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Erschienen in: Small Business Economics 4/2022

08.01.2022

The interplay between human, social and cognitive resources of nascent entrepreneurs

verfasst von: Abeer Alomani, Rui Baptista, Suma S. Athreye

Erschienen in: Small Business Economics | Ausgabe 4/2022

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Abstract

This study integrates human, social and cognitive capital traits into a model of nascent entrepreneurial capital. We hypothesise that the cognitive traits of nascent entrepreneurs play a significant role in moderating the impact of human and social capital endowments on the outcome of the nascent entrepreneurship process. Thus, differences in cognitive traits explain why nascent entrepreneurs with similar stocks of human and social capital resources may experience different outcomes. Using data from the Panel Study of Entrepreneurial Dynamics (PSED), we find that models incorporating cognitive capital traits as moderating variables of human and social capital have a greater explanatory power of nascent entrepreneurship outcomes than models that consider these components of nascent entrepreneurial capital in isolation. The implications of our results are important for researchers, nascent entrepreneurs, venture capitalists and SME support agencies.

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Fußnoten
1
One of the measures widely used to determine the point of success in forming a business is the measure of the sales generated. However, according to Davidsson (2006), nascent ventures may generate first sales even before engaging in the business founding activities, and hence this indicator may not reflect a conclusive measure in this respect.
 
2
The actual question used in the PSED was: ‘How would you classify the status of your firm? (1) operational and running; (2) you are still setting up the business; (3) you have temporarily delayed your start-up effort; (4) you have completely abandoned your start-up effort’. It is worth noting that, while such subjective measures are widely recognised and used in management studies research (e.g. Parker and Belghitar, 2006; Van Gelderen et al., 2000), variables based on self-perceptions are subject to distortion (Kessler et al., 2012).
 
3
This variable is never significant throughout our estimations.
 
4
Data sets stemming from large surveys using long and complex questionnaires (as is the case of the PSED) contain high-dimensional data, hindering the researcher’s ability to visualise patterns (Hair et al., 2006). Examination of a reduced dimension data set allows the researcher to spot trends, patterns and outliers in the data.
 
5
We acknowledge that there are some inherent issues with this reduction technique which have caused a reduction in its use. For example, there is no guarantee that the dimensions are interpretable. Moreover, variance maximization in the dimensional space of the data under a quadratic constraint may result in a large number of variables loading on the first principal component. In order to overcome these limitations, we use Varimax and Orthomax rotation (Hair et al., 2006).
 
6
The PCA analyses conducted for HC, SC and CC are summarised in Appendix 1, available from the authors upon request.
 
7
The level of correlation considered worthy of a variable’s inclusion is usually r ≥ 0.3. The correlation matrix was examined for any variable that does not have at least one correlation with another variable where r ≥ 0.3 (Field, 2013).
 
8
The criteria used are (a) the interpretability criterion, (b) the eigenvalue-one criterion, (c) the proportion of total variance accounted for and (d) the scree plot test. In particular, visual inspection of the scree plots indicates that four components should be retained for HC, six for SC and four for CC. The scree plots are presented in Appendix 2, available from the authors upon request.
 
9
A stepwise selection procedure was used with backward elimination. The procedure starts with a complex model and removes terms sequentially until a further deletion leads to a significantly poorer fit (Field, 2013).
 
10
In step 2, a list of possible pairs of variables in the main effects model that have some scientific basis to interact with each other were tested. This consists of all possible pairs. The interaction terms were added one at a time, in the model containing all the main effects.
 
11
Logit regression results for human and social capital main effects are presented in Appendix 3, available from the authors upon request.
 
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Metadaten
Titel
The interplay between human, social and cognitive resources of nascent entrepreneurs
verfasst von
Abeer Alomani
Rui Baptista
Suma S. Athreye
Publikationsdatum
08.01.2022
Verlag
Springer US
Erschienen in
Small Business Economics / Ausgabe 4/2022
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI
https://doi.org/10.1007/s11187-021-00580-8

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