A Future Outlook on Simulation Games to Address Change Management and Innovation Diffusion

More than 25 years ago we set up to develop the first computer-based simulation games addressing the challenge of spreading IT-enabled innovations in organizations.

1. A successful case of Learning Technology design and diffusion

At that time, the failure rate of such projects was estimated to be more than 50% [1]. The hardware, software, and networking technology to design and develop powerful, user-centered IT solutions to redesign and enhance organizational processes – including those aimed at learning and the development and diffusion of relevant competencies –  was becoming available and affordable. The real challenge was to create the conditions for the successful adoption and diffusion of such innovative solutions.

We started with a clear vision in terms of pedagogical objectives [2], with extensive research on resistance patterns and organizational change barriers to be integrated into the dynamic model underlying our simulations, and profited from the fact that technology was starting to provide us the opportunity to integrate multimedia elements and exploit sound, images, and animation [3].

Our initial bet was to design simulations reproducing complex organizational dynamics to enable learners to experience first-hand a number of critical challenges such as:

  • Trying to persuade individuals to change the way they work / operate / process information / take decisions
  • Addressing successfully different individual articulations of change resistance motivated by different types of fears
  • Understanding and leveraging formal and informal influence networks across the organization, in order to be able to accelerate adoption leveraging epidemic diffusion techniques
  • Diagnosing organizational barriers to change and culture-specific obstacles to be addressed
  • Selecting the appropriate organizational initiatives to implement (ranging from direct interactions with key players and influencers, to timely organization-wide communication to increase awareness, interest, and willingness to try/experiment, to setting up workshops and pilots, or the appropriated or counter-productive deployment of compulsive approaches).

Demonstrating that “serious games” can have a long life span, today our change-related simulations – or similar ones developed over time by colleagues and consulting companies – are still used extensively to train managers in top academic institutions and organizations worldwide, preventing them to fall into a number of well-documented traps determining the success or failure of their innovation projects. Over time, new versions of our initial change management simulations have become even more sophisticated by:

  • integrating new research insights (for instance, leading and driving change successfully in a “Western” organization is significantly different from addressing the same challenge in a Chinese or Middle-East organization [4] [5])
  • exposing learners to an even larger set of individual attitudes, behaviors, and organizational dynamics, and
  • taking advantage of emerging technologies such as interactive video and VR/AR to enhance user interface design, or different Artificial Intelligence techniques to enhance the overall learning process as well as its debriefing and translation into actionable insights. 

As an example, our most recent simulation version – the Boost AI Simulation [9] – addresses the challenge of AI Diffusion to enhance the performance of several organizational functions (from HR, to Finance, R&D, Production, and all the commercial functions). Its advantage is to be able to address insights on successful change management and innovation diffusion, and at the same time provide an overview of how new Data Analytics and AI techniques enable us to redesign and improve (see for instance [6], [7] and [8]) the performance of organizational processes and how key decisions are taken – in different organizational units as well as at the top management and board level. In addition, AI has found its way into the simulations themselves, in the form of an integrated intelligent agent able to analyze dynamically the simulation state and the learners’ behavior, compare it to a large set of simulation runs and knowledge base, and stimulate learners with advice, recommendations or relevant questions to be addressed.

2. Future Developments: Two Open Questions

The recent epidemic has boosted the deployment of online learning in general including highly engaging simulation-based learning experiences like the ones we have described – which are typically run by co-located or distributed teams with the instructor/professor being able to monitor remotely the learners’ teams and intervene when necessary. But two important questions related to future developments and appropriate diffusion of such simulation games remain open.

2.1 Future Outlook: Will ChatGPT and genAI help us progress?

It is highly unlikely and it would be really disappointing if 10 years from now simulation games addressing change management and innovation diffusion were still very similar to the ones we designed and developed more than 25 years ago. When it comes to future developments views might differ significantly. For instance, the emergence of ChatGPT and genAI has helped everybody to imagine a future in which we will have access to intelligent conversational agents able to first understand the specific organizational context in which we are operating and the innovations we aim at injecting, to then provide us personalized advice and continuous monitoring on how to best proceed to make change happen in our organizations.

This vision sounds appealing, but let us also remember that calculators have not removed the teaching of arithmetic from our educational curricula. Intelligent conversational agents could greatly facilitate access to relevant knowledge and insights related to effective innovation diffusion, but every organization is different. Although useful, such AI-based assistants are unlikely to be really effective if their users lack a deep understanding of the dynamics which unfold at the individual, business unit, and organizational level once the decision has been taken to leverage new technologies and embark on significant change and transformation journeys. Will simulation games like the one discussed here still remain the most effective learning technologies to address innovation diffusion and change management skills? How will they evolve? And with which audiences should they actually be best deployed?

2.2 Future Outlook: Just for managers, or for all of us too?

Simulation games like the ones addressed here have spread naturally and have had a significant impact in educational settings involving management students as well as executives and decision-makers operating in organizations worldwide. But contrary to our initial predictions, these learning solutions did not – at least until now – naturally spill over to other audiences that could equally profit from innovation diffusion insights: Those who are in charge of designing and developing IT systems, i.e. software engineers and architects, instructional designers together with all those contributing with their technical know-how to the design of effective IT-based solutions to be deployed in organizational settings – including new learning tools and platforms.

In the past, we have witnessed how the focus on “user centeredness” (vs the exclusive concentration on the underlying technology and system functionality) has led to better IT systems and increased adoption speed. Isn’t it time to extend the curricula of technical experts to focus on “context awareness” too? When it comes to innovation diffusion, such “context awareness” goes much beyond “user centeredness” and boils down to developing even in this target group of learners the capability to better understand the complex dynamics of the organizational contexts in which their IT-enabled solutions will be injected. What has prevented until now engineering and technical schools and universities to adopt and profit from learning technologies developed originally for management audiences? And how could such obstacles be removed? The answer to this question might help us develop a better vision of the importance given to non-technical but critical subjects in the curricula of software engineers, designers, and architects in the near future.

Concluding, effective Innovation Diffusion and IT-enabled Change Management in organizations are still very challenging. Identifying new IT-based learning solutions which are even more effective than the simulation games discussed here and which can be successfully deployed not only with managers remains a critical open question.


[1] Reengineering the Corporation: A Manifesto for Business Revolution, M. Hammer and J. Champy, Harper Business, NY, 1993.

[2] Business Navigator: The Next Generation of Management Development Tools, A. Angehrn, Y. Doz and J. Atherton, International Consortium for Executive Development Research (ICEDR), White Paper 93-03, 1993.

[3] Understanding Organizational Dynamics of IT-Enabled Change: A Multimedia Simulation Approach, J.-F. Manzoni and A. Angehrn, Journal of Management Information Systems, 14(3), 1998. https://www.researchgate.net/publication/220591509_Understanding_Organizational_Dynamics_of_IT-Enabled_Change_A_Multimedia_Simulation_Approach

[4] Chinese Firms: Overcoming Resistance, A. Angehrn, P. Leliaert, S. Zhao, L. van Geffen, and H. Yang, INSEAD Quarterly 2005. http://www.alpha-simulations.com/AAAINSEAD/eis/LingHe/material/changingChineseFirms.pdf

[5] Understanding the Organizational Dynamics of Change in Middle Eastern Organizations: Insights from an Explorative Study, A. Angehrn and F. Schloederer, Academy of International Business (AIB MENA Chapter) Conference Proceedings, 2012. https://www.alpha-simulations.com/AAAINSEAD/eis/GulfCom/GulfComRelatedArticle.pdf

[6] Artificial Intelligence for the Real World, T. Davenport and X. Ronanki, HBR, 2018.

[7] Building the AI-powered Organization, T. Fountaine, B. McCarthy and T. Saleh, HBR 2019. https://hbr.org/2019/07/building-the-ai-powered-organization

[8] Augmenting organizational decision-making with deep learning algorithms : Principles, promises and challenges, Y.R. Shrestha, V. Krishna and G. von Krogh, Journal of Business Research, 2021. https://arxiv.org/ftp/arxiv/papers/2011/2011.02834.pdf

[9] Boost AI: The Artificial Intelligence Diffusion Challenge, A. Angehrn, 2022. https://www.alpha-simulations.com/AAAINSEAD/eis/BoostAISimulationIntroPPT.pdf

Written for OEB Global 2023 by Albert Angehrn

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