Emotions and Values in Equity Crowdfunding Investment Choices 2

Christian Goglin is a Doctor of Management Sciences and a research associate at the CREGO Laboratory, part of the University of Burgundy – France-Comté, France. His research and academic publications focus on behavioral finance and financerelated investor choice.

Introduction ix

Chapter 1. Modeling: The Explanatory Model and the Individual Predictive Model 1

1.1. Preliminary assumptions 1

1.1.1. Nature of uncertainty 1

1.1.2. Consequentialism 3

1.1.3. Rationality of the equity crowdfunding investor: a triptych of rationality 4

1.2. Explanatory model 8

1.2.1. Choice of model variables by the combined hypotheticodeductive and inductive/abductive approach 9

1.2.2. Confirmatory study of the variables of the model on qualitative material 21

1.2.3. Operationalization: development of the measurement model, choice, adaptation of scales and return to data 35

1.2.4. Other hypotheses of the model 73

1.2.5. Reading grids for the explanatory model 83

1.2.6. Conclusion 89

1.3. Individual predictive model 90

1.3.1. Presentation 90

1.3.2. Affective matching theory 93

1.3.3. Theoretical foundations 97

1.3.4. Definitions and operationalization 99

1.3.5. Concluding remarks 107

1.4. Conclusion 108

Chapter 2. Experimentation 109

2.1. Experimental protocol 109

2.1.1. Rationale and objectives 111

2.1.2. Constitution, sample size and recruitment procedure 114

2.1.3. Experimental protocol 117

2.1.4. Experimental design 121

2.1.5. Conclusion 124

2.2. Carrying out the experimental procedure 125

2.2.1. Conducting the experimental procedure 125

2.2.2. Conclusion 128

2.3. Validity and handling of biases 128

2.3.1. Validity 128

2.3.2. Handling biases 129

2.3.3. Concluding remarks 131

2.4. Conclusion 131

Chapter 3. Hypotheses Testing, Results and Discussion 133

3.1. Data prerequisites and the PLS approach 133

3.1.1. Statistical data of the sample 133

3.1.2. PLS-SEM 134

3.1.3. Corrective reprocessing, transformation and adding control variables to the data file 135

3.1.4. Sample size 136

3.1.5. Data review: completeness and data quality 136

3.1.6. Indicator measurement scales: symmetry and equidistance 138

3.2. Preliminary validity of the measurement model 138

3.2.1. Review of the measurement model 138

3.2.2. Single-indicator variables 142

3.2.3. Reflective variables 143

3.2.4. Formative variables 178

3.2.5. Conclusion 191

3.3. Estimation of the explanatory model by structural equations 193

3.3.1. PLS-SEM approach 193

3.3.2. Revision of the structural model 195

3.3.3. Structural model 195

3.3.4. Mediators 214

3.3.5. Invariance and moderators 223

3.3.6. Control variables 247

3.3.7. Conclusion 250

3.4. Experimental design 250

3.4.1. Impact of the Quality of the Business Plan on the Perceived Signal Quality 251

3.4.2. Effect of the business plan on the parameters estimated by multigroup analysis 255

3.4.3. Conclusion 256

3.5. Results of the individual predictive model 259

3.5.1. Reminder of the model 259

3.5.2. Results of the individual predictive model 270

3.5.3. Conclusion 279

3.6. Conclusion 280

Conclusion 283

References 309

Index 339