Content Infrastructure Management (eBook)

Dr. Alexander Benlian ist wissenschaftlicher Mitarbeiter von Prof. Dr. Thomas Hess am Institut für Wirtschaftsinformatik und Neue Medien der Universität München

Foreword 6
Foreword 7
Preface 10
Overview of Contents 12
Table of Contents 14
List of Figures 18
List of Tables 20
List of Abbreviations 22
1 Introduction 23
1.1 Problem statement and motivation 23
1.2 Research questions and objectives 25
1.3 Overview of research methodology 26
1.4 Organization and evolutionary context of study 29
2 Conceptual foundations 33
2.1 Media content and content allocation 33
2.1.1 Media content 34
2.1.2 Content allocation 36
2.2 Publishing companies 39
3 Causal model specification 43
3.1 Generic selection process of theoretical lenses 43
3.2 Logic and selection of reference theories 44
3.2.1 Selection criteria for reference theories 45
3.2.2 Selection of reference theories 46
3.2.3 Applied reference theories in the state-of-the-art literature 54
3.2.4 Framework of comparative institutional performance 56
3.3 Hypotheses from selected theoretical lenses 58
3.3.1 A transaction cost perspective on content allocation 58
3.3.1.1 Origins, application fields, and basic logic of TCT 59
3.3.1.2 Transaction and production costs in the allocation of content 61
3.3.1.3 Deduction of hypotheses based on Transaction Cost Theory 63
3.3.1.4 Synopsis of hypotheses from Transaction Cost Theory 70
3.3.2 A resource-based view on the content allocation problem 71
3.3.2.1 Origins, application fields, and basic logic of the Resource-Based View 72
3.3.2.2 Strategic and operational contribution to competitive advantage 74
3.3.2.3 Deduction of hypotheses based on RBV logic 76
3.3.2.4 Synopsis of hypotheses from RBV 85
3.3.3 A contingency approach to content allocation 85
3.3.3.1 Origins, application fields, and basic logic of Contigency Theory 86
3.3.3.2 Definition of ‘fit’ in Contingency Theory 88
3.3.3.3 Hypotheses from multiple levels of alignment 90
3.3.3.4 Synopsis of hypotheses from Contingency Theory 104
3.4 Interplay and integration of reference theories 105
3.5 Synopsis of hypotheses and path model 108
4 Empirical test of the content allocation model 113
4.1 Fundamentals of structural equation modeling 113
4.2 Operationalization of research constructs 117
4.2.1 Content allocation behavior 119
4.2.2 Comperative advantage variables 121
4.2.3 Content-, production process-, and market-related characteristics 125
4.2.3.1 Content characteristics from TCT theory 125
4.2.3.2 Content characteristics from RBV theory 127
4.2.4 Contingency variables 129
4.2.5 Overview of formative and reflective measurement constructs 135
4.3 Data collection 137
4.3.1 Questionnaire design 137
4.3.2 Sample selection 139
4.3.3 Mailing procedure 140
4.3.4 Survey response 141
4.4 Sample characteristics 143
4.4.1 Key informant demographics 144
4.4.2 Content allocation behavior 145
4.4.3 Descriptives about publisher sub-types 148
4.5 Model estimation and evaluation 151
4.5.1 Estimation procedures in structural equation modeling 152
4.5.2 Selection of appropriate estimation and evaluation procedure 154
4.5.3 Measurement model assessment 157
4.5.3.1 Reflective measurement models 157
4.5.3.2 Formative measurement models 166
4.5.4 Structural model assessment 168
4.5.4.1 Overall model estimation 169
4.5.4.2 Hypotheses testing 172
4.5.4.2.1 Test of direct impacts on content allocation 173
4.5.4.2.2 Test of indirect impacts and working hypotheses 175
4.5.4.2.3 Test of moderator effects 177
4.6 Recapitulation 178
5 Discussion of model findings 183
5.1 Theoretical implications 183
5.1.1 Content allocation behavior of publishing firms 183
5.2 Practical implications 196
5.3 Study limitations 202
5.4 Future research 205
6 Conclusion 209
Literature 213
Appendix 241
Index 267

4 Empirical test of the content allocation model (p. 92)

In this chapter, the mid-range theoretical framework on content allocation, which integrates different theoretical lenses into a coherent whole, will be subjected to an empirical test. This requires to transform the theoretical language into an observable language (Kerlinger/ Lee, 2000, p. 54). In other words, the constructs have to be operationalized as measurable variables. In doing so, it is necessary to satisfy both the theoretical and the empirical requirements. One method that provides a formal structure which allows the matching of theory and data is that of structural equation modeling (SEM). This statistical modeling technique provides rules on how to specify a variance-based theoretical framework in a way that recognizes the requirements of the statistical procedures that are applied to rigorously estimate and evaluate the parameters of the model.

The theoretical groundwork that was laid by developing the theoretical framework facilitates the model building process. However, in order to specify and subsequently test the framework, the requirements of the modeling technique have to be considered a priori. Therefore, the fundamentals of the SEM method will first be introduced in the following chapter (see chapter 4.1). Subsequently, the constructs of the midrange theoretical framework on content allocation will be operationalized (see chapter 4.2). After the development of the measurement instrument, the conduct and analysis of the empirical study will be outlined. This includes the data collection (see chapter 4.3), the presentation of major descriptive characteristics of the sample data (see chapter 4.4), and the more extensive model estimation and evaluation process (see chapter 4.5).

4.1 Fundamentals of structural equation modeling

Within the last twenty years, structural equation modeling (SEM) techniques have become increasingly popular among social scientists (e.g., Chin, 1998a, Hildebrandt/ Homburg, 1998). They allow the rigorous statistical examination of theoretical relationships. Their popularity is essentially attributed to their ability to combine an econometric perspective, focusing on prediction, with psychometric modeling, which focuses on the measurement of not directly observable (i.e. latent) variables by multiple observables – also called indicators or manifest variables (Chin, 1998a, p. vii, Lee/ Barua/ Whinston, 1997, p. 120). The approach is primarily confirmatory in nature. It is generally used to determine whether a pre-specified model is valid, rather than to find a model by exploring the data – although it often includes some exploratory elements in the analysis (e.g., Chin, 1998a, Homburg/ Dobratz, 1991, 1992). SEM techniques allow the researcher to simultaneously test the strength of the relationships between multiple latent variables and the reliability of the measures of the latent variables (Chin, 1998a, p. vii). The relationships between latent theoretical variables form the structural model, which sometimes is also called inner model (see Figure 4.1-1). The structural model equals a variance-based theoretical framework.

Accordingly, the mid-range theoretical framework on content allocation, as illustrated in Figure 3.5-1, represents a structural model of content allocation. As an example, content specificity is hypothesized to have a direct negative impact on the comparative transaction cost advantages of centrally vs. decentrally deployed content (H1a-), and an indirect positive impact (two negative result into one positive relationship) on the allocation of content via comparative transaction cost advantages (H2a-).