CONSUMER BEHAVIOURS AND SATISFACTION OF BEIJING USERS UTILIZING CHINESE WEATHER APPLICATIONS IN CHINA
Keywords:
Consumer Behaviors, Customer Satisfaction, Chinese Weather ApplicationsAbstract
The objectives of this research are 1) to study the relationship between demographic characteristics and Beijing consumer behaviors toward Chinese weather applications, 2) to study the differences in demographic characteristics affecting satisfaction levels of Beijing users on Chinese weather applications, 3) to study the differences in Beijing user behaviors affecting satisfaction with the Chinese weather applications, and 4) to study the relationships between gender and personality types of Beijing users affecting the satisfaction with Chinese weather applications. The population is relatively unknown. The sample size of 385 individuals was established through the utilization of a questionnaire as an instrument for the collection of data along with methods of convenience sampling. The hypotheses were examined using the Chi-square test, the t-test, the one-way analysis of variance, and the two-way analysis of variance. The data were analyzed by frequency, percentage, mean, and standard deviation. The findings showed that 1) personal, gender, occupational, and personality factors are statistically related to how users use weather applications in China at the 0.05 level. 2) Different personal factors, including age and average monthly income, have a statistically significant effect on how satisfied people are with weather applications in China. 3) Users in China behave differently in terms of the number of times they use weather applications and the degree to which satisfied they are with them. It is statistically significant at the 0.05 level. and 4) there is a statistically significant relationship between gender and personality type and satisfaction with weather applications in China when it comes to price.
References
Acebron, L. B., & Dopico, D. C. (2000). The importance of intrinsic and extrinsic cues to expected and experienced quality: An empirical applications for beef. Food quality and preference, 11(3), 229-238.
Aldowah, H., Ghazal, S., Umar, I. N., & Muniandy, B. (2017, September). The impacts of demographic variables on technological and contextual challenges of e-learning implementation. Journal of Physics: Conference Series, 892, 1-12.
Burson, B., & Matthews, K. (1981). The Type A coronary prone behaviour pattern and reactions to uncontrollable stress: An analysis of performance strategies, affect and attributions during failure. Journal of Personality and Social Psychology, 40, 906-918.
Cochran, W.G. (1977). Sampling Techniques (3rd ed.). New York: John Wiley & Sons.
Kantuam, N., Sanmuang, K., & Puangthammarat, K. (2019). Marketing mix affecting consumers’ purchase of online products in Mueang district, Ratchaburi province. Social Science Journal of Prachachuen Research Networks, 1(2), 1-12.
Karaveg, C. (2021). Technology acceptance affecting purchasing behavior among online apparel consumers. Interdisciplinary Research Review, 16(4), 16–23.
Kotler, P., & Keller, K. L. (2016). Marketing management (15th global edition). Edinburgh: Pearson Education.
Mohajerani, P., & Miremadi, A. (2013). Exploring two main perspectives towards customer satisfaction in hotel industry: managers and customers. International Journal of Academic Research in Business and Social Sciences, 3(9), 245-272.
Noh, N. M., Hamzah, M., & Abdullah, N. (2016). The Influence of Demographic Factor on Personal Innovativeness towards Technology Acceptance. Malaysian Online Journal of Educational Technology, 4(1), 68-75.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17(4), 460-469.
Özbek, V., Alnıaçık, Ü., Koc, F., Akkılıç, M. E., & Kaş, E. (2014). The impact of personality on technology acceptance: A study on smartphone users. Procedia-Social and Behavioral Sciences, 150, 541-551.
Pantano, E., & Di Pietro, L. (2012). Understanding consumer acceptance of technology-based innovations in retailing. Journal of technology management & innovation, 7(4), 1-19.
Rerkpichai, C. (2022). Future vision of digital marketing for Metaverse. RICE Journal of Creative Entrepreneurship and Management, 3(1), 66-68.
Sombultawee, K., & Saisanit, M. (2022). The influence of technology adoption and the role of demegraphic as the moderators on Thai consumers intention to buy e-Books. Parichart Journal Thaksin University, 35(2), 129-147.
Sriratana, J. (2021). Consumer behavior through online food ordering applications during the virus covid-19 outbreak. Humanities and Social Science Research Promotion Network Journal, 4(3), 118-128.
Ugwu, K. E., Emerole, I., Duru, E. E., & Kekeocha, M. (2021). Demographic Factor, Adoption of Technology and Competitive Advantage in Nigeria. International Journal of Innovative Science, Engineering & Technology, 8(8), 191-212.
Worapongpat, N. (2022). Behavior of facebook application and Line affecting the purchasing decision to tourists’ products and services: A case study of Don Wai floating market after the covid-19 situation. Journal of Value Chain Management and Business Strategy, 1(2), 41-53.
Yang, K., Choi, J. G., & Chung, J. (2021). Extending the Technology Acceptance Model (TAM) to explore customer behavioural intention to use Self-Service Technologies (SSTs) in Chinese budget hotels. Global Business and Finance Review, 26(1), 79-94.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Social Science Journal of Prachachuen Research Network
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารวิชาการสังคมศาสตร์เครือข่ายวิจัยประชาชื่น
