Approach to service design based on customer behavior data: a case study on eco-driving service design using bus drivers’ behavior data

Kim, Minjun; Lim, Chiehyeon; Lee, Chang-Ho; Kim, Kwang-Jae; Park, Yongsung; Choi, Seung-Hwan

doi:10.1007/s11628-017-0343-8

Cited by 25 publications

(19 citation statements)

References 31 publications

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“…Finally, the findings obtained from text mining should be incorporated with real research and development projects related to smart service systems. We have conducted such projects with the industry and the government, such as those about smart cars and transportation, health, and building systems (e.g., Lim and Kim 2014;Kim et al 2018;Lim et al 2018a, b, c). An integration of the current study and such projects will further facilitate development of smart service systems.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, IoT (Atzori et al 2010), big data (Lim et al 2018b), cloud computing (Iyoob et al 2013), wearable devices (Patel et al 2015), cyber-physical systems (Lee 2008), Industry 4.0 (Hofmann and Rüsch 2017), blockchain (Kshetri 2018), IoT-enabled servitization (Lim and Kim 2015), and service-oriented data use (Kim et al 2018) are Service Science, 2018, vol. 10, no.…”

Section: Literature Reviewmentioning

confidence: 99%

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Data-Driven Understanding of Smart Service Systems Through Text Mining

2018

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Abstract. Smart service systems are everywhere, in homes and in the transportation, energy, and healthcare sectors. However, such systems have yet to be fully understood in the literature. Given the widespread applications of and research on smart service systems, we used text mining to develop a unified understanding of such systems in a data-driven way. Specifically, we used a combination of metrics and machine learning algorithms to preprocess and analyze text data related to smart service systems, including text from the scientific literature and news articles. By analyzing 5,378 scientific articles and 1,234 news articles, we identify important keywords, 16 research topics, 4 technology factors, and 13 application areas. We define "smart service system" based on the analytics results. Furthermore, we discuss the theoretical and methodological implications of our work, such as the 5Cs (connection, collection, computation, and communications for co-creation) of smart service systems and the text mining approach to understand service research topics. We believe this work, which aims to establish common ground for understanding these systems across multiple disciplinary perspectives, will encourage further research and development of modern service systems.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Understanding of Smart Service Systems Through Text Mining

2018

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“…safety of commercial vehicle drivers (i.e., bus, taxi, and truck drivers), and collects vehicle operations data from vehicles using digital tachograph (DTG) devices. The institute wanted to develop data-based services to manage the drivers and transportation companies in terms of driving safety (Kim et al 2018). The service design process in Study 4 is very similar with that in Study 2.…”

Section: Discussionmentioning

confidence: 99%

Multi-factor service design: identification and consideration of multiple factors of the service in its design process

Lim

Kim

et al. 2018

Serv Bus

Self Cite

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Service design is a multidisciplinary area that helps innovate services by bringing new ideas to customers through a design-thinking approach. Services are affected by multiple factors, which should be considered in designing services. In this paper, we propose the multi-factor service design (MFSD) method, which helps consider the multi-factor nature of service in the service design process. The MFSD method has been developed through and used in five service design studies with industry and government. The method addresses the multi-factor nature of service for systematic service design by providing the following guidelines: (1) identify key factors that affect the customer value creation of the service in question (in short, value creation factors), (2) define the design space of the service based on the value creation factors, and (3) design services and represent them based on the factors. We provide real stories and examples from the five service design studies to illustrate the MFSD method and demonstrate its utility. This study will contribute to the design of modern complex services that are affected by varied factors.

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“…Methodological approaches focusing on customer data in particular (e.g. [7,23]) often seek to investigate the encompassing value creation process of data or to develop data-driven business models (e.g. [23,49]).…”

Section: Data-driven Service Innovationmentioning

confidence: 99%

“…Moreover, transforming data into value using analytics is a challenging task where communication barriers between data experts and the business side often complicate the design of data-driven services [11,22]. Based on this problem space, methodological support for a systematic view on accessible data seems reasonable [12,15,16,23]. Customer data especially requires knowledge about data sources as this data often needs to be aggregated across application systems to achieve the desired central customer view and thus an encompassing customer understanding [5,24].…”

Section: Introductionmentioning

confidence: 99%

Customer Data Mapping - A Method for data-driven Service Innovation

Blöcher¹,

Wittwer²,

Alt³

2020

WI2020 Zentrale Tracks

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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.

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Approach to service design based on customer behavior data: a case study on eco-driving service design using bus drivers’ behavior data

Cited by 25 publications

References 31 publications

Data-Driven Understanding of Smart Service Systems Through Text Mining

Data-Driven Understanding of Smart Service Systems Through Text Mining

Multi-factor service design: identification and consideration of multiple factors of the service in its design process

Customer Data Mapping - A Method for data-driven Service Innovation

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