Understanding interorganizational big data technologies: How technology adoption motivations and technology design shape collaborative dynamics

Organizations increasingly employ big data technologies to capture, represent, and analyse complex operational processes at the organizational interface. This provides opportunities to learn about and optimize collaboration processes, which should increase cooperation. Yet, organizations may not learn equally, which could trigger learning races and thereby foster competitive dynamics. This multiple case study of 13 interorganizational relationships reveals four paths that explain how organizations’ technology adoption motivations and different technology designs conjoin to shape collaborative dynamics: where organizations pursue complementary motivations of learning and efficiency, collaborative dynamics are cooperative (path 1). Where organizations pursue shared learning motivations, interaction dynamics are cooperative if big data technologies provide shared analytical processing capability and symmetric transparency (path 2) or competitive where big data technologies provide shared analytical processing capability and asymmetric transparency (path 3) or non‐shared analytical processing capability regardless of transparency (a)symmetry (path 4). These findings advance strategic management literature by showing that big data technologies accelerate interorganizational learning, but that collaborative dynamics depend on organizations’ technology adoption motivations. I also advance learning race theory by introducing transparency as extension to learning races in digital environments.