This study utilized the unified theory of acceptance and use of technology 2 (UTAUT2) to examine usage intentions associated with virtual fitting services. Six independent variables were examined: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit. The study collected responses from 445 participants who had utilized virtual fitting services. Regarding factors related to usage intentions associated with these services, performance expectancy and social influence were found to significantly influence the usage intentions associated with photo-based virtual fitting services. Furthermore, performance expectancy, social influence, and habit significantly influenced the usage intention of avatar-based virtual fitting services. This suggests that higher levels of performance expectancy and social influence positively impact the usage intentions associated with both types of virtual fitting services, while habit influences only avatar-based virtual fitting services. Moreover, the findings confirm that effort expectancy, facilitating conditions, and hedonic motivation from UTAUT2 do not significantly influence usage intentions associated with virtual fitting services. By analyzing factors influencing potential customers’ virtual fitting service usage intentions, this study can suggest effective strategies to increase usage intentions for companies providing virtual fitting services. Additionally, these findings can be utilized in the formulation of virtual fitting service marketing strategies.

The purpose of this study is to suggest a plan to improve the level of acceptance of related technologies and the transition to smart factories of small and medium-sized manufacturing enterprises by using ‘technology readiness’ and ‘integrated technology acceptance model’. To this end, the research hypothesis was verified by collecting questionnaire data from 130 small and medium- sized manufacturing companies in Korea and conducting path analysis. First, optimism affects performance expectations, social influence, and facilitation conditions, innovation affects performance expectations, effort expectations, and social influence, discomfort affects performance expectations, social influence, and facilitation conditions, and anxiety affects effort expectations, social influence and facilitation conditions. has been proven to affect Finally, performance expectations, effort expectations, social influence, and facilitation conditions were verified to have a significant positive effect on the intention to accept technology.

인공지능 기반의 가상비서가 보편화 되면서 음성쇼핑 시장이 확대될 것으로 전망된다. 이 연구에서는 통합기술수용이론(UTAUT)을 활용, 소비자의 음성쇼핑 이용의도를 결정하는 요인을 확인한다. 온라인 쇼핑 주 소비층인 20~40대의 설문조사 데이터를 토대로 분석한 이 연구에서는 UTAUT에서 제시하는 변수 중 성과기대와 노력기대에 쇼핑의 속성을 고려하여 유희기대를 추가하여 음성쇼핑 이용의도에 영향을 미치는 변수로 설정했다. 또 한 이러한 변수에 영향을 줄 것으로 추론되는 4가지 음성비서 속성 즉, 응대정확성, 호환성, 사회적 실재감, 안전성을 포함해 음성쇼핑 이용에 관한 독자적 연구모형을 구축했다. 분석 결과, 성과기대, 노력기대, 유희기대 등 확장된 통합수용모델 변수들은 음성쇼핑의 사용의도에 긍정적 영향을 주는 것으로 확인됐다. 음성쇼핑 속성과 관련해서는, 호환성이 성과기대와 노력기대, 유희기대 모두 긍정적인 영향을 미쳤고, 사회적 실재감은 유희기대에 긍정적 영향을 미쳤다. 안전성은 노력기대와 유희기대에 긍정적 영향을 주는 것으로 확인됐다. 한편, 응대정확성은 성과기대, 노력기대, 유희기대에 모두 유의하지 않은 것으로 나타났다. 이 연구는 음성쇼핑이라는 새로운 방식에 대한 수용 결정요인을 밝혀내어, 일반 전자상거래 업체들과 가상비서 기반 플랫폼 기업이 시장에서 지배력을 창출하기 위해 고려해야 할 혁신의 요인을 제시한다는 점에서 의의를 지닌다.

As advances in digital technologies (e.g., Apps, QR code, RFID tag) is rapidly changing market environment, marketing researches have extended technology acceptance models in different contexts. Since a supermarket chain, “Home Plus,” in Korea, had opened the first QR code virtual store at the subway platform in 2011, many firms attempt to use QR code technology convergence into their retail business. According to a survey report (Embrian, 2012), QR codes are highly familiar with consumers in Korea, and more than 70% mobile phone users have ever scanned QR codes. Due to ubiquitous nature of QR codes, it is obviously implied that marketers would capitalize the QR code technology on their retail business. Yet, it is skeptical for individuals to adopt QR code virtual store because it is in an infant stage in the retail store context. Therefore, marketers need to predict acceptance and use of QR code virtual store from a consumer perspective. Based on UTAUT (Unified Theory of Acceptance and Use of Technology) suggested by Venkatesh et al. (2003; 2012), this study formulated consumer acceptance model with four core determinants of intentions in the context of QR code virtual stores. Specific objectives are (a) identify perceived value in the context of QR code virtual store; (b) test the changes in consumer perceived value by experience over time; and (c) to predict UTAUT model for consumer acceptance to use QR code virtual store across different time points (i.e., from the time of their initial introduction to stages of experience). A longitudinal field study was conducted at the selected QR code virtual stores among individuals using smart phones. A self-administered questionnaire was developed based on literatures the context of consumer use of QR code virtual store. The multi-item scales of perceived value (i.e., performance expectancy, effort expectance, social influence, and facilitating condition) were adapted from Venkatesh et al.’s (2003) UTAUT constructs. Four items of behavioral intention to use QR code virtual store was developed. All items were measured on 5 points rating scale. Sample in this study was obtained from volunteers who participated in three different points in time: initial experience (T1), one month after first experience (T2) and three months after second experience (T3). Participants were required to visit to an assigned QR code store and do shopping task per each time. Then, they were asked to answer the questions on a survey, and there were 123 valid respondents to the first stage of the field experiment. As removed the respondents who did not participate in T2 or T3 point, a total of 309 pooled data across time (103 each time) were analyzed. The sample represents more females (n=66, 68%) than males (n=31, 32%), aging from 19 to 29 years (mean=21.58 years). Preliminarily, using the data pooled across times, factor analysis was conducted to identify underlying constructs for all research variables. As expected, a factor analysis of perceived value constructs revealed four factors: social influence (5 items), performance expectancy (5 items), effort expectancy (4 items), and facilitating condition (3 items). All factor loadings were ranged from .63 to .86, and all internal consistency reliabilities were greater than .80. Also, a unidimensional behavioral intention (4 items) confirmed to be valid (factor loadings=.88 to .93) and reliable (α=.94). Therefore, it is deemed that content validity and reliability were within acceptable ranges for further analysis. Of the four perceived value constructs, there were significant differences in the effort expectancy (F=7.10, p<.01) and facilitating condition (F=8.22, p<.001) across times of experience. Specifically, the perceive effort expectancy tended to be increased over time, indicating a difference of means between T1 and T2 at the significant level of .01 as a result of Duncan’s post-hoc test. With respect to facilitating condition, the mean tended to be increased over time, but post hoc test indicated a significant difference between T1 and T2. The perceived effort expectancy and facilitating condition tended to be changed in positive from T2 to T3 point, which were not significantly different. Therefore, the finding implies that the second stage of experience (i.e., one month after first experience) is a critical point for consumers to increase perceived value of QR code virtual store as a new retail technology. Otherwise, the other factors, such as social influence, performance expectancy and behavioral intention were not significantly increased during the three times of experience. To predict UTAUT model for consumer acceptance to use QR code virtual store, behavioral intention was served as dependent variable and the four factors of perceived value were served as independent variables in regression analysis. In the initial experience (T1), behavioral intention was significantly predicted by social influence (β=.39, t=4.42, p<.001) and performance expectancy (β=.46, t=4.44, p<.001), which explained for 57% of total variance (F=32.08, p<.001, adjusted R²=.57). In the second experience (T2), behavioral intention was significantly predicted by three factors, such as social influence (β=.43, t=5.91, p<.001), performance expectancy (β=.35, t=4.10, p<.001) and facilitating condition (β=.15, t=2.32, p<.05), accounted for 68 % of total variance (F=52.35, p<.001, adjusted R²=.68). In the three month after second experience (T3), behavioral intention was significantly predicted by social experience (β=.36, t=3.33, p<.01) and performance expectancy (β=.33, t=3.33, p<.001), accounted for 52 % of total variance (F=27.65, p<.001, adjusted R²=.52). This study supports the UTAUT model for consumer acceptance of QR code virtual store over time from a theoretical perspective. It should be noted that social influence and performance expectancy appears to be a determinant of behavioral intention to use in most situations. The effect of facilitating condition on behavioral intention was only significant when experienced in second stage. In addition, future study needs to expend technology acceptance model of virtual store in conjunction with digital technological attributes across different product categories.

본 연구에서는 Venkatesh 등(2003)이 제안한 UTAUT모형에서 간과된 플로우(시간적해리, 쾌락적해리)와 마음챙 김(탈중심, 비판단적 수용, 주의집중/현실자각)이라는 개념을 추가하여 보다 완전한 온라인게임의 피로도시스템과 관련된 모형을 검정하고자 하였다. 실증분석결과 첫째, 본 연구에서 설정한 변수들(성과기대, 예상노력, 시간적해 리, 쾌락적해리, 탈중심, 비판단적 수용, 주의집중/현실자각, 사회적 영향, 촉진조건) 중 '예상노력', '쾌락적해리', ' 주의집중/현실자각', '탈중심', '사회적영향'이 사용의도에 유의한 영향을 미치는 것으로 나타났으며 '주의집중/현실 자각', '탈중심', '사회적영향', '촉진조건'이 사용행동에 영향을 미치는 것으로 나타났다. 이러한 결과를 바탕으로 본 연구는 UTAUT모형과 같이 하나의 효과적인 수용모형을 통해 전체 정보기술 및 서비스의 수용을 설명하려는 모형을 제시하기 보다는 서비스의 유형에 따라 각기 다른 수용모형을 제시하고 검증하는 것이 필요할 것이라는 시사점을 제시하였다.

The purpose of this study was to examine the intention of consumer acceptance of technology in agricultural production by applying the unified theory of acceptance and use of technology (UTAUT) to smart farm. In particular, this study analyzed the intention to accept the technology of agricultural students, farmers, start-up farmers, returning farmers, and returnees in the general manufacturing industry and high-tech industries, and in agricultural sectors corresponding to primary industries. The results showed that performance expectancy, social influence, facilitating conditions, IT development level, and reliability had a significant influence on the intention to use smart farm technology. However, effort expectancy and price value were rejected because no significant impact on use intention was tested. In addition, the influences of the variables showing their influence were reliability (β=.569) > IT development level (β=.252) > social influence (β=.235) > performance expectancy (β=.182) > facilitating conditions (β=.134).