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Analysis of a Teleworking Technology Adoption Case: An Agent-Based Model

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Listed:
  • Carlos A. Arbelaez-Velasquez

    (School of Engineering, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Diana Giraldo

    (School of Engineering, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

  • Santiago Quintero

    (School of Engineering, Universidad Pontificia Bolivariana, Medellín 050031, Colombia)

Abstract

An agent-based model for teleworking technology adoption is presented, including the risk of office closure in the event of a lockdown. It analyzes an adoption case using simulations and can be adapted to other cases and teleworking promotion strategies to contribute to sustainability. Simulations produce smooth sigmoidal curves that reasonably fit to real adoption curves. The simulation results suggest that the main reason for the observed increase in the adoption rate is the increase in the risk of office closures, the consequent increase in the usefulness of teleworking technology, and the increase in external influence that motivates them.

Suggested Citation

  • Carlos A. Arbelaez-Velasquez & Diana Giraldo & Santiago Quintero, 2022. "Analysis of a Teleworking Technology Adoption Case: An Agent-Based Model," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9930-:d:885661
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    References listed on IDEAS

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    1. Tahlyan, Divyakant & Said, Maher & Mahmassani, Hani & Stathopoulos, Amanda & Walker, Joan & Shaheen, Susan, 2022. "For whom did telework not work during the Pandemic? understanding the factors impacting telework satisfaction in the US using a multiple indicator multiple cause (MIMIC) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 387-402.
    2. Okubo, Toshihiro, 2022. "Telework in the spread of COVID-19," Information Economics and Policy, Elsevier, vol. 60(C).
    3. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    4. Ann Brewer & David Hensher, 2000. "Distributed work and travel behaviour: The dynamics of interactive agency choices between employers and employees," Transportation, Springer, vol. 27(1), pages 117-148, February.
    5. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    6. Branstad, Are & Solem, Birgit A., 2020. "Emerging theories of consumer-driven market innovation, adoption, and diffusion: A selective review of consumer-oriented studies," Journal of Business Research, Elsevier, vol. 116(C), pages 561-571.
    7. Younghwan Song & Jia Gao, 2020. "Does Telework Stress Employees Out? A Study on Working at Home and Subjective Well-Being for Wage/Salary Workers," Journal of Happiness Studies, Springer, vol. 21(7), pages 2649-2668, October.
    8. Ton, Danique & Arendsen, Koen & de Bruyn, Menno & Severens, Valerie & van Hagen, Mark & van Oort, Niels & Duives, Dorine, 2022. "Teleworking during COVID-19 in the Netherlands: Understanding behaviour, attitudes, and future intentions of train travellers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 55-73.
    9. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    10. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    11. Andrew Hook & Victor Court & Benjamin K Sovacool & Steven Sorrell, 2020. "A Systematic Review of the Energy and Climate Impacts of Teleworking," Working Papers hal-03192905, HAL.
    12. van Oorschot, Johannes A.W.H. & Hofman, Erwin & Halman, Johannes I.M., 2018. "A bibliometric review of the innovation adoption literature," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 1-21.
    13. Kazekami, Sachiko, 2020. "Mechanisms to improve labor productivity by performing telework," Telecommunications Policy, Elsevier, vol. 44(2).
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