E-Book, Englisch, 246 Seiten, eBook
Reihe: Markt- und Unternehmensentwicklung Markets and Organisations
Results of an empirical study in the print industry
E-Book, Englisch, 246 Seiten, eBook
Reihe: Markt- und Unternehmensentwicklung Markets and Organisations
ISBN: 978-3-8350-5700-5
Verlag: Deutscher Universitätsverlag
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)
Dr. Alexander Benlian ist wissenschaftlicher Mitarbeiter von Prof. Dr. Thomas Hess am Institut für Wirtschaftsinformatik und Neue Medien der Universität München
Zielgruppe
Research
Weitere Infos & Material
1;Foreword;6
2;Foreword;7
3;Preface;10
4;Overview of Contents;12
5;Table of Contents;14
6;List of Figures;18
7;List of Tables;20
8;List of Abbreviations;22
9;1 Introduction;23
9.1;1.1 Problem statement and motivation;23
9.2;1.2 Research questions and objectives;25
9.3;1.3 Overview of research methodology;26
9.4;1.4 Organization and evolutionary context of study;29
10;2 Conceptual foundations;33
10.1;2.1 Media content and content allocation;33
10.1.1;2.1.1 Media content;34
10.1.2;2.1.2 Content allocation;36
10.2;2.2 Publishing companies;39
11;3 Causal model specification;43
11.1;3.1 Generic selection process of theoretical lenses;43
11.2;3.2 Logic and selection of reference theories;44
11.2.1;3.2.1 Selection criteria for reference theories;45
11.2.2;3.2.2 Selection of reference theories;46
11.2.3;3.2.3 Applied reference theories in the state-of-the-art literature;54
11.2.4;3.2.4 Framework of comparative institutional performance;56
11.3;3.3 Hypotheses from selected theoretical lenses;58
11.3.1;3.3.1 A transaction cost perspective on content allocation;58
11.3.1.1;3.3.1.1 Origins, application fields, and basic logic of TCT;59
11.3.1.2;3.3.1.2 Transaction and production costs in the allocation of content;61
11.3.1.3;3.3.1.3 Deduction of hypotheses based on Transaction Cost Theory;63
11.3.2;3.3.1.4 Synopsis of hypotheses from Transaction Cost Theory;70
11.3.3;3.3.2 A resource-based view on the content allocation problem;71
11.3.3.1;3.3.2.1 Origins, application fields, and basic logic of the Resource-Based View;72
11.3.3.2;3.3.2.2 Strategic and operational contribution to competitive advantage;74
11.3.3.3;3.3.2.3 Deduction of hypotheses based on RBV logic;76
11.3.3.4;3.3.2.4 Synopsis of hypotheses from RBV;85
11.3.4;3.3.3 A contingency approach to content allocation;85
11.3.4.1;3.3.3.1 Origins, application fields, and basic logic of Contigency Theory;86
11.3.4.2;3.3.3.2 Definition of ‘fit’ in Contingency Theory;88
11.3.4.3;3.3.3.3 Hypotheses from multiple levels of alignment;90
11.3.4.4;3.3.3.4 Synopsis of hypotheses from Contingency Theory;104
11.4;3.4 Interplay and integration of reference theories;105
11.5;3.5 Synopsis of hypotheses and path model;108
12;4 Empirical test of the content allocation model;113
12.1;4.1 Fundamentals of structural equation modeling;113
12.2;4.2 Operationalization of research constructs;117
12.2.1;4.2.1 Content allocation behavior;119
12.2.2;4.2.2 Comperative advantage variables;121
12.2.3;4.2.3 Content-, production process-, and market-related characteristics ;125
12.2.3.1;4.2.3.1 Content characteristics from TCT theory;125
12.2.3.2;4.2.3.2 Content characteristics from RBV theory;127
12.2.4;4.2.4 Contingency variables;129
12.2.5;4.2.5 Overview of formative and reflective measurement constructs;135
12.3;4.3 Data collection;137
12.3.1;4.3.1 Questionnaire design;137
12.3.2;4.3.2 Sample selection;139
12.3.3;4.3.3 Mailing procedure;140
12.3.4;4.3.4 Survey response;141
12.4;4.4 Sample characteristics;143
12.4.1;4.4.1 Key informant demographics;144
12.4.2;4.4.2 Content allocation behavior;145
12.4.3;4.4.3 Descriptives about publisher sub-types;148
12.5;4.5 Model estimation and evaluation;151
12.5.1;4.5.1 Estimation procedures in structural equation modeling;152
12.5.2;4.5.2 Selection of appropriate estimation and evaluation procedure;154
12.5.3;4.5.3 Measurement model assessment;157
12.5.3.1;4.5.3.1 Reflective measurement models;157
12.5.3.2;4.5.3.2 Formative measurement models;166
12.5.4;4.5.4 Structural model assessment;168
12.5.4.1;4.5.4.1 Overall model estimation;169
12.5.4.2;4.5.4.2 Hypotheses testing;172
12.5.4.2.1;4.5.4.2.1 Test of direct impacts on content allocation;173
12.5.4.2.2;4.5.4.2.2 Test of indirect impacts and working hypotheses;175
12.5.4.2.3;4.5.4.2.3 Test of moderator effects;177
12.6;4.6 Recapitulation;178
13;5 Discussion of model findings;183
13.1;5.1 Theoretical implications;183
13.1.1;5.1.1 Content allocation behavior of publishing firms;183
13.2;5.2 Practical implications;196
13.3;5.3 Study limitations;202
13.4;5.4 Future research;205
14;6 Conclusion;209
15;Literature;213
16;Appendix;241
17;Index;267
Conceptual foundations.- Causal model specification.- Empirical test of the content allocation model.- Discussion of model findings.- Conclusion.
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-).
