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With the outbreak of the COVID-19 pandemic, online assessment has become the dominant mode of examination in higher education institutions. However, there are contradictory findings on how students perceive online assessment and its impact on their academic performance. Thus, the purpose of this study is to evaluate the potential impact of online assessment on students' academic performance.

This study proposes a research model based on the task–technology fit theory and empirically validates the model using a survey from students in the UK. In addition, the study conducted four experiments based on paper-based and online assessments and analysed the data using paired sample t test and structural equation modelling.

The findings show that the use of online assessment has a positive impact on students' academic performance. Similarly, the results from the experiment also indicate that students perform better using online assessments than paper-based assessments.

The findings provide crucial evidence needed to shape policy towards institutionalising online assessment. In addition, the findings provide assurance to students, academics, administrators and policymakers that carefully designed online assessments can improve students' academic performance. Moreover, the study also provides important insights for curriculum redesign towards transitioning to online assessment in higher education institutions.

This study advances research by offering a more nuanced understanding of online assessment on students' academic performance since the majority of previous studies have offered contradictory findings. In addition, the study moves beyond existing research by complementing assessment results with the views of students in evaluating the impact of online assessment on their academic performance. Second, the study develops and validates a research model that explains how the fits between technology and assessment tasks influence students' academic performance. Lastly, the study provides evidence to support the wide use of online assessment in higher education.

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.

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.

The service recovery literature provides little guidance to firms on how users of self-service technology (SST) perceive assistance provided by human and non-human service agents following a service obstacle. This research responds by addressing two important research questions about SST recovery: (1) how are perceptions of assistance provided following a service obstacle influenced by a customer's psychological needs? and (2) does supporting the psychological needs of customers positively impact continuance intentions following a service obstacle?

Data are collected to address the research questions via five experiments that explore how assistance provided by a non-human (vs human vs no assistance) service agent contributes to perceptions of psychological support and continuance intentions following a service obstacle while volitionally using SST.

The results show that while users of SST would prefer to do so without an obstacle requiring intervention of a service agent, if assistance is required then the psychological need support elicited from a non-human service agent was vital to an effective recovery. Further, the findings highlight some boundary conditions for this relationship, with the impact of customer perceived need support on continuance intentions found to be sensitive to fit between the task and assistance provided and the complexity of the task being completed.

Much of the prior service recovery literature has emphasized the different types of tactics that can be used (e.g. apologizing, monetary compensation and explaining what happened), failing to appreciate the role of different types of service agents or the underlying psychological process that explain the relative merit of such tactics. The present research shows that for these tactics to influence continuance intentions, they must be provided by a relevant service agent and support a customer's psychological need for autonomy, competence and relatedness. The hypothesized impact of psychological need support on continuance intentions was also observed to be contingent upon the fit between the task and the type of assistance provided, where the level of task complexity attenuated this fit.

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.

This paper aims to propose a user adoption model of human resource information system (HRIS) in the Jordanian public sector by integrating the task technology fit (TTF) model and the unified theory of acceptance and usage of technology (UTAUT).

Using a quantitative approach, survey data were collected using an online survey from employees working in four different public organizations in Jordan, and structural equation modelling has been used to validate the research model.

The study found that among the constructs of the UTAUT model performance expectancy, social influence and facilitating condition have a significant effect on users’ behavioural intention to adopt HRIS. Furthermore, the results also reveal that effort expectancy has an insignificant effect on adoption behaviour. The findings also show that all TTF hypotheses were supported by the data collected. Both task characteristics and technology characteristics have a significant effect on the TTF construct, which further determines users’ adoption behaviour.

These findings contribute to the extant academic literature and have practical implications, improving the understanding of the HRIS adoption and use in public sector organizations.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

Since the advent of augmented reality (AR) technology, “Smart Retailing” has become the dominant business model in the retail sector. Therefore, comprehending the dynamics of AR adoption is essential if retailers are to successfully encourage customers to embrace this extremely innovative form of technology. As a result, the authors propose and evaluate a more comprehensive model, consisting of the task-technology fit (TTF) and unified theory of acceptance and use of technology (UTUAT2) models, for use in low-income countries.

The present research uses variance-based partial least squares structural equation modeling (PLS-SEM) using WarpPLS.7 to examine 398 responses from Egyptian retail consumers.

TTF, performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating condition (FC), hedonic motivation (HM) and customer innovativeness (CI) positively affect shoppers' behavioral intentions (BI) to adopt AR Apps in retail, while perceived risk (PR) negatively affects BI.

The current study is the first to investigate the determinants of shoppers' BI toward AR Apps adoption in the retail context using UTAUT2 and TTF models.

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.

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.