Technology Acceptance Model Constructs Research Articles

Modelling the effects of emotional engagement and peer interaction on the continuous intention to use asynchronous e-learning.

Even though asynchronous e-learning has become popular among universities, few studies have examined how students intend to continue using it for their learning. This study proposed a theoretical model that aims to examine the effects of external factors-emotional engagement and peer interaction-and two constructs of technology acceptance model (TAM) on students' continuous intention to use asynchronous e-learning. A total of 259 students participated in a survey. The data were analysed using two steps in SEM AMOS. confirmatory factor analysis was conducted for assessing model measurement, and structural equation modelling was applied for assessing relationships among constructs and testing hypothesis. The results showed that emotional engagement had a significant effect on students' continuous intention to use asynchronous e-learning through the perceived ease of use of asynchronous e-learning and not through its perceived usefulness. Furthermore, peer interaction had a significant effect on students' continuous intention to use asynchronous e-learning. Moreover, the constructs of the technology acceptance model-perceived ease of use and perceived usefulness-had direct and significant effects on students' continuous intention to use asynchronous e-learning. Several implications and suggestions were discussed.

Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence

In this paper, we discuss how people's attitudes toward accepting Artificial Intelligence (AI) technologies are influenced by their self-reported literacy (i.e., their understanding and ability to use AI technology) and anxiety (i.e., their fear of the AI technology's impact on society). We conducted an anonymous survey, gathering 313 valid responses, and utilized Structural Equation Modelling to examine the relationships between these factors. Our model incorporates the constructs of Technology Acceptance Model (TAM) to provide a more comprehensive overview of AI acceptance. Our findings indicate that while literacy fosters a positive attitude towards acceptance, anxiety has a minimal, although significant, direct negative effect, but plays a significant role in mediating the influence of literacy. Specifically, learning and sociotechnical dimensions of AI anxiety serves as a complementary partial mediator between AI literacy and acceptance, meaning that a portion of the effect of literacy on acceptance is mediated through AI anxiety. In contrast, literacy still explains a portion of acceptance independent from anxiety. Our study also confirms that dimensions of technology acceptance, as suggested by TAM, significantly shape individuals' attitudes towards AI as for others digital technology. In this respect, AI literacy positively influences the perception of ease of use and usefulness, thereby contributing to the overall acceptance of AI-based technology. We also discuss the implications of these findings for the development of critical literacy in the context of AI-based technology.

Feasibility of NepaDengue mobile application for dengue prevention and control: user and stakeholder perspectives in Nepal

BackgroundDengue has emerged as a significant public health issue in Nepal since 2006, necessitating innovative approaches to disseminating timely information and promoting preventive behaviour. Our study focuses on developing and...

Factors affecting the use of climate information services for agriculture: Evidence from Iran

The use of Climate Information Services (CIS) is considered the most important solution for the long-term adaptation of the agricultural sector in dealing with the challenges caused by climate change. While there are examples of successful CIS programs in the agricultural sector of developed countries, there are barriers to successfully using CIS programs for farmers in developing countries. In this regard, this research was carried out with two general objectives: (i) identifying the factors affecting the use of CIS by farmers, and (ii) providing practical policies for applying this information in the agricultural sector of Iran. A comprehensive Technology Acceptance Model (TAM) theory was used as the theoretical framework for this research, and self-efficacy (SE), social norm (SN), and perceived trust (PT) were added as variables. This research was conducted using structural equation modeling (SEM), and a designed questionnaire was used as the data-gathering instrument. The statistical population of this research includes all farmers of Dezful city in Khuzestan province (southwest of Iran). The findings of the research showed that the initial TAM explains 0.537 % of the variance of farmers' behavioral intention in using CIS. The three primary TAM constructs included Attitude, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU), all of which had positive effects on farmers' willingness. Most importantly, by including SE, SN, and PT variables, the developed TAM can increase the model's ability to predict farmers' intentions by 13.5 %.

Perception of Generative Artificial Intelligence in Higher Education Research

Integrating Generative Artificial Intelligence (AI) into higher education research holds great promise, but understanding researchers' perceptions and addressing associated challenges is crucial for success. This study investigates how researchers perceive the potential benefits of Generative AI, its ease of use, and the challenges affecting its adoption in higher education research. It aims to uncover diverse perspectives and insights for effective integration. Using a qualitative case study approach, data was collected from 33 researchers in social sciences at one institution through online surveys and interviews. Thematic analysis, guided by the Technology Acceptance Model (TAM), identified recurring themes. The findings reveal significant engagement with AI adoption, with most participants integrating Generative AI into their research. However, readiness to adopt varied, reflecting different levels of enthusiasm and preparedness. Participants' perceptions ranged from recognizing Generative AI's convenience to expressing concerns about potential negative impacts such as becoming overly reliant, aligning with TAM constructs. Additionally, participants stressed the importance of responsible and ethical Generative AI usage through training and awareness initiatives. These findings lay the foundation for strategies promoting Generative AI adoption and responsible integration in higher education research. Implications for institutions aiming to integrate Generative AI effectively emphasize the need to understand researchers' perspectives and address their concerns.

Involving Patients and Clinicians in the Design of Wireframes for Cancer Medicines Electronic Patient Reported Outcome Measures in Clinical Care: Mixed Methods Study.

Cancer treatment is a key component of health care systems, and the increasing number of cancer medicines is expanding the treatment landscape. However, evidence of the impact on patients has been focused more on chemotherapy toxicity and symptom control and less on the effect of cancer medicines more broadly on patients' lives. Evolving electronic patient-reported outcome measures (ePROMs) presents the opportunity to secure early engagement of patients and clinicians in shaping the collection of quality-of-life metrics and presenting these data to better support the patient-clinician decision-making process. The aim of this study was to obtain initial feedback from patients and clinicians on the wireframes of a digital solution (patient app and clinician dashboard) for the collection and use of cancer medicines ePROMs. We adopted a 2-stage, mixed methods approach. Stage 1 (March to June 2019) consisted of interviews and focus groups with cancer clinicians and patients with cancer to explore the face validity of the wireframes, informed by the technology acceptance model constructs (perceived ease of use, perceived usefulness, and behavioral intention to use). In stage 2 (October 2019 to February 2020), the revised wireframes were assessed through web-based, adapted technology acceptance model questionnaires. Qualitative data (stage 1) underwent a framework analysis, and descriptive statistics were performed on quantitative data (stage 2). Clinicians and patients with cancer were recruited from NHS Greater Glasgow & Clyde, the largest health board in Scotland. A total of 14 clinicians and 19 patients participated in a combination of stage 1 interviews and focus groups. Clinicians and patients indicated that the wireframes of a patient app and clinician dashboard for the collection of cancer medicines ePROMs would be easy to use and could focus discussions, and they would be receptive to using such tools in the future. In stage 1, clinicians raised the potential impact on workload, and both groups identified the need for adequate IT skills to use each technology. Changes to the wireframes were made, and in stage 2, clinicians (n=8) and patients (n=16) indicated it was "quite likely" that the technologies would be easy to use and they would be "quite likely" to use them in the future. Notably, clinicians indicated that they would use the dashboard to enable treatment decisions "with around half" of their patients. This study emphasizes the importance of consulting both patients and clinicians in the design of digital solutions. The wireframes were perceived positively by patients and clinicians who were willing to use such technologies if available in the future as part of routine care. However, challenges were raised, and some differences were identified between participant groups, which warrant further research.

Understanding the factors of mobile payment continuance intention: empirical test in Saudi Arabia

Purpose: The objective of this study is to explore the determinants of individuals' intentions to continue utilize mobile payment services by examining integrated model of the constructs of the Technology Acceptance Model (TAM) (perceived ease of use and perceived usefulness) and the Theory of Planned Behavior (TPB) (attitude, subjective norm, and perceived behavioral control). Furthermore, the study investigates the relationship between user satisfaction and their intention to persist in using mobile payment. Methodology: In this quantitative investigation, a sample of 474 mobile e-wallet users was surveyed online. Partial least squares structural equation modeling was employed for data analysis. Findings: The study reveals that the constructs from the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and user satisfaction collectively influence users' intentions to use e-wallet services. Notably, the model demonstrates its ability to elucidate 59.1% of the variance in behavioral intention to continue utilizing e-wallets. Additionally, both user satisfaction and perceived behavioral control significantly impact the decision to persist in using e-wallets. The insights gleaned from this study can be leveraged by e-wallet stakeholders to inform strategic decisions within the e-wallet ecosystem. Practical implications: The inclusion of TAM and TPB constructs enhances our understanding of e-wallet service utilization. These findings offer valuable insights for service providers, enabling them to tailor their marketing strategies more effectively, thereby ensuring customer satisfaction and continued e-wallet usage. Originality: This study introduces a comprehensive and integrative approach to elucidate the factors influencing the sustained use of e-wallet services. By amalgamating two foundational adoptions above theories, it extends our understanding of mobile payment adoption dynamics.

What factors influence the intention to use electric motorcycles in motorcycle-dominated countries? An empirical study in Vietnam

Transitioning to electric motorcycles (EMs) can reduce dependence on carbon-based fuels, mitigate air pollution, and lower greenhouse gas emissions in countries where motorcycles dominate the transportation landscape. However, the factors influencing fleet electrification in these countries remain largely unknown. Given that this challenge pertains to the prior adoption of new technology, technology acceptance theories can provide valuable insights into the adoption of EMs. Additionally, misinformation about EM risks and self-assessment of knowledge could interact as determinants of adoption. This study integrates the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and perceived risk to elucidate the impact of psychosocial factors on consumers' intentions to use EMs. Furthermore, it examines the direct and moderating effect of subjective knowledge about EM technology on the formation of this behavioral intention. Structural Equation Modeling (SEM) analysis was applied to investigate data collected from 762 respondents through personal interviews using a questionnaire. The results confirm that all original TAM and TPB constructs significantly influence behavioral intention, aligning with theoretical expectations. As hypothesized, perceived risk was found to exert a negative influence on the intention to adopt EMs. Additionally, subjective knowledge was observed to moderate the relationship between perceived usefulness and subjective norms concerning the intention to use EMs. These findings offer a roadmap for developing strategies aimed at encouraging adoption intentions toward EMs, particularly in countries like Vietnam.

Factors influencing the intention of trainee special education teachers to integrate assistive technology into teaching students with disabilities in the United Arab Emirates

Technology has been incorporated into the educational systems of many countries as a teaching and learning tool. Contemporary discourse has focused on how to prepare teachers to utilise technology in their teaching practice. Although most pre-service elementary school teachers in the United Arab Emirates are women, studies have not yet investigated their acceptance of or preparedness to use technology in the classroom. Therefore, it is necessary to investigate whether the training provided to pre-service special education teachers equips them with sufficient skills to support students with disabilities. The present study used the technology acceptance model (TAM) as a theoretical lens to examine the factors that impact female pre-service special education teachers' intention to incorporate assistive technology (AT) in teaching students with disabilities. A survey based on TAM was used to collect data from 138 participants and test four study hypotheses. Regarding data analysis, SPSS and AMOS version 28 software were used to conduct exploratory factor, confirmatory factor and path analyses. Two of the four hypotheses were supported. The results provide support for the four-factor TAM structure, with two predictors – perceived ease of use and computer self-efficacy – supporting pre-service teachers’ intention to use AT to teach students with disabilities in the classroom. Teacher educators and policymakers should consider the TAM construct when preparing pre-service teachers to effectively support all students. Specifically, teacher development and stakeholder engagement with AT resources are needed to optimise the learning of children with disabilities.

Analysis of Australian public acceptance of fully automated vehicles by extending technology acceptance model

There has been an increasing trend in using user acceptance models to explore the public acceptance of automated vehicles (AVs) in different countries. Most of the previous studies have analysed perceived usefulness, behavioural attitude, subjective norms as well as perceived ease of use, but other important factors, such as trust and data privacy, have not been adequately considered. Likewise, public perceptions of fully AVs are limited in the literature as most studies focus on different levels of automation. This study aims to assess the behavioural intention to use fully AVs in Australia by extending the Technology Acceptance Model (TAM) that includes data privacy and trust in the TAM constructs. Based on a survey of 809 adult respondents from Australia, the model was evaluated with Structural Equation Modelling.The research revealed perceived trust and perceived data privacy is the first and second most important variable affecting the attitude, followed by perceived ease of use and usefulness. Perceived data privacy was discovered to positively impact attitude, perceived trust, perceived usefulness, perceived ease of use as well as behavioural intentions. The perceived trust mediated perceived data privacy on the attitudes in this study. Additionally, the two major variables in the proposed model – perceived trust and data privacy- affect attitudes of AVs significantly, with total effects being 0.637 and 0.604, respectively. Attitude is the most significant variable that correlates with behavioural intentions, which leads to acceptance of fully AVs. Multigroup analysis showed the gender, age and income related differences regarding the public acceptance of AVs. Several theoretical and practical implications are discussed in this paper.

Risk perception and adoption of digital innovation in mobile stock trading

AbstractAn increase in retail investors, trading platforms, and smartphones is facilitating digital transformation in stock trading. Investments in mobile‐centered digitalization by trading firms are rapidly becoming a source of competitive advantage. Distinctive psychological aspects involving risks, make stock trading a unique individual decision‐making behavior. The objective of the present research is to examine the determinants of retail investors' behavioral intentions (BI) for mobile stock trading (MoST). Across two studies using a mixed method approach we empirically test a framework comprising cognitive risk‐mitigation factors, perceived risk (PR), perceived financial cost (PFC), and technology acceptance model (TAM) constructs. The results indicate information quality, privacy, and security protection to mitigate the risk perception involved in trading on mobile platforms. Further, we report the mediation of PR in the relationship between the cognitive risk‐mitigating factors and BIs to adopt MoST. Moreover, we find PFC to moderate PR and the TAM constructs' relationship with BIs to adopt MoST. Our research has important theoretical contributions and managerial implications to understand digital adoption behavior by retail investors for MoST platforms.

Assessing the effect of innovation diffusion and technology readiness theories on attitude, behavioral intention and implementation of smart learning

PurposeThis study assesses the impact of innovation diffusion theory (IDT), technology readiness index (TRI) and technology acceptance model (TAM) on the actual use of smart learning. This impact also accounts for the country-digital culture by moderating the effects of resistance to change (RTC) and mediating the role of attitude.Design/methodology/approachThe authors gather data from 301 respondents from various academic institutions in the United Arab Emirates (UAE) by operationalizing established theoretical constructs. The authors adopt a covariance-based structural equation modeling (SEM) approach.FindingsThe results reveal that IDT and TRI significantly and positively affect attitudes toward implementing smart learning. Besides, the attitude fully mediates the relationship between IDT, TRI constructs and behavioral intention (BI). Moreover, this study proves that RTC plays a major role in converging BI to place smart learning into actual use.Research limitations/implicationsThe major limitation of the authors' work is that this work employs cross-sectional data from UAE only, and the data were gathered during the coronavirus disease 2019 (COVID-19) pandemic.Practical implicationsThe stakeholders and administrators in government can benefit from the study findings to improve the efficiency and effectiveness of the implementation of smart learning, which will contribute to achieving stakeholders and administrators' strategic objectives.Originality/valueThe originality of this work stems from the incorporation of IDT, TRI and TAM constructs in the case of smart learning in UAE in post-COVID-19 scenarios.

The Antecedents of the Technology Acceptance Model in Microentrepreneurs' Intention to Use Social Networking Sites.

Social media platforms offer significant growth opportunities for enterprises, particularly microenterprises, due to the chance to establish direct contact with customers. Drawing on the Technology-Acceptance Model (TAM), in the present study, we investigate the psychological reasons that lead microentrepreneurs to use Social Networking Sites (SNSs) for their business. In doing so, we also extend TAM by taking into account entrepreneurs' personalities (e.g., extraversion and openness to experience) and their perceived risk. We collected data by examining 247 microentrepreneurs engaged in the production of handmade objects. Our results confirm that of all the TAM behavioral antecedents tested, perceived usefulness and attitude toward SNSs' usage for business proved to be the best predictors of the intention to use SNSs for business activity. The results also indicate that extraversion, openness to experience, and perceived risk, as external factors, significantly affect the TAM constructs. We discuss implications and suggestions for future research.

Integrating the social support theory and technology acceptance model of social commerce websites

The expansion of social commerce websites has gradually transformed most people’s lives in terms of social interactions during leisure time. Communication has been made easier due to the advancement of information communication technology due to the fact that information sources can efficiently assist in providing the information required. Nevertheless, the possibility struggling to understand online content exists as the information may have inaccuracies and biases. By combining the technology acceptance model (TAM) and social support theory (SST), this study has developed a detailed purchase intention model to analyse relationships related to social commerce websites. Trust has been examined as the mediator in this research. The study has employed an online quantitative approach and has conducted non-probability (convenience) sampling to recruit 392 respondents. The findings demonstrate that different SS forms (emotional and informational) contain significant relationships with the TAM, trust and purchase intention. Nonetheless, perceived usefulness and purchase intention reveal insignificant relationships. Meanwhile, trust significantly mediates the association between SS and TAM constructs in this proposed model. The results also show that the proposed model has achieved a good fit and clearly explains each relationship. Therefore, the integration and extension of constructs may serve as future research paths. The findings could also be applied by marketeers and practitioners when developing social commerce websites.

Impact of the extended version of Technology Acceptance Model on predicting learners’ adoption to synchronous online learning in Sri Lanka

Synchronous online learning has played the prime role in controlling the spread of the COVID-19 throughout the world. Despite the technological advancement, the immediate shift towards the synchronous online learning has challenged availability and accessibility to the quality learning due to the over-reliance on the class-room based teaching and learning. Thus, this study focuses on investigating the impact of factors affecting adoption to synchronous online learning amidst the COVID-19 in Sri Lanka. As the consequence, institutions can pay attention on the essential factors to enhance the adoption to synchronous online learning. In this study, Technology Acceptance Model (TAM) has extended with the external variable of perceived self-efficacy and tested its impact on all other core constructs of TAM. This study used online questionnaire to collect the data from undergraduates of Sri Lankan state universities. Consequently, the study received 382 valid responses. For the data analysis, IBM SPSS statistics 25 and SmartPLS 4 were used and hypotheses were tested using PLS-SEM. The study has identified five variables namely perceived usefulness, perceived ease of use, attitude, perceived self-efficacy and behavioral intention to use. Based on the statistical evidence, all the proposed relationships were supported except attitude and perceived usefulness towards behavioral intention to use. Notably, this study has both theoretical and practical implications. To the best of author’s knowledge, this study firstly attempted to investigate the adoption synchronous online learning with the extended version of TAM using PLS-SEM. Most of the previous studies explored the online learning in general. Furthermore, the findings can be used to establish policies to make synchronous online learning more effective, specifically if the any pandemic occurs in future. 
 Keywords: Synchronous online learning; TAM; Sri Lanka; Adoption to synchronous online learning; Intention to use

Exploring Factors that Influence Physicians’ Usage of mHealth on Physician-Patient Communication

Background: A desirable physician-patient communication is broadly concluded to be able to reduce waste of medical resources and increase the patients’ confidence in recovery. Along with the rapid development of internet of things, hospitals have largely used mHealth as relationship structure to increase information exchange and relationship be- tween bilateral parties. Drawing on the theories of social exchange and social capital, this study extends technology acceptance model (TAM) to investigate social effects on mHealth adoption. Methods: 120 valid hypertension patient samples were collected with the attending aids of a medical center in Taiwan. Results: The result indicates that the conventional TAM constructs of perceived ease of use (PEOU) and perceived usefulness (PU) of mHealth technology are effective in constructing the social capital (mainly relational capital). The social capital (SC) is found to be significant that generated by PEOU and PU in affecting the mHealth adoption. However, negative capital (NC) effect due mainly to excessive structure capital that annoys patients will decrease adoption. Conclusions: While most studies focused so far on the positive outcomes of building relationship with doctors, we have to revealed its dark side as well. And we proof that patients would really suffered in overwhelmed relationships.

Behavioral intention to use electronic resources by distance learners: An extension of the technology acceptance model

This study aimed to discover the factors affecting behavioral intention to use electronic resources by distance learners of the Open University of Sri Lanka, and to develop a model explaining behavioral intention to use such resources. Using the Technology Acceptance Model (TAM) as the theoretical basis, this study explored the effect of five external factors on the behavioral intention of distance learners toward using e-resources. A quantitative research approach was used, and data were collected from a survey of 379 active undergraduates of the Open University of Sri Lanka. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was conducted to obtain the results of the study. The results of PLS-SEM reveal that the distance mode of learning and relevance of information are the two major external factors that affect behavioral intention to use e-resources. Computer self-efficacy and user satisfaction also have a significant influence on the dependent variable. Social influence, on the other hand, shows no significant impact. Of the TAM constructs, perceived usefulness, perceived ease of use, and user attitudes significantly affect e-resource utilization behavior.

Multi-Level Analysis of Learning Management Systems’ User Acceptance Exemplified in Two System Case Studies

There has recently been an increasing interest in Learning Management Systems (LMSs). It is currently unclear, however, exactly how these systems are perceived by their users. This article analyzes data on user acceptance for two LMSs (Blackboard and Canvas). The respective data are collected using a questionnaire modeled after the Technology Acceptance Model (TAM); it relates several variables that influence system acceptability, allowing for a detailed analysis of the system acceptance. We present analyses at two levels of the questionnaire data: questions and constructs (taken from TAM) as well as on different analysis levels using targeted methods. First, we investigate the differences between the above LMSs using statistical tests (t-test). Second, we provide results at the question level using descriptive indices, such as the mean and the Gini heterogeneity index, and apply methods for ordinal data using the Cumulative Link Mixed Model (CLMM). Next, we apply the same approach at the TAM construct level plus descriptive network analysis (degree centrality and bipartite motifs) to explore the variability of users’ answers and the degree of users’ satisfaction considering the extracted patterns. In the context of TAM, the statistical model is able to analyze LMS acceptance on the question level. As we are also very much interested in identifying LMS acceptance at the construct level, in this article, we provide both statistical analysis as well as network analysis to explore the connection between questionnaire data and relational data. A network analysis approach is particularly useful when analyzing LMS acceptance on the construct level, as this can take the structure of the users’ answers across questions per construct into account. Taken together, these results suggest a higher rate of user acceptance among Canvas users compared to Blackboard both for the question and construct level. Likewise, the descriptive network modeling for Canvas indicates a slightly higher concordance between Canvas users than Blackboard at the construct level.

What drives patients’ acceptance of Digital Therapeutics? Establishing a new framework to measure the interplay between rational and institutional factors

BackgroundThe rising incidence of chronic diseases among the population, further exacerbated by the phenomenon of aging, is a primary concern and a serious challenge for the healthcare systems worldwide. Among the wide realm of health digital technologies, the rise of Digital Therapeutics (DTx), which are medical devices able to deliver evidence-based treatments to manage and treat diseases, opens new opportunities. However, their diffusion and usage are still fragmented among countries. As the diffusion results from the adoption of technology from a social system and individual acceptance, this study aims to design and test a theoretical model that investigates the intention to use DTx, with a particular focus on the treatment of obesity, as a widespread and burdensome chronic condition.MethodsThis research is built on 336 answers coming from a survey to test the proposed model, which consists of a combination of organizational mechanisms, derived from Institutional Theory, and rational factors, derived from the Technology Acceptance Model (TAM). The survey has been delivered to patients and former patients of Istituto Auxologico Italiano, a hospital with several locations in northern Italy, recognized as a center of excellence for the treatment of obesity.ResultsThe analyses of the answers, performed through the Structural Equation Modelling (SEM) technique, confirmed the influence of the Perceived Usefulness on Intention To Use, and of the Perceived Ease Of Use on the Perceived Usefulness, confirming the validity of the assumptions derived from the TAM. On the other hand, institutional factors were introduced as antecedents of the Perceived Usefulness, and the Perceived Ease Of Use. Results show that the Regulative Pillar influences both the TAM constructs, the Normative Pillar (peer influence) has a positive effect only on the Perceived Usefulness, and finally, the Cultural Pillar impacts the Perceived Ease Of Use.ConclusionThis study allows filling the knowledge gap regarding the usage of the Institutional as a means to predict individuals’ intentions. Moreover, managerial contributions are available as the results have been operationalized into practical advice to managers and healthcare professionals to foster the adoption, and thus the diffusion, of Digital Therapeutics.

Factors influencing farmed fish traders’ intention to use improved fish post-harvest technologies in Kenya: application of technology acceptance model

Improved fish post-harvest technologies (IFPT) have been promoted as more efficient methods of fish processing, preservation, and value addition than the traditional methods prevalent in developing countries. The adoption rates, however, do not appear to be convincing. The purpose of this study was to determine the socio-demographic and psychological factors that influence intention of Kenyan farmed fish traders to use IFPT. The technology acceptance model (TAM) was used to properly explain the impact of TAM constructs such as perceived usefulness (PU), perceived ease of use (PEOU), and attitude (ATT), as well as socio-demographic factors such as gender, age, education level and fish trading experience on traders’ intention to use the technologies. A cross-sectional survey was conducted to collect data using a semi-structured questionnaire from 146 traders in Busia, Siaya and Kakamega counties. At a significance level of p = 0.05, a linear regression model was used to examine the socio-demographic and psychological determinants of the traders’ behavioral intention to use the improved technologies. The regression analysis revealed that PU (β = 0.443; p = 0.000), PEOU (β = 0.364; p = 0.000) and ATT (β = 0.615; p = 0.000) influence traders’ intention to use IFPT, with ATT having the highest influence on intention. However, the traders’ socio-demographic characteristics have no effect on their intention to use the technologies, as the coefficients for gender (β = 0.148; p = 0.096), age (β = 0.016; p = 0.882), level of education (β = −0.135; p = 0.141) and fish trading experience (β = 0.017; p = 0.869) are all insignificant. These findings show that the traders intend to use IFPT and will use them when it is in their best economic interests.