IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v22y2024i3d10.1007_s10257-024-00675-1.html
   My bibliography  Save this article

Towards a process selection method for embedded analytics

Author

Listed:
  • Tobias Bender

    (University of St. Gallen)

Abstract

Driven by technological progress, business analytics is gaining momentum while paving the path for next-generation business process management. Especially, embedded real-time analytics offers new opportunities for business process intelligence and value creation. However, there are several obstacles that organizations face in their adoption process. A key challenge is to identify business processes that are suitable for embedded analytics and hold relevant value potential. Our research addresses this need by introducing an exploratory BPM method, namely a process selection method. Applying action design research and situational method engineering, we iteratively built, used, evaluated, and refined the theory-ingrained method artifact. The method provides organizations with guidance in selecting operational business processes, for which a reengineering project should be initiated.

Suggested Citation

  • Tobias Bender, 2024. "Towards a process selection method for embedded analytics," Information Systems and e-Business Management, Springer, vol. 22(3), pages 501-525, September.
    • Handle: RePEc:spr:infsem:v:22:y:2024:i:3:d:10.1007_s10257-024-00675-1
    DOI: 10.1007/s10257-024-00675-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-024-00675-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-024-00675-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. G Montibeller & V Belton, 2006. "Causal maps and the evaluation of decision options—a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(7), pages 779-791, July.
    2. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    3. Brocke, Jan vom & Zelt, Sarah & Schmiedel, Theresa, 2016. "On the role of context in business process management," International Journal of Information Management, Elsevier, vol. 36(3), pages 486-495.
    4. Rob Vanwersch & Khurram Shahzad & Irene Vanderfeesten & Kris Vanhaecht & Paul Grefen & Liliane Pintelon & Jan Mendling & Godefridus Merode & Hajo Reijers, 2016. "A Critical Evaluation and Framework of Business Process Improvement Methods," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(1), pages 43-53, February.
    5. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    6. Rob J. B. Vanwersch & Khurram Shahzad & Irene Vanderfeesten & Kris Vanhaecht & Paul Grefen & Liliane Pintelon & Jan Mendling & Godefridus G. Merode & Hajo A. Reijers, 2016. "A Critical Evaluation and Framework of Business Process Improvement Methods," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(1), pages 43-53, February.
    7. Thomas Grisold & Steven Groß & Katharina Stelzl & Jan Brocke & Jan Mendling & Maximilian Röglinger & Michael Rosemann, 2022. "The Five Diamond Method for Explorative Business Process Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(2), pages 149-166, April.
    8. Martin Bichler & Armin Heinzl & Wil M. P. Aalst, 2017. "Business Analytics and Data Science: Once Again?," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(2), pages 77-79, April.
    9. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    10. Ham, Dong-Han & Park, Jinkyun & Jung, Wondea, 2012. "Model-based identification and use of task complexity factors of human integrated systems," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 33-47.
    11. Lepenioti, Katerina & Bousdekis, Alexandros & Apostolou, Dimitris & Mentzas, Gregoris, 2020. "Prescriptive analytics: Literature review and research challenges," International Journal of Information Management, Elsevier, vol. 50(C), pages 57-70.
    12. Buxmann, Peter & Hess, Thomas & Thatcher, Jason Bennett, 2021. "AI-Based Information Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149083, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Lacity, Mary C. & Willcocks, Leslie P., 2016. "A new approach to automating services," LSE Research Online Documents on Economics 68135, London School of Economics and Political Science, LSE Library.
    14. Jan Brocke & Marie-Sophie Baier & Theresa Schmiedel & Katharina Stelzl & Maximilian Röglinger & Charlotte Wehking, 2021. "Context-Aware Business Process Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(5), pages 533-550, October.
    15. Jan Brocke & Wolfgang Maaß & Peter Buxmann & Alexander Maedche & Jan Marco Leimeister & Günter Pecht, 2018. "Future Work and Enterprise Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(4), pages 357-366, August.
    16. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    17. D. J. Power & C. Heavin & J. McDermott & M. Daly, 2018. "Defining business analytics: an empirical approach," Journal of Business Analytics, Taylor & Francis Journals, vol. 1(1), pages 40-53, January.
    18. Peter Buxmann & Thomas Hess & Jason Bennett Thatcher, 2021. "AI-Based Information Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 1-4, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ingo Kregel & Bettina Distel & André Coners, 2022. "Business Process Management Culture in Public Administration and Its Determinants," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(2), pages 201-221, April.
    2. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    3. Latinovic, Zoran & Chatterjee, Sharmila C., 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Journal of Business Research, Elsevier, vol. 144(C), pages 966-974.
    4. Patrick Afflerbach & Martin Hohendorf & Jonas Manderscheid, 0. "Design it like Darwin - A value-based application of evolutionary algorithms for proper and unambiguous business process redesign," Information Systems Frontiers, Springer, vol. 0, pages 1-21.
    5. Marek Szelągowski & Justyna Berniak-Woźny & Piotr Sliż & Natalia Potoczek & Aneta Napieraj & Marta Podobińska-Staniec & Piotr Senkus, 2024. "Exploring the diverse nature of business processes in organisations in Industry 4.0/5.0," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    6. Chen, Yi-Ting & Sun, Edward W. & Lin, Yi-Bing, 2020. "Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability," European Journal of Operational Research, Elsevier, vol. 281(3), pages 687-705.
    7. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    8. Fawwaz Tawfiq Awamleh & Ala Nihad Bustami, 2022. "Examine the Mediating Role of the Information Technology Capabilities on the Relationship Between Artificial Intelligence and Competitive Advantage During the COVID-19 Pandemic," SAGE Open, , vol. 12(3), pages 21582440221, August.
    9. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    10. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    11. Ranjan Chaudhuri & Sheshadri Chatterjee & Demetris Vrontis & Alkis Thrassou, 2024. "Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture," Annals of Operations Research, Springer, vol. 339(3), pages 1757-1791, August.
    12. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    13. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    14. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    15. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    16. Ida Merete Enholm & Emmanouil Papagiannidis & Patrick Mikalef & John Krogstie, 2022. "Artificial Intelligence and Business Value: a Literature Review," Information Systems Frontiers, Springer, vol. 24(5), pages 1709-1734, October.
    17. Osman, Ibrahim H. & Anouze, Abdel Latef & Irani, Zahir & Lee, Habin & Medeni, Tunç D. & Weerakkody, Vishanth, 2019. "A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values," European Journal of Operational Research, Elsevier, vol. 278(2), pages 514-532.
    18. Sanjay Kumar Tyagi & Sujeet Kumar Sharma & R. Krishankumar & K. S. Ravichandran, 2022. "An extension of interpretive structural modeling using linguistic term sets for business decision-making," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1158-1177, September.
    19. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    20. Burger, Katharina & White, Leroy & Yearworth, Mike, 2019. "Developing a smart operational research with hybrid practice theories," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1137-1150.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:22:y:2024:i:3:d:10.1007_s10257-024-00675-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.