Theory Driven Organizational Metrics

Theory Driven Organizational Metrics

W.F. Lawless (Paine College, USA), Joseph Wood (Fort Gordon, USA) and Hui-Lien Tung (Paine College, USA)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-889-5.ch165
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Abstract

The inability to establish first principles has kept organizational theory from being successful. Moreover, due to snapshots in time and researcher biases, case studies are limited to hindsight, rather than serving as a proactive source of solutions to organizational problems. Yet case studies guided by theory have illuminated and tested the first principles that we have discovered. Unlike simple Newtonian mechanics, however, socialpsychological mechanics among organizational members are hidden behind and within explanations and discourse, eluding a science of fundamental interactions. When an interaction stops for measurement (e.g., case studies), significant information from the collapse of organizational interdependence is lost. The path forward is to predict the uncertainty left from the collapse of interdependent variables: planning and execution; or resources and time. In this article, we develop a new organization theory; in a related article (“Restructering a Military Medical Department Research Center” in this encyclopedia), we apply the theory to a case study of a military medical research center (MDRC) with access to advanced information systems (IS), yet struggling to determine the quality of its residents in training, and their scholarly productivity.

Key Terms in this Chapter

Command Decision-Making (CDM): Top-down decision-making, especially the autocratic decision-making practiced in industry, business, and the military, but also by dictators. CDM reduces innovativeness but increases productivity. Further, CDM often employs consensus rules in its decision-making processes, because CR is open to exploitation (Kruglanski, Pierro, Mannetti, & De Grada, 2006).

Majority Rules (MR): Majority rules often lead to faster decisions, more practical decisions, and, counterintuitively, stronger consensuses. The problem with majority rules is that they introduce conflict into decision-making. But if the conflict can be moderated or managed, the result is more learning among the participants compared to consensus rules.

Metrics: The metrics for MDRC are being designed to convert the measurement problem into an organizational metric of performance.

Methodological Individualism (MI): Game theory (Nowak & Sigmund, 2004) has attempted to substantiate the superiority of cooperation based on the belief that cooperation leads to the highest social good, even if it is coerced (Hardin, 1968). However, the lack of substantiating evidence in support of the social worth of cooperation is reviewed in Lawless and Grayson (2004).

Business Models (BM): A business model is a complex plan for business operations and strategy, that when it converges into a consensus, it maximizes productivity, but the stronger the consensus in support of the BM, the more that innovation is reduced. Alternatively, divergence increases opportunities for innovation, but by reducing productivity. This paradox is reduced by creating ambidextrous organizations that can operate in a state of tension between both increasing productivity and innovativeness (Smith & Tushman, 2005).

Measurement Problem/Paradox: Assuming the existence of uncertainty in the four interdependent variables of planning-execution and resources-time, decreasing the uncertainty in either set raises the uncertainty in its correspondingly linked interdependent variable.

Consensus-Seeking Rules (CR): By reducing evidentiary barriers to discussion, consensus-seeking rules often lead to the least common worldview amongst risk perceivers, making it unlikely to reach a practical decision, other than to instantiate past organizational practices, but consequently, reducing innovativeness. Further, consensus rules require considerable time to listen to each risk perception.

Agent-Based Model (ABM): Used to create complex models of organizations and systems; for example, with Monte Carlo methods, ABM’s can be designed to reduce biases in Critical Path Method (CPM models and Program Evaluation Review Technique (PERT)) models.

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