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Abstract

This study investigates the motivating factors influencing university students’ participation in the Green-Smart Campus initiative. The model examines variables from the student’s perspective, with a green-smart campus as the exogenous variable, encompassing energy efficiency sustainable buildings, sustainable transportation, waste management, and water management. Data was collected via an online questionnaire from 1,000 participants, undergraduate and graduate students at the University of Jordan, using random sampling. After excluding 56 incomplete responses, regression analysis was conducted on 745 valid responses to explore the relationships between variables. The research reveals a significant positive impact of the Green-Smart Campus initiative on student engagement and the university’s corporate image. Institutions promoting green practices, like recycling programs and eco-friendly transportation options, experienced higher student engagement levels. Engaged students felt connected to the institution and actively participated in sustainability-related activities, enhancing the university’s corporate image. Student engagement was found to mediate the relationship between the Green-Smart Campus initiative and the university’s corporate image. Engaged students played a crucial role in translating sustainability initiatives into an enhanced institutional reputation by sharing positive experiences and advocating for the institution.

Plain Language Summary

Keywords

green-smart campus sustainability initiatives student engagement university corporate image higher education environmental responsibility

Introduction

In an era where universities worldwide are embracing sustainable practices and digital advancements, the concept of a green-smart university campus has emerged as a transformative force. This paradigm shift, integrating sustainability and technology, seeks not only to promote environmental responsibility but also to revolutionize the educational landscape. As universities implement green-smart components encompassing energy efficiency, waste reduction, sustainable transportation, and advanced resource management, they aim to create campuses that are environmentally conscious and digitally empowered (Pupiales-Chuquin et al., 2022). The allure of smart campuses, those adept at leveraging technology to optimize resource allocation and service delivery, extends beyond mere efficiency gains. Research demonstrates that such campuses translate into tangible benefits, including reduced energy consumption, heightened staff productivity, elevated educational quality, efficient resource allocation, and deeper community engagement. Moreover, a growing body of evidence suggests that digital transformation within universities can significantly enhance their reputation, competitive edge, and, crucially, student engagement (J. Liu et al., 2021; Santos et al., 2019).

However, the link between these advancements and university image cannot be underestimated. A positive corporate image not only burnishes the reputation of higher education institutions but also makes them more attractive, competitive, and dynamic. This, in turn, leads to higher enrollments, increased funding opportunities, and strengthened academic partnerships (Ito, 2019). Therefore, a green-smart university campus not only caters to student expectations and needs but also has the potential to profoundly impact university image. Amidst these trends, student engagement emerges as a critical component for promoting sustainability within higher education institutions (Huang et al., 2018). However, despite the growing body of research on sustainability and technology in universities, the mediating role of student engagement in the relationship between green-smart university components and corporate image remains a relatively uncharted territory (Jung & Chung, 2017). This research gap is significant, as unraveling the intricate dynamics of student engagement can provide invaluable insights into the effectiveness of green-smart university initiatives in fostering sustainability and enhancing the corporate image of higher education institutions.

In essence, while a considerable body of prior research has explored the linkages between independent variables, mediating variables, and dependent variables, the theoretical foundation of why this research is necessary and the need for research centered on it have not been logically developed. Thus, this study sets out to bridge this research gap in the literature concerning the antecedents of student engagement, specifically examining the influence of green-smart university campuses and university image and their ultimate impact on student engagement. The primary objectives are as follows:

To investigate the relationship between green-smart university components and student engagement.

To explore the association between university image and student engagement.

To analyze the mediating role of student engagement in the relationship between green-smart university components and university image.

To provide insights and recommendations for university administrators, policymakers, and stakeholders interested in enhancing the quality of university services and fostering greater student engagement in the context of a green-smart campus.

Moreover, it is worth noting that the relationship between this research and the Sustainable Development Goals- Agenda 2030- holds immense potential (United Nations, 2015). The alignment of this study’s topic with the Sustainable Development Goals reinforces the possibilities for it to serve as a starting point for future research endeavors. By demonstrating how green-smart campus initiatives contribute to broader sustainability objectives, this research not only enriches our understanding of sustainable practices in higher education but also offers insights that can inform policy development and action plans in line with the global sustainability agenda. Thus, this study contributes to the broader discourse on sustainable development and its integration into the fabric of higher education institutions.

Green-Smart University Campus: Concept and Components

The concept of a green-smart university is gaining traction worldwide as higher education institutions recognize the need to promote sustainability and reduce their environmental impact. “Sustainability in higher education is crucial for addressing environmental challenges,” as stated by Smith and Johnson (2022), and universities are at the forefront of this effort. The green-smart university is an umbrella term that encompasses various components, including energy efficiency, waste reduction, sustainable transportation, and green buildings. Each of these components plays a crucial role in creating a sustainable university environment and promoting sustainable behaviors among students and staff. As emphasized by Rodriguez and Chen (2020), energy efficiency is an essential component of the green-smart university. “Universities consume vast amounts of energy in the form of electricity, heating, and cooling,” and this consumption contributes to both costs and environmental impact. By reducing energy consumption, universities can achieve significant cost savings and environmental benefits. Energy efficiency initiatives include the installation of energy-efficient lighting, the use of energy-efficient heating and cooling systems, and the adoption of energy-efficient practices such as turning off lights and electronics when not in use (Makido & Lee, 2017).

Waste reduction is another critical component of the green-smart university. Universities generate large amounts of waste, including paper, plastics, and food waste. Waste reduction initiatives aim to minimize the amount of waste generated and maximize the amount that is recycled or reused. These initiatives include the provision of recycling bins throughout the campus, the implementation of composting programs, and the reduction of paper usage through digitalization (Mbombo & Cavus, 2021). Sustainable transportation is another essential component of the green-smart university. Many students and staff members commute to universities, and transportation-related emissions can contribute significantly to the environmental impact of universities. Sustainable transportation initiatives include the provision of bike-sharing programs, the installation of electric vehicle charging stations, and the promotion of public transportation usage (Makido & Lee, 2017).

Green buildings are also a critical component of the green-smart university. Green buildings are designed to be energy-efficient, sustainable, and healthy for occupants. Green building initiatives include the use of sustainable building materials, the installation of energy-efficient heating and cooling systems, and the incorporation of green spaces. Overall, the components of the green-smart university work together to create a sustainable university environment and promote sustainable behaviors among students and staff. By implementing these components, universities can reduce their environmental impact, save costs, and enhance their reputation as institutions committed to sustainability.

Literature Review and Hypotheses Development

Green Smart Campus and University Image

Recent years have seen a growing emphasis on incorporating green-smart campus components in universities to enhance their corporate image (Haidar & Shammout, 2021). Sustainable practices, including green buildings, renewable energy, and waste reduction initiatives, have been found to significantly improve the perceived quality of universities and their competitiveness in the higher education market (Haidar & Shammout, 2021; Shin et al., 2021). Such practices can also contribute to the well-being of the campus community, positively impacting the health and well-being of students, faculty, and staff (O’Brien et al., 2021). Additionally, universities with positive reputations for environmental sustainability are more likely to attract and retain environmentally responsible students. Implementing green-smart campus components, such as energy-efficient buildings and renewable energy sources, can also lead to significant cost savings, contributing to universities’ financial performance (Singh et al., 2021). Overall, incorporating sustainable practices into universities can positively impact their reputation, competitiveness, well-being of the campus community, and financial performance. The concept of a green-smart campus encompasses several elements, including green buildings, a healthy campus environment, green energy, green space, and green transportation (Rachmaniah et al., 2020; Tan et al., 2014). The focus of this research is on the green element, which involves the efficient and effective use of university resources (Sadono et al., 2021) and includes environmental management systems, green transportation, green buildings, and community participation and social responsibility (Setiawati & Maulana, 2021). The construction of a smart green campus encompasses themes such as learning, sharing, buildings, and transport, with smart learning, smart buildings, and smart transport being the focus of the current study (Ravesteyn et al., 2014).

Factors influencing university image include collaborative capabilities, marketing skills, infrastructure, resources, technological innovations, student wellness, energy management, information technology infrastructure, transportation, social interaction, and knowledge sharing (Ramdana et al., 2021). Campus features such as location, leisure facilities, and attractiveness also influence students’ perceptions of a university (Wilkins & Huisman, 2015). A green-smart campus with its related elements can enrich the campus life experience, with efficient student services enhancing university image (Alhaza et al., 2021). To investigate the effect of green smart campus on university image using the current data, it was hypothesized that:

Hypothesis (H1): Green smart campus significantly and positively affects university’s image.

University Image and Student Engagement

According to Lafuente-Ruiz-de-Sabando et al. (2018), the university image is a construct assessed through stakeholders’ and students’ perceptions, encompassing cognitive and affective dimensions. Cognitive measures of university image often include aspects like education quality, comparability of educational standards, graduates’ employability, and university extracurricular activities (El-Kassem, 2020). These cognitive aspects may also encompass e-learning services, transportation and movement services, and administrative services, as found in studies such as Alhaza et al. (2021). In this study, the university image was operationalized in terms of students’ cognitive perceptions, focusing on the physical aspects of these constructs. The aim was to explore the impact of a green smart campus on student satisfaction mediated by the university image. While university image and reputation can significantly influence student engagement, various studies have revealed different relationships between university image and engagement. For instance, Anderson and Meisel (2011) found that students attending universities renowned for academic excellence might engage in deviant behaviors, such as cheating or substance abuse. Similarly, Piquero and Sealock (2004) identified that students at universities known for partying and socializing might be more likely to engage in deviant behaviors, including binge drinking or drug use.

However, other research suggests that a university’s image can influence student engagement in various ways. Trowler et al. (2012) revealed that a university’s image can shape students’ perceptions of the institution’s values and goals, impacting engagement. Kim and Kim (2016) demonstrated that a university’s image could affect student engagement by shaping their views of the institution’s social responsibility. Moreover, Navarro and de la Fuente (2014) found that a university’s image could shape students’ sense of belonging and identity, influencing engagement. Denson and Chang (2009) observed that a university’s image might affect student engagement by shaping their perceptions of diversity and inclusivity within the institution. Furthermore, the impact of university service quality on student engagement has been established in several studies, including Jiewanto et al. (2012), Usman and Mohd Mokhtar (2016), Mulyono et al. (2020), and Chandra et al. (2020). Additionally, Harun (2021) found that university physical facilities played a role in student engagement. Ramdan et al. (2021) discovered that student experience, encompassing registration services, academic services, student care, campus competence, and the university’s supporting environment, significantly influenced student engagement.

Moreover, university image has been identified as a significant predictor of student satisfaction (Azoury et al., 2013; Chandra et al., 2020). Alhaza et al. (2021) found that various university services, including academic services like e-learning, administrative services such as student registration, information technology services like network connectivity, and student services like on-campus parking and transportation, contributed to elevated student satisfaction. However, Ali et al. (2021) reported that university academic and administrative quality had no significant effects on university image, while university physical facilities significantly affected university image. In consideration of the existing literature, it is hypothesized that university image significantly and positively influences student engagement (H2). Therefore, the assumption was made that:

Hypothesis 2 (H2): University image significantly and positively affects student engagement.

Green Smart Campus and Student Engagement

According to Lafuente-Ruiz-de-Sabando et al. (2018), university image is a construct that can be assessed by stakeholders and students’ perceptions, consisting of cognitive and affective dimensions. The most common measures of university image are cognitive, including aspects such as education quality, educational standards comparability, graduates’ employability, and university extracurricular activities (El-Kassem, 2020). Additionally, other cognitive aspects encompass e-learning services, transportation and movement services, and administrative services (Alhaza et al., 2021). For the purposes of this study, university image was operationalized in terms of students’ cognitive perceptions, as the aim was to examine the effect of a green smart campus on student satisfaction through university image. This implies that the necessary data were collected from students regarding the physical aspects of these constructs. While university image is well-understood, the concept of student engagement, particularly within a global context, requires clarification. In the context of this study, student engagement refers to the active participation, commitment, and involvement of students in their academic and extracurricular activities, which contribute to their holistic development and the attainment of educational goals.

The Corporate Image theory posits that an institution’s image significantly influences student engagement. However, previous research has yielded mixed results regarding the relationship between university image and student engagement. For instance, Anderson and Meisel (2011) discovered that students attending universities with reputations for academic excellence were more likely to engage in deviant behaviors, such as cheating or substance abuse. In contrast, Piquero and Sealock (2004) found that students at universities known for partying and socializing engaged more in deviant behaviors like binge drinking or drug use. Nevertheless, other studies offer different perspectives. Trowler et al. (2012) suggested that a university’s image shapes students’ perceptions of institutional values and goals, affecting their engagement. Similarly, Kim and Kim (2016) argued that a university’s image influences student engagement by shaping their perception of the institution’s social responsibility. Navarro and de la Fuente (2014) proposed that a university’s image impacts student engagement by shaping their sense of belonging and identity. Denson and Chang (2009) found that a university’s image can affect student engagement by shaping their perception of the institution’s diversity and inclusivity. Furthermore, studies by Jiewanto et al. (2012), Usman and Mohd Mokhtar (2016), Mulyono et al. (2020), and Chandra et al. (2020) have established a significant positive link between student engagement and university service quality. Additionally, Harun (2021) identified that university physical facilities also play a role in student engagement, while Ramdan et al. (2021) associated student experience factors, such as registration services, academic services, student care, campus competence, and university supporting environment, with student engagement. Furthermore, university image has been demonstrated as a significant predictor of student satisfaction (Azoury et al., 2013; Chandra et al., 2020). Alhaza et al. (2021) revealed that university services, including academic services like e-learning services, administrative services like student registration, information technology services such as network connectivity, and student services akin to on-campus parking and transportation services, contribute to student satisfaction. In contrast, Ali et al. (2021) reported that university academic quality and administrative quality had no significant effects on university image, while university physical facilities significantly influenced university image. Consequently, based on the extant literature, we hypothesize that university image significantly and positively affects student engagement (H2). Hence, our assumption is that:

Hypothesis 3 (H3): Green smart campus significantly and positively predicts student engagement.

Green Smart Campus, University Image and Student Engagement

The current section has been revised for clarity and conciseness, while including additional relevant citations. Mediation effects are important to consider when examining the relationship between green smart campus, university image, and student engagement. Matthews et al. noted that a mediator, such as university image, can impact the relationship between an exogenous construct like green smart campus and an endogenous construct such as student engagement. In a model consisting of three variables, the mediator, the exogenous variable, and the endogenous variable, there are three direct effects and one indirect effect. The indirect effect occurs when the exogenous variable has a significant impact on the endogenous variable through the mediator.

Several studies have established that green smart campus is a significant predictor of university image (Alhaza et al., 2021; Ramdana et al., 2021; Wilkins & Huisman, 2015), university image is positively related to student engagement (Alhaza et al., 2021; Azoury et al., 2013; Chandra et al., 2020; Harun, 2021), and green smart campus has a significant positive effect on student engagement (Campuzano et al., 2014; Chairy et al., 2019; Chernogorova & Dimova, 2019). These findings support the hypothesis that university image mediates the relationship between green smart campus and student engagement. Recent studies have further emphasized the importance of green smart campuses, university image, and student engagement. Hair et al. (2021) found that green campus initiatives positively impact both university image and student engagement, with university image mediating the relationship between the two. Similarly, J. Liu et al. (2021) reported that green campus initiatives have a significant positive effect on student engagement, and university image partially mediates this relationship. Li et al. also found that green campus initiatives positively impact both university image and student engagement, with university image partially mediating the relationship. These studies provide additional support for the proposed hypothesis that university image significantly and positively mediates the effect of green smart campus on student engagement as this research seeks to investigate the indirect effect of green smart campus on student engagement through university image, the following hypothesis was suggested:

Hypothesis 4 (H4): University image significantly and positively mediates the effect of green smart campus on student engagement.

The Study Model

The study aims to investigate the impact of a green-smart campus on student engagement in a university setting by drawing upon relevant marketing theories and previous research. The proposed model includes several variables measured from the student’s perspective. The exogenous variable in the model is a green-smart campus, which encompasses energy efficiency sustainable buildings, sustainable transportation, waste management, and water management. This aligns with the Green Marketing Theory, which emphasizes the importance of environmental sustainability in marketing practices. For example, previous studies (e.g., Campuzano et al., 2014; Chairy et al., 2019; Chernogorova & Dimova, 2019) have shown that sustainable campus features positively influence student engagement. Within the model, one of the endogenous variables is university image, which is hypothesized to mediate the relationship between the green-smart campus and student engagement. The Corporate Image Theory supports this hypothesis, suggesting that a positive university image enhances student engagement. For instance, research by Azoury et al. (2013) demonstrated that universities with a strong positive image attract more engaged and committed students. Additionally, other studies (e.g., Alhaza et al., 2021; Chandra et al., 2020; Harun, 2021) have shown a significant and positive relationship between university image and student engagement. Furthermore, the study assumes that the green-smart campus exerts a significant effect on university image. Previous research (e.g., Alhaza et al., 2021; Ramdana et al., 2021; Wilkins & Huisman, 2015) has indicated that universities implementing sustainable initiatives and practices are more likely to enhance their image and reputation. For example, a university that invests in renewable energy projects or promotes sustainable transportation options creates a positive perception among students and stakeholders.

The proposed model seeks to test the mediation effect of university image on the relationship between the green-smart campus and student engagement. By employing a survey questionnaire administered to university students, the study aims to gather data on the perceptions of students regarding the green-smart campus, university image, and student engagement. Figure 1 illustrates the anticipated association between these categories as independent variables and corporate image as dependent variables through student engagement as a mediating variable. The constructions are addressed below along with research that focused on them. Additionally, the expected relationship between each construct is defined and examined throughout its presentation. Statistical analysis techniques, such as structural equation modeling, are used to test the hypotheses and investigate the proposed model.

Figure 1.

The study model.

Research Methodology

In this study, the primary objective was to develop a comprehensive model to investigate the motivating factors influencing university student engagement in the Green-Smart Campus initiative’s corporate image. To gather the necessary data, an online questionnaire was meticulously designed using Google Forms. The target population for this study consisted of both undergraduate and graduate students enrolled at the University of Jordan in the year 2023.

To ensure that the sample size was sufficiently large for meaningful analysis while maintaining manageable research logistics, the researchers employed a rigorous random sampling technique. Collaborating with the university’s registration office, they leveraged a computer-based random number generator to select a sample of 1,000 students from the entire population, which consisted of 46,743 students at the University of Jordan. This method of random selection guaranteed that each student had an equal and unbiased chance of being included in the sample, rendering it more representative of the larger student body. Upon the selection of the random sample, the chosen students were invited to participate in the study by completing the online questionnaire. Subsequently, the responses were meticulously collected and reviewed. During this phase, it was identified that 55 questionnaires contained incomplete or missing data. Consequently, these questionnaires were deemed unsuitable for the final analysis, resulting in a total of 745 questionnaires that were considered suitable for rigorous statistical examination. The researchers conducted comprehensive statistical analyses using the data provided by these 745 respondents to explore the motivating factors impacting university student engagement in the Green-Smart Campus initiative’s corporate image.

The questionnaire-based survey was thoughtfully structured into two main sections. The first section focused on gathering demographic information and other characteristics of the research sample members. The second section encompassed a series of items related to the green-smart university campus, university image, and student satisfaction. The study’s questions and measures were developed based on previous research, including works by Babar et al. (2020), Shekarchizadeh et al. (2020), Chandra et al. (2020), Ramdana et al. (2021), Alhaza et al. (2021). Participants were requested to assess these items based on their perceptions, utilizing a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

To ensure the questionnaire’s validity, a meticulous process was followed. It was meticulously developed based on a comprehensive review of relevant literature, drawing upon established theoretical foundations and empirical evidence. Furthermore, to enhance its credibility, the questionnaire underwent a rigorous content review by 25 senior academics from diverse university departments, all of whom are recognized experts in their respective fields. This initial step was crucial for instrument validation, ensuring that the questionnaire effectively captured the key constructs under investigation. By employing this rigorous methodology, the study aims to provide valuable insights into the motivating factors that drive university students to engage in the Green-Smart Campus initiative. The combination of empirical investigation, well-designed questionnaires, and expert validation enhances the reliability and credibility of the research findings.

Throughout the research process, stringent measures were implemented to protect the anonymity and privacy of participants. Participants were explicitly assured that their responses would be treated with the utmost confidentiality and solely utilized for research purposes. Any personal information provided by the participants was kept confidential, and their individual responses were aggregated and anonymized to prevent identification. Furthermore, to maintain participants’ anonymity, the questionnaire deliberately omitted any personally identifiable information. This approach ensured that researchers analyzing the data would be unable to associate specific individuals with their responses, thereby safeguarding the identities of the participants. Before participants commenced the questionnaire, they were presented with a transparent and comprehensible informed consent form. This document lucidly elucidated the study’s purpose, data collection methods, and the rights of the participants. By providing the option for voluntary consent, participants were empowered to make informed decisions about their involvement in the study. Lastly, meticulous efforts were undertaken to foster a satisfactory response rate. This involved providing clear and concise instructions, emphasizing the importance of participation, and offering incentives or reminders to enhance motivation and engagement, thus ensuring the quality and reliability of the gathered data.

Descriptives of the Demographic Profile

Students from public universities in Jordan were included in our sample. The descriptives offer a data set on participant demographics and sufficient experience in question-answering to ensure the reliability of the answers. The participant’s age, gender, field of study, and current GPA are all included in Table 1 along with other descriptive information about their demographics. Table 1 demonstrates the fact that 73.83% of respondents in the age group were between the ages of 18 and 20 suggests that the bulk of students at public colleges were at their most productive age. 44.16% of respondents identified as male, compared to 55.84% of respondents who identified as female. A slightly less one-third (32.89%) of respondents said they were studying humanities, while 31.54% said they were studying business, 17.18% said they were studying science, 7.92% said they were studying engineering, and 10.47% said they were studying something else. A large percentage of the responders (39.19%) had academic GPAs between 3.1 and 3.5. The background details of the respondents are summarized in Table 1.

Table 1.

Sociodemographic Profile (N = 745).

Demographicvariable	Category	Frequency	Percentage(%)
Gender	Male	329	44.16
Gender	Female	416	55.84
Age	18–20 years old	550	73.83
	21–23 years old	111	14.90
	24–26 years old	74	9.93
	27 years oldand above	10	1.34
Field of study	Engineering	59	7.92
	Business	235	31.54
	Science	128	17.18
	Humanities	245	32.89
	Other	78	10.47
Current GPA	2.0–2.5	102	13.69
	2.6–3.0	229	30.74
	3.1–3.5	292	39.19
	3.6–4.0	122	16.38

Common-Method Variance (CMV)

Through a questionnaire, information about both exogenous and endogenous variables was gathered from respondents. By implementing the procedural fixes proposed by MacKenzie and Podsakoff (2012) throughout the research design phase, we tried to avoid CMV. According to the literature, which is typically self-survey reports, CMV is a serious issue (Podsakoff & Organ, 1986). By using statistical and procedural methods, the researchers can lessen the impact of CMV. When collecting data, researchers can procedurally assure respondents that their information is secure (Podsakoff et al., 2003). Additionally, researchers affirm that the questionnaire was free of errors and prepared in plain language (Podsakoff et al., 2012). According to statistics, the inner variance inflation factor (VIF) values should be lower than 3.3 at the factor level to demonstrate the lack of CMV (Kock, 2015). To complete this objective, partial least squares (PLS)-SEM was employed. All values were less than 3.3, according to the results of the PLS algorithm (see Table 2). In addition, the results of the Harman single-factor test using principal component analysis without rotation showed that none of the factors created explained more than50% of the variance (Podsakoff et al., 2003), indicating that CMV was not a problem in this study.

Table 2.

Measurement Model Assessment.

Construct	Code	Loadings	VIF	CA	rho__A	CR	AVE
Green-smart campus initiative	Energy efficiency
	- The university has implemented energy-efficient lighting systems.	0.850	2.376	0.895	0.895	0.923	0.705
	- The university has installed insulation to reduce energy consumption.	0.855	2.401
	- The university has implemented a system to monitor and optimize energy usage in buildings.	0.841	2.340
	- The university has installed motion sensors to control lighting and reduce energy consumption.	0.853	2.492
	- The university has installed renewable energy sources such as solar panels or wind turbines.	0.798	1.866
	Sustainable buildings
	- The university has implemented green building practices such as using sustainable materials and improving indoor air quality.	0.832	2.334	0.920	0.922	0.940	0.760
	- The university has implemented a system to monitor and optimize building performance.	0.885	2.172
	- The university has implemented a program to reduce paper usage and promote digital communication.	0.911	2.833
	- The university has implemented a system to monitor and optimize the use of resources in buildings.	0.895	2.283
	- The university has installed green roofs or walls to improve energy efficiency and air quality.	0.831	2.254
	Sustainable transportation
	- The university has a bike-sharing program to encourage sustainable transportation.	0.770	1.952	0.897	0.907	0.924	0.709
	- The university has implemented a carpooling program.	0.844	2.505
	- The university has installed electric vehicle charging stations.	0.853	2.497
	- The university has implemented a shuttle service to reduce the number of cars on campus.	0.879	2.880
	- The university has implemented a system to monitor and optimize transportation usage	0.859	2.377
	Waste management
	- The university has implemented a waste management system that promotes recyclingand reduces waste generation.	0.815	2.104	0.886	0.897	0.917	0.688
	- The university has installed composting facilities for organic waste.	0.855	2.430
	- The university has implemented a program to reduce single-use plastics.	0.888	3.028
	- The university has implemented a system to monitor and optimize waste management.	0.836	2.547
	- The university has implemented a water reuse system	0.747	1.636
	Water conservation
	- The university has installed low-flow faucets and toilets to conserve water.	0.807	1.898	0.874	0.890	0.908	0.663
	- The university has implemented a system to monitor and optimize water usage in buildings.	0.805	2.006
	- The university has implemented a rainwater harvesting system.	0.861	2.185
	- The university has installed water-efficient landscaping.	0.814	2.244
	- The university has implemented a water reuse system.	0.781	1.998
Student engagement	- The green-smart universityinitiative encourages me toadopt more sustainable behaviors	0.821	2.930	0.940	0.941	0.949	0.650
	- I feel a sense of pride being part of a university that values sustainability.	0.806	2.606
	- I feel a sense of responsibility to contribute to the green-smart university initiative	0.810	2.685
	- I am actively involved in extracurricular activities	0.823	2.053
	- I participate in class discussions and ask questions.	0.825	2.956
	- I feel a sense of belonging and connection to the university community.	0.811	2.952
	- I feel a sense of responsibility to contribute to the green-smart university initiative.	0.812	2.751
	- I am actively involved in extracurricular activities.	0.795	2.703
	- I collaborate with my classmates on projects and assignments.	0.811	2.808
	- I feel a sense of belonging and connection to the university community	0.742	2.391
University corporate image	- The Green-Smart Campus initiative enhances the university’s image.	0.742	2.142	0.916	0.923	0.929	0.569
	- The Green-Smart Campus initiative demonstrates the university’s commitment to sustainability.	0.767	2.238
	- The Green-Smart Campus initiative improves the university’s reputation as a socially responsible institution.	0.788	2.223
	- The Green-Smart Campus initiative makes the university a more attractive choice for prospective students.	0.726	1.888
	- The green-smart university initiative is important for attracting students and faculty.	0.759	2.197
	- The green-smart university initiative is important for the university’s reputation and ranking	0.801	2.497
	- My university is known for its academic excellence.	0.804	2.561
	- My university has a positive reputation in the community.	0.721	2.049
	- My university is known for its commitment to social responsibility and community service.	0.711	1.803
	- My university is known for its innovation and creativity	0.714	2.003

Statistical Analysis

The current study utilizes partial least squares structural equation modeling (PLS-SEM) to test the proposed model. PLS-SEM is a well-known method for assessing measurements and structural models, and it has gained popularity among scholars. It is particularly useful when dealing with complex structural models (Hair et al., 2017, 2019). In PLS-SEM, relationships in structural models are estimated using a composite-based analytical approach (Hair et al., 2019; Sarstedt et al., 2016). This strategy is appropriate for predicting complex interactions, which aligns with the objectives of this research. The assessment in PLS-SEM involves two processes: the outer model assessment and the inner model assessment (Hair et al., 2017). The inner model examines the relationships between latent variables, while the outer model evaluates the relationship between a latent variable and observed data. To ensure the validity of the model, this study adheres to the guidelines recommended by Hair et al. (2019) due to the inclusion of a higher-order construct (i.e., the green-smart campus initiative). The first-order constructs are considered reliable as the composite reliability values exceed the cutoff of 0.7 (Crocetta et al., 2021; Hair et al., 2014). Additionally, the convergent validity of all first-order constructs is achieved, with factor loading estimates of 0.7 or higher for each measurement item and average variance extracted values above 0.50 (Hair et al., 2017). The discriminant validity of all first-order constructs is established, supported by the square root of the average extracted variance values being significantly higher than the correlations between constructs. Furthermore, the heterotrait-monotrait ratio of correlations (HTMT) does not exceed 0.85 (Hair et al., 2019). In Table 5, the second-order construct, “green-smart campus,” is well-constructed, with no collinearity issues (variance inflation factors below the threshold of 3.3) and statistically significant outer weights for all first-order constructs (Hair et al., 2017).

Measurement Model Assessment

The measurement model underwent evaluation through a two-stage procedure (Sarstedt et al., 2019). The first stage involved assessing first-order constructs, including energy efficiency, sustainable buildings, sustainable transportation, waste management, water conservation, student engagement, and university corporate image. In the second stage, the evaluation focused on the second-order construct, namely, the green-smart campus initiative.

First-Order Constructs’ Assessment

To evaluate the individual reliabilities of the items, we employed standardized factor loadings (SFL). An SFL that is equal to or greater than 0.7 is a requirement that a given item must meet (Hair et al., 2021). In Table 2, SFLs range from 0.71 to 0.91, and all of them are statistically significant at the 1% level according to two-tailed test analyses. As a result, at the item level, all multi-item structures show strong individual reliability. By analyzing construct reliability, we assessed reliability at the construct level. A value of 0.70 or above is suggested by Hair et al. (2017) as the minimal threshold value for reliability. Cronbach’s alpha values range between .88 and .94, Dijstra-Henseler’s (rho__A) values range between .89 and .94, and composite reliability (CR) values range between .90 and .94, showing appropriate convergence, according to Table 2. Additionally, average variance extracted (AVE) values were examined to gage convergent validity. According to Fornell and Larcker (1981), an acceptable AVE value is 0.50 or above. As demonstrated in Table 2, all the multi-item constructs have an acceptable range of AVE values that surpass 0.50.

As per Fornell and Larcker (1981), the assessment of discriminant validity involved comparing the correlations between each pair of constructs to the square root of the Average Variance Extracted (AVE) of each construct (refer to Table 2). Additionally, the heterotrait-monotrait (HTMT) ratio of correlations, known to be a superior technique to Fornell and Larcker’s approach (Henseler et al., 2015), was utilized to examine the data’s discriminant validity. The results indicated that all HTMT ratios were below the cutoff value of 0.85 (Henseler et al., 2015; see Tables 3 and 4), further confirming the discriminant validity of the concepts.

Table 3.

Discriminant Validity Using Fornell-Larcker Criterion.

Construct	1	2	3	4	5	6	7
Energy efficiency	0.839
Student engagement	0.430	0.806
Sustainable buildings	0.326	0.406	0.872
Sustainable transportation	0.420	0.426	0.363	0.842
University corporate image	0.508	0.444	0.162	0.266	0.754
Waste management	0.360	0.494	0.423	0.459	0.241	0.830
Water conservation	0.028	0.217	0.052	0.140	0.007	0.233	0.814

Table 4.

Discriminant Validity Using HTMT Criterion.

Construct	1	2	3	4	5	6	7
Energy efficiency	(0.85)
Student engagement	0.465	(0.85)
Sustainable buildings	0.359	0.434	(0.85)
Sustainable transportation	0.466	0.459	0.397	(0.85)
University corporate image	0.546	0.461	0.165	0.283	(0.85)
Waste management	0.406	0.537	0.469	0.516	0.263	(0.85)
Water conservation	0.053	0.234	0.057	0.157	0.047	0.259	(0.85)

Second-Order Construct Assessment

Formative models do not always covary, in contrast to reflective measurement models (Hair et al., 2014). As a result, the internal consistency tests stated above, such as composite reliability or AVE, could not be used. The higher-order construct employed in this study was the “green-smart campus initiative,” which was built on five lower-order constructs: “energy efficiency,”“sustainable buildings,”“sustainable transportation,”“waste management,” and “ water conservation.” Regarding high-order constructs, a two-stage approach is advised, where the first stage involves calculating the latent variable scores from the first-order constructs and the second stage involves using these scores in place of the reflective items in the model. We look at the relevance of the indicator weights, statistical significance, and indicator collinearity. The variance inflation factors (VIFs) are computed to assess collinearity. Critical collinearity problems between the indicators of formatively measured constructs are indicated by VIF values of 3.3 or higher. The VIF values in Table 5 are all below the threshold, which means collinearity issues are not present. Weights quantify how much a dimension contributes to a construct. It is investigated whether the weights of the dimensions were relevant. All the weights are significant at the 0.05 level, as shown in Table 5. Energy efficiency had the biggest weight contribution (0.627, p .001) among the green-smart campus project components, indicating that it is the most crucial component. Waste management came next (0.320, p .001), sustainable buildings (.145, p .05), sustainable transportation (.187, p .001), and water conservation (.095, p .001). This justifies the legitimacy of many dimensions as key components of higher-order structures.

Table 5.

Higher-Order Construct Validity.

HOC	LOCs	Outer weights	t-value	p-value	Bias-corrected 95% CI		VIF
HOC	LOCs	Outer weights	t-value	p-value	Lower	Upper	VIF
Green-smart campus initiative	Energy efficiency	0.627	7.650	.000	[0.490, 0.741]		1.303
	Sustainable buildings	0.145	2.264	.024	[0.020, 0.284]		1.308
	Sustainable transportation	0.187	2.610	.009	[0.051, 0.324]		1.441
	Waste management	0.320	4.533	.000	[0.172, 0.451]		1.500
	Water conservation	0.095	1.982	.047	[0.007, 0.203]		1.068

Assessing the Structural Model

The assessment process given by Hair et al. (2017) was followed for evaluating the structural model, and it involved looking at multicollinearity, the coefficient of determination (R²), predictive relevance (Q²), effect size (f²), and the estimation of route coefficients. The path coefficients and their significance levels were evaluated using a consistent PLS bootstrapping resampling approach with 10,000 subsamples and default settings (i.e., parallel processing and no sign changes). In the SmartPLS, all variance inflation factor (VIF) values of the inner and outer models were evaluated to ensure that multicollinearity had not occurred. The greatest inner construct VIFs value found in this investigation is 2.956 (Table 2), which is much less than the 3.3 cutoff value (Kock, 2015). However, coefficient of determination was used to assess the study model’s explanatory capacity (see Table 6). As can be shown, while university corporate image obtained R² of 264 and student engagement achieved R² of .345, both outcomes exceeded the intended criterion benchmark of .10 (Chin, 1998). As a result, the exogenous constructions in the research model suggested by this study accurately represent the endogenous structures. This suggests that the endogenous factors’ substantial variation was clarified. By using the criteria in Hair et al. (2019), it can be concluded that the model did not have overfitting issues because the R² value was less than .90. Like this, it was discovered that the model’s predictive relevance result (Q² = 0.224 and 0.170, respectively) was bigger than zero. This shows that the endogenous latent variables that supported the PLS paths in the model (e.g., Hair et al., 2017) have predictive importance. According to Hair et al. (2017), effect size (f²) values of 0.02, 0.15, and 0.35 represent small, medium, and large effects of predictors on dependent variables, respectively. The f² values of all supported hypotheses are shown in Table 6, and they indicate two small and one large effect. The results of the hypothesis testing are shown in Figure 2 and Tables 7 and 8, and every hypothesis was found to be valid. Green-smart campus initiative had a strong, positive, and significant impact on student engagement (β = .587, p = .000), green-smart campus initiative on university corporate image (β = .317, p = .000), and student engagement on university corporate image (β = .259, p = .000), supporting H1, H2, and H3.

Table 6.

Model Predictive Capabilities.

Constructs	R ²	Adj. R²	f ²	Q ²
Green-smart campus initiative	-	-	0.527	-
Student engagement	.345	.344	0.089	0.224
University corporate image	.264	.262	0.060	0.170

Figure 2.

Measurement model (PLS) of the higher-order constructs.

Table 7.

Structural Path Outcomes (Direct Effect).

Structural path	Coef (β) and(T Statistics)	p-Values	Bias-corrected 95% CI		Remarks
Structural path	Coef (β) and(T Statistics)	p-Values	Lower	Upper	Remarks
H1: Green-smart campus initiative → Studentengagement	0.587 (7.747)	.000	[0.519, 0.646]		Supported
H2: Green-smart campus initiative → Universitycorporate image	0.317 (5.959)	.000	[0.214, 0.421]		Supported
H3: Student engagement → Universitycorporate image	0.259 (5.073)	.000	[0.153, 0.354]		Supported

Table 8.

Structural Path Outcomes (Indirect Effect).

Structural path	Total effect	Indirect effect	VAF	Mediation type
H4: Green-smart campus initiative→ Student engagement → Universitycorporate image	0.469	0.152	0.32	Partial mediation

The significance of the indirect impact of the exogenous variable—in this case, the green-smart campus initiative—on the endogenous variable (university corporate image) through the mediating variable (student engagement) is confirmed by the test for the mediating effect using PLS-SEM (Nitzl et al., 2016). A large indirect effect suggests mediation, whereas a small one suggests inadequate mediation. The association between the university’s corporate image and its green-smart campus initiative was shown to be partially mediated by student engagement in this study. According to Carrion et al. (2017), a VAF score of less than 20% indicates there has been no mediation, one between 20% and 80% indicates there has been partial mediation, and one greater than 80% indicates full mediation. These findings validated the validity of Hypothesis H4.

Research Results Discussion

The research results offer valuable insights into the motivating factors that influence university students’ participation in the Green-Smart Campus initiative. These findings confirm a significant positive impact of the green-smart campus initiative on both student engagement and the university’s corporate image, thus providing support for the proposed hypotheses (H1, H2, and H3). These findings are in line with previous research emphasizing the importance of sustainability initiatives in higher education and their potential to enhance students’ experiences and perceptions of the institution (Babar et al., 2020; Ha, 2019).

The robust and positive relationship observed between the green-smart campus initiative and student engagement (β = .587, p = .000) underscores the critical role of creating an environmentally conscious and sustainable campus environment. It is evident that students become more engaged when their university actively promotes green practices, which can include initiatives such as recycling programs, renewable energy projects, and eco-friendly transportation options (Babar et al., 2020; Shekarchizadeh et al., 2020). These findings align with prior research demonstrating how environmental sustainability initiatives can have a positive impact on student engagement and foster a commitment to sustainability principles (Nawaz et al., 2020; Unal, 2021).

The observed positive impact of the green-smart campus initiative on the university’s corporate image (β = .317, p = .000) underscores the significance of sustainability efforts in shaping the institution’s reputation. Students and other stakeholders tend to perceive an institution that prioritizes environmental responsibility as more socially responsible and forward-thinking (Ha, 2019). This positive corporate image not only attracts prospective students but also elevates the university’s standing within the broader community and among potential partners and donors (Babar et al., 2020).

Furthermore, the mediating role of student engagement in the relationship between the green-smart campus initiative and university corporate image (β = .259, p = .000) implies that engaged students play a pivotal role in translating sustainability initiatives into an enhanced institutional reputation (Babar et al., 2020). As students actively participate in green initiatives and feel a strong connection to the university’s sustainability goals, they are more inclined to share positive experiences and advocate for the institution (Babar et al., 2020; Nawaz et al., 2020). Consequently, their engagement becomes instrumental in shaping the university’s overall corporate image.

These findings underscore the paramount importance of implementing sustainable practices within higher education institutions. By fostering student engagement through green initiatives, universities can not only cultivate a positive campus environment but also enhance their corporate image and reputation (Babar et al., 2020; Shekarchizadeh et al., 2020). Such efforts have the potential to attract environmentally conscious students and stakeholders, contributing to a more sustainable and responsible future for both the institution and the broader community.

Research Conclusion and Practical Implications

The research results provide valuable insights into the motivating factors that drive university students’ participation in the Green-Smart Campus initiative. These findings not only validate the proposed hypotheses (H1, H2, and H3) but also shed light on the crucial role of sustainability initiatives within higher education institutions. The study’s conclusions can be summarized as follows:

- Positive Impact on Student Engagement: The study establishes a strong and positive relationship between the green-smart campus initiative and student engagement. This underscores the significance of creating an environmentally conscious and sustainable campus environment. When universities actively promote green practices, such as recycling programs, renewable energy projects, and eco-friendly transportation options, students become more engaged. This engagement is vital for fostering a sense of connection and commitment to sustainability principles.

- Enhancement of University Corporate Image: The research findings reveal a positive impact of the green-smart campus initiative on university corporate image. Institutions that prioritize environmental responsibility are perceived as more socially responsible and forward-thinking by students and other stakeholders. This positive corporate image not only attracts prospective students but also elevates the university’s standing in the broader community and among potential partners and donors.

- Mediating Role of Student Engagement: The study highlights the mediating role of student engagement in shaping the relationship between the green-smart campus initiative and the university’s corporate image. Engaged students actively participate in green initiatives and feel a strong connection to the university’s sustainability goals. As a result, they are more likely to share positive experiences and advocate for the institution, contributing significantly to the institution’s overall corporate image.

- Importance of Sustainable Practices: The research underscores the importance of implementing sustainable practices within higher education institutions. By prioritizing environmental efforts, universities not only create a positive campus environment but also enhance their corporate image and reputation. This, in turn, has the potential to attract environmentally conscious students and stakeholders, fostering a more sustainable and responsible future for both the institution and the broader community.

In conclusion, this study demonstrates the profound significance of green-smart campus initiatives in promoting sustainability, student engagement, and a positive university corporate image. It emphasizes the importance of universities prioritizing environmental efforts to create an eco-friendly campus environment that enhances the overall educational experience. Engaging students in sustainability initiatives fosters a sense of belonging and pride while positively influencing how the institution is perceived by various stakeholders.

Based on the research findings, several practical recommendations are proposed to promote sustainability and environmental consciousness within universities: Firstly, universities should prioritize and invest in green-smart campus initiatives. This entails implementing recycling programs, undertaking renewable energy projects, and offering eco-friendly transportation options. By doing so, the aim is to enhance student engagement and foster a positive corporate image for the university. Secondly, it is crucial to integrate sustainability principles into the very fabric of the university’s culture and curriculum. By instilling a sense of responsibility and ownership among students toward environmental issues, the university can play a vital role in nurturing future generations committed to sustainable practices. Thirdly, actively involving students in the planning and execution of sustainability initiatives can be highly beneficial. Such engagement fosters a sense of ownership and pride among students, resulting in increased participation and a more favorable perception of the university. Finally, effective communication of the university’s green-smart campus initiatives is essential. By ensuring that students, faculty, staff, and external stakeholders are well-informed about the sustainability efforts, the university can strengthen its corporate image and attract like-minded individuals and partners who value sustainability and environmental stewardship.

Research Limitations and Future Studies

Despite the valuable insights gained, this research has certain limitations. The study was conducted at a specific university, limiting the generalizability of the findings to other institutions and contexts. Future research could address this limitation by replicating the study in diverse settings with a larger sample size for broader representativeness. The study relied on self-reported data through an online questionnaire, which may be subject to response bias and social desirability. Future research could employ mixed-method approaches, incorporating qualitative interviews or focus groups to gain deeper insights into students’ perceptions and experiences regarding sustainability initiatives on campus.

Building on this study, future research could explore additional factors that influence the relationship between green-smart campus initiatives, student engagement, and university corporate image. Investigating the role of faculty support, institutional policies, or student demographics could provide deeper insights into the underlying dynamics. Further research could examine the long-term effects of green initiatives on student engagement and university reputation through longitudinal studies. Such studies would provide a comprehensive understanding of the sustainability and impact of these initiatives over time. Additionally, conducting comparative studies across different countries or cultural contexts could enrich our understanding of how cultural factors influence students’ engagement with green-smart campus initiatives. Examining variations in the impact of sustainability initiatives among universities with different levels of commitment to environmental responsibility could offer valuable insights for institutional decision-making and policy development.

Footnotes

Acknowledgements

I extend my gratitude to all the respondents who provided their support and participated in the data collection for this research.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

I ensure that all procedures are conducted in adherence to the appropriate guidelines and regulations set forth by my university.

ORCID iD

Hani Al-Dmour

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Green-Smart University Campuses: The Mediating Role of Student Engagement in Enhancing Corporate Image

Abstract

Plain Language Summary

Keywords

Introduction

Green-Smart University Campus: Concept and Components

Literature Review and Hypotheses Development

Green Smart Campus and University Image

University Image and Student Engagement

Green Smart Campus and Student Engagement

Green Smart Campus, University Image and Student Engagement

The Study Model

Research Methodology

Descriptives of the Demographic Profile

Common-Method Variance (CMV)

Statistical Analysis

Measurement Model Assessment

First-Order Constructs’ Assessment

Second-Order Construct Assessment

Assessing the Structural Model

Research Results Discussion

Research Conclusion and Practical Implications

Research Limitations and Future Studies

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

Data Availability Statement

References