Introduction

High-quality education combined with contemporary technology can help create a more advanced digital society. Additionally, the new knowledge created by those participating in officially recognized educational programs and vocal instruction and training will support the expansion of the economy and job market in society1. In the end, how to take advantage of technological advancements to increase the capacity of universities in poor nations is the subject that worries policymakers in the field of higher education2. For a number of decision-makers in universities, the value of knowledge and acquired data is essential3. An organization’s long-term viability is impacted by its effective use of information technology4. Universities are therefore looking for and using a variety of solutions to store and handle their data. In order to analyze high-quality data and manage their data for efficient decision-making, some universities implemented sustainable cloud quality management systems5. A distributed computing paradigm known as “cloud computing” makes virtual resources like computers, networks, storage, development platforms, and apps accessible6.Examining the variables influencing the behavior intention of various university employees to adopt sustainable cloud systems is therefore crucial to this study.

Venkatesh et al.7, examined the literature on IT users’ acceptance and talked about eight well-known theoretical models for IT acceptance and adoption. The social cognitive theory is composed of eight models. The eight models, which used six months’ worth of data from four businesses with three points of measurement, were able to explain 17–53% of the variation in user intentions. In light of these findings, Venkatesh et al.7, created a brand-new, comprehensive model known as the “Unified Theory of Acceptance and Use of Technology” that is divided into four key components and four moderators. After testing with the original data, the new model, UTAUT, was exposed to perform superior to eight theoretical models. The model demonstrates that four factorsperformance expectancy, EE, SI, and facilitating conditions (FC) have a major impact on a user’s desire to adopt and use IT systems. Additionally, the theoretical framework model UTAUT was expanded by Venkatesh et al.8, to examine IT adoption and use in a context. UTAUT2 is the name of the recently suggested model. Three innovative UTAUT constructs the domestic motivation, price value, and habit are included in the UTAUT2 model. Age, gender, and experience are examples of individual variations that are thought to mitigate the impact of these forecasters on a user’s intention to adopt or utilize a certain technology. When compared to the UTAUT model, the prolonged UTAUT2 model significantly improved the variance described in behavioral intention (BI) (56 to 74%)9.

Research gap

The study highlights several notable gaps in existing research. First, although cloud computing services have achieved widespread global adoption, there is a limited focus on sustainable cloud-based collaborative systems (SCBCS), particularly within educational settings. For instance, Alzahrani et al. (2017) emphasize that while cloud computing offers numerous educational benefits, the sustainability dimensions such as environmental and long-term resource efficiency—are frequently overlooked in educational cloud system designs10. Second, little is known about the backdrop of developing nations, particularly Jordan. There are issues with generalizability because the majority of current cloud adoption models are based on data from rich countries. In particular, Alhassan et al. (2018) point out that there are few empirical studies on cloud adoption in poor nations and that adoption trends may be considerably influenced by cultural, infrastructure, and economic factors2. Third, while being a popular framework, not many research have tried to incorporate the Unified Theory of Acceptance and Use of Technology (UTAUT) with system design approaches to enhance its contextual relevance and predictive power. According to Dwivedi et al. (2019), UTAUT’s application to dynamic, collaborative cloud settings in academia is limited by a lack of multidisciplinary integration between it and system design methodologies11. Lastly, research relating the use of cloud computing in education to more general sustainability objectives, including encouraging resource optimization and energy conservation, is conspicuously lacking. Research on green or sustainable IT in the educational sector, particularly with regard to cloud platforms, is still scattered and immature, claim Amini and Bakri (2020)12. This emphasizes the necessity of comprehensive models that close the gap between sustainable development and the adoption of technology in higher education.

Objectives

It aims to develop a predictive model for the adoption of sustainable cloud-based collaborative systems (SCBCS) by identifying the critical factors that influence Jordanian academics’ behavioral intentions. To enhance the relevance and effectiveness of the model, the study seeks to extend and adapt the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating it with system design methodologies, thereby addressing the specific context of SCBCS adoption in Jordan. The research also involves the empirical validation of the proposed model through the collection and analysis of cross-sectional data from 500 university professors. In addition, the study intends to assess how behavioral intentions toward SCBCS impact the quality and sustainability of teaching and learning processes. Ultimately, it aims to provide practical and policy-oriented recommendations that can support the broader adoption of sustainable educational technologies in developing countries.

Jordan holds a distinctive position within the Arab region that makes it particularly well-suited to address the research gaps identified in the adoption of sustainable cloud-based collaborative systems (SCBCS). As a developing country with a growing emphasis on educational reform and digital transformation, Jordan represents a critical case for understanding how emerging economies in the region navigate technology adoption challenges. Its higher education sector is relatively advanced compared to many neighboring countries, with a mix of public and private universities actively seeking innovative solutions to improve teaching and learning processes. Moreover, Jordan faces unique socio-economic and infrastructural conditions, including limited digital resources and varying levels of IT expertise, which provide a valuable context for studying the moderating factors influencing technology acceptance. These contextual factors allow the findings to be more broadly applicable to other Arab nations sharing similar developmental trajectories, educational structures, and cultural dynamics. Consequently, research conducted in Jordan can offer important insights and practical guidance for policymakers and educators across the Arab region, enhancing the relevance and impact of sustainable cloud technology adoption in higher education.

Major contribution

  • To meet the objectives, the proposed model incorporates the modified UTAUT2 with additional factors such as, SI, Perceived security risks (PSR) and Perceived Trust (PT). This novelty can improve the performance of the cloud services with sustainable improvement. Compared to earlier studies carried out in developing environments, it offers solid empirical evidence that using the UTAUT/OOA model significantly improves the variance explained in behavioral intention.

  • First, the findings demonstrate the strength of the expanded UTAUT/OOA research model. In particular, it was demonstrated that Attitude toward Using Cloud Services is significantly impacted by behavioural control, EE, and SI. As a result, the suggested model offers greater descriptive power than earlier studies in developing markets and business literature.

  • Our proposed UTAUT-OOA model is the evolution of UTAUT and OOA approaches which takes advantages of both the model. The major reason for using OOA is to handle the complex data records and retains that as objects. Each object has attributes and methods. The methods explain the behavior of any data object in the form of procedures or functions, while the attributes formulate its qualities. The different heterogeneous data from the cloud is fetched using Materialized view of the OOA model which can effectively accelerate the support service decision during the queries importing. Followed by this, the data management framework constructed using UTAUT is deployed in the web based approaches over the cloud computing services.

Paper structure

The paper’s structure is set out as follows: First, the “Introduction” provides background information and highlights the main concerns of cloud manufacturing and cloud-edge cooperation. The “Literature Review” discusses a summary of the pertinent literature about the plan to employ the cloud-based collaborative system and the UTAUT. The framework of the cloud collaboration mechanism and affecting elements are explained in the section headed “Research Model.” The techniques for training the UTAUT and object-oriented model for long-term prediction of the aim to use cloud services were explained in the “Data used and Method” section. The statistical results produced by the proposed model were explained and supported in the “Results and Discussions” section. The model results, limits, and future directions were discussed in the “Conclusion” section.

Related work

This section discusses some related literatures describing the cloud based collaborative systems. Gómez et al.13, suggested LoT@UNED (Laboratory-of-Things@UNED) framework to incorporate all of these learning tools for IoT settings, from cloud computing to edge computing. In particular, this platform integrates a set of sustainable capabilities (security, availability, and scalability), helping to address the SDG 4 issue by offering an effective and low-complexity solution for high-quality distance learning13. This work also examines the inspiration of several UTAUT/TAMacceptance elements over the teaching and learning process for students in order to assess the impact of our suggested solution. By conducting exploratory and confirmatory analyses to accomplish this goal14, this study experimentally examines customer intention to utilize a service robot by developing an integrated model that incorporates the theory of planned behavior, expectation confirmation model, diffusion of innovation, and robot appearance. 349 responses obtained from consumers who visit retail establishments are used to experimentally test the study hypothesis. According to statistical findings, R2 80.1% of the variance in consumer attitudes toward using service robots was explained by customer innovativeness, compatibility, BC, expectation confirmation, service robot appearance, and individual standards. In order to increase client acceptance of robot services, this research has practically recommended that policymakers focus on innovativeness, compatibility and perceived behavioral control.

Chang et al.15develop an exceptionally well agricultural learning platforms with the goals of (1) achieving learning diversity, improving users’ learning ability and motivation to learn, and eliminating geo-restrictions. It also creates cloud-based e-learning resources and uses satisfaction surveys to assess how well they teach. Real-time streaming analysis is used to determine network string flow while users view videos, and dynamic allocation is used to optimize server efficiency in a cloud-based, highly efficient educational site for the agricultural community. A prediction model of the major factors influencing Jordanian academicsto use sustained cloud-basedsystems is what Dajani et al.16, hope to provides data was used to assess a comprehensive research model that was based on the UTAUT2 and the Theory of Planned Behavior (TPB).The research sample comprises 23 Jordanian public and private universities, whereas the unit of analysis comprises 500 academics.The research modifies and extends the TPB and UTAUT2 model to describe behavioral intention to use sustained cloud-ased systems in developing countries. The three factors that are proven to be important predictors are perceived behavioral control, expectation of performance, and supportive conditions; subjective standards and mindset toward the conduct are not.

The primary methodological issue in UTAUT is the development of the scales used to test the fundamental components. For the final measurement development, Marikyan and Papagiannidis17 used the highest loading elements from each scale. By examining the effects of hedonic motivation, engineers and product managers can alter the offering to either boost the hedonic value of technology or enhance hedonic cues for product marketing. Finally, the moderation effects of UTAUT2 assist practitioners in identifying which user segment need additional marketing efforts to address behaviors, offer hedonic value, and demonstrate better value for money. Given the aforementioned limitations, Venkatesh et al. proposed UTAUT2, an extension of UTAUT18. First of all, unlike all earlier attempts to broaden the paradigm, UTAUT2 was not designed to have a specific focus (such as new technology or geographic area). Instead, the idea sought to offer a comprehensive framework for examining technology uptake. The plugin was designed to give a more precise description of user behavior.

A unique object-oriented design based on three stages is presented by Omari et al.19 for merging different databases into a single, highly adaptable database: The “materialized view” (MV) is used to collect data from a variety of sources during the preparation phase. In the “OODB-construction” stage, there is just one unified database. In the last stage, “web-based and cloud computing,” the required services are set up and dispersed in accordance with the cloud-hosted environment. The proposed paradigm was evaluated on a real-world example. The efficacy of MV was analyzed and compared to the conventional perspective. Next, utilizing comparison results, the behavior of our proposed model is contrasted with that of several conventional database models.

In order to take advantage of cloud computing’s affordances for teaching and learning in a higher education setting, Verma et al.20, attempt to investigate the key aspects of its nature and educational potential. Today’s education is increasingly linked to information technology in terms of communication, collaboration, and content delivery. Universities, colleges, and institutions have very high demands for servers, storage, and software. Despite the fact that cloud computing adoption offers a number of advantages to a company, its successful implementation necessitates knowledge of many dynamics and domain expertise, especially in educational institutions. The goal of this study is to present a roadmap for cloud computing for higher education (CCHE), which outlines several phases for cloud computing adoption. Madhioub et al.21, offers a logical comprehension of the cloud computing models utilized in higher education institutions. We differentiate between the numerous advantages and difficulties that come with utilizing various cloud computing providers. Second, we suggest a cloud-based environment in which a number of cloud services and deployment models are coordinated to produce a logical setting with all the required resources. This setting applies a variety of specialties and broadens the understanding of both teachers and pupils. The results of this investigation will aid researchers, educators, and scholars in comprehending the possibilities of using cloud computing environments from the standpoint of an engineering school.

A closer look reveals a number of enduring constraints, even if previous research has greatly improved our understanding of cloud computing adoption through frameworks like UTAUT. Most notably, previous research has tended to treat UTAUT as a static, survey-driven model, concentrating mostly on perceptual variables such as effort expectancy and performance expectancy without sufficiently taking into account system-level design aspects or dynamic, real-time behavioral interactions. Studies like16,17,18 for instance, successfully model adoption in developed contexts but ignore important contextual nuances in developing nations, especially in areas like Jordan, where policy-driven trust concerns and infrastructure limitations can change the logic of adoption.Additionally, while concepts like enabling conditions and social impact have been studied, they are rarely expanded to include sustainability-focused factors or institutional trust aspects, which are becoming more and more important in cloud-based learning settings. There are hardly any attempts to combine behavioral models and system design approaches, suggesting a conceptual gap between user adoption theories and technical system functionality. The lack of mechanisms like Perceived Sustainability Relevance (PSR) and Policy Trust (PT) in current models prevents them from fully capturing the range of adoption motivators in academic contexts that are socially and ecologically concerned. By suggesting an OOA-informed, context-specific extension of UTAUT, this study overcomes these drawbacks and provides a more comprehensive and useful paradigm designed for Jordanian higher education’s sustained cloud adoption.

Research model

Based on the constructers of the studies related to UTAUT mentioned in literatures, the research model of the proposed system is illustrated in Fig. 1. In the model, we replaced the original UTAUT’s intention to use and actual application with attitude to use and purpose to use. It is proposed that FC and attitude toward use impact intention to use, much like facilitating conditions and intention to use affected actual usage in the original UTAUT paradigm.The suggested model mimics the effects of facilitating conditions and intention to use on actual usage from the original UTAUT model by using behavioral controls, attitude, and facilitating conditions to influence intention to use. OOA and UTAUT are the main factors taken into account in the current study with the goal of using certain behaviors that influence the BI to utilize sustainable cloud-based solutions in Jordanian institutions. In this case, the various factors influencing the students learning and teaching process is analyzed. The initial UTAUT model is extended by adding more factors such as behavioral factors and attitude towards the inclusion of the cloud service usage. A deep exploratory analysis is performed initially. From our knowledge, there are no literatures are related to integrating the OOA and UTAUT to enhance the cloud services. This work enhance the cloud service usage in education context.

Research questions

This study aims to address the following research issues, which are based on the expanded UTAUT framework and situated within Jordanian higher education:

RQ1: How well does the enlarged UTAUT model, which includes Perceived Sustainability Relevance (PSR) and Policy Trust (PT), explain Jordanian academic staff members’ behavioral intention (BI) to embrace sustainable cloud-based collaborative systems (SCBCS)?

RQ2: How do Behavioral Intention (BI) and Usage Behavior (UB) relate to the original UTAUT constructs such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) in the context of SCBCS, and how are these effects influenced by demographic factors like age, academic rank, and IT experience?

RQ3: In the link between UTAUT core components and Behavioral Intention (BI) among academic and administrative users, what moderating effects do Perceived Sustainability Relevance (PSR) and Policy Trust (PT) have?

RQ4: How may user-specific implementation strategies be informed by which user segments (e.g., early-career versus senior academics, or users with high versus low IT knowledge) show the largest variety in adoption drivers?

RQ5: In the Jordanian higher education system, how well do the core and extended UTAUT components (PE, EE, SI, FC, PSR, and PT) predict behavioral intention (BI) and usage behavior (UB)?

Fig. 1
figure 1

Proposed UTAUT-OOA research model (Bold colored lines indicates the novelty of this study).

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Influencing factors and hypothesis

Core variables

  1. (a)

    Attitude.

The degree to which a person judges an activity positively or negatively is referred to as their attitude22. The extent to which a faculty member believes they can use SCBCS effectively or inadequately is reflected in their attitude. Researchers have put up a number of models and theories to look into the connection between conduct and attitude23. Faham24 looked into the connection between behavior and attitude and found that it might not be there.

According to Hale et al.25,, unhappiness with prior experiences causes a limited correlation among attitude and behavior in aintended setting. Additionally, studies have shown that one’s attitude toward the habit has a good impact on behavioral intention to utilize SCBCS26through the cloud services. Yaseen et al.27, evaluates the variables influencing Jordanian SMEs’ adoption of cloud computing. This study accomplishes this by creating a research model based on the Diffusion of Innovation (DOI) and the Technology Organization Environment (TOE).Regarding the traits that support the adoption explanation, these hypotheses are similar. Data from 244 SMEs registered in Jordan was gathered via an online survey. The suggested model was examined using a multiple regression test. The findings show that the adoption of cloud computing by SMEs is highly influenced by seven factors. The best predictor among these variables was cost reduction, whereas business size was the weakest predictor. The firm’s size, however, had no bearing on Jordanian SMEs’ embrace of cloud computing. When it comes to adopting cloud computing, this finding may help SMEs make more informed decisions.

Hypothesis 1

Attitude has a significant positive effect on the behavioral intention to use sustainable cloud-based collaborative systems (SCBCS).

  1. (b)

    Effort Expectancy (EE).

When compared to other particular combinations in the UTAUT models to employ new technology, EE was determined to be the most effective driver of behavior intention28. A performance expectation is the belief held by academic staff members that using Supply Chain Quality Management Software (SCQMS) will help them with their administrative and instructional responsibilities. Consequently, this study suggests that behavioral intention to utilize SCQMS is influenced by performance expectancy. Consequently, the following theory is established.

Hypothesis 2

Effort expectancy has a significant positive effect on attitude, which in turn influences the behavioral intention to use sustainable cloud-based collaborative systems (SCBCS).

  1. (c)

    Social Influence.

Additionally, UTAUT2 was validated in a variety of nations with disparate economies, cultures, and degrees of technological use. Social influence has no effect on the adoption of mobile banking in Jordan29. The importance of the impacts and the intensity of the correlations varied among samples when comparing the use of educational technology in the US, Japan, and Korea. Gibreel et al.30, investigates the applicability of factors that have been found to be obstacles to the broad use of cloud computing, specifically with regard to digital transformation and IT adoption by Kuwaiti SMEs. This study examines social elements influenced by cultural characteristics, such as the power differential between superiors and subordinates and the desire to avoid ambiguity, as well as independent economic factors, such as the degree of trust in an organization and the significance of cost-saving measures. We’ll also look at how business-to-business IT adoption is affected by cyber security and IT complexity. According to our findings, the adoption of cloud computing by SMEs is influenced by a number of socioeconomic factors, including trust in supervisors, economic factors, such as cost savings, social-cultural factors, such as power distance and uncertainty avoidance, justice factors, such as procedural justice, security factors, such as cyber security, and technical knowledge, such as complexity. With contrast, the considered SCBCS impacts the social influence due to the usage of cloud services and form the Hypothesis 3 as,

Hypothesis 3

Social Influence (SI) has a positive effect on the behavioral intention to use Sustainable Cloud-Based Collaborative Systems (SCBCS).

  1. (d)

    Perceived Trust (PT).

Abdalla et al.31, done an online survey approach that included open-ended questions to gather qualitative insights, multiple-choice questions for category data, and Likert scale questions to gauge attitudes and opinions. 314 helpful replies were obtained from the target population, which consists of 300–350 small and medium-sized businesses (SMEs) in Bahrain that already use cloud computing technologies. Convenience sampling was used to start a mixed two-step sampling procedure. The inclusion of different SME categories was then ensured through the use of snowball sampling, which ensures representativeness. The 10,000-sample bootstrapping was performed to assess the structural model utilizing path coefficients, standard errors, t-values, and p-values. The results of the study show that Cloud Computing Adoption (CCA), which in turn enhances the performance of Bahraini SMEs, is significantly influenced by both Perceived Ease of Use (PEU) and Perceived Usefulness (PU). While PEU and PU have a direct effect on CCA, their increased use of cloud computing indirectly improves Organizational Performance (OP).

Hypothesis 4

Performance Trust (PT) has a positive influence on attitude and significantly impacts the behavioral intention to use Sustainable Cloud-Based Collaborative Systems (SCBCS).

  1. (e)

    Perceived Security Risk.

Mondal and Goswami32 focuses on evaluating the Economic Impact (EI) of Cloud Computing (CC), a potent technology that has the potential to revolutionize corporate operations and spur economic expansion. This study evaluates the EI of CC, a technology that has become a game-changer, using a narrative literature review methodology. It starts by looking at the financial advantages of CC, such as lower costs, more innovation, and higher efficiency. It then examines the difficulties in evaluating CC’s EI, including interoperability problems, data privacy and security issues, and the requirement for new legal frameworks. The study also sheds light on the opportunities and difficulties that CC poses for several economic sectors, such as government, healthcare, and banking.

Hypothesis 5

Perceived Security Risk (PSR) has a negative effect on attitude but a positive influence on the behavioral intention to use Sustainable Cloud-Based Collaborative Systems (SCBCS).

  1. (f)

    Facilitating Conditions.

The degree of accessibility to the resources and equipment needed to finish a task is referred to as a “facilitating condition”33. When academic staff members have access to enough resources, they may do their work efficiently. It has been confirmed that facilitating conditions are an important guide for the acceptance and application of SCBCS34. Everything that helps with the assessment method’s execution, such as organizational, technical, or administrative support, knowledge, and other resources, is considered a facilitating circumstance28.

Hypothesis 6

Facilitating Conditions (FC) have a positive influence on the behavioral intention to use Sustainable Cloud-Based Collaborative Systems (SCBCS).

  1. (g)

    Perceived Behaviroal control (PBC).

Perceived behavioral control is a person’s belief in their own ability to exert external control over a particular behavior35. Therefore, it is hypothesized that BI to employ SCBCS is influenced by perceived behavioral control. The following proposition is proposed as a result:

Hypothesis 7

Perceived Behavioral Control (PBC) has a positive influence on the behavioral intention to use Sustainable Cloud-Based Collaborative Systems (SCBCS).

  1. (h)

    Behavioral Intention(BI).

According to one definition, BI is a gauge of how strongly someone wants to carry out a specific action36. Venkatesh33 asserts that behavioral purpose significantly affects how people use technology. Consequently, the following theory is put forth:

Hypothesis 8

Behavioral intention (BI) positively influences the actual use of Sustainable Cloud-Based Collaborative Systems (SCBCS).

Moderating variables

(a) Gender.

Men are generally more task-oriented than women, according to the UTAUT paradigm37. The authors then suggest that men are more affected by performance expectations than women are. However, gender did not appear to have any moderating effects on the role of performance expectancy on BI, according to Venkatesh et al. findings explaining consumers’ adoption of mobile Internet37. In light of our model, we put up the following theory which is illustrated in Table 1.

Table 1 Hypotheses formulated on the basis of influential Determinants.
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Data used and methodologies

Data collection

The aim of this study is to look into Jordanian universities’ BIs regarding the usage of proposed SCBCS. The degree to which key elements (attitude, SI, PBC, EE and FC) can account for BI to utilize SCBCS in Jordanian universities has been investigated by a number of research hypotheses. This study employed purposive sampling to select participants who are most relevant to the research objective—specifically, academics in Jordanian universities who are likely to use sustainable cloud-based collaborative systems (SCBCS). The target population included faculty members from 23 Jordanian universities (12 private and 11 public), as they represent the primary users of SCBCS in higher education settings. The selection criteria were based on academic roles, ensuring the inclusion of individuals with direct involvement in teaching and technological adoption. The final sample consisted of 500 academicians, including 40 lecturers, 168 assistant professors, 164 associate professors, and 88 full professors, reflecting a diverse academic background. The study utilized a self-administered questionnaire distributed online, and achieved an 88% response rate, with 500 valid responses out of 568 distributed questionnaires. By focusing on academics who are actively engaged in higher education and have varying levels of experience and academic ranks, the purposive sampling approach ensured the collection of data from informed respondents capable of providing meaningful insights into behavioral intentions toward SCBCS adoption. Male respondents made up 83.6% of the sample, while female respondents made up 16.4%. 40% of the sample consisted of respondents, most of whom were between the ages of 40 and 49. In the meantime, 35.2% of those surveyed have five to nine years of experience. Specifically, the survey was distributed online using institutional mailing lists and academic communication channels within 23 Jordanian universities (12 private and 11 public). Potential participants were contacted via email with an explanation of the study’s purpose and a link to the self-administered questionnaire. To ensure the relevance of responses, we targeted faculty members who were likely to have exposure to or experience with digital tools, including cloud-based platforms. Participants’ familiarity with information management was inferred based on their academic roles and disciplines, particularly those in fields related to computer science, information systems, education technology, and business administration. This alignment was clarified through a screening question in the survey asking respondents to indicate their area of expertise or teaching. Table 2 denotes the profile of this collected data.

Table 2 Respondents profile.
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Ethical considerations and measurements

All methods were carried out in accordance with relevant guidelines and regulations. The experimental protocols were approved by the Ethics Committee of the School of Information Science and Technology, National University of Malaysia, Selangor. Informed consent was obtained from all participants and/or their legal guardian(s) prior to participation in the study. Participants were informed of their right to withdraw from the study at any time without any negative consequences.

The majority of the measures were modified from earlier studies. Table 3 provides a summary of the demographic factors, means, medians, standard deviations, skewness, and percentage of missing values for each item.

Table 3 Overview of constructed measurements.
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Data analysis with OOA and cloud

OOA method

In this study, two phases of OOA approaches are used. Initially, Materialized view has been adopted to efficiently fetch the user queries followed by web based technology with cloud is utilized. In essence, the materialized view (MV) is a potent DB-object approach that is used to cache and retrieve the necessary data from different broad database Table38. Therefore, the query results are physically included in the MV. By lowering the cost of query operations, it is designed to enhance the query results of traditional views in relational databases. Taking advantage of MV capabilities to integrate with the OOA context is one of the primary goals of the current study.In order to significantly improve the heterogeneous RDB fetching process and efficiently speed up the decision support service during online importing inquiries, this work adapts and develops an effective OOA approach in data caching termed MV. OOA offers a conceptual and methodological foundation for structuring the model, especially in integrating behavioral and system design factors.

The first usage of MV, a potent database object, is as a physical structure for data caching and storage. By lowering the cost of query processing, it is utilized to increase the data access time when gathering the intermediary results. Utilizing the MV’s capabilities for caching and gathering the necessary data tables that can be accessed remotely via a different set of queries on other servers and/or users is the primary aspect of this adaptation39. In the meantime, it offers a method that can expedite the analytical procedure for query extraction while gathering interim results40. One further potent aspect of MV is that it keeps the imported data in a physical object, something that the traditional normal-view is unable to41. The resultant data is periodically stored by MV in a set of tables. It provides the measurable foundation for testing the hypotheses and validating the constructs statistically.

Measurement Variables are the specific indicators or items used to operationalize the constructs in your research model—for example, constructs like Behavioral Intention (BI), Social Influence (SI), or Perceived Behavioral Control (PBC). Each construct is measured by a set of variables or survey items (often using Likert scales). These variables are crucial because: They translate abstract theoretical concepts (like attitude or facilitating conditions) into measurable data. They enable quantitative analysis through statistical methods (e.g., exploratory and confirmatory factor analysis), which validates whether the variables accurately represent the constructs. T hey help establish the reliability and validity of the constructs in the model, ensuring the model is robust and empirically sound. By carefully designing and selecting MV, the model can more precisely predict behavioral intention and usage of SCBCS.

Object-Oriented Analysis is a methodological approach used in system design that helps break down the system (in this case, the SCBCS adoption process) into interacting components (objects) based on their properties and behaviors. Its role in the research model includes: Providing a structured framework to conceptualize complex interactions between constructs, such as how social influence or facilitating conditions ‘interact’ within the system. Supporting the integration of technical and behavioral components by modeling both user behaviors and system features as objects, thus linking the theoretical model with practical system design considerations. Allowing for the incorporation of system design factors into the acceptance model, thereby extending traditional frameworks (like UTAUT) with new elements relevant to sustainable cloud systems.

Cloud based web application

Figure 2 shows the architecture of the cloud based solutions for the collaborative systems. It consists of three phases such as input data from devices or database is processed with MV based OOA concept, second phase have cloud structure such as data storage, API server and analytic tools etc.,. The web services having the increased computation capabilities are employed in the cloud. Third phase is the dashboard where the decision algorithms of UTAUT based solutions are employed as software applications.

Fig. 2
figure 2

Cloud-Based Web Application Architecture Integrated with the Extended UTAUT Model.

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The idea behind adopting CC is that end consumers may use IT technology based on abstract concepts without having a thorough grasp and expertise of the same technology, just as the average user can operate a car without being a mechanic. Therefore, CC discusses the distribution of various computational resources as services, allowing end users to rent a variety of IT programs as services rather than purchasing them as goods and setting them up on their PCs. Setting up database services was difficult a few years ago, but more and more big businesses are realizing that CC is essential to their operations.Since CC integrates and makes use of cutting-edge hardware advancements to minimize the operating costs of the newly developed software at any encountered organization, it may be the most intrusive and revolutionary discovery of this era42. Due to the implementation of OOA concepts the cloud services efficiency is improved.

The theoretical underpinning of our work is UTAUT2, which helps us comprehend and forecast users’ behavioral intents toward using Sustainable Cloud-Based Collaborative Systems (SCBCS). The perceptions and attitudes of users that influence adoption decisions are represented by the UTAUT2 constructs of performance expectancy, effort expectancy, social influence, enabling conditions, and perceived behavioral control. Conversely, the functional capabilities and technical infrastructure that allow the SCBCS to work efficiently are embodied in the cloud-based system architecture. We use system design approaches like Object-Oriented Analysis (OOA) to bridge the gap between these two domains. OOA enables us to describe system components and their interactions as “objects” that correlate to or impact the behavioral constructs revealed in UTAUT2. The availability and usability of technological resources and assistance integrated into the cloud architecture, for instance, are directly related to the facilitating conditions in UTAUT2. In a similar vein, system features that improve user productivity and collaboration are linked to performance expectations. Our study model shows how technical architecture facilitates and supports the behavioral intentions indicated by UTAUT2 by clearly mapping these constructs to system components and capabilities.

Extension of UTAUT with new constructs and system design integration

  1. (a)

    Performance trust and perceived security risk as theoretical extensions.

Performance expectancy, effort expectancy, social influence, and facilitating conditions are the four main factors that determine behavioral intention and use behavior in the original UTAUT paradigm. Although it is useful for simulating user acceptability, it falls short in addressing issues unique to cloud computing environments, especially those pertaining to security and trust, two important aspects that significantly impact adoption choices in digital and collaborative platforms.

  • Performance Trust (PT): Users’ confidence that the cloud-based system will operate dependably, produce the desired outcomes, and sustain service quality over time is captured by PT. The expanded model takes into consideration users’ trust in the system’s ability to function well by incorporating PT. This is especially important in academic settings where system failure can have a direct impact on the continuation of teaching and research.

  • Perceived Security Risk (PSR): PSR highlights worries about information breaches, illegal access, and data privacy related to cloud adoption. By adding this construct, the model is able to account for users’ concerns regarding the inherent vulnerabilities in cloud infrastructure, particularly in organizations with weak data protection procedures. Its twin function as a positive indirect driver of BI and a negative predictor of attitude contributes to a more nuanced understanding of how risk perception affects user intention.

Novel constructs of PT and PSR:

Perceived sustainability relevance (PSR)

In Jordan, where environmental issues and resource limitations are becoming more urgent, decisions about technology adoption are frequently impacted by how well a system complies with sustainability principles (such as digital equity, environmental stewardship, and energy efficiency).

  • PSR measures the user’s perception that SCBCS use promotes sustainability, which affects behavioral intention in ways that go beyond more conventional concepts like effort or performance expectancy.

  • It is in line with the SDGs that Jordan’s Ministry of Higher Education has approved and represents cultural and national sustainability concerns.

Policy trust (PT)

Adoption is greatly influenced in many developing nations by trust in institutional policies and government digital efforts. In Jordan, people’s and organizations’ readiness to adopt cloud technology is influenced by perceived fairness, data privacy guarantees, and clear policies.

  • PT gauges support for regional and national regulations governing cloud computing in education.

  • It captures contextual trust dependencies that are not fully represented by classic UTAUT elements, such as enabling conditions.

  • The model provides a more thorough and practical behavioral framework for SCBCS adoption in higher education by incorporating PT and PSR, which better capture context-specific user concerns in cloud environments.

  1. (b)

    Object-Oriented analysis (OOA) as a bridge between behavioral modeling and system design.

Although UTAUT and other behavioral intention models explain why users decide to use a system, they don’t address how system components are made to meet user expectations and needs. The study incorporates Object-Oriented Analysis (OOA) into the research paradigm in order to close this gap. OOA facilitates the mapping and conception of system elements, allowing for the creation of cloud-based solutions that are:

  • The extended UTAUT model’s behavioral insights serve as the foundation for the user-centered approach.

  • Functionally aligned: aspects of the system, such as security features and performance dependability, should represent what users truly value.

  • Adaptable to academic procedures, which enables improved system integration into administrative, research, and teaching workflows.

Even while UTAUT is still a commonly used framework for comprehending technology adoption, its applicability to dynamic, real-time systems such as sustainable cloud-based collaboration systems (SCBCS) is noticeably limited. The granularity required to model changing user behaviors, technical interactions, and system context is frequently lacking due to its dependence on static, survey-based data. Originally created for software system modeling, object-oriented analysis (OOA) provides a supplementary method by making it possible to:

  • Dynamic, modular depictions of people, things, and procedures,

  • Monitoring of interactions between various system components (such as cloud, mobile, and IoT).

  • Modeling of decision-making processes and real-time behavior patterns that change as the user experience does.

Beyond what self-reported perceptions can provide, researchers may track feedback loops, simulate and display user interactions, and produce realistic behavioral insights by combining OOA with UTAUT. The transition from perception-based acceptance models to behavior-informed system design is made possible by this hybrid approach, which is particularly important in intricate sociotechnical systems such as SCBCS.

OOA ensures that SCBCS platforms are both technically feasible and aesthetically pleasing by acting as a translation layer between system architectural design and user behavior insights. This all-encompassing strategy improves the model’s usefulness for higher education IT developers and decision-makers.

Results and discussions

An examination of the measurement and structural equation model for examining direct impacts is offered along with empirical findings. Group comparisons were used to examine moderating variables for indirect effects.

Analysis of UTAUT measurement model

PLS-SEM (partial least squares structural equation modeling) version 3.0 was used to analyze the data. For small-scale exploratory study, SmartPLS is helpful. Numerous business research domains have made use of partial least squares equation modeling, or SEM43. While the structured model represented the connections (paths) among the research components, the measurement model provided information regarding the validity and reliability of the scales44.

The measurement model’s fit was examined using confirmatory factor analysis (CFA). We began by examining the measuring setup of Venkatesh et al.‘s expanded UTAUT33. Table 4 shows the Squared Multiple Correlation (SMC), significance, standard errors (SE), and normalized factor loadings.

Table 4 Factor loadings, significance, SE and SMC of all the constructs.
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Three different approaches The uniqueness of the research’s model construct is assessed using the Heterotrait-Monotrait (HTMT) ratio, cross-loadings, and the Fornell and Larker criterion. Table 5 shows the Fornell-Larker criterion. Table 6 shows that the square root of the average variance extracted (AVE) for each latent construct is higher than its strongest correlation with other constructs.

Table 5 Fornell-Larker criterion
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Table 6 Measurement models.
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A specific piece of each construct should have a higher loading on its build than on other constructs in terms of cross-loading. Each item’s factor loading value likewise meets the criteria of (0.71), as indicated in Table 5. The composite dependability values, as displayed in Table 6, have an average extracted value of greater than 50% and vary from 0.729 to 0.919. A common metric for demonstrating the construct’s convergent validity is AVE. More than half of the variation in its indicators can be explained by the concept if its AVE value is 0.52 or higher44.

Fig. 3
figure 3

Structural result analysis.

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Analysis of UTAUT structural model- hypothesis

Path coefficients (β), the cross-validation (CV) community duplication indices, and coefficients of determinator (R2) values are crucial metrics for evaluating the study’s reflective structural model. Cross-validation, the blind folding-based validated duplication measure, and the statistically significant nature of the route coefficients are used to evaluate the structural model’s validity. Each construct must have a positive index44.The findings of the structural model study are shown in Fig. 3, together with the R2 value of the determination coefficient and the projected path coefficients (β). The variance in endogenous constructs described by exogenous constructions is explained by R2. Additionally, an HTMT value above 0.90 indicates a lack of discriminant validity, according to Henseler et al.45. According to Fig. 3, the research model explains 0.127 of the differences in user behavior and 0.478 of the variances in BI. Table 7 demonstrates the hypothesis outcomes which are tested.

Table 7 Results of structural models in term of hypothesis results.
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Discussions

The results of the investigated hypotheses are stated in Table 7. Six hypothesis H1, H2, H3, H6, H7, and H8 show a significant favorable impact, according to the results, however H4 and H5 are ‘NOT SUPPORTED’. The outcome for BI are therefore β = 0.412, T_Value = 3.026, and p < 0.05 for H1. This outcome denotes the proposition’s support. Consequently, attitude has an beneficial effect on BI to adopt SCBCS, and H1 is supported. The EE results for H2 are β = 0.382, T_Value = 2.291, and p < 0.05. This outcome denotes the proposition’s support.

As a result, H2 is supported and BI to utilize SCBCS is not positively impacted by the subjective norm. At the significant level of p < 0.05, the route coefficient for PBC, H3, is (β = 0.162) with a value of (T_Value = 3.453). This result means that the hypothesis has been accepted. We can conclude that SI has a positive impact on BI to utilize SCBCS. As a result, H4’s perceived trust findings are (β = 0.014), T_Value(0.432), and p > 0.05. This outcome denotes the proposition’s rejection. Consequently, PT has no beneficial effect on BI to utilize SCBCS, and H4 is not supported. For H5, the perceived security risk values are (β = 0.013), p > 0.05, and T_Value(0.726). This outcome denotes the proposition’s rejection. As a consequence, the PSR has no beneficial effect on BI to utilize SCBCS, and H5 is not supported. Based on reduced path coefficient β value, minimum T values and P value greater than the threshold 0.05, these two hyptohese H4 and H5 are not supported. At a significant threshold of p < 0.05, H6’s path coefficient for the facilitating state is (β = 0.452) with a value of (T_Value = 2.008). This result means that the hypothesis has been accepted. One could draw the conclusion that FC has a favorable impact on BI to utilize SCBCS. At aimportant level of (p < 0.05), the path coefficient for PCB in H7 is (β = 0.352) with a value of (T_Value = 3.923). This result means that the hypothesis has been accepted. One could draw the conclusion that PB has a helpful impact on BI to utilize SCBCS. At a significant threshold of p < 0.05, the BI pathcoefficient, or H8, is (β = 0.463) with a T_Valueof 9.283.

This result denotes that the hypothesis has been accepted. One could conclude that BI has a beneficial impact on cloud service use.From this results, it has been observed that the attitude, BI towards the use of cloud services is significantly influenced by effort expectancy, social influence, perceived behavior control and facilidating conditions. Perceived trust and perceived security risk did not show the influence on BI and attitude to use the cloud services. Therefore, hypothesis 4 and 5 are rejected. These factors show the direct effects on the cloud services.

The moderating factors such as gender, age, academic, higher education level and IT experience. The first moderating variable in the results is gender. Accordingly, Performance Expectancy considerably affects Attitude to Use for both men and women, with the effect being much greater for women. Hypothesis H9a must be disproved as our original hypothesis projected a greater effect among men. Another finding indicates that men and women experience different effects from effort expectation on attitude toward use, with males experiencing a stronger influence. It is necessary to reject hypothesis H9b as it projected a greater effect among women. Women’s attitudes toward using cloud services are significantly influenced by perceived trust, suggesting that women view cloud services as more reliable, which supports hypothesis H9c.The second moderating variable under investigation is age. Thus, among older respondents, Perceived Trust significantly influences Attitude toward Use. Hypothesis H10a is supported by this. An additional finding, though not predicted, is that the influence of Attitude toward Use on BI differs for both young and old age groups, with the effect being greater for the younger respondents, and the effect of FC on BI differs for the older respondents. Hypothesis H10b, H10c, H11a, H11b and H11c must be discarded because there is no discernible difference between young and senior respondents with experience for the remainder of the construct.

Implications

  1. (a)

    Theoretical Implications.

The theoretical development of technology adoption research is advanced in a number of significant ways by this study, especially when it comes to sustainable cloud-based systems in higher education.

In order to promote sustainable system use, it first expands on the Unified Theory of Acceptance and Use of Technology (UTAUT) by adding Attitude, Perceived Behavioral Control (PBC), and Behavioral Intention (BI). Recent research has highlighted UTAUT’s limits in addressing psychological and normative issues beyond its core dimensions, despite the fact that it has demonstrated efficacy in predicting adoption behavior16. Through the incorporation of concepts frequently found in the Technology Acceptance Model 37 and the Theory of Planned Behavior, this study provides a more sophisticated explanatory model appropriate for examining intention creation in intricate socio-technical contexts.

Second, as a supplementary lens to UTAUT, the study creatively integrates Object-Oriented Analysis (OOA). Although system design frameworks and user behavior theories have been the focus of previous study on their own17,18, little is known about how they function together. By using OOA to describe task dependencies, real-time user interactions, and system feedback loops, our work closes that gap and brings behavioral constructs and technological design into alignment. A design-aware behavioral model that is better suited to iterative system development and simulation is produced by this interdisciplinary approach.

Third, a recognized shortcoming in the geographical and situational generalizability of technology adoption models is addressed by contextualizing the expanded UTAUT model within Jordanian higher education. The majority of previous UTAUT applications have been for developed countries (e.g., the United States, the United Kingdom, Qatar and Korea), where institutional support, digital literacy, and infrastructure readiness are very different from those in developing countries22,23. This study shows that conventional models require adaptation by applying the model to a resource-constrained, policy-sensitive environment. It also identifies sustainability-based and trust-based constructs, like Perceived Sustainability Relevance (PSR) and Policy Trust (PT), as context-specific enablers.

Lastly, the study finds high-impact variables, such as attitude, facilitating conditions, and performance expectancy, and empirically confirms their impact in the context of SCBCS. Through the ranking of variable relevance and the provision of a roadmap for focused interventions in technology deployment techniques, this insight aids in the improvement of UTAUT and similar models. Additionally, it establishes the foundation for future extensions that integrate policy, cultural, and environmental aspects into models of mainstream acceptance, a growing emphasis in the literature on sustainable educational technology25.

  1. (b)

    Practical Implications.

Practically speaking, university administrators, legislators, and designers of instructional technology can all benefit from the study’s conclusions. The findings point to important elements that affect faculty members’ intentions to embrace Sustainable Cloud-Based Collaborative Systems (SCBCS), including user expectations, existing support infrastructure, and perceived utility. To enhance adoption outcomes, institutions can utilize this information to make targeted investments in faculty training, technical assistance, and IT infrastructure.

Additionally, the study encourages resource-optimized and energy-efficient cloud solutions, supporting national and institutional efforts to line with the Sustainable Development Goals (SDGs). Adoption of SCBCS supports ecologically sustainable teaching methods in addition to technical upgrading.

Lastly, this research offers a guide for creating user-centric cloud apps that satisfy the operational and pedagogical requirements of academic staff by incorporating system design approaches (via OOA). More adoption and long-term usability of educational technology are ensured by this alignment of system capabilities with user expectations.

  1. (c)

    Limitations.

The generalizability and internal validity of the study’s conclusions may be impacted by a number of respondent profile restrictions, even with efforts to guarantee a representative sample. First, there is a disproportionately low number of early-career professionals (e.g., under 30 or within 5 years after PhD graduation), with the age distribution tilted toward academics in their mid- to late-career. Given that technology attitudes frequently differ greatly by generational familiarity and digital confidence, this mismatch may skew the outcomes of behavioral intention (Venkatesh et al., 20129). Second, there is uncertainty in the distribution of educational attainment levels, especially when it comes to differentiating between respondents who have professional or master’s degrees and those who have terminal academic degrees (such as a PhD). This reduces the accuracy of subgroup analysis according to research inclination or academic seniority.

Third, measurement validity is called into question, especially in multigroup comparisons, due to possible discrepancies in the way variables such as years of IT experience or institutional function were recorded and computed. All of these characteristics make it difficult for the study to adequately capture diversity across institutional hierarchies and user types. To address underrepresented academic groups, future research should use quota-based methods or stratified random sampling.

Conclusion

Our study was primarily designed to answer the question of whether cloud usage has moderating effects. As moderating factors, Hereby, gender, age, academic background, IT experience and higher education were considered. In order to gain a more comprehensive and nuanced knowledge of the moderating effects, we also included BI to use cloud services as an extra variable in the research model. Both direct (structural pathways) and indirect (moderating) effects were the subject of hypotheses. We extended UTAUT with OOA and added new components together with integrated BI towards Use to create and empirically validate a new research model in order to address these problems. Additionally, the data show how each major predictor contributed to the BI to utilize SCBCS. Returning to the performance expectancy predictor, the standardized coefficient’s largest beta value is 0.463. This variable aids in the explanation of behavior intentions with a quality management system-based sustainable cloud. Facilitating condition (0.452) and Attitude (0.412) come next. Notwithstanding the insightful information this study provided, a number of limitations must be noted. First, the study was carried out in particular cultural and geographic setting Jordanian universities which would have limited the findings’ applicability to other areas or educational frameworks. Second, even though the study included a number of moderating factors, including age, gender, academic background, IT experience, and educational attainment, it might have missed other factors that could have had a significant impact, like digital infrastructure, institutional policy support, or cultural attitudes toward technology. By using a longitudinal strategy to evaluate behavioral intention changes over time and investigate the long-term effects of SCBCS adoption, future study could overcome these limitations. The findings’ generalizability might be enhanced by broadening the study to encompass various educational and cultural contexts. To further understand what motivates sustainable cloud adoption in education, future models might include examine other variables as corporate culture, perceived risk, or environmental factors. Lastly, qualitative techniques like focus groups and interviews could be employed to enhance quantitative results and provide deeper understanding of user attitudes and SCBCS experiences.