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This study aims to provide a nuanced understanding of the relationship between task-technology fit and employee innovative behavior, as well as the role of creative self-expectations.

Hierarchical regression analysis was used to test the proposed multilevel model on a sample of 407 employees working in Chinese companies.

Task-technology fit stimulates employee innovative behavior through the regulation of creative self-expectations and positive emotions. When creative self-expectations is low, the promoting effect of task-technology fit on innovative behavior is enhanced. However, when creative self-expectations is too high, this effect is reversed due to employees’ preference for challenging and complex work.

In the process of enterprise digital transformation, managers should not only focus on the alignment between employees' skills and individual task expectations, but also pay attention to employees' emotions and individual trait differences, to enhance the likelihood of innovative behavior occurrence and achieve successful enterprise digital transformation.

This study enriches the research on task-technology fit and provides recommendations for organizations to achieve digital transformation.

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.

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 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.

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.

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.

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.

Integrating transaction costs economics and task-technology fit theory, this study distinguishes two categories of social media–enabled interactions, namely task-related interactions and tie-related interactions, and explores the match between these two and firms' use of contracts in achieving safeguarding and coordinating purposes in interfirm governance.

Two studies were conducted to test the hypotheses. In Study 1, this study collaborated with a professional market research firm and collected responses from Chinese manufacturing firms in a survey. In Study 2, this study designed a scenario-based experiment and collected 239 participants from the Credamo platform.

This study categorized social media–enabled interactions into task-related interactions and tie-related interactions and conducted two studies to reveal that the safeguarding purpose of contract specificity is amplified by tie-related interactions, whereas the coordinating purpose of contract specificity is strengthened by task-related interactions.

This study assumes that firms permit and encourage the use of social media. However, some firms might prohibit the use of social media due to risk issues, or their partners may be prohibited from using social media.

Given that social media–enabled interactions have joint effects with contracts in achieving safeguarding and coordinating purposes, a firm's employees should match their goals with an appropriate type of social media–enabled interactions.

This study enriches the interfirm governance literature by uncovering the roles of these two types of interactions in matching contract specificity to achieve safeguarding and coordinating purposes, which provides actionable insights for managers in governing interfirm relationships.

Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.

The purpose of this study is to propose and test a model to explain users’ intention to adopt m-health devices and divide the importance of antecedents for users to adopt m-health devices based on the stimulus-organism-response (S-O-R) framework.

This research conducted an online survey with m-health app users and collected 562 valid responses. A hybrid SEM-ANN approach was employed to evaluate the research model and hypotheses.

The results show that motivation (M), opportunity (O), and ability (A) affect users’ flow experience and loyalty and further affect their adoption intention of m-health technology. Opportunity plays a more critical role in m-health adoption intention than ability.

This study comprehensively examined the factors that affect users’ deep engagement and m-health adoption from the perspective of MOA. It used the hybrid SEM-ANN method to divide the critical role of motivation, opportunity and ability, providing a new analysis approach for studying information technology (IT) behavior.

This paper aims to investigate the variables that could contribute to facilitating or hindering FinTech adoption in Jordan and how that will affect human well-being (quality of life [QoL]).

A conceptual framework was formulated through the integration of “the unified theory of acceptance and use of technology” (UTAUT), “task-technology fit” (TTF) model and two additional factors, namely, “financial literacy” (FL) and “quality of life” (QoL). A cross-sectional online survey was used to obtain data from 378 FinTech users employing a quantitative method. AMOS 26.0 was utilized to analyse the data based on “structural equation modelling” (SEM).

The analysis of the structural path found that UTAUT constructs including “performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI)”, and TTF were significant determinants of FinTech adoption. Only technology characteristics (TECH) was a significant predictor of TTF. Also, the analysis of empirical data revealed a significant mediating impact of FinTech adoption on the association between FL and QoL, underlining the important role of digital FL in digitalizing societies. Likewise, FL affected the QoL directly.

This research will be beneficial for “FinTech service providers” (FSPs) and policymakers to offer thorough insights regarding the current relatively low acceptance rates of FinTech, contributing to strategies’ formulation that could promote FinTech usage by Jordanian customers, where FinTech is still considered an innovative technology. In addition, FL needs to integrate digital literacy to utilize state-of-the-art technologies for more effective financial management. This is with being able to make decisions facilitating the management of life outcomes which could result in better QoL.

To the best of the author’s knowledge, this research is the first research paper that integrates the UTAUT and TTF models and also adds two additional constructs, namely, FL and QoL, to investigate the FinTech in the Jordanian setting. This study could contribute to the literature on IT adoption by considering FinTech usage and incorporation into individuals’ life in Jordan.

In the aftermath of disasters, humanitarian actors need to coordinate their activities based on accurate information about the disaster site, its surrounding environment, the victims and survivors and the supply of and demand for relief supplies. In this study, the authors examine the characteristics of radio frequency identification (RFID) technology and those of disaster relief operations to achieve information visibility and actor coordination for effective and efficient humanitarian relief operations.

Building on the contingent resource-based view (CRBV), the authors present a model of task-technology fit (TTF) that explains how the use of RFID can improve visibility and coordination. Survey data were collected from humanitarian practitioners in India, and partial least squares (PLS) analysis was used to analyze the model.

The characteristics of both RFID technology and disaster relief operations significantly influence TTF, and TTF predicts RFID usage in disaster relief operations, providing visibility and coordination. TTF is also a mediator between the characteristics of RFID technology and disaster relief operations and between visibility and coordination.

The many recent humanitarian disasters have demonstrated the critical importance of effective and efficient humanitarian supply chain and logistics strategies and operations in assisting disaster-affected populations. The active and appropriate use of technology, including RFID, can help make disaster response more effective and efficient.

Humanitarian actors value RFID technology because of its ability to improve the visibility and coordination of relief operations. This study brings a new perspective to the benefits of RFID technology and sheds light on its antecedents. The study thus expands the understanding of technology in humanitarian operations.

Over the last few decades, electronic banking has been used widely to manage financial transactions worldwide. More recently electronic wallet (e-wallet) banking has been identified as an innovative mode of e-payment and enhances e-banking customer experience. Although e-wallet banking service is more secure, fast, convenient and cost effective, compared to conventional web-based banking, adoption of e-wallet application is limited among e-banking consumers. To address this issue, the current study has conceptualized task technology fit (TTF) model, diffusion of innovation (DOI) theory and protection motivation theory towards adoption of e-wallet service. Moreover, pandemic risk is studied as moderating factor between the relationship of e-wallet and use of e-wallet banking among banking consumers.

The research design of this study is based on positivism research paradigm. This study is cross-sectional and used deductive level of theory to formulate hypotheses. Research survey was conducted towards e-banking users. For statistical findings research framework is tested with 280 numerical responses. Data are estimated through partial least square structural equation modeling (PLS-SEM) approach.

Statistical results demonstrates that collectively factors underpinned protection motivation theory, TTF and DOI have shown large variance R² 65.7% in adoption of e-wallet. The effect size f² analysis has revealed that compatibility is one of the most influential factors in determining individual behavior to adopt e-wallet. Similarly, Geisser and Stone Q² analysis has disclosed substantial predictive power to predict adoption and use of e-wallet service.

Theoretically, this study integrates protection motivation theory, DOI theory and TTF model toward adoption of e-wallet service and hence contributes to information system literature. To practice this, research has suggested that factors such as pandemic risk, perceived severity and compatibility are most influential factors and hence need policy makers' attention to boost e-wallet adoption.

This study is original as the study develops an integrative research model to investigate e-banking user behavior to adopt of e-wallet service. Moreover, pandemic risk is tested as moderating factor between adoption and use of e-wallet which, in turn, enhance the value of this study and directs how to deal with existing and future pandemic crisis.

Employee voice is crucial for organizations to identify problems and make timely adjustments. However, promoting voice in organizations is challenging. This study aims to investigate how social media use (SMU) in the workplace affects employee voice by examining its intrinsic mechanisms and boundary conditions. Specifically, this study examines the mediating roles of social identifications and the moderating effects of job-social media fit on the relationship between SMU and social identifications.

This study conducted a survey of 348 employees in China.

First, SMU affects voice through social identifications. Second, distinct identifications have different effects on voice, such that organizational identification positively affects employee voice, while relational identification positively affects promotive voice and negatively affects prohibitive voice. Third, when social media is highly suitable for the job, the positive effect of work-related SMU on organizational identification is strengthened, while the positive effect of social-related SMU on organizational identification is weakened.

The results indicate that different identifications have distinct impacts on voice. Additionally, this study reveals a double-edged sword effect of SMU on voice through different social identifications. Further, job-social media fit moderates the relationship between SMU and social identifications. These findings have important implications for organizations adopting social media.

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.