Task-technology Fit Model Research Articles

Mediating effect of ambiguity tolerance in automated writing evaluation research model

In light of the advancement of generative artificial intelligence (GenAI) technology-empowered automated writing evaluation (AWE) system represents a revolutionary paradigm at the forefront of mobile assisted English learning (MAEL). Numerous empirical studies have been carried out to test the efficacy of automated writing corrective feedback in writing assistance. However, it has seldom been investigated from the cognitive stance of AWE technology enhanced embodied learning. Embodied cognitive linguistics intensifies that learning experience is enhanced in the process of conceptualizing the empirical world. Therefore, the current study explores technology characteristics and individual characteristics incorporating the psychological construct of ambiguity tolerance. Emanating from psychology, ambiguity tolerance describes people’s preponderance to manage uncertainties and unpredictable challenges in the learning process. This study utilized the partial least square structural equation modeling (PLS-SEM) method to analyze 679 valid questionnaire responses via SPSS 29 and SmartPLS 4. The results elucidated that the AWE research model incorporating the technology acceptance model (TAM model), task technology fit model (TTF model), and individual characteristics could predict the user’s adoption of AWE software in the completion of writing tasks. Moreover, ambiguity tolerance functions as an effective mediator in the GenAI technology-empowered automated writing evaluation research model. This study provides technical implications for AWE developers to design AWE software suitable for individual characteristics. Future research could combine qualitative research methods with multivariant statistical approaches to meticulously investigate the interaction of literacy cultivation and emotional intelligence in the utilization of AWE technology for academic purposes in the GenAI era.

Establishing an Evaluation Indicator System for User Satisfaction With Hypertension Management Apps: Combining User-Generated Content and Analytic Hierarchy Process.

Hypertension management apps (HMAs) can be effective in controlling blood pressure, but their actual impact is often suboptimal. Establishing a user satisfaction evaluation indicator system for HMAs can assist app developers in enhancing app design and functionality, while also helping users identify apps that best meet their needs. This approach aims to improve the overall effectiveness of app usage. This study aims to systematically collect data on HMAs and their user reviews in the United States and China. It analyzes app usage patterns and functional characteristics, identifies factors influencing user satisfaction from existing research, and develops a satisfaction evaluation indicator system to provide more accurate recommendations for improving user satisfaction. We conducted a descriptive statistical analysis to assess the development status of HMAs in both countries and applied the task-technology fit model to evaluate whether the app functionalities align with business needs. We separately summarized the factors influencing user satisfaction in both countries from previous research, utilized the analytic hierarchy process to develop an evaluation indicator system for HMA user satisfaction, and calculated satisfaction levels. Based on these findings, we propose improvements to enhance app functionality and user satisfaction. In terms of current development status, there were fewer HMAs and user reviews in China compared with the United States. Regarding app functional availability, fewer than 5% (4/91) of the apps achieved a demand fulfillment rate exceeding 80% (8/10). Overall, user satisfaction in both countries was low. In the United States, user satisfaction was lowest for advertising distribution, data synchronization, and reliability. By contrast, Chinese apps need improvements in cost efficiency and compatibility.

A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation.

The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system. This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data. A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version. The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13). The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.

Evaluating the effect of face recognition technology on citizens’ happiness

ABSTRACT Face recognition technology (FRT) has a significant impact on citizens’ lives. Based on involvement theory, interaction ritual chain theory, social interaction theory, and the task-technology fit (TTF) model, this study evaluates the effect of FRT on citizens’ happiness. We conducted data collection in a Chinese region widely using FRT, with 653 effective samples, using structural equation models to evaluate the data. Finally, we draw five novel and valuable conclusions. First, task characteristics have a significant positive influence on TTF, but a similar effect is not evident between TTF and technical characteristics. Second, cognitive involvement positively affects positive emotion, which subsequently positively affects actual use. Third, TTF has a significant positive impact on actual use, which can also be transmitted through positive emotion. Fourth, actual use and positive emotion have a significant positive effect on feedback interaction. Fifth, feedback interaction has a significant positive effect on happiness, and this impact can also be transmitted through social connection. A potential contribution of this study is its theoretical framework and empirical model, which integrate the similarities and differences between FRT and other technologies used to meet citizens’ needs, revealing respondents’ reactions to and appreciation for FRT.

What drives the adoption of digital technology in the micro, small, and medium enterprises sector in a developing economy?

PurposeThis study aims to explore the determinants of digital technology adoption within micro, small and medium enterprises (MSMEs) operating in a developing economy. Grounded in the theoretical frameworks of fit-viability and task-technology fit models, this study investigates the impact of environmental fit, task-technology fit and viability on the intention to adopt digital technologies among MSMEs.Design/methodology/approachThis study validates the theoretical model using structural equation modeling, using data collected from 280 experienced respondents employed in the MSMEs in India.FindingsThe results indicate that both functional and symbolic benefits positively impact managers’ intention to adopt digital technology. However, subgroup analysis reveals that in the case of service enterprises, only functional benefits have a positive influence on managers’ intention to adopt digital technology. In addition, the findings underscore the crucial role of viability in shaping the intention to adopt digital technologies among MSMEs. This study highlights how functional and symbolic benefits motivate digital technology adoption in MSMEs.Originality/valueThere is a dearth of empirical studies investigating the factors influencing the adoption of digital technology by MSME firms, especially within the context of developing economies and specifically within the MSME domain. This study contributes to the theoretical discussion surrounding digital technology adoption among MSMEs in India. Through empirical research, it expands on the fit-viability model and formulates a technology adoption model within the MSME context.

The influences of four dimensions of perceived fit on individuals’ utilisation of SPOCs: an extension of the task-technology fit model

ABSTRACT Small Private Online Courses (SPOCs) platform enables individuals to carry out their online and offline learning activities. In order to understand individuals’ utilisation of SPOCs, this study develops a research model to examine the joint influences of four dimensions of perceived fit manifested in perceived technology-task fit (TTF), perceived individual-technology fit (ITF), perceived online-offline task fit (OTF), and perceived online-offline interactivity fit (OIF). A survey is conducted at a famous university in China, and 371 data are collected from students who select courses on the SPOC platform. Structural equation modelling method is used to examine the research model. The empirical results suggest that ITF is the most significant antecedent of individual performance expectancy, followed by OTF, TTF, and OIF. Furthermore, individual performance expectancy positively influences satisfaction and continuance intention in the SPOC platform. Moreover, this study incorporates self-regulation as a moderator to explore behavioural differences between individuals with high and low self-regulation. This study extends the traditional perceived fit framework by introducing OTF and OIF and uncovers the antecedents and boundary conditions of individuals’ utilisation of SPOCs in the emerging research context.

How to build better environments that reinforce adaptation of online learning?——Evidence from a large-scale empirical survey of Chinese universities

This study is based on a comprehensive review of existing literature. Drawing upon the theories of task-technology fit (TTF) and technology adoption model (TAM), an integrated theoretical framework is developed. The framework places adaptability as a critical outcome variable and quality standard. This study constructs a student-centered online learning process model oriented towards effectiveness. The research is intended to utilize data from the "National Survey of Undergraduate Education Teaching Quality in Ordinary Universities" encompassing 123,894 Chinese university students from 272 undergraduate institutions. Through a questionnaire-based survey, the study comprehensively investigates the influence paths and mechanisms of several key factors, including environmental support (ES), teaching satisfaction (TS), perceived ease of use (PEU), task-technology fit (TTF), and learning adaptability (LA). The structural equation model largely validates the study's hypotheses, demonstrating significant correlations among the variables. The task-technology fit (TTF) and adaptability of university students are found to be influenced by teaching satisfaction (TS), perceived ease of use (PEU), and environmental support (ES). task-technology fit (TTF) emerges as a pivotal process variable in online learning. teaching satisfaction (TS) and perceived ease of use (PEU), as foundational elements, positively influence learning adaptability (LA) through the mediating role of task-technology fit (TTF). However, environmental support (ES) negatively predicts task-technology fit (TTF). The study findings offer empirical support for designing an environment conducive to enhancing students' adaptability in online learning. Furthermore, the outcomes guide policy formulation and practical strategies in various aspects, including optimizing teacher instruction in the online setting, enhancing students' perceived usability of digital technologies, and promoting the supply of online technological functionalities. Ultimately, these efforts contribute to the welfare of students' learning experiences.

Computerized accounting information systems: An application of task technology fit model for microfinance

This study investigates the factors of Computerized Accounting Information Systems (CAIS) and the application of Task Technology Fit (TTF) in 190 decision-makers at accredited microfinance in Indonesia that apply technology adoption in decision-making processes. The results of analysis with SEM AMOS highlighted some findings: (1) The system quality of the computerized accounting information system and task analyzability have a significant effect on Task Technology Fit (TTF), (2) Task Technology Fit (TTF) has a positive relationship with perceived usefulness and sustainable performance. Furthermore, this study contributes to the development of TTF theory in optimizing integrated technological capabilities, accounting information systems, and task characteristics toward Sustainable Performance. The prominent contribution of this research is the importance of technology (including CAIS and Core Micro Banking System). Finally, this study will enrich references in the field of Accounting and Finance, especially for the application of technology for Sustainable Performance in Microfinance.

Factors influencing consumers’ continuance usage intention toward Food Delivery Apps during COVID-19 in Bangladesh

The aim of this research is to identify the factors that are crucial to formulating consumers’ attitude toward using food delivery apps (FDAs) continuingly during the coronavirus pandemic. Due to the pandemic, the catering enterprises changed their way of doing business significantly and gone through a digital transformation to survive. Consequently, this marked a shift in consumers’ attitude towards FDAs. To understand the shift and how consumers’ technological and mental perceptions are contributing to continuance usage intentions, a hypothetical model was proposed using variables from Unified Theory of Acceptance and Use of Technology (UTAUT), Expectancy Confirmation Model (ECM) and the Task-Technology fit Model. A structured questionnaire was used to collect feedback from the repeat users of the FDAs through online social media sites and food review groups. In total 198 valid responses were gathered and analyzed. Structural Equation Modeling (SEM) through AMOS 26 was utilized to test the proposed model fit. Some items in the constructs had to be dropped off to make the model fit. Initially four hypotheses were proposed. Among them only two were validated. The outcomes show that satisfaction and social influence have significant positive impact in determining continuance usage intention toward FDAs among Bangladeshi consumers. While perceived Task-technology fit and trust have non-significant relationship with consumers’ continuance intention. These insights might prove helpful for both online food aggregators and catering businesses in designing marketing plan and formulating effective strategies to build a sustainable consumer base. Future research can extend the model by adding more variables to understand relevant technology’s continuance usage intention. This paper is a unique contribution to the existing literature on FDA continuance intention on coronavirus emergency because no previous studies were undertaken to investigate Bangladeshi consumers. So, this is an original piece of work according to the best of the researchers’ knowledge

Employee Acceptance of Human Resource Management Information System: Integrated UTAUT and TTF Model in a Selected Public Firm in Sri Lanka

Aim: In the modern digital world, many companies are moving towards technology-based applications to perform human resource administration work. Employees nowadays are capable of accessing a web-based Human Resource Management Information System (HRMIS) to obtain critical data from recognized techniques such as System Applications and Products (SAP), PeopleSoft, Bann and Lawson. This study aims to investigating the factors affecting the adopting intention of HRMIS in a selected public firm in Sri Lanka.
 Design: Acknowledging the vital concepts on accepting or resisting technology, six components (performance expectancy, effort expectancy, social influence, facilitating conditions, task characteristics, and technology characteristics) from the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Task Technology Fit (TTF) model were contextualized to investigate the drivers influencing HRMIS adoption intentions in a selected public firm in Sri Lanka. Target population was identified as the Executives, Supervisors/Technical Officers, Clerical and Allied employees of the chosen public company in Sri Lanka. A structured online questionnaire, including 30 items, was used to collect data.
 Findings: Multiple regression analysis results revealed that the factors of Performance Expectation, Task Characteristics, and Technology Characteristics have a positive influence on users’ adoption intention to HRMIS. Effort Expectancy, Social Influence, and Facilitating Conditions had negative influence.
 Implications: The outcomes highlight the necessity of combining TTF components with technology acceptance theories when evaluating the factors influencing acceptance of HRMIS or other information systems. The study's findings will aid management in making the required organizational changes to encourage employees to use the HRMIS application.

How do privacy concerns impact actual adoption of central bank digital currency? An investigation using the e-CNY in China

<abstract> <p>Central Bank Digital Currencies (CBDC) are being researched in academia and piloted by central banks around the world. Initial research highlights the importance of privacy concerns on adoption intention in CBDC. We took one step further and investigated the link between privacy concerns and adoption using the Chinese CBDC and digitalized version of the Yuan, the e-CNY. We integrated and applied the established Antecedent Privacy Concerns and Outcomes (APCO) model with the Task-Technology Fit model in a quantitative online-questionnaire with 682 Chinese participants to study the influence of privacy concerns on CBDC usage. The data was analyzed using partial least squares structural equation modeling (PLS-SEM) to identify significant path coefficients and effects in the developed model. The findings demonstrated that several antecedents significantly influenced privacy concerns, which in turn influenced e-CNY usage. In particular, perceived vulnerabilities impacted privacy concerns, while soft and hart trust factors were found to neither impact concerns or usage. When compared to prior research, the distinction between intention to use and usage of CBDC, under consideration of privacy concerns, seemed to be negligible. The often discussed 'privacy-paradox' could not be observed for CBDC. Observed differences in antecedents and other factors may have been due to cultural, political, and demographic factors, as well as different CBDC design choices. For practitioners, the results further emphasized the need for a privacy-friendly implementation of retail CBDC, which efficiently communicated user benefits while rebutting perceived vulnerabilities.</p> </abstract>

AI Chatbot Adoption in Academia: Task Fit, Usefulness and Collegial Ties

Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians’ intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on academicians’ adoption behavior is limited. This study proposes an integrated model incorporating TTF factors and social network characteristics like density, homophily, and connectedness to understand academics’ continuance intentions. Methodology: A qualitative study involving 31 interviews of academics from India examined attitudes and the potential role of social network characteristics like density, homophily, and connectedness in adoption. Results showed positive sentiment towards chatbots and themes on how peer groups accelerate diffusion. In the second phase, a survey of 448 faculty members from prominent Indian universities was conducted to test the proposed research model. Contribution: The study proposes and validates an integrated model of TTF and social network factors that influence academics’ continued usage intentions toward AI chatbots. It highlights the nuanced role of peer networks in shaping adoption. Findings: Task and technology characteristics positively affected academics’ intentions to continue AI chatbot usage. Among network factors, density showed the strongest effect on TTF and perceived usefulness, while homophily and connectedness had partial effects. The study provides insights into designing appropriate AI tools for the academic context. Recommendations for Practitioners: AI chatbot designers should focus on aligning features to academics’ task needs and preferences. Compatibility with academic work culture is critical. Given peer network influences, training and demonstrations to user groups can enhance adoption. Platforms should have capabilities for collaborative use. Targeted messaging customized to disciplines can resonate better with academic subgroups. Multidisciplinary influencers should be engaged. Concerns like plagiarism risks, privacy, and job impacts should be transparently addressed. Recommendation for Researchers: More studies are needed across academic subfields to understand nuanced requirements and barriers. Further studies are recommended to investigate differences across disciplines and demographics, relative effects of specific network factors like size, proximity, and frequency of interaction, the role of academic leadership and institutional policies in enabling chatbot adoption, and how AI training biases impact usefulness perceptions and ethical issues. Impact on Society: Increased productivity in academia through the appropriate and ethical use of AI can enhance quality, access, and equity in education. AI can assist in mundane tasks, freeing academics’ time for higher-order objectives like critical thinking development. Responsible AI design and policies considering socio-cultural aspects will benefit sustainable growth. With careful implementation, it can make positive impacts on student engagement, learning support, and research efficiency. Future Research: Conduct longitudinal studies to examine the long-term impacts of AI chatbot usage in academia. Track usage behaviors over time as familiarity develops. Investigate differences across academic disciplines and roles. Requirements may vary for humanities versus STEM faculty or undergraduate versus graduate students. Assess user trust in AI and how it evolves with repeated usage, and examine trust-building strategies. Develop frameworks to assess pedagogical effectiveness and ethical risks of conversational agents in academic contexts.

Exploring the factors affecting behavioral intention to adopt wearable devices

The study explores factors like healthcare, fashionability, ease of use. affecting adoption of wearable devices, with the intent to contribute to the current global health research. ObjectiveThe objective of this study is to identify the effect of different factors on behavioral intention to adopt wearable device. With regard to the intention to utilize smart wearables, we explicitly proposed a theoretical model based on the task-technology fit (TTF) model, the technology acceptance model (TAM), and external factors. MethodsPrimary data was collected from 139 respondents and structural equation modeling was performed for getting insights regarding intention to adopt wearable devices. SmartPLS software was used to analyse the data. ResultsThe hypothesized model explains 76.9 % variance of BI. 73.5 % of PEOU and 79.3 % of PU. Task and technology characteristics explain 88.9% of TTF construct. Connectivity and health care have a significant impact on TTF. TTF model has effect on TAM model leading to Behavioral Intention to adopt smartwatch. In addition, fashionability had a significant effect on BI. However as per study results, perceived privacy did not impact BI. ConclusionsIn conclusion we document that our findings are in consistency with the original TAM and TTF model. Therefore, this provides an opportunity to extend previous findings of users’ adoption. From this study the reliability and validity of integrated models is also verified. Furthermore, the aspects of wearable devices such as fashionability, ease of use, connectivity and healthcare turned out to be significant in the intention to adopt wearable devices.

Successful Electronic Consultation Service Initiative in Quebec, Canada With Primary Care Physicians' and Specialists' Experiences on Acceptance and Use of Technological Innovation: Cross-Sectional Exploratory Study.

Electronic consultation (eConsult) is an eHealth service that allows primary care providers (PCPs) to electronically consult specialists regarding their patients' medical issues. Many studies have demonstrated that eConsult services improve timely access to specialist care; prevent unnecessary referrals; improve PCPs', specialists', and patients' satisfaction; and therefore have a large impact on costs. However, no studies have evaluated PCPs' and specialists' acceptance of eConsult services in Quebec, Canada, and worldwide. This exploratory study aims to identify factors affecting eConsult service acceptance by PCPs and specialists in urban and rural primary care clinics across 3 regions in the province of Quebec, Canada, by integrating the Unified Theory of Acceptance and Use of Technology and Task-Technology Fit (TTF) models and user satisfaction. This research was designed to broaden and assist in scaling up this effective eHealth service innovation across the province. A cross-sectional web-based survey was sent to all PCPs (n=263) and specialists (n=62) who used the eConsult Quebec Service between July 2017 and May 2021. We proposed a unified model integrating the Unified Theory of Acceptance and Use of Technology model and TTF model and user satisfaction by endorsing 11 hypotheses. The partial least squares was used to investigate factors influencing the acceptance of the eConsult Quebec Service. Of the 325 end users, 136 (41.8%) users responded (PCPs: 101/263, 38.4%; specialists: 35/62, 57%). The results of the analysis with partial least squares method indicate that 9 of our 11 hypotheses are supported. The direct relationships uniting the various constructs of the model highlighted the importance of several key constructs and predominant correlations. The results suggest that satisfaction is the key driver behind the use of the eConsult Quebec Service. Performance expectancy (P<.001) and effort expectancy (P=.03) can have a positive impact on behavioral intention (BI), and BI (P<.001) can impact adoption. TTF has an influence on performance expectancy (P<.001), adoption (P=.02), and satisfaction (P<.001). However, the results show that there is no direct effect between social influence (P=.38) and BI or between facilitating conditions (P=.17) and adoption. This study provides a better understanding of the factors influencing PCPs' and specialists' intention to adopt the eConsult Quebec Service. Furthermore, this study tests a research model and a technology that have never been explored in Quebec until now. On the basis of the results, the service is a good fit to meet the users' need to improve access to specialized medical advice. Therefore, the results of our study have made a valuable contribution to the implementation of the service by policy makers in order to maximize acceptance, use, adoption, and success across the province of Quebec. Moreover, after 4 successful years, the eConsult Quebec pilot project is now the Conseil Numérique digital consultation service.

A SWOT analysis of smart governance applications amid the COVID-19 pandemic

Smart governance technologies are emergent information and communication technologies (ICT), which amid the COVID-19 pandemic have been widely used to govern COVID’s impacts. However, very few studies have provided insight into the strengths, weaknesses, opportunities, and threats (SWOT) of smart governance technologies under COVID-19. This prevents the widespread usage of smart governance technologies across cities and countries and impedes their usefulness during emergencies. This study combines knowledge of emergency management theory and planning support systems (PSS) to frame the functional usefulness of smart governance technologies in handling practical tasks amid the COVID-19 state of emergency into a task-technology fit model. By employing qualitative content analysis to collect views and insights from the international news-based cases (n= 69), we identify SWOT of smart governance technologies under COVID-19. Results indicate that usefulness of smart governance technologies is reflected in task-handling in the ex-post emergency phases of response (e.g., provide e-health service, tracing and tracking) and recovery (e.g., improve e-government services, enhance collaboration for renewal), whereas few applications focus on tasks in the ex-ante emergency phases of mitigation (e.g., hazard analysis, vulnerability assessment) and preparedness (e.g., planning, training and exercises). Main findings lead to a conclusion that hidden capacities of smart governance technologies can be fully achieved if policy makers and practitioners can focus on the full range of tasks and demands before, amid, or after emergencies and identify collaborative opportunities for technological development.

Factors Influencing Interest in Continuing Use of e-Wallet Using the Technology Acceptance Model and Task-Technology FIT

An electronic wallet (e-Wallet) application allows users to make transactions electronically by storing user money on a server. The use of e-wallets is increasing daily, increasing the number of transactions and users. This research aims to analyze the factors related to the intention to reuse e-wallet applications so that they can contribute to the development of e-wallet applications. This quantitative research combined the Technology Acceptance Model and Task-Technology Fit models using the PLS-SEM method and SmartPLS software. Data was collected using a Likert scale questionnaire based on Google Forms. Data analysis technique using inferential statistical analysis. The research results obtained after testing the Outer Model, Inner Model, and Hypothesis Testing show that task characteristics, technological characteristics, task and technology suitability, and users' perceived usability significantly influence the intention to reuse e-wallet applications. The user's perceived ease of use factor does not significantly influence the intention to reuse the e-wallet application. Based on the results obtained, this study has a high predictive relevance value, thus confirming that the combination of the TAM and TTF models has an effective theoretical basis for analyzing the sustainable interest of e-wallet applications.

Understanding consumer behavior toward adoption of e-wallet with the moderating role of pandemic risk: an integrative perspective

PurposeOver the last few decades, electronic banking has been used widely to manage financial transactions worldwide. More recently electronic wallet (e-wallet) banking has been identified as an innovative mode of e-payment and enhances e-banking customer experience. Although e-wallet banking service is more secure, fast, convenient and cost effective, compared to conventional web-based banking, adoption of e-wallet application is limited among e-banking consumers. To address this issue, the current study has conceptualized task technology fit (TTF) model, diffusion of innovation (DOI) theory and protection motivation theory towards adoption of e-wallet service. Moreover, pandemic risk is studied as moderating factor between the relationship of e-wallet and use of e-wallet banking among banking consumers.Design/methodology/approachThe research design of this study is based on positivism research paradigm. This study is cross-sectional and used deductive level of theory to formulate hypotheses. Research survey was conducted towards e-banking users. For statistical findings research framework is tested with 280 numerical responses. Data are estimated through partial least square structural equation modeling (PLS-SEM) approach.FindingsStatistical results demonstrates that collectively factors underpinned protection motivation theory, TTF and DOI have shown large variance R2 65.7% in adoption of e-wallet. The effect size f2 analysis has revealed that compatibility is one of the most influential factors in determining individual behavior to adopt e-wallet. Similarly, Geisser and Stone Q2 analysis has disclosed substantial predictive power to predict adoption and use of e-wallet service.Practical implicationsTheoretically, this study integrates protection motivation theory, DOI theory and TTF model toward adoption of e-wallet service and hence contributes to information system literature. To practice this, research has suggested that factors such as pandemic risk, perceived severity and compatibility are most influential factors and hence need policy makers' attention to boost e-wallet adoption.Originality/valueThis study is original as the study develops an integrative research model to investigate e-banking user behavior to adopt of e-wallet service. Moreover, pandemic risk is tested as moderating factor between adoption and use of e-wallet which, in turn, enhance the value of this study and directs how to deal with existing and future pandemic crisis.

AN EMPIRICAL STUDY ON ACADEMIC SUSTAINABILITY OF MOBILE LEARNING AT UNIVERSITY LEVEL

This study proposes an investigation model for merging Technology Acceptance Model (TAM) for mobile learning utilization in the field of academic sustainability. Mobile education is not fully employ in the universities of Pakistan, so that is the reason why students/teacher Attitude Towards Technology (ATT) and learning teaching through empirical knowledge is at very low level in universities. The grounds of present study were to investigate state of contemporary adaptation of models in M- learning. This work supported the sustainability of portable learning through m-learning by mastery the elements that affect intention of users to use mobile education prior to apply it to make certain continuity or success. The key goal was to facilitate universities a comprehend sight of those elements that can affect the degree to which learners or students adopt any new technology. Resulted outcomes of present study can help academics along with managers to fully acknowledge the way mobile educating system utilization can affect learners’ educational sustainability as well as academic performance. To achieve these results, an enlarge Technology Acceptance Model (TAM) was evolved, which bring out literature relevant to Mobile learning use in Pakistan universities. Furthermore there was no such research in this perspective in Pakistan which has integrated three models as TAM (Technology Acceptance Model), BIM (Behavior Intention Model), and TTF (Task Technology Fit Model) for acceptance of Mobile learning. The aim of present work was to couple essential features of TAM and constructivism with academic sustainability

Information Systems Adoption and Performance: Evidence from Canadian Firms Linking Individual and Organizational Performance

ABSTRACT Implementing a new information system is bound to be a risky decision for any company. Studying the main factors that can help to improve the adoption success is crucial to help mitigate the risk of failure. This paper explores information systems adoption in a Canadian context and evaluates the main factors that drive performance using two models: 1) the task-technology fit (TTF) model and the technology acceptance model (TAM).

Individual performance and mobile banking: Role of task technology fit model

The study examines how task technology fit factors determine the individual performance of mobile banking application users. There isn’t any study about individual performance in the context of mobile banking using the task technology fit model in Nepal, thus it will be a novel study from a Nepalese perspective. The study investigates the factors of the task technology fit model and collects 181 data using a google survey questionnaire. Data were then analyzed using structural equation modeling by AMOS. The findings indicate that task characteristics have a positive and significant effect on task technology fit and the use of mobile banking has a positive and most promising effect on individual performance. However, a positive but insignificant impact can be seen of technology characteristics on task technology fit and task technology fit on the use of mobile banking. This study provides insight into the financial institutions in Nepal regarding the perception of their users toward mobile banking application services.