GoPeaks.org | intellectual foundation
Call for joint efforts: I welcome research collaboration ideas/proposals directly contributing to these themes. Please email email@example.com.
Many of my works (mostly co-authored) seek to contribute to building the intellectual foundation of GoPeaks initiative. I grouped these works below by the GoPeaks component as illustrated in the left figure.
Grand Design of the Overall System
· Vision paper: the key elements of a new business science-practice ecosystem –with Michael Hitt and Denise Rousseau. This is a summary note of a 2017 Academy of Management Symposium with five former Academy presidents.
· The knowledge accumulation problem of the current top scientific journal-centric system of B-schools and ideas on reinventing this system (under review) –with Michael Hitt.
· General principles for “best practices” of a research paper
Knowledge Synthesis Meta-Frameworks
· : In the context of equity-based foreign entry mode, and this project has developed a general template on comparing and integrating multiple theories to prescribe evidence-based “best practices”. It helps consolidate the stock of knowledge from academic research, identify context boundaries, and help analytics professionals to develop a training model for managing and predicting foreign entry performance. This is a work-in-progress –with Yuanyuan Li, George Banks, and Erik Gonzalez-Mulé.
· : This is a framework to synthesize the literature across disciplines to understand the deep roots of the diversity in institutions (formal rules, and informal constraints, cultures) across societies (under R&R)–with John Cantwell.
· : This project is to explain the co-evolution between multinational enterprise (MNE) and institutional environments. It is to develop a general approach to analyze and predict dynamics of MNEs and institutions and their interactions. It is a work-in-progress –with John Cantwell and Harald Bathelt.
· A multi-theoretical/multi-level meta-framework to organize key causes, their interactions, underlying mechanisms, and meta-analytic results in explaining/predicting key performance indicators (i.e., Peaks) concerning all primary stakeholders of a corporation. Here “explaining” is based on meta-analytic results of the past, and “predicting” is “explaining” plus explicit discussions of the applicability of underlying mechanisms in emergent, untested/untheorized contexts. A Professional Development Workshop (PDW) proposal related to this topic is currently under review at 2019 Academy of Management Meeting.
· A methodology based on meta-analysis to rate the replicability and relative weights of scholars, theories, meta-frameworks, and outlets in explaining/predicting key performance indicators (i.e., Peaks) concerning all primary stakeholders of a corporation.
Pluralistic Business Performance Matrix
· : This project is based on a meta-analysis of 138 primary studies on country-level predictors for enhancing multi-stakeholder performance indicators. The purpose is to reconcile the debate between a multi-objective view of stakeholder-agency theory and a trade-off view of shareholder-agency theory. It helps to build up a comprehensive matrix of performance indicators to satisfy multiple stakeholders. The matrix will be the basis of the pluralistic, multi-dimensional “performance” in Peaks. It is a work-in-progress, with all the primary studies fully coded –with Marc van Essen, Steve Sauerwald, and Michael Hitt.
· : This paper conceptualizes diverse shareholder interests along with the market- and nonmarket logic, and shareholder influence through residual claimant and social means. Because the criteria of measuring business performance are largely influenced by powerful shareholders (or their fiduciary agents in corporate governance), understanding the full spectrum of their interests and influence helps us understand how business performance measures might be selected and implemented in actual corporate governance. The paper is a work-in-progress –with Ruth Aguilera.
o This work is derived from a series of my publications and working papers on shareholder heterogeneity and conflicts.
· An ontology of constructs of business performance measures concerning all primary stakeholders of a corporation. It helps researchers to compare and group similar constructs, definitions, and measures regarding performance measures.
Knowledge Mining Algorithms
· This is a series of projects that seek to train a Machine Reading system to automatically extract, compare, and cluster constructs, variables, and their causal relationships from academic publications in organizational management. Results will be presented as an accumulative map of causes-and-effects to predict multi-stakeholder key performance indicators (KPIs), hosted and curated on a cloud-based platform with a search engine for external users. In collaboration with Industry Partners MetricStream (Co-PIs: Mikael Hagstroem, Vidya Phalke) and Syntelli (Co-PI: Rishi Bhatnagar), an extended proposal with Wlodek Zadrozny has been nominated by UNC Charlotte to apply for a grant at National Science Foundation.
· A research finding is reliable for prescription (i.e., confidence) if it has qualities of: (1) causality (ruling out endogeneity); (2) reproducibility; (3) replicability in external/independent data. A machine detector is needed to rate a massive volume of publications (at the same precision or higher than a human detector) and to assign a confidence score for each research finding.
Data Analytics Platforms at Workplaces
· I am in active conversations with several analytics firms (e.g., MetricStream and Syntelli) for the possibility of developing workplace platforms to integrate and monitor Peaks matrix as well as key predictors and their interactions, connected to meta-theoretic and meta-analytic works in academia. The idea is to develop a shared observatory where fragmented analytics data and/or results are harmonized and integrated, and researchers can have their individual hypotheses tested in a quasi-experiment at a large-scale, while controlling for all other variables in a holistic network of causes-and-effects.
· The practical value:
o For analytics-based decision makers: to integrate management/social sciences into data for explainable analytics, thus closing the gap between technical teams and nontechnical executives.
o For the individuals/organizations offering data: to be treated with rigorous management/social sciences-based prescriptions, recommendations, and services.
· The academic/research value:
o Retests and meta-analysis of integrative meta-frameworks, concluding a debating field, filling knowledge gaps, and identifying context boundaries.
o Constructing a digital experimental field for conducting large-scale randomized quasi-experiments to identify new insights.
· A real-time database of companies on key performance indicators (i.e., Peaks) concerning all primary stakeholders of a corporation. This data can be used to replicate a set of hypotheses from the literature for their predictive reliability. This data will complement the current business databases largely based on financial or ESG reporting that do not speak directly to each primary stakeholder group and are relatively disconnected with the accumulative knowledge in management/social sciences. I am in the process of getting a 24-month free access to TruValue Labs’ API service of event data of all US public firms and midcap firms in 50 international markets. These text data can potentially be quantified into a GoPeaks Company Database based on stakeholder sentiment analysis.
· An index of equities/securities based on the predictors (i.e., causes) in explaining/predicting key performance indicators (i.e., Peaks) concerning all primary stakeholders of a portfolio corporation. This index complements the current indices of investment (ESG, etc.) largely based on past performance (i.e., outcomes) rather than predictors (i.e., causes).
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