Task-technology Fit Theory Research Articles

Investigating factors influencing AI customer service adoption: an integrated model of stimulus–organism–response (SOR) and task-technology fit (TTF) theory

PurposeArtificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.Design/methodology/approachThe study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.FindingsThe PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.Practical implicationsThe study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.Originality/valueThe originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.

Exploring the Application of ISSM and TTF Theories: A Literature Review

The Information System Success Model (ISSM) and Task-Technology Fit (TTF) theories provide valuable frameworks for assessing how effectively technology supports tasks within a system. These models are widely recognized and have been applied separately in numerous studies. However, there has been limited effort in the existing literature to integrate ISSM and TTF into a cohesive framework. This study aims to (1) review the relevant literature applying ISSM and TTF in higher education settings, (2) determine which model is more frequently utilized, and (3) evaluate studies that have integrated both models, with a particular focus on digital libraries. The results indicate that most research has centered on theory development or practical real-world applications, especially in public universities. The integration of ISSM and TTF in digital library contexts remains relatively rare despite their potential to complement one another. ISSM assesses system success through dimensions like service quality, user satisfaction, and net benefits, while TTF focuses on how well the technology fits the tasks it is intended to support, making it highly relevant for evaluating digital services. This study contributes to a deeper understanding of how ISSM and TTF are applied, offering valuable insights for future research. It highlights the untapped potential of combining these models to enhance technology adoption, user satisfaction, and overall system performance in digital libraries and other educational settings. By identifying research gaps, this paper lays the groundwork for further exploration and refinement of these theoretical frameworks to improve technology implementation in education.

The implications of blockchain for logistics operations and sustainability

Empirical studies based on detailed, theory-based analyses are essential for a deep understanding of technology adoption. This study provides an overview of blockchain applications in logistics management, employing a comprehensive theoretical framework. Blockchain is considered a critical digital infrastructure for logistics operations due to its distinctive characteristics, including decentralization, transparency, immutability, real-time information sharing, reliability, and end-to-end visibility. These characteristics address many contemporary logistics challenges. The study introduces a research model that integrates the fit-viability model (FVM) and task technology fit theory (TTF), demonstrating blockchain's suitability for enhancing logistics operational functions and sustainability performance. To validate the model, data were collected from logistics managers of 576 companies and analyzed using partial least squares (PLS) regression. This research offers valuable insights for managers, policymakers, and decision-makers on practical challenges and potential solutions in logistics through the application of blockchain. Furthermore, the study demonstrates that the implementation of blockchain can improve the alignment, resilience, transparency, integration, and sustainability of logistics tasks.

Exploring willingness to use adverse drug reaction reporting systems: a multicentre qualitative study in China based on the technology acceptance model and task-technology fit integration approach

BackgroundAdverse drug reaction (ADR) reporting systems are critical for monitoring and managing drug safety. However, various factors influence the willingness to use these systems. This study aimed to investigate the...

Student Perceptions of Generative Artificial Intelligence: Investigating Utilization, Benefits, and Challenges in Higher Education

This research explores the use of Generative Artificial Intelligence (GenAI) tools among higher education students in Saudi Arabia, aiming to understand their current perceptions of these technologies. This study utilizes the Technology Acceptance Model (TAM) and the theory of Task-Technology Fit (TTF) to examine students’ utilization, perceived benefits, and challenges associated with these tools. A cross-sectional survey was conducted, yielding 859 responses. The findings indicate that 78.7% of students frequently use GenAI tools, while 21.3% do not, often due to a lack of knowledge or interest. ChatGPT emerged as the most widely used GenAI tool, utilized by 86.2% of respondents, followed by other tools like Gemini, Socratic, and CoPilot. Students primarily use these tools for defining or clarifying concepts, translation, generating ideas in writing, and summarizing academic literature. They cite benefits such as ease of access, time-saving, and instant feedback. However, they express concerns about the challenges, including subscription fees, unreliable information, plagiarism, reduced human-to-human interaction, and impacts on learning autonomy. This study underscores the need for increased awareness, ethical guidelines, and robust academic integrity measures to ensure the responsible use of GenAI tools in educational settings. These findings highlight the need for a balanced utilization of GenAI tools in higher education that maximizes benefits while addressing potential challenges and guides the development of policies, curricula, and support systems.

Lower than expected but still willing to use: User acceptance toward current intelligent conversational agents

Intelligent conversational agents (ICAs) are revolutionizing how humans interact with information systems. Designed to provide human-like service, ICAs are generally evaluated by users in comparison to their human counterparts, often resulting in less-than-expected user experiences. Our research investigates user acceptance of ICAs in this suboptimal condition of commercial customer service. Drawing from the dual perspectives of expectancy confirmation theory and task technology fit theory, we theorize and test an integrated research model on the collective impact of user expectancy confirmation regarding ICA capabilities and their assessment of service-ICA fit on user acceptance. Results from a field survey of 350 users of five ICAs deployed by major Canadian telecom service providers reveal the significant influence of both user expectancy confirmation with ICA capabilities and their assessment of ICA fit-to-service, with the latter playing a more prominent role in shaping user acceptance. Even though ICA performance may not always meet user expectations, users are still willing to engage with ICA services when they perceive the ICA as a fitting solution for their specific service complexity and availability requirements.

What influences user continuous intention of digital museum: integrating task-technology fit (TTF) and unified theory of acceptance and usage of technology (UTAUT) models

Digital museums play a crucial role in facilitating users' access to and exploration of digital cultural heritage resources. However, exploring the factors influencing user engagement with these digital museums from a user experience perspective remains essential. This study evaluates the factors driving user continuous behavioral intention towards the digital museum of Beijing’s central axis, integrating the new task-technology fit (TTF) and the new unified theory of acceptance and use of technology (UTAUT) models, and introducing perceived enjoyment, design aesthetics, and perceived cultural value as additional variables. Analyzing survey data (n = 377) utilizing structural equation modeling (SEM), the study identifies the following key findings: (1) the task and technology characteristics of digital museums significantly impact the TTF; (2) performance expectancy, effort expectancy, design aesthetics, perceived enjoyment, and perceived cultural value all positively impact user continuous behavioral intention; (3) the technological characteristics of digital museums were observed to positively impact users' effort expectancy; but (4) the TTF and social influence did not have no significant impact the user continuous behavioral intention. These findings offer valuable insights into the factors driving users' continuous behavioral intention to use digital museums of cultural heritage, offering practical guidance for future development and optimization of these digital museums, and highlighting specific implications and suggestions for enhancing the user experience.

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.

When Chatbots Struggle with Customer Queries: Can Humans Help Them?

ABSTRACT Chatbots are becoming increasingly popular for customer service due to their speed and cost-effectiveness. However, chatbots have many limitations; for example, they cannot resolve all consumer queries and rely on human help. This study focuses on two key research questions. First, for what type of queries would customers prefer chatbots to traditional channels like phone calls? Second, when the chatbot cannot resolve customer queries, what is the best way to provide human help so that consumers adopt them in the future? We have used task technology fit and decision point theory to address these research questions. The results of two experiments found that consumers preferred chatbots more for non-complex and non-urgent queries. The results also found that a reduction in decision points led to higher adoption of chatbots in the future. In addition to contributing to theory, this study has significant managerial implications that can help companies effectively employ chatbots.

Study of Consumer’s Intention to Use of E-Wallet: with Extended UTAUT2 Reward and Technological Innovation

Consumers in the 21st century are living in a world transformed by contactless transactions, a product of the digital revolution. This technology is woven into the fabric of daily life, offering solutions for everything from morning coffee purchases to late-night bill payments. From shopping and salons to education, food delivery, money transfers, and public transport, contactless payments are ubiquitous. This constant presence fosters a consumer behavior that embraces new technologies and innovation. The rise of mobile payments, driven by advancements in technology, has fundamentally altered the payment landscape. Consumers now have the power to turn their smartphones into convenient payment tools. The emergence of the Internet and smartphones in the 21st century has made information technology an essential aspect of human existence, which were mostly accessible to organizational users in the late 20th century. Research on technology adoption and diffusion is well-established in the current literature on information systems (IS). IS researchers are constantly seeking to comprehend the different factors that influence individuals' acceptance and utilization of emerging information technology (IT) (Hughes et al., 2016, 2017, Hughes et al., 2020). This extensive research field has seen a variety of research methods being used to study a wide range of technologies in different countries. The existing literature has uncovered numerous theories, contexts, units of analysis, and research methods (Dwivedi & Williams, 2008; Choudrie & Dwivedi, 2005; Williams et al., 2009). The diverse research contexts, which include technology, user type, location, adoption time, and task done, have led to the emergence of numerous conflicting theories and models. The Technology Acceptance Model (TAM), Diffusion of Innovation (DoI), Theory of Planned Behaviour (TPB), and Task Technology Fit (TTF) Theory have been commonly used to study various aspects of adoption and diffusion (Dwivedi et al., 2006, 2007; Dwivedi and Weerakkody, 2007; Kapoor et al., 2014). Venkatesh et al. (2003) created the Unified Theory of Acceptance and Use of Technology (UTAUT) by thoroughly examining eight prominent technology adoption models. This theory focuses on the practical value (external motivation) that technology brings to users in an organizational setting. It eliminates any overlapping or repetitive concepts to streamline the theory (Venkatesh et al., 2003). The emergence of consumer technologies required the expansion of the UTAUT model to include the consumer context, with a focus on the hedonic value (intrinsic motivation) of technology users. As a result, three additional concepts, namely hedonic motivation, price value, and habit, were included in the original UTAUT model. This updated version is known as UTAUT2. However, in UTAUT2, the concept of voluntariness of use was eliminated as a moderator because customers do not have any organizational mandate. Additionally, in many cases, consumer behavior is voluntary (Venkatesh et al., 2012). The UTAUT2 hypothesis has a significantly greater predictive power compared to UTAUT. It can explain around 74 percent of the variation in customers' behavioral intention and 52 percent of the variation in consumers' technological usage of the focus technology (Venkatesh et al., 2016).

Enhancing Police Operations: The Impact of CCTV in Monitoring, Incident Response, and Crime Investigation in Nairobi City County, Kenya

This study assessed the impact of Close Circuit Television (CCTV) on police operations in Nairobi, Kenya, focusing on its role in public space monitoring, incident response, and crime investigation. Employing a convergent parallel design informed by the Task-Technology Fit Theory, it collected data from 403 purposefully and cluster-sampled police officers through questionnaires, key informant interviews, and focus group discussions. Quantitative data analysis utilised descriptive statistics, while thematic analysis helped to uncover qualitative insights. CCTV was found to be valuable for incident awareness, traffic management, and crime detection and significantly aided in incident response and investigations. However, limited camera coverage and technical issues reduced effectiveness. The study concluded that Nairobi's CCTV system is a valuable tool, but improvements like expanding coverage and providing storage solutions for investigators are recommended.

Assessing the Impact of Multicultural Curriculum on Student Performance in Beijing High Schools

Considering increasing diversity and the need for culturally responsive education, this study examines the integration of technology within multicultural curricula in Chinese high schools. This study focuses on the interplay between task characteristics and technological capabilities and their impact on student performance. Grounded in the TaskTechnology Fit theory, the analysis employs structural equation modelling (SEM) to assess the relationships among these variables. This study aims to identify how aligning educational tasks with technological resources can enhance student outcomes in multicultural learning environments. The research design involved collecting data from a purposive sample of teachers and students in Beijing, and the analysis revealed significant relationships among task characteristics (TaC), technology characteristics (TeC), tasktechnology fit (TTF), and student performance (SP). The findings highlight that optimal alignment between educational tasks and technological tools is crucial for enhancing academic performance and fostering deeper engagement with the multicultural aspects of the curriculum. These results emphasise the critical need for strategic selection and integration of technology in educational settings. This study underscores the importance of developing strategies that consider both the pedagogical aspects of the curriculum and the technological tools used for its delivery. This research provides empirical insights into the effective use of technology in multicultural education and offers valuable guidance for educators and policymakers. Furthermore, it plays a role in attaining sustainable development goal 4 and 10. The findings contribute to the ongoing discourse on educational technology and multicultural education, with practical implications for enhancing teaching and learning in diverse educational contexts.

Assessing the Curvilinear Relationship in Employee Digital Performance: A Task-Technology Fit Perspective

In the context of the digital economy, one of the most important issues facing business managers is that of how to improve employee digital performance (EDP), however, extant studies exploring this problem are quite limited. The purpose of this paper is to investigate how the characteristics of task and technology represented by enterprise social media (ESM) affect task-technology fit (TTF), which in turn affects EDP. Using TTF theory, this paper proposes a research model to validate the research question. An ordinary least squares method is used to analyze 284 questionnaire responses drawn from 33 provinces in China. The results indicate that task complexity has an inverted U-shaped effect on TTF, whereas task interdependence has no effect on TTF. Work-related function of ESM has a U-shaped effect on TTF, while social-related function of ESM has a positive effect on TTF. TTF positively influences both employee digital-enabled task performance and employee digital-enabled innovative performance. Employee digital literacy strengthens the impact of TTF on employee digital-enabled task performance, but does not moderate the relationship between TTF and employee digital-enabled innovative performance. Organizational digital culture strengthens the impacts of TTF on both employee digital-enabled task performance and employee digital-enabled innovative performance. From a theoretical perspective, this paper makes a contribution to TTF theory by revealing the curvilinear relationship between the characteristics of task, the characteristics of ESM technology and TTF. This paper also contributes to the EDP literature by clarifying how TTF affects EDP, and by identifying the boundary conditions (namely employee digital literacy and organizational digital culture) between TTF and EDP. From a practical point of view, this paper provides managers with the insights to improve EDP in terms of task assignment, employee training, enterprise culture building, etc.

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.

How does big data affect organizational financial performance in turbulent markets? The role of customer-linking and selling capabilities

The information age provides vast opportunities for organizations to improve their efficiencies by relying on big data-AI empowered analytics (BDA). The purpose of this study is to address the critical question of how BDA-fit affects organizational financial performance, taking into consideration differences between markets and customer change caused by technology turbulence. Using data collected at the firm level and building on Task-Technology Fit Theory and Dynamic Capabilities Theory, empirical results indicate that BDA-fit is important to drive organizational financial performance through the development of customer-linking and selling capabilities. These results vary across markets, with BDA-fit having stronger effects on selling capabilities and organizational financial performance in highly turbulent markets. BDA allows organizations to upturn challenges and hindrances caused by market turbulence into opportunities to increase sales revenues. Based on the findings, theoretical contributions and managerial implications are provided.

EXTENDING TASK-TECHNOLOGY FIT WITH DUAL INFORMATION PROCESSING MODES IN DISASTER DECISION SUPPORT

ABSTRACT Leveraging the analytic and heuristic modes of information processing (i.e. the dual information processing modes) equips disaster professionals with valuable insights into decision support processes during a disaster. Drawing from task-technology fit (TTF), we explore the fit between the dual information processing modes and the influence of different task conditions on perceived disaster decision support by conducting an empirical study involving 136 disaster professionals. The findings of this study extend the theory of TTF by incorporating the dual information processing modes and examine how disaster professionals evaluate these modes in disaster decision support processes.

Unraveling Knowledge-Based Chatbot Adoption Intention in Enhancing Species Literacy

Aim/Purpose: This research investigated the determinant factors influencing the adoption intentions of Chatsicum, a Knowledge-Based Chatbot (KBC) aimed at enhancing the species literacy of biodiversity students. Background: This research was conducted to bridge the gap between technology, education, and biodiversity conservation. Innovative solutions are needed to empower individuals with knowledge, particularly species knowledge, in preserving the natural world. Methodology: The study employed a quantitative approach using the Partial Least Square Structural Equation Modeling (PLS-SEM) and sampled 145 university students as respondents. The research model combined the Task-Technology Fit (TTF) framework with elements from the Diffusion of Innovation (DOI), including relative advantage, compatibility, complexity, and observability. Also, the model introduced perceived trust as an independent variable. The primary dependent variable under examination was the intention to use the KBC. Contribution: The findings of this research contribute to a deeper understanding of the critical factors affecting the adoption of the KBC in biodiversity education and outreach, as studies in this context are limited. This study provides valuable insights for developers, educators, and policymakers interested in promoting species literacy and leveraging innovative technologies by analyzing the interplay of TTF and DOI constructs alongside perceived trust. Ultimately, this research aims to foster more effective and accessible biodiversity education strategies. Findings: TTF influenced all DOI variables, such as relative advantage, compatibility, observability, and trust positively and complexity negatively. In conclusion, TTF strongly affected usage intention indirectly. However, relative advantage, complexity, and observability insignificantly influenced the intention to use. Meanwhile, compatibility and trust strongly affected the use intention. Recommendations for Practitioners: Developers should prioritize building and maintaining chatbots that are aligned with the tasks, needs, and goals of the target users, as well as establishing trust through the assurance of information accuracy. Educators could develop tailored educational interventions that resonate with the values and preferences of diverse learners and are aligned closely with students’ learning needs, preferences, and curriculum while ensuring seamless integration with the existing educational context. Conservation organizations and policymakers could also utilize the findings of this study to enhance their outreach strategies, as the KBC is intended for students and biodiversity laypeople. Recommendation for Researchers: Researchers should explore the nuances of relationships between TTF and DOI, as well as trust, and consider the potential influence of mediating and moderating variables to advance the field of technology adoption in educational contexts. Researchers could also explore why relative advantage, complexity, and observability did not significantly impact the usage intention and whether specific user segments or contextual factors influence these relationships. Impact on Society: This research has significant societal impacts by improving species literacy, advancing technology in education, and promoting conservation efforts. Species knowledge could raise awareness regarding biodiversity and the importance of conservation, thereby leading to more informed and responsible citizens. Future Research: Future works should address the challenges and opportunities presented by KBCs in the context of species literacy enhancement, for example, interventions or experiments to influence the non-significant factors. Furthermore, longitudinal studies should investigate whether user behavior evolves. Ultimately, examining the correlation between species literacy, specifically when augmented by chatbots, and tangible conservation practices is an imperative domain in the future. It may entail evaluating the extent to which enhanced knowledge leads to concrete measures promoting biodiversity preservation.

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.

Transforming Healthcare Delivery through Innovative Inventory Control Technology Strategy: Lessons from Bushenyi District, Uganda

Transforming healthcare service delivery through technology-enhanced inventory control has emerged as a crucial strategy for improving efficiency in resource-limited environments. This study explored the impact of technology integration into inventory control management systems within healthcare facilities in Bushenyi District, Uganda. Grounded in Task-Technology Fit (TTF) Theory, the research utilized a quantitative approach with a correlational design. The target population comprised 192 permanent healthcare workers, from which 130 respondents were selected using the Yamane formula. The study collected primary data through a structured self-administered questionnaire, subsequently analyzed using Statistical Package for Social Scientists (SPSS) version 25. A pilot test involving 13 respondents ensured the validity and reliability of the instrument. Both descriptive and inferential statistics facilitated data analysis. Findings revealed a positive significant relationship between technological integration and service delivery (t=2.226, P=0.028<0.05). The study concludes that integrating technology significantly enhances service delivery in healthcare facilities. Recommendations urge healthcare facilities to prioritize technology integration within their service delivery processes to boost operational efficiency and improve patient outcomes. Policymakers can utilize these findings to implement initiatives that promote and invest in technological integration within healthcare systems, ultimately improving service delivery and enhancing patient care and operational efficiency.

Understanding the Continuance Intention of College Students toward New E-Learning Spaces Based on an Integrated Model of the TAM and TTF

The emergence of educational video platforms has led to microlearning resources becoming increasingly mainstream. These platforms offer unique ecosystems and resource designs that better cater to the needs of learners. In this study, we examined the technology acceptance model (TAM) and task-technology fit (TTF) theory and conducted an empirical analysis of user satisfaction with new online learning spaces. We learned that perceived usefulness, perceived ease of use, and task-technology fit had significantly impacted user satisfaction, with these three factors collectively contributing to 78.2% of the variance in user satisfaction. Additionally, user satisfaction and task-technology fit significantly influenced the continuance intentions of users toward using these spaces, with both factors contributing to 66.7% of the variance in continuance intention. Overall, our findings revealed that the future development of new online learning spaces should consider the task requirements of learners and improve the platforms accordingly.