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Sociotechnical interdependencies and tipping‐point dynamics in data‐intensive services

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  • Navid Ghaffarzadegan
  • Sarah Mostafavi
  • Hyunjung Kim

Abstract

Service science theories do not fully explain failure cases in data‐intensive services – high‐technology services that utilize large volumes of data and provide customized information for users. In these service systems, the technological and social elements are highly interconnected: firms cannot maintain databases and analytic capabilities if they lack market penetration, which itself is influenced by performance of analytic capabilities and databases; a positive feedback loop. Informed by a case study, we develop a simulation model, postulate that market adoption is highly sensitive to the launch conditions, specifically with respect to the initial states of the system, and offer a dynamic theory in which highly nonlinear relationships among the initial states of the system drive outcomes of the market adoption of data‐intensive services. We argue that a tipping point exists at which small technological differences in the launch period differentiates between market success and failure. Furthermore, our analysis points to a strong self‐fulfilling mechanism whereby initial positive customer perception can increase the service's likelihood of success by indirectly influencing technological improvement. In contrast to the conventional belief, having a large number of initial adopters may negatively influence market adoption in the long run. A major source of the patterns observed is the high level of interdependencies between social and technological elements of data‐intensive services. © 2022 System Dynamics Society.

Suggested Citation

  • Navid Ghaffarzadegan & Sarah Mostafavi & Hyunjung Kim, 2023. "Sociotechnical interdependencies and tipping‐point dynamics in data‐intensive services," System Dynamics Review, System Dynamics Society, vol. 39(1), pages 5-31, January.
    • Handle: RePEc:bla:sysdyn:v:39:y:2023:i:1:p:5-31
    DOI: 10.1002/sdr.1724
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