Design principles for a hybrid intelligence decision support system for business model validation

Abstract

One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of early-stage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.

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Notes

  1. 1.

    For further details of the problem identification, demonstration, and evaluation see Appendix.

  2. 2.

    For a comprehensive review of design features for decisional guidance, see Morana et al. (2017).

  3. 3.

    For further details of the problem identification, demonstration, and evaluation phases, see Appendix.

  4. 4.

    For further information please see www.vencortex.com

References

  1. Al-Debei, M. M., & Avison, D. (2010). Developing a unified framework of the business model concept. European Journal of Information Systems, 19(3), 359–376.

    Google Scholar 

  2. Alvarez, S. A., & Barney, J. B. (2007). Discovery and creation: Alternative theories of entrepreneurial action. Strategic Entrepreneurship Journal, 1(1–2), 11–26.

    Google Scholar 

  3. Alvarez, S. A., Barney, J. B., & Anderson, P. (2013). Forming and exploiting opportunities: The implications of discovery and creation processes for entrepreneurial and organizational research. Organization Science, 24(1), 301–317.

    Google Scholar 

  4. Andries, P., & Debackere, K. (2007). Adaptation and performance in new businesses: Understanding the moderating effects of independence and industry. Small Business Economics, 29(1), 81–99.

    Google Scholar 

  5. Armstrong, J. S. (2001). Principles of forecasting: A handbook for researchers and practitioners: Springer science and business media.

  6. Arnold, V., Collier, P. A., Leech, S. A., & Sutton, S. G. (2004). Impact of intelligent decision aids on expert and novice decision-makers’ judgments. Accounting and Finance, 44(1), 1–26.

    Google Scholar 

  7. Attenberg, J., Ipeirotis, P. G., & Provost, F. J. (2011). Beat the machine: Challenging workers to find the unknown unknowns. Human Computation, 11(11).

  8. Baer, J., & McKool, S. S. (2014). The gold standard for assessing creativity. International Journal of Quality Assurance in Engineering and Technology Education (IJQAETE), 3(1), 81–93.

    Google Scholar 

  9. Bailey, D. E., Leonardi, P. M., & Barley, S. R. (2012). The lure of the virtual. Organization Science, 23(5), 1485–1504.

    Google Scholar 

  10. Baker, J., Jones, D. R., & Burkman, J. (2009). Using visual representations of data to enhance sensemaking in data exploration tasks. Journal of the Association for Information Systems, 10(7), 533.

    Google Scholar 

  11. Bazerman, M. H., Moore, D. A. (2012). Judgment in managerial decision making.

  12. Benbasat, I., Dexter, A. S. , Todd, P. (1986). An experimental program investigating color-enhanced and graphical information presentation: An integration of the findings. Communications of the ACM, 29(11), 1094–1105.

    Google Scholar 

  13. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A. , Venkatraman, N. V. (2013). Digital business strategy: Toward a next generation of insights.

    Google Scholar 

  14. Blank, S. (2013). Why the lean start-up changes everything. Harvard Business Review, 91(5), 63–72.

    Google Scholar 

  15. Blattberg, R. C., & Hoch, S. J. (1990). Database models and managerial intuition: 50% model+ 50% manager. Management Science, 36(8), 887–899.

    Google Scholar 

  16. Blohm, I., Riedl, C., Füller, J., & Leimeister, J. M. (2016). Rate or trade? Identifying winning ideas in open idea sourcing. Information Systems Research, 27(1), 27–48.

    Google Scholar 

  17. Bouwman, H., Zhengjia, M., van der Duin, P., & Limonard, S. (2008). A business model for IPTV service: A dynamic framework. info, 10(3), 22–38.

    Google Scholar 

  18. Brynjolfsson, E., Geva, T., & Reichman, S. (2016). Crowd-squared: Amplifying the predictive POWER of SEARCH trend data. MIS Quarterly, 40(4), 941–961.

    Google Scholar 

  19. Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455.

    Google Scholar 

  20. Cavalcante, S., Kesting, P., & Ulhøi, J. (2011). Business model dynamics and innovation:(re) establishing the missing linkages. Management decision, 49(8), 1327–1342.

    Google Scholar 

  21. Cheng, J., Bernstein, M. S. (2015). Flock: Hybrid crowd-machine learning classifiers. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing, 600–611.

  22. Cohen, S., & Hochberg, Y. (2014). Accelerating startups: The seed accelerator phenomenon.

  23. Colton, S., Wiggins, G. A. (Ed.) 2012. Computational creativity: The final frontier?: IOS Press.

  24. Creamer, G. G., Ren, Y., Sakamoto, Y., & Nickerson, J. V. (2016). A textual analysis algorithm for the equity market: The European case. The Journal of Investing, 25(3), 105–116.

    Google Scholar 

  25. Daas, D., Hurkmans, T., Overbeek, S., & Bouwman, H. (2013). Developing a decision support system for business model design. Electronic Markets, 23(3), 251–265.

    Google Scholar 

  26. Dellermann, D., Lipusch, N., Ebel, P. (2017a). Developing design principles for a crowd-based business model validation system. En A. Maedche, J. Vom Brocke A. Hevner (Eds.). Designing the digital transformation: 12th international conference, DESRIST 2017, Karlsruhe, Germany, may 30 – June 1, 2017, proceedings (pp. 163–178). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-59144-5_10.

    Google Scholar 

  27. Dellermann, D., Lipusch, N., & Ebel, P. (Eds.). (2018). Heading for new shores: Crowdsourcing for entrepreneurial opportunity creation.

  28. Dellermann, D., Lipusch, N., Ebel, P. A., Popp, K. M. , Leimeister, J. M. (2017b). Finding the Unicorn: Predicting Early Stage Startup Success through a Hybrid Intelligence Method. Proceedings ICIS 2017, Seoul.

  29. Demil, B., & Lecocq, X. (2010). Business model evolution: In Search of dynamic consistency. Business Models, 43(2), 227–246. https://doi.org/10.1016/j.lrp.2010.02.004.

    Article  Google Scholar 

  30. Demil, B., Lecocq, X., Ricart, J. E., & Zott, C. (2015). Introduction to the SEJ special issue on business models: Business models within the domain of strategic entrepreneurship. Strategic Entrepreneurship Journal, 9(1), 1–11.

    Google Scholar 

  31. Dul, J., Hak, T. (2007). Case study methodology in business research: Routledge.

  32. Ebel, P., Bretschneider, U., & Leimeister, J. M. (2016). Leveraging virtual business model innovation: A framework for designing business model development tools. Information Systems Journal, 26(5), 519–550.

    Google Scholar 

  33. Einhorn, H. J. (1972). Expert measurement and mechanical combination. Organizational Behavior and Human Performance, 7(1), 86–106.

    Google Scholar 

  34. Einhorn, H. J. (1974). Expert judgment: Some necessary conditions and an example. Journal of Applied Psychology, 59(5), 562.

    Google Scholar 

  35. Euchner, J., & Ganguly, A. (2014). Business model innovation in practice. Research-Technology Management, 57(6), 33–39.

    Google Scholar 

  36. Gönül, M. S., Önkal, D., & Lawrence, M. (2006). The effects of structural characteristics of explanations on use of a DSS. Decision Support Systems, 42(3), 1481–1493.

    Google Scholar 

  37. Gordijn, J., & Akkermans, H. (2007). Business models for distributed generation in a liberalized market environment. Electric Power Systems Research, 77(9), 1178–1188.

    Google Scholar 

  38. Gordijn, J., Akkermans, H., & Van Vliet, J. (2001). Designing and evaluating e-business models. IEEE Intelligent Systems, 16(4), 11–17.

    Google Scholar 

  39. Gregor, S., & Benbasat, I. (1999). Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly, 497–530.

  40. Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2).

  41. Gregor, S., & Jones, D. (2007). The anatomy of a design theory. Journal of the Association for Information Systems, 8(5), 312.

    Google Scholar 

  42. Haaker, T., Bouwman, H., Janssen, W., & de Reuver, M. (2017). Business model stress testing: A practical approach to test the robustness of a business model. Futures, 89, 14–25.

    Google Scholar 

  43. Hevner, A. R. (2007). A three-cycle view of design science research. Scandinavian Journal of Information Systems, 19(2), 4.

    Google Scholar 

  44. Hevner, A. , Chatterjee, S. (2010). Design research in information systems: Theory and practice: Springer science and business media.

    Google Scholar 

  45. Hochberg, Y. V. (2016). Accelerating Entrepreneurs and Ecosystems: The Seed Accelerator Model. Innovation Policy and the Economy, 16(1), 25–51.

    Google Scholar 

  46. Huang, L., & Pearce, J. L. (2015). Managing the unknowable: The effectiveness of early-stage investor gut feel in entrepreneurial investment decisions. Administrative Science Quarterly, 60(4), 634–670.

    Google Scholar 

  47. John, T. (2016). Supporting business model idea generation through machine-generated ideas: A design theory. Proceedings of the International Conference on Information Systems,

  48. John, T. , Kundisch, D. (2015). Why fit leads to surprise: An extension of cognitive fit theory to creative problems. Proceedings of the International Conference on Information Systems,

  49. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.

    Google Scholar 

  50. Kahneman, D. (2011). Thinking, fast and slow: Macmillan.

  51. Kamar, E. (2016). Directions in hybrid intelligence: Complementing AI systems with human intelligence. IJCAI, 4070–4073.

  52. Keuschnigg, M., & Ganser, C. (2016). Crowd wisdom relies on agents’ ability in small groups with a voting aggregation rule. Management Science, 63(3), 818–828.

    Google Scholar 

  53. Klein, M., & Garcia, A. C. B. (2015). High-speed idea filtering with the bag of lemons. Decision Support Systems, 78, 39–50.

    Google Scholar 

  54. Larrick, R. P., & Feiler, D. C. (2015). Expertise in decision making. The Wiley Blackwell handbook of judgment and decision making, 696–721.

  55. Larrick, R. P., Mannes, A. E., Soll, J. B., & Krueger, J. I. (2011). The social psychology of the wisdom of crowds. Social psychology and decision making, 227–242.

  56. Leimeister, J. M., Huber, M., Bretschneider, U., & Krcmar, H. (2009). Leveraging crowdsourcing: Activation-supporting components for IT-based ideas competition. Journal of Management Information Systems, 26(1), 197–224.

    Google Scholar 

  57. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22.

    Google Scholar 

  58. Limayem, M., & DeSanctis, G. (2000). Providing decisional guidance for multicriteria decision making in groups. Information Systems Research, 11(4), 386–401.

    Google Scholar 

  59. Magnusson, P. R., Wästlund, E., & Netz, J. (2016). Exploring users' appropriateness as a proxy for experts when screening new product/service ideas. Journal of Product Innovation Management, 33(1), 4–18.

    Google Scholar 

  60. Mahoney, L. S., Roush, P. B., & Bandy, D. (2003). An investigation of the effects of decisional guidance and cognitive ability on decision-making involving uncertainty data. Information and Organization, 13(2), 85–110.

    Google Scholar 

  61. Malone, T. W., Laubacher, R. , Dellarocas, C. (2009). Harnessing crowds: Mapping the genome of collective intelligence.

    Google Scholar 

  62. March, J. G. (1978). Bounded rationality, ambiguity, and the engineering of choice. The Bell Journal of Economics, 587–608.

    Google Scholar 

  63. Massey, A. P., & Wallace, W. A. (1991). Focus groups as a knowledge elicitation technique: An exploratory study. IEEE Transactions on Knowledge and Data Engineering, 3(2), 193–200.

    Google Scholar 

  64. McCormack, J., d’Inverno, M. (Ed.) (2014). On the future of computers and creativity.

  65. Moellers, T., Bansemir, B., Pretzl, M. , Gassmann, O. (2017). Design and evaluation of a system dynamics based business model evaluation method. En a. Maedche, J. Vom Brocke, A. Hevner (Eds.). Designing the digital transformation: 12th international conference, DESRIST 2017, Karlsruhe, Germany, may 30 – June 1, 2017, proceedings (pp. 125–144). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-59144-5_8.

    Google Scholar 

  66. Montazemi, A. R., Wang, F., Nainar, S. K., & Bart, C. K. (1996). On the effectiveness of decisional guidance. Decision Support Systems, 18(2), 181–198.

    Google Scholar 

  67. Morana, S., Schacht, S., Scherp, A., & Maedche, A. (2017). A review of the nature and effects of guidance design features. Decision Support Systems, 97, 31–42.

    Google Scholar 

  68. Morris, M., Schindehutte, M., & Allen, J. (2005). The entrepreneur's business model: Toward a unified perspective. Journal of Business Research, 58(6), 726–735.

    Google Scholar 

  69. Nagar, Y. , Malone, T. (2011). Making business predictions by combining human and machine intelligence in prediction markets.

  70. Nielsen, J. (1997). The use and misuse of focus groups. IEEE Software, 14(1), 94–95.

    Google Scholar 

  71. Nonaka, I., & von Krogh, G. (2009). Perspective—Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory. Organization science, 20(3), 635–652.

    Google Scholar 

  72. Ojala, A. (2016). Business models and opportunity creation: How IT entrepreneurs create and develop business models under uncertainty. Information Systems Journal, 26(5), 451–476.

    Google Scholar 

  73. Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464–473.

    Google Scholar 

  74. Osterwalder, A., & Pigneur, Y. (2013). Designing business models and similar strategic objects: The contribution of IS. Journal of the Association for Information Systems, 14(5), 237–244.

    Google Scholar 

  75. Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present. and future of the concept. Communications of the association for Information Systems, 16(1), 1.

    Google Scholar 

  76. Ozer, M. (2009). The roles of product lead-users and product experts in new product evaluation. Research Policy, 38(8), 1340–1349.

    Google Scholar 

  77. Pagani, M. (2009). Roadmapping 3G mobile TV: Strategic thinking and scenario planning through repeated cross-impact handling. Technological Forecasting and Social Change, 76(3), 382–395.

    Google Scholar 

  78. Parikh, M., Fazlollahi, B., & Verma, S. (2001). The effectiveness of decisional guidance: An empirical evaluation. Decision Sciences, 32(2), 303–332.

    Google Scholar 

  79. Patel, N. (2015) 90% of startups fail: Here's what you need to know about the 10%. Fortune. Recuperado de https://www.forbes.com/sites/neilpatel/2015/01/16/90-of-startups-will-fail-heres-what-you-need-to-know-about-the-10/#175654b96679

  80. Pauwels, C., Clarysse, B., Wright, M., & van Hove, J. (2016). Understanding a new generation incubation model: The accelerator. Technovation, 50, 13–24.

    Google Scholar 

  81. Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.

    Google Scholar 

  82. Petersen, M. A. (2004). Information: Hard and soft.

  83. Riedl, C., Blohm, I., Leimeister, J. M., & Krcmar, H. (2013). The effect of rating scales on decision quality and user attitudes in online innovation communities. International Journal of Electronic Commerce, 17(3), 7–36.

    Google Scholar 

  84. Schneckenberg, D., Velamuri, V. K., Comberg, C., & Spieth, P. (2017). Business model innovation and decision making: Uncovering mechanisms for coping with uncertainty. RandD Management, 47(3), 404–419.

    Google Scholar 

  85. Sengupta, K., & Abdel-Hamid, T. K. (1993). Alternative conceptions of feedback in dynamic decision environments: An experimental investigation. Management Science, 39(4), 411–428.

    Google Scholar 

  86. Shepherd, D. A. (2015). Party on! A call for entrepreneurship research that is more interactive, activity based, cognitively hot, compassionate, and prosocial: Elsevier.

  87. Shepherd, D. A., Williams, T. A., & Patzelt, H. (2015). Thinking about entrepreneurial decision making: Review and research agenda. Journal of Management, 41(1), 11–46.

    Google Scholar 

  88. Silver, M. S. (1991). Decisional guidance for computer-based decision support. MIS Quarterly, 105–122.

    Google Scholar 

  89. Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.

    Google Scholar 

  90. Song, M., Podoynitsyna, K., van der Bij, H., & Im Halman, J. (2008). Success factors in new ventures: A meta-analysis. Journal of Product Innovation Management, 25(1), 7–27.

    Google Scholar 

  91. Sonnenberg, C., & Vom Brocke, J. (2012). Evaluations in the science of the artificial–reconsidering the build-evaluate pattern in design science research. Design science research in information systems. Advances in. Theory Into Practice, 381–397.

  92. Sosna, M., Trevinyo-Rodríguez, R. N., & Velamuri, S. R. (2010). Business model innovation through trial-and-error learning: The Naturhouse case. Long Range Planning, 43(2), 383–407.

    Google Scholar 

  93. Soukhoroukova, A., Spann, M., & Skiera, B. (2012). Sourcing, filtering, and evaluating new product ideas: An empirical exploration of the performance of idea markets. Journal of Product Innovation Management, 29(1), 100–112.

    Google Scholar 

  94. Speier, C., & Morris, M. G. (2003). The influence of query interface design on decision-making performance. MIS Quarterly, 397–423.

  95. Spiegel, O., Abbassi, P., Zylka, M. P., Schlagwein, D., Fischbach, K., & Schoder, D. (2016). Business model development, founders' social capital and the success of early stage internet start-ups: A mixed-method study. Information Systems Journal, 26(5), 421–449.

    Google Scholar 

  96. Strauss, A., and Corbin, J. M. (1990). Basics of qualitative research: Grounded theory procedures and techniques: Sage Publications, Inc.

  97. Surowiecki, J. (2005). The wisdom of crowds: Anchor.

  98. Teece, D. J. (2010). Business models, business strategy and innovation. Long range planning, 43(2–3), 172–194.

    Google Scholar 

  99. Timmers, P. (1998). Business models for electronic markets. Electronic Markets, 8(2), 3–8.

    Google Scholar 

  100. Thaler, R., & Sunstein, C. (2008). Nudge: The gentle power of choice architecture. New Haven: Yale.

    Google Scholar 

  101. Tremblay, M. C., Hevner, A. R., & Berndt, D. J. (2010). Focus groups for artifact refinement and evaluation in design research. Communications of the Association for Information Systems (CAIS), 26(27), 599–618.

    Google Scholar 

  102. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Google Scholar 

  103. Veit, D., Clemons, E., Benlian, A., Buxmann, P., Hess, T., Kundisch, D., et al. (2014). Business models. Business and Information Systems Engineering, 6(1), 45–53.

    Google Scholar 

  104. Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89.

    Google Scholar 

  105. Vessey, I. (1991). Cognitive fit: A theory-based analysis of the graphs versus tables literature. Decision Sciences, 22(2), 219–240.

    Google Scholar 

  106. Vessey, I., & Galletta, D. (1991). Cognitive fit: An empirical study of information acquisition. Information Systems Research, 2(1), 63–84.

    Google Scholar 

  107. Yin, R. K. (2013). Case study research: Design and methods: Sage publications.

    Google Scholar 

  108. Yuan, H., Lau, R. Y. K., & Xu, W. (2016). The determinants of crowdfunding success: A semantic text analytics approach. Decision Support Systems, 91, 67–76.

    Google Scholar 

  109. Zhang, S. X., & Cueto, J. (2017). The study of bias in entrepreneurship. Entrepreneurship Theory and Practice, 41(3), 419–454.

    Google Scholar 

  110. Zoric, J. (2011). Connecting business models with service platform designs: Exploiting potential of scenario modeling. Telematics and Informatics, 28(1), 40–54.

    Google Scholar 

  111. Zott, C., Amit, R., & Massa, L. (2011). The business model: Recent developments and future research. Journal of Management, 37(4), 1019–1042.

    Google Scholar 

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Dellermann, D., Lipusch, N., Ebel, P. et al. Design principles for a hybrid intelligence decision support system for business model validation. Electron Markets 29, 423–441 (2019). https://doi.org/10.1007/s12525-018-0309-2

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Keywords

  • Collective intelligence
  • Machine learning
  • Decision support system
  • Hybrid intelligence
  • Business model
  • Decision making

JEL classification

  • D81