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This research aims to integrate the unified theory of acceptance and usage of technology (UTAUT) with task technology fit to explain users' behavioral intention of using library mobile applications in university libraries.

By integrating the unified theory of acceptance and usage of technology (UTAUT) and the moderator of task-technology fit, this research proposes a library mobile applications usage intention model. The study data come from a convenience sample of 363 undergraduate and graduate students. A structural equation modelling (SEM) technique was conducted to identify causal relationships.

Results showed that the UTAUT model fits the data well. The empirical data reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions determine users' behavioral intention of using library mobile applications. As a determinant in the UTAUT model, the moderating effect of task-technology fit is also significant. Moreover, individuals with different levels of task-technology fit will strengthen or weaken the relationships of determinants in the intention to use library mobile applications in university libraries.

This study addresses self-reported behavioral intentions as part of the survey; as a result, the data may have introduced inaccuracies. The implications of the proposed library mobile applications success model are discussed.

University librarians should reinforce the efficiency of library mobile applications to influence users' willingness to use such applications.

This study combines both the constructs of the UTAUT model and task-technology fit to consider intentions to use library mobile applications in university libraries.

The purpose of this paper is to : first, examine information systems (IS) infusion from a user commitment perspective, and second, examine the formation of user commitment toward the use of IS in terms of job design.

This study adopts a survey approach with structural equation modeling to test the developed research model and hypotheses.

A survey of 236 enterprise system users shows that user commitment has a positive effect on IS infusion. User commitment, in turn, is influenced by task technology fit, technology self-efficacy, and task autonomy. Further mediation and direct effects to IS infusion are explored.

This study offers implications for research, such as explaining a driver of IS infusion; and extending commitment theory by finding antecedents of user commitment.

The results of this study offer suggestions to management on how to improve IS infusion in terms of user commitment and, consequently, how to develop user commitment based on the socio-technical system (STS) design.

The study highlights the critical impact of technology autonomy on IS infusion. An individual user’s authority in using and regulating the system is required for IS infusion.

This study has proposed a theoretical model of IS infusion based on commitment and socio-technical job design factors.

Enterprise social media (ESM) usage has gained considerable momentum within organizations. The purpose of this paper is to seek a better understanding of ESM usage based on the task environments and the technology affordances of ESM.

This study is based on a survey consisting of 556 usable responses. Regression methods are applied to analyze the data.

This study finds strong support for the positive impact of task-technology fit on ESM usage. Considering separately, task equivocality had no impact; task interdependence had a negative effect on ESM usage; bridging social capital had a positive effect; and bonding social capital had a marginal impact on ESM usage.

This research incorporated social capital into discussions of task-technology fit. A 2×2 matrix based on task equivocality and task interdependence was developed, which may be extended to other contexts or technologies.

ESM implementation should account for both task environments and the appropriate technology affordances. Individuals access bridging social capital to a greater extent than bonding social capital using ESM, and they do not use ESM when the task environments alone are considered.

This is an original study that considers task environments and technology affordances in the context of ESM usage. The findings offer valuable and timely contributions to both scholars and practitioners.

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.

The next phase of hardware technology development is focused on alternative ways to manage and store consumers’ personal content. However, even consumers who have adopted Cloud-based services have demonstrated a reluctance to move all of their personal content into the Cloud and continue to resist giving up local hard drives. This paper aims to investigate the characteristics of local hard drives and the Cloud that lead to simultaneous use.

This paper uses content analysis of online comments and ten depth interviews with simultaneous users of local hard drives and the Cloud.

Three factors influence the resistance to giving up local hard drives. Simultaneous users utilize local hard drives as a redundancy system and as a way to ensure the permanence of their digital content. They are unsure of the Cloud’s ability to support their content creation, management and storage activities (task-technology fit).

Study findings are based on qualitative methods and thus the results cannot be considered conclusive.

The authors speculate that it is unlikely that Cloud-only will fully replace hard drives until these factors are understood and addressed by information technology developers. Cloud service providers may not be aware of how little that users understand the Cloud. In contrast to their certainty and confidence in local hard drives, simultaneous users are confused as to what the Cloud is and how it functions. This uncertainty exacerbates their risk perception and need for control.

This is the first study exploring simultaneous use of local hard drives and the Cloud with a view to understanding this behaviour in terms of the relative advantage of the incumbent technology over the new technology.

Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.

Data were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.

Data analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers.

This study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.

The purpose of this study is to propose a synthetic post-adoption model based on the expectation-confirmation model (ECM) and flow theory to examine whether the fit factor, network factors and psychological factors as antecedents to end-users’ beliefs can affect their continuance intention of the robo-advisor.

This study used the research model based on ECM and flow theory to examine the effects of the fit factor, network factors and psychological factors on end-users’ beliefs and continuance intention of the robo-advisor. Sample data were collected from end-users at three financial services companies in Taiwan. A total of 450 questionnaires were distributed and 360 (80.0%) usable questionnaires were analyzed using structural equation modeling.

This study proposes a solid research model that based on ECM and flow theory, three types of factors, namely, fit factor, network factors and psychological factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined and this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that a synthetic post-adoption model can be proposed in this study by introducing the fit factor extracted from task-technology fit model, network factors originated from the theory of network externalities and psychological factors derived from uses and gratifications theory as antecedents to perceived usefulness, confirmation, satisfaction and continuance intention referred in ECM and flow experience derived from flow theory. Thus, this study’s research model and findings can reveal deep insights into the evaluation of determinants in the field of end-users’ continuance intention of the robo-advisor.

According to expectation–confirmation model (ECM) and task-technology fit (TTF) model, the purpose of this paper is to examine the role of TTF in students’ cloud-based e-learning continuance and evaluate whether TTF affects students’ perceived impact on learning of the cloud-based e-learning system within the educational institution.

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 500 questionnaires were distributed in the campus, and 391 (78.2 percent) usable questionnaires were analyzed using structural equation modeling in this study.

This study’s results verified that both task characteristics and technology characteristics affected students’ perceived TTF, which significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, and these in turn directly or indirectly led to their continuance intention of the system and perceived impact on learning; essentially, the results strongly supported the research model integrating ECM and TTF model via positioning key constructs as the drivers with all hypothesized links being significant.

This study contributes to an understanding of the TTF in explaining students’ cloud-based e-learning continuance that is difficult to explain with only their utilitarian perception of the cloud-based e-learning system, and further places considerably more emphasis upon students’ perceived impact on learning greatly driven by their TTF in the system. Thus, this study’s empirical evidence on incorporating ECM and TTF model can shed light on the outcome for cloud-based e-learning continuance and enhance better understanding of a richer post-adoption model.

This study aims to propose a hybrid model based on expectation–confirmation model (ECM), DeLone and McLean IS success model and task-technology fit (TTF) model to examine whether quality factors and TTF as the antecedents to user beliefs can affect organizational users’ continuance intention of cloud enterprise resource planning (ERP).

Sample data for this study were collected from end-users of cloud ERP working in companies in Taiwan. A total of 370 questionnaires were distributed in the 37 sample companies, and 315 (85.1 per cent) usable questionnaires were analyzed using structural equation modeling in this study.

This study’s findings indicated that users’ perceptions of system quality, information quality and TTF all contributed significantly to their satisfaction, confirmation and perceived usefulness (PU) of cloud ERP, which in turn directly or indirectly led to their continuance intention of cloud ERP; that is, the results strongly supported the hybrid model integrating ECM, DeLone and McLean IS success model and TTF model with all hypothesized links being significant.

Empirically, this study’s research model capturing ECM, DeLone and McLean IS success model and TTF model has significantly shed light on the possible formulation of a richer post-adoption model. Noteworthily, considerably more attention should be paid to the fit between information-related characteristics and cloud ERP users’ work goals and needs, while TTF, based on both system-related and information-related technology characteristics, significantly contributes to users’ satisfaction, confirmation and PU, which together directly or indirectly explain their intention to continue using cloud ERP.

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.