Start Date

11-12-2016 12:00 AM

Description

Although we expect managers to use the capabilities of business analytics systems to search for solutions and improve firm performance, we do not have a good understanding of the factors that motivate managers to undertake such search. Drawing on attribution and control theories, we propose a theory that explicates the performance conditions under which managers undertake search activities. Specifically, we theorize that managers are motivated to search for knowledge when both, operational performance and overall organizational performance, are declining. Further, we propose that managers’ search response to sustained organizational performance failure is faster (low search latency) when the magnitude of sustained failure is larger. We tested our hypotheses with longitudinal data collected monthly over a period of four years from seven hospitals. Distributed lag model analysis of the data supports our hypotheses. We conclude with implications for research and practice, and plans for future research.

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Dec 11th, 12:00 AM

How Organizational Performance Influences Managerial Search? – Towards ‘Informating Search’ Theory

Although we expect managers to use the capabilities of business analytics systems to search for solutions and improve firm performance, we do not have a good understanding of the factors that motivate managers to undertake such search. Drawing on attribution and control theories, we propose a theory that explicates the performance conditions under which managers undertake search activities. Specifically, we theorize that managers are motivated to search for knowledge when both, operational performance and overall organizational performance, are declining. Further, we propose that managers’ search response to sustained organizational performance failure is faster (low search latency) when the magnitude of sustained failure is larger. We tested our hypotheses with longitudinal data collected monthly over a period of four years from seven hospitals. Distributed lag model analysis of the data supports our hypotheses. We conclude with implications for research and practice, and plans for future research.