Exploring Human Values and Students’ Aspiration in E-Learning Adoption: A Structural Equation Modeling Analysis

Abstract

This study explores the significance of students’ aspirations as motivation and expectations in adopting e-learning, considering the influence of cultural values. The research utilizes a theoretical framework that integrates the Technological Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and the DeLone and McLean Information System Success Model, along with Schwartz’s human values. Employing a quantitative approach, the study investigates the relationship between social factors and e-learning adoption through a survey of 509 students at King Abdulaziz University in Saudi Arabia using a structured questionnaire and Structural Equation Model for hypothesis testing. Results reveal that students with a proclivity for change and self-enhancement have higher motivation and expectations towards e-learning, while those with conservative perspectives show lower motivation and expectations. Additionally, students prioritizing self-enhancement and openness to change are more likely to actively engage in e-learning adoption. This research contributes to sustainability by highlighting how fundamental human values influence e-learning adoption. It also emphasizes the role of sustainable education and transformative learning processes in shaping attitudes towards e-learning. These insights inform the development of effective e-learning programs, benefiting the field of e-learning research and providing valuable guidance to researchers, policymakers, and decision-makers in creating more inclusive and sustainable educational practices.

1. Introduction

Educational systems have reflected the educational needs of society in each historical period [1]. A brief examination of the historical evolution of educational systems over time shows that these changes have been closely related to larger societal changes in each period [2]. In the agrarian era, for example, simple training was the primary focus of the educational system. Classical education, as expressed by face-to-face interaction, was essential in the educational system during the Industrial Age. The current information society demands that individuals access knowledge through information and communication technology (ICT) to keep pace with advancements. This has led to a transformative shift from traditional teaching methods to virtual education, facilitated by technology [3].

Today, the utilization of ICT marks the beginning of a new era in education [4]. This technology supports and facilitates the shift from teacher-centered to student-centered learning [5]; it has been demonstrated that ICT and e-learning can improve the quality of higher education [6,7]. This shift aligns with the goals of sustainable development by promoting active engagement, motivation, and skill acquisition among students. Innovative methods, such as increasing student motivation, interest, and participation, facilitating skill acquisition, and improving teacher training, can improve the quality of learning outcomes [8]. Therefore, universities must adapt their services and content to align with current societal ICT trends [9]. However, changes in higher education are not solely dependent on technology, but also on human resources and the ability of universities to effectively manage new technologies and their potential [10].

The adoption of e-learning is not just a matter of implementing a technological solution; it is a process that involves a variety of factors, particularly social factors, which play a crucial role in achieving the objectives of using technology in education [11]. As technology becomes more prevalent in our daily lives, the impact of culture on technology adoption becomes increasingly important. This acknowledges the importance of institutional adaptability, faculty training, and leadership in facilitating transformative teaching and learning experiences.

To ensure the success of this new educational system, educational institutions must not only work to create high-quality infrastructure for sophisticated e-learning, but must also work to understand students’ perceptions of this technology-dependent environment and place their aspirations and expectations at the center of the educational process [12]. The models of technology adoption that are most frequently used by researchers typically look at how individuals behave and what their intentions are when using the technology [13]. Despite the importance of social factors, few studies have included them in the acceptance of technology models [14,15] and more particular social factors that focus on specific culture.

In 1980, Hofstede discussed in depth the multilevel nature of culture in Culture’s Consequences [16]. He proposed, using an “onion” diagram, that values are the core of culture, while practices, as expressed in rituals and symbols, are the outer layers [17]. Therefore, values are the most important societal cultural factor and can be used in quantitative research as an indicator of societal cultural variation. Although culture has been incorporated in models of technology adoption [18], researchers have focused on the values that represent the individual and are thus the most appropriate to include in models of technology adoption [19,20]. Furthermore, values are used to describe societies and individuals, track changes over time, and, most importantly, to explain the motivational underpinnings of attitudes and behaviors [21]. There have been few studies attempting to investigate the impact of values on the acceptance of the use of technology in general adoption models, and even fewer in e-learning [22].

This study aims to investigate the role of societal culture, particularly values, in the adoption of e-learning among Saudi Arabian students in higher education. By developing an adaptive model that considers students’ expectations and motivations for using e-learning, this research seeks to understand how cultural values influence technology adoption. The paper is organized into five sections, encompassing a literature review, research questions and hypotheses, methodology and results, outcomes, and conclusions and future work. Through this investigation, valuable insights into the impact of societal culture on e-learning adoption will be gained, providing practical guidance to improve the implementation of this technology in higher education. Additionally, this research contributes to sustainability by exploring the role of sustainable education and transformative learning processes in shaping students’ attitudes towards e-learning, fostering a more inclusive and sustainable approach to education in the digital age.

2. Literature Review

2.1. Basic Human Values

Values are a crucial aspect in the social sciences and play a central role in many areas of psychology. They are used to characterize individuals or communities [23] and play a significant role in fields such as sociology, psychology, anthropology, and others. Changes in communities and individuals’ lives are organized and explained based on their values. Values are pivotal in elucidating the organization and explanation of shifts within communities and individuals’ lives. Their purpose lies in comprehending the fundamental underpinnings of attitudes and behaviors, as well as their developmental trajectories [24]. However, the social sciences had yet to achieve a consensus regarding the concept and nature of these foundational values and lacked a valid empirical framework for their measurement [25]. To bridge this gap, Schwartz [26] introduced the theory of basic human values, transcending cultural boundaries. This theory underwent validation through the Schwartz survey, involving participants from over 70 nations to assess the proposed values’ authenticity [27]. Schwartz’s seminal work in 1992 introduced a comprehensive theoretical model that unveils the intricate interactions between various types of motivational values. This model forms the bedrock of understanding the complexities underlying human values and how they shape attitudes, behaviors, and cultural dynamics.

At the core of Schwartz’s model are ten fundamental human values, each representing distinct motivational goals: hedonism, stimulation, self-direction, universalism, conformity, benevolence, achievement, security, power, and tradition. These values are not isolated entities, but are interconnected along a continuum, forming a circular arrangement that encapsulates their relationships [26], as presented in Figure 1.

The arrangement signifies a dynamic interplay between compatibility and conflict. Values positioned closer to each other on the circle have a higher alignment in their underlying motivations. Such proximity indicates that these values share common core motives, resulting in a higher degree of compatibility and synergy. Conversely, values positioned further apart on the circle reveal disparities in their motivational foundations, indicating potential conflicts or incongruities in their coexistence [27].

The model highlights both the individual and societal dimensions of values. While individuals prioritize and align values differently based on personal circumstances and cultural influences, communities also possess value configurations. These configurations result from the collective alignment of individuals’ values within a particular cultural context [26].

Crucially, the model provides insights into the mechanisms through which values influence attitudes and behaviors. Values serve as cognitive structures that guide individuals’ perceptions of the world, shaping their interpretations and responses. For instance, individuals valuing achievement and self-direction might display a proactive approach to challenges, seeking personal growth and autonomy. Conversely, those prioritizing security and tradition might exhibit a more risk-averse and conformist behavior [28].

Schwartz’s theoretical model not only explicates value interactions within individuals but also extends this to societal contexts. It aids in understanding how the compatibility and conflict between values contribute to cultural dynamics, affecting social norms, group cohesion, and even intergroup relations. Moreover, this model forms the basis for empirical studies that explore the relationships between values and diverse outcomes, offering a framework to decipher the intricate mechanisms through which human values influence various facets of life [21].

2.2. Aspiration

The term “aspiration” has been used with different connotations throughout history [29]. The theory of student aspirations, developed by Quaglia and Cobb [30], encompasses inspiration and ambition. According to this theory, aspirations are defined as “a student’s ability to identify and set future goals while being motivated to work towards these goals in the present”. This perspective acknowledges the role of schools in shaping the aspirations of young individuals [31]. Various factors influence the desire to succeed, and schools can foster an environment where success is celebrated. According to Tani et al. [32], aspirations are a significant aspect of demonstrating a student’s engagement in their education, which is a key component of academic achievement.

Students’ aspirations are a clear indication of their commitment to education and their belief in its significance as a vital step towards their future careers [33]. These aspirations empower students to comprehend the benefits of education for their future and align with their educational expectations, and inspires them and makes the learning process more enjoyable [34]. Research has established a strong connection between students’ expectations, motivations, and enjoyment on one hand, and their goals on the other [35]. The goals of students demonstrate their dedication to obtaining education, which serves as a cornerstone for their future careers. Additionally, aspirations aid students in recognizing the value and benefits of education for their future by meeting their expectations for the learning process, fostering motivation, and making learning an enjoyable experience throughout the process [36].

A student’s aspirations are their expectations for the future. What a learner anticipates will transpire in the future is their anticipation [37]. In his research, Khattab [37] demonstrated that students with higher aspirations or expectations outperform students with lower aspirations or expectations. Furthermore, there is a perfect correlation between high expectations and high aspirations, which is the most significant predictor of students’ future educational conduct [37]. This means that students’ aspirations and expectations can have the same effect on their ability to learn.

The concept of “desire to learn” characterizes students’ motivation [38], and aspirations might be termed “long-term goals” within this context [30]. Aspirations can inspire students to put forth extra effort and complete their assignments in order to reach their intended objectives. According to Olive et al. [39], motivation was an indicator of interest in a career in STEM subjects (science, technology, engineering, and mathematics) among primary students as well as among secondary students.

In light of the reviewed literature, the realm of student psychology illustrates a dynamic interplay involving aspirations, expectations, and motivations that shapes the core of ambition. Aspirations are directed by the fusion of expectations and motivations. Expectations guide students towards goals, while motivations infuse their journey with purpose. This synergy crafts a narrative of resolute purpose, where aspirations rise, expectations anchor, and motivations drive. This symbiotic interweaving propels students towards their aspirations, showing that these aspirations are partly rooted in the fusion of expectations and motivations.

2.3. E-Learning and Technology Adoption

E-learning has shifted the focus from a teacher-centered approach to a more student-centered one [40], and it is seen as a novel method for delivering information and data [41]. Many institutions have been serious about implementing e-learning systems over the past ten years. They knew how important it was for e-learning to change how people learn using technology and the Internet [42]. However, the educational system still requires assistance to ensure the success of the e-learning system, which is critical for tracking the development of key university programs [43].

During the COVID-19 pandemic, e-learning and distant education were deployed in educational institutions throughout the world to promote continuous learning and prevent the spread of this pandemic [44,45]. The understanding of the factors that influence student willingness and the adoption of e-learning is crucial to the success of such systems [36]. However, to effectively integrate technology into education and enjoy its related benefits, greater knowledge of the antecedents of e-learning adoption within e-learning platforms is required [46].

The examination of users’ intentions for utilizing new technology can be accomplished through various frameworks, ranging from basic to complex and in-depth models. Parasuraman [47] introduced a multiple-item scale, known as the Technology Readiness Index (TRI), to assess users’ readiness to adopt new technologies. Davis [48] developed the Technology Acceptance Model (TAM) under the theories of reasoned action (TRA) [49] and planned behavior (TPB) [50] to explain and predict the acceptance of new technology among potential users. TAM is considered the most widely used theoretical framework for evaluating the adoption of new technologies. Venkatesh and Davis [51] expanded on TAM with the creation of TAM2, which describes perceived usefulness and intentions of use in relation to social influence and cognitive instrumental processes. Venkatesh and Bala [52] further developed TAM and TAM2 into TAM3, which identifies and speculates on the common determinants of perceived usefulness and perceived ease of use. The Unified Theory of Technology Acceptance and Use (UTAUT) developed by Venkatesh et al. [53] is widely used and validated and was designed to characterize users’ technology adoption behavior in an organizational setting. UTAUT2 [54] is a more detailed version of UTAUT that examines how people use technology from their perspectives. DeLone and McLean [55,56] created the Information System Success (ISS) Model to identify the most important aspects of an information system and the ways in which they affect user acceptance and benefits.

Several researchers have addressed the issue of e-learning adoption by deploying the previously discussed models in adapted forms, either separately or in combination. Bessadok [57] examined the readiness of students to use the university’s e-learning system based on the TRI model. Tawafak et.al [58] used the Technology Acceptance Model (TAM) and the Confirmation of Expectations Model (ECM) as guiding academic adopted models in their study to identify the major factors influencing student acceptance of e-learning. In their research, Miah [59] used UTAUT to learn more about the factors that affect e-learning acceptance and to make it easier for students to use the system. Based on an extended UTAUT model, Revythi and Tselios [60] concluded that developers and other stakeholders in e-learning should pay attention to certain factors to improve system acceptance and effectiveness in learning management.

Zacharis and Nikolopoulou [61] have extended the Unified Theory of Acceptance and Use of Technology (UTAUT2) model by incorporating the constructs of “learning value” and “empowerment in learning” to examine the factors that predict university students’ intentions to use e-learning platforms in the post-pandemic era.

In recent years, there has been a significant amount of scholarly attention directed towards evaluating the effectiveness of e-learning systems [62]. This interest has been further heightened by the COVID-19 pandemic, which has made e-learning a crucial issue for higher education institutions [63]. To measure the success of e-learning systems, researchers have adopted the Information Systems Success (ISS) model developed by DeLone and McLean [56], and some have also used its expanded version [64].

Several studies have aimed to understand the performance of e-learning systems by incorporating multiple models [65]. For example, Mardiana et al. [66] augmented the Information Systems Success (ISS) model by incorporating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to identify relevant antecedents for the intention to use new technologies. Similarly, Lopes et al. [67] employed a combination of the TAM and UTAUT models to examine how and why individuals use e-learning systems, based on their behavior and intentions. Additionally, Mohammadi [68] integrated the TAM model with the ISS model to investigate the effects of quality features, perceived ease of use, and perceived benefits on user intentions and satisfaction, as well as the mediating effect of usability on the use of e-learning.

The acceptance and adoption of e-learning systems have been widely studied, with much attention given to technology acceptance and adoption within homogeneous cultural groups. However, the impact of individual-level values has received limited attention. Research has sought to explore how values, as a cultural factor, impact students’ motivations and expectations for accepting e-learning systems.

Mehta et al. [22] investigated the influence of individual-level values on e-learning adoption among workers in The Gambia and the United Kingdom. This study incorporated values related to maintaining the status quo and self-enhancement from Schwartz’s theory of human values. Tarhini et al. [69] aimed to examine the impact of individual-level culture on the adoption and acceptance of e-learning tools by students in Lebanon. The study expanded upon the Technology Acceptance Model by including two additional constructs and cultural variables as moderators to understand how cultural context may influence e-learning acceptance.

Despite the significance of values in impacting students’ acceptance of e-learning systems, more research is necessary to examine e-learning technology adoption considering individual cultural aspects. The current study aims to fill this gap by examining the acceptance of e-learning by incorporating students’ aspirations as an indirect effect between values and technology adoption.

In conclusion, studies have shown that the integration of different models can provide a more comprehensive understanding of the factors that influence e-learning adoption. However, most of these studies focused on homogeneous cultural groups, and more research is needed to understand how individual-level values and cultural factors influence e-learning adoption.

3. Research Model and Hypotheses

3.1. Research Model

In this study, we are particularly interested in examining how basic human values affect students’ aspirations, which are expressed by their motivations and expectations with regard to their acceptance and usage of e-learning systems. Our research model, shown in Figure 2, incorporates external variables of Shwartz’s basic human values and students’ aspirations. This model is based on the integration of the TAM, the UTAUT, and the DeLone and McLean Information Systems Success model (as depicted in Figure 2).

More specifically, the research objectives of this study are as follows: (i) to examine the impact of the Schwartz basic human values on e-learning adoption through use aspiration; (ii) to increase our understanding of the influence of values on behavior in the use of technology; and (iii) to increase the analytical potential and predictive precision of a parsimony questionary based on the known TAM model for broader applications in information systems and education technology research.

3.2. Hypotheses

In light of the present research objectives, a set of hypotheses has been formulated based on the foundational constructs of the Technology Acceptance Model (TAM). These hypotheses will be thoroughly elucidated in the subsequent sections.

The proposed research model hypotheses are organized into five components, as follows:

Values ← → Values

The first component of the study comprises Schwartz’s, hypothesis which categorizes the ten values into four groups that should be validated through the research model. The following hypotheses are proposed:

H1a. 

The self-transcendence value is positively correlated with the conservation value.

H1b. 

The openness to change value has a negative correlation with the self-transcendence value.

H1c. 

The self-enhancement value has a negative correlation with the self-transcendence value.

H1d. 

The openness to change value has a positive correlation with the self-enhancement value.

H1e. 

The openness to change value is negatively correlated with the conservation value.

H1f. 

The self-enhancement value is negatively correlated with the conservation value.

Values → Aspiration

Values are ingrained beliefs about the best circumstances or outcomes, independent of the current situation, that can act as a guide for behavior [70]. At the individual level, values explain the reasons for attitudes and behaviors, and placing value structures first at the group level aids in defining culture [20]. The causal relationship between values and adoption constructs describes the effect of Schwartz’s basic human values on a student’s motivations and expectations to engage in an e-learning experience. In this second component, the hypotheses generated from these relationships are as follows:

H2a. 

There is a negative correlation between the conservation value and motivation.

H2b. 

There is a negative correlation between the conservation value and expectation.

H3a. 

There is a positive correlation between the self-enhancement value and motivation.

H3b. 

There is a positive correlation between the self-enhancement value and expectation.

H4a. 

There is a positive correlation between the openness to change value and motivation.

H4b. 

There is a positive correlation between the openness to change value and expectation.

H5a. 

There is a negative correlation between the self-transcendence value and motivation.

H5b. 

There is a negative correlation between the self-transcendence value and expectation.

Aspiration→ Adoption

The third component of the study examines the impact of aspiration factors on adoption factors. Students’ aspirations are heightened when their expectations about the learning experience and motivations are met [36], and this in turn leads them to take action [71]. Similarly, DeLone and McLean [56] define service quality as users’ motivation to ensure long-term success. The following hypotheses are proposed:

H6a. 

Motivation positively influences perceived ease of use.

H6b. 

Motivation positively influences perceived usefulness.

In DeLone and McLean’s [56] model, “system quality” refers to the features and characteristics that users expect to find when using such systems. Furthermore, Santos et al. [72] stress the importance of expectations in “service quality”. Similarly, Venkatesh et al. [54] employ performance expectation and effort expectation in their extended UTAUT2 model to explain better usage performance and user-friendly expectations of users, respectively. In this study, we have summarized the “system quality” and “service quality” factors of the ISS model, as well as the “performance expectancy” and “effort expectancy” factors in the UTAUT2 model, into the “expectations factor” that is used to interpret student expectations from such e-learning experiences. The corresponding hypotheses are:

H7a. 

Students having high expectations positively affects their perceived ease of use of e-learning.

H7b. 

Students having high expectations positively affects their perceived usefulness of e-learning.

Adoption → Adoption

The fourth component of the study focuses on the main adoption factors. According to Davis [48], in the Technology Acceptance Model (TAM), the perceived ease of use factor plays a crucial role in determining students’ readiness to adopt e-learning systems. The following hypothesis is proposed:

H8. 

Perceived ease of use has a positive impact on the perceived usefulness of an e-learning system.

Adoption → Acceptance

In the fifth component, adoption is determined when students perceive that e-learning will be beneficial to them. According to Davis [48], the Technology Acceptance Model (TAM) defines “perceived benefit” as the extent to which the user believes that using a particular system will improve their work performance, while “perceived usability” refers to the mental effort and ease of learning required when using the technology. The following hypotheses are proposed:

H9. 

Perceived usefulness has a positive impact on attitudes towards the intention to use an e-learning system.

H10. 

Perceived ease of use has a positive impact on attitudes towards the intention to use an e-learning system.

4. Methods

The research model, illustrated in Figure 2, aims to investigate the factors that influence students’ acceptance of e-learning systems by examining the impact of basic human values on the aspirations construct through motivations and expectations. The model is operationalized through the use of quantitative techniques, such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), within a structural equation modeling (SEM) framework. This approach allows for a more comprehensive understanding of the causal relationships between the constructs in the model.

4.1. Participants and Procedures

The study utilized an online survey platform, Google Forms, to gather data. The survey consisted of five sections, including socio-demographic information, Schwartz’s basic value elements, aspirations, adoption, and acceptance constructs. The questionnaire was translated into Arabic and distributed to registered students at King Abdulaziz University. Participants were asked to rate the constructs using a 5-point Likert scale that ranged from “strongly agree” to “strongly disagree”. The introduction of the questionnaire emphasized the confidential and anonymous nature of the study and emphasized that participation was voluntary. The Appendix A includes additional information on the metrics used in this study. The data were analyzed using IBM SPSS Version 25 and IBM AMOS Version 24 software. The survey was conducted between September 2021 and March 2021, with a total of 509 questionnaires returned. Demographic information of the participants is provided in

Table 1. Demographic information of respondents.

Demographic	Group	Frequency	Percentage
Gender
	Woman	208	40.1%
	Man	301	59.9%
Age
	<19	8	1.6%
	19–22	336	66.0%
	23–26	98	19.2%
	>26	67	13.2%
Education
	Bachelor’s	317	62.2%
	Master’s	89	17.5%
	Ph.D.	57	11.2%
	Other	46	9.1%
E-learning courses enrolment
	No courses	14	2.9%
	One course	36	24.1%
	More than two courses	459	2.6%

.

4.2. Structural Equation Model

As seen in Figure 2, the proposed research model encompasses a complex array of relationships between various variables. Considering this, SEM was deemed an appropriate method for analyzing these structured relationships, as it offers great flexibility. SEM is a multivariate statistical method that combines CFA and multiple regression analysis. Two types of SEM that are commonly used include partial least squares SEM [73] and CB SEM [74], which are both rooted in the same theoretical framework [75]. The implementation of SEM involves two phases: the evaluation of the measurement model and the evaluation of the structural model [76]. The first stage pertains to how indicator variables define the constructs, while the second stage illustrates how the constructs are interdependent on one another through multiple dependency relationships [77,78].

5. Results

5.1. Measurement Model

In SEM, the measurement model is used to evaluate the validity of the indicators for each construct. EFA is typically employed in the evaluation of measurement models to identify the dimensionality of the construct and to guide the scale development process [79]. Once a well-established scale and prior knowledge of the factor structure has been established, confirmatory factor analysis (CFA) is considered more appropriate [80].

To ensure that the indicators accurately measure the model’s constructs, EFA was used to examine the consistency of each factor several times. Items with low factor loadings below 0.5, as suggested by Bagozzi and Yi [81], were removed as they indicated weak relationships with other indicators, until a clear relationship between the different factors of the model was established. The remaining indicators that measure each construct of the model are presented in

Table 2. The measurement model loading factors *.

Item	Loading Factor
	1	2	3	4	5	6	7	8	9
CV_1	0.806
CV_2	0.898
CV_3	0.910
CV_4	0.943
CV_5	0.872
PE_1							0.857
PE_2							0.749
PE_3							0.825
PE_4							0.819
MV_1			0.853
MV_2			0.866
MV_3			0.835
MV_4			0.888
MV_5			0.683
SE_1				0.897
SE_2				0.926
SE_3				0.972
SE_4				0.928
OC_1					0.898
OC_2					0.810
OC_3					0.729
OC_4					0.878
OC_5					0.657
IU_1						0.874
IU_2						0.925
IU_3						0.759
IU_4						0.754
PU_1		0.804
PU_2		0.852
PU_3		0.955
PU_4		0.949
PU_5		0.810
EP_2								0.709
EP_3								0.765
EP_4								0.881
EP_1								0.761
ST_1									0.782
ST_2									0.741
ST_3									0.762
ST_5									0.726

Note: Extraction method: maximum likelihood. Rotation method: promax with Kaiser normalization. Item: explained in Appendix A, * rotation converged in 6 iterations.

.

To ascertain the robustness of the partial correlation between the constructs of the model, Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests were applied. Values of KMO near 1.0 are considered optimal, while values below 0.5 are deemed inadequate. It is generally accepted that a KMO value of at least 0.80 [77], as presented in

Table 3. KMO and Bartlett’s test.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.877
Bartlett’s Test of Sphericity	Approx. Chi-Square	16,272.699
	df	780
	Sig.	0.000

, is sufficient for factor analysis to proceed.

The assessment of the model constructs’ quality entailed the utilization of reliability and validity measures, as outlined by Fornell et al. [82]. Reliability was determined through Cronbach’s alpha, with a criterion of exceeding 0.7 for each latent variable, in accordance with Jöreskog’s [83] recommendation. Convergent validity was evaluated through composite reliability (CR) and average variance extracted (AVE), adhering to the minimum thresholds of 0.7 and 0.5, respectively, as proposed by Bagozzi and Yi [81]. Discriminant validity, as defined by Anderson and Gerbing [76], assesses the extent to which a specific construct differs from other constructs within the research design. It is considered established when the correlation value between two constructs is lower than the square root of the AVE, as stated by Fornell et al. [82]. The results presented in

Table 4. Validity measures ^a.

Measure	ST	CV	PU	MV	SE	OC	IU	PE	EP
Alpha	0.840	0.950	0.944	0.915	0.963	0.898	0.901	0.886	0.878
CR	0.840	0.950	0.944	0.919	0.963	0.900	0.900	0.887	0.880
AVE	0.568	0.793	0.770	0.694	0.868	0.643	0.692	0.662	0.648

^a Constructs abbreviated as follows—self-transcendence (ST), conservation (CV), perceived usefulness (PU), motivations (MV), self-enhancement (SE), open to change (OC), intention to use (IU), perceived ease of use (PE), expectations (EP); alpha: Cronbach’s alpha. CR: composite reliability, AVE: average variance extracted.

meet the predetermined criteria.

Table 5. The measurement model’s discriminant validity ^b.

Measure	ST	CV	PU	MV	SE	OC	IU	PE	EP
ST	0.754 c
CV	0.108	0.890 c
PU	−0.062	−0.047	0.878 c
MV	−0.073	−0.369	0.092	0.833 c
SE	−0.341	−0.126	−0.031	0.169	0.931 c
OC	−0.237	−0.451	0.026	0.260	0.166	0.802 c
IU	−0.031	0.002	0.368	−0.030	−0.033	0.028	0.832 c
PE	−0.002	−0.118	0.238	0.153	−0.039	0.040	0.100	0.813 c
EP	−0.141	−0.412	0.045	0.635	0.191	0.292	−0.039	0.111	0.805 c

^b Constructs abbreviated as follows—self-transcendence (ST), conservation (CV), perceived usefulness (PU), motivations (MV), self-enhancement (SE), open to change (OC), intention to use (IU), perceived ease of use (PE), expectations (EP). ^c The value of the square roots of the AVE.

showcases the cross-correlation estimates along the diagonal and the square roots of the AVE for the remaining values. As per Fornell et al. [82], the discriminant validity for each item is established when the square root of the AVE surpasses the inter-item correlations. The correlation matrix in Table 5 illustrates that convergent correlations are stronger than discriminative ones, thus supporting both convergent and discriminant validity.

Kline [78] provides an explanation of the evaluation of model fit using a range of indices. These fit indices are categorized into three groups: absolute fit, parsimonious fit, and incremental fit. According to Hair [84], each of these categories comprises several fit indices. Absolute fit indices, including the chi-square statistic, goodness-of-fit index (GFI), and standardized mean residual (SRMR), are employed to assess the extent to which a measurement model accurately replicates the observed data. Parsimonious fit indices, such as the adjusted fit index (AGFI) and root mean square error of approximation (RMSEA), consider the model’s complexity. Incremental fit indices, such as the Comparative Fit Index (CFI), Bentler–Bonett Normed Fit Index (NFI), and Tucker–Lewis Index (TLI), are utilized to evaluate how well the specified model fits in comparison to an alternative benchmark model.

A common method for measuring model fit can be used for a variety of indicators that prove the presence of these relationships between model factors, such as those exposed in

Table 6. The measurement model’s fit indices.

Fit Statistics	Value	Recommended Value
Chi-square/df	1.875	<3
GFI	0.959	>0.9
GFI	0.928	>0.8
NFI	0.958	>0.9
TLI	0.950	>0.9
RMSEA	0.050	<0.06
SRMR	0.051	<0.06

.

5.2. Structural Model

Upon completion of the measurement model step, the next step is to estimate the parameters of the research model in the structural model assessment step. In this second-stage SEM analysis, the evaluation of the structural model was applied to all the causal relationships presented in the research model [85]. To evaluate the appropriateness of the structural equation model, a set of indicators, as recommended by Hair [84], such as those displayed in

Table 7. The structural model’s fit indices.

Fit Statistics	Value	Recommended Value
Chi-square/df	1.939	<3
GFI	0.959	>0.9
GFI	0.928	>0.8
NFI	0.917	>0.9
TLI	0.954	>0.9
RMSEA	0.043	<0.06
SRMR	0.053	<0.06

, were used. These indicators confirm that the recommendations for indicators and the significance of relationships with the structures of the research model have been met.

Figure 3 illustrates that the relationships between the model constructs, except for the associations involving self-transcendence, motivation, expectation, and perceived usefulness, were not statistically significant. However, these relationships exhibited the same direction as hypothesized in the research model, as outlined in

Table 8. Hypothesis test summary.

Hypothesis	Beta	StError	|t-Value|	p-Value	Supported
H1a. ST $\leftrightarrow$ CV	0.108	0.027	2.157	0.031	Yes p < 0.05
H1b. ST $\leftrightarrow$ OC	−0.237	0.031	−4.493	0.000	Yes p < 0.001
H1c. ST $\leftrightarrow$ SE	−0.341	0.038	−6.392	0.000	Yes p < 0.001
H1d. SE $\leftrightarrow$ OC	0.166	0.037	3.452	0.000	Yes p < 0.001
H1e. CV $\leftrightarrow$ OC	−0.450	0.032	−8.279	0.000	Yes p < 0.001
H1f. CV $\leftrightarrow$ SE	−0.126	0.033	−2.706	0.007	Yes p < 0.005
H2a. MV $\leftarrow$ CV	−0.317	0.046	−6.182	0.000	Yes p < 0.001
H2b. EP $\leftarrow$ CV	−0.350	0.037	−6.721	0.000	Yes p < 0.001
H3a. MV $\leftarrow$ SE	0.120	0.032	2.552	0.011	Yes p < 0.05
H3b. EP $\leftarrow$ SE	0.118	0.026	2.509	0.012	Yes p < 0.05
H4a. MV $\leftarrow$ OC	0.109	0.044	2.074	0.038	Yes p < 0.05
H4b. EP $\leftarrow$ OC	0.112	0.035	2.129	0.033	Yes p < 0.05
H5a. EP $\leftarrow$ ST	−0.034	0.037	−0.673	0.501	No
H5b. MV $\leftarrow$ ST	0.025	0.046	0.487	0.627	No
H6a. PE $\leftarrow$ MV	0.137	0.067	2.754	0.006	Yes p < 0.05
H6b. PU $\leftarrow$ MV	0.068	0.041	1.440	0.150	No
H7a. PE $\leftarrow$ EP	0.030	0.086	0.605	0.545	No
H7b. PU $\leftarrow$ EP	−0.022	0.052	−0.461	0.644	No
H8. PU $\leftarrow$ PE	0.230	0.031	4.739	0.000	Yes p < 0.001
H9. IU $\leftarrow$ PU	0.366	0.053	7.583	0.000	Yes p < 0.001
H10. IU $\leftarrow$ PE	0.012	0.033	0.244	0.807	No

.

6. Discussion

The advent of e-learning as a means of instruction has arisen as a result of the digitization of educational systems [86]. It is imperative to comprehend the cultural factors that impact students’ acceptance of e-learning for the successful implementation of an e-learning system. As stated by Hofstede et al. [16], values are a fundamental aspect of culture and have a significant impact on individuals’ attitudes and behaviors, as well as defining a group’s culture [19].

The research model, as depicted in Figure 2, has improved explanatory and predictive abilities by incorporating new constructs such as basic human values and student aspirations, creating a more intricate network of interrelated causal relationships, surpassing the original TAM, UTAUT, Mclean and Delone models.

The significance of this research lies in its contribution to the comprehension of the crucial role played by basic human values and student aspirations in the adoption of e-learning. Our findings indicate that while values such as conservation and self-transcendence have a negative effect on motivation and expectations towards e-learning, values such as openness to change and self-enhancement have the greatest indirect impact on the acceptance of this new form of learning. These insights underscore the intricate relationship between values and e-learning adoption. Meanwhile, Mehta et al. [22] emphasized the indirect influence of the achievement value on learner intentions to use e-learning via adoption factors, further illustrating the multifaceted nature of this relationship. Collectively, these findings contribute to a comprehensive understanding of how individuals’ value orientations shape their attitudes towards e-learning, providing valuable insights for educators and stakeholders in the field.

The study’s findings confirm, through the estimation of model parameters, the total model of the values’ relations of conflict and congruence (H1a, H1b, H1d, and H1f) as postulated by the fundamental theory of human values. In fact, the signs of the first model’s hypothesis (H1a > 0, H1b < 0, H1c < 0, H1d > 0, He < 0 and Hf < 0) support Schwartz’s value continuum, which focuses on the opposite poles of social and personal orientation [87]. The findings also confirm the order of values that describes motivational conflicts and compatibilities, which boil down to two fundamental dimensions: the values of self-transcendence versus the values of self-enhancement (H1c) and the values of conservation versus the values of openness to change (H1e).

The results show the positive role of self-enhancement and openness to change in motivations (H3a = 0.12 and H4a = 0.11) and expectations (H3b = 0.12 and H4b = 0.11). In contrast, the results show the negative impact of conservation value on motivations (H2a = −0.32) and expectations (H2b = −0.35). In fact, a student with a high self-enhancement value will be motivated and expect more from the e-learning experience; on the other hand, the more conservative the student, the less motivated he or she may be, and the lower expectations driven by e-learning he or she may have.

Furthermore, this study underscores the significance of student aspirations in relation to the adoption of e-learning. The findings reveal that students with higher aspirations for their future careers and education display a greater inclination towards adopting e-learning. Notably, the results establish that motivations serve as the most influential factor in predicting the intention to use e-learning, exerting a positive effect throughout the pathway encompassing perceived ease of use and perceived usefulness. Specifically, motivations positively impact perceived ease of use (H6a = 0.14), subsequently leading to a positive influence on perceived usefulness (H8 = 0.23), and ultimately shaping the intention to use e-learning (H10). These outcomes align with prior research that has established a positive association between students’ aspirations and their level of engagement in educational pursuits [32]. Therefore, it is imperative for educational institutions to comprehend and support students’ aspirations in order to effectively implement and leverage e-learning systems.

7. Conclusions

In conclusion, this study offers a more comprehensive perspective on the impact of various values on e-learning adoption. The findings reveal that self-enhancement and openness to change positively influence motivations and expectations towards e-learning, aligning with the adaptability and innovation essential for addressing sustainability challenges, whereas conservation values have a negative effect. This underscores the significance of considering the specific values that may impact student attitudes towards e-learning.

Moreover, the study’s results confirm the vital role of student aspirations in e-learning adoption, as students with higher aspirations are more likely to adopt e-learning. The validation of the pivotal role of student aspirations in e-learning adoption underscores their potential to act as catalysts for transformative learning experiences. Students with higher aspirations exhibit greater receptivity to e-learning, underscoring the significance of nurturing ambitious goals and aspirations that harmonize with sustainability objectives. By harnessing e-learning as a tool to facilitate the realization of these aspirations, a potent link can be forged between educational technology and the pursuit of sustainable development.

Overall, this research highlights the necessity of incorporating cultural and individual factors in the design and implementation of e-learning systems. The imperative of incorporating cultural and individual factors into the design and implementation of e-learning systems echoes the broader objectives of education for sustainable development. This approach recognizes the diversity of cultural perspectives and individual needs, ensuring that e-learning initiatives are inclusive and responsive to the values and aspirations of students. The proposed research model, which includes new constructs related to basic human values and student aspiration, provides a more in-depth understanding of the factors that affect e-learning adoption. This study’s findings have practical implications for educators and policy makers as they can utilize these insights to design and implement e-learning systems that consider cultural factors that influence student adoption, thus fostering an inclusive and efficacious educational environment aligned with sustainability principles. Additionally, the study highlights the importance of understanding and supporting student aspirations to enhance the adoption and effectiveness of e-learning systems.

In totality, this research illumines the pathway towards a more holistic integration of sustainability principles, embracing the roles of sustainable education and transformative learning processes, thus cementing a bridge between contemporary educational paradigms and enduring sustainability objectives.

8. Limitations and Future Perspectives

It is also important to note that this study has some limitations. Firstly, the sample used in this study is limited to a specific cultural context and therefore the findings may not be generalizable to other cultural contexts. Secondly, this study relies on self-reported data, which may be subject to bias and may not accurately reflect participants’ actual attitudes and behaviors. Finally, this study only examines a specific set of factors that influence e-learning adoption, and there may be other important factors that have not been considered in this research.

One potential direction for future research could be to study the impact of e-learning on students from different cultural backgrounds. While this study focused on the role of basic human values and student aspirations in e-learning adoption, it would be interesting to explore how cultural factors may influence these values and aspirations. Additionally, research could be carried out on the effectiveness of e-learning in different educational contexts, such as primary, secondary, and higher education, as well as in different subject areas. Another area of interest could be to examine the use of e-learning in professional development and training programs. Furthermore, it would be interesting to study the impact of e-learning on the development of soft skills, such as critical thinking, problem-solving, and teamwork, and how they relate to student aspiration and values.

Future research could explore the adoption of e-learning in different cultural contexts or in different populations to further understand the relationship between cultural values and technology adoption. Additionally, investigating the long-term effects of e-learning on student achievement and engagement could provide valuable insights into the effectiveness of e-learning as a teaching and learning ecosystem. Studies could also look at the role of individual and organizational factors on e-learning adoption and use, such as the role of the teacher, school culture, and the availability of resources and support.