E-Learning during COVID-19: Understanding the nexus between instructional innovation,E-psychological capital,and online behavioural engagement

Abstract

This current study is to empirically validate the importance of student's behavioural engagement on online teaching during a coronavirus-2019 disease pandemic. The global spread of coronavirus-2019 disease has affected every aspect of business, including education, resulting in the shift of classroom to online teaching. Keeping in view the growing concern about students’ attentiveness, connectivity, participation, and interaction in online classes, the authors underlined the critical need for paying empirical attention to this issue. While addressing a major empirical gap, the present study tested and found the significant role of e-learning efficacy, e-learning resilience, and teachers’ instructional innovation in boosting students` online behavioural engagement. Additionally, the study found a thought-provoking direct and interacting role of teachers’ instructional innovation. Therefore, the implications of the findings indicate that leaders in educational institutions need to invest in psychological resources that emphasize innovation and creativity in instructional methods for teachers to enhance student engagement in an online environment.

Keywords

instructional innovation behavioural engagement online learning COVID-19 pandemic psychological capital

Introduction

The current pandemic situation pushed the higher education institutions to divert from traditional classroom learning to web-based mediums. Therefore, academic institutions worldwide utilized web-based electronic platforms to a great extent (Basilaia and Kvavadze, 2020). Notably, this shift has raised questions on students’ behavioural management and ways to enhance it, since there are limited options for teachers to do so (Waldner et al., 2012). Consequently, these platforms require seasoned technology skills and a tech-friendly attitude to ensure responsive learning. In addition, due to the limited tech skills of the teachers and sudden shift from the traditional classroom-based learning to the web-based teaching, there is a likeliness of questioning technology efficacy and resilience, as underlined by past scholars (e.g. Wang et al., 2013). Moreover, recent scholarly findings suggest that innovative approaches to teaching have become significant, especially in the wake of coronavirus disease 2019 (COVID-19) pandemic (e.g. Ferdig et al., 2020; Woolliscroft, 2020). Given the current COVID-19 pandemic situation, the authors argue that the elements mentioned above can be of prominence to understand how students’ behaviours can be effectively enhanced.

E-learning and instructional innovation

Undeniably, E-learning has been receiving increased attention in the education sector (Al-Samarraie et al., 2018), which has increased in the wake of COVID-19 pandemic, thus making it an integral part of the education system (Radha et al., 2020). Typically, it provides ease of interaction between the learners and the teacher through content sharing via the web as the medium. It is viewed by Li et al. (2008) as a strategy that makes use of up-to-date technology by adapting to traditional teaching approaches to strengthen student engagement. One of the key factors when using E-learning is making the learning accessible anytime and anywhere (Salawudeen, 2010). In general, many scholars defined E-learning to consist of four categories which are:

Communication oriented: E-learning allows people to communicate easily using technology.

System oriented: E-learning relies on electronic means (Li et al., 2008).

Educational paradigm oriented: E-learning is viewed as a modernized way of delivery, and it mainly aims to support and improve students’ learning (Ellis et al., 2009).

Technology-driven: E-learning primarily uses technology. It is viewed as a distant learning, given that it aims at remote learning (Persico et al., 2014).

According to Tyilo (2017), the ‘e’ in the E-learning not only means electronic, but it also means empowering, engaging, and enhancing. E-learning empowers teachers (Romeo, 2006), enabling the capitalization of available resources for teaching via technology to facilitate what Tian et al. (2014) termed as “engaging educational communications” for enhanced student learning and comprehension. However, E-learning does not, by any means, replace the teacher. It is a powerful tool that can facilitate learning and interaction among the learners. Through this, teachers can have new or alternative platforms that provide them with possibilities to be innovative and interactive (Herrington et al., 2009), leading to learning improvement. The use of technology (Rennie and Morrison 2013) makes it possible for learners to process information differently. The web-based discussion forums about the specific themes can be beneficial because they allow for continuous interaction. These also encourage independent learning and even innovation among students. Online education is far beyond the use of some technology-based software such as Microsoft Word, PowerPoint, or an overhead projector (Smith and Greene, 2013). It is more engaging (Smith and Greene, 2013) to both teachers and learners. It also makes it convenient for teachers to evaluate student work with regard to plagiarism, track student progress, evaluate academic progress, and assess comprehension via online assessments marked automatically.

It is important to note that learning online is sometimes not convenient for some learners as pointed out by empirical evidence (Lazim et al., 2021; Muilenburg and Berge, 2005; Oraif and Elyas, 2021). As a result, students must express willingness and dedication toward learning online to overcome these issues. Thus, teachers also have to play a substantial role in being creative in their approaches and teaching styles to ensure that students’ teaching and learning are not marginalized (Clark and Mayer, 2016).

Due to the current pandemic, the education sector including universities was pushed to initiate teaching online quickly as the mode of instruction. Typically, an E-learning environment provides learners with ease of access to course content, flexibility in interaction, and opportunity for discussions (Nortvig et al., 2018). However, the recent rush to E-learning platforms raised several complaints and concerns from all parties involved (e.g. Almanthari et al., 2020; Favale et al., 2020), thus resulting in difficulties for some teachers as well as students to continue learning effectively. Since the pandemic situation likely seems to continue for some time, it has therefore become essential to address this issue empirically. As such, we state that, besides the need to become familiar with technology (e.g. Lee et al., 2013), students need to have the essential psychological resourcefulness (e.g. Siu et al., 2014) toward online learning. Accordingly, we also assert that such a pandemic situation requires creative efforts from teachers to help students remain actively connected during online sessions (Dias et al., 2020; Mishra et al., 2020; Oraif and Elyas, 2021).

More importantly, we also argue that since the students are far from the classroom environment, keeping them behaviourally engaged can be challenging (e.g. Hart, 2012). However, our assertions need to be empirically validated as there is a lack of studies conducted in this context. Therefore, the present study attempts to empirically investigate how students’ online psychological capital can enrich their online behavioural engagement and the role of instructional innovation of teachers in this regard.

Research gaps and significance of the study

The present study attempts to address several scholarly gaps. First, the present study appears to be the first to test the effects of web-based E-learning efficacy and E-learning resilience on students` online behavioural engagement. Although several studies have investigated academic efficacy and resilience with student engagement in general (e.g. Ahmed et al., 2018; Dogan, 2015; Fan and Williams, 2010; Fati et al., 2019), none of the studies have tested their link with behavioural engagement in the context of online learning in particular. In addition, how these interactions would result in the wake of COVID-19 pandemic is another gap the current study attempts to investigate. Second, despite the indispensability of the teacher’s instructional innovation (e.g. Johnson and Aragon, 2003; Richardson et al., 2020), this study appears to be the first to investigate its direct and interaction effects on students’ online behavioural engagement. Furthermore, this study would aid empirical evidence on the notion of innovation that has become much popular recently (Ogalo, 2020). Overall, keeping in view the current pandemic issues beforehand, the present study advances theory development and fortifies research validity especially in the education research domain.

Theoretical framework

The current study is from the perspective of two theoretical lenses. For direct associations between the exogenous and endogenous variables, the present study rests with the Conservation of Resources theory (COR) (Hobfoll, 1989; 2001). The theory outlines how individuals value resources to obtain desired outcomes. The theory categorically outlines four different types of resources, including conditions, personal characteristics, objects and characteristics of others engaging with oneself as energies. Following this assertion, psychological factors such as efficacy and resilience are referred to as personal resource-based characteristics (Hobfoll et al., 2003; Xanthopoulou et al., 2007), enabling individuals to understand their potential to control and influence the environment. Importantly, prior studies have employed the COR theory to examine academic efficacy and resilience behaviours (e.g. Ahmed et al., 2018). Based on these arguments, in the context of online learning, E-learning efficacy and E-learning resilience can be viewed as personal psychological resources necessary for students to boost their online behavioural engagement.

Accordingly, the study also follows the footprints of instructional design theory (Snyder, 2009) to investigate the likely significance of instructional innovation of teachers. There are emerging faculty needs for enhancing student engagement on virtual platforms (Ahmed et al., 2020), whereby the application of innovative prospects while using web-based tools have been found promising in furthering academic outcomes (Boahene et al., 2019). Based on these assertions, the current study speculates that instructional innovation capabilities of teachers will be instrumental in furthering students` online behavioural engagement and instructional design theory appears to be the best theoretical foundation in this regard.

Student engagement

The objective of any educational system is to achieve positive outcomes, and this could be attained by focusing on student engagement (Carini et al., 2006; Coates, 2005; Park, 2005). Student engagement has remained the main focus of educational psychologists (Kahu, 2013). Student engagement can lead to better performance, retention, academic success, and achievement (Appleton et al., 2008; Carini et al., 2006; Junco et al., 2013).

Definitions of student engagement involve active participation in both in-class and out-of-class activities. Krause (2005) defines student engagement as the time, resources, and energy devoted to educational tasks. Likewise, Hu et al. (2008) view student engagement as the quality of efforts and time geared toward contributing to the desired academic outcomes. This current study views student engagement as the ability to achieve learning outcomes by focusing on the students’ cognitive, emotional, psychological, and behavioural reactions to the learning process.

Furthermore, Schaufeli and Bakker (2004) defined engagement as a positive state of mind characterized by dedication and vigor. The concept of engagement was initially referred to as a work-related concept. However, scholars have gradually underlined its prominence in the educational field precipitating the emergence of the related concepts such as academic engagement, student engagement or study engagement. Students are required to attend classes and be active participants in assigned works to achieve a specific goal, such as passing exams. Salanova et al. (2010) state that academic engagement is characterized by feeling vigorous, dedicated to studies, and be absorbed in study-related tasks. It is assumed that students are vigorous when they experience mental resilience and persist in difficulties and challenges. Students’ dedication is characterized by a strong sense of responsibility, enthusiasm, and pride in their studies.

Behavioural engagement

Appleton et al. (2008) state that one of the most common indicators of learners’ dedication that are used in studies is behavioural engagement, since it is easily observable and measurable. Behavioural engagement is, but not limited to, students’ class participation and active participation in educational activities (in and out of class) (Appleton et al., 2006; Finn, et al., 2003; Krause and Coates, 2008). A positive engagement dictates that students are paying attention and they are active in class (asking questions, engage in discussions) (e.g. Fredericks et al., 2004; Handelsman et al., 2005; Sun and Hsieh, 2018). From the context of online classes, the authors argue that behavioural engagement appears to be most important, as students are not physically in a classroom with a teacher. Thus, the teacher has limited options to assess whether students are attentive to the lecture and are participative in activities. Therefore, students need to express behavioural engagement. Online learning requires students to be behaviourally engaged as it helps them attend classes with attention, interact with teachers and other students, follow the rules, put effort into group tasks, and share information with other classmates (Kuzu and Gunuc, 2015).

E-Learning efficacy and behavioural engagement

In academics, efficacy is seen as a combination of self-belief, thinking, self-understanding, and self-motivation (Ahmed et al., 2017). It is mainly linked to one's ability to achieve academic outcomes. Zimmerman et al. (1992) underlined that efficacy helps individuals to encounter challenges and boost personal capabilities. Zimmerman also stressed that for students, efficacy is an important component of academic success. Academic efficacy is reflected as one's ability to achieve the desired level of success in academic tasks (Schunk and Pajares, 2002). The current study thus operationalize E-learning efficacy as self-belief, thinking, and self-motivation in personal capabilities to achieve desired academic goals in an online learning environment. Efficacy is closely linked with how students can overcome challenges when handling educational tasks and activities (Paciello et al., 2016). Critical review of past studies underlines commendable benefits of academic efficacy such as better academic performance (Honicke and Broadbent, 2016); academic motivation (Komarraju and Dial, 2014); academic perfectionism (Yu et al., 2016); etc. Subsequently, students with high efficacy have been found to showcase high level of engagement in general (Ahmed et al., 2017; Galla et al., 2014).

Students with high efficacy are motivated and goal-oriented, and this contributes to their engagement as well. They are willing to make more efforts and deploy energy to complete assigned task(s) (Ouweneel et al., 2011). Notably, the key to engagement is progressing toward the goals rather than attaining them (MacLeod et al., 2008; Sansone and Thoman, 2006). Students feel being effective learners when they see that they can reach future outcomes. Based on these studies, for E-learning, the current study hypothesizes a strong relationship between E-learning efficacy and E-engagement, thus leading the authors to investigate the significance of the link in the face of COVID-19. Often, students are reported not being confident in using technology (Kumar and Kumar, 2003), which may confuse them and affect their overall connectivity and vigor towards learning. This is a serious concern, since students being physically away from the classroom environment can easily get distracted. Thus, we hypothesize the following:

H1: E-learning efficacy will be positively related to students’ online behavioural engagement.

E-learning resilience and behavioural engagement

Resilience refers to the person's ability to handle stress and manage hardships (Hobfoll et al., 2003). Academically, resilient students can handle stress, sustain their motivation, and focus on their studies (Alva, 1991). According to Yeager and Dweck (2012), resilience is an attitude of not giving up on challenges, since any deployed efforts will certainly lead to success. The current study operationalized E-learning resilience as the ability to manage stress and hardships and maintain focus in an E-learning environment. Usually, high achievers are found to be academically resilient (Alva, 1991; Beri and Kumar, 2018). Though resilience is more important for students studying at the universities (Ahmed et al., 2017), children at lower grades for example, primary school children exhibit more resilience (Rojas, 2015). Students would need to be more academically resilient to succeed and manage difficulties (Borman and Overman, 2004; Martin and Marsh, 2008). Generally, students at higher educational levels are under much stress because they have additional responsibilities and several other tasks to complete, such as assignments, projects, reports, presentations, and all these could be challenging to them (Vaez and Laflamme, 2008). Furthermore, students are under the stress of achieving their future goals, requiring more effort. Thus, they have to be more resilient. Similar to other aspects, academic resilience is also related to student engagement (Ahmed et al., 2017; Ahmed et al., 2018). However, due to the shift of study from classroom to the online mode, we speculate that student would require more E-resilience to ensure they overcome any issues and adversities they face coping with online lectures, technology use, and online learning to ensure active engagement in the learning process (behavioural engagement). Sadly, there appears to be no studies on such a relationship, which the present study underlines to be a major gap. Hence, we hypothesize the following:

H2: E-resilience will be positively related to students’ online behavioural engagement.

Instructional innovation

Higher education institutions have been encouraged to use E-learning to improve their teaching and to be up-to-date with their digital pedagogical practices (Sims, 2008). For E-learning to be effective, there has to be a comprehensive pedagogical principle (Smith and Greene, 2013). Most higher education institutions, besides conventional lectures, use learning management systems (LMS) such as Moodle or Blackboard. Hence, E-learning can never replace teachers (Mouzakitis, 2009). Teachers need to make constructive use of different technological tools to prepare their learners. Due to the on-going pandemic and the subsequent shift from conventional teaching to the online mode, we argue that the teacher’s instructional innovation practices could be vital to enhance students` attention and connectivity with online learning, thus enhancing their behavioural engagement. In addition, extant literature has also indicated its significance in furthering learning satisfaction and engagement (Lee, 2008). When teachers use a creative pedagogy of delivery different from conventional practices, this enhances student motivation (e.g. Bolliger et al., 2010). Also, when teachers are innovative in their lecture delivery, style, and content development, this can help enhance their behaviours in online classes. Hence, we posit the following:

H3: Teachers’ instructional innovation will be positively related to students’ online behavioural engagement.

H4: Teachers’ instructional innovation will moderate the relationship between E-learning efficacy and students’ online behavioural engagement.

H5: Teachers’ instructional innovation will moderate the relationship between E-learning resilience and students’ online behavioural engagement.

Instruments

Scales related to E-learning efficacy, E-learning resilience, behavioural engagement, and instructional innovation were adapted and modified from published literature. A five-item scale from Patterns of Adaptive Learning (PALS) (Midgley et al., 2000) was employed to examine E-learning efficacy whereby the statements were adapted from a conventional learning in a classroom to be modified to pose for the online-based e-learning environment. For example, ‘I am certain I can master the skills taught in the online class this semester’. Accordingly, a six-item scale was adapted to assess E-learning resilience from Martin and Marsh (2008). Moreover, a two-item scale was adapted to measure the teacher’s instructional innovation (Lee, 2008). Lastly, online behavioural engagement was measured by adapting a 10-item scale from the study by Gunuc and Kuzu (2015).

Sampling

A total of 144 students registered in the five sections of an undergraduate business studies course in a private university in Oman were sampled for the study. The course teacher had revised the design, delivery, and content of the course with innovative features, and the students were responsively informed of these features. The teacher used different web tools for online activities and lectures. The survey was conducted toward the end of the semester to examine students’ e-psychological capital and the teacher’s instructional innovation in furthering their online behavioural engagement. All the responses were objectively received; however, six were found to be inappropriately filled and were hence discarded. Finally, 138 responses were taken further for analysis and interpretation. The final response rate of the study turned to be 95.8%.

Data analysis

Structural equation modeling using Smart PLS 3.2 was used to examine the model’s robustness and to test the significance of the path coefficients (Hair et al., 2016; Ringle et al., 2012). A two-step approach was used following the recommendations and contemporary trend (Phankhong et al., 2020; Umrani et al., 2019). First, we examined the outer model to confirm internal consistency reliability, convergent validity and reliability, and discriminant validity of the constructs of the study (Hair et al., 2013; 2016). Second, the significance of the hypothesized associations was examined.

Outer model evaluation

The outer model, otherwise known as the measurement model, was examined, and it involved content reliability, internal consistency reliability, convergent validity, and discriminant validity (Hair et al., 2017). The essence of content validity is to indicate the suitability and capability of items (i.e. indicators) spawned for a particular construct in measuring the main concept in a study (Hair et al., 2017). Content validity analysis involves three types of estimations: factor loadings, composite reliability (CR), and average variance extracted (AVE), given the position of Hair et al. (2017), which indicates that factor loadings, composite reliability (CR), and AVE are used to establish convergent validity and internal consistency reliability. The information in Table 1 and Figure 2 confirms the internal consistency reliability, convergent validity and reliability, and discriminant validity of the constructs (Hair et al., 2013; 2016). All the items for each of the constructs have satisfactory loadings ranging from 0.569 to 0.919, thus confirming the content validity. However, the values of two items from E-learning Resilience (ELR) and three items from behavioural engagement (BE) fell below the threshold of 0.5 (Hair et al., 2017) and thus were deleted from the subsequent analysis.

Figure 2.

Inner model.

Table 1.

Internal consistency and convergent validity.

Construct	Loading	BE	CA	CR	AVE
Behavioural Engagement	BE1	0.792	0.880	0.907	0.583
	BE2	0.818
	BE3	0.796
	BE4	0.690
	BE5	0.785
	BE7	0.691
	BE9	0.762
E-Learning Efficacy	ELE1	0.569	0.819	0.865	0.567
	ELE2	0.679
	ELE3	0.767
	ELE4	0.846
	ELE5	0.863
E-Learning Resilience	ELR2	0.868	0.832	0.889	0.668
	ELR3	0.894
	ELR5	0.805
	ELR6	0.686
Teacher’s Instructional Innovation	TII1	0.887	0.775	0.898	0.815
	TII2	0.919

Note: CA: Cronbach alpha; CR:Composite reliability; AVE:Average Variance Extracted.

Moreover, the internal consistency reliability of the constructs was confirmed through CR and Cronbach's alpha values, which are well above the threshold values of 0.7 and 0.6, respectively. The AVE values of the reflective scales ranged between 0.529 and 0.78, thereby exceeding the minimum requirements of 0.5 (Hair et al., 2017). Moreover, discriminant validity is to check the construct validity of the outer model in which it should certify that the measures are not related after conducting the analysis. It also denotes that each measure is more related to its respective construct rather than to other constructs. The discriminant validity assessment was done using the Heterotrait–Monotrait ratio (HTMT) of the correlations (Hair et al., 2017), and it was found that the HTMT values for all pairs of constructs in the matrices fell below the threshold value of 0.90 (see Table 2). This result validates the discriminant validity of this study's constructs.

Table 2.

Discriminant validity (HTMT criterion).

Constructs	BE	ELE	ELR
BE
ELE	0.452
ELR	0.816	0.438
TII	0.469	0.304	0.479

Note: BE:behavioural engagement; ELE: e-learning efficacy; ELR:e-learning resilience; TII: teacher’s instructional innovation; HTMT: Heterotrait–Monotrait ratio.

Inner model evaluation

Table 3 and Figure 2 provide the result of the inner model evaluation and moderating effect testing. The R² value is 0.541 (see Figure 1), indicating that the exogenous latent variables (i.e. E-learning efficacy and E-learning resilience) explain 54% of the variance in the endogenous latent variable (i.e. behavioural engagement), which is considered to be of moderately acceptable level (Falk and Miller, 1992). The direct path regarding relationship between E-learning efficacy and behavioural engagement (ELE -> BE) is significant and positive (β = 0.188, t = 4.377, P < 0.01). Likewise, the direct paths regarding the relationship between E-learning resilience and behavioural engagement and the relationship between instructional innovation of teachers and behavioural engagement are significant and positive (β = 0.529, t = 10.742, P < 0.001; β = 0.128, t = 2.545, P < 0.01), respectively.

Figure 1.

Outer model.

Table 3.

Hypotheses testing.

				Confidence Interval
Hypotheses	Beta	STDEV	t-stat	2.5%	97.5%	p values
ELE -> BE	0.188	0.043	4.377	0.104	0.269	0.000
ELR -> BE	0.529	0.049	10.742	0.427	0.620	0.000
TII -> BE	0.128	0.050	2.545	0.026	0.226	0.011
TII.ELE -> BE	0.083	0.040	2.077	0.016	0.167	0.038
TII.ELR -> BE	−0.080	0.028	2.866	−0.135	−0.022	0.004

Note: BE:behavioural engagement; ELE:e-learning efficacy; ELR:e-learning resilience; TII:teacher’s instructional innovation.

Table 4.

Exogeneous constructs’ effect size on endogenous variables.

Constructs	F²	Effect size
ELE	0.031	Small
ELR	0.610	Large
TII	0.026	Small

Note: BE: behavioural engagement; ELE:e-learning efficacy; ELR:e-learning resilience; TII:teacher`s instructional innovation.

Using the product indicator approach, the moderating effect of teacher`s instructional innovation was estimated and the result, as shown in Table 3 (β = 0.083, t = 2.077, P < 0.05; β = −0.080, t = 2.866, P < 0.05), indicated that teacher`s instructional innovation moderated the relationship between E-learning efficacy and behavioural engagement and the relationship between E-learning resilience and behavioural engagement, respectively. However, the moderating effect of the teacher’s instructional innovation on the relationship between E-learning resilience and behavioural engagement is in the negative direction. These results were tested further using an interaction plot (Dawson, 2014). Figures 3 and 4 indicate that the interaction plot in which line tagged high TII, which indicated the presence of the teacher’s instructional innovation, had a steeper gradient against low TII (lack of instructional innovation).

Figure 3.

ELE-TII interaction effect on BE. Teachers’ instructional innovation strengthens the positive relationship between E-learning efficacy and online behavioural engagement.

Figure 4.

ELR-TII interaction effect on BE. Teachers’ instructional innovation dampens the positive relationship between E-learning resilience and online behavioural engagement.

Thus, this validates the result that instructional innovation moderated the relationship between E-learning efficacy and behavioural engagement and the relationship between E-learning resilience and behavioural engagement. The positive nexus between E-learning efficacy and behavioural engagement will be stronger for teachers with high instructional innovation. Nevertheless, with a teacher who has a high tendency for instructional innovation, E-learning resilience becomes less important for explaining behavioural engagement.

Furthermore, the result in Table 3 indicates that BE is explained by ELE and TII with the effect sizes (f2) of 0.031 and 0.026 (Cohen, 1988; Hair et al., 2016), indicating that the two constructs have small effect on BE. Additionally, BE is explained by ELR with the effect sizes (f2) of 0.610. This signifies that the construct has a large effect on BE.

Discussion

The findings of the present study would help educators and scholars to understand the significance of behavioural engagement and its prediction in the online learning context. The study found a significant association between E-learning efficacy and online behavioural engagement (H1). Although the study appears to be the first in examining this relationship, the result suggests that students who were confident in mastering skills and knowledge learned during online classes; capable and willing to work hard to complete the most difficult tasks given during online classes, were able to remain active in the online class, follow online learning rules, interact with teachers, students, share information and exhibit behavioural engagement. These findings outline that positive self-belief in one’s capabilities would help students to express more immersion, connectivity, attention, and activity during the online classes. Likewise, the study found a significant relationship between E-resilience and online behavioural engagement (H2). This implies that students who are strong enough to face any setbacks during online learning and work hard would be able to improve their dedication, commitment, willingness, and interaction in online classes, and thus exhibiting behavioural engagement. The findings have underlined that resilient behaviours enable individuals to overcome any setbacks, difficulties, and challenges in a given course of action. Conventionally, this can be cross-verified with prior evidence on student efficacy and resilience in academics and their links with students’ engagement in general (e.g. Ahmed et al., 2018; Fan and Williams, 2010; Fati et al., 2019).

Moreover, the findings have also supported the theoretical claims of COR theory (Hobfoll, 1989; 2001). Teachers’ instructional innovation practices have a significant relationship with students’ behavioural engagement in online classes. The results suggest that when teachers strive to be creative in the design, development and delivery of the content, it helps students to be more connected, interactive, and contributory in the online classes, and thereby exhibiting behavioural engagement. Although the current study appears to be the first examining this association, it has confirmed the prominent role of instructional innovation practices in predicting students’ behaviours and outcomes (e.g. Lee, 2008; Rowan and Miller, 2007). The study also found positive moderation of instructional innovation such that it strengthened the positive association between E-learning efficacy and students’ online behavioural engagement. This suggests that efficacious students would be able to learn the most from the innovative instructional capabilities of teachers. In other words, teachers’ instructional innovation helps students build confidence in them to learn while enhancing their online behavioural engagement during virtual classes.

Interestingly, teachers’ instructional innovation dampened the positive association between E-resilience and students’ behavioural engagement. This indicates that students with high E-resilience did not find much vitality of instructional innovation to boost behavioural engagement. Since, the study also reported the highest impact of E-resilience toward behavioural engagement (H2), this also makes the results of H5 sound logical.

Theoretical implications

Theoretically, the study offers six prominent implications. First, the study has advanced the conventional assertions of COR theory (Hobfoll, 1989; 2001). Following the insights of Hobfoll, the study confirms that personal resources like psychological components such as E-learning efficacy and E-learning resilience can be of importance in furthering behaviours and outcomes in an online learning environment. The findings showcase that students who feel high in psychological resources are able to achieve the desired behaviours and outcomes (Ahmed et al., 2017; Xanthopoulou et al., 2007). First, notably, the study strengthens the usefulness and significance of the COR theory in explaining the instrumental role of “e-psychological resources” in predicting behaviours and outcomes. Second, the study has confirmed the vitality of instructional innovation based on instructional design theory (Snyder, 2009). The findings establish that innovative practices while using virtual platforms can help improve student engagement (Ahmed et al., 2020). Henceforth, the current study has strengthened the need for the deployment of innovative approaches to teaching.

Third, the study has offered empirical insights into scarce literature on students’ online behavioural engagement and its prediction. Fourth, this study has empirically contributed toward the concept of E-learning efficacy, E-learning resilience and their prominence in predicting learning engagement. Fifth, scarce literature on innovation has also been enriched by highlighting the direct and interacting effect of instructional innovation toward students’ online behavioural engagement. Finally, the current study has taken steps to respond to the issue of online behavioural engagement, which is very recent and unquestionably a topic of concern for leaders and management in academia to ensure effective online education during the ongoing COVID-19 pandemic.

Findings from this study indicate that resilient students can handle stress, sustain their motivation, and focus in virtual classes despite disadvantages associated with remote learning, which downplays the importance of instructional innovative practices.

Practical implications

The study offers notable practical implications for leader, and policymakers, and how they can provide quality education by improving students` online behavioural engagement, particularly in the higher education sector. First, for leaders in this sector, the study underlines the need to focus on behavioural engagement and potential factors to improving it. Hence, the findings educate that E-psychological resourcefulness can be a key factor helping students improve their online behavioural engagement to achieve teaching and learning objectives during the COVID-19 pandemic. Training interventions for students can be utilized to help them enhance their E-skills necessary to engage in online classes and avoid any anxiety or detachment. In line with this, the online teaching tools may also play a monumental role in enriching student behaviours and online teaching outcomes (Chen et al., 2020; Mohammed et al., 2020). Thus, leaders in academia are encouraged to assess the level of ease of the E-tools and institutional web platforms that they are using to aid students’ psychological confidence in their E-skills to predict online behavioural engagement.

Another important area that requires attention from leaders in this sector is teachers. The findings imply that leaders in the education sector should consider learning opportunities blended with hands-on sessions to facilitate teachers understanding of the different approaches, tools, and practices to bring innovation in their pedagogies.

Important to note is that this may require time, effort, and resource investments from institutions to achieve objective results. Moreover, the teachers may also be encouraged to engage and contribute to instructional innovation process with their ideas, opinions, and insights. Overcoming challenges in online learning is challenging (Adedoyin and Soykan, 2020) and therefore may require a holistic and pragmatic management approach toward teaching, student learning, and engagement. Moreover, if the pandemic situation continues, leaders and management authorities in the education sector may also see a need to rethink their staffing approach and guidelines in the near future to employ people who are not just good at subject matter, but also have technological skills and savvy with instructional innovative practices in teaching. For this, it is important for management entities to give flexibility and empower academic staff members to be creative and innovative in their course design and delivery (Ewing, 2021). In addition, we also imply leadership in the education sector to be able to recognize and adapt to evolving circumstances (Tamrat, 2021), to make effective in-time decisions to avoid any unintended consequences effect on academic goals.

Furthermore, existing empirical evidence has indicated that adopting conventional pedagogical techniques for online learning happen to be less effective and can be seen anachronistic for effective learning and student performance. Flexible innovative instructional methods are essential for online learning, given that pedagogy plays a substantial role in mitigating the negative effects of pandemic on learning (see Orlov et al., 2021). The findings of this study further substantiate this assertion. The findings of this study also underscore the importance of instructional innovation practices in the online teaching realm as this will enhance student engagement and student performance. Therefore, managers of educational institutions and ministries of education across the globe should endeavor to invest in creative and innovative instructional methods for remote learning and teaching methods to flourish.

The overall findings of the study establish that students with E-learning efficacy and E-learning resilience will display online behavioural engagement and likely to reflect a better E-learning experience. Innovative instructional techniques can help E-efficacious students to make the most of what instructors teach in virtual classes. However, students learning abilities differ, underscoring the indispensability of using different instructional techniques for different students. Thus, this study underscores the adoption of flexible innovative instructional techniques for online learning.

Limitations and scope for future studies

Besides notable contributions of this study, the present study presents four main limitations. First, the present study was conducted using a cross-sectional approach, and thus limiting the causal inferences of the findings. Future studies, therefore, are advised to consider looking at different research designs such as the longitudinal approach. Second, the study was conducted in the context of higher education sector, thus ignoring the primary and secondary education levels. Hence, future studies are encouraged to consider examining the conceptual framework across other academic levels such as primary, tertiary, vocational, and secondary. Additionally, the current study tested the moderation of teacher`s instructional innovation. Scholars are encouraged to consider potential mediators to explore the possibility of any intervening effects in the direct relationships investigated in the present study. Lastly, the study only sampled from one university due to restricted social mobility. Therefore, a larger sample is recommended to obtain generalizability of the results.

Conclusion

In conclusion, the study had revealed promising role of E-learning efficacy and resilience in predicting students’ online behavioural engagement. The findings have also presented some interesting insights on the direct and interacting role of the teacher’s instructional innovation. The findings of this study conclude through enriching existing theories (i.e. COR theory and instructional design theory) and create a foundation for new theoretical inquiry that will result in a discovery of new boundary conditions to E-psychological capital and online behavioural engagement relationship.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Mohammed Ali Akour

Abdussalaam Iyanda Ismail

Author biographies

Dr Umair Ahmed is an educationist with a doctorate in human resource management. His area of specialization in research is general HRM, with a particular focus on employee well-being. Dr Umair has a track record of publishing high-impact scholarly work in journals of repute. Besides, he has also authored several book chapters. Recently, Dr Umair also published a book on training and development. He also holds intellectual property rights of the ‘engagement strategy framework’, which he developed to help organizational scholars and practitioners effectively categorize and examine factors that can influence employees’ work engagement. Currently, he is serving as an assistant professor of business administration at Arab Open University, Bahrain.

Dr Iyanda Ismail is a seasoned teaching professional with a doctorate in human resource management from an AACSB accredited institution. He is currently serving as an assistant professor at the University of Fujairah in the United Arab Emirates. His research is inclined toward organizational behaviour, leadership, HR practices, and work wellbeing.

Dr Fati serves at Arab Open University as an assistant professor in the department of language studies. She is a seasoned educationist with research in education and related technology, student well-being, and language topics. Currently, she is working on topics related to changes in instructional strategies in the post-Covid-19 era.

Dr Akour is an associate professor at College of Business Administration, A’Sharqiyah University, Oman. Dr Akour brings a wealth of higher education experience and has been teaching in various regions across the globe. His areas of research include general management, management information systems, personnel management, and education technology.

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