IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p9930-d885661.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/9930/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/9930/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. 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.
    6. Okubo, Toshihiro, 2022. "Telework in the spread of COVID-19," Information Economics and Policy, Elsevier, vol. 60(C).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Kazekami, Sachiko, 2020. "Mechanisms to improve labor productivity by performing telework," Telecommunications Policy, Elsevier, vol. 44(2).
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 29-36, February.
    2. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2019. "Modeling Technological Substitution by Incorporating Dynamic Adoption Rate," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-24, February.
    3. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    4. Guseo, Renato & Mortarino, Cinzia & Darda, Md Abud, 2015. "Homogeneous and heterogeneous diffusion models: Algerian natural gas production," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 366-378.
    5. Scaglione, Miriam & Giovannetti, Emanuele & Hamoudia, Mohsen, 2015. "The diffusion of mobile social networking: Exploring adoption externalities in four G7 countries," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1159-1170.
    6. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    7. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
    8. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    9. Teck-Hua Ho & Shan Li & So-Eun Park & Zuo-Jun Max Shen, 2012. "Customer Influence Value and Purchase Acceleration in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 236-256, March.
    10. Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
    11. Laciana, Carlos E. & Rovere, Santiago L. & Podestá, Guillermo P., 2013. "Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1873-1884.
    12. Chaab, Jafar & Salhab, Rabih & Zaccour, Georges, 2022. "Dynamic pricing and advertising in the presence of strategic consumers and social contagion: A mean-field game approach," Omega, Elsevier, vol. 109(C).
    13. Lacroix, Rachel & Seifert, Ralf W. & Timonina-Farkas, Anna, 2021. "Benefiting from additive manufacturing for mass customization across the product life cycle," Operations Research Perspectives, Elsevier, vol. 8(C).
    14. Singhal, Shakshi & Anand, Adarsh & Singh, Ompal, 2020. "Studying dynamic market size-based adoption modeling & product diffusion under stochastic environment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    15. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    16. Bemmaor, Albert C. & Zheng, Li, 2018. "The diffusion of mobile social networking: Further study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 612-621.
    17. Franses, Philip Hans, 2021. "Modeling box office revenues of motion pictures✰," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    18. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    19. Y. Li & C.J.M. Kool & P.J. Engelen, 2016. "Hydrogen-Fuel Infrastructure Investment with Endogenous Demand: A Real Options Approach," Working Papers 16-12, Utrecht School of Economics.
    20. Guseo, Renato & Guidolin, Mariangela, 2015. "Heterogeneity in diffusion of innovations modelling: A few fundamental types," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 514-524.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9930-:d:885661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.