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Entrepreneurial Motivation Index: importance of dark data

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Abstract

Entrepreneurship is a behavior that influences economic development. There are many effective individual attitudes behind this behavior. According to Global Entrepreneurship Monitor (GEM) reports, the level of individual factors in low-income countries (factor-driven economies) is much higher than high-income countries (innovation-driven economies) which seems to be a contradiction. By applying a method that is developed by statisticians, named the “Dark Data” approach, we attempted to discover an ignored dark data in the GEM’s big dataset. Regarding the wide-ranging experiences of researchers in this field, entrepreneurial motivation was identified as the missing variable that does not be used in the computation of individual factors. Finally, with adding entrepreneurial motivation to the equation of individual factors, as one of the sub-dimensions of individual factors, the result of the relationship between the GDP per capita and individual factors was improved. This study presents a comprehensive method to improve the structure of the Entrepreneurial Motivation Index. According to this study, entrepreneurial motivation can be considered as a specific type of dark data in the GEM dataset (as big data) that ignoring this index in the entrepreneurship studies, knowingly or unknowingly, will impose grave consequences on the results of researches. Ideally, what this study tries to unfold is the importance of dark data that is hidden in many big datasets. Hidden information includes the strengths and weaknesses of a company that needs to be found by applying the methods developed in big data science.

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Data availability

The reports and dataset over individual and environmental factors affecting the entrepreneurship status, released by the Global Entrepreneurship Monitor (GEM) in the year 2015 have been used throughout this study.

Notes

  1. Based on statistical inference, dark data is the particular group of data which often are not tangible, calculable, and collectible but have the most impact on the response variable. For example, some demographic variables (including gender, marriage situation, and age) or even the residence location of individuals may be dark Ddata in a research and it is likely to be overlooked by the researcher. Dark data is a type of unstructured, untagged and untapped data that is found in data repositories and has not been analyzed or processed. It is similar to big data but differs in how it is mostly neglected by business in terms of its value.

  2. The term “this business” refers to the business that the respondent has launched that.

  3. The sub-dimensions creating the individual factor based on the GEM model, are exactly the sub-indexes which have been used in the Eq. (6).

  4. Linear, Logarithmic, Quadratic, Cubic, Inverse, S, Exponential, Power, Growth, Logistic, and Component.

  5. In statistical analysis, often in the regression-based estimations, the coefficient of determination is a suitable scale for measuring the “goodness of fit” of one or more independent variables to the dependent variable.

  6. The Eq. (7) which introduces a six-dimension formula for calculating the individual factor, in addition to all five sub-indexes used in calculating the individual factor presented by the GEM, includes a new sub-index entitled Entrepreneurial Motivation Index. For this reason, the new name designated, arbitrarily, for this improved index is “Improved Individual Factor”.

Abbreviations

GEM:

Global Entrepreneurship Monitor

GDP:

gross domestic product per capita

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Acknowledgements

We are grateful to Dr. Nezameddin Faghih and the reviewers for their wonderful comments and suggestions throughout the review process. We also thank Mr. Moein Heydari for his help in the editing of this study. Additionally, we highly wish to thank our parents and family for inspiriting us during this research.

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Authors and Affiliations

  1. UNESCO Chair Professor Emeritus, Shiraz University, Shiraz, Iran

    Nezameddin Faghih

  2. School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran

    Ebrahim Bonyadi

  3. Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

    Lida Sarreshtehdari

Authors
  1. Nezameddin Faghih
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  2. Ebrahim Bonyadi
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  3. Lida Sarreshtehdari
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Contributions

Nezameddin Faghih: The main ideas behind the paper have been developed by him and also he acted as the supervisor trying to improve the structures of this article.

Ebrahim Bonyadi: Conceptualization, Methodology, Software (including R programing language, and SPSS), Validation, Formal analysis, Data Curation, Writing–Original Draft, Writing–Review and Editing, Supervision, Project administration.

Lida Sarreshtehdari: Investigation, Resources, Formal analysis, Writing–Original Draft, Visualization, Project administration.

Corresponding author

Correspondence to Ebrahim Bonyadi.

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The authors declare no competing interests.

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Faghih, N., Bonyadi, E. & Sarreshtehdari, L. Entrepreneurial Motivation Index: importance of dark data. J Glob Entrepr Res 11, 15–27 (2021). https://doi.org/10.1007/s40497-021-00277-y

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