{"title":"The Online Technology Acceptance Model of Generation-Z People in Thailand during COVID-19 Crisis","authors":"A. Chayomchai","doi":"10.2478/mmcks-2020-0029","DOIUrl":null,"url":null,"abstract":"Abstract This research aims to study the acceptance of the online technology of Thai people in Generation-Z during the incidence of COVID-19 disease. During this period, Thai people must quarantine themselves at home or work from home to prevent the outbreak of this disease and must comply with the laws of the Thai government. The researchers are interested in the Generation Z population because they are highly interested in technology. Previous literature and research used multiple models of acceptance and use of technology such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology model. This study adapted various variables from many models in the past, including personal innovativeness, performance expectancy, effort expectancy, social influence, trust, and behavioral intention to use technology. The research uses questionnaires as a research tool. 457 usable questionnaires from online data collection were used for data analysis. Descriptive and inferential statistical analysis was performed. The researcher tested the hypothesis by assessment of the Partial Least Square-Structural Equation Model. Research findings found that the behavioral intention to use online technology during COVID-19 disease is predicted by three key factors including performance expectancy, effort expectancy, and trust. Effort expectancy positively influences performance expectancy. In addition, personal innovativeness and the trust of users directly significantly affect performance expectancy and effort expectancy. The researcher suggests that the management level can use the findings in the planning of the organization’s management or marketers can utilize the results for the marketing strategy of the organization.","PeriodicalId":44909,"journal":{"name":"Management & Marketing-Challenges for the Knowledge Society","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Management & Marketing-Challenges for the Knowledge Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/mmcks-2020-0029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 29
Abstract
Abstract This research aims to study the acceptance of the online technology of Thai people in Generation-Z during the incidence of COVID-19 disease. During this period, Thai people must quarantine themselves at home or work from home to prevent the outbreak of this disease and must comply with the laws of the Thai government. The researchers are interested in the Generation Z population because they are highly interested in technology. Previous literature and research used multiple models of acceptance and use of technology such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology model. This study adapted various variables from many models in the past, including personal innovativeness, performance expectancy, effort expectancy, social influence, trust, and behavioral intention to use technology. The research uses questionnaires as a research tool. 457 usable questionnaires from online data collection were used for data analysis. Descriptive and inferential statistical analysis was performed. The researcher tested the hypothesis by assessment of the Partial Least Square-Structural Equation Model. Research findings found that the behavioral intention to use online technology during COVID-19 disease is predicted by three key factors including performance expectancy, effort expectancy, and trust. Effort expectancy positively influences performance expectancy. In addition, personal innovativeness and the trust of users directly significantly affect performance expectancy and effort expectancy. The researcher suggests that the management level can use the findings in the planning of the organization’s management or marketers can utilize the results for the marketing strategy of the organization.