Higher Education Digital Academic Leadership: Perceptions and Practices from Chinese University Leaders

Abstract

Digital academic leadership (DAL) is essential for navigating the complexities of digital transformation in higher education institutions. However, empirical studies on how university leaders perceive and implement these practices remain scarce. This study bridges the critical gap in higher education leadership research by empirically investigating DAL through the lens of the unified theory of acceptance and use of technology. This study employs a mixed-methods approach, combining surveys and semi-structured interviews with mid-to-senior university administrators in Zhejiang and Guangdong, China, from whom the research identifies four core dimensions of DAL: digital strategic foresight, resource coordination, technology awareness, and culture building. Quantitative analyses reveal significant variations in DAL perceptions across institutional tiers, gender, and disciplinary cluster, while quantitative and qualitative insights both expose contextual challenges and strategic pathways in effective DAL implementation in Chinese universities for organizational goals. The study contributes a framework for theorizing DAL as a situated practice and offers evidence-based strategies for reconciling technological imperatives with localized leadership dynamics in Chinese universities.

1. Introduction

The rapid digitization of higher education has precipitated a paradigm shift in academic leadership, challenging university leaders globally to reconceptualize institutional governance in an era defined by technological disruption (Kasmia & M’hamed, 2023; Antonopoulou et al., 2021). In China, where higher education functions as both a driver of national innovation and a custodian of cultural heritage, digital transformation extends beyond infrastructure modernization (Xiao, 2019; Cui, 2023). Strategic initiatives such as the Double First-Class project, the Education Informatization 2.0 Action Plan, and the 2024–2035 master plan on building China into a leading country in education underscore the government’s ambition to position universities as globally competitive hubs of digital-era knowledge production (Yan & Yang, 2021). Digital transformation is impacting existing work environments and models in higher educational institutions in an irreversible way, causing the context and mechanisms of the leadership role to change. As digitalization continues to deepen, the complexity of managers’ work is increasing, leadership styles are transforming, and there is a need for leaders to adapt or enhance relevant competencies to meet the demands of the digital age (Sainger, 2018; Benchea & Ilie, 2023). In response, Chinese universities have proactively established specialized digital transformation centers and reformed their administrative frameworks to integrate agile digital governance practices. Moreover, many institutions have launched targeted professional development initiatives to bolster digital literacy among faculty and administrative staff, thereby fostering a culture of innovation and collaborative problem-solving. These measures collectively demonstrate a comprehensive and strategic approach to digital academic leadership that is reshaping the higher education landscape in China.

Against this backdrop, a new leadership style has emerged that delineates the digital era from traditional academic leadership (Dima et al., 2021), which is often rooted in hierarchical authority and disciplinary expertise, as it struggles to accommodate the epistemic fluidity. On the other hand, however, digital academic leadership (DAL), derived from traditional academic leadership, plays a crucial role in the transformation of higher education institutions (HEIs) by facilitating the integration of digital technologies into teaching, learning, and administrative processes (Ghamrawi & Tamim, 2023; Chwen-Li et al., 2022). Digital academic leadership is essential for navigating the complexities of digital transformation in higher education institutions, ensuring that educational objectives are met while adapting to new challenges (Anwar & Saraih, 2024).

Despite growing scholarly attention to digital academic leadership in higher education institutions (Cheng & Zhu, 2021; Zhu & Caliskan, 2021; Ruan et al., 2024), several critical gaps persist in the current literature that necessitate further exploration. Existing studies predominantly focus on Western contexts or K-12 educational settings (Cegielski, 2023; Karakose et al., 2021), leaving a significant knowledge void regarding how digital leadership manifests within the unique socio-cultural and administrative landscapes of Chinese universities, where hierarchical structures and collectivist values may shape leadership practices differently (Cartier, 2016; Xia et al., 2023). Secondly, the emerging body of research has largely concentrated on conceptualizing digital academic leadership through theoretical frameworks and dimension identification (Van Wart et al., 2019; Zupancic et al., 2018), with limited empirical investigation into how these theoretical constructs translate into daily operational practices or contribute meaningfully to organizational goal achievement in university settings. Furthermore, few studies have systematically examined the interplay between digital infrastructure development, policy implementation mechanisms, and leadership cognition within China’s rapidly evolving higher education ecosystem, particularly in the context of national initiatives promoting educational informatization (Miao et al., 2023; M. Liu et al., 2019).

This paper aims to explore the topic from the perspective of Chinese university leaders and address the following two core questions to investigate the development of culturally contextualized models that could inform leadership training programs and institutional transformation strategies in Chinese universities navigating digital transformation challenges: (1) How is DAL perceived by Chinese university leaders and what are the factors contributing to these perception differences? (2) How do universities utilize DAL to achieve institutional strategic goals?

2. Literature Review

2.1. Evolution and Concepts of Digital Academic Leadership

The conceptualization of digital academic leadership (DAL) remains fluid in scholarly discourse, reflecting both the complexity of digital transformation processes and disciplinary divergences in framing technological leadership. While foundational studies by Avolio et al. (2000) positioned electronic leadership (e-leadership) as a sociotechnical influence process mediated by advanced information technologies, subsequent debates have grappled with terminological ambiguity and fragmented operationalizations. Scholars employ overlapping constructs such as technological leadership, ICT leadership, and e-leadership (Eberl & Drews, 2021; Weritz, 2021), often conflating technical infrastructure management with transformative strategic governance. This semantic fluidity, as Oberer and Erkollar (2018) and Sanati et al. (2024) observe, mirrors broader tensions between instrumentalist views of technology adoption and holistic paradigms emphasizing cultural-organizational transformation.

Emerging consensus positions DAL as a distinct leadership modality that synthesizes technical fluency, pedagogical innovation, and adaptive governance to reconfigure academic missions in digitally saturated ecosystems (Ehlers, 2020; Avidov-Ungar et al., 2022; Kawiana, 2023). Building on Msila’s (2022) vision of technology as a catalyst for pedagogical reimagining, contemporary definitions emphasize DAL’s dual role as a driver of systemic change and an enabler of localized practices. Cheng et al. (2024) conceptualize DAL as the systematic orchestration of digital tools, cognitive frameworks, and institutional behaviors to optimize teaching, research, and administrative functions—a process demanding leaders mediate between technological possibilities and academic integrity. Crucially, DAL transcends conventional e-leadership models focused on corporate virtual team management (Ghamrawi & Tamim, 2023) by foregrounding academia’s unique mandate to balance innovation with the ethical stewardship of resources and pedagogical values (Zhan & Jiang, 2023).

Three dominant theoretical lenses structure the existing analyses of DAL’s conceptual terrain. The social influence perspective, rooted in Avolio’s e-leadership framework, interrogates how digital tools reshape power dynamics and collective behaviors across academic communities. In contrast, the leader traits perspective delineates specific competencies—strategic foresight, digital literacy, and adaptive communication (Hebert & Lovett, 2021)—required to navigate institutional resistance and resource disparities during digital transitions. Synthesizing these views, the organizational change perspective positions DAL as a linchpin in overcoming what Cai and Hu (2024) term digital–cultural dualism—the misalignment between legacy academic practices and emergent technological demands. Critical to this synthesis is DAL’s intersection with transformational leadership theory, which underscores leaders’ capacity to articulate compelling digital visions while fostering psychological safety for experimentation (Ratajczak, 2022).

Despite conceptual advancements, theoretical frameworks often underplay the contextual specificities of higher education systems. As Msila (2022) cautions, indiscriminate adoption of corporate-derived digital leadership models risks neglecting academia’s unique governance structures and epistemic cultures. This critique gains urgency in China’s higher education landscape, where socialist pedagogical principles intersect with centralized digital governance initiatives (Zhan & Jiang, 2023), creating leadership challenges distinct from Western contexts. The field’s preoccupation with abstract conceptualizations—evidenced by a proliferation of competing terminologies—has also hindered empirical investigations into how DAL manifests operationally across research management, curriculum redesign, and cross-departmental collaboration. This gap persists despite repeated scholarly calls for granular analyses of leadership praxis in digital transformation cycles, from infrastructure investment to organizational learning outcomes.

2.2. The Reciprocal Relationship Between Digital Transformation and Academic Leadership

Recent studies underscore that digital transformation is not merely a technological shift but a profound organizational process that redefines DAL functions, styles, and capabilities. As B. Wang et al. (2020) note that digital environments reshape core leadership roles including information sourcing and structuring, problem-solving, human resource coordination, and material resource management. Leaders are increasingly expected to navigate this complexity through data-driven decision-making and digitally supported oversight. Henderikx and Stoffers (2022) further highlight a bifurcation in leadership roles; while quantifiable administrative tasks are increasingly delegated to digital systems, leaders themselves shift toward more relational functions, such as motivating, training, and empowering employees. These dynamics have spurred the emergence of diverse leadership modes, including shared leadership (Hoch & Kozlowski, 2014; Chen & Zhang, 2023), platform leadership (Benitez et al., 2022), and distributed leadership (Harris et al., 2021), each reflecting the decentralization, adaptability, and collaboration required in digitally intensive academic environments.

Reciprocally, DAL serves as a critical enabler of institutional digital transformation by orchestrating strategic, structural, cultural, and human-level shifts across the organization. At the organizational level, leaders promote transformation through the design and execution of digital strategies (Porfírio et al., 2021), the cultivation of digital culture (Philippart, 2021), and the restructuring of both internal systems and external interfaces (Warner & Wäger, 2019). At the team level, DAL fosters adaptation and performance through supportive and trust-building leadership styles. For instance, Liao (2017) demonstrates how traditional task- and relationship-oriented leadership (e.g., transformational, empowering) enhances virtual team collaboration and shared mental models. Other emerging styles—such as knowledge-based and authentic leadership—have been shown to improve team IT efficacy and interpersonal dynamics and manage cross-cultural communication and conflict within virtual academic teams (Lin et al., 2019; Y. Zhang et al., 2022), further illustrating the multidimensional impact of DAL in the context of educational digitalization.

2.3. Multidimensional Competencies and Frameworks of DAL

The conceptualization of digital academic leadership (DAL) competencies has evolved through pluralistic theoretical lenses, reflecting the inherent complexity of leading digital transformation in higher education. Current scholarship demonstrates three predominant analytical approaches, each contributing distinct yet complementary perspectives to the competency architecture (

Table 1. Proposed dimensions/key competencies of DAL.

Proposed Dimensions/Key Competencies	Specific Dimensional Indicators	Reference
Three Proposed Dimensions/Key Competencies	Vision for technological integration; resource management; fostering collaboration	Msila (2022)
	Strategic planning capability; organizational mobilization capability; digital infrastructure (resource-, literacy-, institutional-based)	Cai and Hu (2024)
	Trust-building; collaborative team dynamics; adaptive communication	Hebert and Lovett (2021)
	Forward-thinking; data literacy; value recognition	Fang (2019)
Four Proposed Dimensions/Key Competencies	mindful change capability; professional and operational expertise; environmental sensing capability; interactive resonance competence	Duan (2020)
	Data literacy; digital tool proficiency; ethical decision-making in AI; fostering digital citizenship	Jameson et al. (2022)
Five Proposed Dimensions/Key Competencies	Digital awareness; technology vision; technology adoption; collaborative practices; challenge addressing	Cheng et al. (2024)
	5D Typology: Digital competence; digital culture; digital differentiation; digital governance; digital advocacy	Ghamrawi and Tamim (2023)
	Digital leadership; charisma; foresight; influence; decision-making; control	Shan (2023)

).

The three-dimensional frameworks emphasize operational functionality in digital leadership. Msila’s (2022) tripartite model positions technological visioning as the cornerstone, requiring leaders to reconcile resource allocation imperatives with collaborative governance mechanisms—a perspective echoed in Cai and Hu’s (2024) institutional analysis that foregrounds infrastructure development as both technical and sociopolitical endeavor. These frameworks, however, exhibit limited explanatory power regarding individual agency, a gap partially addressed by Pasolong and Setini (2021). through their psychological construct measuring leaders’ cognitive adaptability in risk-laden digital environments.

Emerging four-dimensional typologies signal a paradigm shift toward techno-cultural hybridization. Cheng and Zhu’s (2024) framework breaks new ground by theorizing the dialectical relationship between digital asset utilization and leadership identity reconstruction, positing that effective DAL requires simultaneous mastery of technological affordances and symbolic capital reconstruction. This aligns with Jameson et al.’s (2022) ethical leadership paradigm, which introduces crucial normative dimensions through its emphasis on AI ethics and digital citizenship—elements conspicuously absent in earlier models. The progression from X. Yang et al.’s (2023) data-centric operationalism to Hebert and Lovett’s (2021) communicative constructivism further illustrates the field’s growing recognition of DAL as a boundary-spanning practice.

The most expansive five-dimensional conceptualizations reveal tensions between universalist and contextual approaches. While Cheng et al. (2024) propose a techno-managerial continuum spanning from awareness to problem-solving, Ghamrawi and Tamim’s (2023) 5D typology embeds DAL within institutional power dynamics through its governance-differentiation axis. Notably, Shan’s (2023) psychometric model reintroduces traditional leadership theory elements like charisma into digital contexts, creating theoretical friction with Fang’s (2019) competence-based approach that prioritizes data literacy over personality factors.

2.4. Challenges in Operationalizing Digital Academic Leadership

The operationalization of digital academic leadership (DAL) in Chinese universities presents a complex set of challenges that stem from structural and cultural factors, as well as ethical considerations. These challenges are interconnected and hinder the effective implementation of DAL, which aims to enhance teaching, research, and governance through digital tools and innovative practices.

One of the primary structural barriers is the hierarchical governance system prevalent in Chinese universities, which often conflicts with the decentralized workflows enabled by digital tools (Zhan & Jiang, 2023). For example, attempts to introduce agile decision-making via virtual platforms may flounder on bureaucratic protocols involving multi-layered administrative procedures. Resource disparities further exacerbate these tensions: underfunded institutions struggle to provide the cybersecurity infrastructure or cloud-computing capacities that underpin effective DAL (Jameson et al., 2022).

Cultural resistance plays a significant role in hindering the adoption of technology-mediated pedagogical practices. Senior scholars in Chinese universities often exhibit skepticism toward technology due to concerns about its impact on traditional teaching methods, student engagement, and academic freedom (Pasolong & Setini, 2021). Furthermore, generational divides within institutions exacerbate this resistance as younger faculty may be more open to embracing digital tools compared to their older counterparts.

Ethical dilemmas further complicate the implementation of DAL. The use of learning analytics systems, for example, poses risks to student–teacher relationships by reducing instruction to data-driven algorithms that prioritize individualized attention over holistic educational experiences (Khemtong et al., 2024). Similarly, the proliferation of digital research collaboration tools raises concerns about data sovereignty and intellectual property rights, particularly in transnational partnerships (Cheng et al., 2024). Leaders must navigate these tensions through policies that balance innovation with accountability—a challenge compounded by the absence of global standards for digital academic ethics.

2.5. Strategic Pathways for Effective DAL Implementation

The implementation of digital academic leadership (DAL) in higher education necessitates a strategic approach that aligns institutional goals with technological advancements, ensuring alignment and sustainability. This section synthesizes insights from various studies to identify key pathways that promote effective DAL implementation.

Successful DAL implementation requires leaders to embed digital strategies within existing academic values rather than imposing technocratic solutions. For instance, Peking University’s Digital Humanities Initiative gained faculty buy-in by framing AI text-analysis tools as extensions of traditional philological methods rather than disruptive replacements (X. Yang et al., 2023). Such approaches resonate with Msila’s (2022) emphasis on mission-driven digitization, where LMS adoption serves specific pedagogical goals like increasing rural student access rather than chasing technological novelty.

Another strategic pathway is fostering participatory digital ecosystems. Top-down mandates often fail in academic environments due to the collegiate culture that values collegiality over centralized control (Jameson et al., 2022). To address this, co-design methodologies that engage stakeholders at all levels are essential. Pasolong and Setini (2021) highlight the success of a Malaysian university’s effort to integrate massive open online courses (MOOCs) into its curriculum, which was achieved through workshops involving instructors in course redesign. Similarly, Hebert and Lovett (2021) advocate for digital leadership circles, cross-rank meetings where staff collaboratively critique institutional technology policies, thereby identifying potential barriers to implementation and fostering a more inclusive approach.

Moreover, investing in adaptive capacity building is of significance for effectively implementing DAL. Sustainable DAL demands continuous upskilling tailored to academia’s diverse workforce. Jameson et al. (2022) propose tiered training programs: foundational modules on data literacy for administrative staff, advanced seminars on AI ethics for senior leaders, and peer-mentoring networks to bridge generational tech divides. Crucially, such initiatives must address gendered disparities in digital leadership; Antonopoulou et al. (2021) found male leaders disproportionately dominate tech-related decision-making, underscoring the need for inclusive recruitment in DAL roles.

3. Analytical Framework

The unified theory of acceptance and use of technology (UTAUT), proposed by Venkatesh et al. (2003), integrates earlier technology adoption models to explain individuals’ behavioral intentions and actual technology use. It evolves from, integrates, and optimizes previous technology acceptance models including the theory of planned behavior (TPB), diffusion of innovations (DOI), the technology acceptance model (TAM), and the unified theory of acceptance and use of technology (UTAUT) (

Table 2. The evolution and specific comparison of TPB, DOI, TAM, UTAUT, and UTAUT2.

Theory	Core Constructs	Primary Focus	Theoretical Basis	Moderators	Reference
Theory of Planned Behavior (TPB)	Attitude Subjective Norms Perceived Behavioral Control	Predicting general human behavior	Extension of theory of reasoned action (TRA)	/	Ajzen (1991, 2020)
Diffusion of Innovations (DOI)	Relative Advantage Compatibility Complexity Trialability Observability	Adoption dynamics in social systems	Communication Theory	Social system norms and communication channels	Rogers and Smith (1962); Rogers et al. (2014)
Technology Acceptance Model (TAM)	Perceived Usefulness Perceived Ease of Use	Technology adoption in organizational settings	Rooted in TRA and TPB	/	Davis (1989)
Unified Theory of Acceptance and Use of Technology (UTAUT)	Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions	Organizational technology adoption	Integrates TAM, TPB, DOI, and other models	Age, gender, experience and voluntariness	Venkatesh et al. (2003)
UTAUT2	UTAUT constructs + Hedonic Motivation Price Value Habit	Consumer technology adoption	Evolutionary extension of UTAUT	Age, gender, and experience (expanded)	Venkatesh et al. (2012)

). At its core, UTAUT posits that four direct factors—performance expectancy, effort expectancy, social influence, and facilitating conditions—collectively determine technology adoption behaviors, moderated by age, gender, experience, and voluntariness of use. Based on UTAUT, Venkatesh et al. (2012) further proposed an extended version of the integrated technology acceptance model (UTAUT2). The model further adds the three variables of hedonic motivation, price value, and habit to UTAUT, which more comprehensively covers multiple aspects of factors affecting users’ technology acceptance and use. Performance expectancy reflects the degree to which users believe technology will enhance outcomes; effort expectancy refers to the perceived ease of use; social influence concerns the impact of others’ perceptions; and facilitating conditions encompass institutional support and infrastructure (

Table 3. Core constructs and examples of UTAUT Framework.

Construct	Operational Definition	Example in HE	Theoretical Foundations
Performance Expectancy	The degree to which using a technology will provide benefits in achieving specific job performance	Perceived effectiveness of digital tools in enhancing institutional decision-making	TAM (Perceived Usefulness)
Effort Expectancy	The perceived ease of use associated with technology adoption	Learning curve required to implement AI-driven academic monitoring systems	TAM (Perceived Ease of Use)
Social Influence	The extent to which users perceive that significant referent groups endorse technology adoption	Peer universities’ successful digital transformation impacts adoption willingness	DOI (Social System Norms)
Facilitating Conditions	Available organizational resources and technical infrastructures enabling technology implementation	Government funding for smart campus initiatives	TPB (Perceived Behavioral Control)

Note: Constructs derived from the original UTAUT model (Venkatesh et al., 2003); UTAUT2—specific constructs (hedonic motivation, price value, and habit) are not included as they are not applicable to the context of institutional leadership in this study.

).

In higher education leadership research, UTAUT offers a robust lens to examine how academic leaders perceive and operationalize digital transformation. For instance, J. Liu et al. (2024) demonstrated that performance expectancy significantly predicts educators’ adoption of e-learning systems, emphasizing leaders’ role in aligning technological tools with pedagogical goals. In the context of digital academic leadership, university administrators’ strategic decisions often hinge on their evaluations of whether digital tools will improve institutional efficiency (performance expectancy) or require excessive resources to implement (effort expectancy) (Noureddine et al., 2025). Social influence, in Chinese organizational contexts, carries particular weight in collectivist cultures where peer expectations and hierarchical endorsements heavily shape leadership practices (Huang, 2018). Facilitating conditions in higher education extends beyond mere technological infrastructure to include training programs, policy frameworks, and cultural readiness—factors critically dependent on leadership vision. A study by Xu et al. (2024) highlighted that academic leaders who systematically address facilitating conditions through resource allocation and staff development foster more sustainable digital transitions. This aligns with Shen et al.’s (2020) observation that Chinese university leaders increasingly frame digital leadership as an institutional competency, balancing technological affordances with systemic preparedness.

By applying UTAUT to digital academic leadership, researchers can interrogate how university leaders mediate between technological possibilities and organizational realities. The model’s emphasis on social influence and facilitating conditions, particularly in non-Western contexts, invites an exploration of how cultural norms and institutional histories modulate the enactment of digital leadership strategies. Future studies might investigate how Chinese leaders reconcile performance expectations shaped by global digitalization trends with localized effort expectations rooted in resource constraints.

4. Method

4.1. Research Context

This mixed-methods study employed a sequential explanatory design combining quantitative surveys and qualitative semi-structured interviews to explore the perceptions and practices of digital academic leadership among Chinese university leaders. The participants were senior managers from universities in Zhejiang and Guangdong provinces, including top-tier universities designated as double first-class universities, provincial key universities, and higher vocational colleges, strategically selected due to their economic prosperity, educational quality, and representativeness of China’s higher education landscape, ensuring a coverage of diverse organizational contexts, which enabled us to capture a comprehensive picture of the development of digital academic leadership in Chinese institutions (

Table 4. Information about the interviewed participants.

Participant	Gender	School Type	School Location	Administrative Position
GDDF1	Male	Double First-Class University	Guangdong Province	Dean, School of Automation
ZJPK2	Male	Provincial Key University	Zhejiang Province	Director, Campus Construction and Management Department
ZJHV3	Male	Higher Vocational College	Zhejiang Province	Director, Organization Department (Talent Office)
GDHV4	Male	Higher Vocational College	Guangdong Province	Director, Faculty Development Center
GDPK5	Male	Provincial Key University	Guangdong Province	Party Secretary, School of Data Science and Artificial Intelligence
ZJDF6	Male	Double First-Class University	Zhejiang Province	Director, Yangtze River Delta Smart Oasis Innovation Center
GDHV7	Male	Higher Vocational College	Guangdong Province	Head, Student Affairs Office, School of General Education

). The coding system employs a structured alphanumeric format combining province abbreviations (ZJ for Zhejiang and GD for Guangdong) with institutional category codes (DF for double first-class universities, PK for provincial key universities, and HV for higher vocational colleges), followed by unique participant identifiers. This approach enables immediate recognition of participants’ geographical and institutional affiliations while preserving anonymity, systematically reflecting China’s stratified higher education landscape and regional distribution patterns. The sequential numbering ensures traceability across qualitative analysis processes.

4.2. Data Collection

The survey instrument consisted of the following four parts: (1) basic demographic information; (2) self-assessment of personal digital leadership capabilities using five-point Likert scale items; (3) evaluation of organizational digital leadership practices; and (4) open-ended questions designed to elicit qualitative insights. The items related to personal digital leadership were adapted from Z. Zhang & Zheng’s (2023) digital leadership model, while the organizational-level section drew on prior literature and themes emerging from preliminary qualitative interviews. To enhance content validity and avoid conceptual overlap between sections, two pilot tests were conducted to refine the instrument. The final version of the survey was administered online via Wenjuanxing (a Chinese equivalent of Qualtrics) concurrently with the semi-structured interviews, allowing for methodological triangulation (Creswell & Poth, 2016). Participation was voluntary and anonymous, and respondents were informed about the purpose and scope of the study.

For the interview design, we built upon a review of the existing literature to develop structured questions that aimed to explore both individual perspectives and practices on digital leadership and organizational-level considerations. To ensure consistency in data collection, a standardized interview protocol (Appendix A) was developed based on the literature review and refined through pilot testing. Interviews were conducted synchronously via secure video conferencing platforms (Tencent Meeting), with participants’ consent obtained for audio recording. The interviewers followed a semi-structured guide with open-ended questions, allowing for both the comparability across interviews and flexibility in probing specific areas of interest. The interviews were carried out from December 2024 to February 2025, with each interview lasting approximately 1 h. Participants were explicitly informed about their right to anonymity and the purpose of the study. Transcripts were anonymized to protect participants’ identities. Throughout the data collection process, interviewers maintained reflective memos to capture contextual cues and emergent impressions, which were later incorporated into the qualitative analysis phase to support interpretive depth.

4.3. Data Analysis

Data analysis was conducted using a mixed-methods framework. For the personal digital leadership section (five-point Likert scale items), we utilized IBM SPSS Statistics 26 to compute descriptive statistics, including mean scores and standard deviations. Reliability testing was performed using Cronbach’s alpha to ensure internal consistency of the measurement tool (Cronbach, 1951). For the organizational digital leadership section, open-ended questionnaire items, and interview transcripts, we employed a three-stage thematic analysis approach informed by grounded theory methodology (Charmaz, 2006). This included open coding to identify initial concepts, axial coding to explore relationships between categories, and selective coding to refine overarching themes. NVivo 12 qualitative analysis software was used to manage and visualize the data. Coding was conducted independently by three trained researchers. Inter-coder reliability was ensured through iterative consensus-building discussions rather than statistical coefficients, reflecting qualitative rigor (Miles & Huberman, 1994). Data saturation was reached after analyzing six interviews as no new emergent themes were identified beyond that point. Triangulation of qualitative data sources was applied to enhance validity and ensure analytical coherence (Denzin, 2017; Krippendorff, 2018).

Ethical considerations were fully addressed by obtaining informed consent from all participants, ensuring voluntary participation in both the survey and interviews and adhering to ethical guidelines for human subject research. All participants were provided with a written informed consent form outlining the purpose, procedures, and ethical safeguards of the study. Participation was entirely voluntary and all participants signed the form before the interviews or surveys commenced. The use of mixed-methods allowed us to triangulate data, providing a more robust understanding of the constructs under study while preserving their complexity.

5. Findings

5.1. Empirical Results of the Questionnaire Survey

The digital academic leadership (DAL) scale was subjected to rigorous psychometric validation. Internal consistency reliability was assessed via Cronbach’s α, with all scales exceeding the 0.70 threshold (Nunnally, 1978). Exploratory factor analysis (EFA) using principal axis factoring confirmed construct validity, evidenced by a KMO measure of 0.893 and a significant Bartlett’s test (χ² = 1189.79, p < 0.001), meeting Kaiser’s (1974) criteria for factorability.

The participant profile revealed a strategically stratified sample reflective of China’s higher education hierarchy (

Table 5. Demographic characteristics of questionnaire participants.

Variable	Category	n	%	M	SD
Gender	Male	48	62%	1.38	0.49
	Female	30	39%
Institution Type	Double First-Class	4	5%	2.55	0.60
	Provincial Key	27	35%
	Higher Vocational College	47	60%
Tenure in Position	Below 5 years	48	62%	1.71	0.49
	5–10 years	17	22%
	10–15 years	4	5%
	Over 15 years	9	12%
Disciplinary Cluster	Natural–Applied Sciences (NASs)	23	30%	/	/
	Humanities–Social Sciences (HSSs)	41	52%
	Interdisciplinary–Hybrid Fields (IHFs)	14	18%
	Total	78	100%

Note. Percentages may not sum to 100% due to rounding. M = mean; SD = standard deviation. Natural–Applied Sciences (NASs) encompasses: science, technology, engineering, agriculture, and medicine; Humanities–Social Sciences (HSSs) includes: literature, history, philosophy, economics, law, education, and the arts; Interdisciplinary–Hybrid Fields (IHFs) covers: emerging cross-disciplinary programs.

). Male administrators constituted the majority (62%, n = 48), with female counterparts representing 39% (n = 30), mirroring persistent gender disparities in Chinese academic leadership roles (Hu et al., 2022). Institutional distribution emphasized vocational colleges (60%, n = 47), aligning with national digital transformation priorities for applied education sectors. Double first-class universities were intentionally undersampled (5%, n = 4) to counterbalance their disproportionate representation in existing literature, thereby amplifying voices from provincial key (35%, n = 27) and vocational institutions.

The comparative analysis revealed structurally divergent patterns in digital leadership competencies across gender and institutional strata (

Table 6. Digital competency comparisons by gender and institution type with descriptive statistics.

Competency Domain	Male (n = 48) M(SD)	Female (n = 30) M(SD)	t(df), p	Undergraduate University (n = 31) M(SD)	Higher Vocational College (n = 47) M(SD)	t(df), p
Digital Strategic Change	4.12 (0.72)	4.17 (0.61)	−0.33(76), 0.742	3.98 (0.69)	4.25 (0.66)	−1.72(76), 0.090
Digital Resource Building	3.67 (0.96)	3.23 (0.83)	2.07(76), 0.042 *	3.29 (0.88)	3.65 (0.95)	−1.67(76), 0.100
Digital Ethical Empathy	4.16 (0.74)	4.22 (0.60)	−0.34(76), 0.733	3.98 (0.76)	4.31 (0.61)	−2.12(76), 0.037 *
Digital Cognitive Practice	4.05 (0.74)	3.72 (0.56)	2.07(76), 0.042 *	3.78 (0.72)	4.02 (0.66)	−1.50(76), 0.138

Note. For better analysis, undergraduate university includes double first-class and provincial key universities. * p < 0.05, indicating statistically significant differences.

). Male leaders demonstrated statistically superior performance in technical implementation domains—digital resource building (t = 2.07, p = 0.042, d = 0.49) and cognitive practice (t = 2.07, p = 0.042, d = 0.49)—aligning with global trends of gendered technological capital accumulation in STEM-adjacent fields (Daniels, 2017). Conversely, vocational colleges emerged as unexpected leaders in ethical empathy (t = −2.12, p = 0.037, d = 0.48). Meanwhile, female leaders and comprehensive universities showed non-significant advantages in strategic change competencies (p = 0.742; p = 0.090).

While omnibus ANOVA tests revealed no statistically significant disciplinary differences (p > 0.05), medium-effect trends emerged in specific pairings (

Table 7. Comparative analysis of digital competencies across disciplinary clusters.

Competency Domain	NASs M(SD)	HSSs M(SD)	IHFs M(SD)	F(2,75)	p	Post Hoc Comparisons (t, p, Cohen’s d)
Digital Strategic Change	4.26 (0.53)	4.01 (0.79)	4.33 (0.46)	1.695	0.191	NASs-IHFs: t = −0.40, p = 0.695, d = −0.13 NASs-HSSs: t = 1.36, p = 0.178, d = 0.34 HSSs-IHFs: t = −1.43, p = 0.160, d = −0.50
Digital Resource Building	3.65 (0.88)	3.35 (0.93)	3.71 (0.99)	1.217	0.302	NASs-IHFs: t = −0.19, p = 0.843, d = −0.06 NASs-HSSs: t = 1.27, p = 0.210, d = 0.32 HSSs-IHFs: t = −1.24, p = 0.220, d = −0.35
Digital Ethical Empathy	4.20 (0.59)	4.17 (0.79)	4.20 (0.53)	0.013	0.987	NASs-IHFs: t = −0.00, p = 0.997, d = −0.00 NASs-HSSs: t = 0.13, p = 0.896, d = 0.04 HSSs-IHFs: t = −0.11, p = 0.911, d = −0.05
Digital Cognitive Practice	4.12 (0.57)	3.78 (0.77)	4.02 (0.57)	2.088	0.131	NASs-IHFs: t = 0.51, p = 0.611, d = 0.17 NASs-HSSs: t = 1.89, p = 0.063, d = 0.48 HSSs-IHFs: t = −1.11, p = 0.274, d = −0.34

). Natural–Applied Science (NASs) leaders showed marginally stronger digital cognitive practice capabilities than Humanities–Social Science (HSSs) peers (d = 0.48, p = 0.063), suggesting STEM-adjacent disciplines’ latent advantage in technical implementation. Conversely, Interdisciplinary–Hybrid Fields (IHFs) demonstrated non-significant but consistent advantages over HSS in strategic change (d = −0.50) and resource building (d = −0.35), potentially reflecting cross-disciplinary training benefits. These exploratory findings, though requiring replication with larger samples, challenge assumptions of disciplinary neutrality in digital leadership development.

5.2. Chinese University Leaders’ Perceptions of Digital Academic Leadership

Analysis of interview data reveals four constitutive dimensions shaping Chinese university leaders’ perceptions of digital academic leadership (

Table 8. Dimensions, core capabilities and definitions of Chinese university leaders’ perceptions of DAL.

Dimensions	Core Capabilities	Definitions
Strategic Dimension	Digital Strategic Foresight Capability	To anticipate emerging digital trends and formulate long-term strategies that align with institutional goals
Organizational Dimension	Digital Resource Coordination Capability	Effective integration and management of digital assets across various organizational units
Technological Dimension	Digital Technology Awareness Capability	A comprehensive understanding of current and emerging digital technologies and their potential applications
Cultural Dimension	Digital Culture Building Capability	Cultivating an organizational culture that embraces digital innovation and continuous learning

). Digital strategic foresight capability was defined as the capacity to project institutional trajectories through emerging digital trends. Leaders emphasized proactive planning, with ZJHV3 stating, “We map AI development scenarios to revise doctoral programs every three years”, though GDHV4 cautioned that “budget cycles often limit strategic horizons to five years maximum”. This capability manifested through regular environmental scanning practices, with six participants reporting quarterly technology trend assessments. The second dimension, digital resource coordination capability, concerned systemic integration of digital assets across administrative and academic units. While GDPK5 described successful “cloud platform consolidation serving 20+ departments”, fragmentation persisted, as GDHV7 noted: “Our 15 e-learning systems barely communicate”. Cross-unit collaboration emerged as critical, with GDDF1 explaining their “digital taskforce comprising IT staff, deans, and library directors” as essential for resource alignment. Digital technology awareness capability emerged as leaders’ self-reported competency in discerning technological relevance. All participants referenced monitored emerging technologies through industrial partnerships, with ZJDF6 stating, “Monthly briefings from Huawei/Baidu keep us informed of sector moves”. However, ZJPK2 emphasized the practical translation gap: “Knowing metaverse concepts differs from applying them to virtual labs—that’s our daily challenge”. Finally, digital culture building capability involved nurturing organizational receptivity to digital innovation. Leaders reported multifaceted approaches: GDHV4 instituted “mandatory digital mentoring programs”, whereas ZJHV3 advocated “department-level sandbox experiments”. Despite these efforts, GDPK5 highlighted persistent skepticism: “32% of senior faculty in our survey view VR teaching as educational gimmicks”. The dimension’s operational complexity was summarized by GDHV7: “Culture change requires proving digital tools solve real problems, not just chasing trends”. Interconnection between these capabilities surfaced through leaders’ narratives. ZJPK2 encapsulated this synergy: “Foresight sets direction, resource coordination clears the path, tech awareness chooses the vehicles, and cultural readiness determines how fast we travel”.

5.3. Challenges of Implementing DAL in HEIs

Four subcategories of challenges of implementing DAL in HEIs emerged from our survey and interview findings, including strategic and governance challenges, organizational operational barriers, data and technological bottlenecks, and cultural and cognitive dilemmas (

Table 9. Summary of categories, subcategories and themes from the interview and survey.

Categories	Subcategories	Themes	Frequency
Challenges of Implementing DAL in HEIs	Strategic and Governance Challenges	Lack of digital regulatory and ethic framework	Medium
		High investment costs and limited funding in digitalization	High
	Organizational Operational Barriers	Structural rigidity and cross-departmental collaboration failure	Medium
		Lack of professional teams and expertise	High
	Data and Technological Bottlenecks	Data governance failures, information silos, and real-time update delays	Medium
		Data accuracy and algorithmic ethical risks	Low
	Cultural and Cognitive Dilemmas	Faculty technostress, digital literacy divide, and institutional training gaps	High
		Dynamic tension between technological rationality and pedagogical humanism	Low
Strategies for HEIs Leaders to Achieve Organizational Goals through DAL	Strategic Leadership Dimension	Institutionalizing national policy-embedded innovation	Medium
		Coordinating cross-departmental synergies	Medium
	Organizational Leadership Dimension	Orchestrating hybrid talent deployment	High
		Optimizing adaptive governance and resource allocation	Medium
	Technological Leadership Dimension	Hardware: architecting intelligent campus infrastructures	High
		Software: re-engineering integrated digital ecosystem	High
	Cultural–Emotional Leadership Dimension	Cultivating techno-cultural ambidexterity	Low
		Mediating technophobia through role modeling and structural support	Medium

).

5.3.1. Strategic and Governance Challenges

A recurring theme across interviews was the inadequacy of formalized regulations and ethical guidelines to govern digital practices in academic leadership. Many leaders expressed concerns over the absence of clear policies to govern the ethical use of digital technologies, especially regarding data privacy and algorithmic biases. As one interviewee (GDDF1) noted, “There is no clear regulatory structure that addresses the ethical use of data and technology within the academic setting, leading to confusion and concerns over misuse”. This lack of regulation has resulted in inconsistent practices across departments and a fragmented approach to digital leadership, with leaders struggling to ensure the responsible use of digital tools in teaching and research.

Another major issue is the high investment costs and limited funding for digitalization. While digital technologies are recognized for their potential benefits, the financial burden of implementation and maintenance remains a significant barrier. As GDPK5 stated, “Prestige institutions receive marked budgets for AI labs, while we divert funds from faculty salaries to patchwork IT upgrades. This widens existing inequalities in digital capacity”. Furthermore, funding for digital projects often competes with other priorities, leading to delays or insufficient resources for large-scale digital initiatives. Without sustained financial support, particularly from government or external sources, the ability of universities to keep up with the pace of technological advancements remains compromised.

5.3.2. Organizational Operational Barriers

The implementation of DAL faced significant organizational friction, particularly due to entrenched structural rigidities that hindered agile decision-making processes. Multiple participants described university hierarchies as “multi-layered mazes where digital initiatives get lost between committees” (ZJPK2). These bureaucratic complexities were compounded by chronic failures in cross-departmental collaboration, a concern highlighted by three interviewees. GDDF1 observed that “academic departments, IT services, and administrative units operate like isolated fiefdoms”, while GDPK5 elaborated that “resource allocation disputes regularly paralyze joint digital projects before they begin”. This systemic fragmentation resulted in what GDHV4 termed “innovation islands”—scattered digital initiatives lacking institutional coherence or scalability. The tension between administrative tradition and digital transformation was further exacerbated by what ZJHV3 described as “role ambiguity”, where faculty expected to lead digital pedagogy simultaneously manage ballooning administrative workloads without corresponding support structures.

Concurrently, a pronounced expertise gap emerged as a critical barrier, with only two institutions reportedly maintaining dedicated DAL teams. ZJDF6 lamented that “existing staff members, while administratively competent, lack the data literacy to translate institutional strategies into actionable digital workflows”. Even technologically progressive leaders like GDHV7 acknowledged workforce limitations, admitting their institutions “must outsource 50% of advanced analytics work due to internal capability shortages”. Compounding these mismatches, GDDF1 emphasized the scarcity of hybrid professionals who could “mediate between administrators’ strategic priorities and frontline technical implementations”, noting their institution’s reliance on “overstretched IT staff moonlighting as project managers”.

5.3.3. Data and Technological Bottlenecks

The absence of unified data governance frameworks created critical operational discontinuities across institutions. Multiple interviewees described campus information systems as “digitally feudal territories” (GDDF1), where fragmented data ownership between academic departments, administrative units, and research centers generated structural silos. GDPK5 elaborated that “student attendance records, faculty performance metrics, and laboratory utilization data reside in separate repositories governed by conflicting protocols”, often requiring manual reconciliation that introduced latency. A particularly vexing issue emerged in real-time data synchronization, with ZJHV3 observing that “strategic decisions rely on three-month-old enrollment statistics because live dashboard deployments get vetoed by data security committees”. This temporal disconnect between data generation and leadership utilization generated what ZJPK2 termed “analytical mistrust”—senior administrators doubting the temporal relevance of available datasets when approving digital initiatives. The governance vacuum extended to third-party integrations as universities partnered with edtech providers without standardized data-sharing agreements, leaving institutional researchers to “stitch together incomplete data jigsaws” (GDHV4) to assess program effectiveness.

Even when data became technically accessible, questions regarding its veracity and ethical applications permeated implementation efforts. ZJDF6 disclosed disturbing instances of “phantom datasets”—algorithmic training models being fed artificially inflated international collaboration metrics collected through “creative interpretation of faculty emails”. Such accuracy issues often stemmed from frontline data entry practices. Concurrently, algorithmic ethics emerged as a contentious blind spot. GDHV7 described resistance from academic senates against behavior-tracking AI, arguing that “risk score algorithms developed by corporate vendors encode commercial biases foreign to our educational philosophy”. Crucially, few institutions had established protocols to audit algorithmic fairness, leaving what GDHV4 characterized as an “accountability limbo where technological capabilities outpace our ethical frameworks”.

5.3.4. Cultural and Cognitive Dilemmas

The interplay between technostress and uneven digital competencies emerged as a critical challenge, compounding institutional training inadequacies. While leaders acknowledged the necessity of digital pedagogical innovations, they noted that faculty members often grappled with anxiety over rapidly evolving tools. As ZJPK2 observed, “Adapting to new platforms every semester feels like running a marathon with no finish line… Many feel overwhelmed but hesitate to admit it, fearing stigma around technological incompetence”. This sentiment was exacerbated by a widening generational and disciplinary digital literacy divide, as GDPK5 highlighted: “Senior professors in humanities perceive AI-driven assessments as threatening academic autonomy, whereas younger STEM faculty demand more advanced infrastructure”. Despite these tensions, institutional training programs remained fragmented, often reduced to “one-size-fits-all workshops lacking disciplinary relevance” (GDDF1), failing to bridge knowledge gaps or address discipline-specific needs.

A deeper philosophical conflict surfaced between data-centric technological imperatives and the humanistic ethos of education. Leaders emphasized concerns that technological rationality risked overshadowing pedagogical values. For instance, ZJHV3 critiqued the “uncritical adoption of surveillance tools under the guise of learning analytics”, arguing that it eroded trust and reduced teaching to “a series of metrics divorced from mentorship”. Similarly, GDHV7 warned against framing digital transitions solely through efficiency gains, noting that “when algorithms dictate curriculum design, we risk losing the soul of education—creativity, critical thinking, and empathy”. This tension manifested in resistance to institutional mandates prioritizing digital standardization over flexible, student-centered approaches. However, some leaders, like GDHV4, advocated for “nuanced symbiosis”, proposing hybrid models where technology enriches—rather than replaces—human-driven pedagogy. These findings underscore the contested role of digital tools in balancing institutional efficiency with educational authenticity.

5.4. Strategies for HEIs Leaders to Achieve Organizational Goals Through DAL

Our survey and interview findings reveal that Chinese university leaders adopt a multifaceted approach to achieving organizational goals through digital academic leadership (DAL), encompassing strategic, organizational, technological, and cultural–emotional dimensions (Table 9). This comprehensive framework highlights the need for visionary planning, effective collaboration, digital proficiency, and a supportive institutional culture in driving successful digital transformation.

5.4.1. Strategic Leadership Dimension

University leaders emphasized the centrality of aligning institutional strategies with national directives to drive digital transformation. As ZJHV3 articulated, “Translating macro-level policies into actionable plans requires a hierarchical framework, starting from top-down strategic alignment to grassroots implementation”. This involved embedding policy mandates into curriculum redesign, research incentives, and infrastructure upgrades. For instance, GDHV4 highlighted how their university established a “dual-track digital literacy initiative” merging national smart education goals with localized pedagogical needs. However, leaders cautioned against mechanistic compliance; GDPK5 noted that “Overly rigid interpretations stifle contextual innovation”, underscoring the necessity of balancing fidelity to policy with adaptive execution. Proactive engagement with policymakers was deemed critical, as GDDF1 stressed: “Continuous dialogue ensures institutional priorities resonate with evolving national agendas”.

Achieving organizational goals through digital academic leadership necessitated breaking disciplinary silos and fostering interdepartmental collaboration. ZJPK2 underscored this, stating, “Isolated tech-driven projects often falter; systemic impact demands shared ownership across academic and administrative units”. Leaders highlighted the role of integrative platforms, such as joint committees or digital workflow systems, to harmonize objectives. For example, ZJDF6 described how a cross-functional task force co-developed a unified data analytics dashboard, merging IT, academic affairs, and student services datasets. Yet, challenges persisted in reconciling conflicting priorities. GDHV7 observed, “Departments guard resources; trust-building through transparent governance is non-negotiable”. Successful models coupled structural integration with cultural shifts, incentivizing collaborative KPIs and recognizing interdisciplinary achievements. As GDHV4 summarized, “Synergy isn’t serendipity—it requires intentional architecture and sustained relational investment”.

5.4.2. Organizational Leadership Dimension

HEIs leaders emphasized the strategic integration of diverse expertise to navigate digital transformation complexities. ZJPK2 noted, “Blending tech-savvy early-career staff with seasoned academic administrators fosters innovation while preserving institutional memory”. This hybrid model, as detailed by GDPK5, required “flexible role definitions enabling cross-functional teams to co-design solutions”, such as pairing data scientists with faculty to co-lead AI-driven pedagogy projects. Another strategy is formalizing dual-appointment systems and offering upskilling grants. ZJDF6 highlighted their university’s “digital mentorship circles”, where senior leaders and junior technologists jointly troubleshoot challenges, ensuring reciprocity. Hybrid talent orchestration thus emerged as a balancing act between leveraging diversity and mitigating fragmentation through intentional structural design.

Agile governance requires dismantling hierarchical silos to foster cross-functional collaboration. GDHV4 observed that “defining shared values through iterative workshops” was pivotal in aligning decentralized teams, while ZJDF6 stressed the need for “modular governance frameworks to accommodate rapid decision-making”. Concurrently, dynamic resource allocation emerged as critical for balancing stability and innovation. ZJPK2 advocated for “data-driven resource redistribution to prioritize high-impact initiatives”, a sentiment echoed by GDPK5, who emphasized “leveraging predictive analytics to anticipate shifting demands”. By integrating adaptive governance with fluid resource models, leaders can optimize institutional agility while mitigating resource fragmentation.

5.4.3. Technological Leadership Dimension

University leaders recognized that constructing intelligent infrastructures forms the bedrock of digital academic leadership. Proactive investments in IoTs-enabled devices, big data platforms, and cloud-based systems were prioritized to foster data fluency and seamless connectivity. ZJPK2 highlighted, “Our focus on 5G integration and AI-powered analytics has transformed classrooms into adaptive learning hubs, erasing traditional resource barriers”. However, disparities persisted between resource-rich and less-endowed institutions. GDPK5 cautioned that “without equitable access to high-performance computing, innovation remains siloed”. To bridge gaps, leaders emphasized public–private partnerships for shared infrastructure development, as epitomized by ZJHV3’s institution which collaborated with tech firms to deploy hybrid cloud solutions. Strategic alignment with national digitization policies further amplified scalability and resilience.

The reconfiguration of digital ecosystems emerged as pivotal for centralizing fragmented platforms and enhancing organizational agility. GDHV7 stressed the need for “unified data governance frameworks to dismantle silos and enable cross-departmental collaboration”, with their university adopting middleware interfaces to integrate academic, administrative, and research systems. Similarly, GDHV4 underscored user-centric design, noting that “faculty resistance dissipates when tools align with pedagogical workflows”. Challenges included balancing customization and standardization; GDDF1 advocated for iterative prototyping to “preserve institutional uniqueness without compromising interoperability”. Additionally, embedding AI-driven feedback loops into learning management systems enabled real-time adaptability, a strategy ZJDF6 deemed “critical for sustaining engagement in hybrid environments”. These efforts collectively underscored software’s role in translating infrastructure investments into actionable academic outcomes.

5.4.4. Cultural-Emotional Leadership Dimension

Leaders emphasized harmonizing technological advancement with institutional cultural values to foster techno-cultural ambidexterity. ZJHV3 noted that “digitization often clashes with collective traditions, so we recalibrate tools to align with Confucian principles of community-centric learning”. This involved designing hybrid pedagogies where AI-enhanced platforms coexisted with face-to-face mentorship. ZJDF6 reinforced this, stating, “Our digital literacy workshops integrate storytelling about campus heritage, making tech adoption feel less alienating”. However, tensions arose when rigid cultural norms stifled innovation; ZJPK2 advocated for “dialogue forums to reinterpret traditions in tech contexts”. By framing digital transitions as extensions of cultural identity—such as embedding local academic ethics into plagiarism-detection algorithms—leaders mitigated resistance while preserving institutional uniqueness. Proactive symbiosis between technology and culture thus emerged as a cornerstone for sustainable DAL adoption.

Addressing faculty and student technophobia required empathetic leadership and institutional scaffolding. GDDF1 highlighted that “leadership visibility in experimenting with tools—like livestreaming lectures using VR—normalizes digital risks”, a sentiment echoed by GDHV4, who shared, “When deans host ‘tech sandbox’ sessions, anxieties shift into curiosity”. Structural support further eased transitions: GDPK5’s university instituted “innovation sabbaticals” for staff to prototype digital projects without penalizing failures. Meanwhile, GDHV7 stressed the role of mentorship networks: “Junior-senior pairing systems dissolve hierarchy barriers, allowing tacit tech knowledge transfer”. Despite progress, sporadic generational divides persisted; ZJHV3 advocated tiered training programs to address varying competency levels. Collectively, blending role modeling with systemic safeguards transformed technophobia into collaborative experimentation, fostering an ecosystem where emotional security parallels technical growth.

6. Discussion

The proposed four-dimensional model of digital academic leadership—strategic, technological, organizational, and cultural—synthesizes critical competencies for navigating the complexities of digital transformation in higher education. By integrating the unified theory of acceptance and use of technology (UTAUT), this framework offers a robust theoretical foundation for understanding how leadership practices intersect with technological adoption and institutional agility. To better articulate the institutional pathway from technology acceptance constructs to digital transformation practices, Figure 1 presents an expanded mechanism model that integrates UTAUT constructs, perceived DAL capabilities, institutional challenges, and corresponding strategic responses. This framework illustrates how university leaders’ perceptions of digital leadership mediate between technology adoption pressures and organizational change strategies. Notably, each DAL capability confronts distinct operational and cultural barriers, which are addressed through targeted leadership interventions across strategic, organizational, technological, and cultural dimensions.

Performance expectancy—the perceived utility of digital tools—is fundamentally misaligned between institutional leaders and senior faculty. While leadership visions emphasize digital transformation’s strategic value, veteran academics perceive disjunctures between technology-enhanced pedagogy and Confucian-heritage teaching paradigms prioritizing experiential authority (Deng, 2024). This cognitive gap mirrors A. Liu’s (2011) observations of legacy-oriented meritocracies, where traditional scholarship metrics override innovation incentives. UTAUT’s predictive power here lies in exposing how hierarchical academic cultures correlate seniority with skepticism toward technological disruptors—a phenomenon amplified by age and career stage as moderating variables (Howard & Gutworth, 2020). Leadership communications conflating digital tools with pedagogical modernization paradoxically deepen resistance by threatening senior faculty’s identity as custodians of disciplinary traditions. Furthermore, effort expectancy barriers manifest acutely among senior faculty due to unaddressed cognitive aging differentials (Lai, 2020). The UTAUT framework highlights how institutional training protocols systematically underestimate the cognitive load required for late-career digital skill acquisition. Current one-size-fits-all professional development models (R. Yang, 2016) neglect neuroplasticity decline patterns, resulting in perceived effort–reward imbalances that deter engagement. This explains the observed regression toward administrative dependency—a coping mechanism where technologically anxious faculty bypass skill development through bureaucratic appeals. Crucially, the model identifies age as a critical moderating variable, necessitating differentiated training approaches that account for generational variations in digital literacy baselines and learning modalities (Y. Wang, 2021).

From the UTAUT lens, digital resource coordination capability (organizational dimension) aligns with UTAUT’s emphasis on facilitating conditions, demonstrating how resource optimization and cross-unit collaboration enhance institutional readiness to adopt digital tools. Meanwhile, digital technology awareness capability (technological dimension) resonates with effort expectancy as leaders’ ability to demystify emerging technologies reduces perceived barriers to adoption among stakeholders. The framework extends UTAUT beyond individual acceptance to organizational orchestration, addressing critiques that UTAUT oversimplifies contextual dynamics in institutional settings (Xue et al., 2024).

The synthesis of UTAUT addresses gaps in the literature that often isolate technological adoption from leadership practices. Some prior studies frame digital leadership as either a technical or behavioral construct, while neglecting the interplay of systems and social practices (Hallinger, 2018; Cortellazzo et al., 2019; Erhan et al., 2022). This model, conversely, positions leadership as a dynamic practice–technology nexus, where strategic foresight and cultural adaptability mediate technological integration. Such an approach aligns with recent calls for holistic digital transformation frameworks, while offering empirical pathways to assess how layered capabilities coevolve in academic contexts (Tana et al., 2023; Lakemond et al., 2021). Ultimately, this model contributes to reimagining digital academic leadership as both a structural and socio-technical endeavor, bridging macro-level strategic alignment with micro-level cultural practices—a critical advancement for sustaining institutional relevance in a rapidly digitizing world (Searle & Barbuto, 2013).

In the practice aspect, implementing digital leadership in universities involves navigating a complex landscape of challenges that hinder the effective adoption and integration of digital tools. These challenges are multifaceted, originating from various sources including strategic and governance, organizational operational, data and technological, and cultural and cognitive aspects. The application of UTAUT to Chinese higher education reveals systemic tensions between digital leadership imperatives and faculty adoption dynamics, with three central barriers emerging: attenuated performance expectancy, effort expectancy mismatches, and sociocultural friction in social influence mechanisms and lack of facilitating conditions. These barriers coalesce into a self-reinforcing cycle of digital exclusion, particularly among senior faculty cohorts, demanding culturally attuned reconceptualization of technology adoption strategies.

UTAUT’s social influence construct assumes paradoxical dimensions in China’s collectivist academic culture. While peer networks typically enhance technology adoption, top-down digital mandates trigger performative compliance masking passive resistance. Senior faculty’s implicit authority as Confucian knowledge guardians creates permission structures for collective non-adoption, particularly when digital initiatives are framed as administrative disruptions rather than scholarly evolution (O’Connor et al., 2019; Macfarlane, 2013). This social resistance vector is compounded by tenure systems that decouple technological proficiency from promotion criteria, rendering leadership appeals to institutional loyalty ineffective as change levers. Moreover, the institutional challenges hindering the effectiveness of facilitating conditions in technology adoption can be attributed to three interdependent systemic factors. Firstly, the lack of targeted teacher training programs reflects a misalignment between institutional priorities and pedagogical needs. As Marikyan and Papagiannidis (2021) emphasize, organizational support structures must address skill gaps through context-specific training. However, many institutions prioritize infrastructure procurement over human capital development due to short-term budgetary constraints (Scherer & Teo, 2019), resulting in superficial “technology-first, training-later” implementation models. Secondly, the exclusion of digital competencies from performance evaluation systems perpetuates institutional inertia. This omission signals a tacit devaluation of technology integration by administrative policymakers, thereby disincentivizing educators from adopting pedagogical innovations. Research indicates that assessment frameworks lacking digital teaching metrics fail to create accountability mechanisms for technology utilization, contributing to a cycle of tokenistic compliance rather than meaningful behavioral change (Kaya-Capocci et al., 2022; Chugh et al., 2023). Thirdly, uneven digital infrastructure development stems from fragmented governance approaches. While digital hardware investments and software ecology may satisfy political visibility goals, sustained maintenance and equitable resource distribution often require cross-departmental coordination rarely achieved in centralized bureaucratic systems. Such systemic inefficiencies disproportionately affect marginalized institutions, exacerbating existing digital divides in educational settings (Rahimi et al., 2024).

The implementation of digital academic leadership in Chinese higher education necessitates a nuanced reconceptualization of UTAUT’s core constructs through culturally embedded adaptations. Strategic leadership imperatives collide with deeply rooted Confucian pedagogical epistemologies, where senior faculty’s skepticism toward digital tools emerges not from technological aversion but perceived threats to experiential authority (Deng, 2024). This tension exposes a critical limitation in UTAUT’s performance expectancy construct—its Western-centric assumption that utility perceptions derive solely from functional benefits rather than philosophical alignment. By contrast, our findings demonstrate that effective digital adoption requires hybridizing technological capabilities with traditional scholarship values, akin to Feng’s (2020) adaptive stewardship model, where AI-assisted textual analysis tools are reframed as extensions of classical hermeneutic methods rather than disruptive replacements. Such recalibration addresses Venkatesh et al.’s (2012) call for life-stage contextualization while countering Granić’s (2022) universalist competency frameworks through culturally attuned implementation pathways.

Organizational and technological dimensions further reveal systemic paradoxes: institutional efforts to reduce effort expectancy through standardized training inadvertently exacerbate cognitive load disparities for aging faculty, mirroring Lai’s (2020) findings on neuroplasticity decline yet contradicting R. Yang’s (2016) one-size-fits-all solutions. This necessitates tiered competency frameworks incorporating spaced repetition protocols (Belt & Lowenthal, 2020) while reconstituting facilitating conditions through decentralized governance models that balance centralized infrastructure with departmental autonomy (Scherer & Teo, 2019). Crucially, cultural–emotional barriers manifest as collectivist resistance dynamics—a phenomenon absent in UTAUT’s individualistic social influence paradigm—where senior academics leverage implicit Confucian authority to legitimize non-adoption coalitions (O’Connor et al., 2019). Our proposed generational brokerage systems transcend this impasse by repositioning digital tools as amplifiers of shifu mentorship traditions, thereby aligning technological adoption with Macfarlane’s (2013) scholarship identity preservation theory. These insights collectively advance digital leadership theory by (1) exposing how cultural embeddedness inverts technology adoption mechanisms, (2) identifying age as a neurocognitive moderator rather than demographic variable, and (3) demonstrating philosophical hybridization as critical for reconciling technocratic imperatives with heritage pedagogies—a tripartite contribution addressing Marikyan and Papagiannidis’ (2021) critique of culturally deterministic adoption models.

Reconfiguring digital leadership strategies through UTAUT’s contextual dimensions requires, firstly, performance gap mitigation by implementing discipline-specific demonstration projects showcasing digital tools’ capacity to enhance (not replace) pedagogical authority. Secondly, cognitive load reduction by adopting tiered training systems incorporating spaced repetition and cognitive apprenticeship models tailored to aging learners (Belt & Lowenthal, 2020). Thirdly, social influence recalibration: establishing cross-generational mentoring networks that reframe digital adoption as scholarly adaptiveness rather than generational capitulation. This tripartite approach aligns with Feng’s (2020) adaptive leadership paradigm, positioning digital competence as stewardship of disciplinary evolution rather than disruption of academic traditions. Success ultimately hinges on reconstituting institutional reward structures to valorize digital–philosophical hybridity—where technological proficiency strengthens rather than supplants experiential authority (Granić, 2022).

Collectively, these leadership practices reveal a distinctive Chinese model of digital academic leadership that integrates centralized policy alignment with decentralized implementation practices. The observed emphasis on structural interventions over individual heroics supports the critique of conventional leadership paradigms while offering empirical evidence of alternative models emerging from non-Western contexts.

7. Conclusions

This study contributes novel empirical insights into the emerging discourse on digital academic leadership (DAL) within Chinese higher education institutions (HEIs), bridging the conceptual–theoretical gap between leadership studies and digital transformation practices. By integrating the theoretical lens of UTAUT, our mixed-methods analysis reveals how Chinese university leaders at varying administrative levels perceive, interpret, and enact DAL in their institutional contexts. Three critical contributions emerge from our findings.

First, the identification of four core dimensions of DAL—strategic foresight, resource coordination, technology awareness, and culture building—demonstrates the situated complexity of digital leadership in China’s HEIs. Unlike Western-centric frameworks emphasizing technological determinism, our participants foregrounded contingent negotiation between hierarchical governance structures and the fluid demands of digital academia. The UTAUT constructs, particularly performance expectancy and social influence, elucidate leaders’ instrumental rationale for adopting digital tools.

Second, systematic disparities across gender, administrative tiers, and institutional types underscore the contextualized nature of DAL implementation. Senior leaders prioritized strategic foresight and resource allocation (UTAUT’s facilitating conditions), whereas mid-level managers emphasized material discursive friction in translating digital initiatives into departmental practices. Such stratification suggests that DAL operates not as a monolithic competency but as a distributed, role-contingent practice—a finding challenging conventional technocratic models of leadership.

Third, the identification of implementation challenges reveals structural tensions inherent in China’s HEIs. While leaders acknowledged digitalization’s strategic necessity (reflected in high performance expectancy scores), institutional barriers—including fragmented data ecosystems, risk-averse organizational cultures, and misaligned incentive mechanisms—constrained their ability to reconcile centralized oversight with academic autonomy.

This study proposes an integrated mechanism that delineates how institutional digital transformation unfolds through the interplay of technology acceptance, leadership cognition, organizational barriers, and strategic responses. Specifically, the four core constructs of the UTAUT model—performance expectancy, effort expectancy, social influence, and facilitating conditions—inform university leaders’ perceived digital academic leadership (DAL) capabilities, including strategic foresight, technology awareness, culture building, and resource coordination. These perceived capabilities, in turn, intersect with institution-specific implementation challenges, such as fragmented systems, limited alignment, and resistance to change. Rather than functioning in a linear fashion, the model highlights a recursive relationship in which emerging challenges reshape leaders’ understanding and prioritization of DAL competencies. The process culminates in a set of context-sensitive leadership strategies aimed at navigating constraints and mobilizing digital reform across governance, structure, technology, and culture.

The implications of our research are manifold. Theoretically, our employment of UTAUT advances leadership scholarship by synthesizing individual-level technological acceptance with collective, context-embedded practices. This dual lens captures how digital leadership emerges from the interplay of structural affordances (e.g., resource allocation) and agentic sensemaking (e.g., cultural reinterpretation). Practically, the proposed four-dimensional DAL framework offers HEIs a diagnostic tool for targeted capacity-building. For practitioners, our study underscores the importance of developing targeted training programs and strategic initiatives that enhance the digital competencies of university leaders. The findings suggest that fostering an organizational culture that supports continuous digital learning and cross-departmental collaboration is critical for overcoming existing challenges, such as inadequate digital regulatory frameworks and high investment costs. Moreover, the empirical evidence presented herein offers valuable guidance for policymakers in formulating supportive policies that can bridge the gap between traditional academic leadership and modern digital demands.

Despite these contributions, the study is not without limitations. The sample is geographically confined to universities in Zhejiang and Guangdong provinces, which while strategically significant limits the generalizability to less economically developed regions. Additionally, the reliance on self-reported perceptions necessitates caution in interpreting declarative data. Future research could adopt longitudinal designs to track DAL evolution amidst China’s shifting policy landscapes or employ comparative case studies to disentangle regional disparities. Extending the UTAUT to other cultural contexts might further reveal how digital leadership adapts to divergent governance ideologies.

Ultimately, as HEIs worldwide grapple with post-pandemic digital acceleration, the practice of DAL in universities is essentially a revolution in the paradigm of education and its success not only relies on technological inputs but also requires leaders to reconfigure the organizational culture, balancing the efficiency with the humanistic values. This study positions DAL not as a prescriptive competency checklist but as a dynamic negotiation between technological possibilities, organizational realities, and the human agency of academic leaders—a perspective demanding continued scholarly interrogation.