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.

中文翻译：

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了解组织间大数据技术：技术采用动机和技术设计如何塑造协作动态

组织越来越多地采用大数据技术来捕获、表示和分析组织界面上的复杂操作流程。这提供了了解和优化协作流程的机会，这应该会增加合作。然而，组织可能不会平等地学习，这可能会引发学习竞赛，从而促进竞争动态。这个对 13 个组织间关系的多案例研究揭示了四个路径，这些路径解释了组织的技术采用动机和不同的技术设计如何共同形成协作动态：在组织追求学习和效率的互补动机的情况下，协作动态是合作的（路径 1）。在组织追求共享学习动机的地方，如果大数据技术提供共享分析处理能力和对称透明度（路径 2）或竞争性，如果大数据技术提供共享分析处理能力和不对称透明度（路径 3）或不共享分析处理能力而不管透明度（a ) 对称性（路径 4）。这些发现通过显示大数据技术加速组织间学习来推进战略管理文献，但协作动态取决于组织的技术采用动机。我还通过引入透明度作为数字环境中学习种族的扩展来推进学习种族理论。