Journal of Business Venturing Insights最新文献

Entrepreneurship is multifaceted and there is considerable acknowledgement of the relevance of human agency and individual behavior – since entrepreneurship has proven fundamentally personal. While entrepreneurial behaviors are traditionally aligned with high growth, start-up firms, an evolving body of research recognizes that entrepreneurial behaviors can manifest in non-traditional, ‘heterodox’ contexts. One such context is the Daigou phenomenon. Daigou translates to ‘buying on one's behalf’ – Daigou serve as important ‘middlemen’ – connecting Chinese customers with Western brands by deviating from accepted or orthodox standards or beliefs to exploit free-market networks and engage in cross-border exporting. Daigou, in the context of this research functions as a novel heterodox backdrop to probe manifestations of entrepreneurial behavior, thus identifying how entrepreneurial actors behave ‘entrepreneurially’ in heterodox settings. Our review of the literature delivers three meaningful contributions. Firstly, the findings contribute by advancing understanding of entrepreneurial behaviors in non-traditional and heterodox contexts. Secondly, by unveiling and incorporating new insights to the entrepreneurial behavior literature by integrating Daigou as a novel context for advancing understanding of meaningful heterodoxies. And thirdly, contributing back to the marketing literature by integrating insights from entrepreneurial behavior to inform conceptualizations of Daigou.

What inspires new entrepreneurial theory? And, what should it be – new entrepreneurial phenomena, new and different scholarly voices, or something else? There is recent evidence that it is a ‘something else’ – something that may be interpreted as unusual-yet-too-familiar – where established voices are stretching fictional stories and language games to get to their preferred models and definitions. While diversity and extension of ideas is more than welcome in our field, perhaps what is even more important is that that emerges from a more diverse and stretched set of authors, pedigrees and schools of thought. So, what is there to do? First, we can show that this current approach is not working through example. Second, we can suggest how to be better inspired. Third, we can consider the commonalities in these examples to comment on what that means for progress in the field as it is, and then to suggest ways for improvement at the field level. We take these three steps with an eye to the scientific mindset and a set of expectations about what makes good entrepreneurship theory

In this study, we examine age and race as important predictors to entrepreneurial outcomes. Drawing on social identity theory, we examine a phenomenon among entrepreneurs in the board game industry, where our findings are counterintuitive to previous research on age and race in entrepreneurs, suggesting a shift in identity and social norms in the industry. With 69 entrepreneurs surveyed, and over 1000 data points of product play testing feedback, we find games designed by younger, minority entrepreneurs were rated higher by the play testers on innovation and the intent to buy. Additionally, we find that being a minority attenuated the negative relationship between age and entrepreneurial outcomes, such that older minority entrepreneurs did not experience a similar decline in outcomes as did their white counterparts. We discuss the implications of our findings.

In creating innovative entrepreneurial opportunities, entrepreneurs are characterized by limited information processing capabilities and local search routines that are immanent to humans. Machine learning (ML) offers the opportunity to overcome these limitations while reshaping the innovation process. It is indisputable that ML alone cannot realize entrepreneurial opportunities, but that close collaboration with the human entrepreneur is required. While entrepreneurs usually act according to the principles of effectuation logic in situations of high uncertainty and according to the principles of causation logic in situations of risk, it remains unclear how co-creation with ML affects the entrepreneur's decision-making behavior. By contrasting the functionalities of four different ML paradigms with the principles of the two decision logics, it is shown that supervised ML supports causation logic, while unsupervised and reinforcement ML support effectuation logic in their approach. As a fourth, semi-supervised ML is classified somewhere between effectuation and causation. However, in relation to the situational context of different types of uncertainty, ML may also prove limiting for effectuation by transitioning to causation in the medium-term.

A central prerequisite for market entry and the subsequent economic performance of academic startup ventures is that the product on which the business model is based must be sufficiently well developed. The present research aims to provide answers in the quest to identify the explanatory factors in the development stage of the product of academic spin-offs in the pre-seed phase of their ventures. This is based on various factors that play a role in the innovation process and the development of new products – including characteristics of the project, characteristics of the R&D work and business model development, previous freelance experience, networking, financing, and technology field. The empirical analysis of data from academic startup ventures enrolled in a large German startup support program – using structural equation modeling – revealed both direct and indirect statistically significant effects, showing that a large proportion of the selected variables can be employed to explain the development stage of the product. The findings indicate that the R&D work stage has a medium-sized effect, while business model development stage, the degree of networking, project feasibility, and previous freelance experience connected with the product all have small effects.

To better understand the role of artificial intelligence in the development of entrepreneurial rhetoric, we examine how generative language models such as ChatGPT serve as viable tools for content creation. Using an established framework for examining CEO celebrity (Creator, Transformer, Rebel, and Savior), we illustrate how such models can effectively produce and refine elevator pitches, social media pitches, and crowdfunding pitches commonly used in the study of entrepreneurial rhetoric. We demonstrate ChatGPT's ability to mimic each celebrity CEO archetype by prompting language in the style of exemplars, including Elon Musk, Indra Nooyi, Tony Hsieh, and Lisa Su. Implications of prompt engineering—the fine-tuning of inputs fed into language models to produce precise output—for entrepreneurship research and practice are discussed. We conclude by advancing the idea that the emergent and enduring value of generative models is, at its core, dependent on effective prompt engineering.

Despite plentiful research into venture capital (VC), the process of obtaining funding still frustrates both VC firms and startups. While uncertainty is necessary for the returns that VC investors desire, there are inefficiencies in the process that stem from information asymmetry. We articulate those inefficiencies from both the founder and the VC perspective and offer some pathways toward reducing those inefficiencies to drive better outcomes for startups, VCs, and society as a whole.

While corporate venture capital funds (CVCs) are commonly analyzed as homogenous units, they display significant heterogeneity across various organizational aspects, which affect them and subsequently their portfolio firms. Using a sample of 383 European portfolio firms from the longitudinal VICO dataset, we first investigate the impact of investor type (independent vs corporate) on firm operating efficiency. We show that firms backed by CVCs suffer reductions in productivity. We then account for CVC heterogeneity and find that these significant reductions in operating efficiency only occur for ventures backed by endoisomorphistic CVCs, which resemble more corporate structures. By contrast, firms backed by exoisomorphistic CVCs, which resemble more independent venture capital structures, do not show significant differences in productivity compared to ventures that receive independent venture capital backing.

Research and public interest on economic inequality have grown over the last years. Family firms and the concentration of wealth and power in the hands of a few wealthy entrepreneurial families have been discussed as both a cause and a consequence of economic inequality. Yet, so far, we lack knowledge about the relationship between economic inequality and the prevalence of family firms in an economy. Our study investigates how the share of family-controlled public firms correlates with various measures of income and wealth inequality. The results show that a higher share of public family-controlled firms leads to more income inequality in a country. This effect is particularly pronounced for the middle of the income distribution as opposed to the top quantiles. Redistribution only mitigates this effect to some extent, as the effect is significant for market income and disposable income. We also find that a higher share of family-controlled firms contributes to an increase in wealth inequality. Our results are of economic relevance as, for instance, a one standard deviation change in the share of family-controlled firms leads to an increase of around 1.3–1.5 percentage points in the Gini coefficients for market income, disposable income, and wealth.