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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 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.

Constant and frequent technological changes within organizations call for further scholarly attention, as behavioural intentions need to be coupled also with future learning intentions to predict the present and prospective individual adaptations and performance. This study, grounded on the technology acceptance model, aims to examine the association between training opportunities and behavioural and future learning intentions also taking into account the role of task–technology fit as a moderator.

A survey was carried out within a single organization in the water processing sector on a sample of 200 workers who recently experienced a technological change through the adoption of System Application and Product in data processing. A moderated–mediation model was estimated through regression analyses with bootstrapping.

The results were consistent with study hypotheses. In particular, task–technology fit amplified the positive association between perceived ease of use and training opportunities as well as the indirect effect of this latter on both behavioural and future learning intentions through perceived ease of use and perceived usefulness. In sum, the hypothesized moderated–mediation model was confirmed.

Three novelty factors of this study can be stressed: it is among the few studies carried out on Italian workers in the realm of technology adoption, it expanded the technology acceptance model by including traditional behavioural intentions and future learning intentions as outcome variables and it integrated the task–technology fit perspective within the technology acceptance model.

This study aims to investigate the relationship between information and communication technologies (ICTs) use and work performance during the coronavirus disease 2019 (COVID-19) pandemic. Specifically, it aims to understand what the role of task–technology fit is, and what effect this has on feelings of loneliness of individuals and their subsequent work performance. As a large proportion of workers are required to work from home during the ongoing COVID-19 pandemic, understanding what aspects contribute to higher performance and reduced negative psychological outcomes is of increased practical and research interest.

To explore these questions, a quantitative approach that employed a sample population of 357 individuals who worked from home during the COVID-19 pandemic was used. Using a convenience, purposive and snowball sampling approach the authors collected data through a custom-built online questionnaire, and analyzed the data using structural equation modeling (SEM).

The results highlight the effect that high task–technology fit has in both directly and indirectly influencing work performance of individuals. The authors find that by designing ICTs based on task–technology fit principles, individuals are less likely to experience feelings of loneliness while working from home and are more prone to perform better in their work-related tasks.

This study offers a new perspective on the role fit of tasks with technology have on influencing psychological states, and indirectly influencing work-related outcomes. The authors, therefore, expand the understanding about why task–technology fit is sought after by explaining part of the psychological mechanisms through which it has an effect on work performance.

Using the extended task‐technology fit (TTF) model, this paper aims to examine technology resistance, technology satisfaction and internet usage on students' performance.

The study was conducted at Universiti Teknologi MARA, Johor, Malaysia and questionnaires were distributed to 354 undergraduate students.

The structural equation modelling (SEM) results indicate that technology satisfaction and the internet usage significantly explains the variance on students' performance. Task‐technology fit is not a predictor of technology resistance but it does predict the internet usage. The internet usage has greater impact on technology satisfaction than technology satisfaction on the internet usage. Finally, technology resistance is not a predictor of students' performance.

The study focuses only on education in Malaysia and concentrates only on the students' performance and the relationship between technology resistance, technology satisfaction and the internet usage.

The results provide insights on how Malaysian education systems of a similar structure could improve upon their internet adoption.

This study is perhaps one of the first to address internet adoption in education using an extended task‐technology fit model (task‐technology fit, internet usage, technology resistance, technology satisfaction) to investigate their influences on students' performance.

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 advancement in Internet technology has played a significant role in revolutionizing the Internet banking services. Therefore, little is discussed about factors that motivate technology user to continue the use of Internet banking services. The current study investigates Internet banking user continuance behavior toward the use of Internet banking services with the integration of two-well known information system (IS) theories namely task technology fit (TTF) and technology continuance theory (TCT).

The research design of this study is based on positivist paradigm and followed quantitative research approach. Data were collected from 360 Internet banking users of commercial banks across Pakistan. The research model was tested with structural equation modeling (SEM).

The research model had explained 53.9% variance in Internet banking user continuance intention. Next to this, the predictive relevance of the research model was tested with Stone-Geisser's Q² values using blindfolding procedure. Results revealed that the newly developed integrated technology continuance research model has substantial power to predict Internet banking user continuance intention. Moreover, the effect size analysis revealed that factors like satisfaction and user expectation were the most important factors in determining Internet banking user continuance intention.

For practical implications importance performance matrix analysis (IPMA) has used to see the importance and performance of the underpinned factors. Findings indicate that managers and policy makers should focus on user satisfaction, perceived usefulness and expectation confirmation in order to enhance the Internet banking user continuance intention toward the use of Internet banking services. Some of the ways banks can do this is to develop esthetic Internet banking website with charm of novelty, relevant information and smooth flows with less complex redirects.

Unlike prior studies that focus on Internet banking user pre-adoption issues, the current study examines post-adoption issue of Internet banking users and investigates Internet banking user continuance intention. This study is significant as it integrates two-well known theories namely TCT and TTF in Internet banking user continuance intention and augments the IS literature by developing an integrated technology continuance model (TCM).

The performance of the World Wide Web is evaluated as an information resource in a specific information domain. The theoretical framework underpinning this approach recognises the contribution of information‐seeking behaviour from the discipline of information science and models of information systems success from the discipline of information systems in explaining World Wide Web usage as an information resource. A model integrating the construct of uncertainty and the task‐technology fit model is presented. A questionnaire‐based empirical study is used to test this integrated approach. Our results confirm that richer models representing the broad context of World Wide Web usage are required to evaluate usage of the World Wide Web as an information resource. Usage for travel tasks, uncertainty reduction, the Web as an information resource, and mediation all have a significant impact on users' perception of performance, explaining 46 per cent of the variance.

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.

Although the use of e-book readers has become increasingly widespread, there are few studies to evaluate e-book user behavior and satisfaction with commercial e-book stores, and even fewer approaches from the perspective of task-technology fit (TTF). In order to fill this gap, the purpose of this paper is to adopt the TTF theory to explore the factors that affect the behavior satisfaction of users of commercial e-book stores.

A survey was conducted to collect data from 183 e-book users. Data were collected from an online survey. The results were analyzed via the structural equation model.

The results show that functional service, mobility, convenience, and searching task are the important factors that influence users’ TTF behavior. Moreover, TTF may improve user satisfaction, flow, and scanpath. Finally, satisfaction was affected by TTF, scanpath, and flow factors. An analysis of the research explained 46 percent of the variance for the users’ TTF, and 59 percent of the variance for satisfaction of using the e-book store.

The research model modifies utility and performance from TTF theory in order to focus on individual flow, scanpath, and user satisfaction measurement because general e-book store users are not typically concerned about work performance issues for their leisure activities. These results provide a new perspective to e-book researchers and can help e-book store managers and designers in making policies and designing platforms.