AI Summary & Key Takeaways
AI InsightThe studyfindings also extend the scope of upper echelons literatures by establishing the relevance of upper echelonstheory for a novel dependent variable (i.e., learning from alliance partners)
📌 3 Key Takeaways
Abstract
Purpose – Strategic alliances are essential vehicles for knowledge acquisition from alliance partnersespecially in the technology related sectors. This research examines the interplay between topmanagement team (TMT) functional background diversity and CEO duality on learning from alliancepartners.Design/methodology/approach – Data for 1012 strategic alliances from Information and Communicationssectors (computer equipment, electronics and telecommunications) in US are collected from multipledatabases: Firm data is retrieved from Compustat database, strategic alliance date is retrieved from SDCPlatinum database, patent data is retrieved from NBER database and finally the TMT information is codedfrom U.S. Securities and Exchange Commission (SEC) database. We tested our hypotheses using thenegative binomial regression model since our dependent variable is discrete and non-negative (patentcitation counts). Findings – CEO duality moderates the relationship between TMT functional background and learningfrom alliance partner such that the effect of TMT functional background on learning from alliance partneris positive when CEO does not have dual roles and negative when he or she does.Discussion – This study contributes to the strategic alliance and inter-organizational learning literatureby demonstrating the roles of influential actors in the strategy process (i.e., the CEO and TMT). The studyfindings also extend the scope of upper echelons literatures by establishing the relevance of upper echelonstheory for a novel dependent variable (i.e., learning from alliance partners)