• The Journal of Industrial Distribution & Business
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• v.12 no.4
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• pp.27-34
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• 2021

Purpose: While previous studies mainly focus on one shopping expectancy in the context of e-commerce or m-commerce, this study examines the relationship between consumers' performance and effort expectancy and their shopping intentions in the omnichannel retail environment in which both online and offline shopping channels are utilized concurrently in a single shopping journey. Research design, data and methodology: This study measured consumers' performance expectancy, effort expectancy, attitudes, and intentions toward an omnichannel shopping service. A survey was developed using an online survey platform and distributed to U.S. consumers for a 3-week period and 470 usable responses were obtained. The Confirmatory Factor Analysis and Structural Equation Modeling were performed to test the reliability and validity of the measurement model and research model portraying the hypothesized relationships among constructs. Results: The results confirm that both performance and effort expectancy from shopping affected consumers' attitudes toward omnichannel shopping. The positive attitudes increased their omnichannel shopping intentions. Conclusions: Retailers should promote omnichannel strategies as effective shopping tools to improve consumers' shopping experiences and outcomes. This study suggests that retailers should implement omnichannel strategies that synchronize the retail channels they offer and promote the strategies as effective means to enhance customers' shopping outcomes and experiences.

• The Journal of Bigdata
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• v.9 no.1
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• pp.17-30
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• 2024

This study categorizes the factors that affect the intention to continue using ChatGPT into positive motivational factors (personalization, social influence) and negative motivational factors (privacy concern, perceived risk) and investigates whether they affect the intention to continue using ChatGPT through information trust, performance expectancy, and effort expectancy. To this end, a survey was conducted among 265 adults in their 20s and above who have used ChatGPT service. The results showed that personalization and social influence had a defining effect on information trust, performance expectancy, and effort expectancy. For privacy concern, we found a negative effect of wealth on performance expectations, but not on effort expectations. Perceived risk had a negative effect on performance expectancy and effort expectancy. In addition, information trust, performance expectancy, and effort expectancy have a defining effect on continuance intention. This study extends the scope of existing research that focuses on positive factors, deepens our the understanding of ChatGPT. It also provides useful suggestions for continued use of ChatGPT.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.454-468
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• 2015

The purpose of this study is to investigate the relationship between product attributes of smart device and user acceptance of technology. Moreover, the authors examined mediating effect of utilitarian and hedonic motivation between product attributes and user acceptance. Previous researches focused on user attitude and behavior intention of new technology based on Technology Acceptance Model(TAM). However, differently from previous researches, which are focused on user attitude like perceived usefulness and perceived ease of use, this research emphasized the relationship product attributes such as product functionality, design, and convenience and user motivation both utilitarian(effort expectancy and performance expectancy) and hedonic motivation(perceived enjoyment) according to UTAUT model. The results shows that 3 motivation variables(effort expectancy, performance expectancy, and perceived enjoyment) have positive impacts on user acceptance intention of smart device. product design has positive relationship to perceived enjoyment and product convenience has positive relationship to both effort and performance expectancy. Furthermore, 3 motivation variables play a full mediation between product attributes and user acceptance intention of new technology. However, product functionality has no relationship to both effort and performance expectancy. Research contribution, implications, and further research directions are discussed at the end of the paper.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.97-108
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• 2018

The objective of this study will investigate the approach to increase the intention to use Internet primary bank for those who do not use it at present. This study establishes a theoretical model that includes five independent variables - performance expectancy, effort expectancy, social influence, channel trust and trust toward bank - and one dependent variable - use intention -. The empirical results obtained in a sample of 145 university students are followings; The use intention is positively influenced by the performance expectancy, social influence and channel trust. The performance expectancy is the strongest predictor of the use intention. Effort expectancy and trust toward bank has no effect on intention to use Internet primary bank.

• The Journal of Information Systems
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• v.25 no.2
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• pp.27-50
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• 2016

Purpose The purpose of this study is to examine the effect of the acceptance intention towards healthcare wearable device in Korea. Specifically, this study attempted to identify the relationships among the acceptance intention towards healthcare wearable device, technical characteristics and user's personal characteristic based on UTAUT(Unified Theory of Acceptance and Use of Technology) model. Design/methodology/approach For this purpose of research, we conducted against consumers who use the wearable devices and we collected data from 219 wearable device users. The analyses were conducted using SPSS and AMOS19.0 which is powerful structural equation modeling (SEM) software. Findings The main findings are as followed; First, functional diversity, wearability, trend sensitivity and health perception are significantly related to performance expectancy, while visual aesthetic and enjoyment are not significantly related. Second, performance expectancy and effort expectancy are significantly related to acceptance intention towards healthcare wearable device. Third, effort expectancy is significantly related to performance expectancy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.185-198
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• 2012

This paper is to analyze consumer's acceptance phase of innovative IT product on the ground of dividing into product and application phase. In the acceptance phase of product determinants to consumer's satisfaction were analyzed and TAM(Technology Acceptance Model) were applied to the acceptance phase of application. It is analyzed that how well expected usefulness and perceived satisfaction to product have positively influence its performance and perceived satisfaction respectively in the acceptance phase of the product. In the acceptance phase of application, factors to consumers' satisfaction were analyzed to investigate its influence to the usefulness of application. Usefulness expectancy to the application and product were analyzed to figure out consumers'intention to use the product and effort and cost going into application use were also analyzed. Empirical study was implemented aimed at smart phone users. As a result, perceived usefulness expectancy to product have positively influence on perceived product performance and perceived product performance also affects perceived product satisfaction significantly. Although product satisfaction doesn't affect usefulness expectancy to the application, usefulness expectancy to the product and perceived product performance have positively influence on usefulness expectancy to the application significantly. And also usefulness expectancy to the both of application and product have positively influence on consumer's intention to use the product but consumer's effort and cost going into application doesn't affect to it, it's hold forth the possibility that consumer's effort affect consumer's intention to use the product.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.129-143
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• 2020

The purpose of this study is to explain users' wearable healthcare device adoption using performance expectancy, effort expectancy, facilitating condition, hedonic motivation and price value of UTAUT2, and to identify the causal relationship between intention to use wearable healthcare device and innovativeness. The research model proposed in this study is based on UTAUT2(Extended Unified Theory of Acceptance and Use of Technology). In specific, performance expectancy, effort expectancy, facilitating condition, hedonic motivation and price value of UTAUT2 and innovativeness are adopted in our research model. To validate the research model, we carry out the analysis of the survey data using Smart PLS 3.0 to test the hypotheses. According to the empirical analysis results, this study confirms that Innovativeness have significant effects on the performance expectancy, effort expectancy, Facilitating condition, Hedonic motivation, and price Value of wearable healthcare devices. It also finds that the performance expectancy, effort expectancy, Facilitating condition, hedonic motivation, and price value affects the intention to use wearable healthcare devices.

• The Journal of Information Systems
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• v.28 no.3
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• pp.159-202
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• 2019

Purpose The purpose of this study is to explain users' wearable healthcare device adoption using performance expectancy, effort expectancy, the hedonic motivation and price value of UTAUT2, and to identify the causal relationship between intention to use wearable healthcare device and innovation resistance formed by perceived risks. Design/methodology/approach The research model proposed in this study is based on UTAUT2(Extended Unified Theory of Acceptance and Use of Technology) and MIR(Model of Innovation Resistance). In specific, performance expectancy, effort expectancy, hedonic motivation and price value of UTAUT2 and innovation resistance formed by perceived risks of MIR are adopted in our research model. To validate the research model, we carry out the analysis of the survey data using Smart PLS 3.0 to test the hypotheses. Findings According to the empirical analysis results, this study confirms that the performance expectancy, effort expectancy, hedonic motivation, and price value have significant effects on the intention to use wearable healthcare devices. It also finds that perceived risk affects innovation resistance and in turn, innovation resistance affects the intention to use wearable healthcare devices.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.79-90
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• 2021

The purpose of this research is to identify factors affecting the acceptance and use of e-tax services among medium taxpayers in Phnom Penh, Cambodia. The researcher conducted the study based on a quantitative approach by using multi-stage sampling method, which selects a sample size by two or more stages. The first stage sampling was the stratified random sampling and the subsequent stage was purposive sampling. In this study, the stratified random sampling was first used, followed by purposive sampling. The data were collected from 450 medium taxpayers who experienced using e-tax services located in three tax branches in Phnom Penh. This study adapted the confirmatory factor analysis (CFA) and structural equation model (SEM) to analyze the model accuracy, reliability and influence of various variables. The primary result showed that behavioral intention has a significant effect on user behavior of e-tax services among medium taxpayers in Phnom Penh, Cambodia. Moreover, the results revealed that performance expectancy, effort expectancy, social influence, and anxiety have significant impact on behavioral intention. In addition, social influence has the strongest impact on behavioral intention, followed by anxiety, performance expectancy and effort expectancy. Conversely, facilitating conditions, trust in government, and trust in internet do not influence behavioral intention.

• Korean Journal of Organic Agriculture
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• v.28 no.3
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• pp.315-334
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• 2020

This study was conducted to analyze factors affecting acceptance of smart farm technology. Smart farm technology is rapidly being introduced to agriculture in accordance with the progress of the 4th Industrial Revolution, but research on this is still little. Therefore, in this study, based on the unified theory of acceptance and use of technology (UTAUT), a research model reflecting the characteristics of smart farm technology was constructed. To test this, empirical analysis was performed. A survey was conducted for students in smart farm technology education and adult male and female farmers who are currently planning to operate smart farms. Valid 204 sample were used for analysis. The hypothesis test was based on multiple regression analysis using SPSS 24 statistical package. For the mediating effect and moderating effect, Process Macro 3.4 based on the regression equation was used. The results of testing the hypothesis are as follows. First, in the causal hypothesis test, it was shown that performance expectancy, social influence and price value have a significant positive effect on the intention to use smart farm technology. On the other hand, effort expectancy, facilitating conditions were not tested for a significant influence on the use of smart farm technology. As a result of analyzing the mediating effect of trust, it was found that trust plays a mediating role between performance expectancy, effort expectancy, social influence, facilitating conditions, price value and intention to use smart farm technology. In particular, the effort expectancy has not been tested for a direct significant effect on intention to use smart farm technology, but it has been shown to have an impact through trust. Trust was found to be a full mediating between the effort expectancy and the intention to use the smart farm technology. The current IT level of prospective users has been shown to play a moderating role between performance expectancy, facilitating conditions and intention to use smart farm technology. In particular, the IT level was found to strengthen the relationship between performance expectancy and intention to use smart farm technology. Based on the results of these studies, academic and practical implications were suggested.