Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives

This study investigates how generative AI affects employee job performance in small and medium-sized enterprises (SMEs) within Ghana's education sector, using the Task-Technology Fit (TTF) framework integrated with Technology Acceptance Model 2 (TAM2). It addresses three main objectives: evaluating the fit between AI technology and employee tasks, examining how this fit and AI usage impact job performance, and exploring how perceived usefulness influences intention to use AI. A quantitative approach was used, with data from 246 SME employees in Greater Accra, analyzed through structural equation modelling. All seven proposed hypotheses were supported. Task characteristics strongly influenced task-technology fit, which positively affected both AI usage and task performance. Generative AI usage significantly improved performance, while social influence shaped perceived usefulness, which in turn influenced usage intention. The study highlights the importance of aligning AI tools with task needs and enhancing perceived usefulness to boost adoption and job performance in educational SMEs.

Over the past decade there has been a notable increase in the use of Task-Technology Fit (TTF) theory within the field of information systems. This theory argues that information system use and performance benefits are attained when an information system is well-suited to the tasks that must be performed. As such, it seeks to offer an account of two of the key outcomes of interest to information systems (IS) researchers. Continued interest in the application of TTF theory is therefore expected and, as a result, the following chapter aims to provide a brief overview of the theory and how it has been applied in prior work. Readers are presented with an overview of the diverse range of research contexts and methodologies that have been used to test and extend TTF theory. Key outcomes of interest to TTF researchers are also examined as are the various approaches that researchers have used to operationalize the notion of TTF. It is hoped that this overview will serve as a sound basis for future research and simultaneously help to ensure that IS research does not continue to tread the same ground.

Objective of the study: This study identifies emerging technological trends and key advancements in AI patents related to project management (2018–2024). Using Task-Technology Fit (TTF) theory, it examines how AI innovations address challenges across the five major phases of project management: Initiating, Planning, Executing, Monitoring and Controlling, and Closing. Methodology/approach: Patent data from Lenz.org was filtered for project management-related terms, yielding 1,044 patents. The study applied temporal trend analysis, jurisdiction-based analysis, technology classification, and phase-specific problem-solution mapping using TF-IDF vectorization. Patents were categorized based on PMI's five project management phases using keyword-based classification, enabling systematic assessment of innovation patterns and task-technology alignment. Originality/Relevance: While previous research explored isolated AI applications, this study provides a systematic analysis of AI’s alignment with project management tasks. Applying TTF theory, it assesses AI's role in enhancing efficiency and decision-making across project phases, pioneering the application of TTF theory to patent analysis. Main Results: AI-driven innovations strongly align with Planning and Monitoring, enhancing scheduling, resource allocation, and risk management. The Executing phase shows evolving AI adoption, while Initiating and Closing exhibit weaker alignment. The study highlights jurisdictional trends, with the U.S. leading AI patent filings. Theoretical/methodological contributions: This study applies TTF theory to AI patent analysis in project management, offering a replicable framework for examining technological advancements and assessing innovation-task alignment across project phases. Social/management contributions: Findings provide insights for project managers, organizations, and policymakers on AI adoption. The study highlights AI’s potential to improve efficiency while identifying gaps requiring further technological development, particularly in human-centric project phases.

This study integrates the educators’ digital competency (DC), as an individual characteristic construct of the task-technology fit (TTF) theory, to examine a better fit between Moodle using and teaching task, and to investigate its effect on both Moodle’s utilization and their task performance. For assessing our proposed hypotheses, an online survey was conducted with 238 teaching staff from different departments of universities in Malaysia. Using Structural Equation Modelling (SEM), our analysis revealed that all the proposed components (i.e., technology literacy, knowledge deepening, presentation skills, and professional skills) of digital competency significantly influenced the TTF. The Task-Technology Fit was also found as an influential construct, which positively and significantly affected both Moodle’s utilization and teachers’ task performance. Besides, Moodle’s utilization was confirmed to be a substantial determinant of the performance impact. In the end, this study included limitations and future directions based on how the study’s contribution can support academics and practitioners for assessing and understanding what particular components of digital competency impact TTF, which in turn may influence the system’s utilization and performance impact.

PurposeThe purpose of this paper is to investigate the effect of human-computer interaction (HCI) on customers’ perceived electronic service (e-service) value and the mediating role of task-technology fit (TTF) in that effect.Design/methodology/approachThis paper develops a model based on service-dominant logic (SDL) and TTF theory, and validates it using a hierarchical regression with the data collected from 634 online banking customers in Guangdong Province and Guangxi Zhuang Autonomous Region in China.FindingsThe findings reveal that HCI in e-service contexts comprises five components. Three fundamental components (i.e. technology functionality, customer technology readiness and task routine) contribute to value co-creation. Two core components (i.e. interaction between customer technology readiness and technology functionality, and interaction between task routine and technology functionality) are inhibitors, but the inhibitory effect of the former is only significant in the Guangdong sample. TTF takes a mediating role in these relationships, but the mediating effect of the former core component is only significant in the Guangdong sample.Originality/valueThis paper explains two basic questions about the trigger points of value co-creation in e-service contexts (i.e. what their operational definitions are and how to measure them) and unlocks the “black box” of value co-creation by taking TTF as a mediator. SDL and TTF theory are extended. The paper provides suggestions for how practitioners can efficiently advance value co-creation with customers.

Purpose – The purpose of this paper is to investigate the determinants of mobile banking (m-banking) for individual performance and whether or not there are any age or gender differences. Design/methodology/approach – The research model is based on the task-technology fit (TTF) theory, which integrates elements of task and technology characteristics, technology usage, and individual performance, while combining the age and gender subsamples. The empirical approach was based on an online survey questionnaire of 256 individuals. Partial least squares based on the multi-group analysis were used to analyse the proposed framework construct relationships. Findings – The results reveal that TTF and usage are important precedents of individual performance. The authors find statistically significant differences in path usage to performance impact for the age subsample, and no statistically significant differences for the gender subsample. Originality/value – The paper highlights the TTF model to understand the determinants that influence the individual performance of m-banking, and whether or not there are any age or gender differences. While most earlier research focuses on m-banking adoption, the approach diverges from the majority of the work by examining the individual performance, which has not been considered in earlier studies.

The study sought to examine the task-technology fit of ChatGPT for healthcare services. The research model was based on an integration of the task-technology fit (TTF) model and the technology acceptance model 2 (TAM2). The study used a quantitative research design. Data were collected from 265 health workers who have used ChatGPT in the performance of their work. Data analysis was done using structural equation modelling. Six of the seven hypotheses were accepted. The results show significant positive relationships between task characteristics and task-technology fit, task-technology fit and ChatGPT usage, ChatGPT usage and performance impacts, social influence and perceived usefulness, perceived usefulness and usage intention, as well as usage intention and ChatGPT usage. There was an insignificant positive relationship between task-technology fit and performance impacts.

The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (β = 0.25, p < 0.001), behavioral engagement (β = 0.25, p < 0.001), and emotional engagement (β = 0.27, p < 0.001). Behavioral engagement was affected by TTF (β = 0.31, p < 0.001) and ITF (β = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (β = 0.49, p < 0.001; β = 0.19, p < 0.001; β = 0.12, p = 0.001, respectively) and cognitive engagement (β = 0.28, p < 0.001; β = 0.34, p < 0.001; β = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.

Numerous studies have employed the Task-technology fit (TTF) theory to examine the impact of information systems used for training. While some of these studies reported positive outcomes, others presented mixed results on the impact of the systems improving users' performance in problem solving and learning. One possible explanation for these inconsistencies is likely caused by additional factors beyond the task and technology characteristics emphasized in TTF. This research is a preliminary study to enhance understanding of TTF and its impact on system usage and work performance. The study argues that task-technology alignment should also consider users' skills and the fit between the task and technology. Therefore, this research explores the effects of task characteristics, technology characteristics, and users' skills on system usage and work performance. Data from 210 participants of the Revenue Department of Thailand was analyzed using a two-way factorial multivariate analysis (Factorial MANOVA) to assess the impact of Task-technology-skill fit (TTSF) on system usage and work performance. Additionally, a simple regression analysis was conducted to examine the effect of system usage on work performance. The findings indicated that the fit among tasks, technology, and skills significantly impacts system usage and work performance. However, the system usage does not directly affect work performance. This study proposed a factorial design to depict the task-technology-skill fit, the first to introduce this integrated framework.

Data quality management (DQM) gains importance due to the increasing amount and diversity of data as well as data’s critical impact on organizational success. Most organizations use enterprise resource planning (ERP) systems, establishing an enterprise-wide platform to integrate data and processes. There is a lack of sector-specific research of how ERP systems are used for DQM tasks in the insurance sector despite the sector’s specific characteristics. DQM plays a considerable role in the information-intensive insurance sector, with its numerous regulations, complex products and distribution structure. Following the task-technology fit (TTF) theory, we conducted semi-structured expert interviews in order to examine ERP systems’ use for DQM tasks and the interdependency of ERP systems and DQM. First, this paper shows that in the insurance sector, ERP systems are used to fulfill regulations and provide a starting point for data analysis. However, cultural and managerial challenges need to be addressed in order to lever the potential of diverse data for analysis. Second, we present a qualitative study applying the TTF theory. By doing so, we show that a qualitative application of TTF theory allows for rich insights and identi-fies potential causes of context-specific TTF problems. Furthermore, we present areas for further research on DQM and ERP systems and provide guidance for improving future use of ERP systems with respect to DQM.

Cancer related social network communities have the potential to develop an interactive environment where virtual relationships among cancer patients can be made. Few studies have looked at the role of Social Network Sites (SNS) for cancer patients despite their potential to deliver health messages to large audiences. The purpose of this study is to understand factors that affect the effectiveness of the SNS as fast growing source for online cancer support using Social Cognitive Theory (SCT) and the theory of Task Technology Fit (TTF). A semi-structured interview designed through reviews of relevant literature was analyzed using thematic analysis. Most respondents believed the effectiveness of SNS is significant on information and emotional support. Although, the central issues appeared to be more focused on the self efficacy, past experience, social supports, system and task characteristics in explaining the way factors affect on the effectiveness of SNS for cancer patients. Consequently, more rigorous research should be focused on explaining the best practices that may help speed up effective intervention of social network technology in healthcare.

Early evidence of digital labor in accounting: Innovation with Robotic Process Automation

Objectives: This study aimed to identify the role of the Project Management Office (PMO) in enhancing organizational performance in INGOS. Methods: This study adopted a quantitative research approach. The study population consisted of project managers, project management office (PMO) managers, team leaders, and consultants working in INGOS, with a focus on PMO employees. A simple random sampling method was used, and the sample size was set at 210 participants to ensure statistical significance and reliability of the results. SPSS was used to process and analyze the collected data and test the results of the hypotheses related to the impact of the PMO dimensions on organizational performance. Results: The study concluded that governance and control have a significant positive impact on organizational performance. Resource management and risk management also have a significant impact, but monitoring, controlling and evaluation do not appear to have an impact on organizational performance

This study examines the use of information and communications technology (ICT) in remote work practice during the COVID-19 pandemic by integrating task-technology fit theory and the post-acceptance model of IS continuance into a research framework. In addition, it operationalizes the technological characteristics of TTF (task-technology fit) with the technology acceptance model (TAM) and the diffusion of innovation (DOI) theory. The methodology to test the research model takes support from the partial least squares structural equation modeling (PLS-SEM) method using a sample of 320 employees. The results show that TAM significantly explains TTF. Furthermore, there is a positive impact of ICT use on individual and organizational performance. User satisfaction has the most significant effect on individual performance, organizational performance, and IS continuance intention. The authors provide some managerial implications for addressing the challenges of remote work related to ICT disruptions for the post-COVID-19 period.

This paper fills an important gap related to employee perceptions of teleworking during and after the COVID-19 pandemic. Drawing on the work/family border and task-technology fit theories, we propose and empirically test a new model using data collected from 483 employees. Our findings suggest that social well-being, work-family balance and task-technology fit during the pandemic are positively related to teleworking performance. In addition, teleworking performance during the pandemic affects employees’ intention to continue to telework and career engagement after the pandemic. Also, we offer evidence of the impact of the moderating effect of factors contributing to the digital divide in this context. Our findings contribute to the teleworking literature, by proposing a model which provides insights into employees’ perceptions of teleworking during the pandemic and how this affects their intention to telework and career engagement after the pandemic. Our research has multiple implications for employers, policy makers and technology developers.