Call for Papers

The landscape of entrepreneurship has changed strongly in recent years and a new phenomenon is spreading rapidly, namely transformational entrepreneurship. This emergent phenomenon is linked to the need to implement effective and efficient entrepreneurial behaviors that address global challenges (including unemployment, economic underperformance and societal evolution) (Maas & Jones, 2019). In this vein, entrepreneurial activity continues to evolve due to technological enhancement. In particular, recent developments in technology (Duan et al., 2019; Dwivedi et al., 2019) have led to new forms of entrepreneurship (Nambisan, 2017).

As the information systems (IS) discipline continues to mature and subsume various domains of research, opportunities to examine prior research, synthesize findings, resolve inconsistencies, identify gaps in knowledge, and determine directions for future research assume greater importance. These can be handled using meta-analysis, a family of methods which enables the aggregation and analysis of prior empirical findings (Glass et al. 1981; Hedges and Olkin 1985; Hunter and Schmidt 1990; Rosenthal 1991; Lipsey and Wilson 2001; Borenstein et al. 2009). Despite general criticisms such as “apples and oranges” and “garbage in, garbage out,” meta-analysis has received considerable attention in IS research over the last few decades (Jeyaraj and Dwivedi 2020).


The availability of and access to big data has changed, as digital transformation initiatives are increasingly maturing globally, assisted by the growth of computational capabilities (Grover et al., 2020). Whilst data availability and access used to be a major challenge for information systems (IS) research, the current abundance of big data has now resolved this considerably. The theoretical building blocks of IS research come mainly from management theory, organization theory, behavioural theory, computer science theories, and systems theory (Barki, Rivard and Talbot, 1993). Apart from the core computer science theories, the other related theories enable IS researchers to explain how users interact with technology artefacts within individual, organizational, social, and political contexts and the impact of such interaction. Theory building, however, seems to have been disrupted by the current trends in big data-driven research, whereby the essence of contributing to theory is increasingly seen to be lacking at all levels of analysis. Concurrently, big data-driven research may inspire contributions towards design science and action research, whereby innovative solutions may also be created which help to define ideas, capabilities, practices, and innovative products or services through big data analysis (Angelopoulos et al., 2020; Hevner et al., 2004).

The Global South (GS), a term both contested and misunderstood, has received renewed attention today. Collaboration among stakeholders representing—or claiming allegiance (e.g., China) to—the GS, is on the rise. The velocity and scope of business-to-business (B2B), sectoral, and industry collaboration prompts new modes of cooperation and new ways of thinking about the GS. A new subtle balance of power and influence in the GS permeates business and politics. In a number of ways, e.g., due to the FDI and GVC channels, the ‘Global North’ is influenced by these developments too. Certainly, there is more to it than that. The inroads of the broadly defined AI and the promises it bears will accelerate processes and developments already throbbing in the GS.

In the current digital era, small and medium-sized enterprises (SMEs) use digital technologies to grow their online businesses alongside their offline operations. The digital environment provides numerous opportunities for entrepreneurs who start and run SMEs to have an impact and grow their businesses. Retail industries and operations have had to adapt to the digital environment in order to adjust to the needs of customers around the world during the COVID-19 lockdown. There is a strong need for digital technologies in order to grow businesses with a restricted budget and limited resources. Emerging technologies, especially in data analytics (Sun et al., 2020), enterprise communication and artificial intelligence, are paving the way for SMEs’ growth and optimization.

By 2025, nearly a quarter (24%) of global GDP will come from digital technologies such as AI (World Economic Forum). AI has been claimed to offer almost unlimited potential across sectors and industries, ranging from reinventing business models to redesigning the customer experience to decision-making (Duan et al., 2019) to changing the nature and the future of work (Schwartz et al., 2019).

AI in the workplace environment may affect the nature of work, replace specific roles currently performed by humans, or even better, enhance human capabilities (e.g. AI-enabled recruiting) (Dwivedi et al., 2019). It is estimated that till 2030, automation and AI will significantly impact the one third of our work activities (Manyika et al., 2017). This means that the current way we work and operate might be altered radically, which can potentially cause significant societal changes.