
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12473-5
10.1016/j.heliyon.2024.e36442
e36442
Research Article
Digital social responsibility towards digital education of international higher education students' institutions: Digital culture as mediator
Mothafar Nora A. a
Zhang Jingxiao zhangjingxiao964@126.com
a⁎
Alsoffary Amani b
Masoomi Behzad c
AL-Barakani Abdo d
Alhady Osama S. e
a School of Economics and Management, Logistics Engineering and Management Department, Chang'an University, Xi'an, China
b Management Department, Sana'a Community College, Sana'a, Yemen
c Industrial Management Department, Islamic Azad University, Firoozkooh Branch, Tehran, Iran
d School of Global Business, Chongqing College of International Business and Economics, Hechuan, Chongqing Municipality, China
e School of Economics and Management, Lanzhou University, Lanzhou, China
⁎ Corresponding author. zhangjingxiao964@126.com
22 8 2024
15 9 2024
22 8 2024
10 17 e364425 11 2023
2 8 2024
15 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Digital social responsibility (DSR) has emerged as a wise strategic decision for all higher education institutions during the new crown pandemic. This study explores whether the DSR dimensions (social, economic, legal, and environmental) enhance digital education (DE) in universities. Specifically, this paper intends to investigate the impact of digital culture (DC) on these relationships as a mediator variable. By adopting a cross-sectional approach, data are gathered through an online survey among a sample of 181 students from different nationalities in higher education institutions in Chang'an-China. The partial least squares-structural equation modelling has been used to perform the statistical analyses. Results revealed that all DSR dimensions directly and indirectly affect DE. DC also significantly influences students' DE during the new crown pandemic. Furthermore, our findings show that DC mediates the link between the social, economic, and environmental DSR aspects and students' DE. The study suggests Chinese universities review their DSR policies and student DC perspectives, and design social digital responsibility education programs. These measures can predict the success of student DE and provide a competitive edge as social media technology advances.

Keywords

Digital social responsibility
Digital culture
Digital education
New crown epidemic
Structural equation modelling
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pmc1 Introduction

The new crown outbreak in 2022 disrupted the Chinese academic calendar, causing chaos and prompting educational and industrial shifts. In 2022 it created challenges in education, culture, and society, notably digitization, which enhanced socialization in organizations and led universities to recognize internet platforms as crucial for engaging in socially responsible actions [1,2]. Digital social responsibility (DSR) emerged as a pivotal framework to address these challenges [3].

DSR focuses on using digital practices to benefit society. It involves applying digital tools and platforms in a morally responsible manner, upholding social justice, respecting human rights, safeguarding the environment, and promoting individual and societal well-being [4,5]. Research demonstrates that a commitment to social responsibility (SR) could strengthen stakeholder engagement [6].

China, focusing on online learning, recognized the importance of DSR in achieving higher digital education standards and improving digital education scores [7]. It implemented an educational policy system to evaluate governance and teacher policies [8]. As universities adapt their higher education systems, SR criteria and priorities are shifting. This necessitates urgently updating curricula, ensuring high professionalism and conscientiousness among university teachers, offering retraining on online education platforms, and improving the quality of human capital. These steps are essential for meeting evolving educational standards and responsibilities [9].

Research shows universities adopting DSR to balance digital efforts across social, economic, legal, and environmental dimensions [10]. This shift aims to ensure that each university action is SR and goal-oriented, including inclusion among students, faculty, and staff, promoting digital innovation, compliance with data security regulations, and encouraging sustainable and environmentally friendly technologies. Fostering a socially responsible and ethical digital environment becomes imperative [11]. The theoretical underpinning of this study draws from DSR research and its relevance in addressing the challenges posed by the pandemic (including economic effects, social activity, and healthcare system burdens) [12,13]. This research views DSRs as a management technique and a valuable strategic instrument for academic institutions that are crisis-stricken to enhance their performance. As a result, investigating the role of DSR dimensions in DE is the first objective of this study.

Digitization has not only transformed organizations into a digital social responsibility but also established an electronic culture called digital culture (DC) [14]. DC aims to transform elements of bolt traditional literacy and culture into new digital forms and environments that affect various aspects of life (i.e., education, entertainment, work, and politics) [15]. This digital transformation extends to taking a holistic approach to educational institution's internal and external operations, as well as how they provide services to students. However, the cultural redefinition of the way one deals puts pressure on institutions to adapt to the evolving needs of students within diverse social and cultural contexts. It is essential to recognize that the relationship between technological development and its impact on society is still underappreciated, and more research is needed. To grasp digital culture, one must first comprehend how technology is socially constructed and how it interacts with human behavior, values, and societal norms.

Researchers have highlighted the positive impact of DSR across its various dimensions and how its adoption can inspire (innovation, leadership, autonomous learning, competencies, communication, and collaboration) among learners and teachers in facing challenges and opportunities in the digital world. One possible role is addressing issues like data privacy, digital inclusion, platform governance, and digital literacy [16]. The educational digital divide, which became even more evident during the pandemic, is not solely a technological issue but a social construct influenced by economic disparities, geographic location, and policy decisions. DSR is expected to create social value co-creation among students and teachers, facilitate learning, and bridge students' needs, expectations, and labor market demands. Despite that, researchers have noticed the availability of modern means of communication requires new norms and practices that reflect changes in educational culture. Therefore, how to reshape the concepts of real and virtual in relation to culture to meet the needs and expectations of stakeholders is what requires defining the role of responsible universities towards it. Therefore, this research aims to examine the effect of DC as a mediator in the relationship between DSR dimensions and DE in the higher education sector during the new crown pandemic.

The reasons for DC's mediating role in the association of universities DSR and DE are founded on two logics. One reason is that students/stakeholders rapidly engage in DSR activities through social media, laying the foundation for significantly enhanced DE [17]. Digitalized university activities are successfully only when universities ensure student protection by considering their engagement and feedback on the DSR program, thereby enhancing DE. This study aims to motivate researchers to explore what makes this link plausible [18].

The results of this study contribute in the following ways. First, the impacts of DSR are examined in light of the current educational crisis and offer a fresh angle and use for DSR research. Second, this research is the first to examine the impact of DSR dimensions (i.e., social, economic, legal, and environmental), on student DE, based on the social construction of technology (SCOT) theory, it aims to resolve the theoretical debate on DSR's potential to change students' attitudes and engagement. Third, this study aims to identify DC's role in mediating between DSR dimensions and DE. Finally, the paper develops a new mindset based on DSR for institutions that adopt DC and establish DE amid the new crown epidemic, providing empirical data and theoretical insights on factors' interrelationships. China is used as a case study given its growing positioning of DE despite new epidemic dominance in the DSR and DE. A literature survey was conducted identifying factors and sub-factors of the proposed model, and a structural equation model (SEM) was used to analyze them.

The remainder of this study is organized as follows. Section 2 presents a literature review of the theoretical framework and research gaps. Development hypotheses and conceptual framework are also put forward here. Section 3 describes research methodology. Sections 4, 5, 6 present the findings, discussion, and implications, respectively. Lastly, Sections 7 and 8offer conclusions, discuss limitations and strengths, and suggest future research directions.

2 Literature review

2.1 Theoretical framework

Our research incorporates two significant theories to address various facets of the study comprehensively. Stakeholder theory elucidates the influence of DSR dimensions on DE. In contrast, the social construction of technology explains how DSR dimensions and DC impact on DE.

Other scholars [19] have concurred that SR policies in universities should be grounded in stakeholder theory to balance the needs and priorities of various constituents. According to that, it should take into account the interests and expectations of relevant parties, i.e., students, faculty, researchers, administrators, and managers; through transparent and ethical behavior, is committed to the university's long-term development, take responsibility for the social, economic, and environmental effect of university decisions and activities, and integration of this responsibility into the daily work of the entire university [20]. Transparency means that universities should be transparent about their decisions and activities that impact society and the environment systematically, clearly, and unambiguously through their digital platforms. Disclosing SR practices is a means through which the information asymmetry gap between universities, teachers, students, and policymakers is reduced. Furthermore, universities must operate ethically, ensuring fairness and justice in their management and educational practices. It is the responsibility of the university to enhance the awareness of SR and capacity building in it, providing support for which instructional strategies teachers might use, as well as determine the direction of SR within it, which requires university leaders to reflect the requirements of SR in vision, making it a fundamental consideration in their decision-making process. Integrating SR into the university will be the most effective way universities practice SR [21]. many stakeholder groups into academic administrations.

The social construction of technology (SCOT) theory is situated within the sociology of science and posits that outcomes are shaped by human behavior rather than technology itself. This theory contends that a comprehensive understanding of technology usage requires an appreciation of its social context, and that each technology holds distinct significance for different social groups. SCOT provides a sociological perspective on how technology is not just a neutral tool but is influenced by social, cultural, and contextual factors. SCOT offers a coherent and inclusive approach to interrogating the complex realities of interactions between people, technology, and institutions in empirical settings [22]. Research studies that adopt the lens of SCOT in DE are relatively scarce. The need for communication with DC stems from knowing how technology influences instructional practices and how pedagogical decisions affect technology design and use to enable practical learning experiences for their students. Separated by time and space, it is intimately connected to the culture in which Stakeholders (including principals, administrative staff, teachers, students, etc.) are organized. Education cannot be studied apart from the cultural context in which it is embedded and in which it prepares students for constant engagement with the sociocultural environment and technology [23,24]. It emphasizes that technologies are not determined solely by their functionality but are influenced by various social groups' perspectives, interests, and values.

SCOT also underscores the importance of considering the broader societal impacts of technological innovations, emphasizing that technology is not static but rather a dynamic artifact that evolves in response to societal needs and values, especially in responsible digital technology usage [25]. This evolution results from interactions among various stakeholders, including universities, students, educators, policymakers, and broader society, through the means of action, interaction, and communication. As such, those who develop and deploy technologies have a social responsibility to understand and address the potential consequences and implications of their creations on society, including ethical, environmental, and equity concerns [26]. SCOT theory can help inform discussions about the social responsibility of technology developers and users.

Stober's perspective on the social institutionalization of new technologies explains that new technological means are not only the result of technical invention but are achieved through a two-stage process: invention and social institutionalization. The process of social construction not only transforms and adapts technological innovations but also opens new communication and education opportunities. The community shapes these new technologies to meet changing educational demands and ideals, creating new economic models and widespread collective acceptance. This acceptance includes a legal and regulatory framework for responsible use. These communication methods stay unchanged, but users shape their instructional uses. Adaptation improves specific functions to achieve their educational goal, while pre-adaptation creates new educational functions. In this complex interaction, socio-political, legal, and technological systems work independently but interact. Multifaceted engagement is key to the social building of new technologies, where the community actively develops tools and their instructional roles [27]. Study variables interact within this comprehensive framework in a dynamic and developing way, demonstrating the transformative power of societal influence on technology and education.

The diversity of theoretical approaches on which the research relies helps arrive at a new theoretical framework that explains the mechanism of action of digital education tools and platforms, including curricula, tools, and means, without harming stakeholders. The reason for choosing this consensus framework is due to the comprehensiveness of the study topic. Table 1 presented the review of research studied.Table 1 Review of research studied.

Table 1Authors	Responsibility	Methods	Culture	Education	Study	
	DR	SR		C	DC	Others	E	DE	Others		
[28]		✓	PLS		✓					China	
[29]	✓		AMOS	✓						Thailand	
[30]	✓		SPSS	✓						Australia	
[31]		✓	Qualitative			✓		✓		Portugal	
[3]	✓		PLS-SEM	✓						Saudi Arabia	
[32]		✓	Review	✓				✓		BRICS	
[33]		✓	Qualitative			✓			✓	Saudi Arabia	
[34]	✓		AMOS			✓			✓	Thailand	
[17]		✓	Qualitative	✓						Italy	
[35]		✓	SPSS			✓		✓		Spain	
[36]		✓	Review		✓	✓			✓	India	
[37]		✓	PLS	✓				✓		Spain	
[38]		✓	MySQL	✓						UK	
Proposal work	✓	✓	AMOS		✓	✓	✓	✓		China	

2.2 Research gaps

According to a literature review, investigations of student satisfaction, service learning, personal, professional, and civic education of students, media literacy, social media, and social responsibility of teachers are the subjects of educational empirical research. Many researchers have researched the education sector compared to other sectors. Empirical research on digital social responsibility focuses on customer attitudes, buy intent, brand choice, brand appreciation, and purchase intent. As a result, in impacted countries, the role of digital responsibility in digital education, especially in the face of global challenges, such as the new crown (Omicron BA.2.2.1) variant of the SARS-CoV-2 virus, is relatively restricted and unclear.

Based on the paradigm proposed in the paper, this study tried to fill the following holes in prior works by doing so: First, the impact of DSR on altering students' attitudes and involvement has been questioned, and there is limited study on how various DSR dimensions enhance DE effectiveness in the context of the new crown epidemic [39]. Second, because the boundary conditions and factors that determine DSR on student education have not been empirically investigated, the study considered the mediation influence of DC [39]. Third, studies into the impact of DSR during the present DE pandemic are lacking, so the current study investigated contemporary phenomena - namely, DSR dimensions and DE during the ongoing pandemic in higher education. Fourth, the new crown pandemic has significantly impacted the macro and local levels of the educational sectors' sustainability [40], and there is not enough empirical research on university students' DSR in crisis conditions in DE [41].

2.3 Hypothesis development

2.3.1 Digital social responsibility

SR has been recognized as a valuable strategic instrument by academic institutions that are crisis-stricken. The operational context of educational institutions is characterized by several challenges, as outlined in Ref. [42], which encompass, among other things, the diversification of financial resources, the widespread expansion of universities, and the increasing commercialization. These shifts and hurdles are believed to influence the academic environment, university autonomy, the quality of education, and societal obligations. This underscores the crucial necessity for higher education institutions to embrace and adapt to SR. Previous studies have confirmed the benefits of SR initiatives in academic institutions. SR can enhance innovation, progress, and social-economic, cultural, and environmental [43]. They can also contribute through research collaborations and knowledge transfer relationships with other universities [44]. Socially responsible universities can also aid sustainable digital development by facilitating a learning culture and developing knowledge-sharing processes and networks within educational institutions and society. Knowledge sharing by the stakeholders can be crucial to the promotion of digital education [45]. According to many scholars, the commitment of the universities to SR requires the adoption of four dimensions, where each of these plays a role in shaping the responsibilities of organizations and stakeholders. Social (privacy and ethical concerns that pertain to how technology affects society and individuals; Economic (giving underprivileged people access to digital technologies in a fair manner); legal (data protection and intellectual property); environmental (sustainability and reducing carbon footprint) [13,46].

The new crown pandemic is one of the factors that most motivated and forced institutions into digital transformation. A lot of improvisation has led to schools and universities adopting DE methodologies quickly and transforming elements of traditional literacy and culture into novel digital forms and environments that affect community expectations of education. Universities have a responsibility to consider the interests and needs of all stakeholders when making decisions about digital technology. For instance, academic centers in China, as illustrated by Ref. [47], devised a strategic plan that accommodated online education activities and assessments in accordance with their pandemic alert levels, allowing students to fulfill their academic obligations on time. The choice of digital medium used to communicate and share information can impact the overall culture and create a digital culture among stakeholders. For example, some universities have implemented virtual sustainability initiatives, such as online campaigns to reduce waste and promote energy conservation, to continue promoting social responsibility in the absence of face-to-face activities [48]. Any SR endeavors on digital platforms can be considered DSR.

Furthermore, universities are responsible for preparing students for a rapidly evolving digital world, equipping them with the skills to navigate technology safely, ethically, and effectively, and exposing them to diverse digital perspectives and opinions. Several higher education institutions have used social media to implement SR initiatives, such as disseminating knowledge and culture about the global experience for adherence to ethical principles in daily activities [49]. Therefore, DSR emphasizes universities' obligation to consider a wide range of stakeholders when making decisions about digital technology, aids in delivering DE in a way that benefits society, and safeguards the rights and interests of all parties involved. Given these circumstances, the following hypotheses can be formulated.H1 DSR's dimensions have a positive, relationship with DE.

H2 DSR's dimensions have a positive relationship with DC.

2.3.2 Digital culture and digital education

DC is deﬁned as a holistic integration of human knowledge, beliefs, and behaviors rooted in the capacity for learning and knowledge transmission across generations [50]. It is increasingly recognized in international scientific sources as a pivotal factor influencing both social and professional progress, thereby driving significant shifts in the behavior of the present generation [51]. In the context of university systems, the decision to transition the culture of university leaders, educators, administrators, and students toward a digital paradigm necessitates a comprehensive approach to preparing them. This preparation involves equipping individuals with the modern digital tools, knowledge, skills, and competencies required for information acquisition and manipulation. Digital transformation frameworks have highlighted the pivotal role of leadership as critical actor in developing a digital culture within education systems, supported by empirical evidence from previous studies demonstrating the relationship between cultural transformation and digital education [52]. In our study, digital technology related activities can be split in terms of their impact on the value created for the university [53]. This approach aims to establish a coherent culture with a clear vision, define individual and team responsibilities within a collaborative atmosphere, and promote deep learning while ensuring accountability. Accountability measures consist of three primary factors, namely educational outcome assessments, student engagement information, and consumer data, as identified by Ref. [54]. These metrics offer valuable insights stakeholders use to evaluate the educational processes within universities.

The uptake of digital cultural change has outpaced its integration into education systems, widening the gap between educational offerings and societal demands. This growing disparity highlights the critical relationship between technology and society, a key issue in sociological research. According to Ref. [55], understanding the needs of stakeholders as an integral part of a new cultural and organizational strategy yields evidence and insights that can bolster the justification for transformative actions. For instance, curriculum developers are crucial in equipping teachers and students with the requisite skills, knowledge, and attitudes to address local and global societal challenges. Effective digital transformations commence with a change in mindset across all stakeholder levels. This shift cultivates a culture shift that empowers institutions to become more adaptable, risk-tolerant, and collaborative in addressing the challenges posed by digital education, as highlighted by Ref. [56].

Prior papers have primarily concentrated on investigating the direct effect of DSR and DE from either a conceptual or an empirical perspective over the past years [57,58]. These studies have overlooked the potential mediating role of DC on this relationship. Stakeholders suggest that commitment from key players in fostering DC can establish connections with a diverse range of stakeholders, enhancing overall outcomes [52].

Moreover, SCOT suggests that dedication to meeting the expectations of stakeholders and responding to political, economic, social, and cultural changes in digital transformation may manifest in the form of ethical and environmentally responsible may contribute to expanding technological knowledge and achieving advantages over competitors, such as technological innovation encompassing novel teaching methods and assessment approaches. It is advisable to involve stakeholders directly in the innovation process, as recommended by Ref. [59]. This proactive approach could influence the link between DSR and DE, potentially having a beneficial mediating effect on their relationship and enhancing DE outcomes.

These research works provide insights into technology and country-specific characteristics, including political, social, and cultural environments, contributing to technology knowledge and understanding [60]. mentioned the role of DC as a mediating variable in digitizing new projects. Nevertheless, no study to date has examined the mediating effect of DC in the existing studies that deal with DSR and DE. Considering the above, the following hypotheses were formulated.H3 DC has a positive relationship with DE.

H4 DC mediates the effect of DSR's dimensions on DE.

2.4 Conceptual model

Conceptual model examines the relationship between DSR dimensions (main predictor), and digital education (the criterion) was developed based on prior literature. The digital culture construct is included in the proposed model to test the moderation effect of digital culture (mediating construct) on the links between DSR and DE. The proposed conceptual model was depicted in Fig. 1.Fig. 1 Conceptual model of the research.

Fig. 1

3 Methodology

This study employs a quantitative approach through the preparation of a questionnaire form to deal with the research variables and test the hypotheses. For analyzing and testing the data, AMOS 26.0 software was used. The quantitative approach adheres to the positivist paradigm, which emphasizes monitoring and researching variables as well as employing objective and measurable data to reach findings that can be replicated [61]. Fig. 2 shows the flowchart of the methodology.Fig. 2 Flowchart of the methodology.

Fig. 2

3.1 Participants and data collection

The participants in the cross-sectional research survey were international students from Chang'an University, which served as the epicenter of the second wave of the Omicron subvariant BA.5 outbreak in China, Shaanxi Province, and Xi'an City. The researcher attracted participants from all faculties to ensure a fair and balanced representation of the study population. This study was conducted during the second semester of the academic year 2022 with a final sample of 181 students after excluding missing values or incomplete data (at least 179 foreign university students according to the Krejcie and Morgan table at a population of 441 sample units) [62].

The inclusion criteria for selecting participants were: (1) full-time undergraduate, master's, and doctoral students in the second semester of the 2022 academic year; (2) Have a smartphone or laptop to access social media apps. The majority of the participants were males (71.82 %), which reflects their representation in the research population. (98 %) of them were aged between 21 and 41, and they were the most influential groups in the university. The largest group (55 %) held a master's degree, which enhances the reliability of the questionnaire data and provides a better context for its validity. The profile of the respondents is depicted in Table 2.Table 2 Profile of the respondents.

Table 2Category	Research Sample		
	Frequency	RATE	
	181	100 %	
Gender	
Male	130	71.82	
Female	51	28.17	
Age Crosstabulation	
21–30	90	49.72	
31–40	89	49.17	
41–60	2	1.10	
Education	
Bachelor	41	22.65	
Master	96	53.03	
PhD	44	24.30	

The researcher surveyed a set of questions using Wen Xin Jian from February 2022 to May 2022. They shared the link to the form with their WeChat and QQ contacts and asked them to forward it to other students from the same university. The survey was conducted during the second wave of the new crown pandemic and the government's social distancing measures. The participants could access the form through the link but were instructed to fill it out only once. Since data collection via face-to-face communication was not feasible, it is permissible to evaluate attitudes and behaviors by using online student samples [63]. Table 3 shows the technical data.Table 3 The technical data.

Table 3Study Universe	441 university students	
Geographical Scope	Chang’ an University, China	
Data Collection Method	Structured questionnaire distributed to students via WeChat and QQ	
Sample Unit	Foreign Students	
Sample	181 students	
Technique used	AMOS-SEM	

3.2 Questionnaire design and development

By thoroughly examining theories related to social responsibility, digital culture, and digital education, and referencing various studies to identify and define crucial dimensions for our investigation, data collection involved self-questionnaires with two parts. The first part gathered socio-demographic information with three items, while the second part measured key variables, focusing on the independent variable DSR and the dependent variable DE. This study used DC as a mediation variable. DSR was operationalized through its four dimensions: social, economic, legal, and finally environmental indicators, while DE was measured through indicators assessing digital literacy and the effectiveness of educational resources. As a mediator, DC was measured by capturing cultural attitudes toward digital technologies. The overall model included 30 observable variables and three latent variables. Participants were thoroughly informed about the study aim, focus, and consent procedures.

A five-point Likert scale (ranging from 1 = strongly disagree to 5 = strongly agree) was employed for variable measurement, ensuring consistency across assessments and preventing respondent confusion, a practice supported by previous research [64]. For content validity, the questionnaire underwent rigorous evaluation by three experts in IT and management, adhering to established guidelines [65]. Variable definitions and measurement model is represented in Table 4, while questionnaire factors and sub-factors are represented in Table 5.Table 4 Variable definitions and measurement model of research variable.

Table 4Variable	Definition	Measurement Methods	References	
Digital Social Responsibility (DSR)	The ethical and responsible utilization of digital technology and data spanning multiple dimensions.	Social: Digital inclusivity, ethical data practices. Economic: Contribution of digital strategies to sustainable economic growth.
Legal: Assessments of legal awareness of digital regulations or adherence in digital spaces. Environmental: Ecological footprint of digital operations, such as energy consumption or e-waste management.	[57,61,62]	
Digital Education (DE)	Use of digital tools and platforms to enhance learning and educational outcomes.	Engagement: Student participation on digital platforms. Learning Outcomes: Academic performance, skill acquisition, students' digital literacy levels. Access and Inclusivity: Platforms for People with Disabilities, user-friendly interfaces, and adaptive learning tools.	[40,64]	
Digital Culture (DC)	Values, practices, attitudes, and norms associated with digital technology use and adoption in a society.	Technology Acceptance and Use: Attitudes and usage patterns. Organizational Change and Adaptability: Extent of digital transformation in everyday life. Innovation and Creativity: Level of creativity in addressing challenges in a digital environment.	[44,63]	

Table 5 Questionnaire factors and sub-factors.

Table 5Factors	Sub-factors/item	Author & year	
	Your university upholds the societal customs and traditions (SR1)
Your university utilizes scientific research and its findings to address community concerns (SR2)
It shows respect for the cultural diversity within the community (SR3)
It promotes student engagement in community service (SR4)		
Digital Social Responsibility and Social (DSR_SR)	The university integrates the principles of social responsibility into its curriculum plans (SR5)
It actively supports community activities in all their forms(SR6)
Your university establishes connections between scientific research and the needs of the job market (SR7)
It firmly believes in the principle of equal opportunity (SR8)
The university contributes to the development of new disciplines that cater to society's needs (SR9)	[28,31,66]	
	It enhances public awareness in comprehensive societal development projects (SR10)		
	The university embraces the concept of sustainable development throughout its operations (SR11)		
Digital Social Responsibility and Economic (DSR_ER)	The university provides students with education on their obligations and entitlements towards the community and the university (ER1)
Students are treated equitably, with the university enforcing its laws and regulations (ER2)
The university abides by the prevailing legal norms in society (ER3)
The university promotes democratic values among young individuals (ER4)	[30,38]	
Digital Social Responsibility and Legal (DSR_LR)	The university endeavors to change students' unfavorable conduct (LR1)
It highlights the societal repercussions of academic dishonesty for students engaged in scientific research (LR2)
It fosters the ethical values of students (LR3)
The university strictly adheres to the principles of scientific research ethics in its academic pursuits (LR4)	[34,67]	
Digital Social Responsibility and Enviromental (DSR_En_R)	The university arranges lectures and symposiums focused on environmental conservation (En-R1)
Field studies are conducted that contribute to environmental preservation (En-R2)
Research is conducted on various aspects of waste recycling (En-R3)
The university strictly follows local environmental regulations and guidelines (En-R4)
Collaborations with international research centers are established to conduct environmental studies (En-R5)
The university actively participates in green initiative programs (En-R6)
It prioritizes the dissemination of global and local environmental laws and regulations, as well as methods of preservation (En-R7)
The university plays a role in promoting health awareness among different segments of society (En-R8)	[28,58,67]	
Digital culture (DC)	ICT has revolutionized the global cultural landscape (DC1)
Your university is actively promoting digital literacy across various domains and raising awareness about the significance of digitalization (DC2)
Your university is engaged in developing legislation and regulations to keep up with the ongoing transformations in the digital realm (DC3)
Your university possesses the capability to conduct all activities electronically and facilitate student engagement in them (DC4)
Your university allocates financial resources to implement digitalization processes (DC5)
Your university offers a digital electronic library that supports students in their respective fields of study (DC6)
Your university is dedicated to integrating information technology into educational curricula, programs, and lifelong learning initiatives (DC7)	[15,68,69]	
Digital Education (DE)	The e-learning methodologies employed are efficient and encompass all facets of the curriculum (DE1)
The university offers appropriate technical support to facilitate the integration of technology into educational materials (DE2)
Students are provided with a guide on how to utilize the educational material site (DE3)
The e-learning system enables direct communication among all stakeholders within the educational system (administration, teachers, students (DE4)
The university administration consistently assesses the efficacy of distance teaching methods (DE5)
Teachers demonstrate adaptability and willingness to embrace the changes necessitated by the current circumstances (DE6)
The quality of online learning content, including courses and multimedia materials such as audio and video, is of a high standard (DE7)
Ensuring the integrity of student assessment procedures and providing constructive feedback is prioritized (DE8)	[7,68,70,71]	

4 Results

To analyses the proposed conceptual model, structural equation models based on SEM-AMOS (Analysis of Moment Structure) were utilized. These models are advanced algorithms that blend elements of multiple regression and factors to estimate interrelated dependency relationships simultaneously [72]. At the same time, they support a psychometric approach by allowing the inclusion of latent or unobservable variables derived from indicators, as well as an econometric perspective focused on prediction using directly observed variables [73]. According to the relevant literature, These second-generation multivariate methods offer several advantages in research: (i) incorporate abstract constructs that aren't directly observable [74]; (ii) researchers can assess how well measurable variables describe latent variables; (iii) the models provide insights into relationships among multiple predictor variables and criteria; and; (iv) hypotheses from prior theoretical knowledge can be combined and compared with empirical data for a comprehensive analysis.

4.1 Descriptive statistics

Our analysis commenced with an examination of the descriptive statistics, including the means and standard deviations, to provide an overview of the data's central tendencies and variability. The mean scores of the dimensions of DSR were above average, with the highest score for social responsibility towards the economy (3.301) and the lowest score for social responsibility (3.195). The mean scores of these variables exceeded the theoretical mean of the five-point Likert scale, indicating a high level of awareness of DC and DE among the university staff and students. The standard deviation values that were lower than the correct ones demonstrated a high degree of agreement among the sample members on the variables under investigation. The results of descriptive statistics are presented in Table 6. Pearson's binary correlation test was used to determine the significance of the correlation between the research variables; most of the variables under study showed a positive, significant, and strong correlation with each other. The correlation coefficients were generally high, medium, or low, as indicated in Table 6. Moreover, these correlations were in line with the expected directions of the relationships, which suggests the feasibility of testing these relationships using the structural equation model [75]. The summary results in descriptive statistics of variables are shown in Table 6.Table 6 Descriptive statistics of variables.

Table 6Variables	N	Mean	Std. Dev				
DSR_SR	181	3.195	0.789				
DSR_ER	181	3.301	0.791				
DSR_LR	181	3.216	0.657				
DSR_ En-R	181	3.224	0.654				
DC	181	3.989	0.567				
DE	181	3.418	0.649				

4.2 The measurement models

A three-phase statistical approach is adopted for evaluating model. Initially, Exploratory Factor Analysis (EFA) is employed. This technique aids in identifying the interconnections among variables within the model. It assists in clarifying and simplifying the construct model by analyzing the characteristics or patterns of the constructs, thereby reducing the quantity of latent constructs from a larger pool [76]. To evaluate the appropriateness of the data for the structure of the construct, the Kaiser-Meyer-Olkin (KMO) and Bartlett's Test measure were utilized. The KMO measure of sampling adequacy was found to be 0.832, indicating that the data were well-suited for analysis. A value close to 1 in the KMO test suggests a high degree of adequacy for factor analysis, with values above 0.60 generally considered acceptable [77,78]. Additionally, Bartlett's test of sphericity yielded a significant result with a value of 3331.992 (p < 0.001) for 903 degrees of freedom, further confirming the data's appropriateness for factor analysis [79]. Consequently, the principal components analysis method was employed to explore the data's factor structure and establish the scale's construct validity. The analysis also considered absolute measures when interpreting the correlations between item and total scores. Table 7 shows KMO and Bartlett's test results.Table 7 KMO and Bartlett's test result.

Table 7Kaiser-Meyer-Olkin Measure of Sampling Adequacy	832	
Bartlett's Test of Sphericity	Approx. Chi-Square	3331.992	
	df	903.	
	Sig.	<0.001	

In the following, the principal component analysis (PCA) outcomes indicate that the initial six components possess eigenvalues exceeding 1, as shown in Table 8. The first component dominates the variance, accounting for 22.330 %, while successive components contribute progressively smaller proportions. The cumulative explanation of the dataset's variance by the first six components is 50.770 %. After six iterations, the rotated component matrix was established. All constructs displayed high loadings (>0.5), and no instances of cross-loading were identified for any variable. Table 8 shown total variance explained result.Table 8 Total variance explained result.

Table 8	Initial Eigenvalues		Extraction sums of squared loading	
Component	Total	% of variance	Cumulative%	Total	% of variance	Cumulative%	
1	9.610	22.330	22.330	9.610	22.330	22.330	
2	3.914	9.103	31.452	3.914	9.103	31.452	
3	2.675	6.220	37.672	2.675	6.220	37.672	
4	2.178	5.063	42.737	2.178	5.063	42.737	
5	1.892	4.401	47.138	1.892	4.401	47.138	
6	1.562	3.633	50.770	1.562	3.633	50.770	

Subsequently, confirmatory factor analysis (CFA) is executed to validate the relational structure rooted in the intrinsic connections among variables. allowing each construct to be precisely adjusted and fitted within the model. CFA finding show the fit indicators of the quality of agreement for the fundamental measurement model is good after making a set of modifications suggested by the modification indicators. This modification resulted in the overall modified measurement model for the research, in which the CMIN/DF value in the default model is 1.31. A value of less than two is generally regarded as an appropriate fit, with an increase in the value of other indicators of the Comparative Fit Index (CFI = 9.09) and Incremental Fit Index (IFI = 0.912), which is higher than (0.90) according to Ref. [80]. When comparing the stated model to the null model, the Tucker-Lewis Index (TLI) refers to the value in the default model as 0.9, indicating a decent fit [77]. Finally, the Root Mean Square Error of Approximation (RMSEA), the value in the default model, is 0.05, which is less than the required threshold of 0.08, suggesting a satisfactory fit. The default model matches the data well, with appropriate CMIN/DF and reasonable values for IFI, TLI, CFI, and RMSEA. Table 9 presents the fit indices of the overall measurement model, while Fig. 3 displays the CFA path diagram of the research variables.Table 9 Fit indices of the overall measurement model.

Table 9Model	CMIN	x2/df	P	IFI	NFI	TLI	CFI	RFI	RMSEA	
Default model	1021.81	1.31	0.000	0.912	0.711	0.9	9.09	0.681	0.05	

Fig. 3 CFA path diagram.

Fig. 3

A measuring model was created using their respective indicators to assess the accuracy and dependability of the various constructs. Reliability and validity outcomes of the measurement model are shown in Table 10. The first step was to determine the convergent validity of each concept by calculating the average variance extracted (AVE) values using standardized factor loading scores. Component loading quantifies the proportion of an item's volatility that may be accounted for by a specific element or idea [81]. Factor loading above 0.7 is excellent, 0.6 is good, and below 0.5 to 0.4 is accepted. The AVE values in every instance exceeded the cutoff point of 0.50, supporting the scales' converging validity [82]. Second, the Pearson's binary correlation test was used to examine the discriminant validity of the latent constructs [83]. The paired correlation coefficients were contrasted with the squared roots of the AVE scores. The comparisons showed that, in every instance, the squared roots of the AVE scores were higher than the paired correlation coefficients, confirming the discriminant validity. Moreover, these correlations were in line with the expected directions of the relationships, which suggests the feasibility of testing these relationships using SEM. Table 11 shows the discriminant validity of correlation coefficient matrix. Finally, Cronbach's alpha (α) and composite reliability scores were computed to assess the measures' reliability. The composite reliability and Cronbach's alpha (α) values were both higher than the threshold value, or 0.70 [76]. The findings demonstrate that the measures used had good dependability. In our study, the conditions for convergent validity have been met, as shown in Table 10. Furthermore, Table 10, Table 11 demonstrate that the square root of AVE for each variable exceeds its correlation with other variables, which indicates acceptable discriminant validity.Table 10 Reliability and validity measurement model.

Table 10Construct	Items	Loading	AVE	CR	α	
Digital Social Responsibility and Social (DSR_SR)			0.761	0.962	0.894	
	SR_1	0.455				
	SR_2	0.576				
	SR_3	0.626				
	SR_4	0.753				
	SR_5	0.587				
	SR_6	0.55				
	SR_7	0.687				
	SR_8	0.768				
	SR_9	0.713				
	SR_10	0.658				
	SR_11	0.644				
Digital Social Responsibility and Economic (DSR_ER)			0.770	0.910	0.766	
	ER_1	0.738				
	ER_2	0.557				
	ER_3	0.688				
	ER_4	0.618				
Digital Social Responsibility and Legal (DSR_LR)			0.780	0.920	0.787	
	LR_1	0.656				
	LR_2	0.653				
	LR_4	0.723				
	LR_4	0.743				
Digital Social Responsibility and Environmental (DSR_En_R)			0.785	0.959	0.889	
	EN_R_1	0.607				
	EN_R_2	0.528				
	EN_R_3	0.615				
	EN_R_4	0.575				
	EN_R_5	0.656				
	EN_R_6	0.788				
	EN_R_7	0.688				
	EN_R_8	0.754				
Digital Culture (DC)			0.770	0.950	0.860	
	DC_1	0.614				
	DC_2	0.504				
	DC_3	0.623				
	DC_4	0.632				
	DC_5	0.571				
	DC_6	0.566				
	DC_8	0.548				
Digital Education (DE)			0.800	0.956	0.877	
	DE_1	0.517				
	DE_2	0.655				
	DE_3	0.566				
	DE_4	0.55				
	DE_5	0.44				
	DE_6	0.605				
	DE_7	0.899				
	DE_8	0.568				
N = 181. Significant difference (**p < 0.01).

Table 11 Discriminant validity of correlation coefficient matrix.

Table 11	SR	ER	LR	En_R	DC	DE	
DSR_SR	1						
DSR_ER	0.746**	1					
DSR_LR	0.717**	0.620**	1				
DSR_ En-R	0.723**	0.624**	0.649**	1			
DC	0.637**	0.729**	0.703**	0.656**	1		
DE	0.549**	0.514**	0.524**	0.623**	0.478**	1	
Note: **p < 0.01.

4.3 The structural model

The final phase of choosing the structural model. SEM is employed to uncover any potential hidden constructs and to analyze the overall fit of the model. The fit indices of all possible alternative models were compared to test the distinctiveness of the variables, which demonstrate satisfactory results for our data sample. This process is crucial for testing the assumptions underlying our research hypotheses and for ensuring the integrity of the model's structure.

The evaluation of the structural model involved examining the coefficient of determination [R2] and the significance of path coefficients. The outcomes demonstrated that the R2 for DE was 0.53, signifying that DSR dimensions account for approximately 53 % of the variance in DE. The computed value of Cohen's [f2] is around 1.128, signifying a substantial effect size as per Cohen's benchmarks, given that it exceeds the threshold of 0.35. This implies that within the scope of the study, DSR exhibits a robust association with DE.

Based on this foundation, we further explored the direct relationships among the constructs and the corresponding hypothesis testing (i.e., DSR dimensions, DC, and DE). Our analysis indicated that three direct relationships significant at the 0.01 level, H1a, H1b, and H1d, based on P-values ≥2.69 and ≥ 1.645, and legal DSR (β = 0.111, p < 0.05) is positively related to DE. As for HP2, the results of DSR dimensions effect on DE indicate that social DSR (β = 0.14, p < 0.05), environmental DSR (β = 0.161, p < 0.05) are positively related to DC, therefore, accounting for 53 % of the variation in DC. The R2 value of 0.53 exceeds the 0.50 value recommended by Ref. [84], which implies a moderate model. Thus (H2a and H2d), are supported.

Additionally, there is no significant impact on DC's relationship with economic and legal dimensions, respectively (β = 0.01, p = 0.887; β = 0.086, p = 0.155). So (H2b and H2c), are not supported. In addition, DC significantly influenced DE (β = 0.226, p < 0.01), accounting for 74 % of the variation in DE. Thus, H1a, H1b, H1d, H2a, H2d, and H3 are supported. Table 12 displays the testing of hypotheses.Table 12 Testing of hypotheses.

Table 12Hp	Relationship	Std. Beta	Std. Error	P-value	Decision	
Hypothesis (1)	
H1a	DSRSR ≪ DE	0.549	0.057	4.985 ***	Supported	
H1b	DSRER ≪ DE	0.116	0.051	0.024 **	Supported	
H1c	DSRLR ≪ DE	0.111	0.056	0.045 *	Supported	
H1d	DSREn_R ≪ DE	0.173	0.057	0.002 **	Supported	
Hypothesis (2)	
H2a	DSRSR ≪ DC	0.14	0.07	0.046*	Supported	
H2b	DSRER ≪ DC	0.01	0.067	0.887ns	Not Supported	
H2c	DSRLR ≪ DC	0.086	0.061	0.155ns	Not Supported	
H2d	DSREn_R ≪ DC	0.161	0.065	0.014*	Supported	
Hypothesis (3)	
H3	DC ≪ DE	0.226	0.086	0.009 **	Supported	
						
Note(s): R2 0.53.

To test the effect of the mediating variable, we employed the bootstrapping method to assess the indirect effects. This method is particularly robust as it does not rely on the assumption of normal distribution of the indirect effect, making it a more reliable approach for the analysis of mediation in SEM [85]. Our analysis showed three significant indirect pathways from DSR (social, economic, and environmental) to DE, with effects of 0.064, 0.026, and 0.039, respectively. The corresponding p-values (0.013, 0.04, and 0.018), and the 95 % bootstrapped confidence intervals ([0.026, 0.132], [0.003, 0.078], and [0.092, 0.018]) not include zero, indicate significant mediation [85]. However, the indirect effect from DSR legal to DE (β = 0.025) was not significant (p-value of 0.115), with a 95 % bootstrapped confidence interval that included zero ([0, 0.082]), indicating no mediation. These findings are detailed in Table 13, where the mediation hypothesis of the research variables are tested, and Fig. 4 shows the structural model path diagram.Table 13 Testing of mediation hypothesis.

Table 13Mediation Hypothesis	Confidence interval		
Hp	Relationship	Std. Beta	Std. Error	P-value	LLCI	ULCI	Decision	
H4a	DSRSR ≪ DC ≪ DE	0.064	0.03	0.013***	0.026	0.132	Supported	
H4b	DSRER ≪ DC ≪ DE	0.026	0.023	0.04***	0.003	0.078	Supported	
H4c	DSRLR ≪ DC ≪ DE	0.025	0.026	0.115ns	0	0.082	Not Supported	
H4d	DSREn_R ≪ DC ≪ DE	0.039	0.023	0.018**	0.092	0.018	Supported	
Note(s): *p < 0.05, **p < 0.01; ns = non-sign; DSRSR= Social DSR; DSREC = Economic DSR; DSRLE = Legal DSR; DSREn_R = Environment DSR; DC = Digital culture; DE = Digital education; LLCI = stands for lower-limit confidence intervals; ULCI = stands for upper-limit confidence intervals.

Fig. 4 The structure model path diagram.

Fig. 4

5 Discussion

The pandemic has proven that DE has significant value, so DSR emerged as a crucial strategic move for the university. The literature review and findings of the present study indicate that this study is one of the few to investigate how SDR dimensions influence DE outcomes in universities during the crisis. Therefore, this study can significantly contribute by determining the ideal level of SDR initiatives in DE in the coming years. The findings validated and supported the four hypotheses that make up our model.

The study shows that not all SDR dimensions have the same effects on DE, and likewise on DE. The DE of students during the new crown pandemic has only been considerably impacted by the social, economic, and environmental dimensions of SDR (H1a, H1b, and H1d), while the social and environmental dimensions of SDR (H2a, H2d) have an impact on DC. This finding is consistent with the results that indicate DSR dimensions are multiple, and each factor plays a unique role in selecting the best digital communication tools for students in the education sector during a crisis [86]. For instance, the social responsibility in the digital environment is to harness current technologies to help students interact with peers and exchange insights, resources, and opinions [87]. As a result, building their digital citizenship abilities and personalize their learning to their preferences and needs [88]. In addition, the new crown pandemic has indirectly helped students to develop a healthy and responsible connection to technology something that often meets environmental standards for socially responsible universities, where students are learning how to utilize technology in a responsible and strategic, which enables them can aid in decision-making and develop self-discipline [89].

Moreover, while DE provides teachers and students access to a massive pool of information resources, university educators have minimized the cognitive burden of learning activities to what is essentially using multimedia learning tools, but at the same time, clear of clutter and distracting content [90]. One of the most critical contributions technologies can make is the instruction of digital competence and responsible use. Therefore, when universities take their SR seriously, they are more likely to embrace and invest in DE to enhance learning and improve outcomes for all members of society. The sense of obligation and commitment, known as a SR to enhance the well-being of society, is positively related to the implementation and adoption of DE [57]. This nuanced understanding of their relationships contributes valuable insights for educational institutions aiming to enhance their social responsibility initiatives in the digital era. This study's results agree with the theoretical framework and the earlier experimental [91]. Accordingly, in the first and second hypotheses, we have discussed that DSR dimensions positively influence the DE and DC in higher educational sectors.

Institutions' adoption of DE for societal benefit highlights the importance of cultivating DC within educational settings. DC is not just an add-on to DE; it's integral to unlocking DE's full potential in nurturing an engaged, informed, and responsible student community. The third hypothesis shows a positive association between DC and DE. Recent studies [92] support this finding by highlighting that DC reflects the diversity of students' preferences, behavior, and activities in DE. Technical tools are shaped by the interaction of technology and culture. Understanding social attitudes, values, conventions, and practices regarding technology in education aids students in using technology and the Internet effectively in learning, encompassing their behavior, etiquette, and participation in educational settings. This finding is consistent with prior literature, as per the SCOT theory, which suggests that DC can spread science and technology knowledge, blend heritage with modern technology, foster scientific awareness, and nurture a scientific culture [93]. Social media advocacy has potential for information dissemination, but students with a cultural digital attachment to their university show higher commitment to educational success. Meeting student and educator expectations and preferences in media usage, enhancing interactivity, and providing feedback are recommended strategies for improving learning outcomes [94]. Thus, DC is crucial to DE in university students.

The findings from the third hypothesis underscore the strategic importance of DC in improving online learning quality and impact. This leads to the critical mediation hypothesis, suggesting that DC indirectly affects the relationship between DSR dimensions and DE. Media choices can vary and impact learning experiences, highlighting the nuanced role of DC in shaping the connection between DSR and DE, which is pivotal for understanding and optimizing online learning outcomes [41]. Consistent with this [55], found that encouraging students to use DE platforms for knowledge sharing, collaboration, and participation in constructive activities promotes a culture of diversity, inclusion, and respect in the online environment. Social media has become a key platform for universities to communicate, interact with, and engage students in social responsibility concerns, initiatives, and activities [91]. This has led to compelling interactivity in online learning, improving student performance through various technological advantages.

This study expanded the existing literature during crisis times, suggesting that DSR not only directly influenced students' DE during the new crown pandemic but also indirectly enhanced it by elevating DC levels first. DC bridges DSR dimensions, particularly social, economic, and environmental aspects, with students' DE. Drawing on SCOT, evolving structural environments, knowledge, and shifting values lead to new technological frameworks and relevant social groups. This positions DC as a future alternative for decision-making, given its strong data processing ability and the capacity to convert data into supporting indicators for decision-making and alternative building. It is considered more effective in reshaping students' attitudes, focusing on factors rarely examined in technological and economic theories of technological change [2]. It is intriguing because it reflects views toward digitalization solutions among individuals who will influence education in the coming years, and it may also be a credible voice in a conversation regarding stakeholders' education. Hence, DC demonstrates a positive association between DSR and DE in the university frame and society. All research hypotheses were proven, and the findings verified our proposed theories.

6 Implications

6.1 Theoretical implications

This paper contributes significantly to the study of how different DSR dimensions influence DE during the new crown epidemic in universities. Firstly, it addresses recent and ongoing events by examining how DSR impacts DE during the new crown pandemic, filling a crucial research gap. Secondly, it takes an empirical approach, making it the first paper to evaluate the effects of DSR dimensions on DE during this crisis, thereby expanding the existing literature on DSR dimensions.

Moreover, this study identifies new factors affecting DE, highlighting a theoretical basis for universities to support student DE amid the new crown pandemic. It also adds to the ongoing debate by suggesting that not all DSR dimensions carry equal weight. Through evidence, it demonstrates that each DSR dimension (social, economic, legal, and environmental) has significant direct and indirect impacts on DE during the new crown pandemic, emphasizing the multifaceted nature of DSR's influence on educational outcomes during crisis situations.

Secondly, our work adds to DSR literature by incorporating technology variables like DC and investigating the mediating role of DC in the positive relationship between DSR dimensions and student educational outcomes [4]. This study contributes to DSR literature by confirming that DC, as a technological process, significantly influences the development of DSR strategies through social media applications, ultimately impacting DE. DC channels students' energy towards creating an independent cultural environment that fosters education [42,59].

Furthermore, the study significantly contributes to the stakeholder theory, which assumes that an organization effectively builds strong relationships with all stakeholders. Stakeholder framework can be used to study the association between DSR dimensions and students' DE and create value in university education during the new crown pandemic [42]. Also, it draws attention to the critical effects of factors such as DSR dimensions and DC connected to the stakeholder framework.

Third, the work contributes to the literature on the impact of DSR on student DE by offering empirical data in a specific industry and national context. Prior studies have been carried out in various contexts, including the Thailand business organization sector [92], the Saudi Arabian SME business sector [40], and the Thailand Electronic industry [41]. These findings from the Chinese university education sector are critical for further reinforcing and validating the growing body of literature investigating the link between DSR and DE, particularly during times of crisis.

Finally, the current literature, based on the SCOT theory, investigates the relationships among DSR dimensions, DC and DE. SCOT theory emphasizes that the meaning and impact of technology are not solely determined through its design but are shaped by users and their cultural contexts. Therefore, the significance of this research involves the presence of a digital innovation strategy for culture and new digital skills that help reshape students' attitudes towards the principles of social responsibility in educational practices. The results can be helpful for universities in China in developing effective frameworks for future research to reach students with different levels of knowledge and create content for distance learning in all sectors [27]. On the other hand, it is a tool to help scholars overcome challenging conditions during the new crown pandemic.

6.2 Practical implications

This study holds significant implications for universities in enhancing student DE, particularly during crises. The positive impact of DSR dimensions on DE underscores the need for universities to invest in human and technological resources. This investment can improve digital competitiveness, enhance operational and functional competencies, and build capabilities to serve education outcomes effectively. Such strategic investments are crucial for universities to navigate challenges and improve overall educational outcomes for their stakeholders [32]. Educators should actively promote DSRs among students by setting ethical technology use guidelines, ensuring data privacy and security, and organizing volunteer activities to mitigate the impact of the new crown pandemic. These activities foster digital social responsibility and student well-being, contributing to a more robust community response to challenges. This proactive approach not only benefits students but also promotes a culture of responsibility and engagement within the community [95,96].

DE practices are crucial for universities to gain competitive advantages and create value. Managing DSR engagement processes and education enhances digital literacy, explaining the effectiveness of DSR activities in universities for expanding educational knowledge [97].

One practical implication is that successful DSR management provides consistent information, meets stakeholder expectations, and supplements students' academic education with responsible practices [98]. Suppose university officials wish to improve the DE of students by implementing DSR practices. In that case, they should carefully monitor the DE of these students, which can be accomplished by engaging in various dimensions of DSR activities and fostering a culture of DSR in the use of media within universities. To avoid adverse outcomes, (1) universities should define their principles, vision, culture, and values in ways that assure socially responsible operations [8], build social ties, and formulate persuasive arguments with stakeholders; (2) Universities must understand the current technological culture and future expectations of modern communication tools to help improve the quality of DE.

7 Conclusion

Our study delves into the contexts of DSR, DC, and DE dimensions to comprehensively understand students' educational experiences during the new crown pandemic. It aims to examine how digital social responsibility affects digital education and the mediating role of digital culture in this relationship. This investigation is intended to help students grasp the concept of DSR and effectively manage DE stressors posed by external factors like the new crown pandemic. For the proposed study questions, we used stakeholder theory to address the first research question of the impact of DSR dimensions on DE and SCOT theory to address the second research question of the mediating role of DC during the new crown epidemic. Toward the end, the empirical findings generated by the path analysis of data obtained from 181 university students in the education sector in China during the new crown pandemic revealed that all dimensions of DSR have (direct and indirect) effects on students' DE. It also reveals that economic and legal DSR dimensions positively influence DC. DC also significantly influences students' DE during the new crown pandemic.

Our study also explored the mediating role of DC. Our findings reveal that DC mediates the relationship between social, economic, and environmental aspects of DSR and students' DE. This suggests that Chinese universities should reevaluate their DSR policies and consider students' DC influenced by social, economic, and political factors. Designing social digital responsibility education programs based on these factors can predict student DE success and provide a competitive edge as social media technology advances. By integrating various DSR dimensions, DC, and DE settings, our study offers a comprehensive understanding of students' educational experiences during the new crown pandemic.

Strengths and limitations

No previous study has explored DC's mediating role in the DSR-DE relationship during China's second wave of the new crown epidemic. This study aims to advance the DSR and DE field, potentially guiding future research. It integrates two key theories to enrich the analysis.

Firstly, Stakeholder theory underpins the study, emphasizing the need to balance diverse university needs while prioritizing ethical and transparent digital practices. Secondly, the Social Construction of Technology (SCOT) theory provides a sociological perspective, highlighting how technology is influenced by social, cultural, and contextual factors. Stober's perspective on the social institutionalization of new technologies further enhances the theoretical framework by illustrating how societal influences can shape technology and its impact on education. These theories together provide a robust foundation for understanding the complexities of DSR, DC, and DE within the evolving educational landscape during the new crown pandemic's second wave in China.

Throughout our research, lockdowns and social distancing measures necessitated a shift to online surveys due to restricted direct access to participants. This transition faced challenges like uneven internet access and lower response rates, potentially biasing our sample. Thus, our study has limitations that could guide future research. Firstly, results are based on self-administered surveys and respondents' perceptions, with a small sample limited to Chang'an University, reducing generalizability. Future studies with larger, culturally diverse samples are recommended for more robust results. Secondly, our data is cross-sectional, implying potential biases in data collection.

Future research could consider alternative methods like longitudinal data or experimental designs to address limitations. Additionally, assessing how students' cultural perceptions of DSR and DE evolved over time was not within this study's scope. Comparative studies could offer insights into cultural shifts during and post-new crown pandemic. Despite limitations, the growing interest in digital higher education presents opportunities to explore DC's role as a mediator between DSR and DE for fresh insights. However, SCOT theory might not fully capture the complexity of new media technologies and platforms, especially in evaluating their desirability, ethicality, and social responsibility.

Ethical statement

This study adheres to ethical guidelines, including the assurance of participant confidentiality and informed consent. Ethical approval was obtained from the ethics committee of Chang'an University (01/2022).

Data availability statement

Data are available at request from the first author's email (2021023931@chd.edu.cn) upon reasonable request.

CRediT authorship contribution statement

Nora A. Mothafar: Conceptualization. Jingxiao Zhang: Supervision, Funding acquisition. Amani Alsoffary: Writing – review & editing, Visualization, Validation, Investigation. Behzad Masoomi: Writing – review & editing. Abdo AL-Barakani: Investigation, Formal analysis. Osama S. Alhady: Formal analysis, Data curation.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jingxiao Zhang reports financial support was provided by Chang'an University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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