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Special Issue Call for Papers Grand Synthesis: Unifying the Fragmented Science of Business for All Stakeholders Letter of inquiry by 25 August 2021 Full papers by 25 February 2022 Guest Editors: Flore Bridoux, Erasmus University, The Netherlands Victor Zitian Chen, The University of North Carolina at Charlotte, USA Carina A. Hallin, ITU Copenhagen, Denmark & Massachusetts Institute of Technology, USA Michael A. Hitt, Texas A&M University, USA Marc van Essen, University of South Carolina, USA Weihua Zhou, Zhejiang University, China We are not doing just another call for papers. We are requesting revolutionary blueprints of our shared future. BACKGROUND The business paradigm in both the academic and the professional worlds is generally shifting towards a pluralistic, multi-objective approach that emphasizes and accounts for “stakeholder values.” While the definitions may vary, such values involve typically economic, social, psychological, physical, and health-related wellbeing for different stakeholders (e.g., investors, customers, suppliers, employees, and communities) (Barney and Harrison, 2018; Bridoux and Stoelhorst, 2014; Freeman, 1984; Mitchell, 2017; Mitchell et al., 1997; Mitchell et al., 2015). Notably, on 19 August 2019, 181 CEOs of the largest US corporations signed the Statement on the Purpose of a Corporation at the Business Roundtable (BRT). This leading influential business lobby has an aggregated revenue more significant than any country’s GDP except the US and China (Business Roundtable, 2019). This Statement officially revised the BRT’s mission from shareholder primacy since 1997 to “commitment to all our 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). PROBLEMS TO BE SOLVED We call for both theory reviews and method reviews to arrive at revolutionary blueprints for the future of business and management scholarship. We call for reviews of theories and methods to create an integrated knowledge system and enable large-scale, interdisciplinary research collaborations across traditional knowledge silos (e.g., economics, sociology, psychology, operations research, etc.). We encourage submissions within the scope of conceptualizing, measuring, predicting, and managing multiple stakeholder values simultaneously. Specifically, each research project should demonstrate its capabilities of knowledge integration to overcome two hurdles that result in a fragmented universe of knowledge. 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, 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). SUBMISSIONS In response to these hurdles above, each research project should review the state-of-the-art of literature, theories, and methods and integrate them into integrated and novel frameworks that can be used as platforms for knowledge accumulation and synthesis as new knowledge emerges: Track A – Theory Reviews In this track, we call for integrated and novel conceptual frameworks that can integrate, navigate, and reason through multiple perspectives, levels, and different stakeholder values simultaneously from the fragmented literature. Examples include, but are not limited to: 1. Developing and unifying taxonomies/ontologies of stakeholder values, their causes, and context boundaries 2. Constructing unified knowledge graphs for causes-and-effects relationships, logics, empirical evidence, and hypotheses 3. Building multilevel, complex conceptual frameworks that simulate the dynamics of the social-ecological system for creating and distributing stakeholder values 4. Developing a meta-framework from the top leadership perspective on defining, measuring, predicting, and managing all stakeholder values 5. Developing a meta-framework that can capture the shared as well as heterogeneous motivations of individuals situated in different stakeholder roles or holding different stakeholder identities Please note that our focus is on conceptual and theoretical integration. However, empirical synthesis such as meta-analyses is welcomed as a supportive approach to substantiating the key relationships and paths in a meta-theoretical framework. According to the aims and scope of IJMR, we do not publish analyses that draw on primary data. We will assess the following merits to evaluate the strength of submission to this track: 1. Meta-theory: Is it discussing and comparing multiple alternative theories concerning all stakeholder values? 2. Synthesizing: Is it organizing all concepts and their relations in a unified network, identifying similarities, reducing redundancies, contrasting differences, and reconciling conflicts? 3. Mapping: Is it listing the most generous set of variables and relationships in a unified causal path network ready for data analytics? 4. Extendability: Is it explicating the behavioral and contextual assumptions so users will have the flexibility of adapting it in the face of new contexts or new evidence? We especially invite reviews that will arrive at holistic, meta-theoretical frameworks. You may refer to Ostrom (2009) and Schlüter et al. (2017) as examples of such frameworks. Track B – Method Reviews In this track, we call for integrated and novel methodological approaches that accelerate and scale the discovery, replication, and synthesis of evidence across distributed sources of data and evidence. Examples include, but are not limited to: 1. Reviewing the existing mathematical methods of meta-analytic and meta-regression techniques and suggest new approaches to incorporate nonlinearity, missing interactive terms, as well as hidden moderators for evidence synthesis. 2. Reviewing the existing meta-machine learning (ML) algorithms to aggregate evidence from multiple data sources that cannot be perfectly merged. 3. Reviewing NLP algorithms that can detect and compare unstructured data sources based on the taxonomies/ontologies helps the massive synthesis of fragmented data and evidence. 4. Reviewing collective intelligence and crowdsourcing engineering techniques that ingrain in four main disciplines of innovation and management: (i) open innovation, (ii) co-creation, (iii) the wisdom of crowds and predictions, and (iv) crowd-work. 5. Developing logic and principles that can accelerate or automate the detection of logic inconsistencies, identification for contextual boundaries, and discovering hidden new hypotheses from complex conceptual frameworks. While we focus on methods used in management research, we welcome reviews of cutting-edge methods in other areas that can be adapted to management research. We especially welcome efforts that review, compare, and integrate machine learning tools that can be used for empirical synthesis in management studies. Please explicitly prescribe guidelines for how future studies on stakeholder values select and use these methods. We will assess the following merits to evaluate the strength of submission to this track: 1. Accessibility: Is it offering highly accessible guidelines on when and how to use each method? 2. Prescription: Is it comparing different methods and prescribing the best applicable scenarios for each? 3. Beyond meta-analysis: Is it offering systematic solutions to the key limitations of the existing meta-analytic methods used in management research? You may refer to Gonzalez-Mulé and Aguinis (2018), Villalta and Drissi (2002), Peng (2020), and Ghezzi et al. (2018) as examples of method reviews. We hope that this special issue will contribute ideas for integrated knowledge systems and hopefully serve as a catalyst for future scholarly horizon changes. SUBMISSION INFORMATION International Journal of Management Reviews (IJMR) is one of the most impactful peer-reviewed journals in management and business (impact factor: 8.631, ranked 5/151 in business and 5/226 in management), and amongst the most impactful open forums for knowledge synthesis. Manuscripts should follow the Author Guidelines set out by IJMR available at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1468-2370/homepage/ForA uthors.html. Additionally, see also: Jones O. & Gatrell C. (2014). Editorial: The Future of Writing and Reviewing for IJMR. International Journal of Management Reviews, 16, pp. 249-264. https://doi.org/10.1111/ijmr.12038 Gatrell C. & Breslin D. (2017). Editors’ Statement. International Journal of Management Reviews, 19, p. 3. https://doi.org/10.1111/ijmr.12133 Breslin D., Gatrell C. & Bailey K. (2020). Developing Insights through Reviews: Reflecting on the 20th Anniversary of the International Journal of Management Reviews. International Journal of Management Reviews, 20, pp. 3-9. https://doi.org/10.1111/ijmr.12219 To get early feedback from the editors before you invest in producing the full manuscripts, please submit a one-page Letter of Inquiry to the Guest Editors. In the letter, please specify the target track and then discuss the topic, the scope and method of your review, and the proposed outcome you expect to deliver (e.g., method guidelines and/or meta-theoretical frameworks) (single space, 12 points) by 25 August 2021. Submission for full manuscripts will be open between 31 January and 25 February 2022. We propose to organize a multi-site (China, Europe, and USA) hybrid (in-person and virtual) seminar and invite authors of selected papers in the first round to participate. All submissions will be made online via http://mc.manuscriptcentral.com/ijmr highlighting that you wish to be considered for the Special Issue “Grand Synthesis.” All submissions should also include a letter to the editors specifying which track they target. Flore Bridoux - [log in to unmask] <mailto:[log in to unmask]> Victor Zitian Chen - [log in to unmask] <mailto:[log in to unmask]> Carina A. Hallin - [log in to unmask] <mailto:[log in to unmask]> Michael A. Hitt - [log in to unmask] <mailto:[log in to unmask]> Marc van Essen - [log in to unmask] <mailto:[log in to unmask]> Weihua Zhou - [log in to unmask] <mailto:[log in to unmask]> REFERENCES Bammer, G. (2013). Disciplining interdisciplinarity: Integration and implementation sciences for researching complex real-world problems. ANU Press. Barney, J.B. & Harrison, J.S. (2020). Stakeholder theory at the crossroads. Business & Society, 59, pp. 203-212. Bititci, U.S., Bourne, M., Cross, J.A., Nudurupati, S.S. & Sang, K. (2018). Editorial: Towards a theoretical foundation for performance measurement and management. International Journal of Management Reviews, 20, pp. 653-660. Bridoux, F., Coeurderoy, R. & Durand, R. (2011). Heterogeneous motives and the collective creation of value. Academy of Management Review, 36, pp. 711-730. Bridoux, F. & Stoelhorst, J.W. (2014). Microfoundations for stakeholder theory: Managing stakeholders with heterogeneous motives. Strategic Management Journal, 35, pp.107-125. Business Roundtable. (2019). Statement on the purpose of a corporation. Business Roundtable, August 19, 19. Chen, V.Z. & Hitt, M.A. (2021). Knowledge synthesis for scientific management: Practical integration for complexity versus scientific fragmentation for simplicity. Journal of Management Inquiry, 30, pp. 177-192. Cohen, P.R. (2015). DARPA’s Big Mechanism program. Physical Biology, 12, 045008. Freeman, R.E. (1984), Strategic management: A stakeholder approach. Cambridge, MA: Ballinger. Gerhardt, B., Griffin, K. & Klemann, R. (2012). Unlocking value in the fragmented world of big data analytics: How information infomediaries will create a new data ecosystem. Cisco Internet Business Solutions Group, 7. Ghezzi, A., Gabelloni <https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Gabello ni%2C+Donata> , D., Martini <https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Martini %2C+Antonella> , A. & Natalicchio <https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Natalic chio%2C+Angelo> , A. (2018). Crowdsourcing: A Review and Suggestions for Future Research. International Journal of Management Reviews, 20, pp. 343-363. Gonzalez-Mulé, E., & Aguinis, H. (2018). Advancing theory by assessing boundary conditions with meta-regression: A critical review and best-practice recommendations. Journal of Management, 44, pp. 2246–2273. Gordon, R.A. & Howell, J.E. (1959). Higher education for business. NYC, New York: Columbia University Press. Hitt, M.A., Beamish, P.W., Jackson, S.E. et al. (2007). Building theoretical and empirical bridges across levels: Multilevel research in management. Academy of Management Journal, 50, 1385-1399. Malone, T.W., Laubacher, R., Dellarocas, C. (2010). The Collective Intelligence Genome. Sloan Management Review, April, Spring 2010. McAfee, A., Brynjolfsson, E., Davenport, T.H. et al. (2012). Big data: the management revolution. Harvard Business Review, 90, pp. 60-68. Mitchell, R.K. (2017). Managing and accounting for multiple stakeholders. Rutgers Business Review, 2, pp. 395-401. Mitchell, R.K., Agle, B.R. & Wood, DJ (1997). Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. Academy of Management Review, 22, pp. 853-886. Mitchell, R.K., Van Buren III, H.J., Greenwood, M., et al. (2015). Stakeholder inclusion and accounting for stakeholders. Journal of Management Studies, 52, pp. 851-877. Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325(5939), pp. 419-422. Peng, H. (2020). A comprehensive overview and survey of recent advances in meta-learning. arXiv preprint arXiv:2004.11149. Piersonm, F.C. (1959). The education of American businessmen: A study of university-college programs in business administration. NYC, New York: McGraw-Hill Company. Porter, L.W. & McKibbin, L.E. (1988). Management Education and Development: Drift or Thrust into the 21st Century? Hightstown, NC: McGraw-Hill Book Company. Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M.A., McAllister, R.R., Müller, B., Orach, K. & Schwarz, N. (2017). A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecological Economics, 131, pp. 21-35. Sumbal, M.S., Tsui, E., Irfan, I. et al. (2019). Value creation through big data application process management: the case of the oil and gas industry. Journal of Knowledge Management, 23, pp. 1566-1585. Vilalta, R. & Drissi, Y. (2002). A perspective view and survey of meta-learning. Artificial Intelligence Review, 18, pp. 77-95. Victor Chen **************************************************************************** ******************************** Victor Zitian Chen, PhD Associate Professor of International Management, Belk College of Business Affiliate Faculty, School of Data Science Affiliate Faculty, Doctoral Program of Organizational Science, School of Liberal Arts and Sciences University of North Carolina – Charlotte Founder and Lead Principal Investigator Global OpenLabs for Performance-Enhancement Analytics and Knowledge System (GoPeaks) <https://www.gopeaks.org/> https://www.gopeaks.org/ **************************************************************************** ******************************** ____ AIB-L is brought to you by the Academy of International Business. For information: http://aib.msu.edu/community/aib-l.asp To post message: [log in to unmask] For assistance: [log in to unmask] AIB-L is a moderated list.