From data to value: A nine-factor framework for data-based value creation in information-intensive services

Lim, Chiehyeon; Kim, Ki‐Hun; Kim, Minjun; Heo, JoonNyung; Kim, Kwang-Jae; Maglio, Paul P.

doi:10.1016/j.ijinfomgt.2017.12.007

Cited by 151 publications

(100 citation statements)

References 77 publications

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“…Digital servitization can also raise productivity in vehicle manufacturing (product development) through the use of advanced technological tools such as business analytics and virtual modeling to optimize decision making [16,65,71], reduce development costs and speed up time to market [14]. To cite one instance, Ford Motors implemented digital prototyping into its operations, reducing costs and time of developing components.…”

Section: Digital Servitization (Productivity-oriented Services)mentioning

confidence: 99%

“…Such benefits are directly associated with the technological attributes of digitalization [15,18,65,69,71], which enable companies to identify and virtualize assets, facilitating allocation of resources [73], reducing development costs [72], decreasing planning times [13,65] and shortening time to market [14]. All of the above result in higher operating margins [23][24][25], while implementation of digital servitization gives companies sophisticated mechanisms to exploit value from data [62,63,69,71], facilitating and streamlining decision-making processes to respond to internal and external contingencies in a timely and effective manner [15,17]. Thus, digital servitization enables faster response to customer demands [14,18,65,67] and improves the quality of service provision [16,69], increasing the firm's performance and competitiveness [26,27].…”

Section: Developing Testable Hypothesesmentioning

confidence: 99%

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Uncovering Productivity Gains of Digital and Green Servitization: Implications from the Automotive Industry

2018

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Abstract:The growing industrial concern about sustainability challenges has driven vehicle and auto parts manufacturers to adopt service capabilities as a way to maintain competitiveness in compliance with environmental regulations. As a result, automakers have progressively integrated digital and green service initiatives to support operations and address environmental issues effectively. The present study examined the effect of digital and green servitization on the firm's productivity. To test their effect quantitatively, this study used the ORBIS database to construct a multi-country sample containing 228 companies in the automotive industry. Our findings indicate that implementation of digital and green servitization is positively associated with higher productivity outcomes once the two forms of servitization coexist and operate jointly. Moreover, the results of the study underscore the importance of establishing a successional pathway of implementation priorities. Our evidence suggests that firms willing to offer green services should consider offering digital services first, as this is the only way to obtain productivity gains from green servitization.

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Section: Digital Servitization (Productivity-oriented Services)mentioning

confidence: 99%

Section: Developing Testable Hypothesesmentioning

confidence: 99%

Uncovering Productivity Gains of Digital and Green Servitization: Implications from the Automotive Industry

2018

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“…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%

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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“…A factor is ''one of the things that affects an event, decision, or situation' ' (Collins Cobuild 2009), and understanding key factors that affect the system in question is a prerequisite to its analysis and design (Lim et al 2018b). Service has a multi-factor nature, that is, the nature that involves multiple and interdependent factors (Lee 2017;Lüftenegger et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…For example, researchers have investigated multiple factors related to the experience-centric service design (Zomerdijk and Voss 2010; Ponsignon et al 2017), product-service system design Lim et al 2012), information-intensive service design (Lim et al 2018b), smart service design (Geum et al 2016;Maglio and Lim 2016), and service delivery process design (Bettencourt 2010;Lim and Kim 2014). This is attributed to that dividing blocks (factors) among the objects is useful to see certain functional work of the service (Osterwalder and Pigneur 2010;Lim et al 2018b). However, it is not clear how factors of the service in question can be considered in its design process although the consideration is significant for systematic and solid service design.…”

Section: Introductionmentioning

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

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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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From data to value: A nine-factor framework for data-based value creation in information-intensive services

Cited by 151 publications

References 77 publications

Uncovering Productivity Gains of Digital and Green Servitization: Implications from the Automotive Industry

Uncovering Productivity Gains of Digital and Green Servitization: Implications from the Automotive Industry

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

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