טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
Ph.D Thesis
Ph.D StudentRosen Schwarz Galia
SubjectExtending the Behavioral Theory of R and D Innovation:
Motivational Determinates of Performance Feedback
Learning and the Moderating Effects of
Slack Resources and
DepartmentDepartment of Industrial Engineering and Management
Supervisors Professor Zur Shapira
Mr. Avi Fiegenbaum (Deceased)
Full Thesis textFull thesis text - English Version


Abstract

The Behavioral Theory of the Firm (Cyert and March, 1963) proposes a decision making model based on three stages: performance evaluation, search and decision making. This decision making model has advanced much of the research on performance feedback learning in the last decades. This study extends the foundations of the behavioral theory of the firm along 3 lines; first, I broaden the scope of learning from performance feedback in terms of: motivation (survival versus aspirations), time impact (short versus long term) and comparisons (self versus social comparisons). Then, I develop theory on the impact of performance feedback on both R&D (Research and Development) intensity and R&D quality in 3 different situational zones: underperforming firms threatened by bankruptcy, underperforming firms focused on aspirations, and outperforming firms.  Third, I consider the moderating effect of unabsorbed slack resources and environmental contingencies (munificence and dynamism) on performance feedback learning.

I test my hypotheses on a sample of public U.S firms from three high technology industries over a period of 17 years for R&D intensity data, and 16 years for R&D quality data. I use fixed effects panel regression models. My analysis reveals that (1) performance feedback impacts both R&D search intensity and R&D quality, (2) this impact is conditional upon situational factors and (3) unabsorbed slack resources and environmental munificence and dynamism moderate the relationship between performance feedback and R&D search intensity. Revealing the conditions that frame and constrain R&D search and R&D quality allows me to develop new insights on how firms learn from performance feedback and generate technological innovation.