Factors Affecting Online Grocery Shopping in Indian Culture

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research paper on online shopping in india

  • Ashish Kumar Singh 26 &
  • Nishi Pathak 26  

Part of the book series: Advances in Science, Technology & Innovation ((ASTI))

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Today the online grocery shopping (OGS) is helping customers by making their life convenient by offering best and comfortable deals. Scope of online grocery shopping is increasing exponentially. Therefore, this study aims at examining the influencing role played by personal innovativeness (PI), economic values (EV), design aesthetic (DA), perceived enjoyment (PEJ) and convenience (CON) attributes on development of positive attitude to use OGS by Indian customers. For testing the variables and relationship of the proposed model, a structured questionnaire was formed and dispersed among 351 Ghaziabad and Delhi residents, out of which 232 were used for analysis. The Smart PLS 3.0 programme has been used to provide partial least square structural equation modelling (PLS-SEM) method. Finding a study easy to use (PEOU), perceived usefulness (PU), PI, EV, DA and PEJ and CON have a symbolic quantitative correlation in India with the acceptance of OGS. In contrast, PEJ did not support PEOU. Therefore, the study will provide direction to all online grocery service providers to design their services according to the customer’s expectation and need.

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Adamson, I., & Shine, J. (2003). Extending the new technology acceptance model to measure the end user information systems satisfaction in a mandatory environment: A bank’s treasury. Technology Analysis and strategic management, 15 (4), 441–455.

Google Scholar  

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9 (2), 204–215.

Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41 (6), 731–745.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103 (3), 411–423.

Article   Google Scholar  

Anesbury, Z., Nenycz-Thiel, M., Dawes, J., & Kennedy, R. (2015). How do shoppers behave online? An observational study of online grocery shopping. Journal Consumer Behaviour . https://doi.org/10.1002/cb.1566/pdf.

Apanasevic, T., Markendahl, J., & Arvidsson, N. (2016). Stakeholders’ expectations of mobile payment in retail: Lessons from Sweden. International Journal of Bank Marketing, 34 (1), 37–61.

Aylott, R., & Mitchell, V. W. (1999). An exploratory study of grocery shopping stressors. British Food Journal, 101 (9), 683–700.

Bagozzi R. P., & Yi. Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16 (1), 74–94.

Bagozzi, R. P., & Edwards, J. F. (1998). A general approach to construct validation in organisational psychology: Application to the measurement of work values. Organisational Research Methods, 1 (1), 45–87.

Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40 (1), 8–34.

Bauer, H. H., Falk, T., & Hammerschmidt, M. (2006). eTransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59 (7), 866–875.

Berry, L. L. (2002). The components of department store image. Consumer Behaviour Analysis, 3 , 380.

Bruner II, G. C., & Kumar, A. (2005). Applying TAM to consumer usage of handheld Internet devices. Journal of Business Research, 58 , 553–558.

Carroll, J. M., & Thomas, J. C. (1988). Fun. SIGCHI Bulletin, 19 , 21–24.

Cassil, N. L., Thomas, J. B., & Bailey, E. M. (1997). Consumers’ definitions of apparel value: An investigation of department store shoppers. Journal of Fashion Marketing and Management: An International Journal, 1 (4), 308–321.

Chau, P. Y., & Hu, P. J. H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39 (4), 297–311.

Chau, P. Y., & Lai, V. S. (2003). An empirical investigation of the determinants of user acceptance of internet banking. Journal of Organizational Computing and Electronic Commerce, 13 (2), 123–145.

Chen, L. D., & Tan, J. (2004). Technology adaptation in e-commerce: Key determinants of virtual stores acceptance. European Management Journal, 22 (1), 74–86.

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behaviour. Journal of Retailing, 77 (4), 511–535.

Chin, W. W. (2001) PLS-Graph User’s Guide. CT Bauer College of Business, University of Houston, USA.

Choi, J., Geistfeld, L. V. (2004). A cross-cultural investigation of consumer e-shopping adoption. Journal of Economic Psychology, 25 (6), 821–838. https://doi.org/10.1016/j.joep.2003.08.006 .

Cry, D., Head, M., & Ivanov, A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Information & Management, 43 (8), 950–963.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22 (14), 1111–1132.

Davison, A. C., Hinkley, D. V., & Young, G. A. (2003). Recent Developments in Bootstrap Methodology. Statistical Sciences, 18 (2), 141–157.

Deci, E. L. (1972). Intrinsic motivation, extrinsic reinforcement, and inequity. Journal of Personnlity and Social Psychology, 22 , 113–120.

Driedger, F., & Bhatiasevi, V. (2019). Online grocery shopping in Thailand: Consumer acceptance and usage behavior. Journal of Retailing and Consumer Services, 48 (March 2018), 224–237. https://doi.org/10.1016/j.jretconser.2019.02.005 .

Duane, A., O'Reilly, P., & Andreev, P. (2014). Realising m-payments: modelling consumers’ willingness to m-pay using smartphones. Behaviour and Information Technology, 33 (4), 318–334.

Eastlick, M. A., & Feinberg, R. A. (1999). Shopping motives for mail catalog shopping. Journal of Business Research, 45 (3), 281–290.

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

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: a study of e-commerce adoption. Journal of the Association for Information Systems, 1 (1), 8.

Grewal, D., Gotlieb, J., & Marmorstein, H. (1994). The moderating effects of message framing and source credibility on the price-perceived risk relationship. Journal of Consumer Research, 21 (1), 145–153.

Grewal, D., Krishnan, R., Baker, J., & Borin, N. (1998). The effect of store name, brand name and price discounts on consumers' evaluations and purchase intentions. Journal of Retailing, 74 (3), 331–352.

Gupta, S., & Nayyar, R. (2011). Determinants of Internet buying behavior in India. Asian Journal of Business Research, 1 (2).

Hair, J., Anderson, R., Tatham, R., & Black, C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice-Hall.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19 (2), 139–152. https://doi.org/10.2753/MTP1069-6679190202 .

Hand, C., Harris, P., Singh, J., Rettie, R., & Dall’Olmo Riley, F. (2009). Online grocery shopping: The influence of situational factors. European Journal of Marketing, 43 (9/10), 1205–1219. https://doi.org/10.1108/03090560910976447 .

Hansen, T. (2008). Consumer values, the theory of planned behaviour and online grocery shopping. International Journal of Consumer Studies, 32 (2), 128–137. https://doi.org/10.1111/j.1470-6431.2007.00655.x .

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

Harris, L. C., & Goode, M. M. (2010). Online servicescapes, trust, and purchase intentions. Journal of Services Marketing .

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of Academics and Marketing Sciences, 43 (1), 115–135.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) The use of partial least squares path modelling in international marketing. Advances in International Marketing, 20 (1), pp. 277–319.

Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 41 (7), 853–868.

Hsu, C. L., & Lu, H. P. (2007). Consumer behavior in online game communities: A motivational factor perspective. Computers in Human Behavior, 23 (3), 1642–1659.

Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3 (4), 424.

Hui, T.-K., Wan, D. (2009). Who are the online grocers? Service Industries Journal, 29 (11), 1479–1489. https://doi.org/10.1080/02642060902793334 .

Johnson, V. L., Kiser, A., Washington, R., & Torres, R.: Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M Payment services. Computers in Human Behavior, 79 , 111–122.

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

Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26 (3), 310–322.

Kim, G., Shin, B., Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19 (3), 283–311.

Kurnia, S., Chien, J. A. W. (2003). The acceptance of the online grocery shopping. In Proceedings of the 16th Bled Electronic Commerce Conference, Bled, Slovenia . Citeseer. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?Doi=10.1.1.529.7050&rep=rep1&type=pdf .

Lallmahamood, M. (2007). An examination of individual’s perceived security and privacy of the internet in Malaysia and the influence of this on their intention touse Ecommerce: using an extension of the technology acceptance model. Journal of Internet Banking and Commerce, 12 (3).

Lopez-Nicolas, C., & Molina-Castillo, F. J. (2008). Customer Knowledge Management and E-commerce: The role of customer perceived risk. International Journal of Information Management, 28 (2), 102–113.

Lopez-Nicolas, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information and Management, 45 (6), 359–364.

Lu, H.-P., Hsu, C.-L., & Hsu, H.-Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13 (2), 106–120.

Madan, K., & Yadav, R. (2016). Behavioural intention to adopt mobile wallet: A developing country perspective. Journal of Indian Business Research, 8 (3), 227–244.

Madan, K., & Yadav, R. (2018). Understanding and predicting antecedents of mobile shopping adoption: A developing country perspective. Asia Pacific Journal of Marketing and Logistics, 1 , 139–162.

Malone, T. W. (1981a). Toward a theory of intrinsically motivating instruction. Cognitive Science, 4 , 333–369.

Malone, T. W. (1981b, December). What makes computer games fun? Byte , 258–276.

Mathwick, C., Malhotra, N., & Rigdon, E. (2001). Experiential value: Conceptualisation, measurement and application in the catalog and Internet shopping environment. Journal of Retailing, 77 (1), 39–56.

Merikivi, J., Tuunainen, V., & Nguyen, D. (2017). What makes continued mobile gaming enjoyable? Computers in Human Behavior, 68 , 411–421.

Mortimer, G., Hasan, S. F., Andrews, L., & Martin, J.: Online grocery shopping: the impact of shopping frequency on perceived risk. International Review of Retail, Distribution and Consumer Research, 26 (2), 202–223. https://doi.org/10.1080/09593969.2015.1130737 .

Mun, Y. Y., Hwang, Y. (2003). Predicting the use of web-based information systems: Self efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59 (4), 431–449.

Ng-Kruelle, G., Swatman, P. A., Rebne, D. S., & Hampe, J. F. (2002). The price of convenience: Privacy and mobile commerce. Quarterly Journal of Electronic Commerce, 3 , 273–286.

Nunnally, J. C. (1978). Psychometric theory . New York, NY: McGraw-Hill.

Oh, J., & Sundar, S. S. (2015). How does interactivity persuade? An experimental test of interactivity on cognitive absorption, elaboration, and attitudes. Journal of Communication, 65 (2), 213–236.

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.

Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61 , 404–414.

Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: from the expanded technology acceptance model perspective. Computers in Human. Behaviour, 26 (5), 1111–1119. https://doi.org/10.1016/j.chb.2010.03.015 .

Park, C., & Jun, J. K. (2003). A cross-cultural comparison of Internet buying behavior: Effects of Internet usage, perceived risks, and innovativeness. International Marketing Review, 20 (5), 534–553.

Park, K., Perosio, D., German, G. A., McLaughlin, E. W. (1996). What’s in store for home shopping?

Peng, R., Xiong, L., & Yang, Z. (2012). Exploring tourist adoption of tourism mobile payment: An empirical analysis. Journal of Theoretical and Applied Electronic Commerce Research, 7 (1), 21–33.

Raijas, A. (2002). The consumer benefits and problems in the electronic grocery store. Journal of Retailing and Consumer Services, 9 (2), 107–113. https://doi.org/10.1016/S0969-6989(01)00024-00028 .

Ramus, K., Nielsen, N. A. (2005). Online grocery retailing: What do consumers think? Internet Research, 15 (3), 335–352. https://doi.org/10.1108/10662240510602726(2005) .

Raykov, T. (1997) Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21 (2), 173–184.

Rohm, A. J., & Swaminathan, V. (2004). A typology of online shoppers based on shopping motivations. Journal of Business Research, 57 (7), 748–757. https://doi.org/10.1016/S0148-2963(02)00351-X(2004) .

Schierz, P.G., Schilke, O., & Wirtz. B. W. (2010). Understanding Consumer Acceptance of Mobile Payment Services: An Empirical Analysis. Electronic Commerce Research and Applications, 9 (3), 209–216.

Schuster, A., & Sporn, B. (1998). Potential for on-line grocery shopping in the urban area of Vienna. International Journal of Electronic Markets, 8 (2), 13–16.

Shankar, A., & Kumari, P. (2016). Factors affecting mobile banking adoption behavior in India. The Journal of Internet Banking and Commerce, 21 (1), 01–24.

Shanker, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19 (3), 1–18.

Singh, A. K., Agrawal, B., Sharma, A., & Sharma, P. (2020). COVID-19: Assessment of knowledge and awareness in Indian society. Journal of Public Affairs, e2354 .

Singh, N., Sinha, N., & Liebana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence . International Journal of Information Management , 191–205.

Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2013). Mobile payment adoption: Classification and review of the extant literature. The Marketing Review, 13 (2), 167–190.

Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T. S., & Yew, K. T.: The effects of convenience and speed in M-Payment. Industrial Management and Data Systems, 115 (2), 311–331.

Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science 4 (3): 199-214. Downloaded by HACETTEPE UNIVERSITY, The netherlands. Information & Management, 40 , 541–549.

Townsend, A. M., Demarie, S. M., & Hendrickson, A. R. (2001). Desktop video conferencing in virtual workgroups: Anticipation, system evaluation and performance. Information Systems Journal, 11 (3), 213–227.

Van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11 (4), 342–365. https://doi.org/10.1287=isre.11.4.342.11872 .

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

Xu, G., & Gutiérrez, J. A. (2006). An exploratory study of killer applications and critical success factors in M-commerce. Journal of Electronic Commerce in Organizations (JECO), 4 (3), 63–79.

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Sudeep Tanwar

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Sunil Kumar Pandey

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Gheorghe Matei

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Singh, A.K., Pathak, N. (2021). Factors Affecting Online Grocery Shopping in Indian Culture. In: Singh, P.K., Polkowski, Z., Tanwar, S., Pandey, S.K., Matei, G., Pirvu, D. (eds) Innovations in Information and Communication Technologies (IICT-2020). Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-66218-9_1

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