Significant Predictors Of Behavioral Intention Research Articles

Environmental Responsibility in Urban Forests: A Cognitive Analysis of Visitors’ Behavior

The environmentally responsible behavior of urban forest visitors is a key determinant for the conservation of urban forests. Identifying the determinants of individuals’ behavioral intentions and actual behavior in engaging in environmentally responsible actions is a crucial step in promoting such behavior. This research investigates the determinants of environmentally responsible behavior of urban forest visitors in Tehran using Social Cognitive Theory. Data for the study were collected using 456 questionnaires distributed to visitors of urban forests. The data were analyzed using structural equation modelling, which described a 62.9% variance in behavioral intention and 56.6% in environmental behavior of visitors. The socio-structural factors and the observation of others’ behaviors were the most significant predictors of behavioral intentions. Outcome expectations and self-efficacy significantly influence both behavioral intentions and actual behavior. This study demonstrates that while behavioral intention is a key factor, other determinants such as outcome expectations and self-efficacy play a crucial role in shaping actual environmentally responsible actions. These results underscore the importance of increasing awareness and enhancing the skills of urban forest visitors regarding environmental behaviors. Furthermore, this study highlights the need to remove barriers and provide the necessary facilities to promote sustained environmentally responsible behavior among visitors.

A Bitcoin Conscience: Dispositional Experiences of Nepalese Users

The purpose of this study is to explore the behavioral intention (BI) of Nepalese cryptocurrency users. Perceived ease of use and perceived usefulness are the constructs from the technology acceptance model; similarly, the trust and the perceived risk are integrated constructs based on literature to predict BI. A close-ended questionnaire survey was a five-point Likert scale. Non-probabilistic purposive sampling , 272 valid responses were analyzed using the statistical package for the social science software, which included Pearson correlation and multiple regression. The result reveals that perceived usefulness, trust, and perceived risk are the significant predictors of BI, however insignificant in relation to perceived ease of use. While consumers have shown positive behavioral intentions toward cryptocurrency, the government has insisted that the restriction is in effect. So, there seems to be a conflict between the practicability of government and the psychology of users. To cope with this, policymakers need to conduct real practice-based studies.

What drives the adoption of mobile learning services among college students: An application of SEM-neural network modeling

This research aimed at examining factors influencing college students to adopt mobile learning (m-learning) services. An integrated model combined the information systems success (ISS) and Unified Theory of Acceptance and Use of Technology (UTAUT2), was developed to identify m-learning determinants. A sample of 552 was recruited to test hypotheses using structural equation modeling (SEM). The significant factors explained 70 % of the variance toward Behavioral Intention (BI) based on SEM results. While price value (PV), effort expectancy (EE), performance expectancy (PE), and privacy (PR) were not significant predictors of BI, the results of the neural network model ranked the predictive power of the factors in the following order: information quality, habit (HB), system quality (SYQ), hedonic motivation (HM), facilitating condition (FC), and social influence (SI), positively influenced m-learning adoption. The findings of this study helps the policy makers at higher educational institutions to formulate strategies to enhance students’ learning experience in upcoming crises and place a focus on sustainable mobile learning environment.

Effect of international food tourists’ food experiences on place attachment and behavioral intentions: The moderating role of food variety seeking tendency

This study aims to develop a framework for determining the underpinning factors behind international food tourists’ intentions for food-based visit behavior. Specifically, by engaging food tourists in a systematic study, this study investigates the direct and indirect predictive effects of food tourist local food experiences (LFEs), perceptions of experiential value (EV), and satisfaction with food experiences on place attachment (PA) and behavioral intentions (BIs). Data were collected using a questionnaire from 451 food tourists who participated in a food tour for a day or longer in Istanbul. Structural equation modeling was used to test the proposed model. The findings demonstrate that LFE significantly influences EV, PA and satisfaction (SAS). Second, EV, PA and SAS were found to be significant predictors of BI. Third, food variety seeking (VARSEEK) was found to have a moderating effect between LFE and PA while weakening tourist PA. The study provides new insights into tourist dining experiences and offers a number of recommendations for destination managers and stakeholders to enrich tourist experiences.

Applying an extended theoretical approach to identifying Canadian dental students' acceptance of teledentistry: A cross-sectional study.

Teledentistry is a promising innovation for improving service quality and patient outcomes. While studies have shown the relevance of theoretical frameworks in understanding behaviour change predictors for telehealth implementation efforts, their application in dentistry is limited. This study aimed to test different theoretical approaches to identify the factors affecting dental students' behavioural intention to use teledentistry. This cross-sectional study involved students in their final two years of undergraduate dental programmes, from three Canadian provinces (Quebec, Nova Scotia, and Saskatchewan) using an electronic self-reported questionnaire. Following descriptive analyses, we tested three theoretical models (the technology acceptance model, psychosocial model, and integrated model) using path analysis and multiple linear regression analysis. We analyzed the modifying effect of sociodemographic characteristics and prior use of teledentistry. Out of the 46 students who participated, the majority were female (53.5%) and aged over 25 years (62.8%). The three models successfully explained a substantial portion of the variance in behavioural intention to use teledentistry, ranging from 58.0% to 76.6%. Social role beliefs (p < 0.001) and control beliefs (p < 0.001) were the most significant predictors of behavioural intention to use. Prior use of teledentistry modified the association between control beliefs and behavioural intention to use teledentistry. The original technology acceptance model was a good predictive model of behavioural intention to use teledentistry with perceived use as the strongest predictor. However, the integrated model performed the best in highlighting the relevance of training and education to foster teledentistry implementation in dental schools. The generalizability of the findings is constrained by the modest sample size, warranting larger studies for validation.

“ENHANCING LEARNING POTENTIAL: INVESTIGATING MARKETING STUDENTS’ BEHAVIORAL INTENTIONS TO ADOPT CHATGPT”

ABSTRACT The rapid proliferation of the Internet has sparked a resurgence of attention toward the function of novel artificial intelligence technologies in the higher education. The effective adoption of recent advancements in human-computer interaction is crucial for inclusive education and innovation, resulting in sustainable socioeconomic growth and development. Therefore, the present investigation focuses to examine various factors that exert an influence on acceptance and use of ChatGPT by marketing students. The research’s significance lies in the integrating the concept of system flexibility into the Unified Theory of Acceptance and Use of Technology (UTAUT) model. An adapted questionnaire was administered to gather information, and statistical procedures were conducted on 309 valid responses. The study’s results revealed that habit was the most significant predictor of behavioral intention, with performance expectancy and effort expectancy following closely behind. However, the research shows that perceived risk is not a vital factor for marketing students, as they exhibit a heightened sense of control in regulating their online behavior. Besides, the implications presented in this study hold great significance for policymakers, educators, and top-level management personnel within higher education institutions to evaluate and update existing education policies to accommodate the integration of AI tools like ChatGPT into higher education.

Exploring students’ acceptance of an artificial intelligence speech evaluation program for EFL speaking practice: an application of the Integrated Model of Technology Acceptance

Adapted from the Technology Acceptance Model (TAM), the Integrated Model of Technology Acceptance (IMTA) has been used to examine the perceptions and acceptance of computer-assisted language learning (CALL), such as online learning, mobile learning, and learning management systems. However, whether IMTA can be applied to empirical research on AI-assisted language learning remains unexplored. Therefore, this article intends to analyze an AI speech evaluation system for English speaking practice, in the context of higher education through the IMTA. Research instruments encompassed questionnaires (n = 218) and semi-structured interviews (n = 21). The participants were English as a foreign language (EFL) learners who had used an AI speech evaluation program to practice their speaking skills. The results suggested that (1) most participants found the AI program useful, pleasant, and easy to use. They also had a strong intention to continue using it; (2) perceived usefulness (PU) and perceived enjoyment (PE) are significant predictors of behavioural intention to use (BI). Meanwhile, problems related to user interface design, the accuracy of automatic feedback and especially the lack of face-to-face interaction were reported. This study demonstrates how IMTA could be applied to examine users’ acceptance of AI programs for EFL speaking practice. The findings also offer insights into further research and development in AI tools for EFL speaking practice.

Investigating the unexpected determinants of cryptocurrency adoption in the UAE

PurposeFinancial inclusion provides access to financial infrastructure, facilitating money transfers. Therefore, blockchain and cryptocurrencies might boost worldwide financial acceptance. However, the UAE has one of the lowest cryptocurrency adoption rates. This study explores the UAE customer adoption and use of cryptocurrencies.Design/methodology/approachUsing a scale, the authors created a structural model and obtained 270 responses from a snowball-distributed online questionnaire, assessed by five cryptocurrency specialists.FindingsPerformance expectations (PE), price value (PV), Hedonic motivation (HM) and consumer innovativeness (CI) were the most significant predictors of behavioural intention (BI). Surprisingly, BI is not a reliable indication of actual consumption. Facilitating conditions (FC) are the most accurate predictor of cryptocurrency usage (CU), indicating that adoption might be increased by providing the necessary structures and processes to lead users.Research limitations/implicationsThis research adds to the body of knowledge by examining the adoption and implementation of cryptocurrencies in the UAE and by developing and evaluating new constructs based on current notions. The study also contributes to the current understanding of cryptocurrencies and blockchain adoption.Practical implicationsThe conclusions of the research advise marketers on how to boost the commercialisation of cryptocurrencies in the UAE market and may pave the way for other studies to assist impending developments in the UAE cryptocurrency industry.Originality/valueThis research offers novel insights into significant predictors of cryptocurrency product uptake in the financial and banking business.

The Role of Attitude in the Unified Theory of Acceptance and Use of Technology: A Meta-analytic Structural Equation Modelling Study

The Unified Theory of Acceptance and Use of Technology (UTAUT) model has been widely used to explain user intention and use behavior. Past research has added attitude to the UTAUT framework to examine further how it influences technology adoption. This study utilized a combined meta-analysis and structural equation modelling [approach] to examine how the model, when extended with attitude as a variable, differs from its original UTAUT model and whether there are emerging relationships among the existing constructs. Based on 21 empirical studies that included attitude in various extended models, the results showed that attitude was a significant predictor of behavioral intention and use behavior. The study also revealed that facilitating conditions emerged as the strongest predictor of use behavior in the presence of attitude as a construct. In conclusion, while the inclusion of attitude as a separate construct in the UTAUT model has provided a more comprehensive understanding of technology adoption and usage behavior, this study showed that it did not enhance its explanatory power.

A study on factors influencing Chinese undergraduate EFL learners' self-directed use of mobile English learning resources.

This study aimed to examine the factors that potentially impact the self-directed use of mobile English learning resources (MELR). The participants were 206 Chinese undergraduate EFL learners at Yangzhou University in Mainland China. Applying and modifying the Unified Theory of Acceptance and Use of Technology (UTAUT), this study involved six constructs, including students' performance expectancy, effort expectancy, social influence, facilitating conditions, perceived playfulness, and behavioral intention to use MELR. The structural equation modeling (SEM) technique was adopted to analyze the data collected from the questionnaire. The findings showed that facilitating conditions acted as the most significant predictor of behavioral intention to adopt MELR, followed by effort expectancy, perceived playfulness, and performance expectancy. However, social influence did not have significant effects on students' use of MELR. Pedagogical implications for teachers and students were also presented in the end.

Exploring the intention to use mobile learning applications among international students for Chinese language learning during the COVID-19 pandemic

PurposeThis study aims to improve the Unified Theory of Acceptance and Use of Technology (UTAUT) model by examining technological anxiety and other influential factors on international students' adoption of mobile learning (m-learning) during COVID-19 emergency remote teaching (ERT).Design/methodology/approachThis study utilized the modified UTAUT framework to test hypotheses through a cross-sectional survey method. Participants were university students studying Chinese as a foreign language who were selected using a convenience sampling approach. An online questionnaire was then administered. The data collected from the surveys were analyzed using the partial least squares method with SmartPLS 4 software.FindingsThe study examined 16 hypotheses and found support for six of them. The results confirmed that performance expectancy (PE) is a significant predictor of behavioral intention (BI), and anxiety influences both PE and effort expectancy. The negative effect of social influence on anxiety was found to be significant, while facilitating conditions had a negative impact on learners' self-efficacy. The model fit indices indicated a good overall fit for the model.Research limitations/implicationsThis study presents a valuable contribution to the literature on m-learning in emergency education by incorporating technological anxiety into the enhanced UTAUT model. Examining the relationships between the key factors of the model provides a better understanding of learners' intentions and can inspire researchers to establish new theoretical foundations to evaluate the roles of these factors in diverse educational settings.Practical implicationsThe study found that performance expectations are linked to learners' intentions, and anxiety indirectly affects BIs to use mobile learning platforms. Thus, these platforms should be designed to meet learners' expectations with minimum effort and eliminate anxiety triggers to facilitate ease of use. Language curriculum developers and policymakers should incorporate mobile learning applications to support diverse language skills, address students' needs and encourage their use through professional development opportunities for instructors.Social implicationsSocial factors have been found to significantly influence anxiety levels among learners. Therefore, it is crucial for teachers and family members to play an essential role in mitigating anxiety's adverse effects. Discussing related issues can enhance the quality of mobile learning and stimulate social initiative by providers, ultimately improving the learning experience for all learners, regardless of their location or circumstances. This can also contribute to the growth and development of society.Originality/valueThis study contributes to the originality of m-learning development by proposing an enhanced UTAUT model that considers anxiety and emphasizes the critical role of foreign learners' BIs. It provides fundamental guidelines for designing and evaluating m-learning in ERT contexts.

Does Future Time Perspective Moderate Associations of Instrumental and Affective Attitude With Exercise Behavior? A Three-Wave Longitudinal Survey Among Japanese Older Adults.

To develop effective promotion strategies for exercise behavior in older adults, it is essential to identify the psychological determinants. Previous studies investigated affective and instrumental attitudes as determinants. However, from the conceptual assumptions of socioemotional selectivity theory, affective attitudes might be more influential on the exercise behavior of older adults with an expansive future time perspective, whereas instrumental attitudes would be more influential on those with limited perspectives. This study examined whether the associations of affective and instrumental attitudes with behavioral intention and exercise behavior differed according to future time perspective among older Japanese adults. Data were obtained from a 3-wave longitudinal questionnaire survey of 886 individuals (Wave 1: December 2017-January 2018; Wave 2, 1-year follow-up; Wave 3, 3-year follow-up). Using the data on exercise behavior, affective and instrumental attitudes, and behavioral intention at each wave, and the data of future time perspective and demographic factors at Wave 1, this study performed multiple regression analyses and a multigroup cross-lagged model. Affective attitude was a significant predictor of behavioral intention regardless of future time perspective. Although instrumental attitude significantly predicted behavioral intention among those with a lower future time perspective, it did not predict behavioral intention among those with middle-to-higher future time perspective. Regardless of future time perspective, affective attitude, but not instrumental attitude, significantly predicted exercise behavior. Unlike the socioemotional selectivity theory, this study found that affective attitude was more influential on exercise behavior than instrumental attitude regardless of future time perspective among them.

Human values and traveler behaviors: metaverse for conferences and meetings

ABSTRACT The main purpose of this article is testing the factors that influence the use of the metaverse for conferences/meetings, the moderating effects of human values and the gender, and the age of users on this relationship. A convenience sampling approach was employed in order to gather data in Korea. The findings revealed that the users accepted the metaverse as a valuable technology in regard to virtually assisting conferences and meetings. The performance expectancy, social influence, and a lack of anxiety were specifically the most significant predictors of behavioral intention about the use of the metaverse for conferences/meetings.

Factors influencing mobile banking adoption in India: The role of government support as a mediator

AbstractThis study aims to objectively examine the factors influencing mobile banking adoption in India. There has been a model put forth that is founded on the unified theory of acceptance and use of technology and technology readiness. Data was collected from 536 mobile banking users. The primary determinants influencing mobile banking adoption were assessed using structural equation modeling. The study's conclusions indicate that the most significant predictors of behavioral intention to use mobile banking are factors such as performance expectancy, effort expectancy, social influence, optimism, and innovativeness. The relationship between users' behavioral intentions and adoption of mobile banking is partially mediated by government support. With the help of this study, mobile banking service providers in urban India will be better able to develop new services or modify current ones to boost the adoption of mobile banking.

Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.

Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (β=.453; P=.003) and social influence (β=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (β=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (β=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

A Cross-Sectional Survey on the Post-Pandemic Acceptability of Pharmacist-Delivered Telehealth Services Among HCV-HIV Patients.

The COVID-19 pandemic enhanced the use of telehealth as a means of delivering services to patients who required continued and uninterrupted care. This helped to reduce readmission to hospitals where COVID-19 hospitalization was prioritized. Patients with HCV and HIV and other chronic diseases require this type of care. This study evaluated the post-pandemic acceptability of pharmacist-delivered telehealth services among HCV and HIV monoinfected and coinfected patients in Washington DC. This was a cross-sectional study conducted in a community pharmacy setting in Washington DC whose primary outcome was the acceptability of pharmacist-delivered telehealth services through a proposed platform(docsink). A validated questionnaire, borrowed from the literature was used to determine telehealth acceptability, measured as behavioral intention, among patients who receive care from this pharmacy. The study recruited 100 participants. Descriptive statistics were conducted as well as bivariable and multivariable analyses to assess predictors of telehealth acceptability. In the unadjusted model, PU/EM (OR 0.571, 95% confidence interval (0.45-0.73), P < .0001)), PEOU(OR 0.72, 95% confidence interval (0.61-0.85)) and IM(OR 0.733, 95% confidence interval (0.62-0.87), P = .0003)) were significant predictors of behavioral intention. Overall, the study found that lower Perceived Usefulness/Extrinsic Motivation scores decrease the odds of intending to use pharmacist-delivered telehealth (OR = 0.490, 95% confidence interval (0.29-0.83), P = .008). This study determined that the impact of perceived usefulness and extrinsic motivation was critical to the acceptance of pharmacist-delivered telehealth among a predominantly Black/African American study population.

Acceptance of Automated Shuttles—Application and Extension of the UTAUT-2 Model to Wizard-of-Oz Automated Driving in Real-Life Traffic

Automated shuttles can make public transport more attractive and sustainable. Still, their successful implementation requires a high level of acceptance among users. This study investigates the impact of the predictors performance expectancy, social influence, facilitating conditions, hedonic motivation, and perceived risk of The Unified Theory of Acceptance (UTAUT)-2 on the behavioral intention to use automated shuttles. In earlier work, UTAUT-2 has already been successfully applied to study the acceptance of autonomous public transport. Here, we employed the UTAUT-2 to assess acceptance of a Wizard-of-Oz automated shuttle in real-life traffic, in a study with 35 participants, before and after a first ride and after a second ride on which two incidents occurred. The results show that behavioral intention to use automated shuttles is high even before the first ride and remains high after experiencing automated driving. Performance expectancy was the only significant predictor of behavioral intention for all measurement time points. The explanatory power of the model almost doubles from pre-ride to post-ride. The results indicate a crucial role of performance expectancy for the acceptance of automated shuttles at the current stage of implementation and provide guidance for a successful development and implementation of autonomous public transport.

Explaining information seeking intentions: Insights from a Slovenian social engineering awareness campaign

The human factor remains one of the key challenges in cybersecurity despite effective technical countermeasures in place. This study aims to determine what motivates individuals to seek information about social engineering by investigating the determinants of behavioral intention to follow the materials of a social engineering awareness campaign in Slovenia. A quantitative survey of individuals in Slovenia (N=542) aged 15 or older was administered with participants recruited through University of Maribor students. Data were collected on constructs related to the protection motivation theory (PMT) and the theory of planned behavior (TPB) as well as privacy concerns and perceived performance of authorities. The survey instrument was validated with a confirmatory factor analysis. Covariance-based structural equation modeling (CB-SEM) was used to determine relationships between constructs and analysis of differences between students and employed individuals. Results indicate perceived threat, subjective norm, attitude toward behavior and authorities performance are all significant predictors of behavioral intention. The associations between perceived threat and behavioral intention, and privacy concern and attitude towards behavior was not significant among employed individuals. Among students, trust in authorities was not a significant predictor of authorities performance. This study has several implications. The results of this study suggest that fear appeals may be effective in motivating individuals to seek information about social engineering attacks thus improving the effectiveness of awareness campaigns. They also offer some insights into how to improve messaging towards the target populations. Messaging emphasizing perceived threat may directly increase information seeking intention while messaging emphasizing coping with social engineering may do so indirectly through attitude towards behavior. This study also indicates that messaging should be tailored to the target population (e.g., messaging emphasizing perceived threat may be much less effective for employed individuals than students).

Using an extended technology acceptance model to determine students’ behavioral intentions toward smartphone technology in the classroom

The modern classroom takes on numerous forms and expands beyond the traditional brick-and-mortar walls. Educators are increasingly expected to integrate information and communications technology (ICT) and e-learning into the modern classroom. Several variables may influence instructors’ decision-making processes about ICT integration in the classroom. Smartphone technology provides students with accessibility, the ability to communicate with others, as well as to engage with classroom material. The study aims to examine how a variety of factors (perceived usefulness, perceived ease of use, subjective norm, and attitude) influence behavioral intention toward the use of smartphone technology. Two hundred and ninety survey responses were analyzed to identify the relationship between these factors based on an extended technology acceptance model. The empirical results of the study revealed that subjective norms and attitudes are significant predictors of behavioral intention toward the use of smartphone technology. The article concludes by presenting implications for educators, policymakers, and education researchers derived from the academic and practical discussions based on the findings.

The Role of the Theory of Planned Behavior in Explaining the Energy-Saving Behaviors of High School Students with Physical Impairments.

People with physical impairments can help solve energy problems by participating in diverse energy-saving behaviors, such as switching off lights or turning off an air conditioner when not in use; however, they may struggle to participate in some behaviors due to mobility impairments. This study aims to examine factors that impact the energy-saving behaviors of high school students with physical impairments. The theory of planned behavior (TPB) was used to test whether attitudes towards energy-saving behaviors, subjective norms, and perceived behavioral control could affect intentions, which then leads to performance of energy-saving behaviors. The participants were 330 high school students with physical impairments in Thailand. A questionnaire was employed to measure energy-saving behaviors and TPB constructs. A confirmatory factor analysis (CFA) was performed to validate all study variables; structural equation modeling (SEM) was then used to test causal relationships among TPB constructs and energy-saving behaviors. The results showed that the TPB could be used to explain the energy-saving behaviors of students with physical impairments, that subjective norms were the most significant predictor of behavioral intentions, and that intentions significantly impacted energy-saving behaviors. While perceived behavioral control did not have a direct effect on behaviors, it had a significant effect on intentions. Under the TPB construct, of the studied variables, attitude had the lowest power to predict students’ intentions to perform the concerned behaviors; however, the impact of attitude was still statistically significant. The results suggest that all TPB variables can predict energy-saving behaviors of high school students with physical impairments, but their power to predict the behaviors is different. To promote student participation in energy-saving behaviors, it is important to create subjective norms and eliminate obstacles that students with physical impairments might face when performing energy-saving behaviors.