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Understanding Barriers to Adoption of Grass-Root Innovations—A Case Study of RUTAG Technologies

  • Aishwarya ChauhanEmail author
  • Arpan Kumar Kar
Conference paper
Part of the Design Science and Innovation book series (DSI)

Abstract

Technology adoption by specific user groups has been an area of research and study for a long time. This article focusses on the barriers that are encountered during the process of product as well as technology adoption for grass root innovations. With the research of Handrich and Heidenreich, we explore the types of barriers in the form of the Active and Passive Innovation Resistance. This article also explores strategies that would help the organizations and RuTAG, IIT Delhi for able marketing by knowing the type of target audience the innovators and the heads are looking at. Secondary resources have been used to identify the problems encountered by the rural audience in adopting the technologies in order to better understand them. This article highlights the dominance of Passive Innovation Resistance among the rural masses for the innovations that are launched keeping them as the target audience.

Keywords

Active innovation resistance Passive innovation resistance Better mousetrap fallacy 

References

  1. 1.
    Joseph N, Somal HS, Ilavarasan PV, Kar AK (2017) Non-uptake of a low cost retail management solution by small businesses: an empirical analysis. Procedia Comput Sci 122(2017):1001–1008CrossRefGoogle Scholar
  2. 2.
    Hargittai E (2003) The digital divide and what to do about it. New economy handbook. Elsevier, Academic Press, pp 821–839Google Scholar
  3. 3.
    McKinsey Global Institute, Offline and falling behind: barriers to Internet adoption. Accessed https://www.mckinsey.com/industries/high-tech/our-insights/offline-and-falling-behind-barriers-to-internet-adoption
  4. 4.
    Joseph N, Kar AK, Ilavarasan PV (2017) A model for prioritization and prediction of impact of digital literacy training programmes and validation. In: Kar A et al (eds) Digital nations – smart cities, innovation, and sustainability, I3E 2017. Lecture Notes in Computer Science, vol 10595. Springer, ChamCrossRefGoogle Scholar
  5. 5.
    Stratigea A (2017) ICTs for rural development: potential applications and barriers involved, Netcom [En ligne], 25-3/4| 2011, mis en ligne le 29 mars 2013, consulté le 22 décembre 2017. http://journals.openedition.org/netcom/144,  https://doi.org/10.4000/netcom.144
  6. 6.
    Leiper N, Lamont M (2011) The better mousetrap fallacy: a case study of the Bali Pathfinder tourist map. J Vacat Mark 17(2):95–103CrossRefGoogle Scholar
  7. 7.
    Kuester S, Homburg, Christian and Robertson, Thomas S., Retaliatory Behavior to New Product Entry (October 1, 1999). Journal of Marketing, Vol. 63, Issue 4, pp. 90–106,1999. Available at SSRN: https://ssrn.com/abstract=2508231CrossRefGoogle Scholar
  8. 8.
    Heidenreich S, Kraemer T (2015) Innovations—doomed to fail? Investigating strategies to overcome passive innovation resistance. J Econ Psychol 51:134–151CrossRefGoogle Scholar
  9. 9.
    Heidenreich S, Handrich M, Schmidt T (2011) Consumers’ resistance to innovations – investigating the cases of passive and active innovation resistance. In: Yi Z, Xiao JJ, Cotte J,Price L, Duluth MN AP - Asia-Pacific advances in consumer research vol 9. Association for Consumer Research, pp 230–232Google Scholar
  10. 10.
    Heidenreich S, Handrich M (2015) What about passive innovation resistance? Investigating adoption-related behavior from a resistance perspective. J Prod Innov Manag 32:878–903.  https://doi.org/10.1111/jpim.12161CrossRefGoogle Scholar
  11. 11.
    Jarvis CB, MacKenzie SB, Podsakoff PM (2003) A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J Consum Res 30(2):199–218CrossRefGoogle Scholar
  12. 12.
    Ministry of Electronics & Information Technology Government of India. Accessed http://digitalindia.gov.in/content/about-programme
  13. 13.
    Khatwani G, Anand O, Kar AK (2015) Evaluating internet information search channels using hybrid MCDM technique. In: Panigrahi B, Suganthan P, Das S (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science, vol 8947. Springer, ChamCrossRefGoogle Scholar
  14. 14.
    Swilley E (2010) Technology rejection: the case of the wallet phone. J Consum Mark 27(4):304–312CrossRefGoogle Scholar
  15. 15.
    Goldberg LR (1992) The development of markers for the big-five factor structure. Psychol Assess 4(1):26–42.  https://doi.org/10.1037/1040-3590.4.1.26CrossRefGoogle Scholar
  16. 16.
    Nov O, Ye C (2009) Users’ resistance to change and the adoption of digital libraries: an integrative model. J Am Soc Inform Sci Technol 60(8):1702–1708CrossRefGoogle Scholar
  17. 17.
    Bearden WO, Shimp TA (1982) The use of extrinsic cues facilitate product adoption. J Mark Res 19(3):229–239CrossRefGoogle Scholar
  18. 18.
    Chhonker MS, Verma D, Kar AK (2017) Review of technology adoption frameworks in mobile commerce. Procedia Comput Sci 122:888–895CrossRefGoogle Scholar
  19. 19.
    Rokeach M (1960) The open and closed mind. Basic Books, New YorkGoogle Scholar
  20. 20.
    Oreg S (2003) Resistance to change: developing an individual differences measure. J Appl Psychol 88(4):680–693CrossRefGoogle Scholar
  21. 21.
    Ram S, Sheth Jagdish N (1989) Consumer resistance to innovations: the marketing problem and its solutions. J Consum Mark 6(2):5–14.  https://doi.org/10.1108/EUM00000000042CrossRefGoogle Scholar
  22. 22.
    Mundy S (2017) Reliance Jio’s big giveaway gamble reshapes India’s mobile world. Financial Times. Accessed https://www.ft.com/content/f71f345e-e150-11e6-9645-c9357a75844a
  23. 23.
    Singh BP, Grover P, Kar AK (2017) Quality in mobile payment service in India. In: Kar A et al (eds) Digital nations – smart cities, innovation, and sustainability, I3E 2017. Lecture Notes in Computer Science, vol 10595. Springer, ChamCrossRefGoogle Scholar
  24. 24.
    Kanitkar A (2017) Technology for non-profits: caution against digital evangelism. Accessed http://idronline.org/technology-for-nonprofits-caution-digital-evangelism/
  25. 25.
    Chatterjee S, Kar AK (2017) Effects of successful adoption of information technology enabled services in proposed smart cities of India: from user experience perspective. J Sci Technol Policy Manag 9(2):189–209.  https://doi.org/10.1108/JSTPM-03-2017-0008CrossRefGoogle Scholar
  26. 26.
    El Houssi, Morel, and Hultink (2005); Gregan-Paxton and Moreau (2003)Google Scholar
  27. 27.
    Hess S (2009) Managing consumer’s adoption barriers. Dissertation, University of MannheimGoogle Scholar
  28. 28.
    Atkin T (2006) Garcia R, Lockshin L (2006) A multinational study of the diffusion of a discontinuous innovation. Australas Mark J (AMJ) 14(2):17–33CrossRefGoogle Scholar
  29. 29.
    Feiereisen S, Wong V, Broderick AJ (2008) Analogies and mental simulations in learning for really new products: the role of visual attention. J Prod Innov Manag 25:593–607.  https://doi.org/10.1111/j.1540-5885.2008.00324.xCrossRefGoogle Scholar
  30. 30.
    Heiman A, Muller E (1996) Using demonstration to increase new product acceptance: controlling demonstration time. J Mark Res 33(4):422–430.  https://doi.org/10.2307/3152213CrossRefGoogle Scholar
  31. 31.
    Reinders MJ, Frambach RT, Schoormans JPL (2010) Using product bundling to facilitate the adoption process of radical innovations*. J Prod Innov Manag 27:1127–1140.  https://doi.org/10.1111/j.1540-5885.2010.00775.xCrossRefGoogle Scholar
  32. 32.
    Chatterjee S, Kar AK (2015) Smart Cities in developing economies: a literature review and policy insights. In: Proceedings of the international conference on advances in computing, communications and informatics (ICACCI), Kochi, pp. 2335–2340Google Scholar
  33. 33.
    Chand R (2016) e-platform for national agricultural market. Econ Polit Wkly 51(28):15Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Management StudiesIIT DelhiNew DelhiIndia

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