Digital Learning Orientation and Entrepreneurial Competencies in Graduates: Is Blended Learning Sustainable?

Abstract

The emerging literature demonstrates the significance of digital learning in developing sustainable employability skills in learners. In the modern scenario of digitally transforming business and entrepreneurship education (EE), the study examines the role of digital learning orientation (DLO) for the development of entrepreneurial competencies (ECs) in graduates while considering the effects of blended learning (BL) behavior. The study data came from a survey of 317 graduate students in Saudi Arabia, where digitalization and entrepreneurship are positioned as new agendas for sustainable development in the education sector. The data analysis results from partial least squares structural equation modelling (SmartPLS 3.0) revealed that DLO has a direct impact on the development of ECs in graduates. However, the effects of BL on ECs were not proven. Nevertheless, BL was found to moderate the relationship between DLO and ECs. As a result, the study produced new theoretical and practical implications underpinning digital learning and EE in the contemporary digitalization context.

1. Introduction

The emergence of digital technologies, platforms, and infrastructures has transformed both innovation and entrepreneurship in significant ways, with broad organizational, educational, and policy implications [1]. Organizations are increasingly strategizing the digital aspects of their business to stay competitive [2]. Educational institutions have represented this trend by undertaking the digital transformation of educational systems [3]. The emerging literature accordingly demonstrates the significance of digital learning, digital literacy, and digital ethics in developing competent yet sustainable learners and educational institutions within the digitally transforming business and entrepreneurship scenario [4]. As a result, digital or online education along with blended learning (BL) (a hybrid of face-to-face and online learning) has become increasingly popular [5,6]. Several empirical studies exhibit the positive impacts of BL on students’ academic achievement [7], interest in learning [8], engagement [9] (Su et al., 2023), and other abilities [10]. Nevertheless, critical scrutiny of the literature reveals inconsistent conclusions. For instance, BL is reported by some authors as having no significant improvement [11] or even a negative impact on students’ academic performance [12]. Some studies revealed that students found it more difficult to participate in a blended curriculum, less motivated to learn, and only briefly engaged because it required too much work to switch between the traditional and online models [13]. Additionally, students’ digital orientation does not always result in learning and better academic outputs; rather, it might have an impact on incidental outcomes, such as merely serving as a tool to support academic tasks [14,15]. Remarkably, the psychological mechanisms underlying how digital learning affects academic performance are largely ambiguous, which is further complicated by the general lack of theoretical support underpinning BL [16]. Considering the need to understand online learning behavior, scholars have recently attempted to explore digital learning orientation (DLO) as determining the students’ general digital aptitude concerning the utilization of digital technologies and information-seeking practices in the digital era [17,18]. As such, DLO is a novel yet promising construct for understanding digital learning behavior in the context of not only students but entrepreneurs as well [19]. The ability of individuals to learn and retain digital knowledge is directly influenced by their effectiveness as learners [20]. DLO will therefore demonstrate the ability to use technology sensibly for social interactions, learning in a virtual environment, and interpersonal connections [21]. This ability enables an entrepreneur to work with technology more effectively and pick up new functions while being acutely aware of how they affect people and society [22]. Accordingly, entrepreneurs are required to demonstrate competencies for driving creativity and innovation in the digital space of entrepreneurship [23].

Although DLO remains underrepresented in educational research, the emerging shreds of evidence favor the impact of DLO on students’ academic creativity or innovation outcomes [16] and overall readiness for change [4], thereby lending credence to the development of entrepreneurial competencies in students [24]. Yet, the literature is underdeveloped to answer how DLO determines the development of some crucial business educational outcomes, such as students’ entrepreneurial competencies (ECs). Universities are seen as a promising source of future entrepreneurs [25], given that one goal of higher education is to influence the development of entrepreneurial intentions [26,27]. Indeed, education has a crucial role in enabling individuals to acquire ethical and environmental values, as well as skills, attitudes, and behaviors that align with sustainable development [28].

As such, the need and significance of fostering ECs across a variety of professions have been strongly underlined in entrepreneurship literature [29]. In the meantime, existing studies reflect a sense of earnestness to impart professionals’ essential digital skills that favor the development of ECs [30]. This is consistent with the classical theories that contend that an entrepreneur’s aptitude for learning indicates their likelihood of success [17]. As a result, aspiring entrepreneurs must demonstrate the skills necessary to foster creativity and innovation in the digitally evolving entrepreneurship space [18]. This is evidenced by the recent examples of some entrepreneurs who used innovative approaches to digitizing for social value creation during the COVID-19 pandemic [31]. Nevertheless, the current literature is underdeveloped to answer how digital learning might help with entrepreneurial competency development [19]. Similarly, while scholars started interacting more with entrepreneurship education (EE) that was delivered entirely online because of the push for online learning during the COVID-19 crisis [32], there has not been much discussion of BL in EE [33].

Secondly, developing ECs in students is the priority of many governments and higher education initiatives across both developed and developing countries, including Saudi Arabia [34]. With a focus on initiatives to foster innovation and entrepreneurship competencies under Vision 2030, Saudi Arabia has rolled out a digitalization plan in the education sector [35]. Many higher education institutions, including universities, will primarily use BL programs as part of this plan [36]. Although, with advancements in educational technologies, digital learning has been argued to foster students’ employability skills [37], academia has largely underrepresented the concerns of how digital learning impacts the development of ECs in graduate students. According to the existing literature, the business and educational sectors have been challenged by the rapid digital transformation [38], which has left a significant disparity between the knowledge gained by students and the actual competencies needed by entrepreneurs and businesses [39]. Similarly, when it comes to EE, there are widespread disparities about the selection of face-to-face instruction over virtual or hybrid formats [40]. As such, the existing literature does not adequately explain how BL is conceptualized in higher education or how it relates to scholarly conversations about the development of entrepreneurial competency in graduates.

Considering the above ambiguous outcomes of BL and the need for integrating knowledge of digital learning and entrepreneurship, this research examines whether DLO fosters the development of ECs in graduates, along with considering the role of BL in influencing this relationship. To address the above gaps, the current study draws on the overarching background of the job demand–resource model [41] and resource-based theory [42] to build and test a conceptual framework. Since the study is the first to examine the above relationships, it generates several theoretical and managerial implications underpinning the BL and entrepreneurship competency literature in the digital era.

2. Theoretical Background

2.1. Digitalization and Blended Learning

Businesses are currently dealing with Industry 4.0, or the fourth industrial revolution [43], which is exemplified by the advent of transformative digital technology, applications, and other smart products. As a result, digital transformation emerges as one of the most important socio-technical changes influencing all kinds of enterprises, including educational organisations [2,3]. Specifically, within the education sector, digitalization has enabled digital learning, which specifically facilitates learning management systems that offer significant flexibility to access and review content in a BL environment [44]. Thus, BL is regarded as distinctive, since it preserves the benefits of face-to-face learning, while optimizing the advantages of technology-enhanced online learning [5]. However, historically, a wide range of BL techniques and applications have been used globally in both academic and for-profit training and development facilities [45]. On the other hand, there is now more diversity in definitions and model discussions due to the widespread use of BL. Higher education lacks a consistent and easily accessible foundation to facilitate BL in all academic areas. Nevertheless, BL has become an increasingly popular practice in the higher education sector [5], with the proliferation of its adoption following the COVID-19 outbreak [6]. As a result, there has been a recent surge in scholarly interest in investigating the application and effects of BL [33]. However, while some researchers contend that BL can greatly enhance students’ academic performance [7], others reveal that BL has no discernible effects or even has the opposite effect [11,12]. Moreover, BL is reported to expose the university fraternity, including students, to a great deal of pressure and resultant stress, which may impact student learning negatively [46]. Additionally, the literature has identified different challenges concerning the use of BL. For instance, BL is effective only when students have high motivation, technology skills, self-regulation, etc. [47]. Likewise, BL effectiveness is challenged by the students’ as well as the teachers’ digital divide [5], along with the institutional capability to acquire and maintain educational technologies. Consequently, there are discrepancies in the existing literature concerning the outcomes of BL, particularly when contrasted with face-to-face learning. This is further complicated theoretically as BL is ahead of theory, considering the rapid advancements in educational technologies. As a result, while BL is widely utilized, there are currently few widely recognized theoretical notions of its deployment [48] and none that attempt to integrate business and entrepreneurship paradigms with educational theories.

2.2. Digital Learning Orientation and Entrepreneurial Competencies

Digitalization represents a significant shift in the business environment and calls for the creation of new technological innovation capabilities and competencies at different operational and institutional levels of firms and economies worldwide [49,50]. The digital transition in the academic environment can be seen most clearly in the increased emphasis on acquiring the digital skills, ECs, and general employability skills that are necessary to meet the challenges of the modern job market and economy [51]. According to [52], EE is essential because it addresses the expanding interdisciplinary approach that is useful for students of all disciplines concerning today’s socioeconomic and political concerns. As such, there is no agreement on a single set of ECs. The management and entrepreneurial literature use the term “entrepreneurial competency” in a variety of ways. However, researchers largely agree that ECs represent the sum and integration of an individual’s knowledge, abilities, and personal characteristics that are appropriate for entrepreneurial tasks [53]. Such competencies comprise the fundamental qualities, knowledge, self-image, motivations, social roles, and abilities of entrepreneurs that are associated with the initiation, continuation, and expansion of entrepreneurial endeavors [54]. Therefore, ECs are closely related to people’s attempts to attain these competences through work, education, and training [54,55]. Accordingly, university offerings such as EE and other pools of entrepreneurship and employability-related resources have been found effective in influencing student entrepreneurship intentions and behaviors [25].

Generally, EE places a greater emphasis on learners’ employment orientation by offering knowledge, professional skills, and competencies for sustainable employability skills, whether in a corporate position or as self-employed entrepreneurs. Numerous studies have identified the impact of entrepreneurial skills and education on graduates’ general professional development [56]. In an ever-evolving labor market marked by swift technological progress and shifting economic conditions, ECs are in high demand among employers. Recent graduates who possess entrepreneurial skills are more adept at maneuvering through ambiguity, recognizing favorable circumstances, and making substantial contributions to their respective organizations or initiating their own entrepreneurial endeavors. In this regard, fostering entrepreneurial skills in students has gained widespread acceptance in higher education as a dynamic field of study that can boost employment rates and create jobs, thus contributing to the economic and social development of a country [57]. Meanwhile, in the realm of employment, digital literacy and the capacity to effectively utilize digital tools have emerged as indispensable qualities. For instance, to capitalize on digital value propositions in the modern digital age, the entrepreneurship literature has stressed the need for the development of an entrepreneur’s digital literacy as a means of promoting value creation in entrepreneurship [58]. However, there is still a need for EE and business literacy among the younger generation, particularly regarding the information and abilities needed to succeed as an entrepreneur in the digital era. For example, they need a mix of technical know-how and modern technologies to overcome the obstacles posed by changing global economies [51].

Digital learning in its various forms has accordingly grown in popularity due to the options it presents and the role it plays in fostering employability skills [59]. It has been discovered that digital learning influences students’ motivation, intellectual receptivity, work ethic, conscientiousness, self-evaluation, cognitive processes, knowledge, and creativity in a positive way [7,10]. Likewise, digital learning is argued to empower both aspiring and established entrepreneurs to use a variety of digital platforms, tools, and artifacts (applications) to further their intentions or agendas of innovation and entrepreneurship [60,61]. Thus, from an entrepreneurship perspective, digital learning is considered a crucial capacity for individuals in the digital age [60,62], with accelerated research of entrepreneurs’ digital literacy [58], information-search behavior [18], etc. Nonetheless, while digital skills represent the ability to use digital tools, digital learning leverages these skills to create a meaningful learning experience. However, a person’s effectiveness as a learner directly influences their ability to learn and retain digital information [63]. The digital learning attitude, which is operationalized as the DLO [4], encompasses the mindset and behaviors learners bring to the digital learning environment. As such, an individual who is digitally oriented not only has a basic understanding of technology but also demonstrates proficiency in utilizing digital platforms, participating in online learning, and adjusting to ever-changing digital resources and strategies. This approach goes beyond mere technical expertise and prioritizes the growth of an individual’s digital literacy, self-control, and problem-solving skills within the framework of technology-facilitated education. However, the individual’s digital learning attitude and orientation remain overlooked in entrepreneurship research [64]. Nevertheless, akin to its role in fostering cognitive processes and creativity in students [16], DLO is equally important in the workplace because managers in the current digital environment are eager to continuously reskill and upskill themselves; as a result, the demands and resources placed on both employees and students are considered nearly equal [4].

Considering the above theorizing, it can be posited that while DLO is argued to enhance efficacy and motivation [65], with positive impacts on students’ cognitive processes and creativity, it may help graduate students develop competencies relevant for entrepreneurship. Alternatively, if it can be demonstrated that digital learning can help students develop their ECs, then digital learning programs may be a useful tool for teaching students how to be successful entrepreneurs [66]. Further referencing [67] theoretical arguments, which agree that future research should study how digitalization affects innovation processes, it is viable to examine the role of DLO from the students’ EC perspective. Accordingly, the following hypothesis is proposed:

Hypothesis 1. 

DLO has significant influence on the development of ECs in graduates.

2.3. Blended Learning and the Development of Entrepreneurial Competencies

The term BL is variously describable because it refers to the great majority of educational practices that incorporate digital elements [68]. For instance, BL could be in the form of concept-based or collaboration-oriented BL in higher education [69]. Although BL comes in a variety of forms, there is no widely recognized taxonomy for it, especially in the field of higher education.

Nevertheless, BL is widely considered as an instructional approach that integrates online and in-person instructional components. Accordingly, along with in-person sessions, BL comprises online learning modules, workbooks, digital resource links, simulations, and online self-assessments, as well as self-paced learning via these mediums [70]. Considering the widespread digital transformation, BL is the predominant instructional approach utilized by academic institutions due to its perceived efficacy in providing learners with opportunities for flexibility, continuity, and timely progress [5,6]. Accordingly, scholars are increasingly interested in investigating the multi-aspect significance of BL. For example, from the student perspective, some studies found BL raised students’ academic performance as well as their learning motivation, self-efficacy, critical thinking skills, and learning satisfaction [9,10]. The focus of self-directed learning is precisely on helping students plan, carry out, and assess their learning. This requires them to mobilize their self-efficacy, self-discipline, and motivation when learning online [71]. Second, in their meta-cognitive activity that focuses on learning itself, students employ various learning strategies. This may have strategic implications for the multifaceted field of EE [24]. For instance, according to some scholars, a BL platform could present opportunities for more inclusive forms of EE by providing access to a wider range of students who would not otherwise be able to enroll in existing on-campus courses on entrepreneurship because of time or other constraints [72]. Likewise, it could also facilitate the integration of remote expertise, such as specialized instructors from different educational institutions [73]. The rollout of asynchronous BL activities, such as video recording, prepares learners for in-person classes and thereby helps them to engage actively with the subject matter and saves time for in-depth discussions [74]. To provide context for experience-oriented aspects of the entrepreneurial process, such as business model creation, educators can also employ video materials or even whole lecture series [75,76]. Blended education can also be made more practical by fostering experience-based learning through the combination of in-person reflective or discussion-based sessions with a game or simulation that is played between classes [77]. As a result, BL can encompass a wide range of entrepreneurship-oriented subjects and content. For instance, instructors can cover subjects like opportunity identification [78], business modeling [75], value propositions [79], social entrepreneurship [80], case-based discussions), marketing, firm growth, and exit [81]. Likewise, digital learning platforms could be employed to support skill-oriented formats like project-based teaching [82] and pitching competitions [83]. Remarkably, students can work on projects [79] or ventures [84] under the guidance of synchronous online mentors who are either seasoned business owners or other pertinent professionals. By shifting mentorship to the internet, mentors can now provide more flexibility and on-demand mentoring while also investing less of their time. Finally, in blended education, instructors can utilize a wide range of digital tools, including smartphone apps, online encyclopedias, and online discussion boards where students contribute articles on entrepreneurship [85]. Additionally, the digital mode will enable cross-university and cross-cultural team projects [86] and allow the recording of peer evaluation entrepreneurship pitches, which encourages reflection [83]. As a result, certain authors discovered that digital learning in a blended setting had positive effects on the development of competencies (such as organization, opportunity identification skills, etc.) and entrepreneurial mindset [84]. Likewise, within the BL context, a recent study demonstrated that various learning styles operate as intermediaries between the student’s self-directed learning experience and learning satisfaction [87]. As such, the following hypothesis is proposed:

Hypothesis 2. 

BL has significant influence on the development of ECs in graduates.

2.4. Digital Learning Orientation and Entrepreneurial Competencies in a Blended Learning Context

BL is argued to encourage deep learning, which makes students more capable of participating in BL activities [88]. Nevertheless, authors also believed that students found it more difficult to participate in a blended curriculum, were less motivated to learn, and only briefly engaged because it required too much work to switch between the traditional and online models [13]. Likewise, BL effectiveness is challenged by the students’ as well as the teachers’ digital divide [5]. As such, student characteristics like attitudes, self-regulation, etc. are essential predictors of student satisfaction in a BL context [47]. Students with strong self-regulation abilities, adequate digital literacy, and a strong sense of community are necessary for effective BL; in addition to the course, the instructor, and the organization, the student is seen as a key component [89]. Alternatively, students’ DLO can be regarded as a precursor to capitalizing on BL features. As such, BL might offer the platform needed to demonstrate entrepreneurial skills. This is because, with the increased use of web technologies to launch and run a small business [90], graduates strongly value e-learning courses with traditional entrepreneurship content [91]. When students make use of their DLO—innate abilities, dispositions, and expertise—digital learning platforms have the potential to enhance an individual’s entrepreneurial or employability skills [92]. Nevertheless, it is still unclear how university students will use BL [87]. However, given the discussion above, it seems reasonable to think of BL as a facilitator for the development of ECs in students demonstrating the required DLO. Thus, the following hypothesis can be proposed:

Hypothesis 1a. 

BL moderates the relation between DLO and ECs in graduates.

Figure 1 illustrates the hypothesized relationships.

3. Methodology

3.1. Context and Data Collection

The study employed a survey methodology to gather primary data from graduate students of five public universities that have either fully or partially implemented the BL strategy in their graduate programs. The context of Saudi Arabia is justified considering that the country has rolled out a digitalization plan in the education sector, with many universities predominantly using BL programs, along with a focus on initiatives to foster innovation and entrepreneurship skills under Vision 2030 [35,36]. In particular, the National e-Learning Center was created under the ambitious Vision 2030 “agenda for education” with the goal of improving outcomes in important areas with respect to the quality of digital learning [35], wherein Saudi universities are deemed to play a key role in promoting innovation and entrepreneurship [34] as fundamental cornerstones of the country’s development plan [35]. As a result, many national governments and higher education programs prioritize helping students improve their ECs [34,93]. This further qualifies the Saudi Arabian context to comprehend the unique relationship between DLO and ECs.

As part of the scale validation procedure, the survey was translated into corresponding Arabic and English, and pre-test evaluations and independent back translation were completed by two independent Arabic- and English-speaking academicians. The target respondents comprised both current and former students who were enrolled in or had graduated from the selected universities. They had taken at least one course in entrepreneurship and innovation management, such as corporate culture, business planning, financing and appraisal, team building, managing businesses, business development, succession planning, etc.

The survey was electronically distributed to 682 students between November 2023 and January 2024 after receiving institutional approvals. After multiple follow-up emails, the process yielded only 317 genuine responses, or 37% of the total. Of the 317 legitimate cases, 219 were male (69.30 percent) and 98 were female (30.91 percent).

3.2. Measures

The study adopted the measurements developed by earlier research using a five-point Likert scale ranging from strongly disagree to strongly agree. For DLO, the scale originally developed by [94] proposed a comprehensive measurement of different aspects of DLO involving digital literacy, visual, kinesthetic, multi-tasking, social orientation, etc. The authors [4,95] validated this scale using student samples. The same scale was recently modified by [96] for use in the context of entrepreneurs as well. Likewise, the items for BL behavior were taken from the scale originally developed by [97] for medical students, which was used and validated in other academic contexts by [98]. Lastly, the validated instrument for the measurement of entrepreneurial competencies was adopted from the work of [99,100]. It is important to remember that the study used a survey approach instead of measuring the competencies objectively. Perception-based evaluation of firm and individual attributes is the main method used in entrepreneurship research [101]. Given the behavioral and process perspectives on competences, it is advised to investigate management competencies using qualitative methods as opposed to quantitative ones [102]. Competency is conceptually seen as elusive and hard to grasp since it is not directly observable, is subject to change over time, and appears differently depending on the situation [103]. Because of this, researchers studying entrepreneurship usually depend on opinions rather than objective sources of information. As a result, survey tools and multi-item scales are claimed to attain a high degree of discriminant validity and convergence in entrepreneurship research [104].

A summary of the study instruments is presented in

Table 1. Constructs and item statements.

Construct	Code	Items	Reference
Digital learning orientation	Digital literacy		[4,94,95]
	DL1	I am comfortable using computers, the internet, and other information and communication technologies for a variety of reasons.
	Connectedness
	DL2	I feel like I am always connected to friends because of technologies such as cell phones and the internet.
	Multitasking
	DL3	I am used to doing several tasks at the same time
	Experiential learning
	DL4	I prefer to learn by exploring and trying things out myself.
	DL5	I prefer to learn by doing rather than being told what to do.
	Structure and goal-orientedness
	DL6	I prefer to get clear instructions and information before I try something new.
	DL7	I have clear goals in life.
	Working in groups
	DL8	I prefer to work in groups when doing my work.
	DL9	I enjoy discussing ideas with peers.
	Social
	DL10	I enjoy meeting new people.
	DL11	I enjoy talking about myself to people I meet.
	Preference for images
	DL12	I do not like reading a large amount of text.
	DL13	I prefer images, videos and other multimedia elements over text.
	Community mindedness
	DL14	I get involved in projects and activities that make a difference to society.
	DL15	I believe that science and technology can resolve problems in society.
	Need for immediacy
	DL16	I expect to be able to get information to answer my query quickly.
	DL17	I rely on classmates and lecturers to respond to my questions within a few hours.
Blended learning behaviour	Resources: accessibility and guidance		[97,98]
	BL1	I find the audiovisual online resources provided by the faculty crucial for my learning.
	BL2	Accessibility to faculty lectures online enhances my independent learning.
	BL3	Access to online material off-campus enables me to structure my independent learning.
	BL4	I use faculty lecture material as a guide for what to learn.
	Resources: delivery of content
	BL5	I actively seek online resources to prepare my learning materials before a learning activity (tutorial/lecture/department presentation).
	BL6	I find external audiovisual online resources very important to my learning.
	BL7	Flexibility to use a variety of online material motivates my independent learning.
	BL8	I learn more efficiently when I am able to access online resources using different devices.
	BL9	Specific external online resources are vital to my independent learning.
	BL10	I often integrate a variety of faculty and external online resources to support my learning.
	Learning: social and contextual
	BL11	I find small group work enhances my understanding about a particular concept.
	BL12	I am able to consolidate my learning following a small group activity.
	BL13	My study is stimulated by group discussions.
	BL14	My study habits are influenced by my peers/social interaction.
	BL15	I set up study goals that organise/structure my learning.
	Motivation: intrinsic and extrinsic
	BL16	My use of study resources differs leading up to exams.
	BL17	My motivation to study increases leading up to exams.
	BL18	My study is influenced by the fact that I need to maintain my image (among peers/supervisors).
	BL19	Some online resources are efficient because they are well summarized.
Entrepreneurial competencies	Strategic competency		[99,100]
	EC1	I always monitor progress towards strategic goals.
	EC2	I prioritize work in alignment with business goals.
	EC3	I usually assess and link short-term, day-to-day tasks in the context of long-term direction.
	EC4	I evaluate results against strategic goals.
	EC5	I align current actions with strategic goals.
	Conceptual competency
	EC6	I understand the broader business implications of ideas, issues, and observations.
	EC7	I translate ideas, issues, and observations into the business context.
	EC8	I take reasonable job-related risks.
	EC9	I monitor progress towards objectives in risky actions.
	EC10	I am well prepared in making decisions.
	EC11	I remain proactive and responsive to changes.
	Opportunity competency
	EC12	I seek high-quality business opportunities.
	EC13	I take an idea or concept and make something out of it.
	EC14	I scan the environment to explore opportunities.
	Learning competency
	EC15	I learn proactively.
	EC16	I learn as much as I can in my field.
	EC17	I keep up to date in my field.
	EC18	I apply learned skills and knowledge to actual practice.
	Personal competency
	EC19	I maintain a positive attitude.
	EC20	I prioritize tasks to manage my time.
	EC21	I recognize and work on my own weaknesses.

Source: Authors own elaboration of the adopted scale items.

.

3.3. Descriptive Statistics

A descriptive and normality analysis of the data was carried out with the aim of ensuring the broader applicability of the findings.

Table 2. Descriptive Statistics.

Variables	Mean	SD	Skewness	Kurtosis
ECs	4.44	0.61	1.227	3.32
DLO	4.03	0.83	2.068	3.98
BL	4.38	1.32	1.193	3.55

Source: Extracted from SmartPLS output.

displays the results of computing the descriptive mean and standard deviation. Skewness and kurtosis are calculated to confirm that the collected data are normal. The values fall within the cut-off levels [105].

3.4. Statistical Examination and Hypothesis Testing

Using the SmartPLS 3.0 software, partial least squares (PLS) analysis was performed for the study model’s empirical validation [105]. Structural equation modeling (SEM) is a highly recommended methodological option for assessing causal relationship between the variables [106]. In addition, SEM has been used for similar research objectives within entrepreneurship and business administration research [107]. Furthermore, several factors affected our choice to employ SEM. First, PLS may impose fewer restrictions on sample size and distribution and test and specify route models with latent constructs [108]. Consequently, according to [105], a sample size of 136, combined with a formative construct of two indicators and α = 0.05, was deemed adequate for the SEM analysis. Besides, PLS–SEM can be used to solve multicollinearity issues that are typically connected to multivariate regression analysis and can analyze both the measurement and the theoretical structural model at the same time [108]. As a result, PLS–SEM offers several advantages over traditional regression techniques [109]. Operationally, the PLS model is typically run in two stages: the measurement model (to assess the validity and reliability of the constructs) and the structural model (to look into possible relationships between the theoretical constructs). Subsequently, hypotheses are only approved if they have statistically significant routes in the structural model, resulting in acceptable levels of reliability and convergent and discriminant validity in the measurement mode [105,108].

3.5. Measurement Model

The assessment starts with the “measurement model” approach, which assesses convergent validity using average variance extracted (AVE) scores, discriminant validity using cross-loading and HTMT ratios, and internal consistency using composite reliability and Cronbach’s alpha scores [110]. First, the preliminary interpretation was carried out based on the results of the reliability indicator assessment. Most factors have indicator-loading values greater than 0.70 and AVE values greater than 0.500. Second, the results of the internal consistency check revealed the Cronbach’s alpha and composite reliability scores for most indicators are equal to or greater than the threshold values as suggested by [111] guidelines. Nevertheless, some items DL4, DL7, DL11, and DL12 within DLO; BL2, BL6, BL7, BL12, and BL17 within BL; and EC5, EC9, EC12, EC16, and EC21 with EC dimensions failed to achieve the desired values of >0.70 (

Table 3. Measurement model evaluation.

Latent Variable	Indicator (Table 1)	Convergent Validity Loading > 0.70	AVE > 0.50	Internal Consistency Reliability Composite Reliability > 0.70	Cronbach’s Alpha > 0.70	Discriminant Validity HTMT
DLO	DL1	0.737	0.728	0.706	0.885	<1
	DL2	0.721
	DL3	0.858
	DL5	0.919
	DL6	0.937
	DL8	0.709
	DL9	0.880
	DL10	0.725
	DL13	0.901
	DL14	0.876
	DL15	0.842
	DL16	0.836
	DL17	0.776
BL	BL1	0.721	0.664	0.775	0.798	<1
	BL3	0.974
	BL4	0.850
	BL5	0.891
	BL8	0.790
	BL9	0.762
	BL10	0.785
	BL11	0.871
	BL13	0.767
	BL14	0.920
	BL15	0.842
	BL16	0.971
	BL18	0.945
	BL19	0.864
EC	EC1	0.923	0.701	0.825	0.733	<1
	EC2	0.886
	EC3	0.958
	EC4	0.718
	EC6	0.821
	EC7	0.725
	EC8	0.853
	EC10	0.707
	EC11	0.962
	EC13	0.786
	EC14	0.826
	EC15	0.854
	EC17	0.915
	EC18	0.841
	EC19	0.848
	EC20	0.961

Source: SmartPLS Output.

) and were thus excluded from further analysis. Third, discriminant validity was ascertained by using the HTMT (Heterotrait–Monotrait Ratio of Correlations) ratio and a cross-loading matrix [110]. The test statistics indicated that the square root of AVE (diagonal) in the Fornell–Larcker matrix is greater than all other values, even though the HTMT value is less than one (

Table 4. Fornell and Larcker’s criterion (indicating validity and correlations among constructs).

Construct	ECs	DLO	BL
ECs	0.696
DLO	0.222	0.742
BL	0.609	0.466	0.772

Source: SmartPLS Output.

). As a result, the level of discriminant validity of the “measuring model” is considered appropriate [106,110]. All AVE values are higher than the cut-off of 0.50 [106]. Furthermore, the convergent loadings (greater than 0.70) of the outer model of the PLS models validate all the variable indicators. Therefore, at this point, there was no need to rule out any more indicators.

3.6. Common Method Bias

Harman’s single-factor test was used to look at the reflecting constructs in the study’s common method bias variance. [112] states that common method bias only appears when one factor from factor analysis appears and explains more than half of the variance’s eigenvalue. As a result, the unrotated matrix yielded three factors and a first-factor eigenvalue that explained 41.68% of the variance, or less than the 50% threshold. As a result, no discernible signs of common method bias were identified.

3.7. Structural Model

3.7.1. Coefficient of Determination (R²)

R² values are used to assess how well the exogenous variables can predict variance in the endogenous variable(s). Every endogenous variable’s R² value (ranging from 0 to 1) indicates how well it can be predicted. As per the guidelines of [111], the R² values of 0.75, 0.50, and 0.25, respectively, show that the endogenous variables have a strong, moderate, and weak ability to predict the models. Based on the R² values in

Table 5. Variance explained in the endogenous latent variable (R-square).

Variables	R-Squared	R-Squared Adjusted
ECs	0.518	0.513

Source: SmartPLS output.

, the endogenous factor of ECs has a reasonable capacity to predict the model (R² = 0.518). The interpretation enabled us to conclude that 51.8% of the endogenous factors of ECs can be predicted by the model’s exogenous variables (DLO and BL), and the remaining could be influenced by variables beyond the study’s scope.

3.7.2. Structural Equation Modeling

Using SmartPLS software (3.0), the structural equation model was measured through the 5000 bootstrapping iteration method [113]. The structural equation modeling results are illustrated in

Table 6. Path coefficient and effect size (assessment of the direct relationship of the structural model).

	Path Coef.	t-Value	p-Value	F2	Hypotheses
DLO → ECs	0.739	11.034	0.000	0.488	Supported
BL → ECs	0.127	6.723	0.068	0.354	Not Supported

Source: SmartPLS Output.

. The results demonstrate the decision indicators such as t and p values, along with the path coefficients for each of the tested hypotheses. Similar to regression coefficients, path coefficients show how strongly constructs or latent variables are related to one another. The significance of each route coefficient can be determined using the bootstrapping methods in PLS–SEM, which are comparable to indicator weight analysis in regression [114].

The literature has demonstrated that path coefficient values ranging from <0.30 to 0.30 to 0.60 and >0.60 indicate the presence of moderate, strong, and extremely strong effects, respectively [115]. In light of this, and given the results in Table 6, the following interpretation could be drawn:

• H1: DLO positively impacts the development of ECs—Confirmed [path coefficient = 0.739; t-value = 11.034; p-value = 0.000; f² = 0.488].
• H2: BL positively influences the development of ECs—Not Confirmed [path coefficient = 0.127; t-value = 6.723; p-value = 0.068; f² = 0.354].

3.7.3. Moderator Analysis

Moderation shows how the endogenous variable (ECs) is impacted by the interaction between the exogenous variable (DLO) and the moderator (BL). The way that exogenous and endogenous factors interact varies between models with and without the moderation effect [109]. This indirect link in the structural equation was assessed using the moderation test procedure for interaction terms. The following interpretations could result from

Table 7. Test of moderation (interaction terms).

Hypothesis	Relationship	SD	t-Value	Path Coeff.	p-Value	Decision
H1a	DLO ∗ BL → ECs	0.087	5.148	0.369	0.003	Supported

Source: SmartPLS Output.

’s conclusions.

• H1a: BL mediates the relation between DLO and the development of ECs. Confirmed [path coefficient = 0.369; t-value = 5.148; p-value = 0.003].

4. Results and Discussion

In the modern scenario of digitally transforming business and EE, the study examines the role of DLO in the development of ECs in graduates while considering the effects of BL behavior. The theoretical linkage of DLO, ECs, and BL is in line with the overarching background of the job demand–resource model [41] and the resource-based theory [42]. For exploring the empirical evidence, the study considered the direct effect of DLO and BL on the development of ECs, along with considering how BL moderates this relationship among graduate students.

The results from the structural model report show that DLO directly influences ECs in graduates (H1). Therefore, DLO plays a beneficial role in the development of students’ ECs by assessing their general digital aptitude regarding the use of digital technologies and information-seeking practices in the digital era. Alternatively, the capacity to effectively utilize digital tools is an indispensable ability to succeed as an aspiring entrepreneur in the digital era. Although the study results of DLO for EE are pioneering, they corroborate the existing literature, where digital learning is considered a crucial capacity for individuals and entrepreneurs in the digital age [60,62]. Entrepreneurs can increase total organizational learning and consumer value by proactively learning to incorporate digital assets and business resources and use the digital networks to produce products, services, and processes [116]. Thus, akin to their role in fostering cognitive processes and creativity in students [16], digital learning programs may be a useful tool for teaching students how to be successful entrepreneurs [66]. This extends the existing research into the phenomenon of digital innovation and entrepreneurship, entrepreneurs’ digital literacy [58], and information-search behavior in the digital age [18]. Meanwhile, the new knowledge about DLO’s role in EE could address the academic demand for the transmission of essential digital skills that promote EC development [30]. Alternatively, although the rapid digital transformation has presented challenges for the business and educational sectors [38], graduates must utilize their DLO to foster creativity and innovation in the digitally demanding entrepreneurship space. Thus, from a digitally evolving entrepreneurship perspective, while digital learning is considered a crucial capacity for individuals in the digital age [60,62], students’ digital learning attitude and orientation as DLO emerge as precursors to developing ECs.

Meanwhile, considering the widespread adoption of the BL approach in higher education [5,6], it was intriguing to examine how BL contexts impact the entrepreneurship skills development of graduates (H2) while considering its role in moderating the DLO–EC relationship (H1a). These investigations were warranted because the existing literature on the effectiveness of BL is largely ambiguous and inconsistent [11,16], with a clear lack of examination of how BL might help with entrepreneurship competency development in graduates [19,64]. At the outset, it was believed that BL directly influenced the development of ECs in graduates. In other words, the context for the manifestation of ECs is better with the BL. However, statistical testing of the different paths could not support the above hypothesis. Alternatively, BL may not always support the development of ECs. The result is unexpected considering the recent growth among scholars in support of EE that is delivered entirely online because of the push due to the COVID-19 crisis [32]. Although some existing studies have begun to examine the role of BL in entrepreneurship development, current scholarly understanding is scant and ambiguous [33]. While a few advocates of BL primarily assert the positive effects of BL on the delivery of a variety of EE content, such as opportunity identification [78], business modeling, value propositions [79], and entrepreneurial pitching competitions [83], the integration of knowledge of BL and entrepreneurship is yet to emerge. In this regard, a structured understanding of our study’s outcome (H2) will remove the ambiguities surrounding the role of BL in entrepreneurship competency development. Meanwhile, BL has been shown by numerous authors to enhance students’ academic performance as well as their critical thinking abilities, learning motivation, learning satisfaction, etc. [7,10], our study’s conclusions imply that these advantages might not truly translate to the development of ECs. This could help us make the case that, in the complex field of EE, BL contexts need to be handled strategically and with caution.

Subsequently, we intend to deepen our understanding by examining the moderating role of BL in the DLO–EC relationship. Statistical analysis approved the above hypothesis. This partially corroborates a recently established fact that when students make use of their DLO—innate abilities, dispositions, and expertise—digital learning platforms have the potential to enhance an individual’s entrepreneurial or employability skills [92]. While this moderation result is new, it can be somewhat related to [87] findings, which showed that different learning styles function as a bridge between students’ self-directed learning experience and their level of learning satisfaction in a BL context. Therefore, we can argue that BL might provide the platform required to demonstrate entrepreneurial skills. The students’ DLO can be thought of as a step before leveraging the BL features because traits like attitudes and self-regulation, among other things, are critical indicators of student satisfaction in a BL context [47]. Accordingly, prior research has discovered that the digital divide between teachers and students [5], in addition to other BL challenges [47], poses a threat to BL effectiveness. Because of this, even though BL’s contribution to the development of ECs is conditional, the study’s findings may clarify how university students will use BL to accomplish the desired learning objectives.

5. Conclusions

In Industry 4.0, leveraging digital technologies through digitalization represents one of the significant socio-technical transformations influencing businesses of all types, including educational institutions. As a result, digital or online education along with BL has become an increasingly popular practice. The emerging literature accordingly demonstrates the significance of digital learning, digital literacy, and digital ethics in developing sustainable institutions and competent learners within the digitally transforming business and entrepreneurship scenario. However, studies looking into how digital learning might help with entrepreneurship skills development are still in their infancy [19]. Specifically, although DLO holds great promise for comprehending digital learning behavior, individual DLOs are still underutilized in research [64], and there is an absolute lack of literature examining their applicability for developing ECs in students. This is further complicated by the wide spread of discrepancies and inconsistent conclusions about the effects of BL on students’ learning and competency development [7].

In the context of the digitally evolving education and entrepreneurship scenario, the current study examines the role of DLO for the development of ECs in graduates while considering the effects of the BL approach. This study is the first of its kind to develop and investigate a theoretical model for students (aspiring entrepreneurs, for that matter) that incorporates DLO, BL, and ECs. Specifically, the study tested the direct effects of DLO and BL on the development of ECs in graduate students. Meanwhile, underpinning the resource-based perspective, the study also considered how BL behavior moderates the DLO–EC relationship in graduates. By doing this, the study contributes to the existing knowledge base in multiple ways.

To begin with, the study is the first of its kind to explore the scope of BL in EE, which interprets ECs in students as driven by their DLO. The study, meanwhile, is also the first to scientifically demonstrate that BL as a digital approach to EE has an indirect favorable role in moderating the DLO–EC relationship in graduates. Meanwhile, given the expanding necessity for individuals and institutions to develop and execute digital learning strategies, the results offer students and other practitioner directions on how to adjust and capitalize on DLO from the strategic standpoint of ECs. It is imperative that learners strategically utilize their digital aptitudes, such as DLO, to attain the skill development relevant to becoming entrepreneurs. Alternatively, the results shift the scholarly focus from the academic performance perspective of digital learning to competency-driven components to successfully seize digital learning opportunities for entrepreneurship development [40]. Furthermore, the results hold significance for contemporary patterns in the demand for BL practices in diverse educational and organizational learning configurations. The conclusions specifically suggest enhancing the DLO through BL strategies to speed up EC development. This has implications for sustainable development of academic institutions in Saudi Arabia, where universities are persistently striving to promote entrepreneurship and innovation skill development amid the widespread implementation of BL strategies to support the Vision 2030 goals. In conclusion, the study advances the EE and digital learning literature by highlighting that the integration of EE with DLO can foster ECs among students. This organized knowledge can help educators and policymakers better include these concerns in EE initiatives.

Limitations and Future Research

The results of this study are limited to Saudi Arabian universities that use BL and digital learning as their main methods of instruction. Firstly, even though DLO emerges as contributing to the development of ECs, it is crucial to investigate how applicable it is to programs and institutions that have not yet implemented BL strategies. Thus, to validate the findings of this study, samples from various sociocultural origins and academic levels should be included in future investigations. Likewise, future research might focus on a greater range of entrepreneurship development courses and employ a larger sample size to enhance the generalizability of the study constructs. Similarly, further studies can explore the influence of control variables (gender, age, etc.) that were not considered in this study due to the likelihood of collinearity errors and endogeneity. Extremely skewed item responses in each category could have made this more complicated and decreased the model’s conciseness.

Considering the huge diversity in digital learning programs, BL design, and activities, researchers may need to take contextual variables into account, although our model excluded country-specific features. Subsequent studies need to delve deeper into the connections between BL and ECs. For example, since the blended courses in question for this research are general EE courses, the relevance of individual BL activities and programs could be explored for specific EE contents [76]. Since self-reported data are used in this study, the general method of variance effects might influence the outcomes. Thus, obtaining and using objective measures independently could be beneficial for future research. In this regard, given the multidimensional nature of these relationships and the resulting variability in the effects of BL, longitudinal investigation is necessary, even though the study explains how the DLO–ECs relationship is positively moderated by BL. Accordingly, more research may examine mediation models where BL functions as a precursor to enhance the relationship between DLO and ECs. Alternatively, more investigation into our hypotheses as well as those of other mediators and moderators might contribute to a more thorough understanding of the connection between BL and ECs.