Transforming Industrial Competitiveness through Digital Service Innovation (DSI): Enabling Factors and Expected Outcomes

Closes:
Submissions open: 30th April 2023

Overview

The role of services in manufacturing industries has become increasingly relevant as industrialized nations have progressively transitioned towards service-based economies (Hofmeister et al., 2023). Such growing importance of services in industrial settings has prompted the adoption of services-led strategies by manufacturing companies to enrich their value proposition and thus create sustainable competitive advantage (Kamal et al., 2020). As a result, to date, nearly 40 percent of manufacturers globally supplement their core products with value enhancing services (Nayyar and Davies, 2023).

At the same pace, the transition towards digitalization of business´s structures, processes, models, as well as environments has enabled companies to move from traditional product or service-centric models to digitally unified offerings—labelled as Digital Service Innovation (DSI)—which fuse products, services, and software/hardware systems with higher value-generating potential (Opazo-Basáez el al., 2021). Fundamentally, DSI is built on digital elements that improve the operation of the product, and/or facilitate access to data in real time that allow reconfiguring the use of the product (Vendrell-Herrero et al., 2021). Hence, the technological building blocks underpinning the service entity enable—based on analytical outcomes—the continuous reconfiguration of the service design to the benefit of user/customer experience (Witell et al., 2021). In other words, the service itself has become a digital innovation source that can be completely revamped in relation to the service’s specificities and their expected outcomes (Gebauer et al., 2021).

Additionally, the ability to connect remotely with physical assets/devices has currently enabled the intensification of DSI across business industries, driven by synergies between information system advancements and the widespread adoption of Internet of Things (IoT), big data, artificial intelligence (AI) /Machine Learning (ML), and cloud computing (Narwane et al., 2021; Jamwal et al., 2022; Siegfried et al., 2022). This has promoted the development of innovative data-driven digital service offerings (based on contextual/environmental/consumer patterns) in multiple industrial settings, enabling firms to align business model component configurations dynamically with business and customers’ needs (Heller et al., 2021). Prominent examples of DSI are: condition monitoring and failure prediction services (Xiao et al., 2023), predictive maintenance services (Rapaccini and Adrodegari, 2022), remote assistance services (Fang et al., 2020), remote control and production optimization services (Mourtzis et al., 2022), monitoring and system upgrading services (Copani and Behnam, 2020), among others.

Although research has been reported on the adoption of DSI by companies in supply chain (SC) environments (Soto Setzke et al., 2021; Opazo-Basáez el al., 2021; Raddats et al., 2022), from an organizational standpoint, there is still limited research into what are the necessary technological requirements (e.g., Enterprise Information System [EIS]) for the implementation of DSI, as well as, the organizational capabilities necessary for its adoption. Likewise, from a technological viewpoint, it is necessary to adequately clarify which technologies are appropriate for the deployment of DSI, but also how these technologies facilitate the development and deployment of DSI across industries. On the other hand, it is important to elucidate, at a strategic level, how DSI can be effectively implemented in organizations in order to render competitive advantage in the digitalized world. Finally, it is important to disclose the possible benefits that the implementation of DSI might generate in organizations at financial, organizational, sustainable, and social level, to name a few.

The Guest Editors invite papers with an original perspective and advanced thinking on DSI enabling factors and/or expected outcomes, or both. This special issue is restricted to original contributions that have not been published yet and are neither under consideration by any other journal at present. Also, this special issue is connected – but not restricted – to the International Conference on Business Servitization (ICBS). The participants of the 10th ICBS are invited to submit their papers on the theme of this special issue. Contributions arising from papers given at a conference should be substantially extended and should cite the conference paper where appropriate.
 

List of topics

All researchers worldwide working on the topics indicated above are also invited to contribute. Such studies might be focused on, but not limited to, the following areas of research and related topics. In all research areas DSI antecedents and outcomes is a tying theme. Theoretically-sound and empirically-based papers are encouraged for submission. Moreover, we recommend that authors pay explicit attention to the managerial implications of their findings.

  • Digital Servitization (DS) as a facilitator/enabler for DSI
     
  • The role of service automation and service robots in DSI deployment
     
  • Operation/operative effects of DSI in Digital retailing
     
  • The use of Digital platforms as a key driver for DSI
     
  • Adoption of Artificial Intelligence (AI)/Machine Learning (ML) applications for DSI
     
  • Implications of Business Analytics for DSI strategies
     
  • Adoption of DSI strategies in Industry 4.0, Manufacturing 4.0 contexts
     
  • Development/configuration of Digital ecosystems aimed at DSI deployment
     
  • Autonomous solutions as facilitators for DSI strategies
     
  • DSI as a catalyst for circularity/circular econom (CE) in operations
     
  • DSI-enabled Green supply chain management (GSCM)
     
  • Opportunities of DSI for Social Innovation (SI), Business ethics, and Sustainability
     

Submission Information

Submissions open: 15th August 2023
Submissions deadline: 30th April 2024


Further inquiries should be sent to the Guest Editors:

Marco Opazo-Basáez
University of Deusto - Spain
[email protected]

Ferran Vendrell-Herrero
University of Edinburgh - UK
[email protected]

Yancy Vaillant
Toulouse Business School - France
[email protected]
 

References

Copani, G., & Behnam, S. (2020). Remanufacturing with upgrade PSS for new sustainable business models. CIRP Journal of Manufacturing Science and Technology, 29, 245-256.

Fang, D., Xu, H., Yang, X., & Bian, M. (2020). An augmented reality-based method for remote collaborative real-time assistance: from a system perspective. Mobile
Networks and Applications, 25, 412-425.

Gebauer, H., Paiola, M., Saccani, N. and Rapaccini, M. (2021), “Digital servitization: crossing the perspectives of digitization and servitization”, Industrial Marketing
Management, Vol. 93, pp. 382-388.

Heller, J., Chylinski, M., de Ruyter, K., Keeling, D. I., Hilken, T. and Mahr, D. (2021), “Tangible service automation: decomposing the technology-enabled engagement
process (TEEP) for augmented reality”, Journal of Service Research, Vol. 24 No. 1, pp. 84-103.

Hofmeister, J., Kanbach, D. K., & Hogreve, J. (2023). Service productivity: a systematic review of a dispersed research area. Management Review Quarterly, 1-33.

Jamwal, A., Agrawal, R., Sharma, M., Kumar, A., Kumar, V. and Garza-Reyes, J.A.A.(2022), "Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research", Journal of Enterprise Information Management, Vol. 35 No. 2, pp. 566-596. https://doi.org/10.1108/JEIM-09-2020-0361.

Kamal, M. M., Sivarajah, U., Bigdeli, A. Z., Missi, F., & Koliousis, Y. (2020). Servitization implementation in the manufacturing organisations: Classification of strategies, definitions, benefits and challenges. International Journal of Information Management, 55, 102206.

Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). Development of a PSS for smart grid energy distribution optimization based on digital twin. Procedia CIRP, 107, 1138-1143.

Narwane, V.S., Raut, R.D., Yadav, V.S., Cheikhrouhou, N., Narkhede, B.E. and Priyadarshinee, P. (2021), "The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries", Journal of Enterprise Information Management, Vol. 34 No. 5, pp. 1452-1480. https://doi.org/10.1108/JEIM-11-2020-0463.

Nayyar, G., & Davies, E. (2023). Services-Led Growth. World Bank.

Opazo-Basáez, M., Vendrell-Herrero, F. and Bustinza, O.F. (2022), "Digital service innovation: a paradigm shift in technological innovation", Journal of Service Management, Vol. 33 No. 1, pp. 97-120. https://doi.org/10.1108/JOSM-11-2020- 0427.

Raddats, C., Naik, P., & Bigdeli, A. Z. (2022). Creating value in servitization through digital service innovations. Industrial Marketing Management, 104, 1-13.

Rapaccini, M., & Adrodegari, F. (2022). Conceptualizing customer value in data-driven services and smart PSS. Computers in Industry, 137, 103607.

Siegfried, N., Rosenthal, T. and Benlian, A. (2022), "Blockchain and the Industrial Internet of Things: A requirement taxonomy and systematic fit analysis", Journal of Enterprise Information Management, Vol. 35 No. 6, pp. 1454-1476. https://doi.org/10.1108/JEIM-06-2018-0140.

Soto Setzke, D., Riasanow, T., Böhm, M., & Krcmar, H. (2021). Pathways to digital service innovation: The role of digital transformation strategies in established organizations. Information Systems Frontiers, 1-21.

Vendrell-Herrero, F., Bustinza, O.F. and Vaillant, Y. (2021), “Adoption and optimal configuration of smart products: the role of firm internationalization and offer hybridization”, Industrial Marketing Management, Vol. 95, pp. 41-53.

Witell, L., Kowalkowski, C., Perks, H., Raddats, C., Schwabe, M., Benedettini, O. and Burton, J. (2020), “Characterizing customer experience management in business markets”, Journal of Business Research, Vol. 116, pp. 420-430.

Xiao, H., Hu, W., Liu, G., & Zhou, H. (2023). Edge computing-based unified condition monitoring system for process manufacturing. Computers & Industrial Engineering, 177, 109032.