Leveraging lecturers’ intelligence for student engagement enrichment in blended learning courses

Abstract

An increasing focus has been placed on enhancing accounting student engagement (ASE) in blended learning courses (BLCs) because to its advantageous effects, particularly in the context of the pandemic. The primary objective of current study is to identify the distinct forms of intelligence possessed by lecturer that are relevant to teaching methods, and to investigate how these intelligences may affect student engagement in blended learning environments. The present study made use of a mixed-method technique. Consequently, the goal of analyzing the expert interviews’ qualitative perspectives was to determine how the constructs were put into practice and to have a better grasp of various issues related to the proposed model. The structural equation modelling was conducted using AMOS 28.0 software to analyze a theoretical model that explores the relationships between the mentioned components. Statistical response data was collected from a convenient and snowball sample of 323 informants from public higher education institutions. The conducted outcome analyses highlighted significant and positive relationships between the hypothesized constructs related to significance and effect size. Specifically, creativity intelligence had the highest path coefficient, followed by passion quotient, while adversity quotient had the lowest path coefficient among the drivers of ASE in BLCs. From a policymaking perspective, the current research recommended implementing necessary policy measures to make informed decisions and developing future action plans aimed at increasing and enhancing the intelligence of lecturers. Alternatively, the observations may provide practitioners and policymakers with fresh perspectives to develop specific tactics that can improve the implementation of BLCs.

Keywords:

• Accounting
• blended learning
• engagement
• lecturers’ intelligence
• student

REVIEWING EDITOR:

• Debra Laier Chapman, School of Computing, University of South Alabama, Mobile, USA

SUBJECT:

• Asian Education
• Accounting Education
• Adult Education
• Technology in Education

1. Introduction

Accounting education and the field of management as a whole are being shaped by a number of other global phenomena, including the rise of cyber-attacks and artificial intelligence, the establishment of global capital markets, and the growing significance of sustainability standards (Sollosy & McInerney, 2022). In order for industries to stay competitive in today’s dynamic market, public higher education institutions (PHEIs) have a responsibility to equip their students with the knowledge, skills, and competencies needed to tackle the new challenges posed by this dynamic environment. As a result of their reliance on antiquated methods of instruction that fail to take advantage of technological advancements, traditional pedagogies are ill-equipped to meet the demands of today’s universities (Wanner & Palmer, 2015). Remarkably, global health crisis has brought into sharp focus the importance of looking ahead to anticipated changes in educational and professional pursuits related to accounting, business, and management (Hosseini et al., 2021; Mantai & Calma, 2022). Online education’s adaptability and the opportunities it presents to students have been highlighted by the COVID-19 pandemic (Lasekan et al., 2024). Blended learning, which intentionally combines online and in-person training, keeps the best features of both types of learning environments (Chen et al., 2021; Moradimokhles & Hwang, 2020). According to Singh et al. (2021), blended learning has become increasingly popular in higher education as a way to supplement students’ in-person classroom learning with online pre-class learning options. It is well-established that blended learning offers students numerous benefits, including a more adaptable, ongoing, and helpful educational experience (Kardipah & Wibawa, 2020). Blended learning lets students access instructor-curated materials before in-class activities, unlike traditional lecture-based training (Hrastinski, 2019). This frees up time during in-person classes to focus on learning activities (Shin et al., 2018). This method of instruction suggests a different physical location for learning and was extensively employed to deal with COVID-19-related restrictions on human contact and lockdowns. According to the study conducted by Tetteh et al. (2023), online accounting courses offer a distinct benefit over traditional classroom instruction in light of the current COVID-19 epidemic. This is because students can avoid the virus by engaging in online pedagogy.

The majority of prior research has shown that blended learning has the potential to increase student engagement (Cao, 2023). Research has shown that learning engagement plays a crucial role in blended learning and positively impacts academic performance, persistence, and completion rates (Baranova et al., 2022; Phan et al., 2016). Boelens et al. (2017) and Fisher et al. (2021) note that one of the ongoing goals and challenges of blended course design is to improve student participation. Due to the preference for in-person learning environments and the feelings of isolation they may experience from reduced classroom interaction with instructors and classmates, learners may be maladaptive to navigating between online and face-to-face learning activities, which can lead to low engagement (Heo et al., 2022; Zhong et al., 2022).

A wide line of precedent research has placed emphasis on the determinants of blended learning implementation such as the institutional shifts towards blended learning deployment (Adekola et al., 2017); blended learning design (Owston & York, 2018; Tsankov & Damyanov, 2017), student perspective and context (Vanslambrouck et al., 2017), and issues related to the reinforcement for teachers in blended learning adoption (Graham et al., 2013). According to Bond and Bedenlier (2019) as well as Heilporn et al. (2022), it is assumed that engagement can be changed through the use of contextual facilitators. Tetteh et al. (2023) found that students had negative reactions to using online learning platforms for accounting education due to some professors’ attitudes toward them and a lack of administrative support. To this end, creating interactive learning environments to maximize student engagement is a fundamental component of the challenging task of building blended learning experiences for teachers (Boelens et al., 2017; Lima et al., 2021). It should be noticed that teacher always played a vital role in any phase of educational transformation (Guskey, 2002). Accordingly, teacher journey for an outstanding blended learning adoption claimed much more than solely focusing on obtaining novel skills or replacing pedagogical duty (Philipsen et al., 2019).

Lecturers’ intelligence plays an essential role in students’ learning and also in teachers’ teaching careers and their well-being. Lecturers’ intelligence is seen as an important bridge connecting teachers’ perceptions of their teaching goals and student behaviors, and teachers’ interpersonal behavior. Investigating lecturers’ intelligences is important, however, to date, little is known about how lecturers’ intelligence would influence on students’ engagement in the classroom. The success of students’ education, as well as lecturers’ own professional and personal lives, hinges on the intelligence of these lecturers. Intelligent lecturers are considered as a vital link in the chain that begins with lecturers’ beliefs about their own pedagogical aims and ends with students’ actions in class, as well as lecturers’ interpersonal conduct. Researching lecturers’ intelligence is crucial, yet little is known about how lecturers’ intelligence affects class participation. With those in mind, this research set its sight on investigating the impact of lecturers’ intelligences on the accounting student engagement (ASE) in the blended learning courses (BLCs) among the PHEIs. In doing so, the present work has a stab at ironing out the research questions as follows.

• RQ1. What types of lecturers’ intelligence impact ASE in BLCs?

• RQ2. To what extent do these types of lecturers’ intelligences impact ASE in BLCs?

Although engagement in blended learning is essential for increasing student motivation, enhancing learning outcomes, and establishing a positive learning atmosphere that fosters academic achievement (Cao, 2023), academic works on what helps students stay engaged in blended learning environments is limited (Heilporn et al., 2021; Liao et al., 2023). The culmination of the current studies is to expand the frontier of knowledge on the critical role of ASE in BLCs. In doing so, the current research makes a huge contribution to the accounting education literature by offering novel evidence of ASE within BLCs among PHEIs in developing economies. Indeed, although modern technology has been extensively unopposed for fruitful improvement in the educational realm, nevertheless, teacher could be unable to be altered by any advanced electronic gadgets, instead, these gadgets could act as an additional part together with the teacher to upgrade the learning experiences (Maria Josephine Arokia Marie, 2021). To the best of the understandings of researchers, this study has the potential to be the first scholarly effort to shed light on the distinct effects of different kinds of lecturers’ intelligence on ASE in BLCs. Through empirically delving into these issues, the current research challenges these managerial points of view, and the obtained observations will enable policymakers and governmental influencers in making appropriate transformation.

The whole research ensues as follows. The present research starts by highlighting the research motivation and shedding light on the research orientation prior to depicting the review of literature comprising theoretical background and conceptualization in Section 2. The interpretation of research model and hypotheses development is laid out in Section 3. Section 4 covers a discussion of research methodology. A summary of the obtained observations as well as discussions and implications are illuminated in Section 5. Section 6 puts an end with the conclusion and inherent limitations along with fruitful recommendations for future works.

2. Literature review

2.1. Self-determination theory

Based on the assumption of Deci and Ryan (1985), numerous of human behavior theories have so far failed to cast light on the way to which behavior was encouraged. Self-determination theory (SDT) has been deliberated as a common background of human incentive that placed an emphasis on the degree to which behaviors were comparatively autonomous versus moderately supervised (Deci & Ryan, 1985; 2000). the magnitude of humans ‘developed inner resources for psychological development and behavioral self-regulation were underlined in SDT (Astakhova & Porter, 2015). Accordingly, motivation has been supposed to be the psychological energy oriented at certain goal (Patrick & Williams, 2012). This was because SDT was rested on the fundamental humanistic postulation that persons instinctively and actively direct themselves toward development and self-organization. Differently put, the individuals bended over backward to bolster and grasp themselves by combining novel experiences; at once pulling out all the stops for demand satisfaction and also reacting to the circumstances of the environment which either reinforced or thwarted needs (Legault et al., 2017). In term of the education environment, lecturers have been considered the main actors to propped up student engagement by satisfying their requirements both in classrooms and in virtual studying atmospheres (Bedenlier et al., 2020; Chiu 2022, 2023; Vollet et al., 2017).

2.2. Conceptual respect

2.2.1. Lecturers’ intelligence

The goal of accounting education and training is to develop skilled professionals that have a beneficial impact on their professional community and society throughout the course of their careers (Wilkin, 2022). Accounting educators must stay updated on evolving regulatory standards, implement new teaching methods while aligning them with traditional ones, handle larger and more diverse classes, conduct research in the field, address financial constraints, and meet professional bodies’ educational standards (Duff et al., 2020; Sollosy & McInerney, 2022). Blended learning, which combines in-person and online instructional activities, has been the standard in higher education since the start of the COVID-19 pandemic (Chiu, 2022; Moradimokhles & Hwang, 2020). More remarkably, the shifting novel instructional stages into teachers’ practices was indivisible from their sentiments (Saunders, 2013) and their intellectual characteristics.

Intelligence can be defined as the ability to effectively solve problems or create products and services that are valued in various cultural or communal settings (Gardner, 1987). In other words, it demonstrated the proficiency of individuals in utilizing knowledge in a personalized way to efficiently engage with their environment. Intelligence has traditionally been regarded as a gauge of how well someone has absorbed and assimilated the knowledge and methods of a particular culture or group, enabling them to effectively engage and navigate within that context. Intelligence Quotient (IQ) and Emotional Intelligence Quotient (EQ), as identified by Gardner and Hatch (1989), encompass a total of seven distinct intelligences. Consequently, the intelligences of logical-mathematical, linguistic, musical, spatial, and bodily-kinesthetic were encompassed within the concept of IQ. On the other hand, the interpersonal and intrapersonal intelligences were classified under the concept of EQ, as proposed by Gardner and Hatch (1989). In the current research, creative intelligence, passion quotient and adversity quotient which are categorized under the concept of EQ are supposed to be the most critical characteristics of lecturer in the context of BLC implementation. More concretely, creativity has been well-acknowledged to play the main part in all the realms in the globalized world and has been pondered as one of the key skillfulness (Voogt & Roblin, 2012). Creativity could be comprehended and identified in a variety of approaches resting on the setting. It could be grounded on the culture, the individuals’ understanding, and idiosyncratic expertise, so that a wide range of communities might have divergent definitions of creativity (Amabile, 1982). In the meanwhile, the notion of work passion has attracted the growing concerns of the researchers in the organizational behavior and management field (Ho et al. 2011; Vallerand et al. 2003). Work passion could be understood as a strong intention toward work-related practices that a person felt like and in which he would invest much more his own time and energy to complete (Vallerand & Houlfort 2003). On the other hand, adversity quotient has been so far cogitated as a type of intelligence which was treated as the background of individual’s achievement in coping with challenges (Muztaba et al., 2020). Terminologically, adversity quotient was identified as the competence of a person to exert oneself in processing the complexities into challenges to be addressed (Hidayat et al., 2018) and also transform them into the fruitful chances for better accomplishment (Stoltz, 1997). It comprised of the such four elements as control, origin and ownership, reach, and endurance (Tansiongco & Ibarra, 2020).

2.2.2. Blended learning

Blended learning, also known as hybrid learning, is a method of instruction that combines online and in-person components; it was first proposed in 2001 (Milheim, 2006). Rested on the continuum boundary of the such two approaches, those were, face-to-face and online learning, the BLCs have suffered from lacking of consensus of the academician and practitioner communities in unified definition achievement (Bernard et al., 2014). Admittedly, blended learning notions was supposed to be challenging as they were confusing and comprised of a wide range of teaching activities with little agreement on what they contained (Smith & Hill, 2019). While blended learning was identified as the integration of face-to-face and online learning approaches (Garrison & Kanuka, 2004) by means of the combination of a web-based technique for teaching and face-to-face experiences without the thorough deprivation of face-to-face interaction (Maria Josephine Arokia Marie, 2021), numerous researchers has gone beyond and put accent on the solicitously planned and solid combination of face-to-face and online learning techniques (Garrison & Kanuka, 2004). Several more accurate descriptions detailed the course modalities featured as blended by enumerating the allocation of face-to face and online learning practices. Additionally, a balancing share in the proportion of online and classroom directions was proposed by Bernard et al. (2014) which was propped up by numerous works (Asarta & Schmidt, 2015; Hilliard & Stewart, 2019). On the other hand, a diversity of proposed comparable distributions was also documented in the literature. Several researchers demonstrated the reduction in the number or length of face-to-face practices (Stein & Graham, 2020) whilst the others were bewildered about this facet (Boelens et al., 2017). The term ‘blended learning’ refers to an instructional strategy that integrates multiple pedagogical tenets, most notably those that make use of information and communication technology (Alkhatib, 2018). Blended learning also encourages merging traditional and other learning styles in order to cater to students’ preferred methods of learning (Low et al., 2023). In this regard, BLC can be understood as a course in which a combination of online and face-to-face training, a mix of instructional modalities, and a mix of instructional approaches could be leveraged by lecturers to enhance the students’ performance and engagement.

Taken together, the notion for BLCs employed in the present research rested on the suggestion of Heilporn et al. (2021) which was an integration of face-to-face and online learning practices covering with a contraction in the amount of face-to-face learning activities.

2.2.3. Accounting student engagement

The term ‘engagement’ has first been discovered by the work of American psychologist Ralph Tyler since 1930s which subsequently turned out to be seven principles of effective experiences in undergraduate education of Arthur Chickering and Zelda Gamson (Chickering & Gamson, 1991). In addition, through the College Student Experiences Questionnaire which was formulated in the 1970s by Pace, students’ experiences were concluded to increase when their investment on time and energy in educationally purposeful tasks became greater (Kuh 2009). Hinged on the Tyler and Pace’s contribution, Astin (1984) stressed on the effect of participation on student accomplishment. Since then, student endeavor and time on duties to a wide range expected outcomes of education institution has drawn the attentions of the academician community (Pascarella & Patrick 2005; Pike 2006). Student engagement was identified as the time and exertion students place to the practices which were empirically related to expected results of education institutions and what institutions conducted to encourage the students to take part in these practices (Kuh, 2009) and their doggedness in and contentment with studying (Fredricks et al., 2004). Alternatively, the terminology ‘student engagement’ could be treated as a wide construct aimed at including striking academic as well as specific non-academic facets of the student experiences (Coates, 2007). It also helped to forecast how better students would become in learning pertaining to academic accomplishment and well-being and generated lecturers the chance to capture regular feedback for giving rise to more effective directions (Reeve, 2013). Succinctly put, according to Halverson and Graham (2019) as well as Mandernach (2015), students are considered to be actively engaged learners when they put up a high level of mental and physical effort to succeed in class.

In the current research, ASE is specified as the amplitude of concern, attempt, involvement curiosity, enthusiasm, and passion illustrated by students when they took part in educational program of the accounting major. Remarkably, student engagement has been contemplated a complicated and disputed construct with multifarious theories as well as a plethora of reviews (Trowler & Trowler, 2010). On the other hand, this type of engagement herein has been pondered as a multidimensional psycho-social course of action (Kahu, 2013) which generally contemplated to comprise of behavioral, emotional, cognitive, and agentic elements (Chiu, 2022, 2023; Fredricks, 2011; Reeve, 2013). More concretely, the behavioral engagement was defined as involvement of students in learning practices in the internal and external environment of the classroom (Fredricks et al., 2004). Emotional engagement covered with sentimental responses of the students to their classmates, lecturers, learning practices, and educational institutions (Fredricks et al., 2004). Cognitive engagement identified as mental endeavor of student to fulfill responsibilities utilizing a deep, self-disciplined, and strategic way to grasp, rather than superficial learning practices (Chiu, 2022). Agentic engagement inferred to proactive attempt to fruitfully devote to studying and instructing (Reeve, 2013; Reeve & Tseng, 2011).

3. Hypothesis development and research model

3.1. Hypothesis development

Blended learning has been well-recognized as an innovative teaching approach that combined the conventional classroom and modern technology application where learners were engaged in an active manner (Maria Josephine Arokia Marie, 2021). It integrated conventional techniques with advanced technologies to gain learning performance. It inserted novel evolving open online resources and free instruments and combined several teaching forms which drew the students’ engagement and enhanced their motivation degrees (Maria Josephine Arokia Marie, 2021). This was because blended learning possessed the benefits of face-to-face and e learning to meet the individual differences. Blended learning allowed the lecturers to seek for fruitful solution to assist their learners in reaching the studying objectives and offering them with the greatest possible studying and teaching experiences, as well as revamping lecturers in their parts.

Given that student engagement was provocatively labeled with the newest higher education buzzword, creativity intelligence would enable the higher education lecturers to create new and ingenious ideas to resolve difficulties in term of situations requesting effective options and solutions to obtain behavioral, emotional, cognitive and agentic engagement of their students in the BLC. This was because behavioral, emotional, cognitive and agentic engagement have been recognized as energized by intrinsic motivation, comprehended as indispensable conditions for students to participate and involve in studying (Reeve, 2013).

Building on a standpoint of cognitive psychology (Matlin, 2014), creativity was linked to the trouble-solving realm and was generally referred to the capability to create novel and helpful ideas and resolutions which were suitable, functional, accurate, and valuable (Walia, 2019). The ability to envision one’s ideal environment and the steps necessary to make it a reality is essential for individuals to adapt to a variety of environmental challenges (Trigueros et al., 2020). The creativity intelligence would also condition for lecturer to acquire the freedom of thinking and encouraging communications. By doing so, they could be able to generate an attractive classroom environment which made their students feel free to experiment in their learning (Trigueros et al., 2020). Mulling over all the above, the research hypothesis is formulated as follows.

Hypothesis 1 (H1). Creativity intelligence illustrates a substantial impact on ASE in a direct and positive manner.

Blended learning could generate the optimal learning by offering different learning media that could draw the attentions of the learners to participate in their learning practices and evolve their knowledges (Prahmana et al., 2021). Principally, blended learning was established by the integration between face-to-face form with modern information, communication, and technology, which engendered blended learning possesses numerous advantages. More specifically, the integration of various educational instruments and technologies adoption could ameliorate academic capacities. Moreover, it could be employed to learners with heterogeneous and independent studying manners and enabled cost savings and lowers education expenses. Eventually, through taking advantage of an integration of face-to-face approach and other procedures, learners could obtain in-depth insights anytime and anywhere. There were the such three notifications in blended learning which were suggested to take into consideration as searching information and vitally equipped sources of information relied on the pertinence, rationality and trustworthiness, and academic simplicity of content. While the procurement of data referred to learners’ individually or groups jointly trying to seek, comprehend and collate information captured from information suppliers with ideas that already occurred in their thoughts, the synthesizing of knowledge mentioned on formulating knowledge through the procedures of integration and adjustment from the outcomes of the analysis, communication, and constitution of inferences on the information collected.

In the theoretical facet, work passion represented a positive origin of activity contribution that engendered to performance achievement (Vallerand et al., 2007). The higher of passion quotient that an individual acquired, the more he would pursue his career in voluntary manner and not owing to any pressures or career-related exigencies. This was because the individual who was passionate about what he did would have more cognitive awareness and concerns when performed the responsibilities (Rothbard & Edwards, 2003). Alternatively, employees that are passionate about their profession are more likely to seek out new information, be creative, and connect with others both inside and outside of the company to have access to cutting-edge ideas and perspectives. In this regard, the passionate lecturers would voluntarily internalize the career into his identity which led to the efficiency and effectiveness in completing his tasks. As such, the lecturer with higher passion quotient would take a stab at keeping on seeking much more effective approaches to obtain the ASE in the BLC. This was because work passion also resulted in innovation and creativity and thus drove the staff to search for novel sources of knowledge and to shape associations in the organizational internal and external environment to exploit the advanced insights. Cogitating all the above, the research hypothesis is shaped as follows.

Hypothesis 2 (H2). Passion quotient illustrates a substantial impact on ASE in a direct and positive manner.

Adversity quotient has been the most important components in evaluation an individuals’ competence to reach in a certain career. Indeed, numerous works were investigated with the same research stream - exploring the impact of adversity quotient on lecturers’ performance (Sanusi, 2017; Sekreter, 2019). This was because adversity quotient has been pondered as part of the viewpoints that was anticipated to shape a vigorous basement of feature, was required by persons in meeting the demands of educational institutions recently (Muztaba et al., 2020) and has been employed for the assessment of individual capability of reaching the success in the educational environment (Tansiongco & Ibarra, 2020). The growing body of literature chronicled that in the education field, the performance of lecturers was greatly impacted by determinant, that was, adversity quotient (Puspitacandri et al., 2020; Sigit et al., 2019; Singh & Parveen, 2018; Wang et al., 2021; Zhao et al., 2022). Admittedly, the higher adversity quotient that a lecturers obtained, the more likelihood that they would be responsible for giving rise to better generation (Ristiana et al., 2020). The adversity quotient has been contemplated to be the starting point in allowing lecturers subdue matters or challenges they might encounter during the BLCs implementation in term of accounting educational program. In BLCs, the lecturers would play a facilitator and media during this learning process through supplying directions or studying materials as well as providing guidelines for students to conducting learning practices and making use of the modern technologies for learning (Prahmana et al., 2021) to reap the greatest engagement of students. These procedures also resulted in several difficulties in deploying. Consequently, the lecturers with higher adversity quotient would perform optimally when coping with these adversities. The lecturer could deal effectively with adversities and believe that these matters were just temporary. Under this circumstance, adversities became a critical part to help lecturer always be active. When lecturer took an active part in this process the ASE would be optimized. To put it different, the lecturer with high adversity quotient would be definitely able resolve the troubles related to experiential learning to gain the ASE in term of BLCs. Deliberating all the above, the research hypothesis is formed as follows.

Hypothesis 3 (H3). Adversity quotient illustrates a substantial impact on ASE in a direct and positive manner.

3.2. Research model

The hypothesized model stressed on the impact which creativity intelligence, passion quotient, adversity quotient had on ASE in PHEI in the Southern areas of Vietnam was demonstrated in Figure 1.

Figure 1. The proffered hypothesized model. (Source: Authors’ recommendation, 2021).

By contemplating the above-discussed components, the impact of those types of intelligences on the ASE in BLCs were analyzed. In doing this, the methodology employed in this work was detailed in the next section.

4. Research methodology

4.1. Research design

In order to overcome the shortcomings of both qualitative and quantitative methods, researchers are increasingly turning to mixed-methods approaches (Creswell & Creswell, 2018). Reason being, using many research methods in a single study provides for approval, cross-validation, and corroboration of data, which in turn allows for appreciation of the phenomenon from multiple angles (Chiparausha et al., 2022). The exploratory sequential mixed approaches (Creswell, 2014) were selected for this inquiry because they can go beyond the restrictions of a mono-method and offer a thorough explanation of phenomena (Agyeiwaah, 2022).

In order to have a thorough grasp of the topic, the investigation began by reviewing relevant literature. The researchers identified the empirical codes that would form the basis of the study using the information obtained during the preliminary stage of the semi-structured interviews. A fundamental set of criteria might be more easily established, and the results could be better contextualized, as a result. The next step in developing the study’s final model was to formulate a hypothesis for each empirical code. The study tested the hypotheses using quantitative cross-sectional surveys to add to the existing empirical evidence supporting the theoretical framework.

4.2. Qualitative phase

According to Bouteraa et al. (2022), the main objective of qualitative research is to obtain comprehensive understanding about a subject. Consequently, qualitative research typically employs non-random sampling (Creswell & Poth, 2018). Following the recommendations of numerous qualitative scholars, this study employed a purposive sample strategy to select informants who were both appropriately suited to the topic of investigation and able to articulate it (Creswell & Creswell, 2018; Creswell & Poth, 2018; Sekaran & Bougie, 2019). Researchers can get high-quality expert judgments with little time, money, or human resources with this strategy, while it has significant limitations due to its subjective nature (Karmaker et al., 2023). For this reason, the purposive sample strategy was employed in the current research to identify the most useful informants from working professionals or academics in the relevant subject, as well as individuals employed by public sector organizations. Building on the perspectives of recent qualitative scholars, researchers’ subjective assessments determine the sample size and that they are aware of when saturation has been reached in relation to the size of the qualitative sampling (Creswell & Creswell, 2018; Creswell & Poth, 2018; Sekaran & Bougie, 2019). In other words, according to Creswell and Poth (2018) as well as Sekaran and Bougie (2019), research can be halted after the material has been thoroughly examined and no new themes have been identified.

The most fruitful method has been conducting semi-structured interviews with specialists in the field. This approach made it possible to gather structured data within the predetermined parameters of the study, facilitated the acquisition of insights that would have been impossible to achieve without conducting interviews, and opened the floor for further discussion. Accordingly, the semi-structured interviews were performed to interview and discuss with practitioners and academicians with extensive research expertise on blended learning and the intelligence of lecturers. Once the data reached saturation, or no new information surfaced, the interviewing process came to an end. Saturation of this research was achieved with nine interviews because no fresh information could be exploited. The demographic information of panel of expert was illustrated in Table 1.

Table 1. Profile of panel of expert.

Item	Frequency	Valid (%)	Item	Frequency	Valid (%)
Gender			Organization
Female	6	66.67	University	8	88.89
Male	3	33.33	Department of Finance	1	11.11
Experience (Years)
16 – 26	7	77.78
27 – 37	2	22.22
Career			Education
Deputy	1	11.11	PhD degree	7	77.78
University Lecturer	8	88.89	Associate Professor	2	22.22

Participants were interviewed using semi-structured questions by phone at a time that was convenient for both parties. The interviews were scheduled from January to April 2021 based on the workload and the changing work hours. Each interview lasted anything from thirty to forty-five minutes. For reasons of uniformity, the lead investigator conducted all of the interviews. The notes include a detailed account of each interview, with all identifying information removed to facilitate analysis. After the subjects were finalized, the instances that would demonstrate transparency in reporting were selected. The quantitative and qualitative data are both included in the convergent joint that is displayed in Table 2.

Table 2. Summary of convergent joint presentation of quantitative and qualitative outcomes.

Qualitative results	Meta-inference	Quantitative results
Exemplar quotation		Dimension
…I think…the very first thing…to become a creative intelligent lecturer …is that lecturer should characterize by creative personality and curiosity as well as flexibility …to create new solutions…to gain his students’ engagement during the class… (P01, Lecturer, public university)	Convergence: Creativity intelligence induces a positive impact on ASE	Creative intelligence
I think …adversity quotient is a critical intelligence factor…that influence the success of blended learning course …because…I think …a lecturer with high adversity quotient will try his best to search for a suitable approach to help his learners…to feel curious about …what they are learning…especially in the COVID-19 pandemic… (P02, lecturer, public university)	Convergence: AQ induces a positive impact on ASE	Adversity quotient
In my opinion…passion quote is the necessary aptitude in blended courses…because…it will help the lecturer totally feel excited to …undertake the teaching in these courses and… invest in creating much more effective course material… (P04, lecturer, public university)	Convergence: Passion quotient induces a positive impact on ASE	Passion quotient
…As you know…Adversity quotient will …enable the lecturers to try …to underline all of their potentials …and capabilities, …enhance the amount of working time …in appropriate with their own ability …to become active lecturers …with responsibility for their own teaching… (P05, Deputy of administration and public non-business finance division, Department of Finance)	Convergence: Adversity quotient induces a positive impact on ASE	Adversity quotient
…I do agree that …passion quotient is the main concerns of all employers when recruit an employee…I mean…the lecturers in higher education institutions…in some facets…as it makes him… always eager to seek more about …how he can …undertake his duties effectively…and contribute the most for his organization …and in this case …design a successful course…I mean …blended learning course… (P06, lecturer, public university)	Convergence: Passion quotient induces a positive impact on ASE	Passion quotient
…Of course …creative intelligence is very important…the lecturers with higher creative intelligence …can discover interconnections between the adoption of computers, digital technologies …and they can realize and manage these impacts …on their students, …on their teaching and… on the whole society. (P08, lecturer, public university)	Convergence: Creativity intelligence induces a positive impact on ASE	Creative intelligence

The strategy for analyzing the data was based on thematic analysis. The researchers meticulously entered all observational notes and interview data sets into Microsoft Word files to document the qualitative data. In order to protect their anonymity, the participants were referred to as P1, P2,…, P8. The suggested model’s construct dimensions were subject to an expert consensus round. The sociodemographic data for the expert panel is shown in Table 3.

Table 3. Profile of panel of expert.

Item	Frequency	Valid (%)	Item	Frequency	Valid (%)
Gender			Organization
Female	5	62.50	Public sector organization	5	62.50
Male	3	37.50	University	3	37.50
Experience (Years)
Under 15	5	62.50
15 – under 25	3	37.50
Career
Manager	2	25.00	Education
Chief accountant	3	37.50	Master’s degree	6	75.00
University Lecturer	3	37.50	PhD degree	2	25.00

Based on the responses from the expert panel, Table 4 displays the information on the constructs and dimensions of each construct in the proposed model.

Table 4. Summary of dimensions extracted.

Construct	Experts								Total experts	Valid (%)
	P1	P2	P3	P4	P5	P6	P7	P8
Creative Intelligence	√	√	√	√	√	√	√	√	8	100
Creative personality and curiosity	x	√	√	√	√	√	√	x	8	75.00
Knowledge and skills expansion	√	√	√	√	√	√	√	√	8	100
Flexibility	√	√	√	√	x	√	√	√	8	87.50
Elaboration	√	√	√	√	√	√	x	√	8	87.50
Passion Quotient	√	√	√	√	√	√	√	√	8	100
Work enjoyment	√	√	√	√	√	√	√	√	8	100
Self-motivation	√	√	√	x	√	√	x	√	8	75.00
Self-identity	√	√	√	√	√	√	√	√	8	100
Sense of learning	√	√	√	√	√	x	√	√	8	87.50
Adversity Quotient	√	√	√	√	√	√	√	√	8	100
Control	√	√	√	√	√	√	√	√	8	100
Ownership	√	√	√	√	x	√	√	√	8	87.50
Reach	√	√	√	√	√	√	√	√	8	100
Endurance	√	√	√	√	√	√	√	√	8	100
Accounting student engagement	√	√	√	x	√	√	√	√	8	87.50
Behavioral and emotional engagement	√	√	√	√	√	√	√	√	8	100
Cognitive engagement	√	√	x	√	√	√	√	√	8	87.50
Agentic engagement	√	√	√	√	√	√	√	√	8	100

Note. ‘√’: agree; ‘x’: disagree.

4.3. Quantitative phase

4.3.1. Operationalization of variables for measurement

The elements of the proposed model were derived from previous studies and developed independently throughout the qualitative phase to align with the current research context. The translation-back-translation procedures were implemented to ensure the utmost precision and excellence of the translation (Brislin, 1983). The translation technique involved initially translating all the measure items into Vietnamese by two researchers from Vietnam, followed by a translation back into English by two language experts. Preliminary testing was carried out before completing the main survey. A small-scale pilot survey was conducted with a total of 30 respondents, who were conveniently selected from the pool of participants targeted for the main study. The purpose of the pilot survey was to determine if there were any potential issues in the questionnaire, such as wording faults, statements that were difficult to understand, biased statements, or confusing statements. The questionnaire was subsequently modified and thoroughly based on the feedback and recommendations provided by the participants. However, these 30 responses were subsequently excluded from the final dataset.

The five-points Likert scale from ‘ 1 = vigorously disagree’ to ‘5 = vigorously agree’ was employed for all of the constructs of the proposed model to evaluate participants’ points of view due to its advantages namely lower average fulfillment time (Chyung et al., 2017) and the capability of creating the perfect model fit with substantially greater reliability. The summary of constructs with corresponding indicators in the proposed model was depicted in detail in Table 5.

Table 5. Summary of constructs with corresponding indicators.

Construct	Indicator	References
Creative Intelligence
Creative personality and curiosity		Lehmkuhl et al. (2021)
	CPC1: On occasion, I feel that the best way to address an issue is to give it a lot of mental space and keep trying different approaches.
	CPC2: There are always fresh and practical ways that I may aid others.
	CPC3: The inner workings of things intrigue me.
Knowledge and skills expansion
	KSE1: I have no problem expanding my knowledge.
	KSE2: I can tackle a new challenge using what I know.
	KSE3: Every mistake teaches me a lesson.
Flexibility
	FLE1: Somehow, I always manage to find new methods to mix concepts that no one else has considered.
	FLE2: Making something from scratch is more my speed than doing the same old thing over and over again.
	FLE3: In the event that a needed resource is unavailable, I make every effort to discover an alternative.
Elaboration
	ELA1: I focus on every detail when I am enthusiastic about a subject.
	ELA2: When I combine what I already know, I can come up with new solutions.
	ELA3: There are connections between computer use and societal effects that I can uncover.
Passion Quotient
Work enjoyment		Johri et al. (2016)
	WE1: I really love my work.
	WE2: While working, I am not bored or fatigued.
	WE3: My job makes me feel wonderful and gives me energy.
Self-motivation
	SM1: Even if the money was lower, I would still do this job.
	SM2: It is the labor itself, rather than the potential benefits, that motivates me.
	SM3: I frequently put in extra hours without receiving any explicit request to do so.
Self-identity
	SI1: The things I like about myself are reflected in my work.
	SI2: The people I work with make me feel like I belong.
	SI3: It gives me great joy to do the things that I do.
Sense of learning
	SL1: When I work, I always aim to perform better.
	SL2: When I am working, I am constantly looking for ways to improve my methods.
	SL3: Every time I work, I strive to do my best.
Adversity Quotient
Control		Matore et al. (2020)
	CON1: Despite my anxiety, I am determined to give it my all.
	CON2: I do my best to avoid dwelling on the things that have the potential to derail my life.
	CON3: My future action plan is prepared meticulously
Ownership
	OW1: I intend to be sensitive to the feelings of others.
	OW2: Other individuals occasionally question my abilities, and I get that.
	OW3: Boosting my confidence can only be done by myself.
Reach
	RE1: I make an effort to showcase my whole range of skills and abilities.
	RE2: My ability to work more hours will determine how much time I put in.
	RE3: When things go wrong, I need to quit pointing the finger at myself right away.
Endurance
	EN1: I am able to get enough work done because I arrange my time well.
	EN2: Without a doubt, I will not be one of the less capable groups within our organization.
	EN3: If my loved ones constantly urge me to be careful, I will be less likely to mess up.
Accounting student engagement
Behavioral and emotional engagement
	BEE1: My students have an insatiable need for knowledge.	Skinner et al. (2009)
	BEE2: When I begin a new unit in class, my students put forth a lot of effort.
	BEE3: My students always look forward to class because they are interested in learning new things.
Cognitive engagement
	CE1: My students are typically able to come up with solutions to their study problems when they encounter them.	Wang et al. (2016)
	CE2: My students have taken an active role in my teaching and are accountable for my progress.
	CE3: My students are confident in their abilities.
Agentic engagement
	AE1: The curriculum development process has considered the feedbacks of my students	Reeve (2013)
	AE2: My students voice their choices and thoughts in class.
	AE3: The top goal of my students is devoting time and energy to learning the content.

4.3.2. Sampling procedures and data collection

This research aims to examine lecturers in public universities who possess the ability to provide incisive analysis and profound expertise on relevant subjects from their unique perspectives. Candidates must possess a comprehensive understanding of various forms of human intelligence and a BLCs to be eligible for participation. In addition, it was required that they have a minimum of six years of experience working for their respective organizations, demonstrating their active engagement in the field of education. In order to ensure that participants have the necessary information to complete the survey, they are also required to assess their familiarity with various forms of human intelligence and the integration of blended learning programs. By adhering to these procedures, the researchers may ensure that the dataset is devoid of individuals who are unaware of these concerns. The target participants were contacted by telephone.

4.3.3. Ethical consideration

The participation in this research was founded purely on a volunteer basis and no monetary incentive was offered. Building on a stipulation based on the ethical considerations raised by Saunders et al. (2012), respondents were required to fully grasp the contents of the cover letter accompanying the questionnaire in order to participate. The participants were given the opportunity to freely and without penalty discontinue their participation in the study at any time; also, they were informed that their responses would remain anonymous, private, and entirely voluntary. The current research is the only intended use of the collected data, which were handled in a confidential and anonymous manner.

Methods such as convenience and snowball sampling were used to get the study sample. A nonprobability selection technique known as ‘convenience sampling’ picks community members according to how accessible and available they are. To recruit people who are difficult to reach or who are already familiar with the study, researchers have recognized and used snowball sampling. This strategy is being used to recruit people to take part in the current study by scouring their personal social networks. As stated by Singh and Srivastava (2018), the certain sample size for CB-SEM deployment has suffered from the broad consensus. In particular, whilst Urbach and Ahlemann (2010) advocated a sample size of 200–800 respondents for CB-SEM instrument, the flawless sample size ranging from 1:4 to 1:10 was suggested by Hinkin (1995). Along this line, Tinsley and Tinsley (1987) proposed the number of informants should be fluctuated from 5 to 10 times higher than the number of measure items. The surveys were delivered to instructors in the faculty of accounting and faculty of finance at PHEIs in South Vietnam from July 2021 to December 2021. The remaining sample size for analysis consisted of 323 cases following the careful examination and evaluation of questionnaires, resulting in a data loss rate of 17.18 percent. The statistical data in the current research was analyzedby means of the support of SPSS version 26.0 and AMOS version 28.0. The socio demographic information vindicated that more than half of the sample were females, while males accounted for 17.03 percent. Regarding to the age of informants, the group ‘31–40’ took up 82.04 percent of the sample, which was followed by the group ‘41–50’, constituted around 17.96 percent. Concerning to academic capability, all of respondents possessed a minimum of graduate level qualification. These respondents obtained experience of more than 10 years working as a lecturer of accounting faculty.

5. Result analysis and discussion

5.1. Evaluation on measurement model

5.1.1. Construct reliability and Convergent validity

The construct validity was implemented to warrant all the items evaluated what the research was operationalized to assess (Dewi et al., 2020). This type of validity could be measured through convergent validity and discriminant validity. Convergent validity has been contemplated as the capacity of a scale in combining or loading together as a single construct which was assessed by investigating each loading for each group of indicators (Osman & Sentosa, 2013). The convergent validity was evaluated by means of factor loading, composite reliability (CR) of each scale, and average variance extracted (AVE) for each construct as well (Huma et al., 2017). Concretely, the factor loadings were recommended to be equal to 0.70 or higher (Hair et al., 2017). The AVE indexes of each construct should meet the satisfactory degree of 0.5 or greater (Hair et al., 2017; Zhang et al., 2014). As a rule of thumb, CR was substantiated when the CR coefficients exceeded the minimum demand of 0.7 (Oduro & Haylemariam, 2019; Zhang et al., 2014). On the other hand, the power of the measurement model could be assessed through the reliability (Mahmud et al., 2021). The Cronbach’s alpha value was proffered to be greater than the minimum requested value of 0.7 (Lin & Huang, 2008; Nunnally & Bernstein, 1994). The statistical outcomes of data set in Table 6 underlined that the scales employed for this hypothesized model possessed convergent validity.

Table 6. Construct reliability and validity.

Construct	Item acronyms	Convergent validity		Construct reliability		Inference
		Factor Loadings Ranges	AVE	Cronbach’s Alpha	Composite Reliability
Lecturers’ intelligence
Creative Intelligence	CI
Creative personality and curiosity	CPC	0.778 - 0.848	0.653	0.848	0.849	Retained
Knowledge and skills expansion	KSE	0.792 - 0.806	0.648	0.846	0.846	Retained
Flexibility	FLE	0.708 - 0.933	0.662	0.847	0.853	Retained
Elaboration	ELA	0.515 - 0.821	0.500	0.695	0.718	Retained
Passion Quotient	PQ
Work enjoyment	WE	0.685 - 0.862	0.606	0.819	0.821	Retained
Self-motivation	SM	0.692 - 0.812	0.546	0.780	0.782	Retained
Self-identity	SI	0.711 - 0.789	0.566	0.794	0.796	Retained
Sense of learning	SL	0.595 - 0.861	0.572	0.793	0.798	Retained
Adversity Quotient	AQ
Control	CON	0.735 - 0.781	0.597	0.816	0.816	Retained
Ownership	OW	0.759 - 0.792	0.606	0.819	0.822	Retained
Reach	RE	0.719 - 0.870	0.664	0.852	0.855	Retained
Endurance	EN	0.668 - 0.852	0.553	0.782	0.787	Retained
Accounting student engagement	ASE
Behavioral and emotional engagement	BEE	0.577 - 0.770	0.523	0.762	0.766	Retained
Cognitive engagement	CE	0.666 - 0.811	0.583	0.796	0.806	Retained
Agentic engagement	AE	0.718 - 0.783	0.565	0.793	0.795	Retained

5.1.2. Discriminant validity

The following phase related to the discriminant validity investigation. Discriminant validity has been identified as how far each latent variable was stood apart from other concept in the proposed paradigm (Hair et al., 2017). The first approach was grounded on Fornell-Larcker criterion. Accordingly, diagonal values which were the square root of the AVE for each concept was greater than the greatest correlation coefficient of the off-diagonal components in both groups (Fornell & Larcker, 1981; Henseler et al., 2016; Mahmud et al., 2021). Succinctly put, a latent variable elucidated the variance of its own indexes which was more excellent than that of other latent variables (Hair et al., 2017). Concerning to second approach, discriminant validity was examined through which the loading of each indicator on its concept was more than its cross-loading with other concepts (Chin, 1998). Rested on the statistical results exposed in Table 7, it could be concluded that there was no discriminant validity matter.

Table 7. Results summary for discriminant validity on Fornell-Larcker criterion.

	FLE	RE	CPC	KSE	OW	CE	WE	CON	SL	AE	EN	SI	SM	BEE	ELA
FLE	1
RE	0.130	1
CPC	0.289	−0.011	1
KSE	0.217	0.105	0.331	1
OW	0.053	0.192	0.158	0.328	1
CE	−0.028	−0.045	0.018	0.126	0.179	1
WE	−0.028	0.158	0.077	0.038	0.064	0.130	1
CON	0.019	0.317	0.175	0.040	0.341	0.106	0.135	1
SL	0.068	0.047	0.146	0.061	0.028	0.021	0.319	0.122	1
AE	0.072	0.082	0.103	0.063	0.071	0.018	0.106	0.202	0.200	1
EN	0.026	0.129	0.051	0.210	0.232	0.099	0.039	0.361	0.018	0.125	1
SI	0.077	−0.014	0.042	0.013	0.014	0.076	0.371	−0.033	0.266	0.025	0.074	1
SM	0.055	0.050	0.065	0.126	0.077	0.179	0.305	−0.022	0.158	0.139	0.178	0.158	1
BEE	0.212	0.171	0.299	0.162	0.167	0.300	0.340	0.368	0.231	0.385	0.046	0.163	0.225	1
ELA	0.214	0.113	0.337	0.266	0.208	0.045	0.004	0.139	−0.014	0.102	0.036	0.029	0.125	0.248	1

To ensure a comprehensive examination of discriminant validity, it is recommended to include the assessment of Heterotrait–monotrait ratio of correlations (HTMT) values, in addition to the Fornell–Larcker test (Wijaya et al., 2022). In this regard, this study additionally assessed the HTMT value as it is considered superior to the Fornell-Larcker method in different scenarios (Henseler et al., 2015). When using the HTMT criterion, a construct is said to possess robust discriminant validity if the HTMT value remains below the threshold of 0.9. The data reported in Table 8 clearly indicated that there was no issue with discriminant validity, as suggested by Henseler et al. (2015). Thus, the results demonstrate a strong level of discriminant validity among the components utilized in the model.

Table 8. Results summary for discriminant validity on Heterotrait–Monotrait Ratio.

	FLE	RE	CPC	KSE	OW	CE	WE	CON	SL	AE	EN	SI	SM	BEE	ELA
FLE
RE	0.132
CPC	0.294	0.013
KSE	0.205	0.110	0.330
OW	0.062	0.198	0.164	0.318
CE	0.040	0.019	0.025	0.131	0.182
WE	0.033	0.161	0.071	0.030	0.061	0.119
CON	0.006	0.326	0.172	0.037	0.343	0.111	0.128
SL	0.085	0.053	0.150	0.058	0.046	0.011	0.321	0.123
AE	0.082	0.094	0.095	0.063	0.072	0.027	0.101	0.205	0.205
EN	0.033	0.146	0.058	0.214	0.245	0.091	0.039	0.366	0.051	0.126
SI	0.103	0.021	0.035	0.016	0.013	0.078	0.377	0.033	0.267	0.030	0.077
SM	0.043	0.057	0.044	0.133	0.075	0.193	0.309	0.016	0.157	0.137	0.191	0.163
BEE	0.210	0.183	0.302	0.163	0.168	0.310	0.355	0.363	0.234	0.395	0.047	0.172	0.225
ELA	0.196	0.100	0.359	0.288	0.204	0.056	0.010	0.143	0.018	0.099	0.032	0.023	0.124	0.260

5.1.3. Evaluation of overall model fit

The goodness of fit evaluation was recommended to conducted at the beginning of model assessment (Henseler et al., 2016). In doing so, multiple fit indices comprised of chi-square to degree of freedom (CMIN/DF), comparative fit index (CFI), Tucker Lewis index (TLI), goodness of fit index (GFI), root mean square error of approximation (RMSEA) were determined. Accordingly, the CMIN/DF should be drastically lower than 2 (Byrne, 1989). The value of CFI, GFI and TLI were recommended to be well above the ideal threshold of 0.9 (Byrne, 1989). The RMSEA value was suggested to be well under the greatest value of 0.08 (Browne & Cudeck, 1992). Rested on the basis of the outcomes delineated in Table 9 placed an emphasized that the measurement and structural models impeccably fit the gathered data as all the fit measures lied within the widely suitable range. To put it different, the goodness-of-fit statistics reached the compatibility with the empirical data captured.

Table 9. Analysis of Goodness of model fit.

The goodness of fit measures	CMIN/DF	GFI	CFI	TLI	RMSEA
Recommended threshold	≤3	≥0.9	≥0.9	≥0.9	≤0.08
Measurement Model	1.274	0.919	0.974	0.969	0.023
Structural Model	1.380	0.904	0.960	0.958	0.027

5.2. Evaluation on structural model

5.2.1. Hypothesis testing

Building on the result analysis gleaned from Table 10, CI demonstrated vigorous influence on ASE (β = 0.351; p < 0.001) ​Likewise, PQ (β = 0.409, p < 0.001) was also found to be positively correlated with ASE. Nevertheless, AQ have a significantly desired relationship with ASE but with the lowest path coefficients (β = 0.323, p < 0.001).

Table 10. Structural relationships and hypothesis testing.

Hypothesis No	Hypothesized path			Standardized Coefficients	S.E	C.R	p-value	Inference
H1	CI	→	ASE	0.351	0.098	4.776	0.000	Buttressed
H2	PQ	→	ASE	0.409	0.079	5.294	0.000	Buttressed
H3	AQ	→	ASE	0.323	0.090	4.429	0.000	Buttressed

5.2.2. Robust analysis

The bootstrapping method was initially presented by Bollen and Stine (1992). Due to its ability to analyze determinants, estimate structure coefficients, and assess outcome invariance across samples, this technique has been widely incorporated into SEM techniques (Nevitt & Hancock, 2001). It allows academics to focus on statistical analysis rather than on individuals whose sample distribution was derived theoretically (Diaconis & Efron, 1983). An advantage of this approach is its ability to produce an empirically derived sampling distribution, which can then be utilized for descriptive or inferential purposes, or both (Zientek & Thompson, 2007). The utilization of the bootstrapping technique leads to a more precise and dependable model due to the reduced variability in the findings it produces. The current study utilized the bootstrapping technique, employing 10,000 random observations, to generate a selection bias-corrected bootstrapping strategy. This approach was used to estimate the hypothesized model with 95% confidence intervals (Figure 2). The stability of parameter estimates was assessed by examining standard errors (SEs), comparing the sample statistics to the average bootstrap results, and calculating the ratio of the average bootstrap results to the SEs (Zientek & Thompson, 2007). The results analyses in Table 11 confirm that the hypothesized model proposed in this research is characterized by precision, accuracy, and dependability.

Figure 2. The proffered hypothesized model.

Table 11. Results of bootstrapping estimation.

Hypothesis no	Hypothesized route of path			Standardized	Bootstrap estimation			Discrepancy
					Mean	SE	SE (SE)	Bias	SE (Bias)	CR
H1	CI	→	ASE	0.351	0.350	0.068	0.000	0.000	0.001	0
H2	PQ	→	ASE	0.409	0.408	0.078	0.001	−0.001	0.001	−1
H3	AQ	→	ASE	0.323	0.319	0.074	0.001	−0.001	0.001	−1

5.3. Discussion and implication

5.3.1. Theoretical implication

Given that gaining student engagement has been indubitably a prominent goal of curriculum establishment (Halverson et al., 2014; Spring et al., 2016), the in-depth insights into the determinants of student engagement in the BLCs were very imperative for the successful design of this type of courses. However, student engagement has been also realized as being influenced by context in a direct manner (Lawson & Lawson, 2013; Manwaring et al., 2017). Therefrom, it was required to be mulled over in certain contexts like blended courses (Manwaring et al., 2017) in accounting education. On the other hand, as student engagement was adaptable through the ways employed by teachers (Kahu, 2013; Lawson & Lawson, 2013), the requests of the strategies for student engagement increasing in the blended courses were stressed by several attempts of previous researchers (Halverson et al., 2014; Henrie et al., 2015; Manwaring et al., 2017; Taylor et al., 2018). Surprisedly, the work related to the standpoints of teachers in term of blended learning courses has been sparse and far between (Smith & Hill, 2019; Taylor et al., 2019) regardless of the significance of the role of teacher in this type of course (Boelens et al., 2017; Taylor et al., 2018; Zhu, 2017). More remarkably, the shifting novel instructional stages into teachers’ practices was indivisible from their sentiments (Saunders, 2013) and their intellectual characteristics. In light of what is currently known, this academic work could be the potential to be the first of its kind to classify lecturers’ intelligence and to shed light on the unique ways in which each lecturers’ intelligence type impacts ASE in BLCs. However, the obtained findings of this research also enriched the burgeoning body of literature focus on the prerequisite role of human intelligence in the context of digital transformation (i.e. Huy and Phuc, 2021; Huy and Phuc, 2023a; Huy and Phuc, 2023b).

In line with the expectation of the researchers, the statistical evidence revealed that there was a substantial influence of creative intelligence on ASE. In other words, PHEIs with greater creative intelligent teaching staff will have an edge on reaching ASE in BLCs implementation. This is because the creativity intelligence will enable individual to envision one’s ideal environment and the steps necessary to make it a reality (Trigueros et al., 2020). More concretely, creative intelligence was considered to be paramount for lecturers in on several dimensions. Accordingly, creative intelligence would enable the higher education lecturers to give rise to new and ingenious ideas to puzzle out difficulties in term of situations requesting effective options and solutions to obtain behavioral, emotional, cognitive and agentic engagement of their students in the BLCs. The creativity intelligence would also condition for lecturer to acquire the freedom of thinking and encouraging communications. By doing so, they could be able to generate an attractive classroom environment which made their students feel free to experiment in their learning (Trigueros et al., 2020). In consonance with the anticipation of the researchers, the result analysis underlined the potential role of passion quotient in enhancing the effectiveness of ASE. To put it different, PHEIs with greater passionate teaching staff will have an edge on reaching ASE in BLCs implementation. More particularly, this outcome inferred that the lecturer with high passion quotient would be prone to strive to carry on seeking much more effective approaches to obtain the ASE in the BL course. This was because work passion also resulted in innovation and creativity and thus drove the staff to search for novel sources of knowledge and to shape associations in the organizational internal and external environment to exploit the advanced insights. Admittedly, passionate workers are essential to any organizational success because they are able to rise to the challenge of changing circumstances while keeping their eye on the goals (Gubman, 2004). In the same vein, according to Johri et al. (2016), firms cannot advance in their pursuit of continuous innovation unless they hire people who are truly passionate about what they do.

In accordance with the researchers’ expectations, adversity quotient was highlighted to demonstrate significant effect on ASE. Differently put, PHEIs with greater adversity quotient teaching staff will have an edge on reaching ASE in BLCs implementation. In conformity with a hand full of previously relevant research validating the significantly positive interconnection between adversity quotient and the performance of teachers (Puspitacandri et al., 2020; Sigit et al., 2019; Singh & Parveen, 2018; Wang et al., 2021; Zhao et al., 2022), this research outlined that adversity quotient could boost the lecturer to perform optimally when coping with adversities pertaining to supplying directions or studying materials as well as providing guidelines for students to conducting learning practices and making use of the modern technologies for learning (Prahmana et al., 2021), which thus further intensify his capacity to iron out the troubles related to experiential learning to enhance the ASE in term of BLCs.

5.3.2. Practical implication

Building on the practical facet, numerous implications could be drawn from the observations obtained in this research. Firstly, it has been evident that the lecturers with high intelligences would likely to handle almost all of the problems in the BLC deployment project. As such, when a wide range of approaches to advance the intelligences of lecturers were executed, then sterling ideas would definitely be created and integrated in thrilling and helpful ways. More specifically, the PHEIs should place the concentration on offering appropriate training programs or workshops to give the direction on strengthening variety of intelligence that their lecturers could employ in the BLC. Secondly, balanced work surroundings and equal chances for development were encouraged to be set up in all the departments of the organizations. Thirdly, management educators were suggested to place their concerns on structural changes in term of the provision of technologies that were compulsory to reap the best result in deployment of BLCs (Arbaugh, 2000). Fourthly, it was advisable for all of the lecturers to take into consideration on the fact that what conclusively inspired lecturers to re-plan their context was not only novel scientific understandings on how to undertake their responsibilities, but also an intensified intelligence to deeply seize and effectively resolve matters when the BL courses were put into practice. In doing so, this would boost the lecturers to continuously seek for efficient and effective approaches to determine the suitable training programs to gain their intelligences in the such three aspects as creative intelligence, passion quotient, and adversity quotient, simultaneously, enable them to generate innovative and fruitful techniques to gain the engagement of the student, which would give rise more benefits to the PHEIs’ training quality enhancement. Finally, it was indispensable to fortify learners’ points of view on the BL platform to gain their engagement in learning (Gao et al., 2020). As such, both the policymakers and influencers in the government along with the PHEIs were recommended to reevaluate how to generate the distribution of higher education completely flexible and felicitous with students (Webb et al., 2021).

6. Concluding remark

According to Moghavvemi et al. (2023), the education system and lecturers were forced to swiftly and unexpectedly shift to online teaching and learning due to the COVID-19 pandemic. Blended learning’s potential has been brought to light by the COVID-19 pandemic. Blended learning involves a mix of virtual and physical learning environments (Al-Qatawneh et al., 2020; Yu et al., 2022). Although the majority of prior research has shown that blended learning has the potential to increase student engagement (Cao, 2023), Tetteh et al. (2023) found that students had negative reactions to using online learning platforms for accounting education due to some professors’ attitudes toward them and a lack of administrative support. As such, creating interactive learning environments to maximize student engagement is a fundamental component of the challenging task of building blended learning experiences for teachers (Boelens et al., 2017; Lima et al., 2021). Accordingly, teacher journey for an outstanding blended learning adoption claimed much more than solely focusing on obtaining novel skills or replacing pedagogical duty (Philipsen et al., 2019). This has sparked the current study to identify the specific types of intelligence required by lecturers and its influence on ASE in BLCs. The conducted outcome analyses highlighted significant and positive relationships between the hypothesized constructs related to significance and effect size. Specifically, creativity intelligence had the highest path coefficient, followed by passion quotient, while adversity quotient had the lowest path coefficient among the drivers of ASE in BLCs. From a policymaking perspective, the current research recommended implementing necessary policy measures to make informed decisions and develop future action plans aimed at increasing and enhancing the intelligence of lecturers. Alternatively, the observations may provide practitioners and policymakers with fresh perspectives to develop specific tactics that can improve the implementation of BLCs.

Although every effort was made to ensure the integrity of this research, it is important to consider the inherent constraints that may affect the interpretation of the study’s results. However, these limitations may provide new directions for future research in this field. Firstly, as this research only used data from South Vietnam, the conclusions can only be applied to other regions once similar investigations have been conducted there. The second constraint stemmed from the very small sample size, which, in several situations, was insufficient to make definitive conclusions. Therefore, increasing the sample size was only suggested based on the factors of time and effort. The third significant constraint pertained to the composition of the sample. The current study primarily focused on samples obtained specifically from a limited group of informants, namely teaching staff in the accounting and finance faculties. It is uncertain whether the observations obtained from this manuscript may be applied to professors in other fields of study. Therefore, the inclusion of a wider array of teaching personnel and diverse departments would enhance the acquisition of useful perspectives. Fourthly, the deployment of the survey questionnaire could lead to the emergence of self-serving bias among the informants, despite efforts made to enhance the objectivity of the participants through various methods. Therefore, it is recommended to utilize secondary data in future research to overcome this limitation. More importantly, previous social psychological studies on passion have identified two distinct types of work passion: harmonious passion and obsessive passion (Vallerand et al., 2003; Vallerand & Houlfort, 2003). Meanwhile, this study focused extensively on the shared enthusiasm for work, but it was understood that the variables evaluated were not exhaustive. However, the instructions utilized in this study embodied the most exemplary practices of scholars up to this point. In the future, more studies will necessitate more detailed examination of work passion in order to account for the continually evolving technologies and working environment. Last but not least, the cross-sectional data posed a significant obstacle in making definitive conclusions regarding the result analysis. Put simply, the current study was limited to providing static perspectives on fit, resulting in the ability to make conclusions solely on the shared relationships among the variables under consideration. Hence, future studies should focus on doing a comprehensive longitudinal analysis using advanced statistical techniques.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by University of Economics Ho Chi Minh City (UEH).

Notes on contributors

Quang Huy Pham

Huy Quang Pham is an Associate Professors and Doctor as well as Advanced Lecturer in Public Sector Accounting at the School of Accounting, University of Economics Ho Chi Minh City, Vietnam. He has authored several chapters for books, contributed to a multitude of scholarly journals, and delivered speeches at conferences both domestically and internationally. He has been awarded the Young Scientific Talent in Vietnam issued by the Ministry of Education and Training together with Typical Young Teachers in Ho Chi Minh City for six consecutive years (i.e., from 2012 to 2017) as well as obtaining the Certified Public Accountant from the Ministry of Finance. Huy is a formidable force at work, inspiring others to put in the effort necessary to achieve with his upbeat outlook and boundless enthusiasm.

Kien Phuc Vu

Phuc Kien Vu is a lecturer at the University of Economics Ho Chi Minh City, Vietnam, where he is affiliated with the Faculty of Accounting. Her primary research interests lie in the fields of accounting and management. She published a number of articles and conducted research for international conferences.