Key Influencers of Attitude and Intention to Shop Online Through Live Broadcasting Platform Among Middle-Aged Consumer’s in Chengdu, China
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Purpose: This paper mainly discusses the significant influencing factors of middle-aged consumers’ attitudes and intentions toward online shopping live broadcasting platforms in Chengdu, China. The conceptual framework gives the causal relationship between variables, which include 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 analytical method to explore the factors that influence the attitudes and intentions of middle-aged consumers in Chengdu towards online shopping live streaming platforms. The authors distributed 30 questionnaires to some of the respondents who met the sample unit characteristics. After data collection, confirmatory factor analysis (CFA) was used to evaluate the convergent and discriminant validity. Then, the suitability of all hypotheses and models was tested using structural equation modeling (SEM). Results: The result indicates that attitude significantly impacts the intention to shop online, followed by the subjective norm. Perceived ease of use, perceived usefulness, and trust significantly impact attitude, with perceived ease of use being the most affected, followed by trust. Conclusions: It is recommended that online shopping live broadcasting platform operators pay attention to the shopping experience of middle-aged consumers, improve the platform's ease of use, increase consumer trust, and take further measures to attract consumers to the platform.
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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., & 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.
Aref, M. M., & Okasha, A. E. (2020). Evaluating the online shopping behaviour among Egyptian college-educated community. Review of Economics and Political Science, 5(1), 21-37. https://doi.org/10.1108/reps-10-2018-0013
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.
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
Brookfield, S. (2005). The Power of Critical Theory (1st ed.). Jossey-Bass.
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
Choi, S. K. A. S. M. (2012). Credibility cues in online shopping: An examination of corporate credibility, retailer reputation, and product review credibility. International Journal of Internet Marketing and Advertising, 7(3), 217 – 236. https://doi.org/10.1504/ijima.2012.047425
Cyr, D., Head, M., & Ivanov, A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Information & Management, 43(8), 950-963. https://doi.org/10.1016/j.im.2006.08.009
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
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
Forsythe, S. M. A. S. B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56, 867–875. https://doi.org/10.1016/s0148-2963(01)00273-9
Fu, J.-R., Lu, I. W., Chen, J. H. F., & Farn, C.-K. (2020). Investigating consumers’ online social shopping intention: An information processing perspective. International Journal of Information Management, 54(3), 102189 https://doi.org/10.1016/j.ijinfomgt.2020.102189
Ghose, A., & Bhatnagar, S. (2004). A latent class segmentation analysis of e-shoppers. Journal of Business Research, 57(7), 758–767. https://doi.org/10.1016/s0148-2963(02)00357-0
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall, Upper Saddle River.
Hakan, H. (2006). Information and Self-Organization: A Macroscopic Approach to Complex Systems (1st ed.). Springer, Berlin-Heidelberg, New York.
Han, B., Kim, M., & Lee, J. (2018). Exploring consumer attitudes and purchasing intentions of cross-border online shopping in Korea. Journal of Korea Trade, 22(2), 86–104. https://doi.org/10.1108/jkt-10-2017-0093
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. Serie Research Memoranda 0029, VU University Amsterdam, Faculty of Economics, 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, C. L., Chuan‐Chuan Lin, J., & Chiang, H. S. (2011). The effects of blogger recommendations on customers’ online shopping intentions. Internet Research, 23(1), 69-88. https://doi.org/10.1108/10662241311295782
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
Hsu, M.-H., Chuang, L.-W., & Hsu, C.-S. (2014). Understanding online shopping intention: the roles of four types of trust and their antecedents. Internet Research, 24(3), 332-352. https://doi.org/10.1108/IntR-01-2013-0007
Joshi, Y., & Rahman, Z. (2015). Factors Affecting Green Purchase Behaviour and Future Research Directions. International Strategic Management Review, 3(1-2), 128-143. https://doi.org/10.1016/j.ism.2015.04.001
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
Khare, A., & Rakesh, S. (2011). Antecedents of online shopping behaviors in India: An examination. Journal of Internet Commerce, 10(4), 227–244. https://doi.org/10.1080/15332861.2011.622691
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, R. B. (1998). Principles and practice of structural equation modeling (1st ed.). Guilford Press.
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
Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268.
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
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
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
Schepers, J., & Wetaels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
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
Shih, Y., & Fang, K. (2006). Effects of network quality attributes on customer adoption intentions of internet banking. Total Quality Management, 17(1), 61–77. https://doi.org/10.1080/14783360500249661
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge psychological tests and testing research. Nova.
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. https://doi.org/10.1108/ijrdm-06-2013-0128
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
Usakli, H. (2010). Early Childhood Education: The Case of Turkey. Contemporary Issues in Early Childhood, 11(2), 215-218. https://doi.org/10.2304/ciec.2010.11.2.215
Wang, J. H., Lakshman, K., & Malthouse, E. C. (2018). When reward convenience meets a mobile app: Increasing customer participation in a coalition loyalty program. Journal of the Association for Consumer Research, 3(3), 314–329. https://doi.org/10.1086/698331
Wen, I. (2009). Factors affecting the online travel buying decision: A review. International Journal of Contemporary Hospitality Management, 21(6), 752–765. https://doi.org/10.1108/09596110910975990
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