ABSTRACT

While multiple mechanisms for value creation from big data analytics (BDA) exist, their application in everyday life can create trade-offs, particularly in the context of service robotics where the dispersion of autonomous digital technologies creates potentials for data-driven efficiency gains. Attending to personal preferences in such contexts, increasingly vital for customer acceptance, may run counter to efficiency, thus constraining value creation and rendering efficiency-personalization trade-offs a key managerial challenge. For the case of autonomous vehicles (AVs), we formalize this trade-off and design a machine-learning approach to handle it by drawing on a unique dataset comprising 35,000 drives by 1,850 users. We consider the real-time dynamics and user interactions that affect the decisions of AVs and develop a model extension that allows for leveraging the properties of sharing business models to make better-informed decisions. Our study contributes to information systems (IS) AV research by providing an artifact that targets both efficiency and personalization of AV operations as well as the dynamic balance between the two. With the focus on everyday life contexts, our study points to the value of incorporating trade-offs between competing goals as well as human-centered perspectives in information systems designs for research on BDA value creation. For practitioners, our work provides a practical and generalizable approach to realize the potentials of service robots without risking customer acceptance.

摘要

尽管存在大数据分析（BDA）创造价值的多种机制，但它们在日常生活中的应用可能会产生取舍，特别是在服务机器人领域，其中自主数字技术的分散为数据驱动的效率提升创造了潜力。在这种情况下，对个人喜好变得日益重要，这对于提高客户的接受度可能会与效率背道而驰，从而限制了价值创造，并使效率-个性化的权衡成为关键的管理挑战。对于自动驾驶汽车（AVs），我们正式权衡了这一取舍，并设计了一种机器学习方法，通过利用一个由1850个用户组成的35,000个驱动器的独特数据集来进行处理。我们考虑了影响AV决策的实时动态和用户交互，并开发了一个模型扩展，该模型扩展允许利用共享业务模型的属性来做出更明智的决策。我们的研究通过提供针对AV操作的效率和个性化以及两者之间的动态平衡的工件，为信息系统（IS）AV研究做出了贡献。我们着重研究日常生活中的环境，指出在BDA价值创造研究的信息系统设计中，将相互竞争的目标以及以人为本的观点之间的取舍结合起来的价值。对于从业者，我们的工作提供了一种实用且可通用的方法，以实现服务机器人的潜力，而不会冒客户接受的风险。