A Proposal and Call for Funding for GoPeaks.org Initiative
Global OpenLabs for Performance-Enhancement Analytics and Knowledge System
“Without a common framework to organize findings, isolated knowledge does not cumulate.”
…2009 Nobel Laureate Elinor Ostrom
After more than six decades of scientific research of business administration, the stock of our knowledge is at a volume that makes knowledge synthesis for business applications possible, and meanwhile the structure of this knowledge has reached a degree of fragmentation that makes knowledge creation activities more confused without a shared knowledge navigation system. Like the pharmaceutical industry which historically emerged as a new ecosystem to synthesize scientific research into medical interventions as well as to guide and fund basic research, time is ripe to (re)invent a science-practice ecosystem in business administration to synthesize disperse scientific findings into business analytics tools and to more systematically guide and fund academic research. I thus propose a new industry-academia research partnership named “Global OpenLabs for Performance-Enhancement Analytics and Knowledge System” (or “GoPeaks” for short). The plural term “Peaks” reflects the underlying philosophy that humans are driven to reach their peak of multiple goals and values.
Distinct from any formal organizations, GoPeaks initiative seeks to build a unique community. Consisting of many globally coordinated labs in both the academia and the industry, GoPeaks’ goal is to consolidate disperse theoretical and empirical efforts from all research sources (journals, books, working papers, teaching cases, patents, analytics data, etc.) into a unified digital library that organizes and updates all variables and models to comprehensively predict business performance at multiple levels and from multiple stakeholder perspectives. It is also an Amazon-style exchange platform for knowledge creators (researchers) and knowledge users (decision makers, analytics professionals) to communicate and share resources (e.g., model for data; data for model; merging data into data platforms; integrating models into meta-frameworks, etc). Essentially, this initiative is an interface between consolidated knowledge (e.g., meta-frameworks) from academia and consolidated resources (e.g., merged big data platforms from multiple sources, etc.) from the industry.
The value of this new research partnership is twofold. First, from a business perspective, the existing practice of analytics lies in the advancement of data science and software engineering, whereas a key component at the decision maker’s level is missing. Prior to data and IT is the articulation of the business problem, and translation of it into a comprehensive analytic framework on what data to collect, whom to ask/survey, how to accurately measure variables, how to organize these variables into a predictive/process model, and what the existing empirical efforts (mostly in academic sources) say about the hypotheses. While analytics professionals do consult decision makers to develop their databases and models, the current practice is relatively disconnected from our knowledge development in management and social sciences. This new research initiative will take on this task by constantly organizing and presenting existing scientific findings as a common pool for business decision makers to draw ideas and build (or to verify their own) comprehensive frameworks given any business problems.
Second, from a knowledge perspective, it enables the accumulation of old knowledge and guides the discovery/creation of new knowledge. Most scientific research in business administration tends to be based on discipline- or specialty-specific silos (due to the themes of academic trainings and outlets) and partial testing (due to difficulties in accessing full big data and replications). Without integrating these partial efforts together, it is difficult to timely translate scientific research into reliable analytic frameworks as mentioned above. While systematic reviews and meta-analysis are feasible techniques that can integrate scientific publications into a relatively unbiased and comprehensive model, the current incentive systems of the business school/university and academic funding agencies typically motivate scientific researchers to focus on fundamental/ground-breaking projects, discouraging knowledge integration. With its own funding sources and incentives, this new research initiative will help to fill this gap. To organize existing knowledge and suggest knowledge gaps, this initiative also forms a shared knowledge navigation system to guide new knowledge discovery and creation activities. Prior to the publication of knowledge creation/discovery, it provides an independent reference for both authors and journals to judge whether a new manuscript is built on the solid foundation, novel in its contribution, and important in its target of the knowledge gaps. After the publication, through constant (re)testing using big data, meta-analysis, and relative weight analysis (of predictors and competing theories) in a performance-predicting meta-framework, the new system enables a new incentive system to rate researchers, journals, and editors based on replicability and relative weight in predicting key performance measures, which are not captured by the current business school/university system based on publication counts, citations, and impact factors.
While this initiative is still at the concept level, over the years I have been conducting several studies to build up the intellectual foundation and built a network of prominent scholars and industry partners to enable this initiative.
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Victor Zitian Chen, PhD
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