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
  • Philipp Brauner
  • André Calero Valdez
  • Ralf Philipsen
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10294)


Supply Chains and production networks are complex sociotechnical cyber-physical 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 well understood, the influence of interface and human factors on supply chain performance is currently insufficiently explored. In this article, we analyze how performance is determined by the correctness of Decision Support Systems and specifically, how correct and defect systems influence subjective and objective performance, subjective and objective compliance with the system, as well as trust in the system. We present a behavioral study with 50 participants and a business simulation game with a market driven supply chain. Results show that performance (−21%), compliance (−35%), and trust (−25%) is shaped by the correctness of the system. However, this effect is only substantial in later stages of the game and occluded at the beginning. Also, people’s subjective evaluations and the objective measures from the simulation are in congruence. The article concludes with open research questions regarding trust and compliance in Decision Support Systems as well as actionable knowledge on how Decision Support Systems can mitigate supply chain disruptions.


Compliance Trust Decision support system Supply chain management Enterprise resource planning Human factors Business simulation game Sociotechnical Cyber-Physical systems Internet of production 



We thank all participants for their willingness to contribute to our research and our colleagues Sebastian Stiller, Marco Fuhrmann, Hao Ngo, 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” (EXC 128) and the integrated cluster domain ICD-D1 [30].


  1. 1.
    Snyder, L.V., Atan, Z., Peng, P., Rong, Y., Schmitt, A.J., Sinsoysal, B.: OR/MS models for supply chain disruptions: a review. IIE Trans. 48, 89–109 (2016)CrossRefGoogle Scholar
  2. 2.
    Trent, R.J., Monczka, R.M.: Pursuing competitive advantage through integrated global sourcing. Acad. Manage. Executive 16, 66–80 (2002)CrossRefGoogle Scholar
  3. 3.
    Brauner, P., Philipsen, R., Fels, A., Fuhrmann, M., Ngo, H., Stiller, S., Schmitt, R., Ziefle, M.: A game-based approach to meet the challenges of decision processes in ramp-up management. Qual. Manage. J. 23, 55–69 (2016)Google Scholar
  4. 4.
    Lee, H.L., Padmanabhan, V., Whang, S.: Information distortion in a supply chain: the bullwhip effect. Manage. Sci. 43, 546–558 (1997)CrossRefzbMATHGoogle Scholar
  5. 5.
    Blackhurst, J., Craighead, C.W., Elkins, D., Handfield, R.B.: An empirically derived agenda of critical research issues for managing supply-chain disruptions. Int. J. Prod. Res. 43, 4067–4081 (2005)CrossRefGoogle Scholar
  6. 6.
    Tang, C.: Robust strategies for mitigating supply chain disruptions. Int. J. Logistics 9, 33–45 (2006)CrossRefGoogle Scholar
  7. 7.
    Blum, M., Runge, S., Groten, M., Stiller, S.: Interrelationships between product quality and different demand cases in ramp-up scenarios. Procedia CIRP 20, 81–84 (2014)CrossRefGoogle Scholar
  8. 8.
    Forrester, J.W.: Industrial Dynamics. MIT Press, Cambridge (1961)Google Scholar
  9. 9.
    Sarkar, S., Kumar, S.: Demonstrating the effect of supply chain disruptions through an online beer distribution game *. Decis. Sci. J. Innovative Educ. 14, 25–35 (2016)CrossRefGoogle Scholar
  10. 10.
    Brauner, P., Runge, S., Groten, M., Schuh, G., Ziefle, M.: Human factors in supply chain management – decision making in complex logistic scenarios. In: Yamamoto, S. (ed.) HIMI 2013. LNCS, vol. 8018, pp. 423–432. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-39226-9_46 CrossRefGoogle Scholar
  11. 11.
    Gorry, G.A., Morton, M.S.S.: A framework for management information systems. Sloan Manage. Rev. 13, 50–70 (1971)Google Scholar
  12. 12.
    Kimball, R., Ross, M.: The data warehouse toolkit: the complete guide to dimensional modelling. Wiley, New York (1996)Google Scholar
  13. 13.
    Codd, E., Codd, S., Salley, C.: Providing OLAP to User-Analysts: An IT Mandate (1993)Google Scholar
  14. 14.
    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
  15. 15.
    Phillips-Wren, G.: AI tools in decision making support systems: a review. Int. J. Artif. Intell. Tools 21 (2012)Google Scholar
  16. 16.
    Brauner, P., Ziefle, M.: Human factors in production systems – motives, methods and beyond. In: Brecher, C. (ed.) Advances in Production Technology. LNPE, pp. 187–199. Springer, Cham (2015). doi: 10.1007/978-3-319-12304-2_14 Google Scholar
  17. 17.
    Calero Valdez, A., Brauner, P., Schaar, A.K., Holzinger, A., Ziefle, M.: Reducing complexity with simplicity - usability methods for industry 4.0. In: 19th Triennial Congress of the International Ergonomics Association (IEA 2015), Melbourne, Australia (2015)Google Scholar
  18. 18.
    Ben-Zvi, T.: The efficacy of business simulation games in creating Decision Support Systems: an experimental investigation. Decis. Support Syst. 49, 61–69 (2010)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    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.) HCIBGO 2016. LNCS, vol. 9752, pp. 16–27. Springer, Cham (2016). doi: 10.1007/978-3-319-39399-5_2 CrossRefGoogle Scholar
  21. 21.
    Brauner, P., Ziefle, M.: How to train employees, identify task-relevant human factors, and improve software systems with business simulation games. In: Dimitrov, D., Oosthuizen, T. (eds.) Proceedings of the 6th International Conference on Competitive Manufacturing 2016 (COMA 2016), pp. 541–546. CIRP, Stellenbosch (2016)Google Scholar
  22. 22.
    Sterman, J.D.: Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Manage. Sci. 35, 321–339 (1989)CrossRefGoogle Scholar
  23. 23.
    Wu, D.Y., Katok, E.: Learning, communication, and the bullwhip effect. J. Oper. Manage. 24, 839–850 (2006)CrossRefGoogle Scholar
  24. 24.
    Sarkar, S., Kumar, S.: A behavioral experiment on inventory management with supply chain disruption. Int. J. Prod. Econ. 169, 169–178 (2015)CrossRefGoogle Scholar
  25. 25.
    Goldratt, E.M., Cox, J.: The goal: a process of ongoing improvement. North River Press, Great Barrington (1992)Google Scholar
  26. 26.
    Jian, J.-Y., Bisantz, A.M., Drury, C.G.: Foundations for an empirically determined scale of trust in automated system. Int. J. Cogn. Ergon. 4, 53–71 (2000)CrossRefGoogle Scholar
  27. 27.
    Philipsen, R., Brauner, P., Stiller, S., Ziefle, M., Schmitt, R.: Understanding and supporting decision makers in quality management of production networks. In: Advances in the Ergonomics in Manufacturing. Managing the Enterprise of the Future 2014: Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics, AHFE 2014, pp. 94–105. CRC Press, Boca Raton (2014)Google Scholar
  28. 28.
    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
  29. 29.
    Niels, A., Guczka, S.R., Janneck, M.: The impact of causal attribution s on system evaluation in usability tests. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3115–3125 (2016)Google Scholar
  30. 30.
    Schlick, C., Stich, V., Schmitt, R., Schuh, G., Ziefle, M., Brecher, C., Blum, M., Mertens, A., Faber, M., Kuz, S., Petruck, H., Fuhrmann, M., Luckert, M., Brambring, F., Reuter, C., Hering, N., Groten, M., Korall, S., Pause, D., Brauner, P., Herfs, W., Odenbusch, M., Wein, S., Stiller, S., Berthold, M.: Cognition-enhanced, self-optimizing production networks. In: Brecher, C., Özdemir, D. (eds.) Integrative Production Technology - Theory and Applications, pp. 645–743. Springer International Publishing, Heidelberg (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Philipp Brauner
    • 1
    • 2
  • André Calero Valdez
    • 1
    • 2
  • Ralf Philipsen
    • 1
    • 2
  • Martina Ziefle
    • 1
    • 2
  1. 1.Human-Computer Interaction Center at RWTH Aachen UniversityAachenGermany
  2. 2.Chair of Communication ScienceRWTH Aachen UniversityAachenGermany

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