Benchmarking An International Journal · 2019 · 56 citations · 85 references
ManagementInternational ManagementStrategic ManagementBusiness StrategyCorporate StrategyStrategic AlliancesAlliance PerformanceFirm CapabilitiesJoint VenturesOrganizational CapabilitiesStrategic FitStrategic PartnershipsFlexibilityFuzzy Analytic Hierarchy ProcessInter-partner ConflictInterpretive Structural ModelingPartner Fit AssessmentStrategic Alliance CapabilitiesStrategic Fit In AlliancesStrategic Joint Ventures
Purpose The purpose of this paper is to identify the inter-partner dynamics-based enablers of joint venture (JV) competitiveness. In addition, this paper models the interactions among identified enablers/factors to project the strength of their relationship with JV competitiveness. Design/methodology/approach ISM- and total interpretive structural modeling (TISM)-based fuzzy TISM approach has been used to examine the interactions and strength of interactions among identified enablers of JV competitiveness. Findings The analysis concludes that inter-partner dynamics-based enablers, such as partner fit, power symmetry and trust, have strong driving power and low dependence power and are at the lowest level of hierarchy in fuzzy TISM model. Variables like collaborative communication, organizational learning and absorptive capacity are linkage variables and they have high dependence as well as driving power and they lie in the second level of fuzzy TISM hierarchy. Strategic flexibility is found to have high dependence power and has weak driving power. The outcome variable JV competitiveness found to have zero driving power and highest dependence power. Practical implications The findings have implications for practitioners and policy makers. JVs may achieve competitiveness by managing identified enablers (inter-partner dynamics). Originality/value Present paper is one among the few efforts that address the issue of JV competitiveness (post-formation of JV). Methodologically also, this study is one among few initial efforts of using modified fuzzy TISM to explore and understand the linkage among enablers and outcome variables. Modified fuzzy TISM process carries out transitivity checks along with the successive pair-wise comparisons and simplifies the fuzzy TISM approach.
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