Drivers of purchase intention in Instagram Commerce

  1. Doaa Herzallah 1
  2. Francisco Muñoz-Leiva 2
  3. Francisco Liebana-Cabanillas 2
  1. 1 University of Derby, Derby, UK
  2. 2 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

Revista:
Spanish journal of marketing-ESIC

ISSN: 2444-9695,  2444-9709

Any de publicació: 2022

Volum: 26

Número: 2

Tipus: Article

DOI: 10.1108/SJME-03-2022-0043 DIALNET GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Spanish journal of marketing-ESIC

Resum

Purpose This study aims to analyze the factors that drive purchases via Instagram and contribute to the growth of Instagram Commerce and examine the moderating role of gender, age and experience in Instagram use in the proposed relationship between six variables derived from commitment–trust theory, the technology acceptance model and consumer decision-making theory. Design/methodology/approach A survey was completed by respondents after watching a video about Instagram Commerce. A total of 404 valid responses were collected. The research model was analyzed using partial least squares structural equation modeling. Findings This study makes numerous contributions to Instagram Commerce and holds significant implications for professionals in the social commerce field. Among other results, we found that trust, attitude, perceived usefulness and alternative evaluation significantly affected consumers’ purchase intentions. However, this study found no relationship between trust or ease of use and purchase intention. Finally, it demonstrates the moderating role of gender, age and experience on some of these relationships. Originality/value This research centers on an analysis of consumer purchase behavior on Instagram Commerce, taking a highly innovative approach. The particular originality of this study lies in the proposed model of adoption of social commerce via Instagram, based on a critical framework. This study also provides an original analysis of the moderating effect of the classification variables: gender, age and experience in Instagram use.

Referències bibliogràfiques

  • Abed, S.S. (2020), “Social commerce adoption using TOE framework: an empirical investigation of Saudi Arabian SMEs”, International Journal of Information Management, Vol. 53, p. 102118.
  • Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, p. 411.
  • Assadam, E. (2020), “Online impulse buying: who had suggested you to buy on Instagram”, MEC-J Management and Economics Journal, Vol. 3 No. 3, pp. 231-244.
  • Athapaththu, J.C. and Kulathunga, K.M.S.D. (2018), “Factors affecting online purchase intention: effects of technology and social commerce”, International Business Research, Vol. 11 No. 10, pp. 111-128.
  • Baker, E.W., Hubona, G.S. and Srite, M. (2019), “Does ‘being there’ matter? The impact of web-based and virtual world’s shopping experiences on consumer purchase attitudes”, Information and Management, Vol. 56 No. 7, p. 103153.
  • Barclay, D., Higgins, C. and Thompson, R. (1995), “The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration”, Technology Studies, Vol. 2 No. 2, pp. 285-309.
  • Beyari, H. and Abareshi, A. (2016), “The conceptual framework of the factors influencing consumer satisfaction in social commerce”, The Journal of Developing Areas, Vol. 50 No. 6, pp. 365-376.
  • Bugshan, H. and Attar, R.W. (2020), “Social commerce information sharing and their impact on consumers”, Technological Forecasting and Social Change, Vol. 153 No. 2, p. 119875.
  • Busalim, A.H. (2016), “Understanding social commerce: a systematic literature review and directions for further research”, International Journal of Information Management, Vol. 36 No. 6, pp. 1075-1088.
  • Casaló, L.V., Flavián, C. and Ibáñez-Sánchez, S. (2017), “Understanding consumer interaction on Instagram: the role of satisfaction, hedonism and content characteristics”, Cyberpsychology, Behavior and Social Networking, Vol. 20 No. 6, pp. 369-375.
  • Casaló, L.V., Flavián, C. and Ibáñez-Sánchez, S. (2020), “Influencers on Instagram: antecedents and consequences of opinion leadership”, Journal of Business Research, Vol. 117, pp. 510-519.
  • Casaló, L.V., Flavián, C. and Ibáñez-Sánchez, S. (2021), “Be creative, my friend! Engaging users on Instagram by promoting positive emotions”, Journal of Business Research, Vol. 130, pp. 416-425.
  • Chin, W.W. (1998), “The partial least squares approach to structural equation modeling”, Modern Methods for Business Research, Vol. 295 No. 2, pp. 295-336.
  • Cho, E. and Son, J. (2019), “The effect of social connectedness on consumer adoption of social commerce in apparel shopping”, Fashion and Textiles, Vol. 6 No. 1, p. 14.
  • Chong, A.Y.L. (2013), “Mobile commerce usage activities: the roles of demographic and motivation variables”, Technological Forecasting and Social Change, Vol. 80 No. 7, pp. 1350-1359.
  • Curtis, L., Edwards, C., Fraser, K.L., Gudelsky, S., Holmquist, J., Thornton, K. and Sweetser, K.D. (2010), “Adoption of social media for public relations by nonprofit organizations”, Public Relations Review, Vol. 36 No. 1, pp. 90-92.
  • Dabbous, A., Aoun Barakat, K. and Merhej Sayegh, M. (2020), “Social commerce success: antecedents of purchase intention and the mediating role of trust”, Journal of Internet Commerce, Vol. 19 No. 3, pp. 262-297.
  • Dabholkar, P.A., Bobbitt, L.M. and Lee, E.J. (2003), “Understanding consumer motivation and behavior related to self‐scanning in retailing”, International Journal of Service Industry Management, Vol. 14 No. 1, pp. 59-95.
  • Davis, F.D. (1989), “Perceived usefulness, perceived ease of use and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-340.
  • Djafarova, E. and Bowes, T. (2021), “‘Instagram made me buy it’: Generation Z impulse purchases in fashion industry”, Journal of Retailing and Consumer Services, Vol. 59, p. 102345.
  • eMarketer Report (2020), “Worldwide social network users update: eMarketer’s estimates and forecast for 2016–2021, with a focus on Instagram”, available at: www.emarketer.com/Report/Worldwide-Social-Network-Users-UpdateeMarketers-Estimates-Forecast-20162021-with-Focus-on-Instagram/2002170
  • Engel, J.F., Blackwell, R.D. and Miniard, P.W. (1995), Consumer Behavior, 8th ed., The Dryden Press, Fort Worth, TX.
  • Falk, R.F. and Miller, N.B. (1992), A Primer for Soft Modeling, The University of Akron, US.
  • Featherman, M.S. and Hajli, N. (2016), “Self-service technologies and e-services risks in social commerce era”, Journal of Business Ethics, Vol. 139 No. 2, pp. 251-269.
  • Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA.
  • Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
  • Ganguly, B., Dash, S.B., Cyr, D. and Head, M. (2010), “The effects of website design on purchase intention in online shopping: the mediating role of trust and the moderating role of culture”, International Journal of Electronic Business, Vol. 8 Nos 4/5, pp. 302-330.
  • Geisser, S. (1975), “The predictive sample reuse method with applications”, Journal of the American Statistical Association, Vol. 70 No. 350, pp. 320-328.
  • Hair, J.F. Jr., Hult, G.T.M., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage publications, Thousand Oaks, CA.
  • Hair, J.F., Jr, Sarstedt, M., Hopkins, L. and Kuppelwieser, V.G. (2014), “Partial least squares structural equation modeling (PLS-SEM): an emerging tool for business research”, European Business Review, Vol. 26 No. 2, pp. 106-121.
  • Hauk, N., Hüffmeier, J. and Krumm, S. (2018), “Ready to be a silver surfer? A meta-analysis on the relationship between chronological age and technology acceptance”, Computers in Human Behavior, Vol. 84, pp. 304-319.
  • Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D. (2004), “Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?”, Journal of Interactive Marketing, Vol. 18 No. 1, pp. 38-52.
  • Henseler, J., Hubona, G. and Ray, P.A. (2016), “Using PLS path modeling in new technology research: updated guidelines”, Industrial Management and Data Systems, Vol. 116 No. 1.
  • Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, Vol. 43 No. 1, pp. 115-135.
  • Henseler, J., Ringle, C.M. and Sinkovics, R.R. (2009), “The use of partial least squares path modeling in international marketing”, New Challenges to International Marketing, Emerald Group Publishing.
  • Henseler, J., Dijkstra, T.K., Sarstedt, M., …. Calantone, R.J. (2014), “Common beliefs and reality about PLS: comments on rönkkö and evermann (2013)”, Organizational Research Methods, Vol. 17 No. 2, pp. 182-209.
  • Herzallah, D., Leiva, F.M. and Liébana-Cabanillas, F. (2021), “To buy or not to buy, that is the question: understanding the determinants of the urge to buy impulsively on Instagram commerce”, Journal of Research in Interactive Marketing.
  • Hettiarachchi, H.A.H., Wickramasinghe, C.N. and Ranathunga, S. (2018), “The influence of social commerce on consumer decisions”, The International Technology Management Review, Vol. 7 No. 1, pp. 47-58.
  • Hsu, M.H., Ju, T.L., Yen, C.H. and Chang, C.M. (2007), “Knowledge sharing behavior in virtual communities: the relationship between trust, self-efficacy and outcome expectations”, International Journal of Human-Computer Studies, Vol. 65 No. 2, pp. 153-169.
  • Hu, P.J., Chau, P.Y., Sheng, O.R.L. and Tam, K.Y. (1999), “Examining the technology acceptance model using physician acceptance of telemedicine technology”, Journal of Management Information Systems, Vol. 16 No. 2, pp. 91-112.
  • Hubona, G.S. and Kennick, E. (1996), “The impact of external variables on information technology usage behavior”, Proceedings of the 29th Annual HI International Conference on System Sciences, Maui, HI, Vol. 4, pp. 166-175.
  • Jackson, L.A., Ervin, K.S., Gardner, P.D. and Schmitt, N. (2001), “Gender and the internet: women communicating and men searching”, Sex Roles, Vol. 44 Nos 5/6, pp. 363-379.
  • Joines, J.L., Scherer, C.W. and Scheufele, D.A. (2003), “Exploring motivations for consumer web use and their implications for e‐commerce”, Journal of Consumer Marketing, Vol. 20 No. 2.
  • Kasilingam, D.L. (2020), “Understanding the attitude and intention to use smartphone chat bots for shopping”, Technology in Society, Vol. 62, p. 101280.
  • Kemp, S. (2022), “Digital 2022: global overview report – DataReportal – global digital insights”, DataReportal, available at: https://datareportal.com/reports/digital-2022-global-overview-report (accessed 5 March 2022).
  • Kim, B. and Kim, Y. (2019), “Facebook versus Instagram: how perceived gratifications and technological attributes are related to the change in social media usage”, The Social Science Journal, Vol. 56 No. 2, pp. 156-167.
  • Li, S., Glass, R. and Records, H. (2008), “The influence of gender on new technology adoption and use–mobile commerce”, Journal of Internet Commerce, Vol. 7 No. 2, pp. 270-289.
  • Liao, S.H., Widowati, R. and Cheng, C.J. (2022), “Investigating Taiwan Instagram users’ behaviors for social media and social commerce development”, Entertainment Computing, Vol. 40, p. 100461.
  • Liébana-Cabanillas, F., Muñoz-Leiva, F. and Sánchez-Fernández, J. (2018), “A global approach to the analysis of user behavior in mobile payment systems in the new electronic environment”, Service Business, Vol. 12 No. 1, pp. 25-64.
  • Liébana-Cabanillas, F., Singh, N., Kalinic, Z. and Carvajal-Trujillo, E. (2021), “Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach”, Information Technology and Management, Vol. 22 No. 2, pp. 133-161.
  • Liébana-Cabanillas, F., Muñoz-Leiva, F., Molinillo, S. and Higueras-Castillo, E. (2022), “Do biometric payment systems work during the COVID-19 pandemic? Insights from the Spanish users' viewpoint”, Financial Innovation, Vol. 8 No. 1, pp. 1-25.
  • Lin, K.M. (2011), “e-Learning continuance intention: moderating effects of user e-learning experience”, Computers & Education, Vol. 56 No. 2, pp. 515-526.
  • MacKenzie, S.B. and Lutz, R.J. (1989), “An empirical examination of the structural antecedents of attitude toward the ad in an advertising pretesting context”, Journal of Marketing, Vol. 53 No. 2, pp. 48-65.
  • Martínez-López, F.J., Li, Y., Feng, C. and Esteban-Millat, I. (2020), “Purchasing through social platforms with buy buttons: a basic hierarchical sequence”, Journal of Organizational Computing and Electronic Commerce, Vol. 30 No. 1, pp. 67-87.
  • Mohsin, M. (2020), “Estadísticas instagram 2020: 10 datos curiosos de instagram que no sabías”, available at: www.oberlo.es/blog/estadisticas-de-instagram
  • Moorman, C., Zaltman, G. and Deshpande, R. (1992), “Relationships between providers and users of market research: the dynamics of trust within and between organizations”, Journal of Marketing Research, Vol. 29 No. 3, pp. 314-328.
  • Morgan, R.M. and Hunt, S.D. (1994), “The commitment-trust theory of relationship marketing”, Journal of Marketing, Vol. 58 No. 3, pp. 20-38.
  • Nedra, B.A., Hadhri, W. and Mezrani, M. (2019), “Determinants of customers' intentions to use hedonic networks: the case of instagram”, Journal of Retailing and Consumer Services, Vol. 46, pp. 21-32.
  • Nkoyi, A., Tait, M. and van der Walt, F. (2019), “Predicting the attitude towards electronic banking continued usage intentions among rural banking customers in South Africa”, South African Journal of Information Management, Vol. 21 No. 1, pp. 1-8.
  • Nunnally, J.C. (1994), Psychometric Theory 3E, Tata McGraw-Hill Education.
  • O’cass, A. and Fenech, T. (2003), “Web retailing adoption: exploring the nature of internet users web retailing behaviour”, Journal of Retailing and Consumer Services, Vol. 10 No. 2, pp. 81-94.
  • Olson, J.C. and Reynolds, T.J. (2001), “The means-end approach to understanding consumer decision making”, Understanding Consumer Decision Making: The Means-End Approach to Marketing and Advertising Strategy, pp. 3-20.
  • Rauniar, R., Rawski, G., Yang, J. and Johnson, B. (2014), “Technology acceptance model (TAM) and social media usage: an empirical study on Facebook”, Journal of Enterprise Information Management, Vol. 27 No. 1, pp. 6-30.
  • San José, R. (2007), “Ejecución y eficacia de la publicidad online”, Los sitios web de las agencias de viajes (Doctoral dissertation, Tesis Doctoral).
  • Sarstedt, M., Ringle, C.M. and Hair, J.F. (2017), “Partial least squares structural equation modeling”, Handbook of Market Research, Vol. 26, pp. 1-40.
  • Sawitri, N.L.P.W. and Giantari, I.G.A.K. (2020), “The role of trust mediates the effect of perceived ease of use and perceived usefulness on online repurchase intention”, American Journal of Humanities and Social Sciences Research (AJHSSR), Vol. 5 No. 1, pp. 80-85.
  • Sharma, S., Menard, P. and Mutchler, L.A. (2019), “Who to trust? Applying trust to social commerce”, Journal of Computer Information Systems, Vol. 59 No. 1, pp. 32-42.
  • Sheldon, P. and Bryant, K. (2016), “Instagram: Motives for its use and relationship to narcissism and contextual age”, Computers in Human Behavior, Vol. 58, pp. 89-97.
  • Stafford, T.F., Turan, A. and Raisinghani, M.S. (2004), “International and cross-cultural influences on online shopping behavior”, Journal of Global Information Technology Management, Vol. 7 No. 2, pp. 70-87.
  • Statista (2022), “Previsión del número de usuarios mensuales de instagram a nivel mundial desde 2018 hasta 2023”, available at: https://es.statista.com/estadisticas/1038171/numero-de-usuarios-activos-mensuales-de-instagram-en-el-mundo/ (accessed 22 February 2022).
  • Stone, M. (1974), “Cross‐validatory choice and assessment of statistical predictions”, Journal of the Royal Statistical Society: Series B (Methodological), Vol. 36 No. 2, pp. 111-133.
  • Suraworachet, W., Premsiri, S. and Cooharojananone, N. (2012), “The study on the effect of Facebook's social network features toward intention to buy on F-commerce in Thailand”, 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet, IEEE, pp. 245-250.
  • Wang, W.T., Wang, Y.S. and Liu, E.R. (2016), “The stickiness intention of group-buying websites: the integration of the commitment–trust theory and e-commerce success model”, Information and Management, Vol. 53 No. 5, pp. 625-642.
  • Xie, M., Zhang, J. and Zeng, J. (2009), “M-Commerce in the period of 3G”, 2009 International Conference on Management and Service Science, IEEE, pp. 1-4.
  • Xu, P. and Liu, D. (2019), “Product engagement and identity signaling: the role of likes in social commerce for fashion products”, Information and Management, Vol. 56 No. 2, pp. 143-154.
  • Yadav, M.S., De Valck, K., Hennig-Thurau, T., Hoffman, D.L. and Spann, M. (2013), “Social commerce: a contingency framework for assessing marketing potential”, Journal of Interactive Marketing, Vol. 27 No. 4, pp. 311-323.
  • Yang, C., Ye, X., Xie, J., Yan, X., Lu, L., Yang, Z., … Chen, J. (2020), “Analyzing drivers’ intention to accept parking app by structural equation model”, Journal of Advanced Transportation, Vol. 2020.
  • Yapp, E.H., Balakrishna, C., Yeap, J.A. and Ganesan, Y. (2018), “Male and female technology users”, Acceptance of on-Demand Services. Global Business and Management Research, Vol. 10 No. 1.
  • Zhou, L., Dai, L. and Zhang, D. (2007), “Online shopping acceptance model – a critical survey of consumer factors in online shopping”, Journal of Electronic Commerce Research, Vol. 8 No. 1, pp. 41-63.
Fuente de los datos: Dialnet