A Multi-group Analysis of Gender Difference in Consumer Buying Intention of Agricultural Products via Live Streaming

Bing Zhu(Department of Marketing, Assumption University, Bangkok, 10210, Thailand)
Ping Xu(Department of Educational Psychology, Guangzhou Sport University, Guangdong, 510500, China)
Ke Wang(Department of Educational Psychology, Guangzhou Sport University, Guangdong, 510500, China)

DOI: http://dx.doi.org/10.36956/rwae.v4i1.789

Article ID: 789


This study tries to understand the determinants of Chinese consumers’ purchase behavior and reveal the role of gender in shaping consumers’ buying decisions for agricultural products from live-streaming platforms. For this purpose, an online survey was carried out to collect data in Southern China. Partial least squares structural equation modeling (PLS-SEM) was employed for path analysis and multi-group analysis. The results confirm the substantial influences of consumer attitude, subjective norms and perceived behavioral control on consumer buying intention. Next, gender difference only exists concerning the effect of perceived behavioral control on consumer intention. However, the gap between male and female consumers on this point is small. Furthermore, as each factor affects consumers’ purchase intention differently, corresponding implications are provided.


PLS-SEM; Permutation test; Live-streaming commerce; Gender differences; Agriculture marketing

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[1] USCODE, 2022. 7-Agriculture, Chapter 94-Organic Certification [Internet] [cited 2022 Dec 15]. Available from: https://uscode.house.gov/view. xhtml?req=(title:7%20section:6502%20edition:prelim)#:~:text=(1)%20Agricultural%20product,for%20 human%20or%20livestock%20consumption.

[2] Yu, Z., Zhang, K., 2022. The determinants of purchase intention on agricultural products via public-interest live streaming for farmers during COVID-19 pandemic. Sustainability. 14(21), 13921. DOI: https://doi.org/10.3390/su142113921

[3] Guo, L., Hu, X., Lu, J., et al., 2021. Effects of customer trust on engagement in live streaming commerce: Mediating role of swift guanxi. Internet Research. 31(5), 1718-1744. DOI: https://doi.org/10.1108/INTR-02-2020-0078

[4] Lin, S.C., Tseng, H.T., Shirazi, F., et al., 2022. Exploring factors influencing impulse buying in live streaming shopping: A stimulus-organism-response (SOR) perspective. Asia Pacific Journal of Marketing and Logistics. Ahead of print (ahead of print). DOI: https://doi.org/10.1108/APJML-12-2021-0903

[5] Hu, M., Chaudhry, S.S., 2020. Enhancing consumer engagement in e-commerce live streaming via relational bonds. Internet Research. 30(3), 1019-1041. DOI: https://doi.org/10.1108/INTR-03-2019-0082

[6] Chen, L.L., 2020. Driving factors, effect analysis and countermeasures of the development of China’s live broadcast platform. China Finance and Economic Review. 10(1), 102-116. DOI: https://doi.org/10.1515/cfer-2021-0006

[7] Song, Y.H., 2021. Research on the integrated communication strategy of live streaming of agricultural products [Bachelor’s thesis]. Baoding: Hebei University. Available from: https://cdmd.cnki.com.cn/ Article/CDMD-10075-1021702109.htm.

[8] iResearch, 2022. Taobao Live Annual New Consumption Trend Report [Internet] [cited 2022 Dec 15]. Available from: https://pdf.dfcfw.com/pdf/H3_ AP202207181576267255_1.pdf?1658163843000. pdf.

[9] Ren, H., 2020. Live public service provide new ideas for helping farmers and enterprises. Rural Credit Cooperative of China (Zhongguo Nongcun Jinrong). 15, 61-62.

[10] Ou-Yang, J.W., 2022. 2022 China Agricultural Products E-Commerce Development Report [Internet]. Released-Transformation and Innovation Become a New Hotspot in the Development of Agricultural Products E-Commerce [cited 2022 Dec 15]. Available from: http://www.agri.cn/V20/ZX/nyyw/202204/ t20220421_7842866.htm.

[11] China.org.cn., 2020. Let the “Live Streaming” of Agricultural Products Become a New Fashion for Farmers [Internet] [cited 2022 Dec 7]. Available from: http://guoqing.china.com.cn/2020-08/18/content_76595753.htm.

[12] Todd, P.R., Melancon, J., 2018. Gender and live-streaming: Source credibility and motivation. Journal of Research in Interactive Marketing. 12(1), 79-93. DOI: https://doi.org/10.1108/JRIM-05-2017-0035

[13] Sun, Y., Shao, X., Li, X., et al., 2019. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications. 37, 100886. DOI: https://doi.org/10.1016/j.elerap.2019.100886

[14] Sohn, J.W., Kim, J.K., 2020. Factors that influence purchase intentions in social commerce. Technology in Society. 63(C). DOI: https://doi.org/10.1016/j.techsoc.2020.101365

[15] Wongkitrungrueng, A., Assarut, N., 2020. The role of live streaming in building consumertrust and engagement with social commerce sellers. Journal of Business Research. 117, 543-556. DOI: https://doi.org/10.1016/j.jbusres.2018.08.032

[16] Molinillo, S., Anaya-Sánchez, R., Liebana-Cabanillas, F., 2020. Analyzing the effect of social support and community factors on customer engagement and its impact on loyalty behaviors toward social commerce websites. Computers in Human Behavior. 108, 105980. DOI: https://doi.org/10.1016/j.chb.2019.04.004

[17] Wang, M., Fan, X., 2021. An empirical study on how livestreaming can contribute to the sustainability of green agri-food entrepreneurial firms. Sustainability. 13, 12627. DOI: https://doi.org/10.3390/su132212627

[18] Lim, P.L., Yazdanifard, R., 2014. Does gender play a role in online consumer behavior? Global Journal of Management and Business Research. 14(7).

[19] Hasan, B., 2010. Exploring gender differences in online shopping attitude. Computers in Human Behavior. 26(4), 597-601.

[20] Wang, W., Kim, S., 2019. Lady first? The gender difference in the influence of service quality on online consumer behavior. Nankai Business Review International. 10(3), 408-428.

[21] Pascual-Miguel, F.J., Agudo-Peregrina, Á.F., Chaparro-Pelaez, J., 2015. Influences of gender and product type on online purchasing. Journal of Business Research. 68(7), 1550-1556.

[22] Zhang, L., Shao, Z., Li, X., et al., 2020. Gamification and online impulse buying: The moderating effect of gender and age. International Journal of Information Management. 61, 102267.

[23] Yang, N., Wang, Z.H., 2022. Addressing as a gender-preferential way for suggestive selling in Chinese e-commerce live streaming discourse: A corpus-based approach. Journal of Pragmatics. 197, 43-54.

[24] Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 50(2), 179-211. DOI: https://doi.org/10.1016/0749-5978(91)90020-t

[25] Arora, S., Singha, K., Sahney, S., 2017. Understanding consumer’s showrooming behaviour: Extending the theory of planned behaviour. Asia Pacific Journal of Marketing and Logistics. 29(2), 409-431. DOI: https://doi.org/10.1108/APJML-06-2016-0111

[26] Meng, B., Lee, M.J., Chua, B.L., et al., 2022. An integrated framework of behavioral reasoning theory, theory of planned behavior, moral norm and emotions for fostering hospitality/tourism employees’ sustainable behaviors. International Journal of Contemporary Hospitality Management. 34(12), 4516- 4538. DOI: https://doi.org/10.1108/IJCHM-02-2022-0151

[27] Ham, M., Jegern, M., Ivković, F.A., 2015. The role of subjective norms in forming the intention to purchase green food. Economic Research-Ekonomska Istraživanja. 28(1), 738-748. DOI: https://doi.org/10.1080/1331677X.2015.1083875

[28] Xu, P., Zhu, B., Wang, K., 2022. Consumers’ intention to buy agricultural products via livestreaming platforms in southern China. HCII: Design, operation and evaluation of mobile communications. Lecture Notes in Computer Science. 13337, 286-297. DOI: https://doi.org/10.1007/978-3-031-05014-5_24

[29] Ajzen, I., Fishbein, M., 2005. The influence of attitudes on behavior. The handbook of attitudes. Lawrence Erlbaum Associates Publishers: Hillsdale, NJ. pp. 173-221.

[30] Roseira, C., Teixeira, S., Barbosa, B. et al., 2022. How collectivism affects organic food purchase intention and behavior: A study with Norwegian and Portuguese young consumers. Sustainability. 14, 7361.

[31] Allport, G.W., 1935. Attitudes. Handbook of social psychology 2. Clark University Press:Worcester. pp. 798-844.

[32] Han, H., Hwang, J., Lee, S., 2017. Cognitive, affective, normative, and moral triggers of sustainable intentions among convention-goers. Journal of Environmental Psychology. 51, 1-13. DOI: https://doi.org/10.1016/j.tourman.2014.09.014

[33] Gidaković, P., Koklič, M.K., Zečević, M., et al., 2022. The influence of brand sustainability on purchase intentions: The mediating role of brand impressions and brand attitudes. Journal of Brand Management. 29, 556-568. DOI: https://doi.org/10.1057/s41262-022-00280-y

[34] Shihab, M.R., Putri, A.P., 2018. Negative online reviews of popular products: Understanding the effects of review proportion and quality on consumers’ attitude and intention to buy. Electronic Commerce Research. 19, 159-187. DOI: https://doi.org/10.1007/s10660-018-9294-y

[35] Singh, R.P., Banerjee, N., 2018. Exploring the influence of celebrity credibility on brand attitude, advertisement attitude and purchase Intention. Global Business Review. 19(6). DOI: https://doi.org/10.1177/097215091879497

[36] Armitage, C.J., Conner, M., 1999. The theory of planned behaviour: Assessment of predictive validity and perceived control. British Journal of Social Psychology. 38(1), 35-54. DOI: https://doi.org/10.1348/014466699164022

[37] Sujood, H.S., Bano, N., 2021. Behavioral intention of traveling in the period of COVID-19: An application of the theory of planned behavior (TPB) and perceived risk. International Journal of Tourism Cities. 8(2), 357-378. DOI: https://doi.org/10.1108/IJTC-09-2020-0183

[38] Pakpour, A.H., Lin, C.K., Safdari, M., et al., 2021. Using an integrated social cognition model to explain green purchasing behavior among adolescents. International Journal of Environmental Research and Public Health. 18(23), 12663. DOI: https://doi.org/10.3390/ijerph182312663

[39] Aitken, R., Watkins, L., Williams, J., et al., 2020. The positive role of labelling on consumers’ perceived behavioural control and intention to purchase organic food. Journal of Cleaner Production. 255, 120334. DOI: https://doi.org/10.1016/j.jclepro.2020.120334

[40] Sun, Y., Wang, S., Li, J., et al., 2017. Understanding consumers’ intention to use plastic bags: Using an extended theory of planned behaviour model. Natural Hazards. 89(3), 1327-1342.DOI: https://doi.org/10.1007/s11069-017-3022-0

[41] Roh, T., Seok, J., Kim, Y., 2022. Unveiling ways to reach organic purchase: Green perceived value, perceived knowledge, attitude, subjective norm, and trust. Journal of Retailing and Consumer Services. 67, 102988. DOI: https://doi.org/10.1016/j.jretconser.2022.102988

[42] Ye, L., 2008. The impact of gender effects on consumers’ perceptions of brand equity: A cross-cultural investigation [PhD thesis]. USA: College of Business, University of North Texas. Available from: https://digital.library.unt.edu/ark:/67531/ metadc9007/.

[43] Fischer, E., Arnold, S.J., 1994. Sex, gender identity, gender role attitudes and consumer behavior. Psychology and Marketing. 11(2), 163-182.

[44] Thompson, C., 1996. Caring consumers: Gendered consumption meanings and the juggling lifestyle. Journal of Consumer Research. 2, 388-407.

[45] Szymkowiak, A., Garczarek-Bąk, U., 2018. Gender differences in e-commerce. Handel Wewnetrzny. 4(375), 250-261.

[46] Kanwal, M., Burki, U., Ali, R., et al., 2022. Systematic review of gender differences and similarities in online consumers’ shopping behavior. Journal of Consumer Marketing. 39(1), 29-34. DOI: https://doi.org/10.1108/JCM-01-2021-4356

[47] Foedermayr, E.K., Diamantopoulos, A., 2008. Market segmentation in practice: Review of empirical studies, methodological assessment, and agenda for future research. Journal of Strategic Marketing. 16(3), 223-265.

[48] Kim, D.Y., Lehto, X.Y., Morrison, A.M., 2007. Gender differences in online travel information search: Implications for marketing communications on the internet. Tourism Management. 28(2), 423-433. DOI: https://doi.org/10.1016/j.tourman.2006.04.001

[49] Sanz de, A.L.M.L., Sanz de, A.B.M.T., Cardelle-Elawar, M., 2007. Factors that affect decision making: Gender and age differences. International Journal of Psychology and Psychological Therapy. 7(3), 381–391.

[50] Mitchell, V.W., Walsh, G., 2004. Gender differences in German consumer decision-making styles. Journal of Consumer Behavior. 3(4),331-346.DOI: https://doi.org/10.1002/cb.146

[51] Wiltinger, A., 2009. Female marketing: Frauen ticken anders. Planung & Analyse. Frankfurt, M: Dt. Fachverl: Hamburg. pp. 42-47.

[52] Rajagopal, R.C., 2015. Understanding consumer behavior and consumption experience. IGI Global: USA.

[53] Akhter, S.H., 2003. Digital divide and purchase intention: Why demographic psychology matters. Journal of Economic Psychology. 24(3), 321-327.

[54] Wang, E.S.T., 2010. The effects of browsing frequency and gender on the relationship between perceived control and patronage intentions in e-tail. International Journal of Electronic Commerce. 14(3), 129-144.

[55] Venkatesh, V., Morris, M.G., Davis, G.B., et al., 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly. 27(3). DOI: https://doi.org/10.2307/30036540

[56] Madan, K., Yadav, R., 2018. Understanding and predicting antecedents of mobile shopping adoption: A developing country perspective. Asia Pacific Journal of Marketing and Logistics. 30(1), 139-162.

[57] Alshurideh, M.T., Al Kurdi, B., Masa’deh, R.E., et al., 2021. The moderation effect of gender on accepting electronic payment technology: A study on United Arab Emirates consumers. Review of International Business and Strategy. 31(3), 375-396. DOI: https://doi.org/10.1108/ribs-08-2020-0102

[58] Chen, M.F., Tung, P.J., 2014. Developing an extended theory of planned behavior model to predict consumers’ intention to visit green hotels. International Journal of Hospitality Management. 36, 221-230. DOI: https://doi.org/10.1016/j.ijhm.2013.09.006

[59] Likert, R., 1932. A technique for the measurement of attitudes. Archives of Psychology. 22(140).

[60] Sullivan, G.M., Artino, A.R.J., 2013. Analyzing and interpreting data from likert-type scales. Journal of Graduate Medical Education. 5(4), 541-542. DOI: https://doi.org/10.4300/JGME-5-4-18

[61] Chin, W.W., Dibbern, J., 2010. An introduction to a permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA. In: Vinzi, Esposito, Z., Chin, et al. (editors), handbook of partial least squares concepts, methods and applications. Springer Verlag: Heidelberg. pp. 171-193. DOI: https://doi.org/10.1007/978-3-540-32827-8_8

[62] Hair, J.F., Hult, M.T.G., Ringle, M.C., et al., 2017. A primer on partial least squares structural equation modeling (PLS-SEM) (2nd edition). SAGE Publications, Inc: New York. pp. 118-169.

[63] Nunnally, J.C., Bernstein, I.H., 1994. Psychometric theory (3rd edition). McGraw-Hill: New York.

[64] Hair, J.F., Risher, J.J., Sarstedt, M., et al., 2019. When to use and how to report the results of PLSSEM. European Business Review. 30(1), 2-24. DOI: https://doi.org/10.1108/ebr-11-2018-0203

[65] Dijkstra, T.K., Henseler, J., 2015. Consistent partial least squares path modeling. MIS Quarterly. 39(2), 297-316.

[66] Henseler, J., Ringle, C.M., Sarstedt, M., 2016. Testing measurement invariance of composites using partial least squares. International Marketing Review. 33(3), 405-431. DOI: https://doi.org/10.1108/IMR-09-2014-0304

[67] Ramayah, T., Cheah, J., Chuah, F., et al., 2018. Partial least squares structural equation modeling (PLSSEM) using smartPLS 3.0: An updated and practical guide to statistical analysis, 2nd edition. Pearson Malaysia Sdn Bhd: Malaysia.

[68] Cheah, J.H., Thurasamy, R., Memon, A.M., et al., 2020. Multigroup analysis using smart PLS: Step-bystep guidelines for business research. Asian Journal of Business Research. 10(3). DOI: https://doi.org/10.14707/ajbr.200087

[69] Hult, G.T.M., Ketchen, D.J., Griffith, D.A., et al., 2008. Data equivalence in cross-cultural international business research: Assessment and guidelines. Journal of International Business Studies. (6), 1027-1044. DOI: https://doi.org/10.1057/palgrave.jibs.8400396

[70] Barroso, A., González-López, R.Ó., Sanguino, R., et al., 2018. Analysis and evaluation of the largest 500 family firms’ websites through PLS-SEM technique. Sustainability. 10(557), 14. DOI: https://doi.org/10.3390/su10020557

[71] Toyoda, R., Abegão, R.F., Gill, S., et al., 2021. Drivers of immersive virtual reality adoption intention: A multi-group analysis in chemical industry settings. Virtual Reality. Ahead of print. DOI: https://doi.org/10.1007/s10055-021-00586-3

[72] Henseler, J., Ringle, C.M., Sinkovics, R.R., 2009. The use of partial least squares path modeling in international marketing. Advances in International Marketing. 20, 277-320.

[73] Chen, L.R., 2021. Analysis of the Market Status and Competition Pattern of China’s Live Broadcast e-Commerce Industry in 2021: The Development Gap of Anchors on Different Platforms Appears [Internet] [cited 2022 Dec 15]. Available from: https://www. qianzhan.com/analyst/detail/220/210625-748acc31. html.


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