An Analysis of Factors Affecting Undergraduates' Attitudes and Intention to Use Online Shopping in Zigong, China.

Zhang Yu

ผู้แต่ง

Zhang Yu Assumption University, Bangkok, Thailand

คำสำคัญ:

Keywords: Perceived Usefulness (PU), perceived Ease of Use (PEOU), perceived enjoyment (PE), convenience (CON), Trust (TRU), Attitude (ATT), Intent to Use (INT), Online Shopping.

บทคัดย่อ

Purpose: The core of this paper is to explore and evaluate how many factors affect college students' attitude towards online shopping and their intention to use it. To this end, we construct a conceptual framework that systematically reveals the complex causal relationship path between the pre-factors of perceived usefulness (PU), perceived ease of use (PEOU), perceived enjoyment (PE), convenience (CON), and trust (TRU) with attitude (ATT) and ultimately intent to use (INT). Research design, data and methodology: The researchers used quantitative research methods to conduct a questionnaire survey among students majoring in brewing Engineering, materials Science and engineering, and law, three majors with a relatively large enrollment scale at Sichuan University of Science and Engineering, in Zigong, China, and effectively collected 500 responses. The sampling strategy combines a multi-stage approach, including purposeful sampling, stratified random sampling, convenient and snowball sampling. Data analysis used confirmatory factor analysis (CFA) and structural equation modeling (SEM) to comprehensively evaluate model fit, reliability, and structural validity to ensure the rigor and reliability of the study results. Results: The research results show that in the three majors of brewing engineering, materials science and engineering, and law of Sichuan University of Science and Engineering in Zigong, China, Perceived usefulness (PU), perceived ease of use (PEOU), perceived enjoyment (PE), convenience (CON) and trust (TRU) have significant effects on college students' attitudes about online shopping. Among them, attitude, as a key intermediary variable, also has a significant positive impact on their intention to use online shopping (INT). Notably, convenience (CON) has the most significant impact on attitude (ATT), followed by perceived usefulness (PU) and perceived ease of use (PEOU). Conclusions: The statistical analysis results of this study provide solid and powerful data support for the six research hypotheses, and successfully achieve the established research objectives. Based on these findings, we suggest that in order to further increase the interest and participation of college students in online shopping, all stakeholders, including policy makers, online platform operators and commodity suppliers, should focus on and optimize the above key influencing factors. Especially the three key dimensions of convenience, perceived enjoyment and trust, effectively optimize the user experience in these aspects, effectively enhance the attractiveness of online shopping for college students, so as to improve their willingness to use.

เอกสารอ้างอิง

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Alalwan, A. A. (2018). Investigating the influence of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65–77. https://doi.org/10.1016/j.ijinfomgt.2018.06.001

Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707–733. https://doi.org/10.1108/IntR-05-2014-0146

Ali, M., Raza, S. A., Puah, C. H., & Karim, M. Z. A. (2017). Islamic home financing in Pakistan: A SEM-based approach using modified TPB model. Housing Studies, 32(8), 1156–1177. https://doi.org/10.1080/02673037.2017.1291910

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Adoption of management information systems in context of Yemeni organizations: A structural equation modeling approach. Mediterranean Journal of Social Sciences, 6(1), 1–10. https://doi.org/10.5901/mjss.2015.v6n1p120

Amin, H., Ghazali, M., & Supinah, R. (2010). Determinants of Qardhol Hassan financing acceptance among Malaysian bank customers: An empirical analysis. International Journal of Business and Society, 11(1), 1–16.

Anesbury, Z., Nenycz-Thiel, M., Dawes, J., & Kennedy, R. (2016). How do shoppers behave online? An observational study of online grocery shopping. Journal of Consumer Behavior, 15(3), 261–270. https://doi.org/10.1002/cb.1566

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). Structural equation modeling: Applications in research. International Journal of Marketing and Business Research, 4(1), 1–10.

Azam, A., Fu, Q., Abbas, A. S., & Abdullah, I. M. (2013). Structural equation modeling (SEM) based trust analysis of Muslim consumers in the collective religion affiliation model in e-commerce. Journal of Islamic Marketing, 4(2), 134–149. https://doi.org/10.1108/17590831311329325

Aziz, I. A., & Khan, F. U. (2022). Utilitarian, hedonic, and self-esteem motives in online shopping. Spanish Journal of Marketing - ESIC, 26(2), 231–246. https://doi.org/10.1108/SJME-06-2021-0113

Barney, J., & Hansen, M. (1994). Trustworthiness as a source of competitive advantage. Strategic Management Journal, 15(1), 175–190. https://doi.org/10.1002/smj.4250150912

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

Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and Internet shopping behavior. Communications of the ACM, 43(11), 98–105. https://doi.org/10.1145/353360.353371

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921

Braga, J. N., & Jacinto, S. (2022). Effortless online shopping? How online shopping contexts prime heuristic processing. Journal of Consumer Behavior, 21(4), 743–755. https://doi.org/10.1002/cb.2032

Bugshan, H., & Attar, R. W. (2020). Social commerce information sharing and their influence on consumers. Technological Forecasting and Social Change, 153, 119875. https://doi.org/10.1016/j.techfore.2020.119875

Carter, M., Wright, R., Thatcher, J. B., & Klein, R. (2014). Understanding online customers’ ties to merchants: The moderating influence of trust on the relationship between switching costs and e-loyalty. European Journal of Information Systems, 23(2), 185–204. https://doi.org/10.1057/ejis.2013.15

Chiang, K. P., & Dholakia, R. R. (2003). Factors driving consumer intention to shop online: An empirical investigation. Journal of Consumer Psychology, 13(1–2), 177–183. https://doi.org/10.1207/S15327663JCP13-1&2_16

Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 61, 305–312. https://doi.org/10.1016/j.chb.2016.03.037

Dannenberg, P., Fuchs, M., Riedler, T., & Wiedemann, C. (2020). Digital transition by COVID-19 pandemic? The German food online retail. Tijdschrift Voor Economische En Sociale Geografie, 111(3), 543–560. https://doi.org/10.1111/tesg.12453

Daragmeh, A., Lentner, C., & Sagi, J. (2021). FinTech payments in the era of COVID-19: Factors influencing behavioral intentions of ‘Generation X’ in Hungary to use mobile payment. Journal of Behavioral and Experimental Finance, 32, 100574. https://doi.org/10.1016/j.jbef.2021.100574

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral influences. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022

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. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

Dharmesti, M., Dharmesti, T. R. S., Kuhne, S., & Thaichon, P. (2019). Understanding online shopping behaviors and purchase intentions among millennials. Young Consumers, 22(1), 152–167. https://doi.org/10.1108/YC-12-2018-0922

Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17(1), 4–11. https://doi.org/10.1057/palgrave.ejis.3000726

Dikko, A. G. (2016). Using coefficient alpha to determine the internal consistency of scales: Some considerations. Journal of Social and Behavioral Sciences, 217, 20–25. https://doi.org/10.1016/j.sbspro.2016.02.003

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

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley Publishing Co.

Fornell, C., & 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/10.1177/002224378101800104

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

Gerrard, P., & Cunningham, J. (2003). The diffusion of Internet banking among Singapore consumers. International Journal of Bank Marketing, 21(1), 16–28. https://doi.org/10.1108/02652320310457776

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Prentice Hall International.

Hajiheydari, N., & Ashkani, M. (2018). Mobile application user behavior in the developing countries: A survey in Iran. Information Systems, 77, 22–33. https://doi.org/10.1016/j.is.2018.05.003

Harris, P., Riley, F. D., Riley, D., & Hand, C. (2017). Online and store patronage: A typology of grocery shoppers. International Journal of Retail and Distribution Management, 45(4), 419–445. https://doi.org/10.1108/IJRDM-07-2016-0123

Hernández, B., Jiménez, J., & José Martín, M. (2009). Adoption vs acceptance of e-commerce: Two different decisions. European Journal of Marketing, 43(9–10), 1232–1245. https://doi.org/10.1108/03090560910976465

Huang, H. S., Chiou, C. C., Chiang, H. K., Lai, S. H., Huang, C. Y., & Chou, Y. Y. (2012). Effects of multidimensional concept maps on fourth graders’ learning in web-based computer course. Computers and Education, 58(3), 863–873. https://doi.org/10.1016/j.compedu.2011.10.019

Hua, W., Liu, Y., Zhang, Z., Li, M., & Yu, X. (2024). Exploring users’ adoption intention of virtual try-on apps: How users’ individual characteristics affect post-use feelings. Asia Pacific Journal of Marketing and Logistics. Advance online publication. https://doi.org/10.1108/APJML-09-2023-0920

Hung, S. Y., Chen, C. C., & Huang, N. H. (2014). An integrative approach to understanding customer satisfaction with e-service of online stores. Journal of Electronic Commerce Research, 15(1), 40–57.

Husin, M. M., & Rahman, A. (2016). Do Muslims intend to participate in Islamic insurance? Analysis from theory of planned behavior. Journal of Islamic Accounting and Business Research, 7(1), 42–58. https://doi.org/10.1108/JIABR-05-2014-0012

Hwang, J., & Kim, J. J. (2021). Expected benefits with using drone food delivery services: Its influences on attitude and behavioral intentions. Journal of Hospitality and Tourism Technology, 12(3), 593–606. https://doi.org/10.1108/JHTT-05-2020-0123

Ibrahim, M. A., Fisol, W. N. M., & Haji-Othman, Y. (2017). Customer intention on Islamic home financing products: An application of theory of planned behavior (TPB). Mediterranean Journal of Social Sciences, 8(2), 77–86. https://doi.org/10.1515/mjss-2017-0008

Indarsin, T., & Ali, H. (2017). Attitude toward using m-commerce: The analysis of perceived usefulness, perceived ease of use, and perceived trust: Case study in Ikens Wholesale Trade, Jakarta – Indonesia. Saudi Journal of Business and Management Studies, 2(11), 995–1007. https://doi.org/10.21276/sjbms.2017.2.11.7

Ivanov, S., & Webster, C. (2018). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies – A cost-benefit analysis. In V. Marinov, M. Vodenska, M. Assenova, & E. Dogramadjieva (Eds.), Traditions and innovations in contemporary tourism (pp. 190–203). Cambridge Scholars Publishing.

Jiang, Q., Gu, C., Feng, Y., Wei, W., & Tsai, W. C. (2023). Study on the continuance intention in using virtual shoe-try-on function in mobile online shopping. Kybernetes, 52(10), 4551–4575. https://doi.org/10.1108/K-12-2021-1346

Kaur, G., & Khanam Quareshi, T. (2015). Factors obstructing intentions to trust and purchase products online. Asia Pacific Journal of Marketing and Logistics, 27(5), 758–783. https://doi.org/10.1108/APJML-10-2014-0156

Korvenmaa, P. (2009). The growth of an online social networking service: Conception of substantial elements (Master’s thesis). University of Technology, Helsinki, Finland.

Lavuri, R., Jindal, A., & Akram, U. (2022). How perceived utilitarian and hedonic value influence online impulse shopping in India? Moderating role of perceived trust and perceived risk. International Journal of Quality and Service Sciences, 14(4), 615–634. https://doi.org/10.1108/IJQSS-11-2021-0169

Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006

Maghrabi, T., & Dennis, C. (2011). What drives consumers’ continuance intention to e-shopping? Conceptual framework and managerial implications in the case of Saudi Arabia. International Journal of Retail & Distribution Management, 39(12), 899–926. https://doi.org/10.1108/09590551111183308

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792

Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64. https://doi.org/10.1509/jmkg.64.3.50.18011

Mohiuddin, A. K. (2020). A pandemic review of Covid-19 situation in Bangladesh. The American Journal of Medical Sciences and Pharmaceutical Research, 2(5), 38–50. https://doi.org/10.37547/TAJMSPR/Volume02Issue05-06

Mondal, S., & Hasan, A. T. (2023). Online grocery shopping intentions in the post COVID-19 context: A case of millennial generations in Bangladesh. South Asian Journal of Marketing. Advance online publication. https://doi.org/10.1108/SAJM-01-2023-0001

Monsuwe, T. P., Dellaert, B. G. C., & de Ruyter, K. R. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102–121. https://doi.org/10.1108/09564230410523358

Mummalaneni, V., & Meng, J. (2009). An exploratory study of young Chinese customers’ online shopping behaviors and service quality perceptions. Young Consumers, 10(2), 157–169. https://doi.org/10.1108/17473610910964654

Nuryyev, G., Wang, Y. P., Achyldurdyyeva, J., Jaw, B. S., Yeh, Y. S., Lin, H. T., & Wu, L. F. (2020). Blockchain technology adoption behavior and sustainability of the business in tourism and hospitality SMEs: An empirical study. Sustainability, 12(3), 1–21. https://doi.org/10.3390/su12031256

Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-Qual: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156

Pedroso, C. B., Silva, S. L., & Tate, W. L. (2016). Sales and operations planning (S&OP): Insights from a multi-case study of Brazilian organizations. International Journal of Production Economics, 182, 213–229. https://doi.org/10.1016/j.ijpe.2016.08.035

Perumal, S., Qing, Y., & Jaganathan, M. (2022). Factors influencing attitudes and intentions towards smart retail technology. International Journal of Data and Network Science, 6(2), 595–602. https://doi.org/10.5267/j.ijdns.2022.1.002

Ponte, E. B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tourism Management, 47, 286–302. https://doi.org/10.1016/j.tourman.2014.10.009

Rahmiati, R., & Yuannita, I. I. (2019). The influence of trust, perceived usefulness, perceived ease of use, and attitude on purchase intention. Jurnal Kajian Manajemen Bisnis, 8(1). https://doi.org/10.24036/jkmb.10884800

Raman, P. (2019). Understanding female consumers’ intention to shop online: The role of trust, convenience and customer service. Asia Pacific Journal of Marketing and Logistics, 31(4), 1138–1160. https://doi.org/10.1108/APJML-10-2018-0396

Ray, A., Dhir, A., Bala, P. K., & Kaur, P. (2019). Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. Journal of Retailing and Consumer Services, 51, 221–230. https://doi.org/10.1016/j.jretconser.2019.05.025

Rese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2017). How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions. Technological Forecasting and Social Change, 124, 306–319. https://doi.org/10.1016/j.techfore.2016.10.010

Rodgers, S., & Harris, M. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(3), 322–330. https://doi.org/10.1017/S0021849903030338

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Rosenberg, M. J., & Hovland, C. L. (1960). Cognitive, affective, and behavioral components of attitudes. In C. I. Hovland & M. J. Rosenberg (Eds.), Attitude organisation and change: An analysis of consistency among attitude components (pp. 1–14). Yale University Press.

Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286–301. https://doi.org/10.1016/j.ijinfomgt.2019.08.008

Seckler, M., Heinz, S., Forde, S., Tuch, A. N., & Opwis, K. (2015). Trust and distrust on the web: User experiences and website characteristics. Computers in Human Behavior, 45, 39–50. https://doi.org/10.1016/j.chb.2014.11.064

Shanmugam, A., Savarimuthu, M. T., & Wen, T. C. (2014). Factors affecting Malaysian behavioral intention to use mobile banking with mediating effects of attitude. Academic Research International, 5(2), 236–253.

Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935–943. https://doi.org/10.1016/j.jbusres.2003.10.007

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant validity. Journal of Behavior Therapy and Experimental Psychiatry, 38(2), 239–256. https://doi.org/10.1016/j.jbtep.2006.10.001

Sumi, R. S., & Ahmed, M. (2022). Investigating young consumers’ online buying behavior in COVID-19 pandemic: Perspective of Bangladesh. IIM Ranchi Journal of Management Studies, 1(2), 108–123. https://doi.org/10.1108/IRJMS-09-2021-0127

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144

Teo, T. S. H. (2001). Demographic and motivation variables associated with Internet usage activities. Internet Research, 11(2), 125–137. https://doi.org/10.1108/10662240110695089

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

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. https://doi.org/10.1287/mnsc.46.2.186.11926

Verhoef, P. C., Neslin, S. A., & Vroomen, B. (2007). Multichannel customer management: Understanding the research–shopper phenomenon. International Journal of Research in Marketing, 24(2), 129–148. https://doi.org/10.1016/j.ijresmar.2006.11.002

Vijayasarathy, L. R. (2004). Predicting consumer intentions to use online shopping: The case for an augmented technology acceptance model. Information & Management, 41(6), 747–762. https://doi.org/10.1016/j.im.2003.08.004

William, H. B., Emil, M., & Shailesh, R. (2022). A meta-analytic examination of the antecedents explaining the intention to use fintech. Industrial Management & Data Systems, 123(3), 886–909. https://doi.org/10.1108/IMDS-03-2021-0162

Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers and Education, 68, 570–585. https://doi.org/10.1016/j.compedu.2013.02.014

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information & Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002

Xu, X., & Jackson, J. E. (2019). Examining customer channel selection intention in the omni-channel retail environment. International Journal of Production Economics, 208, 434–445. https://doi.org/10.1016/j.ijpe.2018.12.009

Yang, F., Tang, J., Men, J., & Zheng, X. (2021). Consumer perceived value and impulse buying behavior on mobile commerce: The moderating effect of social influence. Journal of Retailing and Consumer Services, 63, 102683. https://doi.org/10.1016/j.jretconser.2021.102683

Zhang, H., Jabutay, F., & Gao, Q. (2018). E-recruitment adoption among Chinese job-seekers. Kasem Bundit Journal, 19(June), 261–272.

Zhao, Y., Zhao, X., & Liu, Y. (2023). Exploring the influence of online and offline channel advantages on brand relationship performance: The mediating role of consumer perceived value. Behavioral Sciences, 13(1), 16. https://doi.org/10.3390/bs13010016