One size fits all? Using machine learning to study heterogeneity and dominance in the determinants of early-stage entrepreneurship

Graham, Byron; Bonner, Karen

doi:10.1016/j.jbusres.2022.07.043

Cited by 17 publications

(10 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of methodology, we extend Graham and Bonner’s ( 2022 ) call to small business scholars to explore the potential of machine learning. The studies with different approaches, in different contexts and with different datasets could aid in generalisability.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, we draw on decision trees that have already been successfully applied in the real estate context with Feldman and Gross ( 2005 ) as well as in growth determinants with Tan ( 2010 ) and more recently in conjunction with configurational approaches (Graham & Bonner, 2022 ), who also discussed the advantages of these approaches. They stressed their ability to handle large datasets with various data types, missing values and outliers as well as their ability to capture interrelationships between variables in different parts of the measurement space, which is essential given the varying capitalisation and investment decisions.…”

Section: Empirical Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Small Business Property Tax Reductions and Firm Productivity

Matikonis

Gobey

2023

Small Bus Econ

View full text Add to dashboard Cite

We investigate the productivity spillovers from the UK government’s decision to use extensive property tax reductions as a key instrument to stimulate innovation in smaller businesses and drive local growth. To capture the complex interaction and clustering of hierarchical effects, we apply non-parametric Random Effects Expectation Maximisation algorithm that complements more standard econometric estimators, namely matching to control for endogeneity and control functions to estimate total factor productivity. These approaches enabled us to incorporate various contextual configurations in comparing the recipients of these reductions to non-recipients with regard to productivity, in which the UK has experienced a considerably worse performance than its peers since the great recession. Contrary to policy assumptions and business community expectations, we show that generic tax reductions, when significant, are mostly associated with lower productivity and thus have been unsuitably chosen as a policy mechanism to stimulate productivity growth. We further show how instruments that are not built for causality could be beneficial for policy evaluation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Empirical Methodologymentioning

confidence: 99%

Small Business Property Tax Reductions and Firm Productivity

Matikonis

Gobey

2023

Small Bus Econ

View full text Add to dashboard Cite

show abstract

“…The results for this dimension contribute to the developing literature on this subject and the conflicting findings of the knowledge set (Schlaegel et al 2013 ; Valdez and Richardson 2013 ; Fuentelsaz et al 2019 ). According to these results, the possible inference may be that people in both countries have insufficient knowledge and skills related to entrepreneurship to start, manage, and maintain a new business, or that entrepreneurship-related education does not support entrepreneurial intentions (Wales et al 2021 ; Graham and Bonner 2022 ). Additionally, a possible explanation is that entrepreneurial activity is less likely to emerge due to the prevalence of a collectivist culture in developing and transition economies (Dheer Ratan 2017 ; Junaid et al 2022 ).…”

Section: Implications and Conclusionmentioning

confidence: 99%

Institutional and individual determinants of entrepreneurial intentions: evidence from developing and transition economies

Bağış

Altınay²,

Kryeziu³

et al. 2023

Rev Manag Sci

View full text Add to dashboard Cite

This study aims to examine the effect of institutions on entrepreneurial intentions, the mediation of individual motives in this relationship, and the moderating effect of the country. We tested a sample of 678 questionnaires using quantitative research methods. We used confirmatory factor, correlation, multiple regression, mediation, and moderated mediation analyses to analyze the data. Findings show that normative institutions affect entrepreneurial intentions in Turkey and Kosovo. They also reveal that the personal attitude, subjective norm, perceived behavioral control, and need for achievement have a full mediation relationship between regulatory institutions and entrepreneurial intentions and a partial mediation relationship between normative and cultural-cognitive institutions and entrepreneurial intentions. Finally, we found no moderating effect of the country in the relationship between institutions and entrepreneurial intentions. The study contributes to the literature and provides policy and managerial implications on the macro and micro factors that affect entrepreneurial intentions in developing and transition economies.

show abstract

“…Hallman et al (2022) develop a method of detecting bidding by applying a machine learning algorithm to non‐incumbent (i.e., competitor) auditor views of public companies' SEC filings. In the field of management, some literature has used machine learning methods to predict entrepreneurial success (Graham & Bonner, 2022; Kim et al, 2023). In short, supervised learning enhances the predictive accuracy of traditional methods.…”

Section: Literature Reviewmentioning

confidence: 99%

How to achieve the high‐quality development of SRDI enterprises? Evidence from machine learning

Qu,

Bai

2024

Manage Decis Econ

View full text Add to dashboard Cite

Exploring how SRDI enterprises achieve high‐quality development constitutes a pivotal task for small and medium‐sized enterprises (SMEs) and niche leaders. While prior research primarily concentrated on the influence of innovative policies on SRDI enterprises, it has disregarded the intrinsic propelling forces intrinsic to these enterprises. To bridge this research gap, our study leverages a machine learning model that incorporates 19 feature variables spanning four dimensions—“specialization, refinement, distinctiveness, and innovation”—to anticipate high‐quality development in enterprises. Drawing on a sample of 667 A‐share SRDI‐listed enterprises from 2012 to 2022, and after subjecting the data to preprocessing, the study employs the mean of five machine learning models to predict high‐quality development in enterprises. Moreover, we discern pivotal feature variables and dimensions. Notably, outcomes underscore the paramount significance of market share in achieving high‐quality progress within SRDI enterprises, with refinement emerging as the foremost feature dimension among the four. Moreover, during the three stages delineated by SRDI policies, research and development intensity, equity financing, and market share emerge as the preeminent feature variables within their respective stages.

show abstract

One size fits all? Using machine learning to study heterogeneity and dominance in the determinants of early-stage entrepreneurship

Cited by 17 publications

References 95 publications

Small Business Property Tax Reductions and Firm Productivity

Small Business Property Tax Reductions and Firm Productivity

Institutional and individual determinants of entrepreneurial intentions: evidence from developing and transition economies

How to achieve the high‐quality development of SRDI enterprises? Evidence from machine learning

Contact Info

Product

Resources

About