Computational models of technology adoption at the workplace

  • Charalampos Chelmis
  • Ajitesh Srivastava
  • Viktor K. Prasanna
Original Article

Abstract

Popular social networking sites have revolutionized the way people interact on the Web, enabling rapid information dissemination and search. In an enterprise, understanding how information flows within and between organizational levels and business units is of great importance. Despite numerous studies in information diffusion in online social networks, little is known about factors that affect the dynamics of technological adoption at the workplace. Here, we address this problem, by examining the impact of organizational hierarchy in adopting new technologies in the enterprise. Our study suggests that middle-level managers are more successful in influencing employees into adopting a new microblogging service. Further, we reveal two distinct patterns of peer pressure, based on which employees are not only more likely to adopt the service, but the rate at which they do so quickens as the popularity of the new technology increases. We integrate our findings into two intuitive, realistic agent-based computational models that capture the dynamics of adoption at both microscopic and macroscopic levels. We evaluate our models in a real-world dataset we collected from a multinational Fortune 500 company. Prediction results show that our models provide great improvements over commonly used diffusion models. Our findings provide significant insights to managers seeking to realize the dynamics of adoption of new technologies in their company and could assist in designing better strategies for rapid and efficient technology adoption and information dissemination at the workplace.

Keywords

Agent-based computational models Adoption dynamics  Diffusion of innovations Diffusion models Dynamic systems Evolutionary models Influence Technology adoption Social networks 

Notes

Acknowledgments

This work is supported by Chevron Corp. under the joint project, Center for Interactive Smart Oilfield Technologies (CiSoft), at the University of Southern California.

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Charalampos Chelmis
    • 1
  • Ajitesh Srivastava
    • 1
  • Viktor K. Prasanna
    • 2
  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Ming Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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