Christoph Breidbach, Sunmee Choi, Benjamin Ellway, Byron W. Keating, Katerina Kormusheva, Christian Kowalkowski, Chiehyeon Lim and Paul Maglio
The purpose of this paper is to analyze the history and future of service operations, with the goal to identify key theoretical and technological advances, as well as fundamental…
Abstract
Purpose
The purpose of this paper is to analyze the history and future of service operations, with the goal to identify key theoretical and technological advances, as well as fundamental themes that can help to imagine the future of service operations in 2050.
Design/methodology/approach
A review of the service operations literature was undertaken to inform a discussion regarding the role that technology will play in the future of service operations.
Findings
The future of service operations is framed in terms of three key themes – complexity, orchestration, and elasticity. The paper makes three contributions to the service science literature by: reviewing key themes underpinning extant service operations research to frame future trajectories of service operations research; elaborating a vision of service operations in 2050 based on history and technology; and outlining a research agenda for future service operations.
Practical implications
The case of service automation is used to provide an illustration of how the three themes converge to define future service operations, and in particular, to show how technology is recasting the role of the firm.
Originality/value
Service operations in the next 30 years will be very different from what it was in the past 30 years. This paper differs from other review papers by identifying three key themes that will characterize and instill new insights into the future of service operations research.
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Chiehyeon Lim, Min-Jun Kim, Ki-Hun Kim, Kwang-Jae Kim and Paul Maglio
The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to…
Abstract
Purpose
The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services.
Design/methodology/approach
This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature.
Findings
The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature.
Originality/value
“Using data to advance service” is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data.
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Chiehyeon Lim, Min-Jun Kim, Ki-Hun Kim, Kwang-Jae Kim and Paul P. Maglio
The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the…
Abstract
Purpose
The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.
Design/methodology/approach
“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.
Findings
Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.
Originality/value
“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.
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Christoph F. Breidbach and Paul Maglio
The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.
Abstract
Purpose
The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.
Design/methodology/approach
This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.
Findings
The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.
Practical implications
Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.
Originality/value
At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.
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Stephen L. Vargo, Robert F. Lusch, Melissa Archpru Akaka and Yi He