Key Factors of Younger Generation Consumer’s Attitude and Intention to Use Online Shopping Live Broadcasting Platform in Chengdu China
DOI:
https://doi.org/10.14456/shserj.2024.66Keywords:
Online Shopping, Attitude, Perceived Ease of Use, Perceived Usefulness, IntentionAbstract
Purpose: This paper mainly studies the significant influencing factors of younger generation consumers' attitudes and intentions toward online shopping live broadcasting platforms in Chengdu, China. The conceptual framework gives the causal relationship between online shopping intention, attitude, perceived ease of use, perceived usefulness, trust, perceived risk, subjective norm. Research design, data, and methodology: This study used a non-probability analysis method to investigate the factors influencing the attitudes and intentions of younger consumers in Chengdu towards online shopping live broadcasting platforms. Thirty questionnaires were distributed to part of the respondents who met the characteristics of the sample unit. After data collection, convergent validity and discriminant validity were assessed. Finally, structural equation models (SEM) were used to test all assumptions and model applicability. Results: The results showed that attitude and subjective norms significantly affect online shopping intention. Attitude has the most effect, followed by the subjective norm. Perceived ease of use, perceived usefulness, and trust significantly affect attitude, with perceived ease of use being the most affected, followed by perceived usefulness. Conclusions: Six hypotheses were shown to achieve the study objectives. Therefore, it is recommended that operators of online shopping live-streaming platforms pay attention to the shopping experience of consumers and value their positive feedback to enhance their intention to shop. Measures such as secure payment options and clear return policies should establish trust with consumers and stabilize the platform's customer base while attracting more consumers to use the platform.
References
Ahmed, E., & Akhlaq, E. (2014). Digital commerce in emerging economies Factors associated with online shopping intentions in Pakistan. International Journal of Emerging Markets, 10(4), 634-647. https://doi.org/10.1108/ijoem-01-2014-0051
Ajzen, I. (1991). The theory of planned behavior. Journal of Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behaviour (1st ed.). Prentice-Hall.
Akroush, M. N., & Al-Debei, M. M. (2015). An integrated model of factors affecting consumer attitudes towards online shopping. Business Process Management Journal, 21(6), 1353-1376. https://doi.org/10.1108/bpmj-02-2015-0022
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Adoption of management information systems in context of Yemeni organizations: A structural equation modeling approach. Journal of Digital Information Management, 13(6), 429–444.
Aren, S., Güzel, M., Kabadayı, E., & Alpkan, L. (2013). Factors affecting repurchase intention to shop at the same website. Procedia - Soc. Behav. Sci., 99, 536-544. https://doi.org/ https://doi. org/10.1016/j.sbspro.2013.10.523.
Arora, N., & Aggarwal, A. (2018). The role of perceived benefits in formation of online shopping attitude among women shoppers in India. South Asian Journal of Business Studies, 7(1), 91-110. https://doi.org/10.1108/sajbs-04-2017-0048
Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Universiti Teknologi Mara Kelantan.
Beaumont, C. D., Berry, D., & Ricketts, J. (2022). Technology Has Empowered the Consumer, but Marketing Communications Need to Catch-Up: An Approach to Fast-Forward the Future. Businesses, 2(2), 246-272. http://dx.doi.org/10.3390/businesses2020017
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
Bigné‐Alcañiz, E., Ruiz‐Mafé, C., Aldás‐Manzano, J., & Sanz‐Blas, S. (2008). Influence of online shopping information dependency and innovativeness on internet shopping adoption. Online Information Review, 32(5), 648-667.
https://doi.org/10.1108/14684520810914025
Cao, Y., Ajjan, H., & Hong, P. (2018). Post-purchase shipping and customer service experiences in online shopping and their impact on customer satisfaction. Asia Pacific Journal of Marketing and Logistics, 30(2), 400-416.
https://doi.org/10.1108/apjml-04-2017-0071
Chen, Y. T., & Chou, T. Y. (2012). Exploring the continuance intentions of consumers for B2C online shopping. Online Information Review, 36(1), 104-125. https://doi.org/10.1108/14684521211209572
Chetioui, Y., Lebdaoui, H., & Chetioui, H. (2020). Factors influencing consumer attitudes toward online shopping: the mediating effect of trust. Euro Med Journal of Business, 16(4), 544-563. https://doi.org/10.1108/emjb-05-2020-0046
Chiu, C. M., Chang, C. C., Cheng, H. L., & Fang, Y. H. (2009). Determinants of customer repurchase intention in online shopping. Online Information Review, 33(4), 761-784. https://doi.org/10.1108/14684520910985710
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-339. https://doi.org/10.2307/249008
Dellaert, B., & Häubl, G. (2012). Searching in Choice Mode: Consumer Decision Processes in Product Search with Recommendations. Journal of Marketing Research, 49(2), 277-288. 10.2307/23142850.
Eggert, A. (2006). Intangibility and perceived risk in online environments. Journal of Marketing Management, 22(5-6), 553-572. https://doi.org/10.1362/026725706777978668
Fagih, K. (2013). Exploring the Influence of Perceived Risk and Internet Self-efficacy on Consumer Online Shopping Intentions: Perspective of Technology Acceptance Model. ResearchGate, 1(2), 1-10.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: an introduction to theory and research (1st ed.). Addison-Wesley.
Fornell, C. G., & 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/10.2307/3151312
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Hakan, H. (2006). Information and Self-Organization: A Macroscopic Approach to Complex Systems (1st ed.). Springer.
Han, F., & Li, B. (2020). Exploring the effect of an enhanced e-commerce institutional mechanism on online shopping intention in the context of e-commerce poverty alleviation. Information Technology & People, 34(1), 93-122.
https://doi.org/10.1108/itp-12-2018-0568
Hansen, T. (2006). Determinants of consumers' repeat online buying of groceries. The International Review of Retail, Distribution and Consumer Research, 16(1), 93-114. https://doi.org/10.1080/09593960500453617
Heijden, H. (2000). e-Tam: A revision of the technology acceptance model to explain website revisits. Business Administration and Econometrics.
Hibbert, P. (2012). Approaching Reflexivity Through Reflection: Issues for Critical Management Education. Journal of Management Education, 37(6), 803-827. https://doi.org/10.1177/1052562912467757
Hsu, H. C., Wang, C. L., Wang, M., Yang, N., Chen, Z., Sternglanz, R., & Xu, R. M. (2013). Structural basis for allosteric stimulation of Sir2 activity by Sir4 binding. Genes Dev, 27(1), 64-73. https://doi.org/10.1101/gad.208140.112
Ke, W. Y., & Wu, C. C. (2015). An online shopping behavior model integrating personality traits, perceived risk, and technology and acceptance. Social Behavior and Personality, 43(1), 85-98. https://doi.org/10.2224/sbp.2015.43.1.85
Kim, J., Dennis, C., & Forsythe, S. (2009). Adoption of sensory enabling technology for online apparel shopping. European Journal of Marketing, 43(9/10), 1101-1120. https://doi.org/10.1108/03090560910976384
Kline, C. E., Crowly, E. P., & Ewing, G. B. (2011). The effect of exercise training on obstructive sleep apnea and sleep quality: a randomized controlled trial. Randomized Controlled Trial, 34(12), 1631-1640. https://doi.org/10.5665/sleep.1422
Lee, M. K. O., & Turban, E. (2014). A Trust Model for Consumer Internet Shopping. International Journal of Electronic Commerce, 6(1), 75-91. https://doi.org/10.1080/10864415.2001.11044227
Li, Y. (2016). Empirical Study of Influential Factors of Online Customers’ Repurchase Intention. iBusiness, 8, 48-60.
Lin, K. Y., & Lu, H. P. (2011). Why People Use Social Networking Sites: An Empirical Study Integrating Network Externalities and Motivation Theory. Computers in Human Behavior, 27, 1152-1161. http://dx.doi.org/10.1016/j.chb.2010.12.009
Mohamed, N., Hussein, R., Zamzuri, N. H. A., & Haghshenas, H. (2014). Insights into individual's online shopping continuance intention. Industrial Management & Data Systems, 114(9), 1453-1476. https://doi.org/10.1108/imds-07-2014-0201
Monsuwe, T., Dellaert, B., & De Ruyter, k. (2004). What drives consumers to shop online? A literature Review. International Journal of Service Industry Management, 15(1), 102-121. 10.1108/09564230410523358.
Okasha, H. (2019). Risk Factors and Key Principles for Prevention of Surgical Site Infections. IntechOpen2019.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Pelaez, A., Chen, C. W., & Chen, Y. X. (2019). Effects of perceived risk on intention to purchase: a metaanalysis. Journal of Computer Information Systems, 59(1), 73-84. https://doi.org/10.1080/08874417.2017.1300514
Raman, P. (2019). Understanding female consumers’ intention to shop online. Asia Pacific Journal of Marketing and Logistics, 31(4), 1138-1160. https://doi.org/10.1108/apjml-10-2018-0396
Ruyter, K., Jong, A., & Lemmink, J. (2004). Antecedents and Consequences of the Service Climate in Boundary-Spanning Self-Managing Service Teams. Journal of Marketing, 68(2), 18-35. https://doi.org/10.1509/jmkg.68.2.18.27790
SEO Agency China. (2022, July 12). Full Guide for E-commerce live streaming in China. https://seoagencychina.com/full-guide-for-e-commerce-live-streaming-in-china/
Seock, Y., & Norton, M. (2007). Attitude toward internet web sites, online information search, and channel choices for purchasing. Journal of Fashion Marketing and Management, 11(4), 571-586. https://doi.org/10.1108/13612020710824616
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2005.02.010
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Nova.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.
https://doi.org/10.1287/mnsc.42.1.85
Thakur, P., & Kaur, A. (2019). Determinants of tier 2 Indian consumer’s online shopping attitude: A SEM approach. Asia Pacific Journal of Marketing and Logistics. 33(6), 1309-1338. https://doi.org/10.1108/apjml-11-2018-0494
Thakur, R., & Sriwastava, S. (2014). A study on the impact of consumer risk perception and innovativeness on online shopping in India. International Journal of Retail & Distribution Management, 43(2), 148-166.
Tong, X. (2010). A cross-national investigation of an extended technology acceptance model in the online shopping context. International Journal of Retail & Distribution Management, 38(10), 742-759. https://doi.org/10.1108/09590551011076524
Wang, Y., Lu, Z., Cao, P., Chu, J., Wang, H., & Wattenhofer, R. (2022). How Live Streaming Changes Shopping Decisions in E-commerce: A Study of Live Streaming Commerce. Computer Supported Cooperative Work (CSCW), 31, 701-729.
https://doi.org/10.1007/s10606-022-09439-2
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
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. http://dx.doi.org/10.3390/su142113921
Downloads
Published
How to Cite
Issue
Section
License
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
