Table Of ContentEmotions and Values in Equity
Crowdfunding Investment Choices 2
Emotions and Values in
Equity Crowdfunding
Investment Choices 2
Modeling and Empirical Study
Christian Goglin
First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
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Contents
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 hypothetico-
deductive 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
vi Emotions and Values in Equity Crowdfunding Investment Choices 2
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
Contents vii
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
Introduction
The first volume of this work1 made it possible to discover a recent industry:
equity crowdfunding. After having justified our interest in the investors’ decision
and more precisely in the understanding and explanation of their choice of projects,
we began a literature review in the field of entrepreneurial finance. The latter
revealed a rational investor seeking to reduce information asymmetry by identifying
the quality signals of analyzed projects. This cognitive approach seemed insufficient
to us to explain the empirical data in our possession – data collected on a platform
and constituting a corpus of testimonies from investors justifying their choice of
project. It was indeed evident that other affective factors were of significant
importance. Consequently, we have extended our state of the art to other disciplines
of social science, in particular to marketing and HRM, but also psychology by
focusing on theories that appeal to the affect for the understanding of analogous
situations. In so doing, we have developed an original theoretical framework for
interpreting the phenomenon studied, allowing us to confront reality.
The purpose of this second volume is in fact to continue this effort of
conceptualization and modeling by making interdependent hypotheses that
constitute an integrated model. We also introduce an original theory called
“affective matching”, explaining the choice of investors by the attraction of analyzed
projects, what with the strength of this attraction being proportional to the coherence
of the project’s characteristics with the affective and axiological profile of the
investor. Data, collected via a laboratory experiment, allowed us to go beyond the
hypothesis stage and largely confirms the two proposed models. Finally, this second
volume concludes with research perspectives and the managerial implications
induced by this work.
1 Goglin, C. (2020). Emotions and Values in Equity Crowdfunding Investment Choices 1.
ISTE Ltd, London, and John Wiley & Sons, New York.
1
Modeling: The Explanatory Model
and the Individual Predictive Model
This first chapter begins by making preliminary assumptions (section 1.1) of the
modeling, first, of an explanatory model (section 1.2) and second, of an individual
predictive model that derives from it (section 1.3).
1.1. Preliminary assumptions
In this section, three assumptions are made: the first relating to the nature of
uncertainty, the second to the nature of consequences and the last to the nature of
investor rationality.
1.1.1. Nature of uncertainty
The central question of our problem concerns the determinants of the choice of
projects by individual investors. However, a choice between several alternatives is a
decision; therefore, this research is indeed centered on a “decision”. The consequences
of this decision are not deterministic; they are uncertain and depend on hazards. The
question that arises from this observation is that of the nature of the uncertainty at
stake, a question analogous to that posed by the theory of individual decision that,
according to Kast (2002), aims to “propose a framework for studying rational behavior
in the face of uncertainty by distinguishing different types of uncertainties”.
Uncertainty taken in the common sense that concerns us, “the unforeseeable
nature of the result of an action”1, is specified by scientific literature, in particular,
Abbreviations within figures, tables and equations remain in the original French.
1 Source: http://www.cnrtl.fr/definition/incertitude.
Emotions and Values in Equity Crowdfunding Investment Choices 2:
Modeling and Empirical Study, First Edition. Christian Goglin.
© ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.
2 Emotions and Values in Equity Crowdfunding Investment Choices 2
by the oft-repeated distinction, made by Knight (1921), between risk and
uncertainty. For the economist, risk characterizes decisions for which the
consequences are probabilizable, either by a priori calculation or on the basis of
statistics. Conversely, uncertainty characterizes decisions that are not probabilizable,
due to the uniqueness of the situation.
According to Le Heron (2012), uncertainty is not probabilizable in the social
sciences, because human decisions do not come under the law of large numbers.
Moreover, the decisions taken change the state of the world so that their repetition is
not possible.
In the social sciences, in the field of entrepreneurial finance, in the venture
capital sphere, the question of selecting projects financed by the venture capitalist
(screening) is not far from our problem. Differences are due, on the one hand, to the
sophistication of the venture capitalist – a true financing professional – and, on the
other hand, to the stage of development, after the start-up, of the company awaiting
funds.
However, Dubocage (2006) noted the extent of the uncertainty that venture
capitalists face. The latter can base neither their evaluation nor their selection on
objective probabilities. In fact, the business plan offers a limited quantified vision of
the future to understand it and analog methods run up against the singularity of the
evaluated companies, which are generally carriers of disruptive innovation. The
author also decided to use the uncertainty framework proposed by Knight in order to
theoretically clarify the mode of judgment of venture capitalists.
This approach corresponds to that of Keynes (1936) according to whom
decisions relating to distant future horizons cannot, in most cases, be based on
mathematical calculations, due to the lack of data, which can be scarce or even non-
existent. Decision-makers then make the choice they can: “[…] choosing between
the alternatives as best we are able, calculating where we can, but often falling back
for our motive on whim or sentiment or chance”.
For these reasons, Keynes developed the concept of state of trust, that is to say, a
subjective evaluation of the probability of occurrence of a future event that
constitutes, according to him, a more realistic basis of appreciation than that of
mathematical calculation to describe the method of determining the actions of
entrepreneurs.
We can compare Keynes’s concept of state of trust with the successive works of
Ramsey, De Finetti and Savage, all three of whom were critics of frequentist
