Understanding University Students' Intentions to Adopt AI Technology: Key Influencing Factors in the Use of ChatGPT

Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM

Purpose In the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education. Design/methodology/approach A survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements. Findings Findings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks. Research limitations/implications This study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students. Practical implications The practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance. Originality/value This study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.

Purpose The purpose of this paper is to examine a framework integrating the technology acceptance model (TAM), individuals’ task–technology fit (TTF) and perceptions toward adopting automobile telematics devices. Design/methodology/approach This study integrated the TAM with TTF to understand individual perceptions of a technology’s value. In addition, the intrinsic motivational factors toward technology usage, including positive perceptions (perceived enjoyment, personal innovation and perceived uniqueness) and negative perceptions (perceived risk and performance gap), were considered in the model. Furthermore, the moderating effect of driving experience was examined. Findings The perceived usefulness (PU) of telematics as well as perceived ease of use (PEOU) affected drivers’ adoption intentions. PEOU had a greater effect on adoption intentions than PU, and technology characteristics had a greater effect on TTF than task characteristics. Moreover, individuals’ perceptions of perceived enjoyment and uniqueness affected PU and PEOU. The negative perceptions of perceived risk and performance gap affected PU and PEOU, respectively. Furthermore, driving experience significantly weakened the relationship between PU and intentions. Originality/value Telematics is a niche market due to the development of the Internet of Things, but users’ adoption intentions remain unknown. This study constructed a more comprehensive model and tested the impacts of certain variables on telematics adoption, with driving experience as a crucial moderator.

Aim: In the modern digital world, many companies are moving towards technology-based applications to perform human resource administration work. Employees nowadays are capable of accessing a web-based Human Resource Management Information System (HRMIS) to obtain critical data from recognized techniques such as System Applications and Products (SAP), PeopleSoft, Bann and Lawson. This study aims to investigating the factors affecting the adopting intention of HRMIS in a selected public firm in Sri Lanka.
 Design: Acknowledging the vital concepts on accepting or resisting technology, six components (performance expectancy, effort expectancy, social influence, facilitating conditions, task characteristics, and technology characteristics) from the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Task Technology Fit (TTF) model were contextualized to investigate the drivers influencing HRMIS adoption intentions in a selected public firm in Sri Lanka. Target population was identified as the Executives, Supervisors/Technical Officers, Clerical and Allied employees of the chosen public company in Sri Lanka. A structured online questionnaire, including 30 items, was used to collect data.
 Findings: Multiple regression analysis results revealed that the factors of Performance Expectation, Task Characteristics, and Technology Characteristics have a positive influence on users’ adoption intention to HRMIS. Effort Expectancy, Social Influence, and Facilitating Conditions had negative influence.
 Implications: The outcomes highlight the necessity of combining TTF components with technology acceptance theories when evaluating the factors influencing acceptance of HRMIS or other information systems. The study's findings will aid management in making the required organizational changes to encourage employees to use the HRMIS application.

With the rapid advancement of generative artificial intelligence technologies, ChatGPT has increasingly permeated the field of design education. In visual design programs, ChatGPT is widely applied across various stages, including information retrieval, ideation, copywriting, and color matching. Based on an integrated model of the Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) theory, this study investigates the factors influencing visual design students’ continued intention to use ChatGPT. In the TAM framework, perceived usefulness (PU) refers to the extent to which students believe ChatGPT effectively supports their design tasks, while perceived ease of use (PEOU) relates to the simplicity and convenience of operating the tool. The TTF model emphasizes how task characteristics (TAC) and ChatGPT’s technological features (TEC) affect task-technology fit (TTF), the degree to which ChatGPT’s functions align with the needs of design tasks.This study employed a mixed-methods approach, combining structured interviews and a questionnaire survey. Seven visual design students were first interviewed to understand their practical experiences and preferences when using ChatGPT. Based on the interview results, an integrated TAM-TTF model was constructed, and a corresponding questionnaire was developed. A total of 337 valid responses were collected, with results indicating high reliability and validity. Structural equation modeling (SEM) was used to empirically test the proposed model.The findings indicate that task-technology fit (TTF), perceived usefulness (PU), and ChatGPT’s technological features (TEC) are key factors influencing students’ continued use of ChatGPT for design tasks and their willingness to recommend it to others. Based on these findings, this study proposes optimization strategies from an educational perspective—encompassing teaching methods, cognitive scaffolding, institutional support, and practical application. In addition, development strategies are proposed for AI developers, including the design of functional modules, guidance mechanisms, and performance evaluation systems tailored to visual design students. It is hoped that these insights will provide a reference for promoting the intelligent transformation of design education.

Online education has become the norm for higher education institutions (HEIs) during this COVID-19 pandemics. HEIs are required to implement a fully online learning system that is structured and readily accessible with the assistance of a learning management system (LMS), including in developing countries such as the Philippines. This study aims to assess factors that positively influence the perceived satisfaction of engineering students when using the LMS during the COVID-19 pandemic in the Philippines. Additionally, it aims to integrate two models: Task Technology Fit (TTF) and Technology Acceptance Model (TAM), with added variables such as the content of the learning management system, social presence, and social space. Upon deploying the convenience sampling, a total of 1011 engineering students responded in the online survey, which consisted of 81 questions. Structural equation modeling (SEM) showed that the Task Technology Fit was positively influenced by technology, individual, and task characteristics. Moreover, behavioral intention to use LMS was positively influenced by perceived usefulness and perceived ease of use. Furthermore, Task Technology Fit had a significant direct effect on behavioral intention to use LMS, which subsequently led to perceived satisfaction. This study is among the first to explore factors affecting perceived satisfaction among engineering students in using the LMS in the Philippines during the COVID-19 pandemic. To evaluate the perceived satisfaction of students in using the learning management system, future works can be extended and the model can be applied in other countries.

In the context of the digital economy, one of the most important issues facing business managers is that of how to improve employee digital performance (EDP), however, extant studies exploring this problem are quite limited. The purpose of this paper is to investigate how the characteristics of task and technology represented by enterprise social media (ESM) affect task-technology fit (TTF), which in turn affects EDP. Using TTF theory, this paper proposes a research model to validate the research question. An ordinary least squares method is used to analyze 284 questionnaire responses drawn from 33 provinces in China. The results indicate that task complexity has an inverted U-shaped effect on TTF, whereas task interdependence has no effect on TTF. Work-related function of ESM has a U-shaped effect on TTF, while social-related function of ESM has a positive effect on TTF. TTF positively influences both employee digital-enabled task performance and employee digital-enabled innovative performance. Employee digital literacy strengthens the impact of TTF on employee digital-enabled task performance, but does not moderate the relationship between TTF and employee digital-enabled innovative performance. Organizational digital culture strengthens the impacts of TTF on both employee digital-enabled task performance and employee digital-enabled innovative performance. From a theoretical perspective, this paper makes a contribution to TTF theory by revealing the curvilinear relationship between the characteristics of task, the characteristics of ESM technology and TTF. This paper also contributes to the EDP literature by clarifying how TTF affects EDP, and by identifying the boundary conditions (namely employee digital literacy and organizational digital culture) between TTF and EDP. From a practical point of view, this paper provides managers with the insights to improve EDP in terms of task assignment, employee training, enterprise culture building, etc.

An intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians' intentions and their attitudes determine the use and promotion of CDSS in clinical practice. The aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians' intentions to adopt it and by putting forward targeted management recommendations. This study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of "task-technology fit" and "perceived ease of use" on clinicians' intentions to use the CDSS when mediated by "performance expectation" and "perceived risk." We collated and analyzed the responses to the open-ended question. We collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=-0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=-0.281; P<.001) and perceived ease of use (β=-0.377; P<.001) negatively affected perceived risk. Perceived risk (β=-0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians' perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration. Perceived risk and performance expectations were direct determinants of clinicians' adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.

BackgroundHealth care technology adoption is key to improving patient care, enhancing operational efficiency, and ensuring better health outcomes. Examining the determinants that influence the acceptance and sustainable use of health care technologies is crucial for system developers, health care providers, and policymakers. The Unified Theory of Acceptance and Use of Technology (UTAUT) and task-technology fit (TTF) theoretical models offer a comprehensive framework to assess these determinants systematically, with UTAUT focusing on usage intentions (UI) and TTF emphasizing task-technology alignment for system usefulness, usability, and satisfaction.ObjectiveThis systematic review and meta-analysis aimed to identify and analyze the key factors influencing the adoption of health care technologies based on an integrated UTAUT and TTF framework. By synthesizing existing literature, the study seeks to provide valuable insights for stakeholders to implement innovative and effective solutions in the health care domain.MethodsA search was conducted across a range of databases, including MEDLINE and Embase, IEEE Xplore, ScienceDirect, Scopus, CINAHL, Google Scholar, and Web of Science. Inclusion criteria covered studies applying either the UTAUT model, the TTF model, or both to health care technology adoption, published in English between 2012 and 2025. Exclusion criteria included nonquantitative studies, studies not focused on a health care setting, and those lacking sufficient data for meta-analysis. The reviewers collaborated to decide on the final papers for inclusion in the review through Covidence, the Cochrane Collaboration’s platform for systematic reviews. Data collection involved extracting quantitative data (eg, sample sizes, reliabilities, and standardized path coefficients) analyzed using meta-analytic techniques with a random-effects model in R software (R Development Core Team) to combine findings and calculate effect sizes.ResultsA total of 50 studies (35 UTAUT with 20,723 participants and 15 TTF with 4041 participants) met the inclusion criteria, representing various health care technologies, such as electronic health records, telemedicine platforms, and mobile health apps. The meta-analysis revealed that performance expectancy emerged as the most significant predictor of UI (β=.304; P<.001), while UI was the primary predictor of usage behavior (β=.199; P<.001). Other UTAUT predictors included effort expectancy (β=.177; P<.001), social influence (β=.167; P<.001), and facilitating conditions (β=.105; P<.001). For TTF, technology characteristics had the strongest effect on TTF (β=.445; P<.001), followed by TTF on UI (β=.271; P<.001) and task characteristics on TTF (β=.263; P<.001). Variability across settings and regions suggests contextual influences, with high heterogeneity (I²=81.90%‐94.87%).ConclusionsThis study provides valuable insights for enhancing health care technology adoption by integrating UTAUT and TTF, highlighting performance expectancy, effort expectancy, social influence, facilitating conditions, task characteristics, technology characteristics, and TTF as key drivers. The findings, assessing system usefulness, usability, and satisfaction, can guide interventions to improve adoption and health care delivery.

PurposeOff-site construction (OSC) has been regarded as a clean and efficient production approach to help the construction industry towards sustainability. Different levels of OSC technologies vary greatly in their implementations and adoptions. Compared to low OSC level technologies have been applied widely, the adoption of high OSC level technologies (HOSCLTs) in practice remains limited. The adoption mechanism for HOSCLTs by firms has not been clear, hindering their promotion. This study aims to explore the mechanism combining subjective and objective adoption for HOSCLTs.Design/methodology/approachThis study developed an integrated model illustrating mechanism for HOSCLTs adoption based on the technology acceptance model (TAM), which has strong capacity to explain potential adopters' subjective intentions to adoption, and the task-technology fit (TTF) theory, which well describes the linkages between the task, technology and performance in technology adoption. The proposed model was then empirically evaluated through a survey of 232 practitioners in the Chinese OSC industry using partial least squares structural equation modeling.FindingsThe results indicate that both task characteristics (TAC) and technology characteristics (TEC) positively affect TTF of HOSCLTs. TAC, TTF, firm conditions and stakeholder influence have significant positive effects on perceived usefulness (PU), which further positively influence attitude towards adoption. TEC and firm conditions are significantly related to perceived ease of use (PEU). TTF, PEU and attitude towards adoption are good predictors of behavior intention to HOSCLTs adoption. PEU only significantly influences adoption intention and is not observed to influence attitudes and PU, unlike prior research on common OSC adoption.Originality/valueThis study contributes to the body of knowledge by exploring HOSCLTs adoption in the industry based on distinguishing the levels of OSC technologies and supplementing an integrated model for explaining the mechanism with the combination of subjective and objective adoption. The study also provides useful insights into understanding and promoting HOSCLTs adoption for policy makers and stakeholders actively involved in the OSC field.

Prefabricated construction (PC) contributes to the sustainability of the construction sector, with higher levels of prefabrication providing better performance in theory. However, enterprises have great expectations for the performance of high prefabrication level technologies (HPLTs) but poor adoption behaviours in practice. To address this issue, this study explored the mechanisms of HPLTs adoption from the enterprise perspective, by complementing an integrated model that combines expectations and the fit between tasks and technologies, based on the unified theory of acceptance and use of technology and the task-technology fit theory. The significance of paths affecting the adoption behaviour toward HPLTs was identified by the partial least squares structural equation modelling. The results show that adoption intention and task-technology fit are excellent predictors of adoption behaviour toward HPLTs. Social influence, facilitating conditions, task-technology fit, effort expectancy and task characteristics positively affect adoption intention, while performance expectancy is not found to influence adoption intention. The mediating effect analysis indicates that social influence currently has the largest indirect effect on adoption behaviour, followed by facilitating conditions and task-technology fit. The findings contribute to building a bridge between the expectations and adoption behaviours of HPLTs, and provide guidance for the effective promotion of HPLTs.

The purpose of this study is to explore the unused (behavioural and technological) scopes of learning management system acceptance by succeeding an extra inclusive method to address learning management intention adoption. CFA and SEM analyses have been used to examine the data gathered from university students. The study attempts to investigate the part of technological variables in explaining behavioural intention of individuals to adopt learning management by integrating two pre-established frameworks of UTAUT2 (modified) and TTF. The empirical outcomes proven the significant impact of task (TAC) and technology characteristics (TEC) in simplifying task technology fit (TTF). The outcomes of this study also support the significant relationship of task technology fit (TTF) and facilitating condition (FC) with intention to adopt learning management system. The current study offers an all-inclusive method to explain the acceptance of learning management system by joining two recognised theories of technology acceptance. The strength of current study is based on combining behavioural and technological features of learning management system. The explanatory power of our research model suggests the very best explanatory power that described 60.1% of the behavioural intention to adopt learning management.

PurposeThis study aims to examine the effect of task and technology characteristics on the compatibility of technology and tasks, as well as examine the reciprocal effect between the task-technology fit and the use of information systems.Design/methodology/approachThe study took place in 36 star hotels from one-star to four-star hotels in some cities and districts in South Kalimantan Province. There were 24 hotels in Banjarmasin, 7 hotels in Banjarbaru and 1 hotel in each area of Banjar, Tanah Bumbu, Tabalong, Hulu Sungai Utara and Barito Kuala. The hotels chosen were those implemented the information and communication technology as supporting administrative activities to serve hotel customers. The population was the front office staff in the existing hotels as the users of the information technology. The sampling technique used in this research was the questionnaire distribution in accordance with the number of population. Data were collected from the filled questionnaires. From the 239distributed questionnaires, 164 (68.62 per cent) were returned and used as the research data.FindingsTask characteristics and technology characteristics have a significant and positive effect on task-technology fit, in which the higher the task characteristics and technology characteristics, the higher the task-technology fit. The task-technology fit and the use of information systems are positive and reciprocal. This means that the higher the task-technology fit, the higher the use of information systems.Originality/valueThe originality of this study is reciprocal relationship between the variables of use with the task-technology fit. Some researchers have found the compatibility of technological tasks affecting the use of information systems, namely, Lin and Huang (2008), Norzaidi and Salwani (2009), Larsen et al. (2009), McGill and Klobas (2009), D’Ambra and Wilson (2013), Im (2014) and Chang et al. (2015). On the other hand, in task-technology fit theory, Goodhue and Thompson (1995) state that use affects the task-technology fit.

PurposeThis paper aims to propose a user adoption model of human resource information system (HRIS) in the Jordanian public sector by integrating the task technology fit (TTF) model and the unified theory of acceptance and usage of technology (UTAUT).Design/methodology/approachUsing a quantitative approach, survey data were collected using an online survey from employees working in four different public organizations in Jordan, and structural equation modelling has been used to validate the research model.FindingsThe study found that among the constructs of the UTAUT model performance expectancy, social influence and facilitating condition have a significant effect on users’ behavioural intention to adopt HRIS. Furthermore, the results also reveal that effort expectancy has an insignificant effect on adoption behaviour. The findings also show that all TTF hypotheses were supported by the data collected. Both task characteristics and technology characteristics have a significant effect on the TTF construct, which further determines users’ adoption behaviour.Originality/valueThese findings contribute to the extant academic literature and have practical implications, improving the understanding of the HRIS adoption and use in public sector organizations.

The increasing integration of AI technologies such as ChatGPT in educational systems calls for an in-depth understanding of the factors influencing students’ intentions to use these tools. This study explores the factors shaping university students’ intentions to use ChatGPT by analysing three key dimensions: task characteristics, technology characteristics and individual characteristics. Using the task-technology fit (TTF) framework, the research examined how these elements impact the alignment between educational tasks and ChatGPT’s capabilities, ultimately driving students’ behavioural intentions. A survey of 393 students from a Saudi Arabian university was conducted, and structural equation modelling was applied to assess the relationships among the variables. Results indicated that all three characteristics significantly influenced TTF, which in turn had a positive impact on students’ intentions to use ChatGPT. The study highlighted the importance of achieving a strong TTF to encourage the effective use of AI tools in academic settings. The implications of this research suggest that educational institutions should focus on aligning AI technologies with students’ learning tasks to enhance their intent to use these tools, thereby improving academic performance. Furthermore, this study extended the TTF model to the context of AI-powered educational tools, particularly in line with Saudi Arabia’s Vision 2030. This research is one of the first to investigate the factors influencing students’ intentions to use ChatGPT within the unique cultural and technological context of Saudi Arabia’s higher education system. By integrating the TTF framework with local and regional factors, the study provides novel insights into the drivers of AI usage in education, offering guidance for regional policy and broad educational practices.