Universities are increasingly recognized as key players in developing entrepreneurial ecosystems (EEs) that support social entrepreneurs (SEs). This paper explores how universities reconfigure EEs to foster SEs by identifying and categorizing the roles of students, staff, academics, and alumni. Using data from coordination reports of an Erasmus+-funded capacity-building project and workshops conducted at social entrepreneurship-focused academic conferences, the study applies an inductive analysis to develop a typology of dynamic capabilities to which each university actor contributes. The findings highlight how universities strategically deploy resources and align their actors to create a supportive environment for SEs. By clarifying the distinct roles of different university actors, this typology provides practical insights for universities aiming to enhance their impact on SEs and contribute to broader societal change. The study offers both theoretical contributions to social entrepreneurial universities and practical guidance for universities.

This paper examines the outcomes (i.e., persistence or exit) of entrepreneurs suffering from major depressive disorder. The results show that entrepreneurs suffering from MDD use individual coping strategies. While these strategies help entrepreneurs to cope with their depressive symptoms, they also reduce the productivity of their firms. Moreover, employees respond to their bosses' coping strategies. Their behavior leads to lower productivity of their bosses' firms. Furthermore, entrepreneurs suffering from MDD lower their individual performance thresholds. This effect results from the lack of alternative employment opportunities in which they can apply their coping strategies. Thus, entrepreneurs suffering from MDD persist in entrepreneurship even when their firms are underperforming. The availability of alternative income opportunities in which they can apply their coping strategies raises the individual performance thresholds of the entrepreneurs. This circumstance provokes their firm exit

Sustainable Business Models (SBMs) are recognised as among the best strategies to improve business sustainability and contribute to sustainability transitions. However, there is a lack of insights on how exactly businesses can adopt SBMs which is particularly pronounced in the case of small and medium enterprises (SMEs). Based on semi-structured interviews with Australian SMEs that have successfully adopted SBMs, this paper examines how these SMEs navigated the transformation from traditional, profit-centric business models to SBMs, providing insights into the stages and strategies of the transition process. The paper identifies three broad stages in the SMEs journey to adopting an SBM: 1) pre-departure; 2) transition; and 3) SBM take-off. In addition to describing the characteristics of each stage, different strategies that supported the SMEs’ transitions are discussed. This paper responds to calls for much needed insights regarding the transformation of existing business models towards SBMs and works towards unlocking the largely untapped potential of SMEs to contribute to sustainable development.

Artificial Intelligence (AI) technologies, including natural language processing, and machine learning, present the potential a transformative opportunity to disrupt social, economic, and political interactions in Sub-Saharan Africa (SSA) (Dionisio et al., 2024; Dwivedi et al., 2021; Kiemde & Kora, 2022; Truong & Papagiannidis, 2022). As AI technology evolves, its capacity to disrupt and reshape traditional systems and drive social change is increasing attention in management and entrepreneurship research (Autio et al., 2018; Berendt, 2019; Ciulli & Kolk, 2023; Obschonka & Audretsch, 2020; Rawhouser et al., 2022; Weber et al., 2022). However, to fully leverage the potential of AI-driven technologies in SSA, requires a deep understanding of the socio-technical and institutional dynamics that influence their adoption and impact. We define social innovations as new ideas, practices, or outcomes aimed at addressing complex grand challenges associated with sustainable development, best addressed through alternative social relations involving collective and collaborative multi-actor partnerships and networks (Dionisio et al., 2024; Mulgan, 2006; Tracey & Stott, 2017).

While existing literature explores the technological, organisational, and environmental factors that influence AI’s capacity to drive value creation and enhance organisational performance, the potential for AI to fuel social innovation in SSA remains underexplored (Mikalef & Gupta, 2021; Weber et al., 2022). Beyond automation and optimisation, AI-enabled systems within organisational contexts are expanding, driving innovative business practices, novel service propositions, and transformative organisational changes (Berente et al., 2021; Dwivedi et al., 2021; Floridi et al., 2018). These systems are extending their reach into domains that were previously considered the exclusive domain of humans (Dwivedi et al., 2021). The use of AI for societal benefit offers opportunities to address inefficiencies in resource allocation, poverty, social exclusion, healthcare inequities, political participation, and access to education (Adams et al., 2023; Eke et al., 2023; Foster et al., 2023; Vinuesa et al., 2020). However, AI development, access, and deployment gaps persist between developed and developing countries, perpetuating an asymmetric North-South dynamic (Eke et al., 2023; Kiemde & Kora, 2022; Rawhouser et al., 2022).

AI adoption in Africa lags behind other regions, leading to underrepresentation in global indices and studies (Fjeld et al., 2020; Maslej et al., 2023; Oxford Insights, 2023).. Factors such as limited access to capital, regulatory uncertainty, inadequate infrastructure, and a shortage of digital skills are key reasons for this gap (Lay & Tafese, 2023; Sydow et al., 2020; Turker & Altuntas Vural, 2017). These persistent challenges hinder the growth and scalability of tech-driven ventures in SSA, creating higher transaction costs, weaker legal frameworks, inadequate intellectual property protections, and funding limitations (Mair et al., 2012; Sydow et al., 2022). Spatial-contextual-related challenges further complicate the situation, as current technologies often overlook contextual factors (e.g. gender and ethnic aspects) and the lack of quality, accessible datasets in African languages hinders the potential of AI for the continent (Adams et al., 2023).

Despite these challenges, AI deployment is growing in SSA, with countries such as Mauritius, Rwanda, Senegal, and Benin developing national AI strategies (Kiemde & Kora, 2022; Oxford Insights, 2023). Key enablers include a youthful, entrepreneurial population, rising digital penetration, local institutional voids, and increased interest in technology-driven solutions (Rawhouser et al., 2022; Sydow et al., 2020). Entrepreneurs in SSA are creating homegrown tech solutions that foster new markets, challenge traditional norms, and defy existing regulations (Eke et al., 2023; Mair et al., 2012; Tracey et al., 2011). While these efforts provide a foundation for AI’s role in transformative change, significant barriers to scaling AI-driven innovations remain.

AI-driven opportunities come with technical challenges and ethical challenges. The opacity of AI systems (‘Black box’ problems) poses significant concerns, as do data privacy risks and increased inequality (Berente et al., 2021; Floridi et al., 2018). The energy intensity of AI may exacerbate societal disparities, especially in SSA, where access to electricity and internet infrastructure is limited (George & Schillebeeckx, 2022). Moreover, AI can reinforce existing biases and stereotypes when trained on biased datasets or analytics models (Amankwah‐Amoah, 2019; McDuie‐Ra & Gulson, 2020; Pizzi et al., 2020). In some cases, AI technologies can be weaponised for surveillance or control, undermining individual freedoms and deepening social divides (Berente et al., 2021).

AI research often focuses on its technical dimensions or impact on organisations and individuals in isolation (Berente et al., 2021). Given these risks, it is necessary to understanding both the positive and negative externalities of AI. Addressing the technological and social dimensions of AI adoption in SSA will help ensure responsible and inclusive applications (Tracey et al., 2011; Turker & Altuntas Vural, 2017). Additionally, to realise the potential of AI technologies while mitigating the undesirable effects will require institutional change in social relations and economic structures because ‘problems of poverty, exclusion, segregation and deprivation or opportunities for improving living conditions (…) cannot find satisfactory solutions in the “institutionalised field” of public or private action’ (Moulaert, 2010, p. 2).

This paper applies an affordance approach to study how AI can drive social innovation. In our view, social innovation is critical for ensuring AI benefits less privileged people, not just high-skilled people and capital owners. We explore the interplay between AI affordances and social innovation in SSA. Affordances refer to the opportunities that AI technologies, present to individuals or organisations to achieve specific goals (Burton-Jones & Volkoff, 2017; Majchrzak et al., 2016; Strong et al., 2014). However, these affordances must be actualised—triggered or translated into concrete actions through purposeful engagement—to deliver tangible outcomes (Burton-Jones & Volkoff, 2017; Majchrzak et al., 2016; Strong et al., 2014). The Affordance-Actualization (AA) framework offers a lens to examine how AI affordances are recognised and actualised within specific socio-economic contexts, with entrepreneurs and their ventures acting as agents of social innovation.

Our analysis emphasises that AI affordances are not deterministic; their actualisation depends on the interactions between technology and the context in which it is applied. In SSA, the ability to actualise AI affordances is influenced by organisational factors (e.g., strategies, customer needs, values, and regulatory issues ) and structural factors such as access to resources, digital infrastructure, and institutional frameworks (Lehrer et al., 2018; Leonardi, 2011; Volkoff & Strong, 2018). While weak institutions, fragmented markets, and resource scarcity may limit the deployment of AI technologies for social changes, they also encourage creativity among entrepreneurs who deploy the technologies to modify existing norms and regulations (Mulgan, 2006; Tracey et al., 2011).

To address these barriers, tech entrepreneurs engage in ‘institutional work’—creating, maintaining, or disrupting institutional structures to support innovation (Lawrence & Suddaby, 2006). change. In SSA, where formal institutions are often weak or absent, institutional work becomes critical in legitimising AI technologies and creating the conditions necessary for their successful adoption (Sydow et al., 2022). Entrepreneurs must navigate these institutional voids to build supportive environments for AI-driven social innovation. While affordance actualisation theory focuses on how actors perceive and use AI technologies, institutional work theory emphasises how agents create conditions necessary for technology success.

Consequently, we propose the following research questions: How do AI affordances intersect with organisational dynamics and socio-economic contexts to drive social innovation in SSA? What contextual factors influence the successful adoption and implementation of AI technologies in this region?

To address our research questions, we utilised in-depth qualitative methods for exploring under-researched processes, such as how socio-tech entrepreneurs in SSA actualise AI affordances ( (Eisenhardt, 1989). Our inductive approach involved gathering data on current activities and agents’ perceptions to derive theoretical insights (Gioia et al., 2013). We employed purposeful sampling to ensure theoretical density and conducted online searches to identify organisations involved in AI across various AI artefacts (e.g., machine learning, drone technology, computer vision), sectors (e.g., financial services, healthcare and agriculture), and development stages (e.g., scaling, validation). From September 2023 to August 2024, we conducted 23 semi-structured interviews via Zoom with founders, CEOs, project managers, developers, and researchers engaged in AI projects in SSA. Snowball sampling helped expand the participant pool. We supplemented interview data with secondary sources, such as press releases and internal documents. Using an explorative qualitative approach and inductive analysis, we transcribed and analysed the interviews with Atlas. ti (Gioia et al., 2013). The initial coding identified first-order codes reflecting participants’ language, focusing on AI affordances, motivations, opportunities, and challenges. We then performed axial coding to group related codes into larger themes. Finally, we condensed these into aggregate dimensions, ensuring theoretical saturation through iterative data collection and analysis.

This study makes three key contributions to the literature on AI, innovation and entrepreneurship (e.g. (Moren et al., 2022; Obschonka & Audretsch, 2020). First, it applies the affordance-actualisation perspective to SSA, extending the work of Information Systems scholars (e.g. (Du et al., 2019; Strong et al., 2014) by extending the applicability of the theory to social innovation in resource-constrained environments. Second, integrating the institutional work lens enriches the affordance-actualisation framework, highlighting the dynamic interplay between AI, individuals and local institutional contexts. This offers a framework to explore how local institutions influence and shape AI-driven innovations. This approach underscores the importance of aligning technological capabilities with local conditions, institutional structures, and social goals to achieve sustainable and inclusive development. Third, it addresses a gap by focusing on SSA, an underexplored region in technology adoption studies, contributing to a more inclusive understanding of the role of AI in social innovation (Amankwah‐Amoah, 2019; Sydow). Current AI reporting highlights positive high-tech solutions, thus contributing to an overhyped perception of AI potential while neglecting its limitations (Bareis & Katzenbach, 2022). Therefore, we seek to understand the relationship between AI technologies and the underlying usage practices to determine the positive or negative consequences. These findings are expected to inform evidence-based policies and strategies that foster entrepreneurship and (social) innovation in SSA and guide interventions to enhance AI’s impact in resource-constrained settings.

Our findings highlight the need for targeted interventions that address the structural and institutional barriers limiting AI adoption in SSA. Policies to strengthen digital infrastructure, enhance access to capital, and foster AI skills development are essential for realising AI’s potential to drive social innovation in the region. This study provides a foundation for future research and policy discussions on leveraging AI for the common good in SSA by advancing our understanding of how AI affordances are actualised in resource-constrained environments.

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This paper explores how entrepreneurs respond to various, sometimes conflicting, policies in the entrepreneurial ecosystem. Prior research has highlighted the importance of government policy to support rural and regional development. This is particularly important in the context of rural entrepreneurship where the goal is facilitating the economic development and survival of rural economies. However, there has been limited focus on the evolution of entrepreneurial ecosystems and unintended negative consequences of a bundle of policies aimed to support sectoral development. The empirical setting for this paper is a full population analysis of the Irish artisan cheese sector. Our findings demonstrate that policy mix presents significant challenges for entrepreneurs. We reveal three primary responses to a mix of policies, entrepreneurs may ‘navigate’, ‘bridge’ or ‘drown’ in the swamp of policies. We use the swamp metaphor to encapsulate the evolving nature of the policy mix and the challenges faced by entrepreneurs as they navigate the ecosystem.