This study examines management students' perceptions of the key success factors associated with smart campus environments in the Gulf region and contributes to the emerging literature on technology-enabled higher education.
An exploratory sequential mixed-methods design was used in this study. In the qualitative research phase, relevant items were extracted in line with a proposed smart campus framework using inductive and deductive analyses. This was followed by a quantitative research phase to collect data from 215 students in the Gulf countries.
The results from an empirical exercise indicate that smart governance, smart interaction, smart environment and smart technology positively affect students' perceptions of success in higher education. However, students based in the Gulf countries did not perceive that smart infrastructure had a positive impact on students' perceptions of higher education success.
The findings of this study provide insights for policymakers and educational administrators to design effective strategies for smart campuses aligned with evolving market needs. The study also shows the growing importance of digital learning ecosystems in the post-pandemic era.
This study is among the first to empirically investigate management students' perceptions of smart campus success factors across multiple Gulf countries, offering a region-specific perspective on smart education.
1. Introduction
The adoption of digital technologies in higher education has accelerated significantly in recent years, driven by global policy agendas such as the United Nations Sustainable Development Goal 4, which emphasizes inclusive and equitable quality education for all. In response, institutions of higher learning have begun to offer information and communications technology (ICT)-driven business and management programs around the world. These institutions use a wide array of program delivery platforms. Traditional universities have transformed their campuses, and smart campuses have been established. A smart campus is a digitally connected, technology-enabled educational environment that leverages advanced applications to enhance learning, administration and student engagement. A smart campus aims to provide learners with a well-rounded, seamless experience through technology and enhance it more effectively. A smart campus, also sometimes called a next-generation campus, is an interface between smart homes and smart cities.
The rapid expansion of smart campuses received a further boost following several lockdowns during the COVID-19 pandemic. While the shift to online learning was dramatically accelerated by the COVID-19 pandemic, its effects have persisted, contributing to the sustained growth of remote and online education (Zoghbor & Demirci, 2025). The entry of Artificial Intelligence (AI) in education, the rise of smart learning methods and changes in students' preferences for online educational programs have further reinforced this trend. However, COVID-19 is not the only global crisis that has affected the region's education system. In the United Arab Emirates (UAE) and other Gulf countries, weather conditions such as heavy rains and sandstorms have also necessitated the adoption of remote learning, reinforcing the need for resilient and flexible educational infrastructures. The e-learning industry's market size, which reached approximately US$342 billion in 2024, is projected to grow significantly, surpassing US$700 billion by 2030 (IMARC Group, 2025).
Despite these developments, the effectiveness of smart campuses remains a subject of debate. Some studies highlight their potential to enhance accessibility, engagement and learning outcomes (Xu, Luo, & Zhang, 2025), while others argue that digital learning environments may not fully replicate the benefits of traditional face-to-face learning (Roberto & Johnson, 2019). This debate underscores the need to examine students' perceptions, particularly in regions where smart education is rapidly expanding.
The Gulf countries provide a particularly relevant setting for such an investigation. These countries have made significant investments in digital infrastructure and education reform, positioning themselves as emerging hubs for technology-driven learning (Anwar, Sohail, & Al Reyaysa, 2020; Sarea, Alhadrami, & Taufiq-Hail, 2021). Furthermore, the growing importance of management education and the expansion of online business programmes have increased the strategic relevance of understanding how students evaluate the quality and success of smart campus environments (Aparicio, Oliveira, Bacao, & Painho, 2019). However, empirical research on how students in this region perceive smart campus experiences, particularly within management education, remains limited.
Understanding students' perceptions of smart campus environments is therefore essential for both theory and practice. As higher education institutions continue to invest in digital transformation, there is a need to evaluate not only technological capabilities but also their effectiveness from the perspective of end users. This study addresses this need by providing a multidimensional, student-centered analysis of students' perceptions of higher education success factors in the Gulf region.
To address this gap, the study is guided by the following research question: What factors influence management students' perceptions of smart campus success in the Gulf region? Specifically, it examines the role of five constructs, namely, smart governance, smart infrastructure, smart interaction, smart environment and smart technology, which shape perceptions of higher education success. This study makes two main contributions. First, the study extends smart campus literature by offering a multidimensional, student-centered analysis of perceived success factors in the Gulf context. Second, it offers practical insights for educational institutions and policymakers seeking to design and implement effective smart-campus strategies that align with evolving student needs and market demands.
The remainder of the paper is organized as follows. The next section provides an overview of the research setting, followed by a review of the relevant literature and the development of hypotheses. The methodology and results are then presented, followed by a discussion of findings, implications and directions for future research.
2. Research setting
This study examines students' perceptions of smart campus experiences across countries in the Gulf Cooperation Council (GCC) region. The GCC comprises six member states - Kuwait, Bahrain, Qatar, the UAE, Oman and Saudi Arabia – that broadly share similar cultural, economic and institutional characteristics. The present study focuses on four of these countries: Saudi Arabia, the UAE, Bahrain and Kuwait. The rationale for selecting these countries is their advanced adoption of digital education initiatives, the presence of established e-learning institutions, active investment in smart campus infrastructure and demonstrated economic resilience.
Despite economic challenges stemming from oil price fluctuations and the disruptions caused by the COVID-19 pandemic, Gulf economies have demonstrated resilience through diversification strategies and investments in knowledge-based sectors. Within this context, higher education institutions have adapted by introducing flexible, technology-driven programme delivery formats.
Furthermore, the presence of multinational enterprises across the Gulf region has intensified the demand for graduates with globally relevant skills and digital competencies. This has encouraged policymakers and educational institutions to invest in smart education initiatives and expand access to online learning opportunities. As a result, smart campuses have emerged as a strategic response to both economic and technological shifts, positioning higher education institutions in the region to better align with future workforce requirements.
3. Literature review
This study is grounded in constructivist learning theory and the Technology Acceptance Model (TAM). Constructivist theory emphasizes the role of interaction and active engagement in knowledge construction (Vygotsky, 1978), while TAM explains how perceived usefulness and ease of use influence technology adoption (Davis, 1989). Together, these perspectives provide a theoretical foundation for understanding how the dimensions of smart campuses influence students' perceptions of higher education success.
In this study, the concept of “higher education success” is conceptualized from a student-centered perspective, reflecting students' overall evaluation of their learning experience within a smart campus environment. Rather than focusing solely on objective outcomes such as academic performance, success is defined by perceived learning effectiveness, satisfaction with the educational experience and the extent to which digital systems support engagement and learning. This approach is consistent with prior research, which emphasizes that in technology-enabled learning environments, perceived success is closely linked to user experience, engagement and satisfaction (DeLone & McLean, 2003; Davis, 1989).
The use of information technologies to deliver academic and professional training programs has been gaining widespread acceptance (Phutela & Dwivedi, 2020). Research across countries such as the United States, the United Kingdom and Australia shows that the integration of technologies such as learning management systems, AI and digital platforms enhances student engagement, supports personalized learning and improves educational accessibility (Xu et al., 2025; OECD, 2023).
3.1 Conceptualizing smart campus success
Given the multidimensional nature of smart campuses, their success cannot be explained by technology alone but must be understood through a combination of institutional, technological and experiential factors. In this study, higher education success is conceptualized from a student-centered perspective, reflecting students' perceived learning effectiveness, satisfaction and overall experience within smart campus environments (DeLone & McLean, 2003; Davis, 1989).
Prior research suggests that several key dimensions shape this perceived success. Firstly, governance plays a critical role by aligning institutional strategies, policies and stakeholder needs, thereby enabling effective implementation of digital initiatives (Krishnaswamy, Hossain, Kavigtha, & Nagaletchimee, 2019). Secondly, infrastructure provides the technological foundation necessary to deliver smart learning environments, although in highly digitalized contexts it may be perceived as a basic requirement rather than a differentiating factor (Deloitte, 2019).
Thirdly, interaction is a central determinant of learning effectiveness, as smart campuses facilitate learner–learner, learner–instructor and learner–content engagement through digital platforms, enhancing participation and satisfaction (Sandanayake, 2019; Jo, Park, Ji, Yang, & Lim, 2016). Fourth, the learning environment, including sustainability and adaptability, contributes to students' overall experience and engagement (Valks, Arkesteijn, Koutamanis, & den Heijer, 2021). Finally, technology serves as the enabling mechanism of smart campuses, with usability, accessibility and reliability influencing students' perceptions of value and effectiveness (Freestone & Mason, 2019).
Taken together, these dimensions provide a comprehensive framework for understanding how smart campus features influence students' perceptions of higher education success.
3.2 Digital learning and smart campus evolution
Digital learning encompasses a range of delivery formats, including online, virtual, distance and blended learning. Although these formats differ in terms of interaction and physical presence, they share a common reliance on digital technologies to facilitate learning. Research indicates that blended learning environments, which combine face-to-face and online components, tend to produce higher engagement and better learning outcomes than purely online formats (Sandanayake, 2019). This suggests that interaction and experiential elements remain critical even in technology-driven education.
The evolution from digital learning to smart campuses reflects a shift from isolated technological tools to integrated ecosystems. Smart campuses leverage advanced technologies such as cloud computing, big data analytics, Internet of Things and 5G networks to create intelligent, adaptive and context-aware learning environments (Hou, Ho, & Yau, 2023). These technologies enable real-time data processing, personalized learning pathways and seamless communication between stakeholders.
However, the literature also points to implementation challenges. Transitioning from traditional education systems to smart campuses requires significant organizational change, investment and alignment with local contexts (Hojeij, Baroudi, & Meda, 2023). In regions such as the Gulf countries, where education systems are rapidly evolving, the effectiveness of smart campus initiatives may depend on how well they address cultural, institutional and infrastructural factors.
3.3 Research model and hypotheses
Following a review of the literature, this study operationalizes the success of a smart campus into five constructs: smart governance, smart environment, smart interaction, smart infrastructure and smart technology. The study's framework is depicted in Figure 1.
3.3.1 Smart governance
Governance is a critical enabler of effective learning environments in higher education institutions. In the context of smart campuses, governance refers to the institutional structures, leadership practices and policy frameworks that guide the implementation and management of technology-enabled learning systems. Effective governance ensures alignment between institutional objectives, technological initiatives and stakeholder needs. From a constructivist perspective, learning is facilitated within environments that encourage participation, collaboration and engagement (Vygotsky, 1978). Governance plays a central role in shaping such environments by establishing policies and practices that either enable or constrain interaction and active involvement. Transparent, flexible and participatory governance structures can foster a sense of inclusion and trust among students, thereby enhancing their willingness to engage with smart campus systems (Malatji, 2017).
Conversely, weak leadership, rigid decision-making processes and bureaucratic constraints may hinder innovation and reduce user engagement, ultimately limiting the effectiveness of smart campus initiatives (Krishnaswamy et al., 2019). In the Gulf region, where institutional structures may vary in flexibility, governance is likely to play a particularly important role in shaping students' perceptions of educational quality and success. Accordingly, governance mechanisms that support participation, responsiveness and adaptability are expected to positively influence students' perceptions of higher education outcomes.
Smart governance positively influences students' perceptions of higher education success.
3.3.2 Smart infrastructure
The “smartness” of a campus is in part determined by the quality and robustness of its infrastructure. Smart infrastructure refers to the ICT-enabled systems that support the delivery and management of digital learning environments, including network connectivity, data storage and cybersecurity. These components form the technological backbone necessary for the effective functioning of smart campus initiatives. From an information systems perspective, system quality is a critical determinant of user satisfaction and system success (DeLone & McLean, 2003). Similarly, within the TAM, system functionality and ease of access contribute to perceived usefulness and ease of use, which in turn influence user perceptions and acceptance (Davis, 1989). In the context of smart campuses, a robust and reliable infrastructure enables seamless access to digital resources and learning platforms, thereby enhancing students' overall learning experience.
On the other hand, deficiencies in infrastructure–such as unstable connectivity, limited system capacity, or weak data security–can disrupt learning processes and reduce students' confidence in digital systems (Devkota, 2021). Prior research also highlights that smart campuses rely on integrated infrastructure systems, including communication networks, cloud solutions and digital platforms, to support effective teaching and learning (Deloitte, 2019). Accordingly, a high-quality infrastructure that ensures reliability, accessibility and security is expected to positively influence students' perceptions of success in higher education.
Smart infrastructure positively influences students' perceptions of higher education success.
3.3.3 Smart interaction
Interaction is a core component of effective learning environments. Moore (1989) identified three key forms of interaction: learner–learner, learner–instructor and learner–content. According to constructivist theory, learning occurs through active engagement and social interaction (Vygotsky, 1978).
Smart campuses enhance these interactions through digital platforms and collaborative tools, enabling both synchronous and asynchronous engagement. Prior research indicates that such interactions improve student engagement, cognitive development and satisfaction (Sandanayake, 2019; Jo et al., 2016). In digitally mediated environments, where physical presence is limited, effective interaction becomes particularly critical.
Accordingly, it is expected that smart interaction will positively influence students' perceptions of higher education success.
Smart interaction positively influences students' perceptions of higher education success.
3.3.4 Smart environment
A supportive learning environment plays a critical role in shaping educational outcomes. From a constructivist perspective, learning is influenced by the context in which it occurs, with adaptive and engaging environments enhancing student experience (Vygotsky, 1978).
In smart campuses, the environment includes both physical and digital elements, with increasing emphasis on sustainability and efficient resource management (Malatji, 2017; Valks et al., 2021). Such environments, along with mechanisms for incorporating student feedback, contribute to improved learning experiences (HESCA, 2017).
Accordingly, it is expected that a smart environment will positively influence students' perceptions of higher education success.
A smart environment positively influences students' perceptions of higher education success.
3.3.5 Smart technology
Smart campuses rely on advanced digital technologies to deliver and support learning processes. According to the TAM, users' perceptions of usefulness and ease of use are key determinants of technology adoption and satisfaction (Davis, 1989; Venkatesh & Davis, 2000).
In smart campus environments, user-friendly and reliable technologies enhance accessibility, flexibility and engagement. Features such as digital resources, interactive platforms and data-driven systems enable students to manage their learning more effectively. Prior research indicates that convenience, connectivity and control are critical factors shaping students' learning experiences (Freestone & Mason, 2019; Villegas-Ch, Palacios-Pacheco, & Luján-Mora, 2019). Accordingly, technologies that enhance accessibility, are reliable and are easy to use are likely to safeguard students' perceptions of success in higher education.
Smart technology positively influences students' perceptions of higher education success.
4. Research method
4.1 Research design
This study adopts an exploratory sequential mixed-methods design, which integrates qualitative and quantitative approaches to develop and validate measurement constructs (Creswell, 2014). This design is particularly appropriate given the exploratory nature of the research and the limited empirical evidence on perceptions of smart campuses in the Gulf region.
The research was undertaken in two stages. In the first stage, a qualitative approach was employed to identify and refine measurement items related to smart campus constructs. During the second stage, a quantitative survey was administered to test the proposed research model and hypotheses.
4.2 Scale development and validation
The scale development process followed established methodological guidelines (Hinkin, 1995) and involved both deductive and inductive approaches. In the deductive stage, measurement items were generated based on an extensive review of the literature on smart campuses, digital learning and technology-enabled education. These items were aligned with the five constructs identified in the conceptual framework: smart governance, smart infrastructure, smart interaction, smart environment and smart technology.
In the inductive stage, the initial pool of items was refined through a virtual focus group discussion involving twelve management students. While random selection is not typically feasible in qualitative focus group research, participants were selected to ensure diversity in academic level and experience, thereby capturing a broad range of perspectives. This approach enabled the incorporation of user perspectives and ensured contextual relevance. The use of virtual focus groups was also consistent with emerging qualitative research practices in digitally mediated environments (Lin & Hsieh, 2011; Keemink, Sharp, Dargan, & Forder, 2022).
The focus group discussions explored students' experiences with smart campus environments, including perceptions of digital learning platforms, interaction with instructors and peers, accessibility of technological resources and overall learning effectiveness. Thematic analysis of the discussions identified key dimensions related to governance, interaction, environment and technology, which informed the refinement and wording of the measurement items.
The initial item pool consisted of 47 items. The themes identified from the focus group discussions aligned with the proposed conceptual framework and enhanced the clarity and contextual relevance of the measurement items. The scale purification process was conducted in two stages. First, a theoretical purification was undertaken through expert evaluation to assess the content validity, clarity and representativeness of the items. Second, empirical purification was performed using exploratory factor analysis (EFA), where items with low factor loadings or cross-loadings were removed. This two-step process ensured both conceptual rigor and statistical validity of the measurement scales. Five academic experts with experience in scale development evaluated the items for clarity, relevance and representativeness. Based on their feedback, 22 items were removed or revised, resulting in a final set of 25 measurement items. All constructs were measured using a Likert-type scale (1 = strongly disagree to 5 = strongly agree).
4.3 Data collection, sampling method and survey administration
Survey data for this study were collected from E-learning institutions licensed by the Ministry of Education in the respective countries – UAE, Saudi Arabia, Bahrain and Kuwait. The population for this study included undergraduate and graduate students pursuing management studies at those institutions. The study was conducted in accordance with established ethical standards for research involving human participants. Respondents were informed of the study's purpose prior to participation and provided informed consent. Participation was voluntary and respondents were free to withdraw at any stage without consequence. All responses were treated confidentially. Formal ethics committee approval was not required for this study because it involved a voluntary, anonymous survey of adult participants and did not involve sensitive personal data or vulnerable groups. To ensure confidentiality, responses were anonymized and analyzed in aggregate form. All data were used exclusively for academic research purposes.
This study used non-probability convenience sampling, which enabled data collection from participants available at the time. A large number of similar studies have used such a method of data collection. The survey instrument was distributed online to the target students by sharing the link with the heads of departments at E-learning institutions. Filling out the questionnaire was voluntary.
A total of 245 responses were collected from student respondents, of which 215 were usable. Of the total respondents, 67% were male students and the remaining 33% were female students. The majority of respondents (57%) were from the UAE, 17% from Saudi Arabia, 14% from Bahrain and 12% from Kuwait. This distribution reflects differences in response rates across countries, with data collection yielding more responses from the UAE. To address this potential bias, a sensitivity analysis excluding the UAE was conducted. The results remained consistent, suggesting that the overrepresentation did not significantly affect the overall conclusions.
5. Analysis
5.1 Measurement and data analysis
Data were analyzed using IBM SPSS Statistics (Statistical Package for the Social Sciences), Version 30. As a preliminary step, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.713, exceeding the recommended threshold of 0.60, indicating that the data were appropriate for factor analysis (Hair, Black, Babin, & Anderson, 2006). Bartlett's Test of Sphericity was significant (p < 0.05), confirming that the data were appropriate for factor analysis (Hair et al., 2006).
EFA was conducted using principal axis factoring with oblique rotation to identify the underlying factor structure. Factors were retained based on eigenvalues greater than 1.0 and examination of the scree plot. Items with factor loadings below 0.50 or cross-loadings were removed. The resulting factor structure was consistent with the proposed conceptual framework.
Subsequently, Confirmatory Factor Analysis (CFA) was conducted to evaluate the measurement model. The sample size (n = 215) was deemed adequate for CFA, consistent with recommended guidelines for structural equation modeling (Hair et al., 2006). The measurement model was refined iteratively, and two items with low factor loadings were removed to improve model fit. Specifically, one item each from smart governance and smart interaction was excluded because their loadings were below the acceptable threshold.
Reliability and validity were assessed using composite reliability (CR), average variance extracted (AVE) and standardized factor loadings (see Table 1). Convergent validity was established when factor loadings exceeded 0.50, CR exceeded 0.70 and AVE values exceeded 0.50 (Hair et al., 2006; Fornell & Larcker, 1981). Discriminant validity was assessed using the Fornell–Larcker criterion by comparing the square root of AVE with inter-construct correlations.
Constructs, factor loadings, Cronbach's alpha, CR AVE
| Construct and items | Factor loadings | Means | S.D | Cronbach α | Composite reliability | AVE |
|---|---|---|---|---|---|---|
| Smart Governance | 3.899 | 0.984 | 0.689 | 0.861 | 0.557 | |
| GOV1 | 0.679 | |||||
| GOV2 | 0.745 | |||||
| GOV3 | 0.409 | |||||
| GOV4 | 0.675 | |||||
| GOV5 | 0.849 | |||||
| GOV6 | 0.769 | |||||
| Smart Infrastructure | 4.163 | 0.843 | 0.814 | 0.860 | 0.673 | |
| INFRA1 | 0.841 | |||||
| INFRA2 | 0.770 | |||||
| INFRA3 | 0.848 | |||||
| Smart Interaction | 4.019 | 0.872 | 0.756 | 0.807 | 0.559 | |
| INT1 | 0.533 | |||||
| INT2 | 0.776 | |||||
| INT3 | 0.723 | |||||
| INT4 | 0.668 | |||||
| INT5 | 0.665 | |||||
| INT6 | 0.313 | |||||
| Smart environment | 4.020 | 0.953 | 0.748 | 0.879 | 0.710 | |
| ENV1 | 0.742 | |||||
| ENV2 | 0.903 | |||||
| ENV3 | 0.874 | |||||
| Smart Technology | ||||||
| TECH1 | 0.878 | 3.733 | 1.020 | 0.878 | 0.915 | 0.783 |
| TECH2 | 0.903 | |||||
| TECH3 | 0.874 | |||||
| Smart Education | 3.885 | 0.993 | 0.765 | 0.874 | 0.645 | |
| HES1 | 0.842 | |||||
| HES2 | 0.725 | |||||
| HES3 | 0.799 | |||||
| HES4 | 0.655 |
Note(s): Cronbach's alpha >0.7; CR > 0.70 (Chin, 1998); AVE >0.50 (Fornell & Larcker, 1981). All inter-construct correlations were significant at the 0.01 level (2-tailed)
Model fit was evaluated using multiple indices, including the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the comparative fit index (CFI) and the root mean square error of approximation (RMSEA). Acceptable model fit was indicated by GFI, AGFI and CFI values above 0.90 and RMSEA values below 0.08 (Hu & Bentler, 1999). The measurement model demonstrated an acceptable fit to the data (χ2/df = 3.6, CFI = 0.94, RMSEA = 0.07), meeting the recommended thresholds.
Finally, common method bias was assessed using Harman's single-factor test. The results indicated that no single factor accounted for the majority of variance, suggesting that common method bias was not a significant concern.
5.2 Hypotheses testing
To test the proposed hypotheses, regression analysis was conducted to examine the proposed relationships. Although the hypotheses are directional, two-tailed tests were employed as a conservative approach to allow for both positive and negative relationships.
Standardized regression coefficients (β), standard errors, t-values and p-values were used to evaluate the significance and strength of relationships. Hypotheses were considered supported if the relationships were statistically significant at conventional levels (p < 0.05). The regression results are presented in Table 2. These results clearly indicate that all of the hypothesized relationships, except for the effect of smart infrastructure on students' perceptions of higher education success, were validated. The next section provides a discussion and implications of these results.
Regression results
| Path | β | SE | t-value | p-value | Decision |
|---|---|---|---|---|---|
| Smart governance (SMGOV) → Higher education success (HES) | 0.650 | 0.096 | 6.770 | 0.000 | Supported |
| Smart infrastructure (SMINFRA) → HES | 0.093 | 0.083 | 1.124 | 0.262 | Not supported |
| Smart interaction (SMINTER) → HES | 0.271 | 0.096 | 2.821 | 0.005 | Supported |
| Smart environment (SMENV) → HES | 0.236 | 0.085 | 2.778 | 0.006 | Supported |
| Smart technology (SMTECH) → HES | 0.133 | 0.065 | 2.066 | 0.040 | Supported |
6. Discussion
This study examined management students' perceptions of key success factors associated with smart campuses in the Gulf region. The findings provide important insights into how different dimensions of the smart campus environment influence perceived higher education success. The results indicate that smart interaction, smart governance, smart environment and smart technology have significant positive effects on students' perceptions of success. However, smart infrastructure does not exhibit a significant impact. These findings were largely consistent with comparable research conducted in other countries (Valks et al., 2021). The findings of the present study contribute to the growing body of literature on digital education by highlighting the relative importance of experiential and institutional factors over purely technological foundations.
6.1 Interpretation of key findings
The significant effect of smart governance emphasizes the importance of institutional structures and leadership in shaping student experiences. This finding is consistent with prior research suggesting that effective governance facilitates alignment between technological initiatives and stakeholder expectations (Krishnaswamy et al., 2019). From a student perspective, governance mechanisms that promote responsiveness, transparency and flexibility enhance trust in the institution and improve perceptions of educational quality. This suggests that digital transformation in higher education is not only a technological endeavor but also an organizational one.
The positive influence of smart interaction underscores the critical role of engagement in technology-enabled learning environments. From a constructivist perspective, learning is an active, socially mediated process in which knowledge is constructed through interaction and collaboration (Vygotsky, 1978). The present findings suggest that smart campuses that facilitate these forms of interaction can enhance student satisfaction and perceived success. This is particularly relevant in digital learning environments, where the lack of physical presence may otherwise constrain engagement and reduce opportunities for collaborative learning.
Similarly, the significance of smart technology highlights the critical role of technological design in shaping educational outcomes. In line with the TAM, technologies that are perceived as useful and easy to use are more likely to enhance users' attitudes and learning experiences (Davis, 1989; Venkatesh & Davis, 2000). The findings suggest that the convenience, accessibility and reliability of digital tools are key determinants of students' perceived success in smart campus environments. These attributes contribute to greater technology acceptance and sustained engagement, particularly in digitally mediated learning contexts.
The impact of a smart environment suggests that digital learning contexts play an important role in shaping student perceptions. Environmentally responsive and adaptive campuses contribute to a holistic learning experience by enhancing comfort, accessibility and sustainability. This finding extends existing research by demonstrating that environmental factors, often considered peripheral, are integral to the perceived effectiveness of smart campuses.
In contrast, the non-significant effect of smart infrastructure offers an interesting, context-specific insight. One possible explanation is that in the Gulf region, where digital infrastructure is relatively well developed, students may perceive it as a basic expectation rather than a value-added feature. As a result, infrastructure does not significantly differentiate the quality of the educational experience. This finding supports the argument that once a threshold level of technological capability is reached, other factors, such as interaction, governance and user experience, become more salient in shaping perceptions of success.
6.2 Managerial implications
The findings of this study have important implications for institutional leaders, educational administrators and policymakers. Firstly, institutions should prioritize effective governance structures that support digital transformation. This includes fostering participatory decision-making, reducing bureaucratic barriers and aligning policies with student needs. Secondly, enhancing interactive learning environments should be a key strategic focus. Investments in collaborative platforms, real-time communication tools and interactive content can significantly improve student engagement and satisfaction.
Thirdly, institutions should focus on user-centered technology design. Rather than simply adopting advanced technologies, the emphasis should be on usability, accessibility and integration to ensure that technologies enhance, rather than hinder, learning experiences. Fourthly, the role of the learning environment should not be overlooked. Institutions should invest in creating adaptive, sustainable and student-friendly environments that support both physical and digital learning experiences. Finally, given the non-significant role of infrastructure, decision-makers should recognize that infrastructure alone is insufficient to drive success. Instead, a balanced approach that integrates governance, interaction, environment and technology is essential.
6.3 Implications for public policy
From a policy perspective, the findings suggest that governments in the Gulf region should adopt a holistic approach to developing smart education. While investments in digital infrastructure remain important, equal attention should be given to governance frameworks, quality assurance mechanisms and pedagogical innovation. Policymakers should also focus on developing flexible and resilient education systems that can adapt to disruptions such as pandemics or environmental challenges. Smart campuses can play a key role in achieving these objectives by enabling continuity of learning and expanding access to education.
Furthermore, policies should encourage collaboration between the public and private sectors to foster innovation in educational delivery. By leveraging technological advancements and best practices, governments can enhance the quality and competitiveness of higher education systems in the region.
7. Limitations of the study and future research direction
Despite its contributions, this study has several limitations that should be acknowledged. Firstly, the study is based on a non-probability convenience sample, which may limit the generalizability of the findings. Although the sample size is adequate for statistical analysis, future studies could employ probability sampling techniques to enhance representativeness. Secondly, the study focuses on four Gulf countries, and therefore, the findings may not be fully generalizable to other regions with different educational, cultural, or technological contexts. Comparative studies across regions would provide deeper insights into contextual differences.
Thirdly, the research adopts a cross-sectional design, capturing perceptions at a single point in time. Given the rapidly evolving nature of digital education, longitudinal studies are needed to examine how perceptions of smart campuses change over time. Fourth, the study relies on self-reported data, which may be subject to common method bias and response bias. Although statistical tests suggest that common method bias is not a major concern, future research could incorporate objective measures or multi-source data.
Finally, while the study examines five key constructs, other potentially relevant factors, such as student motivation, digital literacy and institutional culture, were not included. Incorporating these variables could provide a more comprehensive understanding of students' perceptions of higher education success.
Building on these limitations, several avenues for future research are suggested. Future studies could explore comparative analyses across countries or regions to identify contextual variations in smart campus adoption and effectiveness. Such studies would help determine whether the findings of this research are specific to the Gulf region or generalizable to other settings. Longitudinal research designs could be used to examine how students' perceptions evolve, particularly as institutions continue to invest in digital transformation and smart technologies.
Further research could also investigate the role of individual-level factors, such as digital competence, learning styles and motivation, in shaping perceptions of smart campus experiences. Integrating these variables would enhance the explanatory power of existing models. In addition, qualitative studies could provide deeper insights into students' experiences in smart campus environments, complementing the quantitative findings of this study.
The authors sincerely thank the editor and reviewers for their valuable and constructive feedback. Their insightful comments greatly improved the clarity, rigor and overall quality of this manuscript.
