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The Interaction of Causal Attribution of Performance and Compliance with Decision Support Systems in Cyber-Physical Production Systems - An Empirical Study Using a Business Simulation Game

  • Philipp Brauner
  • Ralf Philipsen
  • André Calero Valdez
  • Martina Ziefle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 601)

Abstract

Supply Chains and production networks are complex sociotechnical systems whose performance is determined by system, interface, and human factors. While the influence of system factors (e.g., variances in delivery times and amount, queuing strategies) is increasingly well understood, the influence of the interface and human factors is currently insufficiently explored. Previous research has shown that decision support systems may help to enhance performance by improving the interface. In this work, we address the users’ trust in a decision support system. In a user study (n = 40), using a business simulation game, we investigated how four dimensions of attribution theory relate to trust in decision support systems and further to task performance. The results show that human factors, especially trust in automation and attribution theory relate to the performance in the business simulation game. We conclude that attribution relates to job compliance and performance in material disposition tasks and supply chain management.

Keywords

Business simulation game Industrial internet of things Automation Trust in automation Attribution theory User modelling Human factors 

Notes

Acknowledgements

We thank all participants of the presented study for their willingness to contribute to our research and our colleagues Anne Kathrin Schaar, Felix Dietze, Lisa Schwier, Marco Fuhrmann, Sebastian Stiller, Hao Ngo, and Robert Schmitt for support and in-depth discussions on this work. Furthermore, we like to thank Sabrina Schulte for her research support. The German Research Foundation (DFG) founded this project within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” and the Integrated Cluster Domain “Self-Optimizing Production Networks” [29].

References

  1. 1.
    Nguyen, T.T., Maxwell, P.H.F., Loren, H., Joseph, T.: Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 677–686. ACM (2014)Google Scholar
  2. 2.
    Calero Valdez, A., Brauner, P., Schaar, A.K., Holzinger, A., Ziefle, M.: Reducing complexity with simplicity - usability methods for industry 4.0. In: 9th Triennial Congress of the International Ergonomics Association (IEA 2015), Melbourne, Australia (2015)Google Scholar
  3. 3.
    Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. 3, 119–131 (2016)CrossRefGoogle Scholar
  4. 4.
    Moor, J.H.: The nature, importance, and difficulty of machine ethics. IEEE Intell. Syst. 21, 18–21 (2006)CrossRefGoogle Scholar
  5. 5.
    Aven, T.: On the meaning of a black swan in a risk context. Saf. Sci. 57, 44–51 (2013)CrossRefGoogle Scholar
  6. 6.
    Calero Valdez, A., Brauner, P., Ziefle, M., Kuhlen, T.W., Sedlmair, M.: Human factors in information visualization and decision support systems. In: Workshop Human Factors in Information Visualization and Decision Support Systems Held as Part of the Mensch und Computer 2016. Gesellschaft für Informatik (2016)Google Scholar
  7. 7.
    Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decis. Support Syst. 33(2) 111–126 (2002)Google Scholar
  8. 8.
    Gorry, G.A., Morton, M.S.S.: A framework for management information systems. Sloan Manag. Rev. 13, 50–70 (1971)Google Scholar
  9. 9.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modelling. Wiley, New York (1996)Google Scholar
  10. 10.
    Codd, E., Codd, S., Salley, C.: Providing OLAP to User-Analysts: An IT Mandate (1993)Google Scholar
  11. 11.
    Bra, A., Lungu, I.: Improving decision support systems with data mining techniques. In: Advances in Data Mining Knowledge Discovery and Applications. InTech (2012)Google Scholar
  12. 12.
    Phillips-Wren, G.: Ai tools in decision making support systems: a review. Int. J. Artif. Intell. Tools 21(02), 13 pages (2012)Google Scholar
  13. 13.
    Shibl, R., Lawley, M., Debuse, J.: Factors influencing decision support system acceptance. Decis. Support Syst. 54, 953–961 (2013)CrossRefGoogle Scholar
  14. 14.
    Althuizen, N., Reichel, A., Wierenga, B.: Help that is not recognized: harmful neglect of decision support systems. Decis. Support Syst. 54, 719–728 (2012)CrossRefGoogle Scholar
  15. 15.
    Ben-Zvi, T.: Measuring the perceived effectiveness of decision support systems and their impact on performance. Decis. Support Syst. 54, 248–256 (2012)CrossRefGoogle Scholar
  16. 16.
    Brauner, P., Calero Valdez, A., Philipsen, R., Ziefle, M.: How correct and defect decision support systems influence trust, compliance, and performance in supply chain and quality management – a behavioral study using business simulation games. In: HCI in Business, Government, and Organizations (HCIGO), Held as Part of HCI International. Springer (2017, in press). doi: 10.1007/978-3-319-58484-3_26
  17. 17.
    Colman, A.M.: Oxford Dictionary of Psychology. Oxford University Press, Oxford (2015)Google Scholar
  18. 18.
    Weiner, B.: An attributional theory of achievement motivation and emotion. Psychol. Rev. 92, 548–573 (1985)CrossRefGoogle Scholar
  19. 19.
    Graham, S., Folkes, V.S.: Attribution Theory: Applications to Achievement, Mental Health, and Interpersonal Conflict. Lawrence Erlbaum Associates, Hillsdale (1990)Google Scholar
  20. 20.
    Niels, A., Guczka, S.R., Janneck, M.: The impact of causal attributions on system evaluation in usability tests. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3115–3125. ACM (2016)Google Scholar
  21. 21.
    Niels, A., Zagel, C.: Gamification: Der Einfluss von Attributionen auf die Motivation [The Influence of Attributions on Motivation]. In: Prinz, W., Borchers, J., Jarke, M. (eds.) Mensch und Computer 2016 - Tagungsband. Gesellschaft für Informatik e.V. (2016)Google Scholar
  22. 22.
    Arning, K., Ziefle, M.: Understanding age differences in PDA acceptance and performance. Comput. Hum. Behav. 23, 2904–2927 (2007)CrossRefGoogle Scholar
  23. 23.
    Brauner, P., Leonhardt, T., Ziefle, M., Schroeder, U.: The effect of tangible artifacts, gender and subjective technical competence on teaching programming to seventh graders. In: Hromkovic, J., Královiè, R., Vahrenhold, J. (eds.) Proceedings of the 4th International Conference on Informatics in Secondary Schools (ISSEP 2010), Zurich, Switzerland. LNCS, vol. 5941, pp. 61–71. Springer, Heidelberg (2010)Google Scholar
  24. 24.
    Wittland, J., Brauner, P., Ziefle, M.: Serious games for cognitive training in ambient assisted living environments – a technology acceptance perspective. In: Abascal, J., Barbosa, S., Fetter, M., Gross, T., Palanque, P., Winckler, M. (eds.) Proceedings of the 15th INTERACT 2015 Conference. LNCS, vol. 9296, pp. 453–471. Springer, Cham (2015)Google Scholar
  25. 25.
    Stiller, S., Falk, B., Philipsen, R., Brauner, P., Schmitt, R., Ziefle, M.: A game-based approach to understand human factors in supply chains and quality management. Procedia CIRP 20, 67–73 (2014)CrossRefGoogle Scholar
  26. 26.
    Sterman, J.D.: Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Manag. Sci. 35, 321–339 (1989)CrossRefGoogle Scholar
  27. 27.
    Goldratt, E.M., Cox, J.: The Goal: A Process of Ongoing Improvement. North River Press, Great Barringtons (1992)Google Scholar
  28. 28.
    Brauner, P., Calero Valdez, A., Philipsen, R., Ziefle, M.: Defective still deflective – how correctness of decision support systems influences user’s performance in production environments. In: Nah, F.F.-H., Tan, C.-H. (eds.) HCI in Business, Government, and Organizations (HCIGO), Held as Part of HCI International 2016, pp. 16–27. Springer, Cham (2016)Google Scholar
  29. 29.
    Schlick, C., et al.: Cognition-enhanced, self-optimizing production networks. In: Brecher, C., Özdemir, D. (eds.) Integrative Production Technology - Theory and Applications, pp. 645–743. Springer, Heidelberg (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Philipp Brauner
    • 1
  • Ralf Philipsen
    • 1
  • André Calero Valdez
    • 1
  • Martina Ziefle
    • 1
  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany

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