2022
DOI: 10.1016/j.techfore.2021.121451
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My video game console is so cool! A coolness theory-based model for intention to use video game consoles

Abstract: With the outbreak of COVID-19, the video game console market is thriving again. In this study, we attempted to explore users’ intention to use video game consoles by developing a causal model mainly based on coolness theory and the technology acceptance model. To better illustrate user experience for video game consoles, we added several concepts to the causal model, including hedonic motivation, system and service quality, perceived cost, and game variety. Through examining survey-based data from 360 Koreans,… Show more

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Cited by 32 publications
(24 citation statements)
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References 71 publications
(136 reference statements)
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“…Pertaining to the suggestions of prior studies ( Mariani et al, 2021 ; Chang and Lee, 2022 ; Nan et al, 2022 ), this research computed factor loading, average variance extracted (AVE), and composite reliability by performing confirmatory factor analysis with AMOS 23. Additionally, Cronbach’s alpha was computed with SPSS 27.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Pertaining to the suggestions of prior studies ( Mariani et al, 2021 ; Chang and Lee, 2022 ; Nan et al, 2022 ), this research computed factor loading, average variance extracted (AVE), and composite reliability by performing confirmatory factor analysis with AMOS 23. Additionally, Cronbach’s alpha was computed with SPSS 27.…”
Section: Resultsmentioning
confidence: 99%
“…Intention refers to the subjective probability regarding how an individual consciously acts (or does not act) in a particular way ( Warshaw and Davis, 1985 ; Shaw and Sergueeva, 2018 ). Thus, in this study, “intention to use FRP” was conceptualized as “ the subjective probability that individuals will consciously use or not use FRP .” Due to the fact that the intention to use is strongly and notably related to the actual usage ( Venkatesh et al, 2003 ), many user-oriented studies of particular technologies have employed intention to use as a dependent variable and explored the predictors that notably affected the intention to use ( Shaw and Sergueeva, 2018 ; Nan et al, 2022 ).…”
Section: Literature Reviewmentioning
confidence: 99%
“…As shown in Table 4, by calculating the structural and measurement model fit indices without requiring the model's parameters to be equal in both CSCL and FtF collaborative learning, we further verified the model fit. From earlier studies (Byrne, 2001;Nan et al, 2022), the model fits were acceptable.…”
Section: Model Fitmentioning
confidence: 71%
“…As a result, in this research, machine learning techniques showed high accuracy of roughly 90% in predicting user satisfaction, implying that machine learning approaches are effective methods for exploring user experience and satisfaction. In addition, considering that online reviews are easy to obtain and machine learning techniques can effectively process such a large amount of data, the approach used in this research saves money and time (see Kim et al, 2021) compared to survey methods that have been widely used to explore user experience (Ju and Zhang, 2020;Keikhosrokiani et al, 2020;Nan et al, 2022). Overall, the current research can contribute to extending the literature of user experience of healthcare services in the dimensions of big data and machine learning approaches.…”
Section: Discussionmentioning
confidence: 97%