IJMR Call for Papers - Grand Synthesis: Unifying the Fragmented Science of Business for All Stakeholders

The current technologies, outlets, and incentives of business and management scholarship have been incapable of solving such a complex social problem (Chen & Hitt, 2021). Since Gordon and Howell (1959) and Pierson (1959), later reinforced by Porter and McKibbin (1988), the business and management scholarship has been rewarding incremental research that develops and tests coherent hypotheses of interest from a simplified view of complex problems. This reductionist approach is perpetuated by discipline boundaries, peer pressures for granular specialization, limited space, scope, and frequency of periodical outlets such as journals, and lack of diversity in scholarly incentives. As a consequence, both managers and researchers face a knowledge fragmentation conundrum. The literature, data, and communities for different stakeholder values are becoming increasingly fragmented, distributed into silos, and disconnected. It has become exceedingly difficult to develop complete, explanatory frameworks connecting all the knowledge silos, because the effects across these silos and their interrelatedness (e.g., complementarity) are poorly understood. There are an increasing number of specialists and experts focusing on different topics piecewise, but limited solutions to the complex whole.

The problem of knowledge fragmentation has been recently raised by major funding agencies, which attempt to incentivize the integration of currently isolated knowledge advancements. For instance, in the 2017 consultation of its Research Excellence Framework, the UK Research and Innovation, the largest funding agency for higher education institutions, proposed a series of revisions to its old review policies that tend to disadvantage interdisciplinary research. In the US, the National Science Foundation defines Growing Convergence Research, a type of research that seeks to integrate advances across disciplines for solving complex problems on societal needs, as one of its current 10 Big Ideas for investment priorities. More specifically, the Defense Advanced Research Projects Agency (DARPA) in the US carried out a $45 million Big Mechanism program between 2014 and 2017 to fund innovations to integrate fragmented cancer models into a holistic causal framework (You, 2015). Although the business scholarship also suffers significant knowledge fragmentation, systematic efforts to innovate our research foundations have been relatively reticent (Chen & Hitt, 2021).

The first hurdle is fragmented science. As suggested by a recent International Journal of Management Reviews (IJMR) special issue, the theories and methods on organizational performance measurement and management have been advancing within disciplines. A meta-theory has failed to emerge (Bititci, Bourne, Cross, Nudurupati, & Sang, 2018). Creating and distributing stakeholder values is a complex social task, with many levels, disciplines, and heterogeneous stakeholder interests (Hitt et al., 2007; Bridoux and Stoelhorst, 2014; Bridoux et al., 2011). The conventional scientific approach is to study these different components in a piecewise manner using

discipline-based, coherent theory-driven, and reductionist models (Chen & Hitt, 2021; Cohen, 2015; Bammer, 2013). Instead of studying multiple stakeholder values simultaneously, our knowledge about an organization as a whole is fragmented into granular specializations. They often use different assumptions of human behaviors and prioritize some stakeholder values over others (e.g., human resources management for employees, marketing for customers, corporate strategy/finance for investors, operations management for suppliers, and ethics for community/environment).

The second hurdle is distributed evidence and data. Except for some shareholder/financial data, stakeholder data are mostly unstructured (e.g., natural language processing [NLP] data, etc.) and kept in dispersed and uncoordinated sources (McAfee et al., 2012; Gerhardt et al., 2012; Sumbal et al., 2019). Thus, empirical tests and replications are likely to run on incomplete or biased data fractions rather than on a coherent, tightly integrated global sample. New methodological approaches are needed to make sense of fragmented evidence and synthesize the fragments into a complete set of evidence. Such approaches could be meta-analytic, and meta-learning, and collective intelligence (CI) approaches, but not limited to, that can mobilize enhanced evidence aggregation, as well as communication and collaboration of large stakeholder groups using crowdsourcing (Malone, Laubacher, & Dellarocas, 2010), thereby transform research collaborations at scale (Ghezzi et al., 2018).

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