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Advanced theories and methodologies for design and management of digital transformations

Advanced theories and methodologies for design and management of digital transformations

Impact Score 1.32


Special Issue Information

Submission Deadline: 31-01-2021
Journal Impact Score: 1.32
Journal Name: Advanced Engineering Informatics
Publisher: Advanced Engineering Informatics

Special Issue Call for Papers

Focus and Motivation for the Special Issue

Digital transformation (DT) is the process of combining digital technology with existing operating models to generate value, respond to market demand, and make profits (Vial, 2019; Gimpel et al., 2018; Schallmo, 2017). The four essential elements of DT are 1) target entity, i.e., the organization that adopts DT; 2) scope and focus of the transformation; 3) technology adoption and manners, and 4) contexts and benefit goals of the expected change (Vial, 2019; Lee et al., 2019). If an organization undergoes DT, it is said to be “triggering significant changes and effectiveness to its external market strategy and internal organization tactics through combinations of information, computing, communication, and connectivity technologies” (Vial, 2019; Gimpel et al., 2018; Schallmo, 2017). An organization with a high digital transformation maturity means that it has the capability to upgrade and transform in different aspects like operational processes, value proposition, customer experience, and culture, while being market sensitive at the same time (Vial, 2019; Schallmo, 2017; Hess et al., 2016;). In doing so, organizations are found to have changed the perception of customer value and experience at the same time (Schallmo, 2017; Gimpel et al., 2018; Huang & Rust, 2020).

To fully reap the benefits of DT, an appropriate DT strategy is necessary for it to be integrated successfully for every organization across various industries, thereby making the “design and management of DT” critical (Vial et al., 2019; Majchrzak et al., 2016). Such can be achieved by integrating an organization’s resources and business needs, to design a unique and innovative value proposal, based on its situation at any point of time (Wang et al., 2017; Lee et al., 2019). In industries having lesser interaction with customers, organizations can undergo DT by ‘digitalizing’ its operational processes. One such example is the use of improved engineering tools to further improve its efficiency and cost-effectiveness (Schallmo et al., 2017). On the other hand, for customer-centric industries such as finance, travel, retail, and media, organizations may focus on value propositions and discuss the digital expertise and quality services that can be provided in terms of primary and market position (Vial, 2019; Gimpel et al., 2018; Schallmo, 2017).

This dynamic pace of DT development has brought about new challenges related to its implementation across all industries (Wang et al., 2017; Lee et al., 2019). One key challenge is the lack of comprehensive understanding of the DT phenomenon and its in-depth insights into industries which are critical factors for market success. This is consistent with several studies, where researchers adopted different methodologies to design and manage DT based on an organization’s structure, processes, and culture to generate the valid value and find the right paths to achieve transformation results (Vial, 2019; Majchrzak et al., 2016).

Another important aspect of DT involves the use of new and emerging technologies, by converging their advantages to the needs of the different industries (Frank et al., 2019; Schallmo et al., 2017; Wang et al., 2017). These tools are normally applied with the aim of addressing demand-pull factors like value-adding customers or technology-push like the improvement of the manufacturing or engineering process (Trappey et al., 2016; Schallmo et al., 2017; Bharadwaj et al., 2013). Examples of these technologies range from the development of new cutting-edge technologies, like the synchronous tool for electronic surveillance to the application of integrated digital technologies – Internet of Things (IoT), cloud computing, and predictive analytics digital tools (Frank et al., 2019; Wee et al., 2019).

Therefore, this special issue aims to explore the fields related to these DT challenges and solutions, from its design, management, and implementation of tools to provide glimpses of the innovative business models, practical experience, and cutting-edge knowledge of the DT in the industry (Dremel et al., 2017; Lee et al., 2019).

Topics and subjects

Unpublished, original contributions from prospective authors are invited for consideration by the special issue, subject to blind reviews, with main focus on enabling methodologies of digital transformation that support knowledge intensive tasks (e.g., system architectures and designs, service experience design, human factors, modeling and validation, performance evaluation), the digital-technology enabler for digital transformation (e.g., digital data-driven technology, networking-based technology, digital consumer access-driven technology, artificial intelligence-based technology) (Schallmo, et al., 2017; Boueé and Schaible, 2015). Real-world digital transformation scenarios with the integration and convergence of the above technologies particularly for those in the context of advanced manufacturing and services of Industry 4.0, smart city, and governance innovation (Schallmo et al. 2017; Vial, 2019, Govindarajan; 2018) are expected and required. For manuscripts on DT at the organizational level, the design of innovative strategies to embrace the implications of digital transformation for better operational performance must be addressed (Vial, 2019; Nambisan et al., 2019). For example, AR and VR are used in the construction industry's DT for better design support of outside and inside building (Delgado et al., 2020). The current status and future trends are analyzed using big data in the constructions for bringing DT benefits (Bilal et al., 2016). Using the convergence digital-technology enabler to carry out technology evolution and transition in the fuel cell field (Chen et al., 2013).

Comprehensive case studies, in-depth review papers about how to design and manage digital transformation for the new innovation model position, process development and value creation, customer experience and loyalty enhancement and operation efficiency improvement, transformation strategy and digital ethics issues are also welcome. Meanwhile, all SI research papers need to fit the core philosophy and the scope of ADVEI. Research papers with particular emphasis on 'knowledge' and 'engineering applications' are the requirement of ADVEI. Articles must illustrate contributions using examples of digital-enabled automating and supporting knowledge-intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering for bringing DT benefits by advanced technology and engineering transition (Rezgui et al., 2010; Verhagen et al.; Chen et al., 2013). Research contribution must demonstrate the successful adoption of DT with practical engineering context, and real data analytics. Further, the research must highlight its improvement and implications in the context of an industry and engineering discipline.

Topics of the special issue interests and focuses include, but not limited to:

1. Advanced theories and methodologies of design and management topics for digital transformation with advanced technology and engineering transition to support knowledge intensive tasks in the industry:

  • Management, control, and governance of DT resources and capabilities.

  • Changes in strategy, structure, workforce, processes associated with DT.

  • Managing intended and unintended DT outcomes across levels of analysis.

  • DT business strategy, business models, and value creation processes.

  • DT innovations, including tangible digital product, intangible software development, and customer experience design.

  • DT Evaluation associated with organizational activity and revenue flow model.

  • Managing design issues associated with DT in infrastructures, products, services, platforms, ecosystems and markets.

  • Managing policy, ethical, and social implications under the DT context.

  • Digital innovation, digital governance, digital participation and co-creation in public services;

2. The research must present the integration and convergence of multiple advanced digital-technology enabler for digital transformation (as listed below) for the innovative changes. These DT changes must demonstrate with measurable significant outcomes for the given issues in private sectors or public sectors.

1) Digital data-driven technology:

  • Wearable Devices

  • Internet of things

  • Cyber-physical systems

  • Big data analytics

  • Mobile big data analytics

2) Networking-based technology:

  • Smart sensing networks

  • Sensor technology

  • Brain machine interface

  • Cloud computing

  • Edge Computing

  • Mobile crowd sensing systems

3) Digital consumer access-driven technology

  • Immersive technology (Virtual reality/Augmented reality/ Mixed reality)

  • FinTech

  • Blockchain

  • Social media

  • Mobile social media

  • Mobile software systems

  • Mobile internet

  • Context- and location-aware service systems

4) Artificial intelligence-based technology

  • Artificial intelligence

  • Ambient intelligence

  • AIoT (AI+IoT)

  • Machine learning

  • Deep learning

  • AI Robot

  • Knowledge bots/Service bots/Chatbots

  • Context-aware intelligent systems

Submission Guidelines

Only original manuscripts can be submitted, according to the ‘Guide for Authors’ published on the Advanced Engineering Informatics website https://www.journals.elsevier.com/advanced-engineering-informatics. As regards the online submission system of Advanced Engineering Informatics, the authors are invited to follow the link “Submit your Paper”, located in the main page of the Journal website, and submit manuscript to Article Type “VSI: Digital transformations” in Advanced Engineering Informatics.

Please mention the name of the Special Issue in your cover letter. All manuscripts will be peer-reviewed in accordance with the established policies and procedures of the journal. The final papers will be selected for publication depending on the results of the peer review process and the reviews of the Guest Editors.

Guest Editors

Dr. Ching-Hung Lee -- Xi’an Jiaotong University

Email: [email protected]

Dr. John Mo -- RMIT University

Email: [email protected]

Dr. Amy Trappey -- National Tsing Hua University

Email: [email protected]

Dr. Kevin C. Desouza -- QUT business school

Email: [email protected]

Dr. Chien-Liang Liu -- National Chiao Tung University

Email: [email protected]

Important Dates

• Submission open: August 1, 2020

• Final submission deadline: January 31, 2021

• Final acceptance deadline: July 31, 2021


1. Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems.

2. Hess, T., Matt, C., Benlian, A., Wiesboeck, F., 2016. Options for formulating a digital transformation strategy. MIS Quart. Execut. 15 (2), 123–139.

3. Schallmo, D., Williams, C. A., & Boardman, L. (2017). Digital transformation of business models—best practice, enablers, and roadmap. International Journal of Innovation Management, 21(08), 1740014.

4. Gimpel, H., Hosseini, S., Huber, R.X.R., Probst, L., Röglinger, M., Faisst, U., 2018. Structuring digital transformation: a framework of action fields and its application at ZEISS. J. Inform. Technol. Theory Appl. 19 (1), 31–54.

5. Majchrzak, A., Markus, M.L., Wareham, J., 2016. Designing for digital transformation: lessons for information systems research from the study of ICT and societal challenges. MIS Quart. 40 (2), 267–277.

6. C.V. Trappey, A.J.C. Trappey, E. Mulaomerovic, Improving the global competitiveness of retailers using a cultural analysis of in-store digital innovations, Int. J. Technol. Manage., 70 (2016) 26-43

7. Huang, M. H., & Rust, R. T. (2020). Engaged to a Robot? The Role of AI in Service. Journal of Service Research.

8. Wang, Y. H., Lee, C. H., & Trappey, A. J. (2017). Modularized design-oriented systematic inventive thinking approach supporting collaborative service innovations. Advanced Engineering Informatics, 33, 300-313.

9. Lee, C. H., Zhao, X., & Lee, Y. C. (2019). Service quality driven approach for innovative retail service system design and evaluation: A case study. Computers & Industrial Engineering, 135, 275-285.

11. Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review and Deloitte University Press, 14(1-25).

12. Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773.

13. Frank, A.G.; Mendes, G.H.S.; Ayala, N.F.; Ghezzi, A. (2019). Servitization and Industry 4.0 convergence in the digital transformation of product firms: a business model innovation perspective. Technological Forecasting and Social Change

14. Wee, H. J., Lye, S. W., & Pinheiro, J. P. (2019). An integrated highly synchronous, high resolution, real time eye tracking system for dynamic flight movement. Advanced Engineering Informatics, 41, 100919.

15. Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J. C., & Brenner, W. (2017). How AUDI AG Established Big Data Analytics in Its Digital Transformation. MIS Quarterly Executive, 16(2).

16. Lee, C. H., Chen, C. H., & Trappey, A. J. (2019). A structural service innovation approach for designing smart product service systems: Case study of smart beauty service. Advanced Engineering Informatics, 40, 154-167.

17. Govindarajan, U. H., Trappey, A. J., & Trappey, C. V. (2018). Immersive technology for human-centric cyberphysical systems in complex manufacturing processes: a comprehensive overview of the global patent profile using collective intelligence. Complexity, 2018.

18. Gill, M. and VanBoskirk, S. “The digital maturity model 4.0.” Benchmarks: Digital Transformation Playbook (2016).

19. Boueé, C and Schaible, S. (2015). Die Digitale Transformation der Industrie. Studie: Roland Berger und BDI.

20. Delgado, J. M. D., Oyedele, L., Demian, P., & Beach, T. (2020). A research agenda for augmented and virtual reality in architecture, engineering and construction. Advanced Engineering Informatics, 45, 101122.

21. Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., & Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced engineering informatics, 30(3), 500-521.

22. Rezgui, Y., Hopfe, C. J., & Vorakulpipat, C. (2010). Generations of knowledge management in the architecture, engineering and construction industry: An evolutionary perspective. Advanced Engineering Informatics, 24(2), 219-228.

23. Verhagen, W. J., Bermell-Garcia, P., Van Dijk, R. E., & Curran, R. (2012). A critical review of Knowledge-Based Engineering: An identification of research challenges. Advanced Engineering Informatics, 26(1), 5-15.

24. Chen, S. H., Huang, M. H., & Chen, D. Z. (2013). Exploring technology evolution and transition characteristics of leading countries: A case of fuel cell field. Advanced Engineering Informatics, 27(3), 366-377.


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