Examining Online Grocery Purchase Intentions through an Extended TAM Framework : A Mediation Analysis Approach

Authors

  •   Kala Mahadevan Ph.D. Research Scholar, Symbiosis International (Deemed University), Lavale Gram, Mulshi Taluka, Pune - 412 115, Maharashtra. & Faculty Member, IBS – Mumbai, Hiranandani Gardens, Hiranandani Knowledge Park, Opposite Hiranandani Hospital, Off, Technology St, Powai, Mumbai - 400 076, Maharashtra
  •   Krunal K. Punjani Assistant Professor, Narsee Monjee Institute of Management Studies, V. L. Mehta Road, Vile Parle, West, Mumbai - 400 056, Maharashtra
  •   Sujata Joshi Professor (Corresponding Author), Symbiosis International (Deemed University), Lavale Gram, Mulshi Taluka, Pune - 412 115, Maharashtra. & 3 Professor, Symbiosis Institute of Digital and Telecom Management, Lavale Gram, Mulshi Taluka, Pune - 412 115, Maharashtra

DOI:

https://doi.org/10.17010/ijom/2023/v53/i11/170597

Keywords:

Online Grocery

, TAM Model, Mediation Analysis, Convenience, Subjective Norms.

Paper Submission Date

, September 5, 2022, Paper sent back for Revision, May 19, 2023, Paper Acceptance Date, June 15, Paper Published Online, November 15, 2023

Abstract

Purpose : The present study has attempted to extend the TAM framework for online grocery shopping (OGS) by adding convenience (CON) and subjective norms (SN) as exogenous constructs and examines direct and sequential mediation among CON, SN, perceived ease of use (PEOU), perceived usefulness (PU), attitude toward (ATT) OGS, and online grocery purchase intention (OGPI).

Methodology : This study proposed a conceptual model for OGS, for which data from 453 respondents across India were collected. Furthermore, this study employed a sequential mediation approach and tested the proposed constructs’ direct relationships.

Findings : This study revealed that CON significantly impacted PEOU and PU, while PEOU significantly influenced PU and ATT. SN also influenced PU. Additionally, PU significantly affected ATT, and ATT influenced OGPI. Moreover, PU and ATT as sequential mediators significantly affected the relationships among SN–OGPI, PEOU–OGPI, and CON–OGPI, out of which complete mediation was found for PEOU–PU–ATT–OGPI, and the other two were partial mediation. Furthermore, CON–PEOU–ATT–OGPI was also found to have partial sequential mediation. This study added to the literature on OGS by extending TAM with SN and CON, a hitherto under-researched area in this domain.

Practical Implications : The study had practical implications for e-retailers. Grocery e-retailers need to focus on the CON aspects of consumers. Additionally, website design characteristics need to be focused on, which in turn would have an impact on the PEOU of the website, which would favorably impact consumer ATT OGS.

Originality : This study sought to address a critical gap in the research literature on OGS by examining the collective impact of SN and CON on the TAM constructs.

Downloads

Download data is not yet available.

Downloads

Published

2023-11-01

How to Cite

Mahadevan, K., Punjani, K. K., & Joshi, S. (2023). Examining Online Grocery Purchase Intentions through an Extended TAM Framework : A Mediation Analysis Approach. Indian Journal of Marketing, 53(11), 41–57. https://doi.org/10.17010/ijom/2023/v53/i11/170597

References

Agarwal, R. (2021, January 28). Indian E-Grocery: A promising opportunity led by value-first users. Redseer. https://redseer.com/reports/indian-e-grocery-a-promising-opportunity-led-by-value-first-users/

Ahmad, S., Bhatti, S. H., & Hwang, Y. (2020). E-service quality and actual use of e-banking: Explanation through the technology acceptance model. Information Development, 36(4), 503–519. https://doi.org/10.1177/0266666919871611

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice Hall.

Bagla, R. K., & Khan, J. (2017). Customers' expectations and satisfaction with online food ordering portals. Prabandhan: Indian Journal of Management, 10(11), 31–44. https://doi.org/10.17010/pijom/2017/v10i11/119401

Bauerová, R. (2018). Consumers' decision-making in online grocery shopping: The impact of services offered and delivery conditions. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(5), 1239–1247. https://doi.org/10.11118/actaun201866051239

Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737–746. https://doi.org/10.11118/actaun201866030737

Chakraborty, D., & Altekar, S. (2021). What drives people to use grocery apps? The moderating & mediating role of customer involvement and trust. Indian Journal of Marketing, 51(11), 23–37. https://doi.org/10.17010/ijom/2021/v51/i11/166734

Chauhan, V., Choudhary, V., & Mathur, S. (2016). Demographic influences on technology adoption behavior: A study of e-banking services in India. Prabandhan: Indian Journal of Management, 9(5), 45–59. https://doi.org/10.17010/pijom/2016/v9i5/92571

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. https://doi.org/10.1016/S0022-4359(01)00056-2

Clemes, M. D., Gan, C., & Zhang, J. (2014). An empirical analysis of online shopping adoption in Beijing, China. Journal of Retailing and Consumer Services, 21(3), 364–375. https://doi.org/10.1016/j.jretconser.2013.08.003

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

Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of HumanComputer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748

Driediger, F., & Bhatiasevi, V. (2019). Online grocery shopping in Thailand: Consumer acceptance and usage behavior. Journal of Retailing and Consumer Services, 48, 224–237. https://doi.org/10.1016/j.jretconser.2019.02.005

Eastlick, M. A., & Lotz, S. (1999). Profiling potential adopters and non-adopters of an interactive electronic shopping medium. International Journal of Retail & Distribution Management, 27(6), 209–223. https://doi.org/10.1108/09590559910278560

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing, 15(3), 1–13. https://doi.org/10.4018/joeuc.2003070101

Ghazali, E., Mutum, D., & Mahbob, N. A. (2006). Exploratory study of buying fish online: Are Malaysians ready to adopt online grocery shopping? International Journal of Electronic Marketing and Retailing (IJEMR), 1(1), 67–82. https://doi.org/10.1504/IJEMR.2006.010096

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. Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Prentice Hall.

Hansen, T. (2005). Understanding consumer online grocery behavior: Results from a Swedish study. Journal of Euromarketing, 14(3), 31–58. https://doi.org/10.1300/J037v14n03_03

Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behaviour. International Journal of Information Management, 24(6), 539–550. https://doi.org/10.1016/j.ijinfomgt.2004.08.004

Horton, R. P., Buck, T., Waterson, P. E., & Clegg, C. W. (2001). Explaining intranet use with the technology acceptance model. Journal of Information Technology, 16(4), 237–249. https://doi.org/10.1080/02683960110102407

Jasti, D., & Syed, A. A. (2019). Leveraging the internet for grocery shopping: A study of factors influencing the Indian consumer. International Journal on Emerging Technologies, 10(3), 436–443.

Kazi, R., Singh, A., & Sharma, A. (2018). The relationship between perceived Ad morality and behavioral intentions exploring the mediation effect: Indian women's perspective using structural equation modeling. Prabandhan: Indian Journal of Management, 11(3), 7–23. https://doi.org/10.17010/pijom/2018/v11i3/122074

Krishnamurti, S., & Gupta, B. (2017). Changing consumer behavior paradigms: Does gender and marital status influence grocery shopping behavior? An exploratory study. Indian Journal of Marketing, 47(10), 7–18. https://doi.org/10.17010/ijom/2017/v47/i10/118693

Kumar, A., & Malik, G. (2022). Structural equation modeling of airlines service quality: A study of airlines industry in India. Prabandhan: Indian Journal of Management, 15(6), 28–45. https://doi.org/10.17010/pijom/2022/v15i6/170025

Kurnia, S. (2008). Exploring e-commerce readiness in China: The case of the grocery industry. Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 413. https://doi.org/10.1109/HICSS.2008.160

Kurnia, S., & Chien, A.-W. (2003). The acceptance of the online grocery shopping. Proceeding of 16th Bled Electronic Commerce Conference, Slovenia, 219–233.

Lin, G. T., & Sun, C.- C. (2009). Factors influencing satisfaction and loyalty in online shopping: An integrated model. Online Information Review, 33(3), 458–475. https://doi.org/10.1108/14684520910969907

MacKenzie, S. B., & Lutz, R. J. (1989). An empirical examination of the structural antecedents of attitude toward the ad in an advertising pretesting context. Journal of Marketing, 53(2), 48–65. https://doi.org/10.1177/002224298905300204

Mahadevan, K., & Joshi, S. (2021). Trends in electronic word of mouth research: A bibliometric review and analysis. Indian Journal of Marketing, 51(4), 8–26. https://doi.org/10.17010/ijom/2021/v51/i4/158468

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

Padmanabh, B., Jeevananda, S., & Jose, K. G. (2016). A study on factors impeding online buying of household items in Bangalore city. Indian Journal of Marketing, 46(4), 7–23. https://doi.org/10.17010/ijom/2016/v46/i4/90526

Pahari, S., Ghosal, I., Prasad, B., & Dildar, S. M. (2023). Which determinants impact consumer purchase behavior toward online purchasing of organic food products? Prabandhan: Indian Journal of Management, 16(1), 25–41. https://doi.org/10.17010/pijom/2023/v16i1/172667

Phonthanukitithaworn, C., Sellitto, C., & Fong, M. W. L. (2016). An investigation of mobile payment (m-payment) services in Thailand. Asia-Pacific Journal of Business Administration, 8(1), 37–54. https://doi.org/10.1108/APJBA-10-2014-0119

Pramod, & Arora, U. (2021). Effect of gender, age, and marital status of grocery consumers on their perceived retail store image: A study of organized grocery market in India. Indian Journal of Marketing, 51(4), 58–71. https://doi.org/10.17010/ijom/2021/v51/i4/158471

Prasad, S., & Sharma, M. (2016). Demographic and socioeconomic influences shaping usage of online channel for purchase of food & grocery. Indian Journal of Marketing, 46(10), 7–21. https://doi.org/10.17010/ijom/2016/v46/i10/102851

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/BF03206553

Punjani, K. K., & Kumar, V. V. R. (2021). Impact of advertising puffery and celebrity trustworthiness on attitude and purchase intent: A study on Indian youth. Journal of Advances in Management Research, 18(5), 738–757. https://doi.org/10.1108/JAMR-06-2020-0133

Punjani, K. K., & Mahadevan, K. (2022). Transitioning to online learning in higher education: Influence of awareness of COVID-19 and self-efficacy on perceived net benefits and intention. Education and Information Technologies, 27, 291–320. https://doi.org/10.1007/s10639-021-10665-2

Putrevu, S., & Lord, K. R. (1994). Comparative and noncomparative advertising: Attitudinal effects under cognitive and affective involvement conditions. Journal of Advertising, 23(2), 77–91. https://doi.org/10.1080/00913367.1994.10673443

Rao, P., Vihari, N. S., & Jabeen, S. S. (2020). E-commerce and fashion retail industry: An empirical investigation on the online retail sector in the Gulf Cooperation Council (GCC) countries. ICEB 2020 Proceedings (Hong Kong, SAR China), 37. https://aisel.aisnet.org/iceb2020/37

Sangwan, S., Sharma, S. K., & Sharma, J. (2022). Disclosing customers' intentions to use social media for purchase-related decisions. Asia-Pacific Journal of Business Administration, 14(1), 145–160. https://doi.org/10.1108/APJBA-02-2021-0061

Sarmah, R., Dhiman, N., & Kanojia, H. (2021). Understanding intentions and actual use of mobile wallets by millennial: An extended TAM model perspective. Journal of Indian Business Research, 13(3), 361–381. https://doi.org/10.1108/JIBR-06-2020-0214

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007

Sreelata, Narasimham, N. V., & Gupta, M. K. (2016). Segmenting consumers in food and grocery retail. Indian Journal of Marketing, 46(4), 24–38. https://doi.org/10.17010/ijom/2016/v46/i4/90527

Sreeram, A., Kesharwani, A., & Desai, S. (2017). Factors affecting satisfaction and loyalty in online grocery shopping: An integrated model. Journal of Indian Business Research, 9(2), 107–132. https://doi.org/10.1108/JIBR-01-2016-0001

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education Inc.

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

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001

Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229–243. https://doi.org/10.1080/13598660802232779