Acceptance Intention Research Articles

The Influence of Anthropomorphism and Presence on the Acceptance Intention of Makeup Image Optimization Recommendation Systems Utilizing Deep Learning Technology: Focusing on the Mediating Effect of Cognitive Absorption

This study was conducted to understand the relationship between HCI (Human-Computer Interaction) factors, user cognitive absorption, and acceptance intention in makeup image optimization recommendation systems. A preliminary survey and main survey were carried out targeting adults in their twenties, and various statistical methods including frequency analysis, exploratory and confirmatory factor analysis, and structural equation modeling were used to analyze the data using SPSS and AMOS programs. The results of the study confirmed that the survey items consistently and reliably measured each variable through reliability and validity analysis, and the model’s high fit was proven through principal component analysis and confirmatory factor analysis. Correlation analysis showed significant positive relationships between all variables, and most paths in hypothesis testing through structural equation modeling were statistically significant. In particular, a significant partial mediating effect was found in the path where anthropomorphism affects acceptance intention mediated by curiosity. This means that HCI factors such as anthropomorphism and presence positively influence the acceptance intention of the recommendation system through various aspects of cognitive absorption, such as user engagement, temporal dissociation, curiosity, and pleasure. These results provide important implications for user-centered service design and improvement, suggesting that it is possible to move towards increasing user satisfaction and loyalty by accurately reflecting individual user needs and preferences. Additionally, the findings promote a deeper understanding of technological innovation in the beauty industry and user experience design, laying the foundation for effective user-centered service development.

UTAUT2 기반 인공지능 기술이 실제 시스템 사용을 통한 사용자 만족도에 미치는 영향

Artificial Intelligence (AI) is a computer system that performs perception, reasoning, learning, and language abilities similar to humans, making it a core technology of the Fourth Industrial Revolution. Particularly, generative AI, which creates new content such as text, images, and music through large-scale data learning, has garnered significant attention. This technology utilizes artificial neural networks and machine learning to understand user intentions, learn from data, and generate various types of content. AI is defined as a technology that implements human intelligence-related functions such as learning, cognition, and reasoning using the concepts and tools of computer science. This study aims to analyze the impact of AI-based UTAUT2 characteristics on user satisfaction. The research targets general consumers who use AI services, and statistical analysis was conducted using Smart PLS 4.0. The purpose of this study is to analyze the impact of UTAUT2 characteristics in the context of artificial intelligence (AI) on user satisfaction. The research subjects were general consumers who use AI services, and statistical analysis was conducted using Smart PLS 4.0. The results of the study are as follows. First, performance expectancy had a significant positive effect on behavioral intention. Second, effort expectancy had a significant positive effect on behavioral intention. Third, social influence did not have a significant positive effect on behavioral intention. Fourth, facilitating conditions did not have a significant positive effect on behavioral intention. Fifth, trust had a significant positive effect on behavioral intention. Sixth, personal innovativeness had a significant positive effect on behavioral intention. Seventh, behavioral intention had a significant positive effect on actual system use. Eighth, actual system use had a significant positive effect on user satisfaction. Therefore, this study empirically verified the relationships between AI technology’s behavioral intention, actual system use, and user satisfaction based on the UTAUT2 model. Performance expectancy, effort expectancy, trust, and personal innovativeness were identified as significant factors influencing the acceptance intention of AI technology. Furthermore, behavioral intention was shown to lead to actual system use, which, in turn, positively influenced user satisfaction. Based on these findings, AI technology developers and companies should enhance user experience, build trust, and strengthen technical support to maximize AI technology adoption and user satisfaction.

Adoption of Big Data Analytics and Artificial Intelligence Among Hospitality and Tourism Companies: Perceive Performance Perspective

ABSTRACT The objective of the study was to develop a model of Big Data Analytics and Artificial Intelligence (BDA-AI) technology acceptance in the hospitality and tourism industry in Malaysia. The model developed in this study is Comprehensive Theory of Use and Adoption of Technology (CTUAT). This is an empirical and quantitative study based on a unified model developed through a massive literature review. The study adopted a cross-sectional online survey among 343 owners/managers of tourism and hospitality firms. Applying structural equation modeling and using AMOS software, data was purified and analyzed. The study identified that strategic orientation, performance effectiveness; top management support, organizational resources, employee readiness, and technology expectancy are the predictors of the behavioral intention of BDA-AI technology acceptance except for innovation on behavioral intention.

What changes the autonomous vehicle acceptance after COVID-19? Evidence from China

We propose a new theoretical autonomous vehicle (AV) acceptance model and use pre-post data to predict acceptance intention changes in the post-pandemic era. Based on the Technology Acceptance Model (TAM), this paper further incorporated trust, perceived risk, social influence, and perceived COVID-19 risk into the original TAM to explore the public acceptance of AVs. Moreover, combining the structural equation model and ordered logit model, this paper adopted a hybrid choice model including psychological attributes, socio-demographic attributes, travel attributes as well as ethics, legal responsibilities, and vehicle safety levels to clarify determinants of the public AV acceptance changes before and after the pandemic. This study solicited 466 residents in China, and used online surveys to collect stated preference data. The results show that the significant positive impact of COVID-19 on the changes in acceptance of AVs for people's mobility, especially for the higher automation levels. It makes AVs more appealing after COVID-19 outbreak. In addition, perceived usefulness, perceived ease of use, trust, and social influence also have a significant positive impact on the public AV acceptance changes. Gender, age, dedicated lanes, government subsidies, traffic accident experience, travel mode, travel expense, and other factors are related to AV acceptance changes as well. We provide insights for measuring the changes in acceptance of AVs across time due to public health emergencies.

A Study on the Impact of Quality and HCI Factors of a Deep Learning-Based Makeup Recommendation System on Acceptance Intention: Focusing on the Mediating Effects of Users’ Perceived Trust and Immersion

This study analyzed the impact of system quality and HCI factors on user acceptance intention, mediated by perceived trust and immersion, in the context of a deep learning-based makeup recommendation system. The results indicate that system quality and HCI factors significantly influence user acceptance intention through perceived trust and immersion. This underscores the importance of system quality and HCI factors for the successful application of makeup recommendation systems. In particular, factors such as diversity, accuracy, anthropomorphism, and realism enhance user trust and immersion, leading to higher acceptance intention. However, it was found that certain factors, such as personalization, do not significantly influence acceptance intention through immersion. This suggests that the impact of specific factors should be considered in the design of recommendation systems. The study is limited by its focus on a specific age group and gender in the survey, which may restrict the generalization of the results. Additionally, there is a lack of research on users with different cultural backgrounds, necessitating an understanding of the global market applicability. Future research should extend to users with diverse demographic characteristics to derive universal design principles for makeup recommendation systems. Furthermore, the impact of factors such as personalization on user experience should be explored in depth to enhance user-centered, customized beauty services. This research provides a theoretical and practical foundation for the effective design and implementation of makeup recommendation systems and is expected to contribute to the digital transformation of the beauty industry.

The Effect of Self-Determination on Generative AI Acceptance Intention : Focusing on the Mediating Effect of Perceived Usefulness and the Moderating Effect of Perceived Trustworthiness

The Effect of Self-Determination on Generative AI Acceptance Intention : Focusing on the Mediating Effect of Perceived Usefulness and the Moderating Effect of Perceived Trustworthiness

Exploring college students' risk perception and acceptance intention of facial recognition technology in China

In response to China’s ‘smart campus’ trend, facial recognition technology (FRT) in higher education has evolved from identification to emotion recognition, raising various risks and controversies. Understanding college students’ perceptions is key to addressing these concerns and facilitating AI integration. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) model to gauge students’ attitudes towards FRT, introducing three new constructs: ‘personal innovativeness,’ ‘trust in the system,’ and ‘perceived risks.’ Results show that students’ personal innovativeness and trust in the system positively relate to their acceptance intention. Trust in FRT is hindered by perceived privacy, psychological, and performance risks, while perceived social risks are insignificant. Higher education shows less resistance to FRT due to normalized campus surveillance. Compared to Western societies, Chinese students’ Confucian cultural stance of ‘harmony but not uniformity’ reduces concerns about racial or religious discrimination from FRT. These findings are crucial for assessing FRT’s necessity and legality in higher education and devising risk mitigation strategies.

Consumers’ acceptance intention towards interest-free home financing: decomposition approach of TPB model

Purpose This paper aims to determine factors that contribute to consumers’ intention to opt for interest-free home financing or mortgage adapting decomposition approach of the theory of planned behaviour (TPB) model. Design/methodology/approach For this study, judgemental and purposive sampling was used and over 300 respondents who are using or willing to use equity-based Islamic mortgages were selected for analysis. Findings According to the findings of this study, consumers’ intention towards the interest-free mortgage is influenced primarily by their attitude, subjective norm and perceived behavioural control but also by the additional construct of their innovativeness of consumers as well as decomposed variables such as perceived relative advantage and perceived compatibility, but most importantly, peers. Overall, the study’s findings show that the suggested model clarifies a better knowledge of the factors that influence consumers’ choices to accept interest-free mortgage financing. Research limitations/implications Considering this research to be limited in terms of coverage geographically and the theory rendered the context should be given proper attention when interpreting future outcomes. Furthermore, future researchers can extend the direct relationship by using attitude as a mediator and also test adding a moderating variable when conducting a cross-sectional study. Originality/value This work is mainly focused on consumers’ intention to use Islamic mortgages. Current research incorporates and validates consumer innovativeness and introduces decomposed variables, namely, peers, perceived relative advantage and perceived compatibility to the TPB model. To the best of the authors’ knowledge, the decomposition of variables with the main predictors in the setting of an interest-free mortgage in Pakistan, the decomposition approach of the TPB model has not yet been attempted.

Acceptance intention toward kiosk technology in golf courses

This study examined users' intention to accept the introduction of kiosks into golf courses by using the Technology Readiness Index 2.0 and the technology acceptance model. We conducted an online survey of 274 golf course users and used structural equation modeling to analyze the data. The analysis showed that golf course users were ready to accept new technology in the form of kiosks, owing to their greater efficiency and convenience. On the basis of our findings, golf courses should provide a seamless service to users, raising their awareness of kiosks and encouraging them to consider these as easy and convenient to use.

Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform. This paper has been This paper was accepted by Felipe Caro for the Special Issue on the Human-Algorithm Connection. Funding: This study is supported by the National Natural Science Foundation of China [Grants 72172169 and 91646125], Program for Innovation Research at the Central University of Finance and Economic, and Shanghai Pujiang Program. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03833 .

Senior tourists’ acceptance for tourism-related mobile apps: An integrated model based on BWS case 1 and ordered choice data

This paper aims to investigate senior tourists' acceptance for tourism-related mobile apps using a model integrating best-worst scaling (case 1) and ordered logit modeling techniques. A mobile app targeting to provide benefit for senior tourists was designed. An offline survey was carried out targeting the senior group in Xiaogan, China, based on which the model was estimated. The results confirm the reliability and validity of the proposed model and show the following conclusions. First, besides the specific functions of the mobile app, the personal attributes (especially the gender) can also vary senior tourists' weighting toward the functions. In addition, the personal attributes can also influence senior tourists' acceptance intention of the mobile app. Second, the function weights of the mobile app have contributions to the acceptance intention. The higher the function weight, the higher the influence magnitude. Third, social influence also significantly influence senior tourists’ acceptance intention of the mobile app.

Understanding factors influencing ESL student teachers’ adoption of classroom response systems: an integration of TAM and AOI theory

ABSTRACT This study aims to investigate the factors affecting student teachers’ acceptance intention of a gamified classroom response system (CRS) named Bodoudou. Combining the attributes of innovation (AOI) theory with the well-known technology acceptance model (TAM), we raised 19 hypotheses that explain the relationships among eight factors. The proposed model was tested with survey data collected from 240 English-as-a-second-language (ESL) student teachers in China. We first conducted a scale evaluation, which removed eight items measuring a technology’s compatibility and observability, then ran a structural equation modeling (SEM) test to explore the relationships among other factors. Findings revealed that student teachers’ intention to use CRS is mainly determined by the technology’s perceived usefulness (PU), perceived ease of use (PEoU), relative advantages (ADV), complexity (CPL), and trialability (TRI). Moreover, complexity is not a precise antonym to perceived ease of use in the current context, and trialability is not statistically related to perceived ease of use. The underlying reasons need further exploration. Future studies need to validate the scale measuring the five attributes of innovation, as well as the rationale of adding the AOI theory to the TAM model when investigating users’ technology acceptance intention.

온라인 학습환경에서 대학생의 디지털 리터러시가 에듀테크 수용의도에 미치는 영향

Objectives The purpose of this study was to explore the effects of college students’ digital literacy on their intention to adopt EduTech with the mediating effects of social presence in online learning environments. Methods For this purpose, the online survey using Google Docs was conducted for 107 college students who took online classes with digital devices at two universities A, B in Busan, South Korea. Based on the survey results, this study explored the relationships among digital literacy, social presence and acceptance intention of EduTech of college students’ by conducting hierarchical regression analysis of Baron & Kenny(1986) and the Sobel test(1982) to analyze the data. Results The main findings of this study are as follows: First, in the online learning environments, there was a significant positive correlation among college students’ digital literacy, social presence, and acceptance intention of EduTech. Secondly, in the online learning environments, it was revealed that the influence of college students’ digital literacy on their acceptance intention of EduTech is mediated by their social presence. Conclusions These research findings suggested the need to explore the teaching-learning strategies for enhancing various digital related competencies of college students through the customization of curricular and extracurricular subjects based on individual learners’ level of digital literacy. Also, it is emphasized that it is necessary to develop instructional strategies to facilitate the effective utilization of EduTech. Lastly, the study also proposes future research directions based on the limitations of the research.

An Empirical Study on the Acceptance Intention of Blockchain-based Electronic Bills of Lading

An Empirical Study on the Acceptance Intention of Blockchain-based Electronic Bills of Lading

Exploring the recycled water acceptance based on the technological perspective of UTAUT2: a hybrid analytical approach.

The development of advanced sewage technologies empowers the industry to produce high-quality recycled water, which greatly influences human's life and health. Thus, this study investigates the mechanism of individuals' adoption of recycled water from the technology adoption perspective. Employing the mixed method of structural equation modeling and artificial neural network analysis, we examined a research model developed from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. To examine the research model, this study employs a leading web-survey company (Sojump) to collect 308 valid samples from the residents in mainland China. The structural equation modeling results verified the associations between the six predictors (performance expectancy, effort expectancy, social influence, facilitating conditions, environmental motivation, and price value), individuals' cognitive and emotional attitudes, and acceptance intention. The artificial neural network analysis validates and complements the structural equation modeling results by unveiling the importance rank of the significant determinants of the acceptance decisions. The study provides theoretical implications for recycled water research and useful insights for practitioners and policymakers to reduce the environmental hazards of water scarcity.

디지털 리더십과 동적 디지털 역량, 조직의 수용의도가 디지털 전환 수준에 미치는 영향

디지털 리더십과 동적 디지털 역량, 조직의 수용의도가 디지털 전환 수준에 미치는 영향

A Study on the Determinants of the Acceptance Intention of Smart TV Advertising: Focusing on the Application of Internet Advertising Theory

A Study on the Determinants of the Acceptance Intention of Smart TV Advertising: Focusing on the Application of Internet Advertising Theory

The Effects of TikTok Advertisement Characteristics on Advertising Attitude and Advertising Acceptance Intention -The Moderated Mediating Effect of Self-Regulatory Focus-

The Effects of TikTok Advertisement Characteristics on Advertising Attitude and Advertising Acceptance Intention -The Moderated Mediating Effect of Self-Regulatory Focus-

A Study on the Blockchain Acceptance Intention for Improving the Reliability of Student Submission Data and Efficiency of Administrative Processing, such as the College Entrance Screening Department

In this study, we explored the acceptance factors of Blockchain technology, a fourth industrial innovation technology, for efficient management of basic admission data such as school records that require transparency, security, efficiency, and reliability. Factors for accepting Blockchain technology were derived through in-depth interviews with university admissions officers, and a Delphi survey and stratification analysis (AHP) were conducted on a total of 42 people, including admissions officers, university professors, high school seniors' homeroom teachers, and admissions academy counseling experts. carried out. The in-depth interview consisted of an open-ended survey on four aspects, including behavioral affordance, constraints, effectiveness, and efficiency, based on Donald A. Norman, International Standards (ISO), and Nielsen's theory, and closed-ended questions from 42 admissions-related experts. The importance and priority of the factors derived through this study were analyzed. As a result, the importance of the top factors was in the following order: efficiency (0.414), effectiveness (0.309), behavioral affordance (0.154), and constraints (0.123). The sub-factors are: efficiency is 'reduction of admission management burden'(0.235), effectiveness is 'ensuring fairness in admissions'(0.284), behavioral affordance is 'unfairness of entrance exam results'(0.324), and constraint is 'ambiguity and distrust of the admission system'(0.314) showed the highest importance, respectively. The research results showed that the efficient aspect of “how much the burden of admission management is reduced” should be considered as the most important factor in accepting Blockchain technology into the university admissions system.

미술치료의 특성이 치료 수용의도에 미치는 영향 연구

With the rapid development of digital technology, societal changes have a significant impact on mental health and behavior, particularly among China's MZ generation. Their unique life experiences and social backgrounds have led to a high concern for mental health and differing preferences for traditional psychological treatments. This study explores the influence of art therapy characteristics on the treatment acceptance intention of the Chinese MZ generation, focusing on non-verbal nature, form diversity, self-expressiveness, and safety. After reviewing relevant literature and analyzing representative cases, a research model and hypotheses are proposed. A questionnaire survey among the MZ generation with experience or acceptance of art therapy collects empirical data. Multiple regression analysis confirms the impact of art therapy's non-verbal nature, self-expressiveness, and safety on treatment acceptance intention (p-values: 0.000, 0.008, 0.000, respectively, all <0.05), indicating a positive effect. However, form diversity (p-value: 0.116) shows no significant impact, leading to the rejection of its hypothesis.