The restaurant technology market is rapidly evolving and is transforming the restaurant business as a significant sector of tourism and hospitality. Enabled by artificial intelligence (AI), mobile apps, kiosks and chatbots revolutionize the guest experience and robots automate restaurant operations. Despite the increasing interest, the use of AI and robotics in restaurants is still in its early stage and restaurant managers are seeking guidance to leverage these technologies for service excellence. In this high-contact service sector, emotional skills need to be balanced with the possible automation potentials. The present research analyzes the current state of AI and robotics in the restaurant sector and proposes a systematic identification of process innovation potentials. For this purpose, a market analysis of the European AI and robotics market for restaurant operations is conducted, which yields a first knowledge base for future research and conceptual work. Besides detailed empirical data, a reference process is developed for leveraging new technologies for process innovation.
Service providers nowadays face a complex situation, which is characterized by highly-demanding customers on the one and a plethora of potentially relevant data on the other hand. Data-driven service offerings need to be based on a solid understanding of available data in order to design personal value propositions. This research proposes a visual approach to build up data understanding from a customer perspective and highlights the potential of customer data. Based on the Customer-Dominant Logic, it develops the method "Customer Data Mapping" which supports businesses in establishing customer understanding through a structured process in a collaborative setting. It guides participants from capturing customer data along the customer journey to deriving customer understanding as the foundation for data-driven services.
Stimulated by an ongoing digital transformation, companies obtain a new source for digital service innovation: The use of personal data has the potential to build deeper customer relationships and to develop individualized services. However, methodological support for the systematic application of personal data in innovation processes is still scarce. This paper suggests a comprehensive approach for service design tools that enable collaborative design activities by participants with different data skills to identify new service opportunities. This approach includes the systematic development of customer understanding as well as a process to match customer needs to existing personal data resources. Following a design science research approach, we develop design principles for service design tools and build and evaluate a service opportunity canvas as a first instantiation.
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