Acceptance of Mobile Apps among Bottom of Pyramid Customers of Urban Areas

Authors

  •   Pooja Sehgal Tabeck Amity Business School, Sector 125, Amity University, Noida - 201 303, Uttar Pradesh
  •   Anurupa B. Singh Amity Business School, Sector 125, Amity University, Noida - 201 303, Uttar Pradesh

DOI:

https://doi.org/10.17010/ijom/2022/v52/i9/171984

Keywords:

Bottom of the Pyramid

, Mobile Applications, Technology Acceptance Model.

Paper Submission Date

, July 25, 2021, Paper sent back for Revision, May 16, 2022, Paper Acceptance Date, June 15, Paper Published Online, September 15, 2022

Abstract

India houses the world’s leading and most rapidly increasing digital bases, with 560 million internet subscribers, next to China. The emergence of low-cost smartphones and falling internet rates make mobile apps more accessible to people in the lower-income groups. These apps are not only downloaded for entertainment and information, but they also appear to be increasing the income base. The thriving mobile app economy has unlocked opportunities for thousands of low-income customers for income enhancement. Many studies have been conducted to determine the acceptance of mobile phones amongst the bottom of pyramid customers, but acceptance of mobile applications is still in its infancy stage. The paper attempted to understand the acceptance of mobile-based applications among the bottom of pyramid customers in urban areas using the technology acceptance model. Primary data were collected from 296 urban bottom of pyramid customers. Following this analysis, the researchers observed that if the bottom of pyramid customers perceived the mobile application’s usefulness for themselves, it resulted in a significant positive effect on attitude towards usage, which will lead to acceptance. The study also presented breakthrough managerial implications for practitioners working on BOP.

Downloads

Download data is not yet available.

Downloads

Published

2022-09-01

How to Cite

Tabeck, P. S., & Singh, A. B. (2022). Acceptance of Mobile Apps among Bottom of Pyramid Customers of Urban Areas. Indian Journal of Marketing, 52(9), 43–58. https://doi.org/10.17010/ijom/2022/v52/i9/171984

References

Abdin, M. S. (2020). A study to identify and profile consumer segments in the mobile telecommunication services market. Indian Journal of Marketing, 50(5–7), 46–60. https://doi.org/10.17010/ijom/2020/v50/i5-7/152119

Aloudat, A., Michael, K., Chen, X., & Al-Debei, M. M. (2014). Social acceptance of location-based mobile government services for emergency management. Telematics and Informatics, 31(1), 153–171. https://doi.org/10.1016/j.tele.2013.02.002

Alwahaishi, S., & Snásel, V. (2013). Acceptance and use of information and communications technology: A UTAUT and flow-based theoretical model. Journal of Technology Management & Innovation, 8(2), 61–73. https://doi.org/10.4067/S0718-27242013000200005

Anderson, J. L., Markides, C., & Kupp, M. (2010). The last frontier: Market creation in conflict zones, deep rural areas, and urban slums. California Management Review, 52(4), 6–28. https://doi.org/10.1525/cmr.2010.52.4.6

Baishya, K., & Samalia, H. V. (2020). Factors influencing smartphone adoption: A study in the Indian bottom of the pyramid context. Global Business Review, 21(6), 1387–1405. https://doi.org/10.1177/0972150919856961

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Ceci, L. (2022, May 17). Combined global Apple App Store and Google Play app downloads from 1st quarter 2015 to 1st quarter 2022. Statista. https://www.statista.com/statistics/604343/number-of-apple-app-store-and-google-play-app-downloads-worldwide/

Chopra, M., Singh, S. K., Gupta, A., Aggarwal, K., Gupta, B. B., & Colace, F. (2022). Analysis & prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic. Sustainable Technology and Entrepreneurship, 1(2), Article 100012. https://doi.org/10.1016/j.stae.2022.100012

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Duarte, A. L., Macau, F., Flores e Silva, C., & Sanches, L. M. (2019). Last mile delivery to the bottom of the pyramid in Brazilian slums. International Journal of Physical Distribution & Logistics Management, 49(5), 473–491. https://doi.org/10.1108/IJPDLM-01-2018-0008

Edmunds, R., Thorpe, M., & Conole, G. (2012). Student attitudes towards and use of ICT in course study, work, and social activity: A technology acceptance model approach. British Journal of Educational Technology, 43(1), 71–84. https://doi.org/10.1111/j.1467-8535.2010.01142.x

Elshafey, A., Saar, C. C., Aminudin, E. B., Gheisari, M., & Usmani, A. (2020). Technology acceptance model for augmented reality and building information modeling integration in the construction industry. ITcon, 25, 161–172. https://doi.org/10.36680/j.itcon.2020.010

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313

Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565–571. https://doi.org/10.1016/j.jbusres.2008.06.016

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (5th ed.). Prentice Hall.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results, and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Hammond, A. L., Kramer, W. J., Katz, R. S., Tran, J. T., & Walker, C. (2007). The next 4 billion. http://www.mitpressjournals.org/doi/pdfplus/10.1162/itgg.2007.2.1-2.147

Handa, M., & Ahuja, P. (2021). Thus far and no further? An inquiry into adoption of mobile phones by low income women in urban India. Journal of Poverty, 25(2), 173–192. https://doi.org/10.1080/10875549.2020.1783423

Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Hu, P. J., Chau, P. Y., Sheng, O. R., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112. https://doi.org/10.1080/07421222.1999.11518247

Hsu, C.-L., Wang, C.-F., & Lin, J. C.-C. (2011). Investigating customer adoption behaviours in mobile financial services. International Journal of Mobile Communications, 9(5), 477–494. https://doi.org/10.1504/IJMC.2011.042455

Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2019). M-payment adoption for bottom of pyramid segment: An empirical investigation. International Journal of Bank Marketing, 37(1), 362–381. https://doi.org/10.1108/IJBM-01-2018-0013

Ireland, J. (2008). Lessons for successful BOP marketing from Caracas’ slums. Journal of Consumer Marketing, 25(7), 430–438. https://doi.org/10.1108/07363760810915644

Jebarajakirthy, C., & Lobo, A. (2015). A study investigating attitudinal perceptions of microcredit services and their relevant drivers in bottom of pyramid market segments. Journal of Retailing and Consumer Services, 23, 39–48. https://doi.org/10.1016/j.jretconser.2014.12.005

Kaka, N., Madgavkar, A., Kshirsagar, A., Gupta, R., Manyika, J., Bahl, K., & Gupta, S. (2019, March 27). Digital India: Technology to transform a connected nation. McKinsey Global Institute. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/digital-india-technology-to-transform-a-connected-nation

Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213. https://doi.org/10.2307/249751

Khuntia, J., Mayya, R., Mithas, S., & Agarwal, R. (2021). Managing cellphone services for customer satisfaction: Evidence from the base-of-the-pyramid markets. Production and Operations Management, 30(2), 438–450. https://doi.org/10.1111/poms.13276

Kim, S. H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6), 387–393. https://doi.org/10.1016/j.im.2008.05.002

Knoesen, H., & Seymour, L. F. (2019). Mobile enterprise application adoption: A South African insurance study. South African Computer Journal, 31(2), 117–149. https://doi.org/10.18489/sacj.v31i2.690

Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61, 193–208. https://doi.org/10.1016/j.compedu.2012.10.001

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130

Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2006). Marketing research: An applied orientation (3rd ed.). Pearson Education Australia. http://hdl.handle.net/10536/DRO/DU:30010407

Mathur, M. K., Mehta, R., Swami, S., & Bhatnagar, S. (2018). Exploring the urban BoP market. In R. Singh (ed.), Bottom of the pyramid marketing: Making, shaping and developing BoP markets (marketing in emerging markets) (pp. 199–212). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78714-555-920181012

Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2

Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445. https://doi.org/10.1037/0033-2909.105.3.430

Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing-ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001

Nagdev, K., & Rajesh, A. (2018). Consumers’ intention to adopt internet banking: An Indian perspective. Indian Journal of Marketing, 48(6), 42–56. http://doi.org/10.17010/ijom/2018/v48/i6/127835

Okumus, B., Ali, F., Bilgihan, A., & Ozturk, A. B. (2018). Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, 72, 67–77. https://doi.org/10.1016/j.ijhm.2018.01.001

Pipitwanichakarn, T., & Wongtada, N. (2019). Mobile commerce adoption among the bottom of the pyramid: A case of street vendors in Thailand. Journal of Science and Technology Policy Management, 10(1), 193–213. https://doi.org/10.1108/JSTPM-12-2017-0074

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Prahalad, C. K., & Hart, S. L. (2002, January 10). The fortune at the bottom of the pyramid. Strategy + Business, Issue 26, 1–14. https://www.strategy-business.com/article/11518

Purohit, S., & Arora, R. (2021). Adoption of mobile banking at the bottom of the pyramid: An emerging market perspective. International Journal of Emerging Markets, (Vol. ahead-of-print). https://doi.org/10.1108/IJOEM-07-2020-0821

Rahman, M. S., Mannan, M., & Amir, R. (2018). The rise of mobile internet: The adoption process at the bottom of the pyramid. Digital Policy, Regulation, and Governance, 20(6), 582–599. https://doi.org/10.1108/DPRG-05-2018-0024

Raj, K., & Aithal, P. S. (2018). Digitization of India-impact on the BOP sector. International Journal of Management, Technology, and Social Sciences (IJMTS), 3(1), 59–74. https://doi.org/10.47992/IJMTS.2581.6012.0036

Rao, S., & Troshani, I. (2007). A conceptual framework and propositions for the acceptance of mobile services. Journal of Theoretical and Applied Electronic Commerce Research, 2(2), 61–73. https://doi.org/10.3390/jtaer2020014

Reio, T. G. (2010). The threat of common method variance bias to theory building. Human Resource Development Review, 9(4), 405–411. https://doi.org/10.1177/1534484310380331

Reddy, T. T., & Rao, B. M. (2019). The moderating effect of gender on continuance intention toward mobile wallet services in India. Indian Journal of Marketing, 49(4), 48–62. http://doi.org/10.17010/ijom/2019/v49/i4/142976

Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In, An integrated approach to communication theory and research (pp. 432–448). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780203887011-36/diffusioninnovations-everett-rogers-arvind-singhal-margaret-quinlan

Robertson, T. S. (1967). The process of innovation and the diffusion of innovation. Journal of Marketing, 31(1), 14–19. https://doi.org/10.1177/002224296703100104

Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683–696. https://doi.org/10.1016/j.ijhcs.2006.01.003

Sharma, Y., Nasreen, R., & Kumar, A. (2019). Role of social network in defining the impact of marketing-mix on satisfaction from food items at subsistence marketplace. Indian Journal of Marketing, 49(2), 7–24. http://doi.org/10.17010/ijom/2019/v49/i2/141579

Srivastava, R. (2019). Customer expectations at the urban bottom of pyramid in India: A grounded theory approach. In, Rajagopal & R. Behl (eds.), Business governance and society (pp. 55–73). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-94613-9_5

Subrahmanyan, S., & Tomas Gomez - Arias, J. (2008). Integrated approach to understanding consumer behavior at bottom of pyramid. Journal of Consumer Marketing, 25(7), 402–412. https://doi.org/10.1108/07363760810915617

Tabeck, P. S., & Singh, A. B. (2019). Contemporary mobile experience among bottom of pyramid. In X. Xu (ed.), Impacts of mobile use and experience on contemporary society (pp. 213–225). IGI Global. https://doi.org/10.4018/978-1-5225-7885-7.ch01

Tambotoh, J. J., Manuputty, A. D., & Banunaek, F. E. (2015). Socio-economics factors and information technology adoption in rural area. Procedia Computer Science, 72, 178–185. https://doi.org/10.1016/j.procs.2015.12.119

Tavera-Mesías, J. F., van Klyton, A., & Collazos, A. Z. (2022). Technology readiness, mobile payments, and gender - A reflective-formative second order approach. Behaviour & Information Technology, 1–19. https://doi.org/10.1080/0144929X.2022.2054729

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venugopal, S., & Viswanathan, M. (2017). The subsistence marketplaces approach to poverty: Implications for marketing theory. Marketing Theory, 17(3), 341–356. https://doi.org/10.1177/1470593117704282

Wentzel, J. P., Diatha, K. S., & Yadavalli, V. S. (2013). An application of the extended Technology Acceptance Model in understanding technology-enabled financial service adoption in South Africa. Development Southern Africa, 30(4–05), 659–673. https://doi.org/10.1080/0376835X.2013.830963

Wheaton, B., Muthén, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84–136. https://doi.org/10.2307/270754

Williams, L. J., & Brown, B. K. (1994). Method variance in organizational behavior and human resources research: Effects on correlations, path coefficients, and hypothesis testing. Organizational Behavior and Human Decision Processes, 57(2), 185–209. https://doi.org/10.1006/obhd.1994.1011

Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527–540. https://doi.org/10.1108/10662241111176353