Abstract
Designing and delivering useful information for customers are crucial requirements of smart services as they influence the customer perception of the value and appeal of the service. Service information represents the results of data analysis that are delivered to customers. While the importance of transforming data into information has been recognized, the process of information design has been inadequately researched. This study introduces a methodology for transforming data into information to support smart services. The methodology is developed using morphological analysis and text mining. The proposed methodology was applied to a real-world case in smart services for vehicle operations management.
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Data availability
The data that support the findings of this study are available from the first author [minjun@kumoh.ac.kr] upon request.
References
Allmendinger G, Lombreglia R (2005) Four strategies for the age of smart services. Harv Bus Rev 83(10):131
Auh S, Menguc B, Sainam P, Jung YS (2022) The missing link between analytics readiness and service firm performance. Serv Ind J 42(3–4):148–177
Beverungen D, Matzner M, Janiesch C (2017) Information systems for smart services. Inf Syst E-Bus Manag 15(4):781–787
Beverungen D, Müller O, Matzner M, Mendling J, Vom Brocke J (2019) Conceptualizing smart service systems. Electron Mark 29(1):7–18
Buede DM (2000) The engineering design of systems: models and methods. Wiley, New York
Chang V, Doan LMT, Xu QA, Hall K, Wang YA, Kamal MM (2023) Digitalization in omnichannel healthcare supply chain businesses: the role of smart wearable devices. J Bus Res. https://doi.org/10.1016/j.jbusres.2022.113369
Choe EK, Lee B (2015) Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Comput Graph Appl 35(4):28–37
Delen D, Demirkan H (2013) Data, information and analytics as services. Decis Support Syst 55(1):359–363
Dey AK (2001) Understanding and using context. Pers Ubiquitous Comput 5(1):4–7
Dreyer S, Olivotti D, Lebek B, Breitner MH (2019) Focusing the customer through smart services: a literature review. Electron Mark 29(1):55–78
Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37. https://doi.org/10.1609/aimag.v17i3.1230
Field JM, Victorino L, Buell RW, Dixon MJ, Goldstein SM, Menor LJ, Pullman ME, Roth AV, Secchi E, Zhang JJ (2018) Service operations: what’s next? J Serv Manag 29(1):55–97
Fischer M, Heim D, Hofmann A, Janiesch C, Klima C, Winkelmann A (2020) A taxonomy and archetypes of smart services for smart living. Electron Mark 30(1):131–149
Fitzgerald M (2016) General Motors relies on IoT to anticipate customers’ needs. MIT Sloan Manag Rev 57:4
Gaiardelli P, Pezzotta G, Rondini A, Romero D, Jarrahi F, Bertoni M, Wiesner S, Wuest T, Larsson T, Zaki M, Jussen P, Boucher X, Bigdeli AZ, Cavalieri S (2021) Product-service systems evolution in the era of industry 4.0. Serv Bus 15(1):177–207
George G, Haas MR, Pentland A (2014) Big data and management. Acad Manag J 57(2):321–326
Geum Y, Jeon H, Lee H (2016) Developing new smart services using integrated morphological analysis: integration of the market-pull and technology-push approach. Serv Bus 10(3):531–555
Gonçalves L, Patrício L, Teixeira JG, Wünderlich NV (2020) Understanding the customer experience with smart services. J Serv Manag 31(4):723–744
Han M, Park Y (2019) Developing smart service concepts: morphological analysis using a Novelty-Quality map. Serv Ind J 39(5–6):361–384
Hong S, Kim SH, Kim Y, Park J (2019) Big data and government: evidence of the role of big data for smart cities. Big Data Soc. https://doi.org/10.1177/2053951719842543
Huang GQ, Mak KL (1999) Web-based morphological charts for concept design in collaborative product development. J Intell Manuf 10(3):267–278
Huang CC, Liang WY, Wen DW, Ting PH, Shen MY (2022) Qualitative analysis of big data in the service sectors. Serv Ind J 42(3–4):206–224
Jansson DG, Smith SM (1991) Design fixation. Des Stud 12(1):3–11
Kim C, Choe S, Choi C, Park Y (2008) A systematic approach to new mobile service creation. Expert Syst Appl 35(3):762–771
Kim MJ, Lim CH, Kim KJ (2018a) A data-driven approach to designing new services for vehicle operations management. Int J Ind Eng Theory Appl Pract 25(5):604–619. https://doi.org/10.23055/ijietap.2018.25.5.3689
Kim MJ, Lim CH, Lee CH, Kim KJ, Park YS, Choi SH (2018b) Approach to service design based on customer behavior data: a case study on eco-driving service design using bus drivers’ behavior data. Serv Bus 12(1):203–227
Kitchin R (2014) The real-time city? Big data and smart urbanism. GeoJournal 79:1–14
Lee H, Seol H, Min H, Geum Y (2017) The identification of new service opportunities: a case-based morphological analysis. Serv Bus 11(1):191–206
Le-Khac NA, Jacobs D, Nijhoff J, Bertens K, Choo KKR (2020) Smart vehicle forensics: challenges and case study. Future Gener Comput Syst 109:500–510
Li I, Dey AK, Forlizzi J (2011) Understanding my data, myself: supporting self-reflection with ubicomp technologies. In: Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, pp 405–414
Lim CH, Maglio PP (2018) Data-driven understanding of smart service systems through text mining. Serv Sci 10(2):111–214
Lim CH, Kim MJ, Kim KH, Kim KJ, Maglio PP (2018a) Customer process management: a framework for using customer-related data to create customer value. J Serv Manag 30(1):105–131
Lim CH, Kim KH, Kim MJ, Heo JY, Kim KJ, Maglio PP (2018b) From data to value: a nine-factor framework for data-based value creation in information-intensive services. Int J Inform Manag 39:121–135
Lim CH, Kim MJ, Kim KH, Kim KJ, Maglio PP (2018c) Using data to advance service: managerial issues and theoretical implications from action research. J Serv Theory Pract 28(1):99–128
Maglio PP, Lim CH (2016) Innovation and big data in smart service systems. J Innov Manag 4(1):11–21
Maglio PP, Kwan SK, Spohrer J (2015) Commentary—toward a research agenda for human-centered service system innovation. Serv Sci 7(1):1–10
Massink M, Harrison M, Latella D (2010) Scalable analysis of collective behaviour in smart service systems. In: Proceedings of the 2010 the Symposium on Applied Computing, New York, pp 1173–1180
Molina-Solana M, Ros M, Ruiz MD, Gómez-Romero J, Martín-Bautista MJ (2017) Data science for building energy management: a review. Renew Sustain Energy Rev 70:598–609
Moon H, Han SH (2016) A creative idea generation methodology by future envisioning from the user experience perspective. Int J Ind Ergon 56:84–96
Moon H, Han SH, Kwahk J (2019) A MORF-Vision method for strategic creation of IoT solution opportunities. Int J Hum-Comput Interact 35(10):821–830
National Science Foundation (2016) Partnerships for innovation: Building innovation capacity (PFI:BIC). Retrieved from https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504708. Accessed 20 Jan 2023
Nicholl B, Mclellan R (2007) The contribution of product analysis to fixation in students' design and technology work. In: Proceedings of the Design and Technology Association International Research Conference, United Kingdom, pp 71–76
Noh H, Song Y, Park AS, Yoon B, Lee S (2016) Development of new technology-based services. Serv Ind 36(5–6):200–222
Ostrom AL, Parasuraman A, Bowen DE, Patrício L, Voss CA (2015) Service research priorities in a rapidly changing context. J Serv Res 18(2):127–159
Park M, Geum Y (2021) On the data-driven generation of new service idea: Integrated approach of morphological analysis and text mining. Serv Bus 15(3):539–561
Park J, Han SH, Kim HK, Moon H, Park J (2015) Developing and verifying a questionnaire for evaluating user value of a mobile device. Hum Factors Ergon Manuf 25(6):724–739
Paukstadt U, Strobel G, Eicker S (2019) Understanding services in the era of the internet of things: a smart service taxonomy. In: Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden
Porter ME, Heppelmann JE (2014) How smart, connected products are transforming competition. Harv Bus Rev 92:64–88
Pourzolfaghar Z, Helfert M (2017) Taxonomy of smart elements for designing effective services. In: Proceedings of the 23rd Americas Conference on Information Systems, Boston
Ramos J (2003) Using TF–IDF to determine word relevance in document queries. In: Proceedings of the 1st Instructional Conference on Machine Learning, Washington, DC, 29–48
Saarijärvi H, Grönroos C, Kuusela H (2014) Reverse use of customer data: Implications for service-based business models. J Serv Mark 28(7):529–537
Sailer M, Homner L (2020) The gamification of learning: a meta-analysis. Educ Psychol Rev 32(1):77–112
Solazzo G, Elia G, Passiante G (2021) Defining the big social data paradigm through a systematic literature review approach. J Knowl Manag 25(7):1853–1887
Wünderlich NV, Wangenheim FV, Bitner MJ (2013) High tech and high touch: A framework for understanding user attitudes and behaviors related to smart interactive services. J Serv Res 16(1):3–20
Wünderlich NV, Heinonen K, Ostrom AL, Patricio L, Sousa R, Voss C, Lemmink JG (2015) “Futurizing” smart service: implications for service researchers and managers. J Serv Mark 29(6/7):442–447
Yoon B, Park I, Coh BY (2014) Exploring technological opportunities by linking technology and products: application of morphology analysis and text mining. Technol Forecast Soc Change 86:287–303
Zhang D, Wang XH, Hackbarth K (2004) OSGi based service infrastructure for context aware automotive telematics. In: Proceedings of the IEEE 59th Vehicular Technology Conference, Milan, Italy, pp 2957–2961
Acknowledgements
This work was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIT) (No. 2022R1G1A1008312) and the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A2A03065747).
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Kim, M., Trimi, S. Transforming data into information for smart services: integration of morphological analysis and text mining. Serv Bus 17, 257–280 (2023). https://doi.org/10.1007/s11628-023-00526-y
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DOI: https://doi.org/10.1007/s11628-023-00526-y