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This study aims to investigate the influences of task-technology fit on university students’ attitudes towards ubiquitous library-supported learning when they use a mobile library app, Line@Library.

In this study, structural equation modelling to examine 158 valid questionnaires are used. The study aims to examine the effects of task-technology fit (TTF) on university students’ attitudes towards mobile learning (AML) when using Line@Library.

The results show that task-technology fit is an important role that influences the students’ attitudes towards mobile learning. The factor “technology characteristics” is considered when the students attempted to use the mobile app to solve problems or complete tasks. This study also found that the students responded with positive perceptions of the task-technology fit and had positive perceptions of its ease of use. Furthermore, usefulness, ease of use and affection of AML were found to be the most influential predictors of mobile library adoption intention.

From the perspective of learners, this study investigates the relationships of the combination of social media and a mobile library between TTF and AML. This study further found that not only ease of use, usefulness and affection but also task-technology fit can be a predictor that influences students’ attitudes towards mobile learning.

The purpose of this paper is to examine the roles of task-technology fit (TTF), learning-technology fit (LTF) and cognitive absorption (CA) in determining medical professionals’ cloud-based electronic learning (e-learning) system continuance intention and performance outcomes and evaluate whether medical professionals’ perceived impact on learning can affect their perceived impact on tasks within medical institutions.

Sample data for this study were collected from medical professionals at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 373 (62.2%) usable questionnaires were analyzed using structural equation modeling in this study.

In this study, medical professionals’ perceived TTF and LTF as antecedents to their cloud-based e-learning continuance intention and performance outcomes were validated, and medical professionals’ perceived impact on learning had a positive effect on their perceived impact on tasks. Synthetically speaking, this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that this study introduces a new construct, “LTF,” to conceptualize, define and measure it, and further contributes to the application of capturing both expectation–confirmation model and CA (i.e. an intrinsic motivator) for completely explaining medical professionals’ perceived TTF and LTF as external variables to their cloud-based e-learning continuance intention and performance outcomes.

The primary objective of this research is to investigate the impact of task‐technology fit on users’ performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem‐solving programs, and four questionnaires were developed. A laboratory experiment was performed with 292 undergraduate auditing students. The results suggested that the effect of task‐technology fit on accuracy in solving problems was marginal for case‐based reasoning with unstructured tasks. No significant effect was found on problem‐solving accuracy for rule‐based reasoning with structured tasks. The task‐technology fit, however, marginally increased users’ certainty of the correctness of their solutions.

Using the extended task‐technology fit (TTF) model, the purpose of this paper is to examine the impact of intranet usage on middle managers' performance in the port industry.

The study was conducted on 357 middle managers from various organisations in the Malaysian port industry.

The structural equation modelling results indicate that TTF and usage significantly explains the variance on managers' performance. TTF is a predictor of perceived usefulness and usage but it does not predict user resistance. Perceived usefulness is a predictor of usage but it does not predict user resistance. User resistance does not predict managers' performance.

The study focuses only on the port industry in Malaysia and concentrates only on the management perspective of intranet usage.

The results provide insights on how the Malaysian port industry and other organisations of a similar structure could improve on their intranet adoption.

This study is perhaps one of the first to address the intranet adoption in the port industry using a comprehensive, extended TTF model (perceived usefulness, usage, user resistance) to investigate their influences on individual job performance.

This paper aims to examine organizational information systems based on Web 2.0 technology as socio-technical systems that involve interacting relationships among actors, structure, tasks and technology. Alignment within the relationships may facilitate increased technology use; however, gaps in alignment may impede technology use and result in poor performance or system failure. The technology examined is an organizational wiki used for collaborative knowledge management.

Results of a survey administered to employees of an organization providing cloud computing services are presented. The research model depicts the socio-technical component relationships and their influence on use of the wiki. Hierarchical latent variable modelling is used to operationalize the six main constructs. Hypotheses propose that as alignment of a relationship increases, wiki use increases. The partial least squares (PLS) method is used to examine the hypotheses.

Based on the results, increased perceptions of alignment among technology and structure increase wiki use. Further analysis indicates that low usage may be linked to gaps in alignment. Many respondents with lower usage scores also indicated “low alignment” among actor-task, actor-technology, and task-structure.

The sample size is rather small; however, results may give an indication as to the appropriateness of dimensions chosen to represent the alignment relationships. Socio-technical systems theory (STS) is often utilized in qualitative studies. This paper introduces a measurement instrument designed to evaluate STS through quantitative analysis.

User acceptance and change management continue to be important topics for both researchers and practitioners. The model proposed here provides measures that may reveal predictive indicators for increased information system use. Alternatively, practitioners may be able to utilize a diagnostic tool as presented here to assess underlying factors that may be impeding effective technology utilization.

The paper presents a diagnostic tool that may help management to better uncover misaligned relationships leading to underutilization of technology. Practical advice and guidelines are provided allowing for a plan to rectify the situation and improve technology usage and performance outcomes.

The purpose of this paper is to develop a research model examining users’ perceived needs-technology fit of mobile communication software through motivational needs and technological characteristics. The study investigated the effects of perceived needs-technology fit on user satisfaction and intention to continue using mobile communication software.

This study proposes a research model based on task-technology fit theory and uses and gratification theory, incorporating key determinants of users’ continuance intention toward mobile communication software. An online survey instrument was developed to collect data, and 403 questionnaires were used to test the relationships in the proposed model.

The causal model was validated using AMOS 21.0, and all nine study hypotheses were supported. The results indicated that users’ perceived needs-technology fit and satisfaction were crucial antecedents of their intention to continue using mobile communication software and that they mediated the influence of users’ needs as well as technological characteristics.

Mobile communication software practitioners should focus on enhancing users’ perceived needs-technology fit through motivational needs (utilitarian, hedonic, and social needs) and technological characteristics (mobile convenience, service compatibility, and user control) to further boost user satisfaction and intention to continue using mobile communication software services.

This study contributes to a theoretical understanding of factors explaining users’ continuance intention toward mobile communication software.

Using the extended task‐technology fit (TTF) model, this paper attempts to determine whether task‐technology fit, perceived resistance, user resistance and usage influence managers' performance.

The study was conducted on 150 middle managers from various organisations in Malaysia's port industry.

The structural equation modelling results reveal that task‐technology fit is significantly related to usage and perceived resistance, and that perceived resistance is a predictor of usage. Usage predicts performance, but not user resistance. There is no relationship between usage and user resistance, and vice versa.

The study focuses on Malaysia's port industry and concentrates only on the management perspective of intranet usage.

The results provide insights into how the Malaysian port industry and other organisations of a similar structure could enhance their intranet usage.

This study is one of the first to address intranet usage in the port industry, and introduces two importance factors (i.e. perceived resistance and user resistance) that influence managers' task performance.

The internet technology becomes an essential tool for individuals, organizations, and nations for growth and prosperity. The purpose of this paper is to integrate the DeLone and McLean IS success model with task-technology fit (TTF) to explain the performance impact of Yemeni Government employees.

Questionnaire survey method was used to collect primary data from 530 internet users among employees within all 30 government ministries-institutions in Yemen. The four constructs in the proposed model were measured using existing scales. The data analysis starts with initial exploratory factor analysis, then confirmatory factor analysis and lastly structural equation modeling via AMOS.

The results showed that the proposed integrated model fits the data well. Findings of the multivariate analysis demonstrate four main results. First, actual usage has a strong positive impact on user satisfaction, TTF, and performance impact. Second, user satisfaction has a great influence on performance impact. Third, TTF has a strong positive impact on user satisfaction and performance impact. Fourth, both user satisfaction and TTF mediate the relationship between the actual usage and performance impact.

The public sector in Yemen contains three parts: Yemeni prime minister, Yemeni ministries, and government agencies. This study focuses only on the Yemeni employees among Yemeni ministries; hence the results are not necessarily generalizable. Moreover, there are biases when the researcher measures the actual Internet usage variable through asking a participant about their opinion regarding their usage because these are generally found to differ from the true score of system usage.

The findings should be very useful for the Yemeni Government in presenting the importance of information technology effects on individual efficiency and effectiveness. Therefore, the information from these findings should encourage and support the formation of future policy at the organizational level and national level. If the government utilizes these findings by setting up strategies to promote internet usage, this may, in turn, improve professional practice, personal development, and quality of working life.

This paper adds to the existing literature of information systems by combining actual technology usage, user satisfaction, and TTF to predict performance impact within the organizations. Furthermore, this study proposed a second-order model of performance impact in order to increase the power of explaining the output by the model, which contains four first-order constructs: process, knowledge acquisition, communication quality, and decision quality. The predictive power of the proposed model has a higher ability to explain and predict performance impact compared to those obtained from some of the previous studies.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

The purpose of this paper is to propose a computer‐supported collaborative learning (CSCL) research framework.

The framework was developed from a review and synthesis of the literature. More specifically, gaps in the literature were identified and a general framework for future CSCL research was proposed.

This paper proposes a research framework that identifies a fit profile between learning objectives, learning tasks, and technology in CSCL. The fit profile, in turn, is expected to influence users' learning processes and outcomes.

This framework can serve as a foundation for future research in CSCL. More specifically, research should give consideration to the collaborative learning objective and task, which will vary in degrees of complexity and cognitive demands. Also, researchers will want to evaluate the level of sophistication of the CSCL technology, as well as its appropriateness for a given objective and task, which ultimately influences the learning process and outcomes.

Considerations and guidelines for development of CSCL applications are provided.

CSCL is an important research area that has attracted the attention of researchers from various disciplines (e.g. information systems, educational psychology). However, the findings are varied and sometimes conflict with each other. By emphasizing a fit profile between learning objectives, learning tasks, and technology, this paper proposes a theory‐based research framework to address the inconsistencies observed in the literature.