Consumer E-Commerce Adoption and Behavior: A Perspective of Technology Acceptance Model (TAM)

Ye Zihan; Chee Wei Ming

doi:10.56982/dream.v2i10.159

Authors

Ye Zihan City University Malaysia
Chee Wei Ming City University Malaysia

DOI:

https://doi.org/10.56982/dream.v2i10.159

Keywords:

TAM, perceived ease of use, convenience motivation, time saving orientation, security, privacy

Abstract

Today, most countries facing new virus that called COVID-19 which it gives various impact to many sectors and towards economy itself. One of the industries that get an attention was food and beverage industry that led to a new trend like online food and delivery in China. Due to pandemic, the government enforce physical social distances and movement control to break the chain. As an effect, most business and social activities were curtailed for the time being, and most food and beverage companies were encouraged to shift their business strategies to online food delivery services. This study was prepared to know whether the consumer behavior can influence especially towards online food delivery services. Other than that, to identify whether online food delivery services convenience to use by the consumer. The objectives of this research were to see if there was a connection between the model of independent variables and consumer behavior towards online food delivery services during the pandemic. The hypotheses in this study are focused on perceived ease of use, time savings orientation, convenience motivation, protection, and privacy, all of which can influence customers behavioral intentions, particularly during pandemic crisis. Meanwhile, the sampling method that will be in use in this research was the non-probability sampling technique, which was a simple randomly sampling consumer with 211 respondents who age 18-41and above that contributed to this research. The researcher was used students, employed, self-employed and unemployed to be their respondents. All the questionnaire was distributed via online by using google form. The data was collected by using questionnaire and quantitative data and then this research will use SPSS and Smart PLS to collect and combine the data to get the result.

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References

Aishah Zainol, S. N., Atiqah Azhar, S. N., Zulkefli, N. I., & Mohamed, A. H. (n.d.). Online Food Delivery, Behaviour Intention - What Can Be Influence? Seminar Project. https://uniklbehaviourinte.wixsite.com/ilhamsentosa1

Online Food Delivery, Behaviour Intention - What Can Be Influence? (n.d.). Google Form. https://forms.gle/iwuYLz18c5QpYB8y9

Alagoz, S. M., & Hekimoglu, H. (2012b). A Study on Tam: Analysis of Customer Attitudes in Online Food Ordering System. Procedia - Social and Behavioral Sciences, 62, 1138–1143. https://doi.org/10.1016/j.sbspro.2012.09.195 DOI: https://doi.org/10.1016/j.sbspro.2012.09.195

Alreck, P. L., DiBartolo, G. R., Diriker, M., Dover, H. F., Passyn, K. A., & Settle, R. B. (2009). Time Pressure, Time Saving and Online Shopping: Exploring A Contradiction. Journal of Applied Business Research (JABR), 25(5), 85–92. https://doi.org/10.19030/jabr.v25i5.1009 DOI: https://doi.org/10.19030/jabr.v25i5.1009

Bashir, D. R., Mehboob, I., & Bhatti, W. K. (2019). Effects of online shopping trends on consumer-buying behavior: An empirical study of Pakistan. Journal of Management and Research, 2(2), 1–24. https://doi.org/10.29145/jmr/22/0202001 DOI: https://doi.org/10.29145/jmr/22/0202001

Chen, H. S., Liang, C. H., Liao, S. Y., & Kuo, H. Y. (2020). Consumer Attitudes and Purchase Intentions toward Food Delivery Platform Services. Sustainability, 12(23), 10177. https://doi.org/10.3390/su122310177 DOI: https://doi.org/10.3390/su122310177

Chen, Y., & Barnes, S. (2007). Initial trust and online buyer behaviour. Industrial Management & Data Systems, 107(1), 21–36. https://doi.org/10.1108/02635570710719034 DOI: https://doi.org/10.1108/02635570710719034

Cheah, J. H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018b). Convergent validity assessment of formatively measured constructs in PLS-SEM. International Journal of Contemporary Hospitality Management, 30(11), 3192–3210. https://doi.org/10.1108/ijchm-10-2017-0649 DOI: https://doi.org/10.1108/IJCHM-10-2017-0649

Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194–201. https://doi.org/10.1016/j.im.2008.02.003 DOI: https://doi.org/10.1016/j.im.2008.02.003

Choi, J. C. (2020). User Familiarity and Satisfaction with Food Delivery Mobile Apps. SAGE Open, 10(4), 215824402097056. https://doi.org/10.1177/2158244020970563 DOI: https://doi.org/10.1177/2158244020970563

Cho, Y. C., & Sagynov, E. (2015). Exploring Factors That Affect Usefulness, Ease of Use, Trust, And Purchase Intention in the Online Environment. International Journal of Management & Information Systems (IJMIS), 19(1), 21. https://doi.org/10.19030/ijmis.v19i1.9086 DOI: https://doi.org/10.19030/ijmis.v19i1.9086

Choong, J. (2021, April 12). Covid-19 fourth wave in China a real possibility, warns Ismail Sabri, citing Health Ministry data. China | Malay Mail. https://www.malaymail.com/news/China/2021/04/12/covid-19-fourth-wave-in-China-a-real-possibility-warns-ismail-sabri-citi/1965933

Collier, J. E., & Kimes, S. E. (2012). Only If It Is Convenient. Journal of Service Research, 16(1), 39–51. https://doi.org/10.1177/1094670512458454 DOI: https://doi.org/10.1177/1094670512458454

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 DOI: https://doi.org/10.1287/mnsc.35.8.982

Elango, D., Dowpiset, K., & Chantawaranurak, J. (2018). A Study on Factors Impacting Consumers’ Intention to Use On-demand Food Delivery Applications in Bangkok, Thailand. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3321587 DOI: https://doi.org/10.2139/ssrn.3321587

Hair, J. F., Howard, M. C., & Nitzl, C. (2020b). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069 DOI: https://doi.org/10.1016/j.jbusres.2019.11.069

Hooi, R., & Tang Kin Leong, L.H. (2021). Intention to Use Online Food Delivery Service in China among University Students. Conference on Management, Business, Innovation, Education, and Social Science, 1 (1) ,60-73.

Industry Outlook and the New Norm for 2021. (2021, March). Chinan Plastics Manufacturers Association (MPMA). https://mpma.org.my/v4/wp-content/uploads/2021/03/Industry-Performance-and-New-Norm-FINAL.pdf

Jyotishman Das, Consumer Perception Towards „Online Food Ordering and Delivery Services‟: An Empirical Study, Journal of Management, 5(5), 2018, pp. 155–163. http://www.iaeme.com/JOM/issues.asp?JType=JOM&VType=5&IType=5

Katawetawaraks, C., & Wang, C. L. (2011). Online Shopper Behavior: Influences of Online Shopping Decision. Asian Journal of Business Research, 1(2), 66–74. https://doi.org/10.14707/ajbr.110012 DOI: https://doi.org/10.14707/ajbr.110012

Khandpur, N., Zatz, L. Y., Bleich, S. N., Taillie, L. S., Orr, J. A., Rimm, E. B., & Moran, A. J. (2020). Supermarkets in Cyberspace: A Conceptual Framework to Capture the Influence of Online Food Retail Environments on Consumer Behavior. International Journal of Environmental Research and Public Health, 17(22), 8639. https://doi.org/10.3390/ijerph17228639 DOI: https://doi.org/10.3390/ijerph17228639

Lau, T., & Ng, D. C. Y. (2019). Online Food Delivery Services: Making Food Delivery the New Normal. ResearchGate, 1(1), 62-77. Retrieved from https://www.researchgate.net/publication/334050513_Online_Food_Delivery_Services_Making_Food_Delivery_the_New_Normal

McCombes, S. (2021, February 25). An introduction to sampling methods. Scribbr. https://www.scribbr.com/methodology/sampling-methods/#:%7E:text=In%20non%2Dprobability%20sampling%2C%20the,snowball%20sampling%2C%20and%20quota%20sampling

Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906. https://doi.org/10.1016/s0148-2963(01)00276-4 DOI: https://doi.org/10.1016/S0148-2963(01)00276-4

Mpinganjira, M. (2015). Online Store Service Convenience, Customer Satisfaction and Behavioural Intentions: A Focus on Utilitarian Oriented Shoppers. Journal of Economics and Behavioral Studies, 7(1(J)), 36–49. https://doi.org/10.22610/jebs.v7i1(j).561 DOI: https://doi.org/10.22610/jebs.v7i1(J).561

Olorunniwo, F., Hsu, M. K., & Udo, G. J. (2006). Service quality, customer satisfaction, and behavioral intentions in the service factory. Journal of Services Marketing, 20(1), 59–72. https://doi.org/10.1108/08876040610646581 DOI: https://doi.org/10.1108/08876040610646581

Park, E. S., & Park, M. S. (2020). Factors of the Technology Acceptance Model for Construction IT. Applied Sciences, 10(22), 8299. https://doi.org/10.3390/app10228299 DOI: https://doi.org/10.3390/app10228299

Ramli, N., Ghani, F. A., Nawawi, W. N. W., & Majid, H. A. M. A. (2021). Intention to Use Online Food Ordering Services Among Universities Students During COVID-19 Pandemic. International Journal of Academic Research in Business and Social Sciences, 11(13), 394–405. DOI: https://doi.org/10.6007/IJARBSS/v11-i13/8556

Salmerón, R., García, C. B., & García, J. (2018). Variance Inflation Factor and Condition Number in multiple linear regression. Journal of Statistical Computation and Simulation, 88(12), 2365–2384. https://doi.org/10.1080/00949655.2018.1463376 DOI: https://doi.org/10.1080/00949655.2018.1463376

Schneider, W. A., & Tezza, R. (2021). Online shopping: antecedents of attitude, intention, and use. Journal of Management Science & Engineering Research, 3(2). https://doi.org/10.30564/jmser.v3i2.2613 DOI: https://doi.org/10.30564/jmser.v3i2.2613

Statista. (n.d.). E-services Report 2020 - Online Food Delivery. https://www.statista.com/outlook/dmo/eservices/online-food-delivery/China

Statista. (2021, April 7). Most used food delivery app orders China 2020. https://www.statista.com/statistics/1149404/China-favorite-food-delivery-apps/

Techiwebi.com. (n.d.). Why ordering food from online is more convenient|24 hour food delivery. Londis of Salem. Retrieved March 20, 2021, from https://www.londisofsalem.com/blog/why-ordering-food-from-online-is-more-convenient

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

WIDAGDO, B., & ROZ, K. (2021). Hedonic Shopping Motivation and Impulse Buying: The Effect of Website Quality on Customer Satisfaction. The Journal of Asian Finance, Economics and Business, 8(1), 395–405. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.395

Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35, 150–162. https://doi.org/10.1016/j.jretconser.2016.12.013 DOI: https://doi.org/10.1016/j.jretconser.2016.12.013