Open Access
ITM Web Conf.
Volume 41, 2022
International Conference on Exploring Service Science (IESS 2.2)
Article Number 04001
Number of page(s) 14
Section Smart Services’ Innovation Design
Published online 08 February 2022
  1. H. Gebauer, G.-J. Ren, A. Valtakoski, and J. Reynoso, “Service-driven manufacturing,” Journal of Service Management, 2012. [Google Scholar]
  2. C. Kowalkowski and W. Ulaga, Service strategy in action: A practical guide for growing your B2B service and solution business. Service Strategy Press, 2017. [Google Scholar]
  3. H. Lightfoot, T. Baines, and P. Smart, “The servitization of manufacturing: A systematic literature review of interdependent trends,” International Journal of Operations & Production Management, vol. 33, no. 11–12, pp. 1408–1434, 2013. [CrossRef] [Google Scholar]
  4. H. Gebauer, M. Paiola, N. Saccani, and M. Rapaccini, “Digital servitization: Crossing the perspectives of digitization and servitization,” Industrial Marketing Management, vol. 93, pp. 382–388, Feb. 2021, doi: 10.1016/j.indmarman.2020.05.011. [CrossRef] [Google Scholar]
  5. W. Ulaga and W. J. Reinartz, “Hybrid Offerings: How Manufacturing Firms Combine Goods and Services Successfully,” Journal of Marketing, vol. 75, no. 6, pp. 5–23, Nov. 2011, doi: 10.1509/jm.09.0395. [CrossRef] [Google Scholar]
  6. J. Meierhofer, P. Kugler, and R. Etschmann, “Challenges and approaches with data-driven services for SMEs: insights from a field study,” in Proceedings of the spring servitization conference: delivering services growth in the digital era, 2019, pp. 39–49. [Online]. Available: [Google Scholar]
  7. F. Tao, Q. Qi, A. Liu, and A. Kusiak, “Data-driven smart manufacturing,” Journal of Manufacturing Systems, vol. 48, pp. 157–169, Jul. 2018, doi: 10.1016/j.jmsy.2018.01.006. [CrossRef] [Google Scholar]
  8. J. Meierhofer and P. Kugler, “Data4KMU : Data Science für KMU leicht gemacht. Aktuelle Erkenntnisse und Lösungen,” 2020, Accessed: May 27, 2020. [Online]. Available: [Google Scholar]
  9. J. Meierhofer, M. Dobler, K. Frick, and L. Schweiger, “Smart service patterns for small manufacturing enterprises,” Sep. 2020, pp. 88–95. Accessed: Oct. 05, 2020. [Online]. Available: [Google Scholar]
  10. R. Alt, H. Demirkan, J. F. Ehmke, A. Moen, and A. Winter, “Smart services: The move to customer orientation,” Electron Markets, vol. 29, no. 1, pp. 1–6, Mar. 2019, doi: 10.1007/s12525-019-00338-x. [CrossRef] [Google Scholar]
  11. M. Fay and N. Kazantsev, “When Smart Gets Smarter: How Big Data Analytics Creates Business Value in Smart Manufacturing,” Dec. 2018. [Google Scholar]
  12. P. Kristensson, “Future service technologies and value creation,” Journal of Services Marketing, vol. 33, no. 4, pp. 502–506, Jan. 2019, doi: 10.1108/JSM-01-2019-0031. [CrossRef] [Google Scholar]
  13. R. Giannetti, L. Cinquini, and M. Rapaccini, “Un modello di ROI per la valutazione e la gestione della creazione di valore in Industry 4.0.” (accessed Sep. 15, 2021). [Google Scholar]
  14. L. Schweiger and J. Meierhofer, “Data-driven servitization of SMEs : assessment of success factors based on a multiple case study,” in 8th International Conference on Business Servitization (ICBS), San Sebastian, Spain, November 21-22, 2019, Nov. 2019, pp. 85–88. Accessed: Jun. 15, 2020. [Online]. Available: [Google Scholar]
  15. S. C. Brailsford, T. Eldabi, M. Kunc, N. Mustafee, and A. F. Osorio, “Hybrid simulation modelling in operational research: A state-of-the-art review,” European Journal of Operational Research, vol. 278, no. 3, pp. 721–737, Nov. 2019, doi: 10.1016/j.ejor.2018.10.025. [CrossRef] [MathSciNet] [Google Scholar]
  16. L. Lättilä, P. Hilletofth, and B. Lin, “Hybrid simulation models–when, why, how?,” Expert Systems with Applications, vol. 37, no. 12, pp. 7969–7975, 2010. [CrossRef] [Google Scholar]
  17. N. Côrte-Real, P. Ruivo, T. Oliveira, and A. Popovič, “Unlocking the drivers of big data analytics value in firms,” Journal of Business Research, vol. 97, pp. 160–173, Apr. 2019, doi: 10.1016/j.jbusres.2018.12.072. [CrossRef] [Google Scholar]
  18. N. Côrte-Real, P. Ruivo, and T. Oliveira, “Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?,” Information & Management, vol. 57, no. 1, p. 103141, Jan. 2020, doi: 10.1016/ [CrossRef] [Google Scholar]
  19. V. Grover, R. H. L. Chiang, T.-P. Liang, and D. Zhang, “Creating Strategic Business Value from Big Data Analytics: A Research Framework,” Journal of Management Information Systems, vol. 35, no. 2, pp. 388–423, Apr. 2018, doi: 10.1080/07421222.2018.1451951. [CrossRef] [Google Scholar]
  20. B. Chen, J. He, X.-H. Wen, W. Chen, and A. C. Reynolds, “Uncertainty quantification and value of information assessment using proxies and Markov chain Monte Carlo method for a pilot project,” Journal of Petroleum Science and Engineering, vol. 157, pp. 328–339, Aug. 2017, doi: 10.1016/j.petrol.2017.07.039. [CrossRef] [Google Scholar]
  21. S. L. Vargo, R. F. Lusch, and K. Koskela-Huotari, The SAGE Handbook of Service-Dominant Logic. SAGE, 2018. [CrossRef] [Google Scholar]
  22. K. Xie, Y. Wu, J. Xiao, and Q. Hu, “Value co-creation between firms and customers: The role of big data-based cooperative assets,” Information & Management, vol. 53, no. 8, pp. 1034–1048, Dec. 2016, doi: 10.1016/ [CrossRef] [Google Scholar]
  23. M. A. Akaka, K. Koskela-Huotari, and S. L. Vargo, “Further Advancing Service Science with Service-Dominant Logic: Service Ecosystems, Institutions, and Their Implications for Innovation,” in Handbook of Service Science, Volume II, P. P. Maglio, C. A. Kieliszewski, J. C. Spohrer, K. Lyons, L. Patrício, and Y. Sawatani, Eds. Cham: Springer International Publishing, 2019, pp. 641–659. doi: 10.1007/978-3-319-98512-1_28. [Google Scholar]
  24. R. Schüritz, K. Farrell, B. W. Wixom, and G. Satzger, “Value Co-Creation in Data-Driven Services: Towards a Deeper Understanding of the Joint Sphere,” ICIS 2019 Proceedings, Nov. 2019, [Online]. Available: [Google Scholar]
  25. C. Grönroos and P. Voima, “Critical service logic: making sense of value creation and co-creation,” J. of the Acad. Mark. Sci., vol. 41, no. 2, pp. 133–150, Mar. 2013, doi: 10.1007/s11747-012-0308-3. [CrossRef] [Google Scholar]
  26. S. L. Vargo and R. F. Lusch, “Evolving to a New Dominant Logic for Marketing,” Journal of Marketing, vol. 68, no. 1, pp. 1–17, Jan. 2004, doi: 10.1509/jmkg. [CrossRef] [Google Scholar]
  27. S. Leroi-Werelds, “An update on customer value: state of the art, revised typology, and research agenda,” Journal of Service Management, vol. 30, no. 5, pp. 650–680, Jan. 2019, doi: 10.1108/JOSM-03-2019-0074. [CrossRef] [Google Scholar]
  28. J. C. Sweeney and G. N. Soutar, “Consumer perceived value: The development of a multiple item scale,” Journal of Retailing, vol. 77, no. 2, pp. 203–220, Jun. 2001, doi: 10.1016/S0022-4359(01)00041-0. [CrossRef] [Google Scholar]
  29. M. Ehret and J. Wirtz, “Unlocking value from machines: business models and the industrial internet of things,” Journal of Marketing Management, vol. 33, no. 1–2, pp. 111–130, Jan. 2017, doi: 10.1080/0267257X.2016.1248041. [CrossRef] [Google Scholar]
  30. M. Breuer et al., “Data Economy - Datenwertschöpfung und Qualität von Daten,” 2018. (accessed Jan. 11, 2022). [Google Scholar]
  31. K. Möller, B. Otto, and A. Zechmann, “Nutzungsbasierte Datenbewertung,” CON, vol. 29, no. 5, pp. 57–66, 2017, doi: 10.15358/0935-0381-2017-5-57. [CrossRef] [Google Scholar]
  32. D. L. Moody and P. Walsh, “Measuring the Value Of Information-An Asset Valuation Approach.,” in ECIS, 1999, pp. 496–512. [Google Scholar]
  33. L. Holst, F. Groen in’t Woud, J. Frank, and V. Stich, “Towards a Methodology to Determine Intersubjective Data Values in Industrial Business Activities,” in 2021 8th Swiss Conference on Data Science (SDS), Jun. 2021, pp. 39–45. doi: 10.1109/SDS51136.2021.00014. [Google Scholar]
  34. M. E. Porter and J. E. Heppelmann, “How Smart, Connected Products Are Transforming Competition,” Harvard Business Review, Nov. 01, 2014. Accessed: Apr. 07, 2021. [Online]. Available: [Google Scholar]
  35. O. Müller, M. Fay, and J. vom Brocke, “The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics,” Journal of Management Information Systems, vol. 35, no. 2, pp. 488–509, Apr. 2018, doi: 10.1080/07421222.2018.1451955. [CrossRef] [Google Scholar]
  36. J. Rowley, “The wisdom hierarchy: representations of the DIKW hierarchy,” Journal of Information Science, vol. 33, no. 2, pp. 163–180, Apr. 2007, doi: 10.1177/0165551506070706. [CrossRef] [Google Scholar]
  37. A. Dogan and D. Birant, “Machine learning and data mining in manufacturing,” Expert Systems with Applications, vol. 166, p. 114060, Mar. 2021, doi: 10.1016/j.eswa.2020.114060. [CrossRef] [Google Scholar]
  38. A. Popovič, R. Hackney, R. Tassabehji, and M. Castelli, “The impact of big data analytics on firms’ high value business performance,” Inf Syst Front, vol. 20, no. 2, pp. 209–222, Apr. 2018, doi: 10.1007/s10796-016-9720-4. [CrossRef] [Google Scholar]
  39. J. Meierhofer, C. Heitz, and F. Hannich, “Optimizing Service Value Creation with Smart, Connected Products,” in Proceedings of the 2021 Naples Forum on Service, Capri, 2021, p. 13. [Online]. Available: [Google Scholar]
  40. J. Meierhofer, S. Züst, Lu, Jinzhi, Schweiger, Lukas, and Kiritsis, Dimitris, “Enabling Decision Support Services in Industrial Ecosystems by Digital Twins,” in Spring Servitization Conference - Driving Competition through Servitization, Aston University, Florence, May 2021, Birmingham, May 2021, pp. 138–146. [Google Scholar]
  41. “Field Service - Simulation Models in AnyLogic Cloud.” (accessed Nov. 26, 2021). [Google Scholar]
  42. C. G. Cassandras and S. Lafortune, Introduction to Discrete Event Systems. Cham: Springer International Publishing, 2021. doi: 10.1007/978-3-030-72274-6. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.