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Factors influencing indirect adoption of e-Government services: a qualitative study

Author

Listed:
  • Rajiv Kumar

    (Indian Institute of Management Kashipur)

  • Arindam Mukherjee

    (Indian Institute of Management Ranchi)

  • Amit Sachan

    (Indian Institute of Management Ranchi)

Abstract

The intermediary plays an important role in accessing e-Government services on behalf of the citizens, introducing a concept of indirect adoption. In e-Government research, which is a phenomenon for more than the last two decades, there are no apparent theoretical mechanisms to understand indirect adoption in the Indian context. Also, the existing indirect adoption theories are narrow in scope. This study investigates the factors influencing indirect adoption (i.e., adoption via intermediaries) of e-Government services. Grounded theory approach using forty-seven semi-structured interviews is used to propose a theoretical model for indirect e-Government adoption. The proposed model has thirteen factors, out of which access convenience to intermediary, intermediaries’ service charge, risk-averse characteristics, and value-added services are novel factors in the context of e-Government adoption. The study also found some variables moderating the relationship between some of these factors and indirect adoption. While busy lifestyle, cost rationality, resistant to change, and technophobia moderate the relation between intermediaries’ service charge and indirect adoption intention, voluntary usage context was identified to moderate the relationship between lack of resources, lack of computer self-efficacy, perceived difficulty-to-use, lack of multilingual option and indirect adoption intention. The study advances the understanding of indirect e-Government adoption. The findings have potential implications for public administrators and policymakers. As the objective of qualitative research is to obtain an in-depth understanding rather than generalizations, this study draws reasonable conclusion from the small sample. However, further study is required to test the model and for generalizations.

Suggested Citation

  • Rajiv Kumar & Arindam Mukherjee & Amit Sachan, 2023. "Factors influencing indirect adoption of e-Government services: a qualitative study," Information Systems and e-Business Management, Springer, vol. 21(3), pages 471-504, September.
    • Handle: RePEc:spr:infsem:v:21:y:2023:i:3:d:10.1007_s10257-023-00637-z
    DOI: 10.1007/s10257-023-00637-z
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    References listed on IDEAS

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    1. Howells, Jeremy, 2006. "Intermediation and the role of intermediaries in innovation," Research Policy, Elsevier, vol. 35(5), pages 715-728, June.
    2. Nripendra P. Rana & Yogesh K. Dwivedi & Michael D. Williams, 2015. "A meta-analysis of existing research on citizen adoption of e-government," Information Systems Frontiers, Springer, vol. 17(3), pages 547-563, June.
    3. Kiran Gopakumar Rajalekshmi, 2007. "E-Governance Services Through Telecenters: The Role of Human Intermediary and Issues of Trust," Information Technologies and International Development, MIT Press, vol. 4(1), pages 19-35, October.
    4. Lemuria Carter & Vishanth Weerakkody, 2008. "E-government adoption: A cultural comparison," Information Systems Frontiers, Springer, vol. 10(4), pages 473-482, September.
    5. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    6. Z Irani & Y K Dwivedi & M D Williams, 2009. "Understanding consumer adoption of broadband: an extension of the technology acceptance model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1322-1334, October.
    7. Musteen, Martina, 2016. "Behavioral factors in offshoring decisions: A qualitative analysis," Journal of Business Research, Elsevier, vol. 69(9), pages 3439-3446.
    8. Meuter, Matthew L. & Ostrom, Amy L. & Bitner, Mary Jo & Roundtree, Robert, 2003. "The influence of technology anxiety on consumer use and experiences with self-service technologies," Journal of Business Research, Elsevier, vol. 56(11), pages 899-906, November.
    9. Linda Schamber, 2000. "Time‐line interviews and inductive content analysis: their effectiveness for exploring cognitive behaviors," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(8), pages 734-744.
    10. Pratim Datta & Sutirtha Chatterjee, 2008. "The economics and psychology of consumer trust in intermediaries in electronic markets: the EM-Trust Framework," European Journal of Information Systems, Taylor & Francis Journals, vol. 17(1), pages 12-28, February.
    11. Sarkar, Mitrabarun & Butler, Brian & Steinfield, Charles, 1998. "Cybermediaries in Electronic Marketspace: Toward Theory Building," Journal of Business Research, Elsevier, vol. 41(3), pages 215-221, March.
    12. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    13. Weerakkody, Vishanth & El-Haddadeh, Ramzi & Al-Sobhi, Faris & Shareef, Mahmud Akhter & Dwivedi, Yogesh K., 2013. "Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation," International Journal of Information Management, Elsevier, vol. 33(5), pages 716-725.
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