User Behavioral Intention Research Articles

Technology Acceptance and Innovation Diffusion: Are Users More Inclined Toward AIGC-Assisted Design?

Artificial Intelligence Generated Content (AIGC) has shown significant potential in design, driven by advancements in artificial intelligence technology. However, understanding designers’ willingness to embrace this technology and the factors influencing their decision-making requires further research. In this study, we develop a theoretical model of user behavioral intention in AIGC-assisted design, drawing upon the Diffusion of Innovations theory and the Unified Theory of Acceptance and Use of Technology. Through empirical analysis using the PLS-SEM structural equation model, we investigate the mechanisms behind various influencing factors on behavioral intention. Our findings highlight the relative advantage as the most significant positive factor, emphasizing the importance of the innovation’s benefits in the Diffusion of Innovations theory. Designers prioritize the innovation and assistance provided by AIGC technology in the design process and ideas, recognizing the advantages of the innovation itself over mere performance improvement. This study provides valuable insights into the psychological and behavioral mechanisms guiding designers’ decision-making regarding the application of AIGC technology in design.

Unpacking Mobile Website Aesthetics and Its Effect: A Case of M-commerce Website Offering Search and Experience Goods

ABSTRACT A major challenge for mobile commerce operators is how to attract customers to access their websites displayed on a mobile device screen only twice the size of a palm, so making design of such websites is especially important in order to catch customers’ eyes. Based on S-O-R model, the study developed a research model to explain how aesthetics of m-commerce websites affects customers’ continuing access to the websites. The model was evaluated through a stratified survey of 285 participants on m-commerce websites with varying levels of mobile website aesthetics. Structural equation modeling (SEM) was utilized to analyze the data. The results uphold the applicability of visual aesthetics (VisAWI) scale in m-commerce, which has significant positive effect on websites’ perceived usability. The study also finds that mobile websites selling experience goods have higher demand for website aesthetics than those for search goods. The study offers a well-structured practical instrument based on visual aesthetics (VisAWI) scale, helping mobile website designers understand the effect of specific aesthetic elements on user behavioral intention.

MODEL PERILAKU PENGGUNA APLIKASI QRIS DAN HUBUNGANNYA TERHADAP IMPULSIVE BUYING

This type of research is quantitative research that assesses the QRIS user behavior model and its relationship with Impulsive Buying. Research was conducted on people who use QRIS in carrying out online and offline transactions. This research uses the UTAUT approach. This research also supports IKU 1 Polytechnic LP3I Jakarta, namely the work readiness of graduates because of. In this study there are 4 independent variables (X) and 2 dependent variables (Y), namely, performance expectancy (X1), effort expectancy (X2), social influence (X3), facilitating conditions (X4), behavioral intention (Y1), Impulsive Buying (Y2) Respondents in this study were LP3I Jakarta polytechnic students who belonged to generation Z with a birth year range of 2002-2006 who used QRIS as many as 100 people. For more accurate results, respondents taken were those who had used QRIS >5x. The results of hypothesis testing in this research prove that performance expectancy has a significant positive effect on user behavior intention. This means that performance expectancy influences behavioral intention that the use of QRIS digital payments is very fast and efficient. Hypothesis testing in this research proves that effort expectancy has a significant positive effect on user behavior intention. This means that effort expectancy influences behavior intention so that those using QRIS digital payments can learn and become more skilled in using the new information system

Structural Relationship Between Perceived Value, Customer Behavior, and Continuous Use Intention of Sports Business Platform Users

Structural Relationship Between Perceived Value, Customer Behavior, and Continuous Use Intention of Sports Business Platform Users

Influencing Factors Analysis IoT Adoption in Indonesian State Housing Management Information System

The Internet of Things (IoT) has revolutionized asset management in Smart Homes. In Indonesia, one of the biggest struggles in implementing emerging technology in the e-government system is connecting the users' behavioral intention with the suitability of the type of work and the technological convenience offered. A technology acceptance and use model is proposed to address this issue by analyzing the correlation between IoT adoption in asset management. The model draws insights from two complementary methods: user behavioral intention predicts utilization, and task-technology fit predicts performance with moderating factors analysis. The study finds that social influence, hedonic motivation, and price value factors are compelling users' intentions to adopt the Internet of Things in asset management. The IoT technology is deemed worth the price for asset management, particularly for state residences, due to its automation, accuracy, and real-time features. It also enhances decision-making, as the asset information is more reliable and secure. This study enriches moderating factors analysis to study the effect of technology adoption. Additionally, research on asset management systems still needs to be improved, especially in the government sector. In conclusion, this study provides insights into adopting IoT in the government sector and its potential to transform asset management.

Exploring User Attitudes and Behavioral Intentions towards Augmented Reality Automotive Assistants: A Mixed-Methods Approach

As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving user acceptance and engagement were identified. The research reveals that trust in AR technology, perceived utility, and ease of interaction are prioritized by users. Clustering analysis identified three distinct user groups: a ‘Safety-Conscious Group’, a ‘Technology Enthusiast Group’, and an ‘Experience-Seeking Group’, each displaying unique preferences towards AR features. Additionally, a support vector machine (SVM) model effectively predicted user behavior with a training set accuracy of 89.96%. These findings provide valuable insights for the design and marketing of AR automotive assistants, acknowledging both essential features and delighters identified through the Kano model. By understanding user preferences and expectations, tailored AR solutions can be developed to enhance user satisfaction and adoption rates in the automotive sector. Moreover, this research contributes to the sustainable development goals related to the automotive industry by fostering innovation in vehicle technology, promoting eco-friendly driving practices, and enhancing overall mobility efficiency.

Peran Unsur Meta-Utaut, Penerimaan Paylater dan Persepsi Penggunaan Terhadap Antropomofisme Indonesia

Purpose: Applying the integration of UTAUT and MTAM theories, this study investigates the acceptance and usage of paylater from the Indonesian anthropomorphism perspective. Methodology/approach: Quantitative research strategy using methods for gathering data via questionnaires. Using the Lameshow formula, which yields 384.16, the sample size was calculated and rounded to 400 individuals. The combined strategy of the SEM-ANN method is used in this study's data analysis. Findings: The research presented here demonstrates that behavioral intentions are mostly determined by their performance expectations. The elements of effort expectancy, social influence, facilitating condition, hedonic motivation, habit, and trust in shaping user behavioral intentions all support this. However, perceived risk and perceived cost have the effect of decreasing behavioral intention to use. One of the things that encourages usage behavior is its goal. Practical implications: This empirical study breaks out the paylater adoption-influencing aspects from an Indonesian social perspective. IT developers can create user-friendly or widely approved paylater applications in this approach. Originality/value: This study builds pricing value components by combining the UTAUT and MTAM methodologies with structural equation modeling (SEM) and analysis of neural network (ANN) techniques. In recognizing connections that are linear and non-linear.

Understanding Users’ Behavior Intention of Serious Games for Intangible Cultural Heritage Based on the Stimulus-Organism-Response Model

Intangible Cultural Heritage (ICH) represents a major component of the global cultural heritage. It is confronted with challenges in its preservation and transmission. Unlike tangible cultural heritage, which has seen extensive research through serious games, ICH has not received similar attention. Despite the prevalent application of serious games in previous research on cultural heritage conservation, the focus has primarily been on ICH. However, their importance has been largely ignored in the scholarly investigation of ICH preservation. We propose a theoretical model that combines the Stimulus-Organism-Response (SOR) framework with the Technology Acceptance Model (TAM), with a survey of 551 gamers utilized for data collection. Our results indicate that the three stimuli of serious games (enjoyment, cultural identity, and aesthetic quality) significantly impact user perceptions. Furthermore, these perceptions (i.e., perceived usefulness, ease of use, entertainment, and empathy) all positively influence user behavioral intentions. This research increases the understanding of user engagement with serious games for ICH and offers valuable insights for designers and stakeholders in the development of these educational tools.

A Hybrid Deep Learning Model Utilizing Cross-Structural Multi-Behavioral Comparative Recommendation for Sustainable Electric Transportation Infrastructure

Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users’ behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption.

Structural Relationship between User Experience Value, Satisfaction, and Behavioral Intention: A Case Study of the Keep Fitness App in the Post-COVID-19 Pandemic Context

Structural Relationship between User Experience Value, Satisfaction, and Behavioral Intention: A Case Study of the Keep Fitness App in the Post-COVID-19 Pandemic Context

User Behavior on Actual Use of Academic Information System in Makassar State University: Is Information Technology Facilities Have a Moderating Role to Strengthen the Behavior?

This study aims to determine factors that influence the use of information technology in higher education, especially at Makassar State University. The type of information technology studied is academic information systems. This study combines two theories, namely the Technology Acceptance Model (TAM) and The Theory of Planned Behavior (TPB) to explain the user behavior for the use of academic information systems at Makassar State University. The results of this study indicate that perceived usefulness and ease of use significantly affect user behavioral intention to use academic information systems. Behavioral intention significantly affected actual use of the academic information system. An interesting finding is that the facilities available for the use of the system in the campus environment have no significant effect on the use of the academic information system at Makassar State University.

A Collaborative Model in Persuasive Web Design: Multiple case study of Lazada and Shopee

This study examined online persuasion through website design by using the Elaboration Likelihood Model (ELM) and Persuasive System Design (PSD). This study investigates the phenomenon of the success of Lazada and Shopee to provide guidelines for website designers and local sellers to enhance the e-commerce website's persuasiveness. The persuasive design features in Lazada's and Shopee's e-commerce websites were evaluated based on the design criteria of the ELM and the PSD model. This study has examined (1) the influence of central route elements (Dialogue Support and Primary Task Support) towards Lazada and Shopee user's attitude change, (2) the influence of Lazada and Shopee user's attitude change towards behavioral intention and (3) the influence of Lazada and Shopee user's behavioral intention towards actual online buying behavior. A total of 414 samples were analyzed using Partial Least Square Structural Equation Modelling (PLS-SEM) technique. The results from the analysis indicated that Primary task support through navigation design (PTN) was found to be the most influential persuasive design criteria, followed by Social support through connectedness (SSC), and Dialogue support through argument quality (DSA).

Predicting the employment of teledentistry in clinical practice by the Saudi dental community using a theoretical model.

There are very few scholastic studies applying a theory-driven methodology to analyse the employment of teledentistry in clinical practice by the Saudi dental community. The objective of this research was to predict the employment of teledentistry in clinical practice by the Saudi dental community using the UTAUT (Unified Theory of Acceptance and Use of Technology) model. A countrywide survey was executed from November 2022 to April 2023 among the dental community (pre-graduate students, graduates, post-graduate students, general dentists, and specialist dentists) involved in clinical practice. The survey employed the UTAUT model, which has four fundamental constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). These constructs are known to impact the user's behavioural intention (BI). The four fundamental constructs were independent, and BI was the dependent variable. A Likert scale with five scores was used to record each variable. Descriptive statistics were used to describe all the constructs. Cronbach's alpha scores were used to measure the inner consistency of the Likert scale. Simple linear regression and multiple linear regression were used to determine the correlation between all the constructs and the overall model's prediction. The Statistical Package for the Social Sciences was applied for analysis. The study had 80% power and an alpha threshold of .05. The electronic survey was sent to 3000 participants, out of whom 2143 responded (response rate = 71.43%). PE (R2= 26%, p < .01) was the most significant predictor of the Saudi dental community BI to employ teledentistry in clinical practice, followed by SI (R2= 24%, p < .01), EE (R2= 19%, p < .01) and FC (R2= 6%, p < .01). With statistically significant predictive power, the UTAUT model explained 32% of the variance in the BI (R2= 0.32, p < .01). Each UTAUT construct and the entire model were significantly correlated with the employment of teledentistry in clinical practice by the Saudi dental community. PE had the most salient correlation, followed by SI, EE and FC. The participants have perceived the benefits of teledentistry, increasing the future likelihood of its utilisation. The Saudi government could consider the UTAUT constructs to promote teledentistry in tandem with Vision 2030.

User experience and behavioral intention to use e-commerce: A study of digital literacy as a moderating variable

Research about perceived ease of use (PEoU) and perceived usefulness (PU) on behavioral purpose to utilize are generally tested directly (Venkatesh et al., 2012), so it is not clear how PEoU and PU determine behavioral intention to employ, therefore, it is necessary to place user satisfaction (US) as a mediating variable to elucidate the effect of PEoU and PU on behavioral intention. This research is carried out on applications developed by large companies, not on applications of digital entrepreneurship startups. The aim of this study is to look into the elements that have an impact on US and the outcomes resulting from the utilization of digital entrepreneurship startup applications. This study involves conducting a survey to gather research data and information on 122 respondents applying e-commerce microfloriculture. Structural equation modeling (SEM) and SmartPLS are employed for examining the causal connection among constructs. The result of the investigation approves that PEoU, PU, and information quality (IQ) influence continuance to use (CtU) through US and digital literacy (DL) variables, which are proven to moderate relationships of US and CtU. The paper explores the practical implications for managers of digital entrepreneurial startups, considers any constraints or limitations, and offers suggestions for future research.

Augmented Reality Cartography and Urban Economic Dynamics: Quantifying the Synergies Between 3D Mapping Technologies and Cultural Event Tourism

This study investigates the adoption and economic impact of Augmented Reality (AR) cartography in cultural tourism contexts. Drawing on the Technology Acceptance Model and cultural tourism literature, we examine how perceived usefulness, ease of use, and enjoyment influence AR adoption, cultural event experiences, and local economic outcomes. The study employs a quantitative approach, utilizing structural equation modeling to analyze data collected from 487 participants at a major cultural event. Findings reveal that AR cartography adoption significantly enhances cultural event experiences and increases local business spending, both directly and indirectly through extended length of stay. Demographic factors, particularly age, gender, and education level, moderate these relationships. Notably, the study demonstrates that AR use creates a virtuous cycle of improved experiences, longer stays, and increased spending. This research contributes to the literature by providing empirical evidence for the economic benefits of AR in cultural tourism, extending beyond previous studies that focused primarily on user satisfaction and behavioral intentions. It also offers practical implications for destination managers and policymakers, suggesting that investment in AR infrastructure could yield substantial economic returns. The study opens new avenues for research into the role of immersive technologies in driving sustainable economic development through cultural tourism.

Exploring User Behavioral Intentions and Their Relationship With AI Design Tools: A Future Outlook on Intelligent Design

Exploring User Behavioral Intentions and Their Relationship With AI Design Tools: A Future Outlook on Intelligent Design

Users’ Behavioral Intention to Use E-Payment Service in Nepal: Based on SEM Analysis

Background: Digital payments were revolutionized after 1990 in Nepal’s payment system, and the rise of ICT and mobile tech since 2010 led customers to embrace e-payments. While e-payment adoption grows, progress is needed to make bank cards default for online purchases and enhance user awareness and trust for secure digital transactions. Further, understanding user perspectives and influencing factors is crucial. Objectives: This study aims to analyze the intention of users to use e-payment services in the Nepalese context. Methods: Explanatory research design is used to meet the purpose of the study. A total of 295 respondents were selected as users of the e-payment services are increasing and there is no exact number of users identified. A structured questionnaire was developed and a pretest was carried out on 10% of the sample. Data is collected from the survey through the structured questionnaire and used the Kobo Toolbox and Interviewed from Key Informants Interview (KII) method. Results: The study shows mobile banking users are highly increasing yearly, followed by internet banking with growing users. The SEM result depicts the significant relationship between behavioral intention on user satisfaction (β = 0.191, P &lt; 0.05), perceived usefulness (β = 0.099, P &lt; 0.05), perceived ease of use (β = 0.084, P &lt; 0.05), social influence (β = 0.064, P &lt; 0.05) and perceived credibility (β =0.096, P &lt; 0.05). Furthermore, improved credibility and ease in e-payment functions can enhance customer satisfaction. Conclusion: The study concluded that Perceived Credibility (PC), Perceived Usefulness (PU), Perceived Ease of and Social Influence (SCI) have an impact on user satisfaction and Behavioral Intention (BI) to use the e-payment system. Additionally, User Satisfaction was also found to be related to Behavioral Intention (BI) to use an e-payment system.

Collaborative Consumption in an Emerging Market: What Motivates Consumers to Adopt It under Economic and Political Uncertainty?

This study presents a consumption model tailored for emerging markets beyond BRICS, which considers the advantages of a sharing economy service and its impact on user behavioral intention. Moreover, it integrates moderating variables to enhance the understanding of consumer behavior toward adopting collaborative consumption services, making two significant contributions to the existing literature. By drawing data from 270 customers from Chile and utilizing variance-based structural equation modeling along with partial least squares techniques that use SmartPLS, the research findings highlight that convenience and enjoyment play the most pivotal roles in influencing consumer behavioral intention. Additionally, the study reveals that gender significantly moderates the relationship between convenience and purchase intention. Overall, this research sheds light on the potential of sharing economy services in emerging markets, providing valuable insights into consumer preferences and behavior, which can prove beneficial for businesses and policymakers alike.

Determinants of behavioral intention toward telemedicine services among Indonesian Gen-Z and Millenials: a PLS–SEM study on Alodokter application

Telemedicine has become increasingly important in healthcare, especially with the COVID-19 pandemic. Despite, Younger generations are more fluent in using technology, previous study shows that older generations (Gen-X) are more satisfied in using telemedicine compared to younger generations. This study aims to identify the factors influencing user satisfaction and behavioral intention toward Alodokter's telemedicine service application usage among Millennials and Gen-Z in Indonesia. A quantitative cross-sectional study was conducted using a purposive sampling technique. A total of 160 Millenials and Gen-Z respondents who had used the Alodokter telehealth application within the past year were chosen in this study. The data were collected by means of an online questionnaire that was distributed through widely used social media platforms. The questionnaire consisted of 30 questions that assessed variables, such as performance expectancy, effort expectancy, social influence, price value, customer satisfaction, and behavioral intention. Data were analyzed using Partial Least Square–Structural Equation Modeling (PLS–SEM) with SmartPLS software version 3.2.9. The findings reveal that customer satisfaction positively affects behavioral intention. Performance expectancy, effort expectancy, price value, and social influence positively impact customer satisfaction. Price value was shown to have the most positive influence on behavioral intention. This study provides insights into the factors influencing user satisfaction and behavioral intention toward telemedicine service application usage among younger generations in Indonesia. The results can be used to improve telemedicine services and enhance the experience of users, particularly Millennials and Gen-Z.

Attention-Based Time Sequence and Distance Contexts Gated Recurrent Unit for Personalized POI Recommendation

Aiming at the problems resulting from the fact that the existing point of interest (POI) recommendation methods cannot effectively consider the personalized differences of users' mobile behavior in space and time, the author proposes a personalized POI recommendation method using attention-based time sequence and distance contexts gated recurrent unit (ATSD-GRU). First, the author combined the time sequence and distance context with the GRU to extract useful information from users, effectively alleviating the data sparsity. Second, inspired by the attention mechanism, the author introduced the attention model further into the neural network to capture the user's main mobile behavior intention. Finally, the author studied the ATSD-GRU and trained through Bayesian personalized sorting framework and back propagation algorithm. Experiments imply that the proposed method outperforms the comparison method in terms of the F1 index for any recommended number. When the recommendation list length is 15, the proposed algorithm exhibits an accuracy of 9.23% and a recall rate of 14.65%, both higher than the compared algorithm.