Business Intelligence Adoption in Higher Education Institutions: Extending UTAUT with Organizational Learning Culture Evidence from Jordan

Business Intelligence Adoption in Higher Education Institutions: Extending UTAUT with Organizational Learning Culture Evidence from Jordan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Business Intelligence Adoption in Higher Education Institutions: Extending UTAUT with Organizational Learning Culture Evidence from Jordan mohammad Mahmoud alzubi, Suad Abdalkareem Alwaely, Sanna Abd el rahman Yaghi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9367638/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Even though Business Intelligence (BI) has greatly enhanced decision-making in various industries, there is limited literature on its use in institutions of higher learning (HEIs) especially in third world countries. Since the amount of data produced by HEIs is enormous, this paper analyzes the major determinants of BI adoption, and more so, the impact of Organizational Learning Culture (OLC) on the behavioral intentions of the users. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the research paper will build on the model by integrating OLC as a central organizational issue that will affect BI adoption. Quantitative methodology was adopted, and the survey data was gathered with 579 respondents of managerial level working in Jordanian HEIs and analyzed in Structural Equation Modeling (SEM). The results show that the positive impact of Performance Expectancy, Social Influence, and Facilitating Conditions on behavioral intention to adopt BI systems is significant, as compared to the insignificance of the effect of Effort Expectancy. Notably, the Organizational Learning Culture illustrates the strong positive effect on all UTAUT constructs, which is why it is considered a key driving factor in the adoption of BI. The article makes its contribution to the body of literature both by confirming an extended UTAUT model in the context of a developing country and in a higher educational setting and by highlighting organizational learning culture as one of the critical facilitators of technology adoption. The implications on academic leaders, policymakers, and system developers that the results will provide are useful in making decisions based on data and in the process of digital transformation of higher education institutions. Business and commerce/Business and management Social science/Business and management Humanities/Cultural and media studies Social science/Cultural and media studies Social science/Education Business and commerce/Information systems and information technology Social science/Science technology and society UTAUT Culture Business Intelligence Higher Education Figures Figure 1 1. Introduction The use of innovative technology is being requested more often in higher education ((de Souza & Debs, 2024 ); (Alkhwaldi, 2024 )). Using new methods and technologies, IS is now giving priority to delivering valuable educational materials that boost student achievement (Daniel et al., 2024 ;Zhao et al., 2022 ). When HEIs use IT properly, they can reduce costs, ensure resources are used wisely and create a better environment for students to learn(Almanwari et al., 2024 ;Mellors & Vicencio, 2025 ). Business Intelligence (BI) tools provide a range of IT systems to organizations and universities to help gather, combine and review large amounts of information (Sequeira et al., 2024 ); (Al-khateeb, 2024 ). Using data analysis, HEIs can see their own strengths and weaknesses as well as the opportunities and pressures from outside their institutions (McDonald et al., 2025 ). Basically, BI combines business analytics, data mining, data visualization, data tools and infrastructure and best practices to help universities use data for decision-making (Tsiu, 2023). BI was first introduced in the 1990s (Salisu et al., 2021 ). Experts in the field and BI users are currently increasing knowledge by talking about strategies and methods to put BI systems into use. On the other hand, few research efforts have been aimed at how BI works in the HEI sector. Within HEIs, BI means analysing data and using advanced tools for analysis. As a result, institutions are better able to develop sound ways of making decisions (Ahsan, 2025 ); (Gürtl et al., 2024 ). Because the world of higher education is always developing, HEIs need to handle resources efficiently and deliver a good learning experience for students (Ul Hassan et al., 2025 ); (Moore et al., 2025 ). With BI, IT is able to change the way universities operate by transforming data into useful knowledge that enhances decision-making, organization, schooling and results for students (Afzal & Tumpa, 2025 ;Arefin et al., 2021 ). BI systems stand out by taking information from various sources and passing it along to decision-makers in HEIs (Sequeira et al., 2024 ). By using BI, HEIs can boost student teaching-learning processes, better manage their workforce, improve how they operate administratively and cut expenditures (Okpala, 2025 ;Hmoud et al., 2023 ). Because data plays such an important role in higher education, BI has become a valuable topic for experts as well as researchers. The use of BI can make an institution more effective and can help improve the importance of student-centred learning. HEIs in Jordan must continuously adapt to changes so they can find new ways to run efficiently in a competitive education market. More evidence points to the importance of internal culture in allowing innovation, mainly from constant learning and an ability to adapt within the organization (Barjak & Heimsch, 2023 ;Ahsan, 2025 ) M. M. Alzubi et al., 2021 ). This study analyzes organisational culture and the way it affects the integration of new technology in Jordanian HEIs. Schein stated in 1990 that organisational culture refers to the set of values, perceptions and assumptions that employees within an organisation share. Members of the faculty are guided by this culture when making decisions together and in their academic work. Whether technological advancement is supported by or opposed by organisational culture will ultimately decide how effective technology implementation is (Almatrodi & Skoumpopoulou, 2023 ; Chaudhuri et al., 2024 ). Supporting a culture where everyone learns continually is key to successful use of technology in colleges and universities. Acceptance and innovation in technology within academic institutions are partly due to the positive role played by Organisational Learning Culture (OLC). OLC means an institution works to help students learn, spread knowledge, adjust to new circumstances and try new ideas (AlSaied & Alkhoraif, 2024 ). At HEIs, OLC takes the form of workshops for staff members, active sharing of ideas, listening to one another and an open mindset toward classroom changes and new technologies. If properly tended to such a culture helps companies become ready for digital tools like BI. People leading and working in these organisations usually appreciate data, reflect deeply and support changes that make the institution more effective. In other words, OLC might greatly influence the way individuals view and respond to Business Intelligence. Academic research has highlighted that organisational learning is important for digital transformation. Organizations that encourage staff to learn are more likely to handle new problems well, respond to recent changes and be creative (Islam et al., 2025 ; DiBella et al., 2023 ). Additionally, having a good learning environment can make people more likely to accept change, use technology securely and take part actively in the company’s sales and marketing campaigns. For BI, it means that when faculty and administrators use an OLC approach, they are more likely to see benefits in such tools, find them simple, get support from their peers and consider the support for implementation is adequate. These aspects are closely connected to the key features of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT model, created by (Venkatesh et al., 2003 ), is used widely to explain how workers in organisations accept technology. The new model takes key factors from eight different technology adoption models and finds that four main characteristics—performance expectancy, effort expectancy, social influence and facilitating conditions—help forecast user intentions. Experts have studied UTAUT in fields such as healthcare, e-government and higher education (Akhtar et al., 2025 ); (Saboor et al., 2025 ). Even so, experts have advised adding specific elements depending on the environment and organisation to improve the matching of cultures. Although OLC is important, only a small amount of research has looked at its part in driving BI adoption at HEIs in developing countries. Most existing studies look at technology or people, not at the big-picture factors that influence the organisation (Jingyi & Pamintuan, 2025 ;. M. S. Alzubi et al., 2025 ). As a result, we do not fully understand how the culture within an organization can help or hinder the use of advanced analytics technology. To address this, the new UTAUT model under study now adds OLC as an important predictor of performance expectancy, effort expectancy, social influence and facilitating conditions. Integrating OLC into the study allows it to better explain BI adoption in HEIs and discover the important cultural factors for making BI work successfully. For testing this model, the researchers analyze higher education institutions in Jordan, since they are only just beginning to use BI. Data will be gathered from universities to investigate the relationships among OLC and UTAUT variables and the effect they have on users’ intention to use BI. The study is also designed to discover successful strategies and difficulties unique to Jordanian HEIs in developing a learning culture for innovation. This is especially relevant now because institutions want to drive their planning, stand out globally and ensure good achievements by students with the help of technology. To help education and performance in Jordanian HEIs, it is important to promote both learning and the use of BI within organizations. Likely, institutions that have programs for teamwork, try out fresh concepts and emphasize evidence will integrate educational innovation technology more effectively. The study brings value to the literature by outlining the key impact of organizational learning cultures on using BI and by putting forward a model rooted in cultural context for understanding technology application in universities. Thus, this study looks at what influences HEIs to use Business Intelligence systems and how a culture of learning at work affects this decision. It appears from the review that the use of business intelligence can improve observations of how students perform, how well faculty are working and the results of academic programmes. This study suggests, by including OLC in the UTAUT model, a new way to examine the factors in an organisation that aid BI implementation. Researchers expect that what they learn will benefit decision-makers in education, leaders in universities and those working in technology. 2. Theoretical background and hypotheses development The introduction and use of innovative technologies is a lively area of study in the field of IS management. Prior work has looked at various theories trying to explain how individuals accept IT systems, like the Technology Acceptance Model (TAM), the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (Almahri & Saleh, 2025 ;Venkatesh et al., 2003 ). Nonetheless, experts realised that a new model was needed to combine all these older approaches. In response, (Venkatesh et al., 2003 ) suggested the Unified Theory of Acceptance and Use of Technology (UTAUT) by combining the most important elements of eight well-known models. According to the UTAUT, user behaviour and behavioural intention are explained using the four main constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). Furthermore, it accounts for these elements: age, gender, how experienced someone is and whether they used the system by choice (Venkatesh et al., 2003 ;Almahri & Saleh, 2025 ). The research on these dimensions has been carried out in several cases such as BI adoption (Kašparová, 2023 ) and IT use at academic institutions (Twum et al., 2022 ;Teng et al., 2022 ). Despite the introduction of hedonic motivation, price value and habit in UTAUT2, the original UTAUT is more suitable for organisations and educational institutions because of its broad application. Considering the specific environment of HEIs, this study adds a contextual variable – (OLC) – to the UTAUT model, reflecting the usual ways and culture that help the institution learn new things and adapt. OLC is highly relevant when it comes to technology, specifically supporting faculty and staff in trying, adopting and implementing new technologies such as Business Intelligence (BI). Research has shown that BI systems are growing in industries such as healthcare, insurance and SMEs (Salisu et al., 2021 ;Al-maaitah et al., 2025 ). However, the current understanding of how HEIs are incorporating BI systems is still limited, particularly in places like Jordan. Earlier studies centred on the effect of technology and organisation on BI adoption, while we examine the ways that a OLC influences BI usage. Because of various structural and operational troubles within Jordanian HEIs, OLC plays a valuable role in promoting digital transformation. Figure 1 represents the conceptual model used in this research. The next subsections explain the hypotheses connect to each construct. 2.1 Performance Expectancy (PE) The UTAUT model defines performance expectancy (PE) as the extent to which people think using a technology will increase their job performance. In higher education institutions, this construct indicates that faculty and staff believe BI systems can increase their efficiency, the quality of their decisions and their overall results in their work. The same way PE helps encourage new technology use in healthcare, PE is also likely to further implement BI tools in HEIs. BI systems help users understand a lot of information, find emerging patterns and decide on actions that match the institution’s goals. HEIs are more likely to achieve things such as monitoring students’ progress, managing resources wisely, upgrading their curriculum and improving how the institution operates (Hmoud et al., 2023 ;Olubiyi & Akinlabi, 2025 ). When learners believe that technology can help with their main tasks, they tend to embrace it. The same study discovered that PE played a big role in healthcare staff’s intentions to use CDSS, meaning this can be applied to using BI in education contexts. (Kašparová, 2023 ) found that perceived ease of use of BI tools is a strong reason why users accept them, but only in cases where results directly improve productivity. If faculty and HEI staff think BI will boost their job effectiveness by making work more efficient, accurate and boosting productivity, they are probably more likely to have a good attitude toward using BI. Therefore, this study proposes the following hypothesis: "H1. Performance expectancy will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions." 2.2 Effort Expectancy (EE) EE in the UTAUT framework is about how easy someone finds using an IS/IT system (Venkatesh et al., 2003 ); (Tang et al., 2025 ). In higher education settings, EE is viewed as key to adopting new technologies, especially when users’ digital literacy is not equal, according to(Abubakar & Al-Mamary, 2025 ). It is built around how people see the system in terms of usability, ease of understanding and simple controls. Many empirical investigations have found that an effective educational environment leads people to want to use educational technologies (Wang et al., 2024 ). BI-related systems are more likely to be accepted by faculty and staff if they find them easy to use and reasonably easy to install. By contrast, when usage is demanding or the tools are unclear, users may turn away and fail to embrace the system (Srivastava et al., 2025 ). A BI platform that is centered on users is important in promoting uptake, particularly when the system at an organization is not advanced or easily customizable (Isiaku & Adalier, 2024 ), making user interfaces easy to understand and offering clear help features can greatly encourage educators and administrators to adopt technology. Under these conditions, being convenient to use becomes not only what users consider important but also an important part of HEIs’ strategy for rolling out BI systems smoothly. For this reason, this study puts forth the hypothesis that, when BI systems are perceived as clear and easy to use, staff and faculty include them in their academic and workplace tasks. Hence, the following hypothesis is proposed: "H2. Effort expectancy will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions." 2.3 Social influence When using the UTAUT framework, Social Influence (SI) indicates how much a person thinks significant others such as colleagues, supervisors or leaders, think they should start using a specific technology (Venkatesh et al., 2003 ). At Jordanian universities, SI greatly influences attitudes about using Business Intelligence (BI) systems. The investigation found that SI greatly affects behavioural intention, showing how important peer influence, managerial backing and workplace norms are for using BI. As earlier works emphasise (Singh et al., 2024 ;Jaklič et al., 2018 ), teachers and administrators should give collective support to promote the use of technology in schools. In universities and other educational institutions in Jordan that feature cooperative relationships and upper level decision-making, SI becomes important for driving changes. By encouraging both leaders and influential teachers to patronize BI, the adoption of data across departments can occur much faster. "H3. Social influence will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions." 2.4 Facilitating Conditions (FC) As explained by Venkatesh et al. ( 2003 ), having favorable conditions (FC) means a person believes that organizational and technical support is in place for the system. In this study, FC reflects faculty and staff views on whether the institution gives them the necessary resources to use Business Intelligence (BI) well. You will also need a solid IT set-up, qualified technical help and convenient, organised data for Business Intelligence. To use BI, HEIs must have both staff who are willing to use the technology and a strong institutional system in place (Aldogiher et al., 2025 ). With the correct technological environment, users are better able to learn how to use BI tools. When training is provided, help desks are on hand, proper data management rules exist and clear system documentation is available, users become more confident and accepting of technology. (Al-Emran et al., 2025 ) discovered that people with access to basic technology in schools are more ready to use these systems and build useful knowledge. It is also backed up by the study of (Perron et al., 2025 ) which concludes that strong technical support, compatibility and good infrastructure make users more likely to accept learning tools in schools and universities. Likewise, (Olugboyega et al., 2025 ) found that a sufficient amount of hardware/software and resources for staff development help organizations achieve success with using new IT systems. Because technical readiness and institutional backing play a key role in technology use, this study finds that professionals who believe their institutions are prepared will have a stronger intention to use BI systems. Hence, the following hypothesis is proposed: H4. Facilitating conditions will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions. 2.5 Organisational Learning Culture (OLC) Organisational Learning Culture refers to the environment in a company that supports ongoing learning, the sharing of knowledge and the ability to adapt (Ahsan, 2025 ). A supportive OLC helps promote both new ideas and decisions based on data (Gupta et al., 2020 ). Some recent research suggests that workplaces focused on learning are more likely to adopt modern IT solutions, including technology for using big data (Allahawiah et al., 2024 ). Within a university or college, an OLC can set a framework for using BI effectively by pushing staff to try new things, reflect and base their work on research. (Exner & Zunic, 2025 ) reported that focusing on continuous learning is key to building successful BI systems and (Mexhuani, 2025 ) highlight that a learning culture in institutions helps them quickly adopt technology. Besides, OLC may help shape UTAUT elements by improving desired benefits (PE), lessening problems with technology (EE), offering peer assistance (SI) and ensuring technical resources are sufficient (FC). HEIs should focus on staff ongoing professional development, work on cross-departmental efforts, support the use of evidence and introduce continuous learning in all their policies. Moreover, encouraging training, mentoring and sharing knowledge publicly, strengthens the cultural focus on innovation and increases people’s willingness to use BI. Although OLC is very important, the connection between OLC and UTAUT has not been thoroughly examined in the context of higher education BI in most developing countries. Therefore, this research investigates empirically the role of OLC as an explanation for core UTAUT constructs, improving our knowledge of BI adoption. Strengthening organisational learning is thought to enhance stakeholders’ opinions regarding the utility, ease of use, support and preparedness of the BI tools. Based on that idea, the following hypotheses are proposed: H5. Organisational learning culture will have a significant impact on performance expectancy of adopting business intelligence within higher education institutions. H6. Organisational learning culture will have a significant impact on effort expectancy of adopting business intelligence within higher education institutions. H7. Organisational learning culture will have a significant impact on social influence of adopting business intelligence within higher education institutions. H8. Organisational learning culture will have a significant impact on facilitating conditions of adopting business intelligence within higher education institutions. This concludes this section, in which the UTAUT model is revised to guide the study of BI’s use in Jordanian HEIs. Using OLC, the study helps fill a gap in IS literature by highlighting the way cultural aspects within institutions impact the use of technological solutions among educators. By including perceptions of support, the UTAUT framework explains more about user behavior and helps solve a real challenge that benefits policy, strategy and development in colleges and universities. 3. Research Methodology 3.1 Sampling and Data Collection A positivist method and an online survey through Google Forms were utilized in the study. This research involved key members of the university community in Jordan such as academic deans, department heads, directors of internal control, IT staff and information systems managers. Before taking part in the online survey, participants were provided with an outline of Business Intelligence concepts drawn from existing sources and a short educational video outlining the use of BI in universities. Only those respondents who realized what the concept meant and why it affected HEIs were allowed to take part in the survey. From January to March 2024, we conducted the study by collecting data. With no detailed list of the study population, researchers used convenient non-probability sampling by selecting participants easily available to them (Sekaran, 2016 ). Because the study used Structural Equation Modelling (SEM), the amount of data collected was based on recommendations from leading experts in the field (Hair et al., 2019 ). This paper collected 579 valid sets of responses, meeting the needed minimum set by many experts which is equal to five times the observed variables (Kline, 2023 ; Ali et al., 2024 ). With this sample size, the model estimates and tests of significance are considered statistically appropriate. To make certain the content was valid, the survey was first created in English and then underwent a double-translation process using best practices around the world (Ozolins, 2008 ). Both forward-translations to Arabic followed by back-translation to English were used to check for the same meaning. Many research studies show that back-translation helps improve language quality and guarantees that translated tools are accurate when used across different cultures (Arnaud et al., 2025 ). A team of BI experts from Jordanian HEIs went through the instrument to judge if the items are clear, relevant and suitable. After getting their comments, items judged to be redundant were taken out and issues with ambiguous language were sorted out. To review the procedure, 30 individuals from a variety of HEIs took part in a pilot study. By doing this review, we were able to check whether the questions in the survey were presentable, easy to read and easy to understand. Each of the measurement items was assessed using a five-point Likert scale, from 1 (strongly disagreeing) to 5 (strongly agreeing), to look at participants’ views and planned behaviour related to BI. 3.2 Measurement Reliability and validity of the model were accomplished by using validated scales from the UTAUT theory and relevant research. Among these, measurement was done for Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Behavioural Intention (BI) and the contextual variable Organizational Learning Culture (OLC). Measurements for the variables PE, EE, SI, FC and BI were taken from the original UTAUT developed by Venkatesh et al. ( 2003 ) and expanded by (Belhadi et al., 2024 ). Every statement was developed so that it applies to higher education and focused on using Business Intelligence (BI) systems within Jordanian HEIs. Furthermore, learning from relevant studies in the field of technology use in education helped us to adjust the items (Aideed et al., 2025 ; Al-Adwan, 2024 ); Lee et al., 2025 ). This variable was measured with three items taken from existing studies looking at organisational readiness and knowledge environments (Alkhwaldi, 2024 ; (Al-Kfairy et al., 2024 ; Allioui & Mourdi, 2023 ). They were developed to gauge how much HEIs help with continuous learning, knowledge exchange and responding to changes which are essential for the success of BI. 4. Data Analysis and Results 4.1 Demographic Profiles The demographic profile of the respondents is presented in Table 1 . Of the 579 participants, 68.0% were male and 32.0% were female. In terms of age, the largest group was 35–44 years old (36.3%), followed by 45–54 years (31.8%), 55 years and above (18.8%), and 25–34 years (13.1%). Regarding position, department heads represented the largest category (33.0%), followed by IT/IS managers (28.0%), directors/internal control officers (24.7%), and deans (14.3%). In terms of experience, 34.2% of the respondents had 5–10 years of experience, 30.2% had 11–15 years, 24.0% had more than 15 years, and 11.6% had less than 5 years. Concerning academic qualification, most respondents held a master’s degree (42.3%) or a PhD (41.8%), while 15.9% held a bachelor’s degree. These results indicate that the sample included respondents with relevant managerial, administrative, and technical backgrounds, making them suitable for examining Business Intelligence adoption in Jordanian higher education institutions. Table 1 Demographic Profiles Demographic Variable Category Frequency (n) Percentage (%) Gender Male 394 68.0% Female 185 32.0% Age Group 25–34 76 13.1% 35–44 210 36.3% 45–54 184 31.8% 55 and above 109 18.8% Position Department Head 191 33.0% Dean 83 14.3% IT/IS Manager 162 28.0% Director/Internal Control 143 24.7% Years of Experience Less than 5 years 67 11.6% 5–10 years 198 34.2% 11–15 years 175 30.2% More than 15 years 139 24.0% Academic Qualification Bachelor’s Degree 92 15.9% Master’s Degree 245 42.3% PhD 242 41.8% 4.2 Descriptive Statistics The parameters for each construct in the investigation are all displayed in Table 2 . According to the results, everyone’s views about the constructs were generally positive. The mean values for Effort Expectancy (EE) were highest at 3.461, with Facilitating Conditions (FC) ranking second at 3.405 and Organizational Learning Culture (OLC) being 3.360. Social Influence (SI) was rated the lowest out of all factors (2.982). All constructs showed a good measure of internal consistency, since their Cronbach’s alpha scores were greater than 0.70. 4.3 Measurement Model Evaluation The fit of the measurement model was assessed through Confirmatory Factor Analysis (CFA) in AMOS 25.0 and the Maximum Likelihood Estimation (MLE) method was used to analyze the data. The analysis was conducted using the two-step method developed by (F. Hair Jr et al., 2014 ), focusing first on the validation and reliability of the measurement model. Fitness of the model was assessed using chi-square/df, RMSEA, SRMR, GFI, AGFI, NFI and CFI. Regular methods showed that the data followed the model well. Reliability was supported by high Composite Reliability (CR), where every construct was greater than the required 0.70. All Average Variance Extracted (AVE) values were above the suggested threshold of 0.50 by (Hair et al., 2019 ) which confirmed convergent validity. Reliability and convergent validity were acceptable, with CR values from 0.895 to 0.963 and all AVE values larger than 0.653. For every construct, the square root of its AVE was examined against the correlations it shares with other variables. For all subjects, the AVE of each construct was still higher than the correlation between any two constructs, so each variable was clearly separated from the rest. Thanks to the results, it was clear that the constructs of the scale were reliable, consistent, were as predicted and able to separate each area, ensuring the measurement model was suitable for structural analysis. Table 2 Descriptive Statistics Constructs Mean SD Cronbach’s a Performance Expectancy (PE) 3.114 0.972 0.902 Effort Expectancy (EE) 3.461 0.931 0.928 Social Influence (SI) 2.982 0.824 0.861 Facilitating Conditions (FC) 3.405 0.884 0.963 Behavioral Intention (BI) 3.152 1.027 0.826 Organizational Learning Culture (OLC) 3.36 0.918 0.911 4.3 Measurement Model The study explored the relationships between the different parts of the proposed model by following a sequential method. First, it needed to be established that the measurement model accurately included the important variables (F. Hair Jr et al., 2014 ). This investigation used Data Analysis and Statistical Software (SPSS) and AMOS 25.0, together with Maximum Likelihood Estimation (MLE) (F. Hair Jr et al., 2014 ); (Byrne, 2013 ). This research conducted all the analyses with the constructed variance-covariance matrices. Model fit assessment included chi-square/degrees of freedom (χ²/df), Root Mean Square Error of Approximation (RMSEA), Standardised Root Mean Square Residual (SRMR), Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Normed Fit Index (NFI) and Comparative Fit Index (CFI), according to (F. Hair Jr et al., 2014 ). As the statistics in Table 3 indicate, all measures exceeded the thresholds which demonstrates both the measurement and structural models had reasonable goodness of fit. This table (Table 4 ) provides the Composite Reliability (CR), Average Variance Extracted (AVE) and a look at discriminant validity. All Convergent Reliability (CR) values were over 0.895 and all Average Variance Extracted (AVE) values were above 0.653 which meant all samples met the necessary criteria for reliability and convergent validity. In addition, every root AVE was larger than the inter-construct correlations, signaling proper discriminant validity. 4.4 Structural Model When the measurement model was validated, the analysis turned to the structural model to test the hypothesized relationships. According to Table 5 , there were very positive impacts of PE, SI and FC on BI to adopt BI at Jordanian HEIs. Results did not show a significant impact of Effort Expectancy (EE) on BI. Likewise, it was found that Organizational Learning Culture (OLC) strongly contributed to the enhancement of all the main constructs in the UTAUT model, so H5, H6, H7 and H8 were upheld. Table 3 Model Fit Indices Fit Indices Measurement Model Structural Model Acceptable Value χ²/df 2.512 2.517 ≤ 3.00 GFI 0.916 0.918 ≥ 0.9000 AGFI 0.847 0.848 ≥ 0.8000 CFI 0.933 0.934 ≥ 0.9000 SRMR 0.040 0.041 ≤ 0.05 NFI 0.926 0.922 ≥ 0.9000 RMSEA 0.063 0.063 ≤ 0.080 Table 4 Construct Reliabilities, Convergent Validity and Discriminant Validity Constructs CR AVE PE EE SI FC BI OLC PE 0.932 0.671 0.819 EE 0.910 0.694 0.602 0.833 SI 0.895 0.673 0.598 0.415 0.820 FC 0.927 0.819 0.788 0.721 0.769 0.904 BI 0.938 0.784 0.703 0.446 0.588 0.810 0.885 OLC 0.916 0.653 0.356 0.392 0.392 0.516 0.336 0.808 5. Discussion The study explores what influences management experts in Jordanian HEIs to adopt Business Intelligence (BI) systems and gives particular attention to how Organizational Learning Culture (OLC) affects these decisions. The study’s outcomes verify that several expectations set out in the extended UTAUT framework are valid. According to the results, Performance Expectancy (PE) greatly influences a person’s intention to act (confirming H1). This implies that those making decisions in HEIs consider BI systems useful for improving how they make decisions, in keeping with previous work (Venkatesh et al., 2003 ); (Alkhwaldi, 2024 ). SI was found to strongly influence the intention to adopt BI (supporting H3) which means leaders tend to choose BI when encouraged to do so by their work colleagues and supervisors. It points to how HEIs favor decisions that are made by collective effort and top leaders. FC emerged as the greatest factor in determining why people intend to adopt BI systems (H4 shows this result), proving that IT services and professional training are very important for the adoption. The findings fit with those of (Timsina & Bhattarai, 2025 ; Sarantis et al., 2025 ), who stressed that enabling environments are essential for technology acceptance in organisations. However, Effort Expectancy (EE) did not affect Motivation significantly (H2 could not be confirmed). Although the original UTAUT proposition suggests differently, studies by (Kašparová, 2023 )and others who looked at similar settings report that how easy-to-use a system is may not strongly affect adoption intentions in those situations. At Jordanian universities, administrators are perhaps already adept at managing complicated systems, so EE is regarded as playing a smaller part. The findings of the second part highlight that Organizational Learning Culture (OLC) helps form and strengthen PE, EE, SI and FC (this supports H5–H8). This shows why organizations need a culture that helps people share knowledge, always keep learning and accept new technological trends. Like (Mahara et al., 2021 ; Alvi & Khechine, 2025 ); Mahara et al., 2021 ), we found a strong association between cultural readiness and the performance of BI solutions. Institutions that build a strong OLC are more inclined to endorse BI systems, increase users’ confidence in them, promote a positive group effect and improve the supporting infrastructure. A strong culture of learning encourages people to put evidence into practice, accept new technology and merge BI effectively into the institution’s ways of working. Consequently, OLC supports the successful introduction of BI systems within organizations. They help to build the field of education research on Business Intelligence in developing countries. This study indicates that organizational culture is important for enablement and improvement in using and responding to technology. For BI implementation to succeed, HEIs should start by changing their work culture, support ongoing learning and make sure all necessary institutional support structures exist. Table 5 Hypotheses Analysis Hypotheses Path Path Coefficient (β) Outcome H1 PE → BI 0.182** Accepted H2 EE → BI 0.007 Not accepted H3 SI → BI 0.168** Accepted H4 FC → BI 0.229** Accepted H5 OLC → PE 0.364*** Accepted H6 OLC → EE 0.349*** Accepted H7 OLC → SI 0.348*** Accepted H8 OLC → FC 0.389*** Accepted *Note: *p < 0.05, **p < 0.01, ** p < 0.001 6. Theoretical and Practical Implications 6.1 Theoretical Implications The findings offer valuable knowledge about helping HEIs in developing countries use BI, focusing on Jordan. Existing studies mostly looked at technology adoption in businesses, while this research adds to this by looking at BI acceptance within educational institutions using a more complete UTAUT model. This research also adds to theory by associating Organizational Learning Culture (OLC) with the way BI is adopted. By making OLC a key factor in the main UTAUT constructs, this research explains how an organization’s culture can affect the desire to use BI. Unlike earlier work, this method looks at OLC as its own influence on technology use rather than as only a background factor. Also, the study points out that having collective learning, willingness to try new things and easy knowledge sharing between groups in an organization can help people appreciate their BI tools. These new theories help us see how UTAUT can be extended to education and used in different societies. 6.2 Practical Implications These findings are significant for policy makers, university leaders, IT managers and suppliers of business intelligence systems in HEIs. In the first place, designers need to ensure that any BI tool can be easily seen as adding value to help employees make better decisions and improve the organization’s performance. Implementing BI must happen in a way that does not stop current academic practices and should easily merge with current ways of working. Social Influence supports the importance of BI project leaders and vendors partnering with academic stakeholders, including deans, department heads and professional groups, so that they may advocate for the benefits of BI. When institutions and peers encourage staff, staff members are more confident about using BI tools. Results indicated that FC greatly influenced BI intention, leading HEIs to ensure they have proper facilities, good support and easy-to-access training. For this, we offer downloadable guides, free trials, initial workshops and ongoing learning opportunities to prepare users. When IT services support the needs of the academics, the system’s worth is enhanced. That is, the role of OLC makes it clear that for BI to succeed, there must be cultural changes as well as technical improvements. HEIs should make sure staff members learn about data, use BI tools for experiments and rely on data analytics in all important decisions. The metrics for performance should relate to how BI is used so that the institution’s commitment is strengthened. Several helpful recommendations for implementation have been uncovered by this study. It is important for HEIs to design programmes that teach faculty and staff how to collect, review and present data. They are necessary to teach employees about BI tools and to create an environment that values making choices based on data. It is also important for universities to invest in the necessary money, technology and staff to help deploy BI. This requires purchasing software licenses, updating information technology infrastructure and recruiting people with the needed expertise to take care of BI systems. We also need to work together with people from BI, consultancy firms and other universities to achieve our goals. They make it possible for partners to learn from each other, access top strategies and speed up the process with co-trained staff and advice from experts. At last, a strong data governance structure is needed to guarantee data quality, protect it and make sure everyone can use it. For this, the establishment needs to create thorough policies, similar procedures and technologies for handling data that enhance faith in the institution and its overall data utilisation. Ensure you take all these actions for an ecosystem that gives BI adoption the encouragement it needs within your HEI. 7. Limitations and Future Research Directions There are some constraints acknowledged in this study that suggest directions for further investigation. One problem is that using information collected from surveys leads to biases linked with memory loss and the tendency to provide answers that are especially socially acceptable. Limitations within data may decrease how accurately the results reflect the research question. In the next steps, researchers can use multiple forms of data or include hard numbers to increase confidence and validity. Also, the research only measured HEIs in Jordan and thus the study’s findings cannot be generalized beyond. Performing the study in additional settings, for example, healthcare, public administration or private business, could broaden the conclusions. In addition, examining the model in different cultural and technological settings would confirm its usefulness in many countries. The study was also designed as a cross-sectional study which gathers data all at the same time and does not track causes and changes over different times. It would be useful for future research to explore withdrawal and continued acceptance stages by looking at the way BI adoption habits develop as time passes. Additionally, the sampling approach employed in this study might reduce how well the sample fits the population. Despite being a good representation of Jordanian HE, the sample may miss some of the other types of roles, experiences and institutions found there. In the future, more consistent sampling using stratified or random methods would make the outcomes more general. In addition, the current study extends UTAUT by including Organizational Learning Culture (OLC), but the concept could still be developed further. Researchers can add other organisational, psychological or technological dimensions such as being prepared for change, having leaders support it, having advanced data tools or someone unwilling to accept new technology. Using all these variables together enables us to explore factors behind BI use in HEIs and introduce helpful suggestions for educational policy and practice. 8. Conclusion The study aimed to discover what affects the BI systems used in Jordanian HEIs. Integrating Organizational Learning Culture with the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study explains how BI is adopted in higher education. Through empirical analysis, it became clear that the impact of Performance Expectancy (PE), Social Influence (SI) and Facilitating Conditions (FC) on behavioural intention for BI systems is positive and strong. Meanwhile, Effort Expectancy (EE) was shown to be irrelevant to the model by the study. Significantly, OLC strongly aided all the main UTAUT constructs, proving how vital institution culture is in helping people accept and use technology. The study offers new insights by supporting the use of OLC as an additional factor in a revised version of the UTAUT framework that influences BI adoption. The model we created improves our understanding of how groups and organisations use technology and could be broadly applied to other adoptions of technology. Practically, these findings remind all HEIs to develop a culture that encourages lifelong learning, is open to transformation and relies on data when making decisions. Training, upgrading infrastructure and working with businesses in the industry are needed to make users and institutions more capable and prepared. Despite the important findings here, it is still important to recognize what they do not cover. The results cannot be generalized or analyzed for cause-and-effect because convenience sampling and a cross-sectional design were used. Further work should use long-term and statistical sampling methods to build upon the results found here. Investigating certain functions of BI, the user experience and the unique issues in higher education offers a better picture of BI implementation within the campus. In short, encouraging Organizational Learning Culture can motivate BI adoption in HEIs, aid the institution’s results and empower informed choice-making for the digital time. Declarations Authors Mohammad Mahmoud Alzubi 1 , Suad Abdalkareem Alwaely 2 , Sanna Abd el rahman Yaghi 3 , Naheel M badri Haddad 4 , Abdulrahman Nehro Ismail 5, Saddam Rateb Darawsheh 6 , Mohammad Issa Alzoubi 7 Affiliation 1 Department of Business Middle East University, Amman, Jordan 2 College of Education, Humanities and Social Sciences, Al Ain University, UAE, [email protected] 3 Graduate Student, Al Ain University, UAE, [email protected] 4 Graduate Student, Al Ain University, UAE, [email protected] 5 Graduate Student, Al Ain University, UAE, [email protected] 6 Department of Administrative Sciences, The Applied College, Imam Abdulrahman Bin Faisal University Dammam, Saudi Arabia, [email protected] 7 Department of Business Intelligence and Data Analysis, Irbid National University, Jordan, [email protected] Corresponding Author Mohammad Alzubi (Corresponding Author) Email: [email protected] Acknowledgements The authors would like to thank the participating organizations and professionals who contributed their time and insights to this research. No professional writing or editorial services were used in the preparation of this manuscript. Funding Declaration This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Disclosure of Interest The authors declares that there are no competing interests or personal relationships that could have influenced the work reported in this paper. Ethics Approval This study received ethical approval from the Research Ethics Committee of Irbid National University, Jordan (Approval No.: 10-15). All procedures were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations. The participants were managerial-level employees in Jordanian higher education institutions (HEIs), all of whom were adults. Participation was entirely voluntary, and informed consent was obtained electronically from all participants prior to data collection. Participants were fully informed about the purpose of the study, their right to withdraw at any time without penalty, and the confidentiality of their responses. No personally identifiable information was collected, and all data were anonymized and handled with strict confidentiality. Consent for publication Not applicable. There is no personal data of a single person in the manuscript. Data Availability Statement The data that was studied and used during this research can be obtained by the respective author under a reasonable request. Data requests need to be sent to [email protected] . References Abubakar, A. A., & Al-Mamary, Y. H. (2025). Exploring factors influencing the intention to use social media and its actual usage in higher education: a conceptual model of effectiveness, effort, communication, self-awareness, social influence, and facilitating conditions. Interactive Learning Environments , 1–25. Afzal, F., & Tumpa, R. J. (2025). Project-based group work for enhancing students learning in project management education: an action research. International Journal of Managing Projects in Business , 18 (1), 189–208. Ahsan, M. J. (2025). Cultivating a culture of learning: the role of leadership in fostering lifelong development. The Learning Organization , 32 (2), 282–306. Aideed, H., Salem, I. E., Magdy, A., AlAmri, T. K., Alzubaidi, A. S., & Elbaz, A. M. (2025). Beyond reality: Harnessing the metaverse for transformative education through UTAUT-2 and task-technology synergy. The International Journal of Management Education , 23 (2), 101169. Akhtar, S., Alfuraydan, M. M., Mughal, Y. H., & Nair, K. S. (2025). Adoption of Massive Open Online Courses (MOOCs) for Health Informatics and Administration Sustainability Education in Saudi Arabia. Sustainability , 17 (9), 3795. Al-Adwan, A. S. (2024). The meta-commerce paradox: exploring consumer non-adoption intentions. Online Information Review . Al-Emran, M., Al-Qaysi, N., Al-Sharafi, M. A., Khoshkam, M., Foroughi, B., & Ghobakhloo, M. (2025). Role of perceived threats and knowledge management in shaping generative AI use in education and its impact on social sustainability. The International Journal of Management Education , 23 (1), 101105. Al-Kfairy, M., Alomari, A., Al-Bashayreh, M., Alfandi, O., & Tubishat, M. (2024). Unveiling the Metaverse: A survey of user perceptions and the impact of usability, social influence and interoperability. Heliyon . Al-khateeb, B. A. A. (2024). Business Intelligence (BI): A Critical Strategy for University Success and Sustainability. International Journal of Asian Business and Information Management (IJABIM) , 15 (1), 1–15. Al-maaitah, T. A., Masa’deh, R., Al-maaitah, D. A., Abueid, A. I., & Al Smadi, K. (2025). The Impact of Business Intelligence in the Banking Sector. In The Role of Artificial Intelligence Applications in Business (pp. 225–234). Emerald Publishing Limited. Aldogiher, A., Halim, Y. T., El-Deeb, M. S., Maree, A. M., & Kamel, E. M. (2025). The Impact of Digital Teaching Technologies (DTTs) in Saudi and Egyptian Universities on Institutional Sustainability: The Mediating Role of Change Management and the Moderating Role of Culture, Technology, and Economics. Sustainability , 17 (5), 2062. Ali, A., Sharabati, A., Alqurashi, D., Shkeer, A., & Allahha, M. (2024). The impact of artificial intelligence and supply chain collaboration on supply chain resilience: Mediating the effects of information sharing. Uncertain Supply Chain Management , 12 (3), 1801–1812. Alkhwaldi, A. F. (2024). Understanding the acceptance of business intelligence from healthcare professionals’ perspective: An empirical study of healthcare organizations. International Journal of Organizational Analysis . Allahawiah, S., Altarawneh, H., & Al-Hajaya, M. (2024). The Role of Organizational Culture in Cybersecurity Readiness: An Empirical Study of the Jordanian Ministry of Justice. Calitatea , 25 (202), 74–84. Allioui, H., & Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors , 23 (19), 8015. Almahri, F. A. A. J., & Saleh, N. I. M. (2025). Insights into Technology Acceptance: A Concise Review of Key Theories and Models. Innovative and Intelligent Digital Technologies; Towards an Increased Efficiency: Volume 2 , 797–807. Almanwari, H. S. A., Saad, N. H. M., & Zainal, S. R. M. (2024). The influence of environment & location, personal motivation, and fee & price on satisfaction, attituding and behavioural loyalty among international students in Oman. Journal of Open Innovation: Technology, Market, and Complexity , 100285. Almatrodi, I., & Skoumpopoulou, D. (2023). Organizational routines and digital transformation: An analysis of how organizational routines impact digital transformation transition in a Saudi university. Systems , 11 (5), 239. AlSaied, M. K., & Alkhoraif, A. A. (2024). The role of organizational learning and innovative organizational culture for ambidextrous innovation. The Learning Organization , 31 (2), 205–226. Alvi, I., & Khechine, H. (2025). Effect of Cultural Values on Students’ Adoption of Social Media for Collaborative Learning. Journal of Computer Assisted Learning , 41 (3), e70051. Alzubi, M. M., Ismaeel, B., & Ateik, A.-H. (2021). The Moderating Effect of Compatibility Factor in The Usage of E-Government Services Among Malaysian Citizens. 2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE) , 224–232. Alzubi, M. M. S., Alrifae, A. A. M., & Atieh, A. A. (2025). Factors Influencing Business Intelligence Adoption by Jordanian Private Universities. PaperASIA , 41 (1b), 148–167. Arefin, M. S., Hoque, M. R., & Rasul, T. (2021). Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy. Journal of Knowledge Management , 25 (3), 573–594. Arnaud, L., Sander, O., Rednic, S., Mertz, P., Faria, R., Crisafulli, F., Silva-Ribeiro, S., Kawka, L., Sztejkowski, C., & Düsing, C. (2025). European Reference Network (ERN) ReCONNET methodology for the cross-cultural adaptation of instruments for research and care in the context of rare connective tissue diseases (CROSSADAPT). Orphanet Journal of Rare Diseases , 20 (1), 1–7. Barjak, F., & Heimsch, F. (2023). Understanding the relationship between organizational culture and inbound open innovation. European Journal of Innovation Management , 26 (3), 773–797. Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research , 333 (2), 627–652. Byrne, B. M. (2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming . routledge. Chaudhuri, R., Chatterjee, S., Vrontis, D., & Thrassou, A. (2024). Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture. Annals of Operations Research , 339 (3), 1757–1791. Daniel, K., Msambwa, M. M., Antony, F., & Wan, X. (2024). Motivate students for better academic achievement: A systematic review of blended innovative teaching and its impact on learning. Computer Applications in Engineering Education , 32 (4), e22733. de Souza, A. S. C., & Debs, L. (2024). Concepts, innovative technologies, learning approaches and trend topics in education 4.0: A scoping literature review. Social Sciences & Humanities Open , 9 , 100902. DiBella, J., Forrest, N., Burch, S., Rao‐Williams, J., Ninomiya, S. M., Hermelingmeier, V., & Chisholm, K. (2023). Exploring the potential of SMEs to build individual, organizational, and community resilience through sustainability‐oriented business practices. Business Strategy and the Environment , 32 (1), 721–735. Exner, R., & Zunic, A. (2025). Organizing BI Strategy. In The Path to an Intelligent Enterprise: The Art and Practice of Business Intelligence Strategy (pp. 151–188). Springer. F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review , 26 (2), 106–121. Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management , 90 , 581–592. Gürtl, S., Scharf, D., Thrainer, C., Gütl, C., & Steinmaurer, A. (2024). Design and Evaluation of an LLM-Based Mentor for Software Architecture in Higher Education Project Management Classes. International Conference on Interactive Collaborative Learning , 375–386. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review , 31 (1), 2–24. Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Al Zoubi, J. Z. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity , 9 (3), 100111. Isiaku, L., & Adalier, A. (2024). Determinants of business intelligence systems adoption in Nigerian banks: The role of perceived usefulness and ease of use. Information Development , 02666669241307024. Islam, M. T., Mission, M. R., Refat, T. K., & Kynatun, M. (2025). Cybersecurity risk assessment frameworks for engineering databases: A systematic literature review. Strategic Data Management and Innovation , 2 (01), 224–243. Jaklič, J., Grublješič, T., & Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management , 43 , 305–318. Jingyi, X., & Pamintuan, C. (2025). Exploring factors influencing online learning platform usage behavior: a comparative study of southeast Asian learners based on the UTAUT model. Interactive Learning Environments , 1–31. Kašparová, P. (2023). Intention to use business intelligence tools in decision making processes: Applying a UTAUT 2 model. Central European Journal of Operations Research , 31 (3), 991–1008. Kline, R. B. (2023). Principles and practice of structural equation modeling . Guilford publications. Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding Psychosocial Barriers to Healthcare Technology Adoption: A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT Frameworks. Healthcare , 13 (3), 250. Mahara, T., Iyer, L. S., Matta, V., & Alagarsamy, S. (2021). Effect of Organizational Culture during Crises on adoption of virtual classrooms: An extension of UTAUT model. Journal of Information Technology Case and Application Research , 23 (3), 213–239. McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans , 100121. Mellors, J., & Vicencio, A. (2025). Widening participation in outward student mobility: Successes, challenges, and opportunities. British Educational Research Journal . Mexhuani, B. (2025). Adopting Digital Tools in Higher Education: Opportunities, Challenges and Theoretical Insights. European Journal of Education , 60 (1), e12819. Moore, R. L., Lee, S. S., Pate, A. T., & Wilson, A. J. (2025). Systematic review of digital microcredentials: Trends in assessment and delivery. Distance Education , 1–28. Okpala, P. (2025). Strategic Management of Online Higher Education Institutions. In Building Organizational Capacity and Strategic Management in Academia (pp. 553–590). IGI Global. Olubiyi, T. O., & Akinlabi, H. B. (2025). Intelligent Decision Making Through Adoption of Business Analytics: Empirical Evidence From Behavioral Intentions of African SMEs. In Generative AI for Business Analytics and Strategic Decision Making in Service Industry (pp. 137–168). IGI Global Scientific Publishing. Olugboyega, O., Chukwudi, C. S., Oseghale, O. B., Omojola, S. O., & Adeyemi, M. (2025). Maintenance requirements and methods for building information modelling infrastructures. Facilities . Ozolins, U. (2008). Issues of back translation methodology in medical translations. Proceedings, FIT [International Federation of Translators] XVIII Congress, Shanghai . Perron, B. E., Hiltz, B. S., Khang, E. M., & Savas, S. A. (2025). AI-Enhanced Social Work: Developing and Evaluating Retrieval-Augmented Generation (RAG) Support Systems. Journal of Social Work Education , 1–11. Saboor, A., Khan, M. Z., Khan, M. N., Hussain, T., Attar, R. W., Alnfiai, M. M., & Almalki, N. S. (2025). Exploring the factors that influence students acceptance and use of online learning technology in higher education institutes of Khyber Pakhtunkhwa Pakistan. Education and Information Technologies , 1–28. Salisu, I., Bin Mohd Sappri, M., & Bin Omar, M. F. (2021). The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review. Cogent Business & Management , 8 (1), 1935663. Sarantis, D., Rizun, N., Alexopoulos, C., & Saxena, S. (2025). Analyzing behavioral intention of open government data adoption across Latvia, India and Poland: does national culture matter? Journal of Science and Technology Policy Management . Sekaran, U. (2016). Research methods for business: A skill building approach . John Wiley & Sons. Sequeira, R., Reis, A., Alves, P., & Branco, F. (2024). Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review. Information , 15 (4), 208. Singh, R. K., Modgil, S., & Shore, A. (2024). Building artificial intelligence enabled resilient supply chain: a multi-method approach. Journal of Enterprise Information Management , 37 (2), 414–436. Srivastava, K., Kumar, M., Verma, R., Singh, S., & Maurya, P. (2025). Analyzing the Drivers of Adoption in Higher Education E-Learning: Exploring Factors Affecting Behavioral Intentions and Actual Usage. In Architecting the Digital Future: Platforms, Design, and Application (pp. 47–82). IGI Global Scientific Publishing. Tang, X., Yuan, Z., & Qu, S. (2025). Factors Influencing University Students’ Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model. Journal of Computer Assisted Learning , 41 (1), e13105. Teng, X., Wu, Z., & Yang, F. (2022). Research on the relationship between digital transformation and performance of SMEs. Sustainability , 14 (10), 6012. Timsina, S. M., & Bhattarai, U. (2025). Identifying factors shaping the behavioural intention of Nepalese youths to adopt digital health tools. Healthcare Technology Letters , 12 (1), e70005. Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management , 13 (3), 713–737. Ul Hassan, M., Murtaza, A., & Rashid, K. (2025). Redefining higher education institutions (HEIs) in the era of globalisation and global crises: A proposal for future sustainability. European Journal of Education , 60 (1), e12822. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly , 425–478. Wang, S., Asif, M., Shahzad, M. F., & Ashfaq, M. (2024). Data privacy and cybersecurity challenges in the digital transformation of the banking sector. Computers & Security , 147 , 104051. Zhao, J., Feng, H., Chen, Q., & de Soto, B. G. (2022). Developing a conceptual framework for the application of digital twin technologies to revamp building operation and maintenance processes. Journal of Building Engineering , 49 , 104028. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 May, 2026 Editor assigned by journal 06 May, 2026 Editor invited by journal 04 May, 2026 Submission checks completed at journal 03 May, 2026 First submitted to journal 03 May, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9367638","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638728783,"identity":"1ab22ef8-4468-4b98-880d-3beb574b1b1a","order_by":0,"name":"mohammad Mahmoud alzubi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACxgMMDAd4+CEcZiA+QFgPWItkA6laGAwOwLUQALoNzA8O/my7I2N87fAxCYYK68QGxsP4rTE7wGZwQLLtGY/Z7bQ0CYYz6YkNDMcSCGgBOsmw7TBQS46ZBGPbYaCWMwYEtLB/OJAI1GI8O/+bBOM/orTwGBw4CNRiIJ3DJsHYQIyWwzwFBxvOPeORuJ1mbJFwLN24jaBfjrdvfPij7I49/+zkhzc+1FjL9ksQCDHUiAAZzyZBQAcWwN9AspZRMApGwSgY3gAADmFNY1k15W8AAAAASUVORK5CYII=","orcid":"","institution":"Middle East University","correspondingAuthor":true,"prefix":"","firstName":"mohammad","middleName":"Mahmoud","lastName":"alzubi","suffix":""},{"id":638728784,"identity":"30e6a7cb-a196-4aa6-8b8e-b07521d937cc","order_by":1,"name":"Suad Abdalkareem Alwaely","email":"","orcid":"","institution":"Al Ain University","correspondingAuthor":false,"prefix":"","firstName":"Suad","middleName":"Abdalkareem","lastName":"Alwaely","suffix":""},{"id":638728785,"identity":"68fa9df3-3500-4fad-89cd-36e125063eb2","order_by":2,"name":"Sanna Abd el rahman Yaghi","email":"","orcid":"","institution":"Al Ain University","correspondingAuthor":false,"prefix":"","firstName":"Sanna","middleName":"Abd el rahman","lastName":"Yaghi","suffix":""},{"id":638728787,"identity":"61fc6326-99e8-49ab-9209-3976361e5776","order_by":3,"name":"Naheel M badri Haddad","email":"","orcid":"","institution":"Al Ain University","correspondingAuthor":false,"prefix":"","firstName":"Naheel","middleName":"M badri","lastName":"Haddad","suffix":""},{"id":638728789,"identity":"6bbc36b3-997f-4bb0-a925-386a7e11b474","order_by":4,"name":"Abdulrahman Nehro Ismail","email":"","orcid":"","institution":"Al Ain University","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahman","middleName":"Nehro","lastName":"Ismail","suffix":""},{"id":638728792,"identity":"2e0ac416-f2ef-447a-9186-6ad05c068f72","order_by":5,"name":"Saddam Rateb Darawsheh","email":"","orcid":"","institution":"Imam Abdulrahman Bin Faisal University","correspondingAuthor":false,"prefix":"","firstName":"Saddam","middleName":"Rateb","lastName":"Darawsheh","suffix":""},{"id":638728794,"identity":"3a64b634-d24b-4fc2-95d0-c6da6a7a7c2a","order_by":6,"name":"Mohammad Issa Alzoubi","email":"","orcid":"","institution":"Irbid National University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Issa","lastName":"Alzoubi","suffix":""}],"badges":[],"createdAt":"2026-04-09 11:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9367638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9367638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109276726,"identity":"b78ff42b-11d1-4521-84d9-c3460cdc1f44","added_by":"auto","created_at":"2026-05-14 15:18:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":458136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed research model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9367638/v1/ac2eeb0a5e5a06188eb96452.png"},{"id":109276749,"identity":"f6f924e2-a82d-46bf-9861-d48063a1fb86","added_by":"auto","created_at":"2026-05-14 15:18:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":853894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9367638/v1/7bd29dda-a0a5-4c01-be13-fa9dcccecd19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Business Intelligence Adoption in Higher Education Institutions: Extending UTAUT with Organizational Learning Culture Evidence from Jordan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe use of innovative technology is being requested more often in higher education ((de Souza \u0026amp; Debs, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); (Alkhwaldi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)). Using new methods and technologies, IS is now giving priority to delivering valuable educational materials that boost student achievement (Daniel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;Zhao et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When HEIs use IT properly, they can reduce costs, ensure resources are used wisely and create a better environment for students to learn(Almanwari et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;Mellors \u0026amp; Vicencio, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Business Intelligence (BI) tools provide a range of IT systems to organizations and universities to help gather, combine and review large amounts of information (Sequeira et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); (Al-khateeb, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Using data analysis, HEIs can see their own strengths and weaknesses as well as the opportunities and pressures from outside their institutions (McDonald et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Basically, BI combines business analytics, data mining, data visualization, data tools and infrastructure and best practices to help universities use data for decision-making (Tsiu, 2023). BI was first introduced in the 1990s (Salisu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Experts in the field and BI users are currently increasing knowledge by talking about strategies and methods to put BI systems into use. On the other hand, few research efforts have been aimed at how BI works in the HEI sector. Within HEIs, BI means analysing data and using advanced tools for analysis. As a result, institutions are better able to develop sound ways of making decisions (Ahsan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); (G\u0026uuml;rtl et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Because the world of higher education is always developing, HEIs need to handle resources efficiently and deliver a good learning experience for students (Ul Hassan et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); (Moore et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With BI, IT is able to change the way universities operate by transforming data into useful knowledge that enhances decision-making, organization, schooling and results for students (Afzal \u0026amp; Tumpa, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e;Arefin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). BI systems stand out by taking information from various sources and passing it along to decision-makers in HEIs (Sequeira et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By using BI, HEIs can boost student teaching-learning processes, better manage their workforce, improve how they operate administratively and cut expenditures (Okpala, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e;Hmoud et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Because data plays such an important role in higher education, BI has become a valuable topic for experts as well as researchers. The use of BI can make an institution more effective and can help improve the importance of student-centred learning.\u003c/p\u003e \u003cp\u003eHEIs in Jordan must continuously adapt to changes so they can find new ways to run efficiently in a competitive education market. More evidence points to the importance of internal culture in allowing innovation, mainly from constant learning and an ability to adapt within the organization (Barjak \u0026amp; Heimsch, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;Ahsan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) M. M. Alzubi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study analyzes organisational culture and the way it affects the integration of new technology in Jordanian HEIs. Schein stated in 1990 that organisational culture refers to the set of values, perceptions and assumptions that employees within an organisation share. Members of the faculty are guided by this culture when making decisions together and in their academic work. Whether technological advancement is supported by or opposed by organisational culture will ultimately decide how effective technology implementation is (Almatrodi \u0026amp; Skoumpopoulou, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chaudhuri et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Supporting a culture where everyone learns continually is key to successful use of technology in colleges and universities. Acceptance and innovation in technology within academic institutions are partly due to the positive role played by Organisational Learning Culture (OLC). OLC means an institution works to help students learn, spread knowledge, adjust to new circumstances and try new ideas (AlSaied \u0026amp; Alkhoraif, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At HEIs, OLC takes the form of workshops for staff members, active sharing of ideas, listening to one another and an open mindset toward classroom changes and new technologies. If properly tended to such a culture helps companies become ready for digital tools like BI. People leading and working in these organisations usually appreciate data, reflect deeply and support changes that make the institution more effective. In other words, OLC might greatly influence the way individuals view and respond to Business Intelligence.\u003c/p\u003e \u003cp\u003eAcademic research has highlighted that organisational learning is important for digital transformation. Organizations that encourage staff to learn are more likely to handle new problems well, respond to recent changes and be creative (Islam et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; DiBella et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, having a good learning environment can make people more likely to accept change, use technology securely and take part actively in the company\u0026rsquo;s sales and marketing campaigns. For BI, it means that when faculty and administrators use an OLC approach, they are more likely to see benefits in such tools, find them simple, get support from their peers and consider the support for implementation is adequate. These aspects are closely connected to the key features of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT model, created by (Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), is used widely to explain how workers in organisations accept technology. The new model takes key factors from eight different technology adoption models and finds that four main characteristics\u0026mdash;performance expectancy, effort expectancy, social influence and facilitating conditions\u0026mdash;help forecast user intentions. Experts have studied UTAUT in fields such as healthcare, e-government and higher education (Akhtar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); (Saboor et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Even so, experts have advised adding specific elements depending on the environment and organisation to improve the matching of cultures.\u003c/p\u003e \u003cp\u003eAlthough OLC is important, only a small amount of research has looked at its part in driving BI adoption at HEIs in developing countries. Most existing studies look at technology or people, not at the big-picture factors that influence the organisation (Jingyi \u0026amp; Pamintuan, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e;. M. S. Alzubi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, we do not fully understand how the culture within an organization can help or hinder the use of advanced analytics technology. To address this, the new UTAUT model under study now adds OLC as an important predictor of performance expectancy, effort expectancy, social influence and facilitating conditions. Integrating OLC into the study allows it to better explain BI adoption in HEIs and discover the important cultural factors for making BI work successfully. For testing this model, the researchers analyze higher education institutions in Jordan, since they are only just beginning to use BI. Data will be gathered from universities to investigate the relationships among OLC and UTAUT variables and the effect they have on users\u0026rsquo; intention to use BI. The study is also designed to discover successful strategies and difficulties unique to Jordanian HEIs in developing a learning culture for innovation. This is especially relevant now because institutions want to drive their planning, stand out globally and ensure good achievements by students with the help of technology.\u003c/p\u003e \u003cp\u003eTo help education and performance in Jordanian HEIs, it is important to promote both learning and the use of BI within organizations. Likely, institutions that have programs for teamwork, try out fresh concepts and emphasize evidence will integrate educational innovation technology more effectively. The study brings value to the literature by outlining the key impact of organizational learning cultures on using BI and by putting forward a model rooted in cultural context for understanding technology application in universities. Thus, this study looks at what influences HEIs to use Business Intelligence systems and how a culture of learning at work affects this decision. It appears from the review that the use of business intelligence can improve observations of how students perform, how well faculty are working and the results of academic programmes. This study suggests, by including OLC in the UTAUT model, a new way to examine the factors in an organisation that aid BI implementation. Researchers expect that what they learn will benefit decision-makers in education, leaders in universities and those working in technology.\u003c/p\u003e"},{"header":"2. Theoretical background and hypotheses development","content":"\u003cp\u003eThe introduction and use of innovative technologies is a lively area of study in the field of IS management. Prior work has looked at various theories trying to explain how individuals accept IT systems, like the Technology Acceptance Model (TAM), the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (Almahri \u0026amp; Saleh, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e;Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Nonetheless, experts realised that a new model was needed to combine all these older approaches. In response, (Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) suggested the Unified Theory of Acceptance and Use of Technology (UTAUT) by combining the most important elements of eight well-known models. According to the UTAUT, user behaviour and behavioural intention are explained using the four main constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). Furthermore, it accounts for these elements: age, gender, how experienced someone is and whether they used the system by choice (Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e;Almahri \u0026amp; Saleh, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The research on these dimensions has been carried out in several cases such as BI adoption (Kašparov\u0026aacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and IT use at academic institutions (Twum et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e;Teng et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the introduction of hedonic motivation, price value and habit in UTAUT2, the original UTAUT is more suitable for organisations and educational institutions because of its broad application. Considering the specific environment of HEIs, this study adds a contextual variable \u0026ndash; (OLC) \u0026ndash; to the UTAUT model, reflecting the usual ways and culture that help the institution learn new things and adapt. OLC is highly relevant when it comes to technology, specifically supporting faculty and staff in trying, adopting and implementing new technologies such as Business Intelligence (BI).\u003c/p\u003e \u003cp\u003eResearch has shown that BI systems are growing in industries such as healthcare, insurance and SMEs (Salisu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e;Al-maaitah et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the current understanding of how HEIs are incorporating BI systems is still limited, particularly in places like Jordan. Earlier studies centred on the effect of technology and organisation on BI adoption, while we examine the ways that a OLC influences BI usage. Because of various structural and operational troubles within Jordanian HEIs, OLC plays a valuable role in promoting digital transformation. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the conceptual model used in this research. The next subsections explain the hypotheses connect to each construct.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Performance Expectancy (PE)\u003c/h2\u003e \u003cp\u003eThe UTAUT model defines performance expectancy (PE) as the extent to which people think using a technology will increase their job performance. In higher education institutions, this construct indicates that faculty and staff believe BI systems can increase their efficiency, the quality of their decisions and their overall results in their work. The same way PE helps encourage new technology use in healthcare, PE is also likely to further implement BI tools in HEIs.\u003c/p\u003e \u003cp\u003eBI systems help users understand a lot of information, find emerging patterns and decide on actions that match the institution\u0026rsquo;s goals. HEIs are more likely to achieve things such as monitoring students\u0026rsquo; progress, managing resources wisely, upgrading their curriculum and improving how the institution operates (Hmoud et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;Olubiyi \u0026amp; Akinlabi, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When learners believe that technology can help with their main tasks, they tend to embrace it.\u003c/p\u003e \u003cp\u003eThe same study discovered that PE played a big role in healthcare staff\u0026rsquo;s intentions to use CDSS, meaning this can be applied to using BI in education contexts. (Kašparov\u0026aacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that perceived ease of use of BI tools is a strong reason why users accept them, but only in cases where results directly improve productivity. If faculty and HEI staff think BI will boost their job effectiveness by making work more efficient, accurate and boosting productivity, they are probably more likely to have a good attitude toward using BI. Therefore, this study proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003e\"H1.\u003c/b\u003e Performance expectancy will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Effort Expectancy (EE)\u003c/h2\u003e \u003cp\u003eEE in the UTAUT framework is about how easy someone finds using an IS/IT system (Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); (Tang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In higher education settings, EE is viewed as key to adopting new technologies, especially when users\u0026rsquo; digital literacy is not equal, according to(Abubakar \u0026amp; Al-Mamary, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is built around how people see the system in terms of usability, ease of understanding and simple controls. Many empirical investigations have found that an effective educational environment leads people to want to use educational technologies (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). BI-related systems are more likely to be accepted by faculty and staff if they find them easy to use and reasonably easy to install. By contrast, when usage is demanding or the tools are unclear, users may turn away and fail to embrace the system (Srivastava et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA BI platform that is centered on users is important in promoting uptake, particularly when the system at an organization is not advanced or easily customizable (Isiaku \u0026amp; Adalier, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), making user interfaces easy to understand and offering clear help features can greatly encourage educators and administrators to adopt technology. Under these conditions, being convenient to use becomes not only what users consider important but also an important part of HEIs\u0026rsquo; strategy for rolling out BI systems smoothly. For this reason, this study puts forth the hypothesis that, when BI systems are perceived as clear and easy to use, staff and faculty include them in their academic and workplace tasks. Hence, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003e\"H2.\u003c/b\u003e Effort expectancy will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Social influence\u003c/h2\u003e \u003cp\u003eWhen using the UTAUT framework, Social Influence (SI) indicates how much a person thinks significant others such as colleagues, supervisors or leaders, think they should start using a specific technology (Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). At Jordanian universities, SI greatly influences attitudes about using Business Intelligence (BI) systems. The investigation found that SI greatly affects behavioural intention, showing how important peer influence, managerial backing and workplace norms are for using BI. As earlier works emphasise (Singh et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;Jaklič et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), teachers and administrators should give collective support to promote the use of technology in schools. In universities and other educational institutions in Jordan that feature cooperative relationships and upper level decision-making, SI becomes important for driving changes. By encouraging both leaders and influential teachers to patronize BI, the adoption of data across departments can occur much faster.\u003c/p\u003e \u003cp\u003e \u003cb\u003e\"H3.\u003c/b\u003e Social influence will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Facilitating Conditions (FC)\u003c/h2\u003e \u003cp\u003eAs explained by Venkatesh et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), having favorable conditions (FC) means a person believes that organizational and technical support is in place for the system. In this study, FC reflects faculty and staff views on whether the institution gives them the necessary resources to use Business Intelligence (BI) well. You will also need a solid IT set-up, qualified technical help and convenient, organised data for Business Intelligence. To use BI, HEIs must have both staff who are willing to use the technology and a strong institutional system in place (Aldogiher et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With the correct technological environment, users are better able to learn how to use BI tools. When training is provided, help desks are on hand, proper data management rules exist and clear system documentation is available, users become more confident and accepting of technology.\u003c/p\u003e \u003cp\u003e(Al-Emran et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) discovered that people with access to basic technology in schools are more ready to use these systems and build useful knowledge. It is also backed up by the study of (Perron et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) which concludes that strong technical support, compatibility and good infrastructure make users more likely to accept learning tools in schools and universities. Likewise, (Olugboyega et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that a sufficient amount of hardware/software and resources for staff development help organizations achieve success with using new IT systems. Because technical readiness and institutional backing play a key role in technology use, this study finds that professionals who believe their institutions are prepared will have a stronger intention to use BI systems. Hence, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4. Facilitating conditions will have a significant impact on behavioural intentions to adopt business intelligence within higher education institutions.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Organisational Learning Culture (OLC)\u003c/h2\u003e \u003cp\u003eOrganisational Learning Culture refers to the environment in a company that supports ongoing learning, the sharing of knowledge and the ability to adapt (Ahsan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A supportive OLC helps promote both new ideas and decisions based on data (Gupta et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some recent research suggests that workplaces focused on learning are more likely to adopt modern IT solutions, including technology for using big data (Allahawiah et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within a university or college, an OLC can set a framework for using BI effectively by pushing staff to try new things, reflect and base their work on research.\u003c/p\u003e \u003cp\u003e(Exner \u0026amp; Zunic, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported that focusing on continuous learning is key to building successful BI systems and (Mexhuani, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlight that a learning culture in institutions helps them quickly adopt technology. Besides, OLC may help shape UTAUT elements by improving desired benefits (PE), lessening problems with technology (EE), offering peer assistance (SI) and ensuring technical resources are sufficient (FC). HEIs should focus on staff ongoing professional development, work on cross-departmental efforts, support the use of evidence and introduce continuous learning in all their policies. Moreover, encouraging training, mentoring and sharing knowledge publicly, strengthens the cultural focus on innovation and increases people\u0026rsquo;s willingness to use BI.\u003c/p\u003e \u003cp\u003eAlthough OLC is very important, the connection between OLC and UTAUT has not been thoroughly examined in the context of higher education BI in most developing countries. Therefore, this research investigates empirically the role of OLC as an explanation for core UTAUT constructs, improving our knowledge of BI adoption. Strengthening organisational learning is thought to enhance stakeholders\u0026rsquo; opinions regarding the utility, ease of use, support and preparedness of the BI tools. Based on that idea, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5.\u003c/b\u003e Organisational learning culture will have a significant impact on performance expectancy of adopting business intelligence within higher education institutions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6.\u003c/b\u003e Organisational learning culture will have a significant impact on effort expectancy of adopting business intelligence within higher education institutions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH7.\u003c/b\u003e Organisational learning culture will have a significant impact on social influence of adopting business intelligence within higher education institutions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH8.\u003c/b\u003e Organisational learning culture will have a significant impact on facilitating conditions of adopting business intelligence within higher education institutions.\u003c/p\u003e \u003cp\u003eThis concludes this section, in which the UTAUT model is revised to guide the study of BI\u0026rsquo;s use in Jordanian HEIs. Using OLC, the study helps fill a gap in IS literature by highlighting the way cultural aspects within institutions impact the use of technological solutions among educators. By including perceptions of support, the UTAUT framework explains more about user behavior and helps solve a real challenge that benefits policy, strategy and development in colleges and universities.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sampling and Data Collection\u003c/h2\u003e \u003cp\u003eA positivist method and an online survey through Google Forms were utilized in the study. This research involved key members of the university community in Jordan such as academic deans, department heads, directors of internal control, IT staff and information systems managers. Before taking part in the online survey, participants were provided with an outline of Business Intelligence concepts drawn from existing sources and a short educational video outlining the use of BI in universities. Only those respondents who realized what the concept meant and why it affected HEIs were allowed to take part in the survey. From January to March 2024, we conducted the study by collecting data. With no detailed list of the study population, researchers used convenient non-probability sampling by selecting participants easily available to them (Sekaran, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Because the study used Structural Equation Modelling (SEM), the amount of data collected was based on recommendations from leading experts in the field (Hair et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This paper collected 579 valid sets of responses, meeting the needed minimum set by many experts which is equal to five times the observed variables (Kline, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With this sample size, the model estimates and tests of significance are considered statistically appropriate. To make certain the content was valid, the survey was first created in English and then underwent a double-translation process using best practices around the world (Ozolins, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Both forward-translations to Arabic followed by back-translation to English were used to check for the same meaning. Many research studies show that back-translation helps improve language quality and guarantees that translated tools are accurate when used across different cultures (Arnaud et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A team of BI experts from Jordanian HEIs went through the instrument to judge if the items are clear, relevant and suitable. After getting their comments, items judged to be redundant were taken out and issues with ambiguous language were sorted out. To review the procedure, 30 individuals from a variety of HEIs took part in a pilot study. By doing this review, we were able to check whether the questions in the survey were presentable, easy to read and easy to understand. Each of the measurement items was assessed using a five-point Likert scale, from 1 (strongly disagreeing) to 5 (strongly agreeing), to look at participants\u0026rsquo; views and planned behaviour related to BI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measurement\u003c/h2\u003e \u003cp\u003eReliability and validity of the model were accomplished by using validated scales from the UTAUT theory and relevant research. Among these, measurement was done for Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Behavioural Intention (BI) and the contextual variable Organizational Learning Culture (OLC). Measurements for the variables PE, EE, SI, FC and BI were taken from the original UTAUT developed by Venkatesh et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and expanded by (Belhadi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Every statement was developed so that it applies to higher education and focused on using Business Intelligence (BI) systems within Jordanian HEIs. Furthermore, learning from relevant studies in the field of technology use in education helped us to adjust the items (Aideed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Al-Adwan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Lee et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This variable was measured with three items taken from existing studies looking at organisational readiness and knowledge environments (Alkhwaldi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; (Al-Kfairy et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Allioui \u0026amp; Mourdi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). They were developed to gauge how much HEIs help with continuous learning, knowledge exchange and responding to changes which are essential for the success of BI.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data Analysis and Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Demographic Profiles\u003c/h2\u003e \u003cp\u003eThe demographic profile of the respondents is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 579 participants, 68.0% were male and 32.0% were female. In terms of age, the largest group was 35\u0026ndash;44 years old (36.3%), followed by 45\u0026ndash;54 years (31.8%), 55 years and above (18.8%), and 25\u0026ndash;34 years (13.1%). Regarding position, department heads represented the largest category (33.0%), followed by IT/IS managers (28.0%), directors/internal control officers (24.7%), and deans (14.3%). In terms of experience, 34.2% of the respondents had 5\u0026ndash;10 years of experience, 30.2% had 11\u0026ndash;15 years, 24.0% had more than 15 years, and 11.6% had less than 5 years. Concerning academic qualification, most respondents held a master\u0026rsquo;s degree (42.3%) or a PhD (41.8%), while 15.9% held a bachelor\u0026rsquo;s degree. These results indicate that the sample included respondents with relevant managerial, administrative, and technical backgrounds, making them suitable for examining Business Intelligence adoption in Jordanian higher education institutions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Profiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePosition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment Head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIT/IS Manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirector/Internal Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eYears of Experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAcademic Qualification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe parameters for each construct in the investigation are all displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. According to the results, everyone\u0026rsquo;s views about the constructs were generally positive. The mean values for Effort Expectancy (EE) were highest at 3.461, with Facilitating Conditions (FC) ranking second at 3.405 and Organizational Learning Culture (OLC) being 3.360. Social Influence (SI) was rated the lowest out of all factors (2.982). All constructs showed a good measure of internal consistency, since their Cronbach\u0026rsquo;s alpha scores were greater than 0.70.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Measurement Model Evaluation\u003c/h2\u003e \u003cp\u003eThe fit of the measurement model was assessed through Confirmatory Factor Analysis (CFA) in AMOS 25.0 and the Maximum Likelihood Estimation (MLE) method was used to analyze the data. The analysis was conducted using the two-step method developed by (F. Hair Jr et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), focusing first on the validation and reliability of the measurement model. Fitness of the model was assessed using chi-square/df, RMSEA, SRMR, GFI, AGFI, NFI and CFI. Regular methods showed that the data followed the model well. Reliability was supported by high Composite Reliability (CR), where every construct was greater than the required 0.70. All Average Variance Extracted (AVE) values were above the suggested threshold of 0.50 by (Hair et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which confirmed convergent validity. Reliability and convergent validity were acceptable, with CR values from 0.895 to 0.963 and all AVE values larger than 0.653.\u003c/p\u003e \u003cp\u003eFor every construct, the square root of its AVE was examined against the correlations it shares with other variables. For all subjects, the AVE of each construct was still higher than the correlation between any two constructs, so each variable was clearly separated from the rest. Thanks to the results, it was clear that the constructs of the scale were reliable, consistent, were as predicted and able to separate each area, ensuring the measurement model was suitable for structural analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s a\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance Expectancy (PE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort Expectancy (EE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Influence (SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitating Conditions (FC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention (BI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganizational Learning Culture (OLC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Measurement Model\u003c/h2\u003e \u003cp\u003eThe study explored the relationships between the different parts of the proposed model by following a sequential method. First, it needed to be established that the measurement model accurately included the important variables (F. Hair Jr et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This investigation used Data Analysis and Statistical Software (SPSS) and AMOS 25.0, together with Maximum Likelihood Estimation (MLE) (F. Hair Jr et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); (Byrne, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This research conducted all the analyses with the constructed variance-covariance matrices. Model fit assessment included chi-square/degrees of freedom (χ\u0026sup2;/df), Root Mean Square Error of Approximation (RMSEA), Standardised Root Mean Square Residual (SRMR), Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Normed Fit Index (NFI) and Comparative Fit Index (CFI), according to (F. Hair Jr et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As the statistics in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicate, all measures exceeded the thresholds which demonstrates both the measurement and structural models had reasonable goodness of fit. This table (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) provides the Composite Reliability (CR), Average Variance Extracted (AVE) and a look at discriminant validity. All Convergent Reliability (CR) values were over 0.895 and all Average Variance Extracted (AVE) values were above 0.653 which meant all samples met the necessary criteria for reliability and convergent validity. In addition, every root AVE was larger than the inter-construct correlations, signaling proper discriminant validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Structural Model\u003c/h2\u003e \u003cp\u003eWhen the measurement model was validated, the analysis turned to the structural model to test the hypothesized relationships. According to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, there were very positive impacts of PE, SI and FC on BI to adopt BI at Jordanian HEIs. Results did not show a significant impact of Effort Expectancy (EE) on BI. Likewise, it was found that Organizational Learning Culture (OLC) strongly contributed to the enhancement of all the main constructs in the UTAUT model, so H5, H6, H7 and H8 were upheld.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructural Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcceptable Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.9000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.8000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.9000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.9000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruct Reliabilities, Convergent Validity and Discriminant Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOLC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe study explores what influences management experts in Jordanian HEIs to adopt Business Intelligence (BI) systems and gives particular attention to how Organizational Learning Culture (OLC) affects these decisions. The study\u0026rsquo;s outcomes verify that several expectations set out in the extended UTAUT framework are valid.\u003c/p\u003e \u003cp\u003eAccording to the results, Performance Expectancy (PE) greatly influences a person\u0026rsquo;s intention to act (confirming H1). This implies that those making decisions in HEIs consider BI systems useful for improving how they make decisions, in keeping with previous work (Venkatesh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); (Alkhwaldi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). SI was found to strongly influence the intention to adopt BI (supporting H3) which means leaders tend to choose BI when encouraged to do so by their work colleagues and supervisors. It points to how HEIs favor decisions that are made by collective effort and top leaders.\u003c/p\u003e \u003cp\u003eFC emerged as the greatest factor in determining why people intend to adopt BI systems (H4 shows this result), proving that IT services and professional training are very important for the adoption. The findings fit with those of (Timsina \u0026amp; Bhattarai, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sarantis et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who stressed that enabling environments are essential for technology acceptance in organisations.\u003c/p\u003e \u003cp\u003eHowever, Effort Expectancy (EE) did not affect Motivation significantly (H2 could not be confirmed). Although the original UTAUT proposition suggests differently, studies by (Kašparov\u0026aacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)and others who looked at similar settings report that how easy-to-use a system is may not strongly affect adoption intentions in those situations. At Jordanian universities, administrators are perhaps already adept at managing complicated systems, so EE is regarded as playing a smaller part.\u003c/p\u003e \u003cp\u003eThe findings of the second part highlight that Organizational Learning Culture (OLC) helps form and strengthen PE, EE, SI and FC (this supports H5\u0026ndash;H8). This shows why organizations need a culture that helps people share knowledge, always keep learning and accept new technological trends. Like (Mahara et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Alvi \u0026amp; Khechine, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Mahara et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we found a strong association between cultural readiness and the performance of BI solutions.\u003c/p\u003e \u003cp\u003eInstitutions that build a strong OLC are more inclined to endorse BI systems, increase users\u0026rsquo; confidence in them, promote a positive group effect and improve the supporting infrastructure. A strong culture of learning encourages people to put evidence into practice, accept new technology and merge BI effectively into the institution\u0026rsquo;s ways of working. Consequently, OLC supports the successful introduction of BI systems within organizations.\u003c/p\u003e \u003cp\u003eThey help to build the field of education research on Business Intelligence in developing countries. This study indicates that organizational culture is important for enablement and improvement in using and responding to technology. For BI implementation to succeed, HEIs should start by changing their work culture, support ongoing learning and make sure all necessary institutional support structures exist.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypotheses Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotheses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePath Coefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE \u0026rarr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE \u0026rarr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot accepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI \u0026rarr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC \u0026rarr; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.229**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLC \u0026rarr; PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLC \u0026rarr; EE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLC \u0026rarr; SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLC \u0026rarr; FC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.389***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Note: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Theoretical and Practical Implications","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Theoretical Implications\u003c/h2\u003e \u003cp\u003eThe findings offer valuable knowledge about helping HEIs in developing countries use BI, focusing on Jordan. Existing studies mostly looked at technology adoption in businesses, while this research adds to this by looking at BI acceptance within educational institutions using a more complete UTAUT model. This research also adds to theory by associating Organizational Learning Culture (OLC) with the way BI is adopted. By making OLC a key factor in the main UTAUT constructs, this research explains how an organization\u0026rsquo;s culture can affect the desire to use BI. Unlike earlier work, this method looks at OLC as its own influence on technology use rather than as only a background factor. Also, the study points out that having collective learning, willingness to try new things and easy knowledge sharing between groups in an organization can help people appreciate their BI tools. These new theories help us see how UTAUT can be extended to education and used in different societies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Practical Implications\u003c/h2\u003e \u003cp\u003eThese findings are significant for policy makers, university leaders, IT managers and suppliers of business intelligence systems in HEIs. In the first place, designers need to ensure that any BI tool can be easily seen as adding value to help employees make better decisions and improve the organization\u0026rsquo;s performance. Implementing BI must happen in a way that does not stop current academic practices and should easily merge with current ways of working. Social Influence supports the importance of BI project leaders and vendors partnering with academic stakeholders, including deans, department heads and professional groups, so that they may advocate for the benefits of BI. When institutions and peers encourage staff, staff members are more confident about using BI tools. Results indicated that FC greatly influenced BI intention, leading HEIs to ensure they have proper facilities, good support and easy-to-access training. For this, we offer downloadable guides, free trials, initial workshops and ongoing learning opportunities to prepare users. When IT services support the needs of the academics, the system\u0026rsquo;s worth is enhanced. That is, the role of OLC makes it clear that for BI to succeed, there must be cultural changes as well as technical improvements. HEIs should make sure staff members learn about data, use BI tools for experiments and rely on data analytics in all important decisions. The metrics for performance should relate to how BI is used so that the institution\u0026rsquo;s commitment is strengthened.\u003c/p\u003e \u003cp\u003eSeveral helpful recommendations for implementation have been uncovered by this study. It is important for HEIs to design programmes that teach faculty and staff how to collect, review and present data. They are necessary to teach employees about BI tools and to create an environment that values making choices based on data. It is also important for universities to invest in the necessary money, technology and staff to help deploy BI. This requires purchasing software licenses, updating information technology infrastructure and recruiting people with the needed expertise to take care of BI systems. We also need to work together with people from BI, consultancy firms and other universities to achieve our goals. They make it possible for partners to learn from each other, access top strategies and speed up the process with co-trained staff and advice from experts. At last, a strong data governance structure is needed to guarantee data quality, protect it and make sure everyone can use it. For this, the establishment needs to create thorough policies, similar procedures and technologies for handling data that enhance faith in the institution and its overall data utilisation. Ensure you take all these actions for an ecosystem that gives BI adoption the encouragement it needs within your HEI.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Limitations and Future Research Directions","content":"\u003cp\u003eThere are some constraints acknowledged in this study that suggest directions for further investigation. One problem is that using information collected from surveys leads to biases linked with memory loss and the tendency to provide answers that are especially socially acceptable. Limitations within data may decrease how accurately the results reflect the research question. In the next steps, researchers can use multiple forms of data or include hard numbers to increase confidence and validity. Also, the research only measured HEIs in Jordan and thus the study\u0026rsquo;s findings cannot be generalized beyond. Performing the study in additional settings, for example, healthcare, public administration or private business, could broaden the conclusions. In addition, examining the model in different cultural and technological settings would confirm its usefulness in many countries.\u003c/p\u003e \u003cp\u003eThe study was also designed as a cross-sectional study which gathers data all at the same time and does not track causes and changes over different times. It would be useful for future research to explore withdrawal and continued acceptance stages by looking at the way BI adoption habits develop as time passes. Additionally, the sampling approach employed in this study might reduce how well the sample fits the population. Despite being a good representation of Jordanian HE, the sample may miss some of the other types of roles, experiences and institutions found there. In the future, more consistent sampling using stratified or random methods would make the outcomes more general. In addition, the current study extends UTAUT by including Organizational Learning Culture (OLC), but the concept could still be developed further. Researchers can add other organisational, psychological or technological dimensions such as being prepared for change, having leaders support it, having advanced data tools or someone unwilling to accept new technology. Using all these variables together enables us to explore factors behind BI use in HEIs and introduce helpful suggestions for educational policy and practice.\u003c/p\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThe study aimed to discover what affects the BI systems used in Jordanian HEIs. Integrating Organizational Learning Culture with the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study explains how BI is adopted in higher education. Through empirical analysis, it became clear that the impact of Performance Expectancy (PE), Social Influence (SI) and Facilitating Conditions (FC) on behavioural intention for BI systems is positive and strong. Meanwhile, Effort Expectancy (EE) was shown to be irrelevant to the model by the study. Significantly, OLC strongly aided all the main UTAUT constructs, proving how vital institution culture is in helping people accept and use technology. The study offers new insights by supporting the use of OLC as an additional factor in a revised version of the UTAUT framework that influences BI adoption. The model we created improves our understanding of how groups and organisations use technology and could be broadly applied to other adoptions of technology.\u003c/p\u003e \u003cp\u003ePractically, these findings remind all HEIs to develop a culture that encourages lifelong learning, is open to transformation and relies on data when making decisions. Training, upgrading infrastructure and working with businesses in the industry are needed to make users and institutions more capable and prepared. Despite the important findings here, it is still important to recognize what they do not cover. The results cannot be generalized or analyzed for cause-and-effect because convenience sampling and a cross-sectional design were used. Further work should use long-term and statistical sampling methods to build upon the results found here. Investigating certain functions of BI, the user experience and the unique issues in higher education offers a better picture of BI implementation within the campus. In short, encouraging Organizational Learning Culture can motivate BI adoption in HEIs, aid the institution\u0026rsquo;s results and empower informed choice-making for the digital time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohammad Mahmoud Alzubi\u003csup\u003e1\u003c/sup\u003e, Suad Abdalkareem Alwaely\u003csup\u003e2\u003c/sup\u003e,\u0026nbsp;Sanna Abd el rahman Yaghi\u003csup\u003e3\u003c/sup\u003e,\u0026nbsp;Naheel M badri Haddad\u003csup\u003e4\u003c/sup\u003e,\u0026nbsp;Abdulrahman Nehro Ismail\u003csup\u003e5,\u0026nbsp;\u003c/sup\u003eSaddam Rateb Darawsheh\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e Mohammad Issa Alzoubi\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Business Middle East University, Amman, Jordan\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eCollege of Education, Humanities and Social Sciences, Al Ain University, UAE, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eGraduate Student, Al Ain University, UAE, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eGraduate Student, Al Ain University, UAE, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eGraduate Student, Al Ain University, UAE,[email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003eDepartment of Administrative Sciences, The Applied College, Imam Abdulrahman Bin Faisal University Dammam, Saudi Arabia, [email protected]\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e7\u003c/sup\u003eDepartment of Business Intelligence and Data Analysis, Irbid National University, Jordan, [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMohammad Alzubi (Corresponding Author)\u003c/strong\u003e\u003cbr\u003e Email: \u003cstrong\[email protected]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participating organizations and professionals who contributed their time and insights to this research. No professional writing or editorial services were used in the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declares that there are no competing interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Research Ethics Committee of Irbid National University, Jordan (Approval No.: 10-15). All procedures were conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations. The participants were managerial-level employees in Jordanian higher education institutions (HEIs), all of whom were adults. Participation was entirely voluntary, and informed consent was obtained electronically from all participants prior to data collection. Participants were fully informed about the purpose of the study, their right to withdraw at any time without penalty, and the confidentiality of their responses. No personally identifiable information was collected, and all data were anonymized and handled with strict confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. There is no personal data of a single person in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that was studied and used during this research can be obtained by the respective author under a reasonable request. Data requests need to be sent to [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbubakar, A. A., \u0026amp; Al-Mamary, Y. H. (2025). Exploring factors influencing the intention to use social media and its actual usage in higher education: a conceptual model of effectiveness, effort, communication, self-awareness, social influence, and facilitating conditions. \u003cem\u003eInteractive Learning Environments\u003c/em\u003e, 1\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eAfzal, F., \u0026amp; Tumpa, R. J. (2025). Project-based group work for enhancing students learning in project management education: an action research. \u003cem\u003eInternational Journal of Managing Projects in Business\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 189\u0026ndash;208.\u003c/li\u003e\n\u003cli\u003eAhsan, M. J. (2025). Cultivating a culture of learning: the role of leadership in fostering lifelong development. \u003cem\u003eThe Learning Organization\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(2), 282\u0026ndash;306.\u003c/li\u003e\n\u003cli\u003eAideed, H., Salem, I. E., Magdy, A., AlAmri, T. K., Alzubaidi, A. S., \u0026amp; Elbaz, A. M. (2025). Beyond reality: Harnessing the metaverse for transformative education through UTAUT-2 and task-technology synergy. \u003cem\u003eThe International Journal of Management Education\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 101169.\u003c/li\u003e\n\u003cli\u003eAkhtar, S., Alfuraydan, M. M., Mughal, Y. H., \u0026amp; Nair, K. S. (2025). Adoption of Massive Open Online Courses (MOOCs) for Health Informatics and Administration Sustainability Education in Saudi Arabia. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(9), 3795.\u003c/li\u003e\n\u003cli\u003eAl-Adwan, A. S. (2024). The meta-commerce paradox: exploring consumer non-adoption intentions. \u003cem\u003eOnline Information Review\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAl-Emran, M., Al-Qaysi, N., Al-Sharafi, M. A., Khoshkam, M., Foroughi, B., \u0026amp; Ghobakhloo, M. (2025). Role of perceived threats and knowledge management in shaping generative AI use in education and its impact on social sustainability. \u003cem\u003eThe International Journal of Management Education\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 101105.\u003c/li\u003e\n\u003cli\u003eAl-Kfairy, M., Alomari, A., Al-Bashayreh, M., Alfandi, O., \u0026amp; Tubishat, M. (2024). Unveiling the Metaverse: A survey of user perceptions and the impact of usability, social influence and interoperability. \u003cem\u003eHeliyon\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAl-khateeb, B. A. A. (2024). Business Intelligence (BI): A Critical Strategy for University Success and Sustainability. \u003cem\u003eInternational Journal of Asian Business and Information Management (IJABIM)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eAl-maaitah, T. A., Masa\u0026rsquo;deh, R., Al-maaitah, D. A., Abueid, A. I., \u0026amp; Al Smadi, K. (2025). The Impact of Business Intelligence in the Banking Sector. In \u003cem\u003eThe Role of Artificial Intelligence Applications in Business\u003c/em\u003e (pp. 225\u0026ndash;234). Emerald Publishing Limited.\u003c/li\u003e\n\u003cli\u003eAldogiher, A., Halim, Y. T., El-Deeb, M. S., Maree, A. M., \u0026amp; Kamel, E. M. (2025). The Impact of Digital Teaching Technologies (DTTs) in Saudi and Egyptian Universities on Institutional Sustainability: The Mediating Role of Change Management and the Moderating Role of Culture, Technology, and Economics. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(5), 2062.\u003c/li\u003e\n\u003cli\u003eAli, A., Sharabati, A., Alqurashi, D., Shkeer, A., \u0026amp; Allahha, M. (2024). The impact of artificial intelligence and supply chain collaboration on supply chain resilience: Mediating the effects of information sharing. \u003cem\u003eUncertain Supply Chain Management\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 1801\u0026ndash;1812.\u003c/li\u003e\n\u003cli\u003eAlkhwaldi, A. F. (2024). Understanding the acceptance of business intelligence from healthcare professionals\u0026rsquo; perspective: An empirical study of healthcare organizations. \u003cem\u003eInternational Journal of Organizational Analysis\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAllahawiah, S., Altarawneh, H., \u0026amp; Al-Hajaya, M. (2024). The Role of Organizational Culture in Cybersecurity Readiness: An Empirical Study of the Jordanian Ministry of Justice. \u003cem\u003eCalitatea\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(202), 74\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eAllioui, H., \u0026amp; Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(19), 8015.\u003c/li\u003e\n\u003cli\u003eAlmahri, F. A. A. J., \u0026amp; Saleh, N. I. M. (2025). Insights into Technology Acceptance: A Concise Review of Key Theories and Models. \u003cem\u003eInnovative and Intelligent Digital Technologies; Towards an Increased Efficiency: Volume 2\u003c/em\u003e, 797\u0026ndash;807.\u003c/li\u003e\n\u003cli\u003eAlmanwari, H. S. A., Saad, N. H. M., \u0026amp; Zainal, S. R. M. (2024). The influence of environment \u0026amp; location, personal motivation, and fee \u0026amp; price on satisfaction, attituding and behavioural loyalty among international students in Oman. \u003cem\u003eJournal of Open Innovation: Technology, Market, and Complexity\u003c/em\u003e, 100285.\u003c/li\u003e\n\u003cli\u003eAlmatrodi, I., \u0026amp; Skoumpopoulou, D. (2023). Organizational routines and digital transformation: An analysis of how organizational routines impact digital transformation transition in a Saudi university. \u003cem\u003eSystems\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(5), 239.\u003c/li\u003e\n\u003cli\u003eAlSaied, M. K., \u0026amp; Alkhoraif, A. A. (2024). The role of organizational learning and innovative organizational culture for ambidextrous innovation. \u003cem\u003eThe Learning Organization\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(2), 205\u0026ndash;226.\u003c/li\u003e\n\u003cli\u003eAlvi, I., \u0026amp; Khechine, H. (2025). Effect of Cultural Values on Students\u0026rsquo; Adoption of Social Media for Collaborative Learning. \u003cem\u003eJournal of Computer Assisted Learning\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), e70051.\u003c/li\u003e\n\u003cli\u003eAlzubi, M. M., Ismaeel, B., \u0026amp; Ateik, A.-H. (2021). The Moderating Effect of Compatibility Factor in The Usage of E-Government Services Among Malaysian Citizens. \u003cem\u003e2021 2nd International Conference on Smart Computing and Electronic Enterprise (ICSCEE)\u003c/em\u003e, 224\u0026ndash;232.\u003c/li\u003e\n\u003cli\u003eAlzubi, M. M. S., Alrifae, A. A. M., \u0026amp; Atieh, A. A. (2025). Factors Influencing Business Intelligence Adoption by Jordanian Private Universities. \u003cem\u003ePaperASIA\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(1b), 148\u0026ndash;167.\u003c/li\u003e\n\u003cli\u003eArefin, M. S., Hoque, M. R., \u0026amp; Rasul, T. (2021). Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy. \u003cem\u003eJournal of Knowledge Management\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 573\u0026ndash;594.\u003c/li\u003e\n\u003cli\u003eArnaud, L., Sander, O., Rednic, S., Mertz, P., Faria, R., Crisafulli, F., Silva-Ribeiro, S., Kawka, L., Sztejkowski, C., \u0026amp; D\u0026uuml;sing, C. (2025). European Reference Network (ERN) ReCONNET methodology for the cross-cultural adaptation of instruments for research and care in the context of rare connective tissue diseases (CROSSADAPT). \u003cem\u003eOrphanet Journal of Rare Diseases\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 1\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eBarjak, F., \u0026amp; Heimsch, F. (2023). Understanding the relationship between organizational culture and inbound open innovation. \u003cem\u003eEuropean Journal of Innovation Management\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3), 773\u0026ndash;797.\u003c/li\u003e\n\u003cli\u003eBelhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., \u0026amp; Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. \u003cem\u003eAnnals of Operations Research\u003c/em\u003e, \u003cem\u003e333\u003c/em\u003e(2), 627\u0026ndash;652.\u003c/li\u003e\n\u003cli\u003eByrne, B. M. (2013). \u003cem\u003eStructural equation modeling with Mplus: Basic concepts, applications, and programming\u003c/em\u003e. routledge.\u003c/li\u003e\n\u003cli\u003eChaudhuri, R., Chatterjee, S., Vrontis, D., \u0026amp; Thrassou, A. (2024). Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture. \u003cem\u003eAnnals of Operations Research\u003c/em\u003e, \u003cem\u003e339\u003c/em\u003e(3), 1757\u0026ndash;1791.\u003c/li\u003e\n\u003cli\u003eDaniel, K., Msambwa, M. M., Antony, F., \u0026amp; Wan, X. (2024). Motivate students for better academic achievement: A systematic review of blended innovative teaching and its impact on learning. \u003cem\u003eComputer Applications in Engineering Education\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), e22733.\u003c/li\u003e\n\u003cli\u003ede Souza, A. S. C., \u0026amp; Debs, L. (2024). Concepts, innovative technologies, learning approaches and trend topics in education 4.0: A scoping literature review. \u003cem\u003eSocial Sciences \u0026amp; Humanities Open\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 100902.\u003c/li\u003e\n\u003cli\u003eDiBella, J., Forrest, N., Burch, S., Rao‐Williams, J., Ninomiya, S. M., Hermelingmeier, V., \u0026amp; Chisholm, K. (2023). Exploring the potential of SMEs to build individual, organizational, and community resilience through sustainability‐oriented business practices. \u003cem\u003eBusiness Strategy and the Environment\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(1), 721\u0026ndash;735.\u003c/li\u003e\n\u003cli\u003eExner, R., \u0026amp; Zunic, A. (2025). Organizing BI Strategy. In \u003cem\u003eThe Path to an Intelligent Enterprise: The Art and Practice of Business Intelligence Strategy\u003c/em\u003e (pp. 151\u0026ndash;188). Springer.\u003c/li\u003e\n\u003cli\u003eF. Hair Jr, J., Sarstedt, M., Hopkins, L., \u0026amp; G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. \u003cem\u003eEuropean Business Review\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 106\u0026ndash;121.\u003c/li\u003e\n\u003cli\u003eGupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., \u0026amp; Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. \u003cem\u003eIndustrial Marketing Management\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e, 581\u0026ndash;592.\u003c/li\u003e\n\u003cli\u003eG\u0026uuml;rtl, S., Scharf, D., Thrainer, C., G\u0026uuml;tl, C., \u0026amp; Steinmaurer, A. (2024). Design and Evaluation of an LLM-Based Mentor for Software Architecture in Higher Education Project Management Classes. \u003cem\u003eInternational Conference on Interactive Collaborative Learning\u003c/em\u003e, 375\u0026ndash;386.\u003c/li\u003e\n\u003cli\u003eHair, J. F., Risher, J. J., Sarstedt, M., \u0026amp; Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. \u003cem\u003eEuropean Business Review\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1), 2\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eHmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., \u0026amp; Al Zoubi, J. Z. (2023). Factors influencing business intelligence adoption by higher education institutions. \u003cem\u003eJournal of Open Innovation: Technology, Market, and Complexity\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 100111.\u003c/li\u003e\n\u003cli\u003eIsiaku, L., \u0026amp; Adalier, A. (2024). Determinants of business intelligence systems adoption in Nigerian banks: The role of perceived usefulness and ease of use. \u003cem\u003eInformation Development\u003c/em\u003e, 02666669241307024.\u003c/li\u003e\n\u003cli\u003eIslam, M. T., Mission, M. R., Refat, T. K., \u0026amp; Kynatun, M. (2025). Cybersecurity risk assessment frameworks for engineering databases: A systematic literature review. \u003cem\u003eStrategic Data Management and Innovation\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(01), 224\u0026ndash;243.\u003c/li\u003e\n\u003cli\u003eJaklič, J., Grublje\u0026scaron;ič, T., \u0026amp; Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. \u003cem\u003eInternational Journal of Information Management\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e, 305\u0026ndash;318.\u003c/li\u003e\n\u003cli\u003eJingyi, X., \u0026amp; Pamintuan, C. (2025). Exploring factors influencing online learning platform usage behavior: a comparative study of southeast Asian learners based on the UTAUT model. \u003cem\u003eInteractive Learning Environments\u003c/em\u003e, 1\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eKa\u0026scaron;parov\u0026aacute;, P. (2023). Intention to use business intelligence tools in decision making processes: Applying a UTAUT 2 model. \u003cem\u003eCentral European Journal of Operations Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(3), 991\u0026ndash;1008.\u003c/li\u003e\n\u003cli\u003eKline, R. B. (2023). \u003cem\u003ePrinciples and practice of structural equation modeling\u003c/em\u003e. Guilford publications.\u003c/li\u003e\n\u003cli\u003eLee, A. T., Ramasamy, R. K., \u0026amp; Subbarao, A. (2025). Understanding Psychosocial Barriers to Healthcare Technology Adoption: A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT Frameworks. \u003cem\u003eHealthcare\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 250.\u003c/li\u003e\n\u003cli\u003eMahara, T., Iyer, L. S., Matta, V., \u0026amp; Alagarsamy, S. (2021). Effect of Organizational Culture during Crises on adoption of virtual classrooms: An extension of UTAUT model. \u003cem\u003eJournal of Information Technology Case and Application Research\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(3), 213\u0026ndash;239.\u003c/li\u003e\n\u003cli\u003eMcDonald, N., Johri, A., Ali, A., \u0026amp; Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. \u003cem\u003eComputers in Human Behavior: Artificial Humans\u003c/em\u003e, 100121.\u003c/li\u003e\n\u003cli\u003eMellors, J., \u0026amp; Vicencio, A. (2025). Widening participation in outward student mobility: Successes, challenges, and opportunities. \u003cem\u003eBritish Educational Research Journal\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eMexhuani, B. (2025). Adopting Digital Tools in Higher Education: Opportunities, Challenges and Theoretical Insights. \u003cem\u003eEuropean Journal of Education\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(1), e12819.\u003c/li\u003e\n\u003cli\u003eMoore, R. L., Lee, S. S., Pate, A. T., \u0026amp; Wilson, A. J. (2025). Systematic review of digital microcredentials: Trends in assessment and delivery. \u003cem\u003eDistance Education\u003c/em\u003e, 1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eOkpala, P. (2025). Strategic Management of Online Higher Education Institutions. In \u003cem\u003eBuilding Organizational Capacity and Strategic Management in Academia\u003c/em\u003e (pp. 553\u0026ndash;590). IGI Global.\u003c/li\u003e\n\u003cli\u003eOlubiyi, T. O., \u0026amp; Akinlabi, H. B. (2025). Intelligent Decision Making Through Adoption of Business Analytics: Empirical Evidence From Behavioral Intentions of African SMEs. In \u003cem\u003eGenerative AI for Business Analytics and Strategic Decision Making in Service Industry\u003c/em\u003e (pp. 137\u0026ndash;168). IGI Global Scientific Publishing.\u003c/li\u003e\n\u003cli\u003eOlugboyega, O., Chukwudi, C. S., Oseghale, O. B., Omojola, S. O., \u0026amp; Adeyemi, M. (2025). Maintenance requirements and methods for building information modelling infrastructures. \u003cem\u003eFacilities\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOzolins, U. (2008). Issues of back translation methodology in medical translations. \u003cem\u003eProceedings, FIT [International Federation of Translators] XVIII Congress, Shanghai\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ePerron, B. E., Hiltz, B. S., Khang, E. M., \u0026amp; Savas, S. A. (2025). AI-Enhanced Social Work: Developing and Evaluating Retrieval-Augmented Generation (RAG) Support Systems. \u003cem\u003eJournal of Social Work Education\u003c/em\u003e, 1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eSaboor, A., Khan, M. Z., Khan, M. N., Hussain, T., Attar, R. W., Alnfiai, M. M., \u0026amp; Almalki, N. S. (2025). Exploring the factors that influence students acceptance and use of online learning technology in higher education institutes of Khyber Pakhtunkhwa Pakistan. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, 1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eSalisu, I., Bin Mohd Sappri, M., \u0026amp; Bin Omar, M. F. (2021). The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review. \u003cem\u003eCogent Business \u0026amp; Management\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 1935663.\u003c/li\u003e\n\u003cli\u003eSarantis, D., Rizun, N., Alexopoulos, C., \u0026amp; Saxena, S. (2025). Analyzing behavioral intention of open government data adoption across Latvia, India and Poland: does national culture matter? \u003cem\u003eJournal of Science and Technology Policy Management\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSekaran, U. (2016). \u003cem\u003eResearch methods for business: A skill building approach\u003c/em\u003e. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eSequeira, R., Reis, A., Alves, P., \u0026amp; Branco, F. (2024). Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review. \u003cem\u003eInformation\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 208.\u003c/li\u003e\n\u003cli\u003eSingh, R. K., Modgil, S., \u0026amp; Shore, A. (2024). Building artificial intelligence enabled resilient supply chain: a multi-method approach. \u003cem\u003eJournal of Enterprise Information Management\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(2), 414\u0026ndash;436.\u003c/li\u003e\n\u003cli\u003eSrivastava, K., Kumar, M., Verma, R., Singh, S., \u0026amp; Maurya, P. (2025). Analyzing the Drivers of Adoption in Higher Education E-Learning: Exploring Factors Affecting Behavioral Intentions and Actual Usage. In \u003cem\u003eArchitecting the Digital Future: Platforms, Design, and Application\u003c/em\u003e (pp. 47\u0026ndash;82). IGI Global Scientific Publishing.\u003c/li\u003e\n\u003cli\u003eTang, X., Yuan, Z., \u0026amp; Qu, S. (2025). Factors Influencing University Students\u0026rsquo; Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model. \u003cem\u003eJournal of Computer Assisted Learning\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(1), e13105.\u003c/li\u003e\n\u003cli\u003eTeng, X., Wu, Z., \u0026amp; Yang, F. (2022). Research on the relationship between digital transformation and performance of SMEs. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), 6012.\u003c/li\u003e\n\u003cli\u003eTimsina, S. M., \u0026amp; Bhattarai, U. (2025). Identifying factors shaping the behavioural intention of Nepalese youths to adopt digital health tools. \u003cem\u003eHealthcare Technology Letters\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), e70005.\u003c/li\u003e\n\u003cli\u003eTwum, K. K., Ofori, D., Keney, G., \u0026amp; Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student\u0026rsquo;s intention to use E-learning. \u003cem\u003eJournal of Science and Technology Policy Management\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 713\u0026ndash;737.\u003c/li\u003e\n\u003cli\u003eUl Hassan, M., Murtaza, A., \u0026amp; Rashid, K. (2025). Redefining higher education institutions (HEIs) in the era of globalisation and global crises: A proposal for future sustainability. \u003cem\u003eEuropean Journal of Education\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(1), e12822.\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., Morris, M. G., Davis, G. B., \u0026amp; Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. \u003cem\u003eMIS Quarterly\u003c/em\u003e, 425\u0026ndash;478.\u003c/li\u003e\n\u003cli\u003eWang, S., Asif, M., Shahzad, M. F., \u0026amp; Ashfaq, M. (2024). Data privacy and cybersecurity challenges in the digital transformation of the banking sector. \u003cem\u003eComputers \u0026amp; Security\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e, 104051.\u003c/li\u003e\n\u003cli\u003eZhao, J., Feng, H., Chen, Q., \u0026amp; de Soto, B. G. (2022). Developing a conceptual framework for the application of digital twin technologies to revamp building operation and maintenance processes. \u003cem\u003eJournal of Building Engineering\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 104028.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"UTAUT, Culture, Business Intelligence, Higher Education","lastPublishedDoi":"10.21203/rs.3.rs-9367638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9367638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEven though Business Intelligence (BI) has greatly enhanced decision-making in various industries, there is limited literature on its use in institutions of higher learning (HEIs) especially in third world countries. Since the amount of data produced by HEIs is enormous, this paper analyzes the major determinants of BI adoption, and more so, the impact of Organizational Learning Culture (OLC) on the behavioral intentions of the users. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the research paper will build on the model by integrating OLC as a central organizational issue that will affect BI adoption. Quantitative methodology was adopted, and the survey data was gathered with 579 respondents of managerial level working in Jordanian HEIs and analyzed in Structural Equation Modeling (SEM). The results show that the positive impact of Performance Expectancy, Social Influence, and Facilitating Conditions on behavioral intention to adopt BI systems is significant, as compared to the insignificance of the effect of Effort Expectancy. Notably, the Organizational Learning Culture illustrates the strong positive effect on all UTAUT constructs, which is why it is considered a key driving factor in the adoption of BI. The article makes its contribution to the body of literature both by confirming an extended UTAUT model in the context of a developing country and in a higher educational setting and by highlighting organizational learning culture as one of the critical facilitators of technology adoption. The implications on academic leaders, policymakers, and system developers that the results will provide are useful in making decisions based on data and in the process of digital transformation of higher education institutions.\u003c/p\u003e","manuscriptTitle":"Business Intelligence Adoption in Higher Education Institutions: Extending UTAUT with Organizational Learning Culture Evidence from Jordan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 15:17:56","doi":"10.21203/rs.3.rs-9367638/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-06T04:36:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T04:33:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T15:41:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-03T07:02:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-05-03T06:58:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fae4677f-987a-4120-a8d6-0b8ccd56849c","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"4","date":"2026-05-06T04:36:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-06T04:33:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T15:41:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-03T07:02:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-05-03T06:58:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67979724,"name":"Business and commerce/Business and management"},{"id":67979725,"name":"Social science/Business and management"},{"id":67979726,"name":"Humanities/Cultural and media studies"},{"id":67979727,"name":"Social science/Cultural and media studies"},{"id":67979728,"name":"Social science/Education"},{"id":67979729,"name":"Business and commerce/Information systems and information technology"},{"id":67979730,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-05-14T15:17:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 15:17:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9367638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9367638","identity":"rs-9367638","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}