Modeling and Predicting Factors Influencing the Intention to Adopt Green Buildings in Tanzania

Higher education must evolve and adapt to technological changes in developing skilled human capital. Nevertheless, each technology has features that may eventually lead to user acceptance. This paper reviews factors that led to technology acceptance in the higher education context based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model since 2009. The related articles were extracted from the Scopus database. A screening process was performed to filter the related articles guided by the PRISMA approach. Specific keywords were used for searching, and 50 articles were identified. Only 44 related articles were reviewed after the identification and screening processes. Bibliometric analysis was used to understand further and highlight the impact of past studies and certain trends of their works that would benefit future research. The review revealed that performance expectancy is the most critical determinant that could affect users' intention to accept technology used in higher education institutions when the original UTAUT and its extended framework were used. However, all variables revealed some inconsistencies. Future studies should further investigate the reason behind this occurrence. Most of the past studies used covariance-based structural equation modeling (CB-SEM) as a data analysis tool when dealing with a complex model. This paper provides insights to policymakers in the higher education sector that are useful for their capital investment decision-making in future technology developments. It triggers that any technology that will be put into practice needs to be efficient and have features that could improve individual performance in performing tasks.

The Intergovernmental Panel on Climate Change recently stated that a swift ecological transition in transport, energy, and construction is required to reach the global emission reduction target. Around 40% of global emissions come from residential and commercial buildings. Therefore, this study developed a green building adoption model based on the Unified Theory of Acceptance and Use of Technology, which encourages firms to implement these novel green building technologies. For this, a survey of 333 respondents from Pakistani construction firms was conducted, and Partial Least Square Structural Equation Modeling approach was applied to validate the study's hypotheses. The findings showed that green building adoption provided invaluable insight by showing the strong associations between green building environmental concern, green building performance expectancy, green building effort expectancy, green building social influence, and green building innovation cost with green building behavioral intentions. Likewise, the findings also showed that green building behavioral intentions play a significant mediating role between the adoption of green buildings and its factors. The findings from this study suggest that intentions to engage in green building activities have a strong direct and mediating effect on green building adoption. Furthermore, the moderating effect of firm size demonstrated the significant differences between small, medium, and large firms size. Based on the study’ findings, the green building adoption model provided novel insights for governments and concerned stakeholders, including construction firms, to adopt green buildings for carbon neutrality in this era of climate emergency.

As an emerging healthcare technology, artificial intelligence (AI) health assistants have garnered significant attention. However, the acceptance and intention of ordinary users to adopt AI health assistants require further exploration. This study aims to identify factors influencing users’ intentions to use AI health assistants and enhance understanding of the acceptance mechanisms for this technology. Based on the unified theory of acceptance and use of technology (UTAUT), we expanded the variables of perceived trust (PT) and perceived risk (PR). We recruited 373 Chinese ordinary users online and analyzed the data using covariance-based structural equation modeling (CB-SEM). The results indicate that the original UTAUT structure is robust, performance expectancy (PE), effort expectancy (EE), and social influence (SI) significantly positively affect behavioral intention (BI), while facilitating conditions (FC) do not show a significant impact. Additionally, perceived trust is closely related to performance expectancy, effort expectancy, and behavioral intention, negatively impacting perceived risk. Conversely, perceived risk adversely affects behavioral intention. Our findings provide valuable practical insights for developers and operators of AI health assistants.

Aim/Purpose: This study aims to examine the ten factors from the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Unified Theory of Acceptance and Use of Technology (UTAUT) theories in order to analyze behavioral intentions to use the Indonesian online capital market investment platforms and the effect of behavioral intentions on actual usage. Background: The potential growth of capital market investors in Indonesia is large, and the low use of the Internet for investment purposes makes it necessary for stakeholders to understand the factors that affect people’s intentions to invest, especially through online platforms. Several previous studies have explained the intention to use online investment platforms using the TAM and TPB theories. This study tries to combine TAM, TPB, and UTAUT theories in analyzing behavioral intentions to use an online capital market investment platform in Indonesia. Methodology: The research approach employed is a mixed method, particularly explanatory research, which employs quantitative methods first, followed by qualitative methods. Data were collected by conducting interviews and sending online surveys. This study was successful in collecting information on the users of online capital market investment platforms in Indonesia from 1074 respondents, which was then processed and analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with the IBM AMOS 26.0 application. Contribution: This study complements earlier theories like TAM, TPB, and UTAUT by looking at the intention to use online capital market investment platforms from technological, human, and environmental viewpoints. This study looks at the intention to use the online capital market investing platform as a whole rather than separately depending on investment instruments. This study also assists practitioners including regulators, the government, developers, and investors by offering knowledge of the phenomena and factors that can increase the capital market’s investment intention in Indonesia. Findings: Attitudes, perceived ease of use, perceived behavioral control, subjective norm, and national pride were found to be significant predictors of the intention to use online investment platforms in Indonesia, whereas perceived usefulness, perceived risk, perceived trust, perceived privacy, and price value were not. Recommendations for Practitioners: All practitioners must be able to take steps and strategies that focus on factors that have a significant impact on increasing usage intentions. The government can enact legislation that emphasizes the simplicity and convenience of investment, as well as launch campaigns that encourage people to participate in economic recovery by investing in the capital market. Meanwhile, the developers are concentrating on facilitating the flow of investment transactions through the platform, increasing education and awareness of the benefits of investing in the capital market, and providing content that raises awareness that investing in the capital market can help to restore the national economy. Recommendation for Researchers: Further research is intended to include other variables such as perceived benefits and perceived security, as well as other frameworks such as TRA, to better explain individuals’ behavioral intentions to use online capital investment platforms. Impact on Society: This study can help all stakeholders understand what factors can increase Indonesians’ interest in investing in the capital market, particularly through online investment platforms. This understanding is expected to increase the number of capital market participants and, as a result, have an impact on economic recovery following the COVID-19 pandemic. Future Research: Future research is expected to investigate additional factors that can influence individuals’ behavioral intention to use an online capital market investment platform, such as perceived benefits and perceived security, as well as the addition of control variables such as age, gender, education, and income. International research across nations is also required to build a larger sample size in order to examine the behavior of investors in developing and developed countries and acquire a more thorough understanding of the online capital market investment platform.

Purpose This study seeks to improve the understanding of the motivation driving the entrepreneurs of micro, small and medium enterprises (MSMEs) to integrate ICTs and why this process is easier for some. The unified theory of acceptance and use of technology (UTAUT) is a suitable framework for this analysis. Our research aims to establish an explanatory typology based on the optimization of individual perceptions of entrepreneurs and usage intentions which enables identification of those groups that possess a greater intention to use ICTs in their businesses. This sheds light on how these factors influence their information and communication technology (ICT) adoption intentions within their businesses. Design/methodology/approach This study adopts an alternative approach and methodology to contribute new insights into academic discourse regarding the Unified Theory of Acceptance and Use of Technology (UTAUT). Building upon the theoretical foundation of the UTAUT, the present study pioneers the application of Data Envelopment Analysis (DEA) to a dataset encompassing 436 Spanish entrepreneurs. The objective is to examine the combination of diverse UTAUT perceptions that optimise the intention to implement ICTs in their companies while minimising anxiety associated with the adoption of new technologies (the outputs). Based on the results obtained, an explanatory typology is established that combines perceptions and usage intentions. Findings When applied to our group of entrepreneurs, DEA reveals that certain individuals can optimise their intentions with their current perception levels. This discovery has enabled us to create an explanatory typology through an examination of the efficiency of various perception-intention and/or anxiety combinations. Our proposed typology aims to shed light on the debate as to why not all perceptions translate into intentions and why certain entrepreneurs have a greater intention to use ICTs. An investigation of these efficient entrepreneurs reveals a wide range of combinations, which encompasses both those in harmony with effective usage behaviour and those deviating from such behaviour. Research limitations/implications DEA provides a snapshot of efficiency at a specific point in time and does not account for dynamic changes or adjustments over time, and DEA efficiency scores are relative measures that depend on the efficiency of other decision-making units in the dataset. The identification of appropriate benchmark units for comparison can be challenging, especially in heterogeneous datasets and cross-cultural analysis. In this respect, the UTAUT literature is lacking in cross-technology and cross-cultural comparisons. Practical implications Our methodology and results have implications for business management, business accelerators and economic policy. A detailed study of these clusters could reveal potential barriers and obstacles hindering the effective implementation of ICTs in MSMEs, thereby enabling researchers to focus on individuals who do not align with the model. Entrepreneurs classified in the most unfavourable typologies can take steps to enhance their perceptions, while administration and business accelerators can focus their efforts on these entrepreneurs. Originality/value The application of the UTAUT model on individual entrepreneurs receives limited coverage in the existing literature. To the best of our knowledge, this is the first study to utilise a DEA methodology within the framework of the UTAUT model (in contrast to the prevalent use of structural equation modelling in previous studies related to UTAUT). This analysis contributes fresh empirical evidence to the academic discourse on technology adoption models among individual entrepreneurs. Our methodology is a tool that reveals potential barriers and obstacles in individual perceptions that hinder the effective implementation of ICT in certain types of entrepreneurs.

The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely used in research involving adoption and acceptance of technologies. The theory considers factors that influence behavioural intention and use behaviour of technology. The authors of UTAUT combined eight competing theories of technology acceptance including the Diffusion of Innovation theory. Despite the involvement of the characteristics that affect technology adoption from the Diffusion of Innovation theory, the authors of UTAUT did not include the process that technology progresses through to be adopted. This paper argues conceptually that technology adoption processes should be included in UTAUT to better predict technology acceptance and that future information systems research should endeavour to include the environment and process in which technologies are used.

Research on technology adoption has identified a large number of factors that can affect individuals’ behavioral intentions to use various technologies, and many models have emerged. Venkatesh et al. (MIS Quarterly, 27(3), 425–478: 2003) aimed to develop an integrated model based on the idea that researchers may miss the opportunities offered by alternative models when they choose only one of the existing models. In the synthesis study, Theory of Reasoned Behavior, Technology Acceptance Model, Motivation Model, Theory of Planned Behavior, Unified Model of Technology Acceptance and Planned Behavior, PC Use Model, Diffusion Theory, and Social Cognitive Theory were discussed, their deficiencies and strengths were compared, and the Unified Theory of Acceptance and Use of Technology (UTAUT) was developed. According to UTAUT, using behavior is determined directly by behavioral intention and facilitating conditions, and performance expectation, which determines behavioral intention, is indirectly determined by effort expectancy, social influence, and facilitating conditions variables. In addition, age, gender, experience, and voluntariness of use variables were added as directing variables to the relationships in the model. To make the model more consumer-centric, Venkatesh et al. (MIS Quarterly 36, 157–178: 2012a) added hedonic motivation, price value, and habit variables to the model and removed the voluntariness of use moderator from the model, thus expanding the UTAUT model to UTAUT2. In this chapter, UTAUT, which examines the variables that affect individuals’ technology acceptance and use with a holistic approach, is examined in an educational context. The reader who completes the section will be able to (a) explain the general structure of the model and for what purpose it was developed (b) define the factors in the model and the relationships between factors (c) give an educational example of the acceptance and use of a technology (d) expand the UTAUT and UTAUT2 models in an educational context, formulate hypotheses/problems, collect and analyze data, and (e) discuss and evaluate the results of a study using the UTAUT and UTAUT2 models.KeywordsICT in educationBehaviorAcceptanceTechnology acceptanceUTAUT

The unified theory of acceptance and use of technology (UTAUT) is a survey instrument used to determine technology usage intention and behaviour. UTAUT consists of four predictor variables; performance expectance, effort expectancy, social influence and facilitating conditions. This study investigates the technology acceptance profiles of university teaching staff, by using the UTAUT predictor variables as clustering variables, in the context of the implementation of a new learning management system (LMS). While students are mostly the focus of research on technology acceptance in higher education, university teaching staff are predominantly overlooked. Using a modified UTAUT questionnaire, 244 university teaching staff from a Belgian university took part in a survey focusing on their acceptance and use of the new LMS. Most studies on LMS acceptance in higher education are variable centred, whereas this research takes a person‐centred approach. This approach will shed new light on how UTAUT can provide information which can be used to interpret the professional development needs during an institution‐wide educational technology implementation. A cluster analysis with the predictor variables of UTAUT as input variables resulted in three distinct groups: a high, moderate and a low scoring cluster. These differences between the clusters were also reflected in the acceptance of the LMS. The results of this study will therefore facilitate decision making and guidelines for the design of institution‐wide professional development initiatives that are targeted towards the needs of specific groups of university teaching staff. Practitioner Notes What is already known about this topic Most research has focused on university students, or on general barriers to technology acceptance. The four core predictors of UTAUT are reliable determinants of intention and attitude, which in turn are direct determinants of use. An alternative to measuring actual use, self‐reported intensity of use, has been found to be a direct determinant of actual use. Previous cluster analyses in connection with technology acceptance have shown that there are meaningful differences between groups of users in the way they accept and use a technology. Clustering gives additional information about technology users that may facilitate follow‐up research or support initiatives designed to suit the needs of the groups. What this paper adds A three‐solution cluster analysis reveals three distinct groups of technology acceptance in university teaching staff: High, moderate and low. High users are most likely to innovate. Moderate and low users most likely need additional support as well as increased social influence from policy and decision makers. Implications for practice and/or policy The professional development support needs of each group can be more easily addressed and targeted separately to suit their specific needs. This is an opportunity for policy and decision makers to revise and optimise policies on LMS use as well as on the supportive initiatives needed to facilitate better and more effective use of the LMS. Innovating will require additional support. While the high usage group is clearly more inclined to use the system more innovatively, the two other groups will require additional resources and social pressure to influence their intentions regarding innovating.

Purpose– The purpose of this paper is to perform a systematic review of articles that have used the unified theory of acceptance and use of technology (UTAUT).Design/methodology/approach– The results produced in this research are based on the literature analysis of 174 existing articles on the UTAUT model. This has been performed by collecting data including demographic details, methodological details, limitations, and significance of relationships between the constructs from the available articles based on the UTAUT.Findings– The findings indicated that general purpose systems and specialized business systems were examined in the majority of the articles using the UTAUT. The analysis also indicated that cross-sectional approach, survey methods, and structural equation modelling analysis techniques were the most explored research methodologies whereas SPSS was found to be the largely used analysis tools. Moreover, the weight analysis of independent variables indicates that variables such as performance expectancy and behavioural intention qualified for the best predictor category. Moreover, the analysis also suggested that single subject or biased sample as the most explored limitation across all studies.Research limitations/implications– The search activities were centered on occurrences of keywords to avoid tracing a large number of publications where these keywords might have been used as casual words in the main text. However, we acknowledge that there may be a number of studies, which lack keywords in the title, but still focus upon UTAUT in some form.Originality/value– This is the first research of its type which has extensively examined the literature on the UTAUT and provided the researchers with the accumulative knowledge about the model.

This paper aims to perform a systematic review of articles that have used the unified theory of acceptance and use of technology (UTAUT) from 2019 to 2023. The results in this research are based on the literature analysis of 10 existing articles on the UTAUT model. This has been performed by collecting data including demographic details, methodological details, limitations, and significance of relationships between constructs from the available articles based on the UTAUT. The findings indicated that general-purpose systems and specialized business systems were examined in the majority of the articles using the UTAUT. The analysis also indicated that the cross-sectional approach, survey methods, and structural equation modeling analysis techniques were the most explored research methodologies whereas Smart PLS was found to be the largely used analysis tool. This is the continued research that examined the literature on the UTAUT for the past five years and provided the researchers with accumulative knowledge about the model.

The unified theory of acceptance and use of technology (UTAUT) is a little over a decade old and has been used extensively in information systems (IS) and other fields, as the large number of citations to the original paper that introduced the theory evidences. In this paper, we review and synthesize the IS literature on UTAUT from September 2003 until December 2014, perform a theoretical analysis of UTAUT and its extensions, and chart an agenda for research going forward. Based on Weber’s (2012) framework of theory evaluation, we examined UTAUT and its extensions along two sets of quality dimensions; namely, the parts of a theory and the theory as a whole. While our review identifies many merits to UTAUT, we also found that the progress related to this theory has hampered further theoretical development in research into technology acceptance and use. To chart an agenda for research that will enable significant future work, we analyze the theoretical contributions of UTAUT using Whetten’s (2009) notion of cross-context theorizing. Our analysis reveals several limitations that lead us to propose a multi-level framework that can serve as the theoretical foundation for future research. Specifically, this framework integrates the notion of research context and cross-context theorizing with the theory evaluation framework to: (1) synthesize the existing UTAUT extensions across both the dimensions and the levels of the research context and (2) highlight promising research directions. We conclude with recommendations for future UTAUT-related research using the proposed framework.

The island of Sumatra ranks second after Java in terms of the number of internet banking users, with a total of 62 million customers. The adoption of mobile banking can be determined and predicted by several factors measuring consumer behavior likelihood. This can be achieved through the application of the UTAUT (Unified Theory of Acceptance and Use of Technology) model. The aim of this research is to examine the usage of mobile banking using the UTAUT model, specifically in Sumatra. The research method employed is quantitative, with data obtained from distributing questionnaires to 225 respondents. The analysis of research findings is assisted by the SEM PLS 3.9 tool. The results indicate that Performance Expectancy and Effort Expectancy variables influence Usage Intention, while Social Influence does not affect usage intention. Facilitating Conditions and Usage Intention affect Usage Behavior. However, Age and Experience do not moderate the relationship between Facilitating Conditions and Usage Behavior. Keywords: Mobile Banking, Unified Theory of Acceptance and Use of Technology (UTAUT), Sumatera Island.

E-wallet is an electronic service used as a payment tool in electronic form where the amount of money will be stored in a certain electronic medium. However, there are still problems related to privacy and data security also network access while using e-wallet. The use of e-wallet is expected to increase efficiency and effectiveness in conducting transactions, helping the smoothness of small but frequent mass transactions. The Unified Theory of Acceptance and Use of Technology (UTAUT) analysis is used to predict a person's attitudes and acceptance of the technology used and provide information on what factors can encourage the user's attitudes. The purpose of this study is to analyze the use of e-wallets in transactions on e-commerce using the UTAUT framework and to determine the impact of using E-wallets as a non-cash payment tool for e-commerce using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework on 109 student respondents of Faculty Economics and Business Satya Wacana Christian University using explanatory research through a quantitative approach. The results of this study prove that there are two accepted hypotheses. Only the social influence and facilitating conditions variables have a positive and significant effect on the user's interest in using the ShopeePay service with an interest in transactions on e-commerce.

With the continuous advancement of artificial intelligence (AI) technology—particularly the widespread adoption of generative AI—the application of AI tools in the design field has become increasingly prevalent, demonstrating considerable potential for innovation. However, existing research has primarily focused on the acceptance and use of AI technologies by general users, while relatively limited attention has been paid to the behavioral mechanisms of professional creative users, such as digital media designers, in subscribing to advanced AI-assisted drawing tools. Prior studies have often overlooked the nuanced decision-making processes and unique cognitive factors that shape subscription behavior within creative work contexts. To address this gap, the present study draws on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and their extended frameworks, integrating grounded theory’s three-stage coding approach with covariance-based structural equation modeling (CB-SEM) to construct and validate an innovative research framework that bridges qualitative exploration with quantitative validation. Seven key variables—perceived trust, perceived usefulness, perceived ease of use, social influence, output quality, personal innovativeness, and price value—were identified through semi-structured interviews and empirically examined based on data from 394 participants. The results indicate that perceived ease of use indirectly influences subscription intention through perceived usefulness, while output quality affects it through the mediating role of perceived trust. Social influence, price value, and perceived trust all exhibit significant positive effects on subscription intention, whereas personal innovativeness does not demonstrate a statistically significant impact. Theoretically, this study extends the applicability of TAM and UTAUT to the relatively under-researched context of the creative industry, achieves methodological innovation through the integration of qualitative and quantitative approaches, and provides practical guidance and a decision-making foundation for AI tool developers and the intelligent transformation of the creative sector.

PurposeGiven the growing importance of blockchain technology (BT), the authors use the unified theory of acceptance and use of technology (UTAUT), which posits that BT adoption intention depends on the complementarity between UTAUT and blockchain transparency (BTRAN) and examine it in a new setting: the boundary condition of perceived helpfulness.Design/methodology/approachThe authors review the major conceptual literature on both UTAUT and BT to identify their principal common factors. They examine the complementarity between UTAUT and BTRAN and further test the moderating effect of perceived helpfulness. The authors used the PLS technique for data analysis because this technique can test the direct and interaction effects.FindingsThe complementarity between UTAUT and BTRAN strongly affects BT adoption intention. The authors further show that perceived helpfulness moderates the relationship between adoption intention and usage behavior. At high levels of perceived helpfulness, usage behavior increases rapidly with adoption intention.Originality/valueThe results indicate that UTAUT is a valuable theory to identify the determinants of adoption intention, confirming its robustness in blockchain-enabled supply chain management. The combination of UTAUT and BTRAN can contribute a plausible approach to the strategy literature: the complementarity effect might create more benefits than adopting a single practice.