouragement at certain times but not at others, and so on. In conclusion, the question of how privacy and creativity are related, has yet to be answered. It is not final and conclusive that they are unrelated, even though no effect was found in two studies. Their relationship may simply be too complex as to be easily elucidated. (Even the suppression of the direct and indirect effect was not obvious before.) As the measure used in this study was already quite sophisticated, the investigation of interaction effects seems to be especially promising.

6.3

Managerial Implications

Employees are an important group of stakeholders and an invaluable asset for modern businesses. Companies can reach their goals more easily if their employees are intrinsically motivated to pursue them. Intrinsic motivation, or empowerment, depends on giving employees freedom and relaxing controls. Granting employees privacy means to relax controls and give them freedom, which is in line with the modern paradigm of management proposed by Thomas and Velthouse (1990). The focus of this study was therefore on the effect of privacy on empowerment, as well as creativity and job satisfaction. Both creativity and job satisfaction are goals that are probably shared by many companies, either per se or because they are positively related to higher-ranking goals. In particular, creativity is a necessary condition for innovation, and successful business goes along with satisfied employees, although it is not perfectly

6.3 Managerial Implications

117

clear how job satisfaction and performance are related. Empowerment, in turn, is not only related to creativity and job satisfaction, but also to other desirable behaviors. Implications for management derive from the support that the empirical analysis offers to these arguments. First, privacy was found to have a positive effect on job satisfaction. More precisely, the better employees are able to achieve as much privacy as they desire in their job, the more they are satisfied with it. Hence, companies should make sure that employees are able to balance their achieved and desired privacy. This does not necessarily mean to increase achievable privacy, because employees can also adapt their expectations. However, they will do so more easily if they know about and understand the restrictions. Invasions of privacy are more acceptable if employees are given advance notice and explained the reasons (see Section 2.2). Second, this study shows, in keeping with earlier research, that privacy does increase empowerment or intrinsic motivation. Empowerment is not an end in itself from the company perspective, but produces outcomes which are desirable, such as OCB. Hence, privacy may help produce “those gestures (often taken for granted) that lubricate the social machinery of the organization” (Bateman and Organ, 1983, p. 588), but which are hard to pinpoint. The effects of privacy on empowerment and job satisfaction are quite stable across different types of organizations and groups of employees, although they differ in detail. Overall, they provide a strong argument for granting employees privacy. On the contrary, the relationship between privacy and creativity is ambiguous. It is clear that an optimum balance between achieved and desired privacy does not coincide with maximum creativity. Note, however, that this does not mean that employees should generally be given less (or more) privacy than desired. Moreover, the effect of privacy may depend on a number of other factors not considered in this study, related to the person, task, or situation. It would therefore be premature to conclude that privacy and creativity are on balance independent, although the evidence refutes a direct effect. It must be left to future research to further elucidate the privacy–creativity link. Privacy is one factor among many that influence the success of companies. While it is certainly not the main driver, it is still relevant and merits consideration. In particular, when trading off the benefits and costs of restrictions of privacy, it is worth thinking about the (hidden) benefits— such as satisfaction or empowerment—which will be won or lost together with privacy.

Appendix A Statistics and Tests A.1

Test of psa and csv for Significance

Anderson and Gerbing (1991) assume that the probability for an item to be randomly assigned to the correct rather than any other construct is .5. Let p denote the level of significance (e.g., .01, .05, and .1), and mp , the critical number of correct assignments. If nc , the number of correct assignments, is such that P (nc ≥ mp ) < p, then one may conclude (with probability of error p) that nc is not random. For a given mp , the critical values of psa and csv are p¯sa =

mp N

and c¯sv =

2mp − 1. N

Table A.1 lists mp , p¯sa , and c¯sv for N = 21 and the typical levels of significance. Table A.1 Critical Values for psa and csv p

mp

p¯sa

c¯sv

.1 .05 .01

14 15 17

.67 .71 .81

.33 .43 .62

Note. N = 21.

A.2

The Sobel Test for Mediation

The SmartPLS software package bootstraps the data to determine tvalues for the direct and total effects. However, no test statistic is provided for the indirect effects. Instead, the procedure proposed by Sobel (1986) can be used to test for mediation (Schloderer et al., 2009).

120

A Statistics and Tests

Sobel calculates from the direct effects and their standard errors a test statistic z, which is approximately normally distributed. If z differs significantly from zero, the indirect effect is significant. Let a and b denote the coefficients of the direct effects which together make up the indirect effect (e.g., the effects of P riv on Emp and of Emp on Crea), and sa and sb their standard errors. Then the indirect effect (of P riv on Crea) is ab, and Sobel’s test statistic, z=p

A.3

ab ∼ N (0, 1) . + a2 s2b

b2 s2a

Test for Moderation

The tests for moderation in this study are limited to multi-group comparisons because all presumptive moderators were dummy variables. Depending on whether the variance of the estimates to be compared are equal or unequal,1 independent 2-sample t-tests for unequal sample sizes and equal or unequal variances were conducted (Schloderer et al., 2009; Nitzl, 2010). Let i, i ∈ {1, 2}, denote the subsample; ni , the size of subsample i; xi , the estimate (e.g., of the coefficient) for subsample i; and si , the standard error of xi . Then the test statistic is for equal variances r n1 n2 x1 − x2 ∼ tn1 +n2 −2 , t= S n 1 + n2 2

S2 =

2

(n1 − 1) s21 + (n2 − 1) s22 n1 + n2 − 2

is the pooled variance of the two subsamples. For unequal variances, the test statistic is t= q

x1 − x2 n1 −1 2 n1 s1

+

n2 −1 2 n2 s2

∼ tv ,

and the degrees of freedom, v, are  v=

1

2

n2 −1 2 n1 −1 2 n1 s1 + n2 s2 n1 −1 4 4 s1 + n2n−1 2 s2 n21 2

− 2.

An F -test was used to check whether the variances are equal (Toutenburg and Heumann, 2008, pp. 139–41).

Appendix B Tables B.1

Correlations between the Privacy Items

Table B.2 below (p. 122) lists the correlations among the privacy items as well as the correlation of each item with the corresponding first-order construct.

B.2

Indirect and Total Effects

Table B.1 lists indirect and total effects that were not hypothesized to complement Table 5.11. Table B.1 Further Indirect and Total Effects Path

Coeff. ???

Clim–Crea .26 Clim–JS .57??? EduL–Crea .20??? EduL–JS .02 JobExp–Crea .02 JobExp–JS .03?? M P S–Crea .06??? M P S–JS .04??? P rivCom–Crea −.01 P rivCom–Emp .04??? Note. N = 395. ? p < .1.

??

Path

Coeff.

P rivCom–JS .08??? P rivEnv–Crea −.02 P rivEnv–Emp .05??? P rivEnv–JS .11??? P rivInf –Crea .00 P rivInf –Emp .01 P rivInf –JS .02 P rivW lb–Crea −.01 P rivW lb–Emp .02? P rivW lb–JS .04?? p < .05.

???

p < .01.

P rivCom1 P rivCom2 P rivCom3 P rivCom4 P rivCom5 P rivCom6 P rivEnv1 P rivEnv2 P rivEnv3 P rivEnv4 P rivEnv5 P rivInf1 P rivInf2 P rivInf3 P rivInf4 P rivW lb1 P rivW lb2 P rivW lb3 P rivW lb4

.53 .29 .36 .48 .39 .26 .27 .28 .19 .23 .15 .23 .13 .15 .08 .14 .05 .20 .15 .18 .11 .18 .09 .16 .08 .14 .09 .14 .08 .31 .20 .16 .22 .04 .07 .09 −.08

2

.64 .43 .30 .26 .22 .15 .17 .18 .25 .24 .23 .19 .22 .20 .20 .03 .03

3

.65 .25 .29 .22 .21 .19 .24 .33 .19 .22 .19 .19 .36 .22 .16 .12

4 6 7 8 9

.80 .65 .77 .34 .46 .85 .31 .41 .78 .79 .34 .44 .72 .79 .85 .15 .31 .54 .58 .53 .38 .46 .60 .55 .56 .36 .40 .37 .34 .34 .34 .39 .43 .39 .41 .34 .39 .41 .39 .39 .36 .39 .41 .38 .42 .23 .17 .16 .09 .10 .16 .06 −.01 −.05 −.01 .00 −.12 −.08 −.12 −.13 .17 .28 .30 .37 .42

5 11

12 13 14 15

.82 .44 .74 .21 .32 .85 .30 .35 .83 .93 .28 .31 .76 .83 .89 .30 .34 .71 .80 .79 .95 .13 .17 .14 .16 .15 .12 .06 .04 .07 .03 .04 .03 .00 −.06 −.06 −.03 −.05 −.09 .25 .27 .15 .25 .25 .29

10

17

18

.82 .25 .47 .33 .13 .41 .03 −.09 −.08

16

.47

—

Note. The correlations of the item scores and the latent variable scores of the first-order constructs appear on the diagonal.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1

Table B.2 Correlations between the Privacy Items

122 B Tables

Appendix C German Questionnaire Befragung zu Privatsphäre und Kreativität am Arbeitsplatz Sehr geehrte Dame, sehr geehrter Herr, vielen Dank für Ihr Interesse an dieser Untersuchung des Instituts für Produktionswirtschaft und Controlling der Ludwig-Maximilians-Universität München (www.controlling.bwl.lmu.de) zu Privatsphäre und Kreativität am Arbeitsplatz. Ziel unserer Untersuchung ist es herauszufinden, ob zwischen Privatsphäre, Kreativität und Arbeitszufriedenheit ein Zusammenhang besteht. Unter Privatsphäre verstehen wir dabei die Möglichkeit, am Arbeitsplatz für sich zu sein, über Dinge, die einen persönlich betreffen, selbst zu bestimmen und über die Grenze zwischen Berufs- und Privatleben entscheiden zu können. Auf den folgenden Seiten bitten wir Sie jeweils, Aussagen zu bewerten, indem Sie angeben, wie gut diese auf Sie zutreffen. Es geht uns immer um Ihre persönliche Einschätzung; deshalb gibt es keine „richtigen“ oder „falschen“ Antworten. Geben Sie einfach diejenige Antwort, die Ihre Einschätzung am besten widerspiegelt. Sollte Ihnen die Auswahl schwer fallen, antworten Sie nach Ihrem ersten Gefühl. Bitte beantworten Sie alle Fragen, denn wir können nur vollständig ausgefüllte Fragebögen verwenden. Sie werden dazu 10–15 Minuten benötigen. Bearbeiten Sie den Fragebogen zügig. Am Ende der Befragung laden wir Sie zu einem Gewinnspiel ein. Wir verlosen Gutscheine im Wert von 100, 50 und 25 Euro. Sämtliche Angaben, die Sie machen, werden absolut vertraulich behandelt. Ihre Antworten sind anonym und können Ihnen nicht persönlich zugeordnet werden. Die Daten werden im Rahmen eines Forschungsprojektes erhoben und dienen ausschließlich wissenschaftlichen Zwecken. Falls Sie Fragen haben, steht Ihnen Herr Andreas Ostermaier jederzeit gerne zur Verfügung. Sie erreichen ihn telefonisch unter der Nummer +49 89 2180 2302 oder per E-Mail unter der Adresse [email protected]. Schon jetzt danken wir Ihnen sehr herzlich für Ihre Mitarbeit! Dipl.-Kulturw. Andreas Ostermaier, MBR

Prof. Dr. Dr. h. c. Hans-Ulrich Küpper

Wissenschaftlicher Mitarbeiter und Projektverantwortlicher

Vorstand des Instituts für Produktionswirtschaft und Controlling

124

C German Questionnaire

Beschreibung Ihrer Tätigkeit Die folgenden 10 Aussagen dienen dazu, eine Tätigkeit allgemein zu beschreiben. Bitte geben Sie an, wie gut jede dieser Aussagen auf Ihre Tätigkeit zutrifft. Falls Ihre Arbeit verschiedene Aspekte umfasst, beziehen Sie sich bitte bei Ihren Antworten auf Ihre Arbeit insgesamt.1 SV +

Meine Arbeit verlangt von mir den Einsatz vieler komplexer oder anspruchsvoller Fertigkeiten.

SV −

Meine Arbeit ist ganz einfach und meine Aufgaben wiederholen sich ständig.

T ID+

Meine Arbeit gibt mir die Möglichkeit, Arbeitsvorgänge ganz von Anfang bis Ende zu bearbeiten.

T ID−

Meine Arbeit ist so gestaltet, dass ich nur einen kleinen Ausschnitt aus einem großen Arbeitsvorgang erledige.

T S+

Wie gut ich meine Arbeit mache, hat Auswirkungen auf viele andere Leute.

T S−

Insgesamt betrachtet ist meine Arbeit nicht sehr bedeutend. +

Meine Arbeit lässt mir viel Freiheit, selbst zu entscheiden, wie ich vorgehe.

AU T O−

Meine Arbeit gibt mir keine Möglichkeit, meine Aufgaben eigenständig zu erledigen.

F J+

Schon bei der Ausführung meiner Arbeitsaufgaben kann ich leicht sehen, wie gut ich arbeite.

F J−

Meine Arbeit gibt selbst sehr wenig Hinweise, ob ich sie gut oder schlecht mache.

AU T O

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.

Arbeitsumgebung In den folgenden 5 Aussagen geht es darum, wie gut Sie über Ihre Arbeitsumgebung bestimmen können. Wenn von „anderen Personen“ die Rede ist, sind alle Personen gemeint, mit denen Sie in und aufgrund Ihrer Arbeit zu tun haben, wie z. B. Kollegen, Vorgesetzte, Kunden usw. 1 The measure was adapted from the German version of the JDS, provided by the Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany.

C German Questionnaire

125

P rivEnv1

Ich kann über den räumlichen Abstand zwischen mir und anderen Personen bestimmen, wie ich möchte.

P rivEnv2

Ich kann mich an meinem Arbeitsplatz aus dem Blickfeld anderer Personen zurückziehen, wenn ich möchte.

P rivEnv3

Ich kann an meinem Arbeitsplatz vermeiden, von anderen Personen gehört zu werden, wenn ich nicht gehört werden möchte (z. B. um vertrauliche Gespräche zu führen).

P rivEnv4

Ich kann darüber bestimmen, ob andere Personen Zugang zu meinem Arbeitsplatz und Arbeitsmaterialien haben, wie ich möchte.

P rivEnv5

Ich kann meinen Arbeitsplatz individuell gestalten, wenn ich möchte (z. B. durch persönliche Gegenstände wie Bilder, Zimmerpflanzen, selbst gewählte Möbel usw.).

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.2

Kommunikation am Arbeitsplatz Die folgenden 6 Aussagen beziehen sich alle auf die Kommunikation am Arbeitsplatz. „Kommunikation“ schließt das persönliche Gespräch ebenso ein wie die Kommunikation per Telefon, E-Mail, Chat usw. Mit „anderen Personen in der Arbeit“ sind Personen gemeint, mit denen Sie bedingt durch Ihre Arbeit zu tun haben, wie z. B. Kollegen, Vorgesetzte, Kunden usw. P rivCom1

Ich kann mit anderen Personen in der Arbeit so viel/wenig kommunizieren, wie ich möchte.

P rivCom2

Ich kann darüber bestimmen, wie ich von anderen angeredet werde und andere anrede (z. B. mit Du oder Sie, Vor- oder Familienname usw.), wie ich möchte.

P rivCom3

Ich kann mit anderen Personen in der Arbeit auf die Weise kommunizieren, wie ich möchte (z. B. Dialekt, Umgangssprache, Wortwahl usw.).

P rivCom4

Ich kann über mein Erscheinungsbild (z. B. Kleidung, Frisur usw.) in der Arbeit bestimmen, wie ich möchte.

Mit „Personen außerhalb Ihrer Arbeit“ sind Personen gemeint, mit denen Sie zwar möglicherweise während Ihrer Arbeitszeit, nicht aber bedingt durch Ihre Arbeit zu tun haben, wie z. B. Angehörige, Freunde, Ihr Arzt oder Frisör usw. 2

The dismissed item P rivEnv6 read “Ich kann darüber bestimmen, ob sich andere Personen direkt an meinem Arbeitsplatz aufhalten.”

126

C German Questionnaire

P rivCom5

Ich kann mit Personen außerhalb meiner Arbeit so viel/wenig kommunizieren, wie ich möchte, während ich in der Arbeit bin.

P rivCom6

Ich kann darüber bestimmen, auf welchem Weg (z. B. Telefon, E-Mail, Chat usw.) ich mit Personen außerhalb meiner Arbeit kommuniziere, wie ich möchte, während ich in der Arbeit bin.

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.3

Persönliche Informationen Gegenstand der nächsten 4 Aussagen ist, wie gut Sie über Sammlung und Verwendung persönlicher Informationen (z. B. Ihr Alter, Wohnort, Gehalt usw.) in Ihrem Unternehmen bestimmen können. Wenn Sie die Informationspolitik Ihres Unternehmens nicht genau kennen, bedenken Sie bitte, dass es um Ihre persönliche Wahrnehmung geht, inwieweit Sie über die Verwendung persönlicher Informationen bestimmen können. Antworten Sie deshalb nach Ihrem ersten Gefühl, wenn Sie nicht sicher sind. Denken Sie bei Ihrer Antwort bitte an Informationen, die Sie als persönlich empfinden. P rivInf1

Mein Unternehmen lässt mich in dem Maße, wie ich möchte, darüber bestimmen, welche Informationen es über mich sammelt.

P rivInf2

Mein Unternehmen lässt mich in dem Maße, wie ich möchte, darüber bestimmen, für welche Zwecke es Informationen über mich verwendet.

P rivInf3

Mein Unternehmen lässt mich in dem Maße, wie ich möchte, darüber bestimmen, wer (innerhalb des Unternehmens) Zugang zu Informationen über mich hat.

P rivInf4

Mein Unternehmen lässt mich in dem Maße, wie ich möchte, darüber bestimmen, an wen (außerhalb des Unternehmens) es Informationen über mich weitergibt.

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“. 3 The dismissed item P rivCom read “Ich kann darüber bestimmen, auf welchem 7 Weg ich mit anderen Personen in der Arbeit kommuniziere (z. B. Telefon, E-Mail, Chat usw.).”

C German Questionnaire

127

Abgrenzung von Berufs- und Privatleben In den nächsten 4 Aussagen geht es darum, wie gut Sie Ihr Berufs- und Ihr Privatleben voneinander abgrenzen können. P rivW lb1

Ich kann über das Verhältnis zwischen meiner Arbeitszeit und Freizeit bestimmen, wie ich möchte. (Denken Sie bei Arbeitszeit auch an Dienstreisen, Bereitschaftsdienst, Weiterbildung usw.)

P rivW lb2

Meine Arbeit lässt mir ausreichend Möglichkeit, darüber zu bestimmen, wo ich lebe. (Eine Einschränkung könnte z. B. ein berufsbedingter Umzug oder Auslandsaufenthalt sein.)

P rivW lb3

Meine Arbeit hindert mich, in meinem Privatleben Dinge zu tun, die ich eigentlich gerne tun möchte (z. B. bestimmte Sportarten, Hobbys usw.).

P rivW lb4

Meine Arbeit zwingt mich, in meinem Privatleben Dinge zu tun, die ich eigentlich nicht gerne tue (z. B. Lektüre von Fachliteratur usw.).

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.4

Ihre Privatsphäre als Mitarbeiter P rivOall

Alles in allem habe ich als Mitarbeiter meines Unternehmens so viel Privatsphäre, wie ich möchte.

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.

Verhältnis zu Ihrer Arbeit Im Folgenden finden Sie eine Liste aus 9 Aussagen über das Verhältnis, das jemand zu seiner Arbeit haben kann. Bitte denken Sie an Ihre eigene Arbeit und geben Sie an, inwieweit jede Aussage auf Sie zutrifft.5 EmpM ean1 Meine Arbeit bedeutet mir sehr viel. EmpM ean2 Ich finde meine Arbeit persönlich sinnvoll. EmpComp1 Ich traue mir zu, meine Arbeitsaufgaben erfüllen zu können. EmpComp2 Ich beherrsche die Fertigkeiten, die für meine Arbeit nötig sind. 4 The dismissed items read as follows: P rivW lb0 —“Ich kann darüber bestimmen, 1 wie lange ich arbeite (einschließlich Dienstreisen, Bereitschaftsdienst usw.) und wie viel 00 Freizeit ich somit habe.” P rivW lb1 —“Ich kann darüber bestimmen, wann ich meine Arbeit erledige (z. B. Früh- oder Spätschicht, gleitende Arbeitszeit usw.) und wann ich somit frei bin, andere Dinge zu tun.” 5 The measure was adapted from that developed by Spreitzer (1995).

128

C German Questionnaire

EmpSDet1 Ich habe viel Freiheit zu bestimmen, wie ich meine Arbeit erledige. EmpSDet2 Ich kann selbst entscheiden, wie ich vorgehe, um meine Arbeit zu erledigen. EmpImpt1

Ich kann wesentlich darüber mitbestimmen, was in meiner Abteilung geschieht.

EmpImpt2

Ich habe großen Einfluss auf die Vorgänge in meiner Abteilung.

EmpOall

Insgesamt gibt mir meine Arbeit das Gefühl, dass ich persönlich etwas bewegen kann.

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.

Unterstützung für Kreativität Für die Entstehung neuer Ideen spielt das Umfeld eine wichtige Rolle. Die folgenden 6 Aussagen beziehen sich auf die Bereitschaft Ihres Arbeitsumfeldes, neue Ideen zu entwickeln und umzusetzen. Bitte geben Sie an, wie stark Sie jeder Aussage zustimmen.6 Clim1

Meine Kollegen und ich werden bei der Entwicklung neuer Ideen bereitwillig unterstützt.

Clim2

Meine Kollegen und ich sind Veränderungen gegenüber aufgeschlossen.

Clim3

Meine Kollegen und ich suchen ständig nach neuen Wegen, Probleme zu lösen.

Clim4

Meine Kollegen und ich nehmen uns die Zeit, die wir brauchen, um neue Ideen zu entwickeln.

Clim5

Meine Kollegen und ich arbeiten zusammen, um neue Ideen zu entwickeln.

Clim6

Meine Kollegen und ich geben uns gegenseitig praktische Unterstützung bei der Verwirklichung neuer Ideen.

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.

6

The measure was adopted from the German version of the Team Climate Inventory (Brodbeck et al., 2000).

C German Questionnaire

129

Kreativität in der Arbeit In den folgenden 10 Aussagen geht es darum, wie kreativ Sie sich selbst einschätzen. Bitte geben Sie an, wie gut jede Aussage auf Sie zutrifft.7 Crea1

Ich finde neue Wege, um Vorgaben oder Ziele zu erreichen.

Crea2

Ich finde neue Wege zur Verbesserung bestehender Verfahren oder Produkte.

Crea3

Ich suche nach neuartigen Verfahren oder Produktideen.

Crea4

Ich finde neue Verwendungen für bekannte Verfahren, Geräte oder Produkte.

Crea5

Ich habe neue und nützliche Ideen mit Bezug zu meiner Arbeit.

Crea6

Bei der Entwicklung neuer Ideen bin ich bereit, Risiken einzugehen.

Crea7

Ich werbe bei anderen für neue Ideen und trete bei anderen für neue Ideen ein.

Crea8

Ich bin bei der Arbeit einfallsreich, wenn man mir dazu Gelegenheit gibt.

Crea9

Ich entwickle zielführende Pläne zur Umsetzung neuer Ideen.

Crea10

Ich gehe Probleme mit ausgefallenen Lösungsansätzen an.

Antwortformat: 7-Punkt-Likert-Skala von (1) „Trifft gar nicht zu“ bis (7) „Trifft voll zu“.

Arbeitszufriedenheit Bitte beantworten Sie nun folgende 8 Fragen zu Ihrer Zufriedenheit mit Ihrer gegenwärtigen Arbeit.8 JS1

Wie zufrieden sind Sie mit Ihren Kollegen?

JS2

Wie zufrieden sind Sie mit Ihren Vorgesetzten?

JS3

Wie zufrieden sind Sie mit Ihrer Tätigkeit?

JS4

Wie zufrieden sind Sie mit Ihren Arbeitsbedingungen?

JS5

Wie zufrieden sind Sie mit der Organisation und Leitung?

JS6

Wie zufrieden sind Sie mit Ihren Entwicklungsmöglichkeiten?

JS7

Wie zufrieden sind Sie mit Ihrer Bezahlung?

JSOall

Wie zufrieden sind Sie mit Ihrer Arbeit insgesamt?

Antwortformat: 7-Punkt-Likert-Skala von (1) „Sehr unzufrieden“ bis (7) „Sehr zufrieden“. 7 The German items are based on measures provided by Sandra Ohly (Institute of Psychology, Goethe University Frankfurt, Germany), and Jennifer Gunkel (Chair of Psychology, Technische Universität München, Germany). 8 The items were adopted from the ABB (Neuberger and Allerbeck, 1978).

130

C German Questionnaire

Selbsteinschätzung Damit wir Ihre Antworten einordnen können, ist es wichtig, dass Sie uns eine Einschätzung von sich selbst bezüglich bestimmter Eigenschaften geben. Wir nennen Ihnen 20 Eigenschaften. Bitte kreuzen Sie all diejenigen Eigenschaften an, die Sie gut beschreiben. (Überlegen Sie, wie z.B. Ihre Freunde oder Kollegen Sie beschreiben würden.)9 CP S1+ CP S2+

klug

+ CP S11

selbstbewusst

sicher

+ CP S12

unkonventionell vorsichtig

egoistisch

CP S1−

CP S4+

humorvoll

CP S2−

unauffällig

CP S5+

individualistisch

CP S3−

konservativ

ungezwungen

CP S4−

rechtschaffen

vielseitig interessiert

CP S5−

wohlerzogen

erfinderisch

CP S6−

seriös

überlegt

CP S7−

folgsam

einfallsreich

CP S8−

misstrauisch

CP S3+

CP S6+ CP S7+ CP S8+ CP S9+ + CP S10

Antwortformat: Ankreuzen der zutreffenden Eigenschaften.

Persönliche Daten Abschließend bitten wir Sie um einige Angaben zu Ihrer Person und zu Ihrer beruflichen Situation. Wir benötigen diese Informationen ausschließlich zur Einordnung Ihrer bisherigen Angaben. Selbstverständlich werden die Angaben auf dieser Seite nicht benutzt, um Teilnehmer dieser Umfrage zu identifizieren.

Geschlecht Weiblich – Männlich

Alter Bitte geben Sie Ihr Geburtsjahr an: ... 9

The measure was adopted from the German version of the Creative Personality Scale, provided by Heinz Schuler (Chair of Psychology, University of Hohenheim).

C German Questionnaire

131

Ausbildung Bitte wählen Sie aus folgender Liste den höchsten Ausbildungsabschluss aus, den Sie erworben haben: • Kein Abschluss • Abgeschlossene Schulausbildung (z. B. Haupt-, Realschulabschluss, Abitur) • Abgeschlossene Berufsausbildung (z. B. Gesellen-, Meisterbrief) • Abgeschlossene Hochschulausbildung (z.B. Diplom (FH), Promotion) Bitte geben Sie nun Ihren höchsten Ausbildungsabschluss möglichst genau an (z. B. Bürokaufmann, Dipl.-Kffr. (Univ.), B.Sc. in Physik): ...

Berufserfahrung Wie viele Jahre lang arbeiten Sie schon in Ihrem gegenwärtigen Beruf? . . . Jahr(e)

Tätigkeit Die Fragen unterschieden sich je nach Organisation. Vgl. Abschnitt 5.2.7.

Führungsverantwortung Haben Sie in Ihrer jetzigen Tätigkeit Führungsaufgaben mit Weisungsbefugnissen? Ja – Nein

Feedback Falls Sie Feedback für uns haben, geben Sie dieses bitte in folgendes Feld ein. Wir freuen uns auf Ihre Anmerkungen! ... Mit dem „Weiter“-Button gelangen Sie zum Gewinnspiel. Bitte beachten Sie, dass Sie nicht mehr zur Befragung zurückkehren können, sobald Sie auf „Weiter“ geklickt haben.

132

C German Questionnaire

Gewinnspiel Wie zu Beginn angekündigt, bieten wir Ihnen nun die Chance, einen Gutschein im Wert von 100, 50 oder 25 Euro zu gewinnen. Um an dem Gewinnspiel teilzunehmen, tragen Sie bitte in folgendes Feld Ihre E-Mail-Adresse ein: ... Falls Sie zu den Gewinnern zählen, benachrichtigen wir Sie gleich nach Abschluss der Erhebung. Bitte beachten Sie, dass selbstverständlich Ihre E-Mail-Adresse nicht mit Ihren übrigen Angaben verknüpft werden kann.

Vielen Dank für Ihre Teilnahme Mit Ihren Antworten haben Sie einen wertvollen Beitrag zu unserer Untersuchung geleistet. Dafür nochmals vielen Dank. Falls Sie an dem Gewinnspiel teilnehmen, wünschen wir Ihnen viel Glück. Weitere Informationen über uns und unsere Arbeit finden Sie auf unserer Homepage: www.controlling.bwl.lmu.de. Wir freuen uns über Ihr Interesse.

Appendix D English Questionnaire

Privacy and Creativity at Work Dear Sir or Madam, Thank you very much for your interest in our survey on privacy and creativity at work. This survey is conducted by the Institut für Produktionswirtschaft und Controlling (Institute of Production Management and Management Accounting) at the University of Munich (www.controlling.bwl.lmu.de). The purpose of this survey is to find out whether privacy, creativity, and job satisfaction are empirically related. According to us, privacy includes the chance to be alone at work, decide on personal matters, and define the boundary between your work life and private life. On the following pages, we will present you with a number of statements and ask you to tell us to what extent you feel these statements apply to you. Our questions are about your personal opinion. There are no “right” or “wrong” answers. Just choose the answer that best reflects your opinion. If you are not sure, answer intuitively. Please answer all questions. We can only use questionnaires that have been completed. It should take you 10 to 15 minutes to complete the entire questionnaire. Please move through it quickly. At the end of the questionnaire, you may take part in a lottery of gift certificates worth e100, 50, or 25. Please note that all your answers will be kept absolutely confidential. This survey is anonymous. We collect these data for the purpose of a research project. We will use the data for scientific research only. We would be glad to answer any questions you may have about this survey. Please contact Andreas Ostermaier (phone +49 89 2180 2302, [email protected]). Thank you very much for your cooperation! Dipl.-Kulturw. Andreas Ostermaier, MBR

Prof. Dr. Dr. h.c. Hans-Ulrich Küpper

Research assistant and project manager

Head of the Institut für Produktionswirtschaft und Controlling

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D English Questionnaire

Description of your job Listed below are 10 statements which could be used to describe a job. Please indicate whether each statement is an accurate or an inaccurate description of your job. Please give an overall assessment of your job. Items: SV + , SV − , T ID+ , T ID− , T S + , T S − , AU T O+ , AU T O− , F J + , and F J −.

Work environment The next 5 statements are about how much control you have over your work environment. When talking about “other people,” we refer to people related to your job, such as your colleagues, supervisor, clients, etc. Items: P rivEnv1 –P rivEnv5 .

Communication at work The next 6 statements refer to communication at work. “Communication” includes any form of communication (face-to-face, phone, e-mail, chat, etc.). By “other people at work” we mean people related to your job, such as your colleagues, supervisor, clients, etc. Items: P rivCom1 –P rivCom4 . By “other people who are not related to my job” we mean people you may deal with during your working hours, but unrelated to your job, such as your family, friends, doctor, hairdresser, etc. Items: P rivCom5 , P rivCom6 .

Personal information The next 4 statements refer to your control over how your company deals with your personal data (e.g., your age, residence, pay, etc.). If you are unsure about your company’s actual data policy, please remember that what matters is how you perceive the way your company deals with your data, so please answer intuitively. Talking about “personal information,” think of any information which you consider personal. Items: P rivInf1 –P rivInf4 .

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135

Work life vs. private life The following 4 statements are about whether you can define the boundary between your work life and your private life as you desire. Items: P rivW lb1 –P rivW lb4 .

Overall privacy as an employee Item: P rivOall.

Relationship to your job Listed below you find 9 statements on how people can experience their job. Please think of your job and indicate how strongly you agree or disagree with each statement. Items: EmpM ean1 and EmpM ean2 ; EmpComp1 and EmpComp2 ; EmpSDet1 and EmpSDet2 ; EmpImpt1 and EmpImpt2 ; EmpOall.

Support for creativity The social environment is an important factor in the generation of novel ideas. The next 6 statements refer to your organization’s readiness to develop and implement novel ideas. Please indicate how strongly you agree or disagree with each statement. Items: Clim1 –Clim6 .

Creativity at work These 10 statements refer to how creative you assess yourself. Please indicate how accurately or inaccurately each statement describes you. Items: Crea1 –Crea10 .

Job satisfaction Please answer now the following 8 questions on how satisfied you are with your current job. Items: JS1 –JS7 and JSOall.

136

D English Questionnaire

Self-assessment For our appraisal of your answers it is important to know how you characterize yourself with regard to a number of properties. Listed below are 20 properties. Please check all those properties which characterize you accurately. (Think of how friends or colleagues of yours might describe you.) + Items: CP S1+ –CP S12 and CP S1− –CP S8− .

Personal data We finally ask you for some data relating to yourself and your job. Note that we need these data to better assess the answers you have just given. Of course, the answers made on this page will not be used to identify participants in this survey.

Gender Female—Male

Age Please indicate your year of birth: ...

Education Please select from the following list the highest level of education/training you have attained: • Did not graduate • Graduated from high school • Completed vocational education/apprenticeship • Graduated from college/university Please indicate your highest degree attained as exactly as possible. Please include your field of education (e.g., Associate of Applied Business, MBA, B.Sc. in Physics): ...

Job experience How many years have you been working in this job? . . . year(s)

D English Questionnaire

137

Your tasks The questions differed depending on the organization. See Section 5.2.7.

Managerial responsibilities Does your position provide you with disciplinary responsibilities? Yes—No

Feedback If you have feedback for us, please use the following field. We look forward to your comments! ... Push the “Next” button to go on to the raffle. Please note that you cannot return to the survey once you have pushed the “Next” button.

Lottery We now offer you the chance to win a gift certificate worth e100, 50, or 25. If you wish to participate in the raffle, please enter your e-mail address in the space below: ... If you win, we will contact you at your e-mail address after finishing our survey. Please be assured that your e-mail address cannot be linked to your answers on the previous pages.

Thank you for your participation Your answers to our questions are a highly valuable contribution to our research project. Once again, thank you very much for your cooperation. If you participate in the lottey, we wish you good luck. Please find further information about us and our research activities on our website at www.controlling.bwl.lmu.de. We look forward to your visit.

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Index ABB 71, 72 Address 16, 27 Autonomy 3, 8, 32–34, 46, 50, 54, 55, 74, 77–79 Average Variance Extracted 86, 93, 96, 97 Behavior 3, 5, 7–11, 14–17, 20, 26, 29, 40, 43, 50, 82, 112, 113, 117 – Calculus of 17 – Deviant 4, 11, 40 – Discretionary 3, 4 – Extra-role 3, 39 – Innovative 47, 48 – Territorial 15, 26 Bias 69, 96, 109, 110 Blindfolding 89 Bootstrapping 89, 90, 119 Business case 4, 5, 111 Climate 38, 55, 57–59, 79, 80 C-OAR-SE 88, 95 Coefficient of determination 89 Coefficient of substantive validity 31, 87 Common method variance 109 Communication 1, 8, 10, 15–17, 19, 20, 25–29, 34, 35 – Channel of 16–18, 26, 27, 42 – Control over 5, 14, 15, 18, 23, 26, 27, 29, 31, 34–36, 42, 46, 59, 67, 90, 100, 104, 108, 114 – Nonverbal 15, 16, 26, 27 – Verbal 15, 16, 26 Condition Index 87, 88, 90, 91, 94, 95 Construct 23 – First-order 23

– Second-order 23 Context factor 37, 38, 51, 74, 101, 116 Control – Perceived 9, 13, 15, 17, 20, 25, 29, 35, 42, 59 – Social 16, 39, 40, 44 Coping – Emotion-focused 9–11 – Problem-focused 9, 10 Covariance-based approach 83–85 CPS see Creative personality Creative performance see Creativity Creative personality 38, 52, 59, 75–77, 88, 100, 110 Creative potential 38, 51–54, 59, 67, 74, 75, 88, 100, 101, 109 Creative process 38, 48 Creative requirement 55, 58, 77 Creativity 5, 6, 36–40, 44, 47–49, 51–59, 67–70, 74–77, 79, 80, 82, 88, 93, 96, 100–103, 105, 106, 108, 109, 111–117 – Climate for 38, 51, 56–59, 67, 79, 80, 88, 96, 100–103, 105, 106, 108 Crowding 9, 11, 15 Disclosure 18, 28, 29, 68 Distance 14–16, 19 – Mental 15, 16, 27 – Physical 14, 25, 26 Education 51, 53, 54, 59, 61, 63–66, 80, 81, 100–102, 105–108, 115 Effect size 86, 89, 96, 98, 101–103 Empowerment 6, 36, 40, 44–55, 57–59, 67, 71–74, 76, 77, 84, 88, 90,

162

Index

94–96, 100–103, 105, 106, 111–114, 116, 117 Enumeration see COARSE Environment 13, 17, 20, 28, 29, 43, 55, 57, 58, 72, 79, 108 – Control over the 5, 14, 23, 25, 29, 31, 32, 34–36, 39, 42, 43, 46, 59, 67, 72, 90, 100, 104, 108, 114 – Physical 42, 56 – Social 19 Ethics 2, 3, 114 Experience 51, 53, 54, 59, 63, 64, 81, 82, 100–102, 105, 106, 108 Expert agreement 24, 87 Expertise 52, 53, 82 Factor analysis 85, 110 Feedback 5, 32, 34, 35, 40, 54–56, 63, 67, 68, 70, 77, 78, 114 Five Factor Inventory 75 Fornell–Larcker criterion 86, 93, 96 Freedom 2, 3, 7, 8, 13, 30, 39, 44, 46, 54, 78, 111, 116 Genetic screening

1, 2

Harman 110 Homo economicus 3 Human Relations Movement Humanization of work 41

41

Index 23 Indicator (variable) 23 Indicator loading 85 Indicator weight 87 Information 1, 8, 13, 16–18, 29, 35, 47, 54, 82, 92, 104 – Access to 18, 28, 29, 92 – Collection of 18, 28, 29, 92 – Control over (personal) 5, 7, 8, 14, 17, 18, 20, 23, 28, 29, 31, 32, 35, 36, 42, 59, 90–92, 100–102, 104–106, 108, 109, 114, 115 – Privacy of 3, 18, 40, 43, 47, 92, 105, 112, 114, 115

– Storage of 18 – Use of 18, 28, 29 Innovation 37, 40, 47, 116 – Climate for 55–57, 79 – Support for 79, 80 Interaction 8, 12, 17, 50, 105, 114, 116 Intrinsic motivation see Motivation Item 23 Job characteristics 49, 54, 55, 71, 77, 78, 82 Job Descriptive Index 71 Job Diagnostic Survey 71, 72, 77 Job satisfaction 6, 11–13, 36, 40–44, 49–51, 54, 55, 58, 59, 67, 71–74, 84, 88, 90, 93, 94, 96, 100–102, 104, 108, 109, 111–114, 116, 117 Kolmogorov–Smirnov test

84

Leadership 37, 48, 51, 56–58, 64, 107, 108 Learned helplessness 9, 11, 42, 50 Marker 25, 26 Measure – Formative 24 – Reflective 24 Mediator 6, 54, 55 Mobility 19 Moderator 105–107, 109, 113, 115, 120 Monitoring 1, 10, 40, 46, 47, 56 Motivating potential 51, 54, 55, 59, 67, 71, 76–79, 82, 88, 100–102, 105, 109 Motivation 11, 44–50, 52, 54, 55, 57, 58, 82, 105, 113, 116, 117 MPS see Motivating potential Multicollinearity 87 NEO-FFI see Five Factor Inventory Norm 2, 4, 113, 114

Index OCB see Organizational citizenship behavior On-call time 19, 30, 34 Open-plan office 13, 17, 43 Organizational citizenship behavior 3, 41, 113, 117 Partial least squares 83–85, 89, 96 Path model 59, 88, 96, 98 Performance 40, 41, 45, 54, 55, 113, 114, 117 – Creative see Creativity Personal space 14, 15, 25 Personality 37, 52–54, 74, 75 Personalize 15, 25, 26, 66 PLS see Partial least squares Power 44, 46, 87 Predictive relevance 89, 101 Privacy – Achievable 11–13, 17, 19, 20, 26, 28, 29 – Achieved (level of) 9–12, 14, 20, 21, 42, 59, 106, 111, 115–117 – Architectural 13, 42, 115 – Desired (level of) 9–12, 14, 20, 21, 26, 28, 29, 42, 59, 106, 111, 112, 115–117 – Invasion of 1, 3, 20, 43 – Objective 12–14, 112 – Perceived 12–15, 112 – Physical 18, 72, 112, 115 – Psychological 13 – Regulation of 9–11, 13, 14, 16, 29, 42 – Right to 2–4, 111 Privacy of data 1, 28, 114 Proportion of substantive agreement 31, 87 Reactance 9, 50 Reliability 24, 86, 88 – Composite 85, 93, 96 – Construct 85, 86

163

– Indicator 85 Routine 39, 40, 77, 102 Scale 23 Seclusion 3, 9, 39 Secrecy 3 Self-efficacy 44, 48–50 Skill 30, 53, 54, 74, 77–79, 106 SmartPLS 83, 85, 86, 89, 90, 96, 98, 119 Sobel test 98, 119, 120 Social density 15 Social isolation 9, 11 Social network 1, 105, 114 Stone–Geisser criterion 89, 101 Structural equation modeling 73, 83, 96, 112 Suppression 92, 100, 116 Surveillance 1, 10 Team climate 79 Telecommunication 19 Telecommuting 19 Territory 14, 15, 23, 25 Turnover 11, 41 Validity 24, 32–34, 47, 86–88, 90 – Discriminant 86 Variable – Latent 23, 24, 35, 77, 80, 83–93, 96, 110, 122 – Manifest 23, 83, 84, 87, 88 Variance-based approach 83–85 Variance Inflation Factor 87, 90, 91, 94, 95 Work–life balance 106 Work–life boundaries 5, 14, 17–20, 23, 27, 29, 31, 34–36, 42, 59, 90, 104, 106, 108, 114 Working time 11, 17, 19, 30, 34, 42, 63, 82