This meta-analysis of 31 studies over 20 years advances our understanding of the gender gap in entrepreneurial bank finance. Findings from previous research on the relationship between entrepreneurs’ gender and bank financing are mixed, which suggests the need to pay particular attention to entrepreneurs’ social context. In this study, we develop a model of how social gender norms explain variation in women entrepreneurs’ (vis-à-vis men entrepreneurs’) access to bank finance. Specifically, we theorize how women’s formal (their nations’ political ideologies) and informal (women’s empowerment) social standing within their societies influence gender discrimination in entrepreneurial bank financing. Consistent with most previous studies, our baseline results show that women entrepreneurs’ business loan applications are rejected to a greater extent than men entrepreneurs’ loan applications. Women entrepreneurs also pay higher interest rates on loans than men entrepreneurs. Further, in societies dominated by a conservative (rather than a liberal) political ideology, the positive relationship between women entrepreneurs and loan interest rates is more positive. Interestingly, gender discrimination in loan rejection and interest rates is magnified in societies with greater women’s empowerment. Taking a social gender-norm perspective, our analysis establishes a gender gap in entrepreneurial bank finance, and we outline an agenda for further research.

Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms.

How should new venture ideas be framed in order to acquire human resources and gain support in times of crisis characterized by struggling or failing institutions and governmental organizations? To answer this question, we analyze 316 new venture ideas aimed at alleviating the COVID-19 crisis in 11 countries. We investigate different linguistic framing configurations and test their persuasive power for human resource acquisition. Our fuzzy-set qualitative comparative analysis (fsQCA) and linguistic analysis reveal that a “common enemy framing” is crucial for obtaining resources in crisis contexts. Non-profit venture ideas, specifically, may acquire resources via two additional paths: adding positive emotional content or using an entrepreneurial hustle framing with concrete calls to action. Our findings provide novel insights into entrepreneurial resource acquisition and idea framing during crises.

Recent dirty work research has begun to explore intersectionality, attending to how meaning is made at the intersection of multiple sources of taint. This research has shown that individuals often construct both positive and negative meanings, which can be challenging to manage because the meanings people construct require a certain coherence to provide a foundation for action. This challenge is intensified when dirty work is intractable—when it is difficult, if not impossible, for a person to avoid doing this work. Our study of meaning-making in the face of intractable dirty work examines ragpickers in Mumbai, India, who handle and dispose of garbage, and are further tainted by belonging to the lowest caste in Indian society, and living in slums. These ragpickers constructed both an overarching sense of helplessness rooted in the intractability of their situation, and a set of positive meanings—survival, destiny, and hope—rooted in specific facets of their lives and enacted through distinct temporal frames. By holding and combining these disparate meanings, they achieved “functional ambivalence”—the simultaneous experience of opposing orientations toward their work and lives that facilitated both acceptance and a sense of agency, and enabled them to carry on in their lives.

As e-commerce has increasingly gained traction in the retail market, many traditional “brick-and-mortar” retailers are innovating their business models and making the transition towards digital business models. While scholars have started to examine the influence of digitalization on various business model elements, they have so far paid little attention to its implications on the external relationships in which firms engage for value creation. Building on a qualitative analysis of seventeen interviews, this study develops a two-stage framework for the transition to digital business models. In Stage 1, retailers collaborate with specialized service providers to implement a digital business model. As firms from the retail ecosystem collaborate with firms from the digital-service ecosystem to create a value proposition for end-customers, a meta-ecosystem emerges. In Stage 2, firms (retailers) seek to differentiate themselves from their competitors in the meta-ecosystem. Physical interactions with the digital service providers, the product suppliers, and the customers are a primary means towards this end. Thus, digitalization does not make physical interactions and close personal ties obsolete. Our study has substantial implications for the academic literature and management practice.

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company’s entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research. © 2020 The Author(s)

Research has focused on the role of entrepreneurial action in alleviating poverty. However, there is a gap between individuals’ short-term outcomes from entrepreneurship overcoming immediate resource concerns and the large-scale impact of entrepreneurship on institutional and system change. Therefore, in this study, we explore entrepreneurs’ beliefs about how entrepreneurial action can alleviate poverty. To do so, we conducted a qualitative study of entrepreneurs of businesses located in Indian slums and identified the impact of expectations, role models, and the subjective value of their children’s education in attempts to alleviate poverty.

Employing a panel setting of 88 counties in the State of Ohio over the five-year period ending in 2006, this study aims to investigate the applicability of the knowledge spillover theory of entrepreneurship in explaining the relationships between flagship enterprises, entrepreneurial clusters, and business entry rates. The study confirms the overall positive relationship between flagship enterprises and startup rates, and the negative relationship between entrepreneurial clusters and startup rates. It further demonstrates that the effect of clusters is moderated by local unemployment rates so that higher rates of unemployment weaken the negative impact of entrepreneurial clusters on startup rates. Based on the evidence collected, policy makers should increase support for flagship enterprises in their regions, and would-be business owners should consider locating their ventures in proximity to flagship companies.

This paper explores how government venture capitalists approve or reject financing applications. Based on longitudinal observations, complemented by interviews, documentation, and secondary data, the findings show the limited influence of the regulative and normative logics (e.g., formal guidelines and accepted behavior) on government venture capitalists’ decisions. Instead, individual decisions are observed to be largely overshadowed by cognitions and heuristics, which dominate formal regulations and socially constructed group-level norms. Although official decision communications state that regulations have been followed, the evidence suggests that the cognitive logic dominates the funding decision-making process through a set of overshadowing forces that restrict the influence of the normative and regulative logics on funding decisions. This research has implications for venture financing and highlights the importance of cognitions in shaping venture capital decisions.