Abstract
Background and Aim: M-commerce cannot be implemented until m-commerce apps become ubiquitous. The Technology Acceptance Model (TAM) uses stated intentions to predict whether a person would use a computer based on their attitudes, perceived utility, perceived ease of use, and other criteria. This study explores the level of the Technology acceptance model (TAM) of Meituan Application in Kunming, China, and suggests the guideline for the technology acceptance model (TAM) of Meituan Application in Kunming, China.
Materials and Methods: This research is a quantitative research method. Three hundred eighty-five customers of the Meituan Application in Kunming, China, will make up the research’s total sample size. The questionnaire was used as a tool to collect data. To create descriptions for the guidelines for the technology acceptance model (TAM) of the Meituan Application in Kunming, China, the research’s categorized findings and descriptive statistics will be used.
Results: All variables had a high level, according to the findings. This study investigates Meituan Application customers’ confidence and acceptance. Trust and danger affect the application’s credibility and adoption. Technology and client demographics lessen trust and risk. Kunming males know Meituan App risk and reliability better than women. Technology, popularity, third-party endorsements, and user satisfaction affect Meituan Application’s reputation.
Conclusion: This study examines Meituan Application users’ trust, risk, and reliability, as well as technology, popularity, endorsements, and user satisfaction. The Meituan Application requires financial institutions to integrate trust-building measures to enhance its usability and efficacy, as highlighted by a study.
Publisher
Dr. Ken Institute of Academic Development and Promotion
Reference48 articles.
1. AdChina. (2021). China E-Commerce Market in 2022 – All You Need To Know. Retrieved from: https://www.adchina.io/china-ecommerce-market/
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes. 50 (2), 179–211.
3. Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. New Jersey: Prentice-Hall.
4. Alagoz, S.M., & Hekimoglu, H. (2012) A Study on Tam: Analysis of Customer Attitudes in Online Food Ordering System. Procedia Social and Behavioral Sciences. 62, 1138–1143.
5. Arvidsson, N. (2014). Consumer Attitudes on Mobile Payment Services—Results from a Proof-of-Concept Test. International Journal of Bank Marketing, 32, 150-170.