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This 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.

The 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.

Task 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.

The 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.

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.

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.

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.

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.

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.

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.

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.

Traditionally, Task–Technology Fit (TTF) theory has been applied to examine the usefulness of technology in the work environment. Can the same approach (based on experience rather than tasks) be applied to non-work, cultural heritage environments? This is the question the authors ask in this study. This study proposes a new variation of TTF based on the experience economy model, namely Experience–Technology Fit (ETF), for the non-work environment, in particular, in the context of cultural heritage, where visitor experience is enhanced by extended reality technology, which combines immersive technologies and artificial intelligence.

Employing a quantitative survey method, the empirical analysis seeks to determine the influence of Mixed Reality (MR) characteristics (interactivity, vividness), Voice User Interface (VUI) characteristics (speech recognition, speech synthesis) and experience economy factors (education, entertainment, esthetic, escape) on satisfaction, revisit intention and actual purchase to propose a new ETF model.

VUI, MR, and experience factors were significantly associated with ETF; when combined with MR-based experience, ETF was significantly associated with satisfaction. This study’s findings further demonstrate the relationship between users' satisfaction when engaging with MR-based experience and revisit intention, while purchase intention was significantly associated with the actual purchase.

The novel contribution of this study is the proposal of the EFT model, a new variation of TTF based on the experience economy model. Overall, this study expands the applications of TTF to an experience-oriented business, thereby broadening the authors’ understanding of technological success with a specific focus on the technology fit of Extended Reality (XR) in the context of cultural heritage.

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.