Exploring the acceptance of online learning among students in technical and non-technical programmes at a higher education institution

Abstract

Higher education institutions have substantially shifted towards online teaching and learning. However, empirical research examining students’ perceptions on online learning in different academic fields is limited, necessitating further research in this area. This study investigates differences in online learning acceptance and technology acceptance factors among undergraduate students in technical and non-technical programmes. A questionnaire survey was employed to gather data from Malaysian undergraduate students enrolled in Technology Management (TM) and Information Technology (IT) programmes. The results suggest a moderate level of acceptance of online learning among undergraduate students. IT students demonstrate a significantly greater inclination towards accepting e-learning than students in the TM field. IT students also perceived the technological acceptance factors (performance expectancy, effort expectancy, social influence, and facilitating conditions) much higher than the TM students. The one-way MANOVA test supports all the hypotheses proposed that there are significant differences between IT and TM students in online learning acceptance, performance expectancy, effort expectancy, social influence, and facilitating conditions. The findings have important implications for addressing challenges related to online learning in higher education institutions. This will lead to a more seamless integration of online learning into current educational practises.

Keywords:

• online learning
• e-learning
• UTAUT
• management education
• Malaysia
• higher education

1. Introduction

Online learning has experienced a significant increase around the world due to the continuing evolution of teaching approaches in higher education institutions (HEIs), driven by the advancements in digital technologies (Maatuk et al., 2022), which are further accelerated by the COVID-19 pandemic in 2020. This shift has had significant effects on students’ communication patterns (Parida et al., 2023), their access to information, and engagement in peer networking (Alami & El Idrissi, 2022). In addition, it has also enhanced HEIs’ adaptability to unforeseen economic and environmental changes (Raaper, 2021). For example, in 2023, one of local university in Malaysia had to temporarily suspend face-to-face classes and transition to online platforms for all undergraduate programmes at its main campus. This was a result of weeks-long flooding in the vicinity of the university caused by heavy rain and high tidal levels. The university’s decision to implement online learning has been regarded as a timely appropriate measure given the prevailing circumstances. The approach offers advantages in terms of its flexibility with respect to both physical location and class scheduling (Shahriar et al., 2023), thereby allowing the university to maintain teaching and learning provision (Briggs et al., 2023).

While such a transition has been acclaimed as a means of ensuring business continuity, numerous researchers have identified significant challenges that have arisen because of the changes (Raaper, 2021), especially when online learning design and teaching approaches are implemented prematurely (Cohen, 2021) and when students are not ready for the pedagogical transition (Crocco & Culasso, 2021). These factors subsequently influence the effectiveness of teaching methods, create negative effect on students’ academic performance (Fang et al., 2023; Raaper, 2021), and the unfavourable responses exhibited by students from different academic backgrounds (Tetteh et al., 2023). Despite the existence of significant challenges, there is a limited body of research that has been undertaken to investigate the level of acceptance of online learning among a wider demographic (Duggal, 2022). It is generally established that students prefer face-to-face learning over online learning due to the difficulties of engaging in online learning (Eringfeld, 2020). This is especially true when it comes to addressing the specific requirements of marginalised students who may have encountered inequalities stemming from the adoption of online learning, such as extended periods of absence from university premises and the presence of a digital divide (Tetteh et al., 2023). According to Helfaya (2019), students demonstrate a positive perception towards the utilisation of online learning when they possess a strong familiarity with the learning system and have received adequate support from their lecturers and the university. Consequently, there is a lower tendency among students enrolled in non-technical programmes to fully embrace online learning, potentially leading to the oversight of certain underlying systemic concerns (Parida et al., 2023).

Given this context, it is essential to conduct a thorough examination of students’ acceptance of online learning across different academic disciplines and determine if they perceive the contextual elements of online learning differently. This will inevitably aid in understanding students’ ability to utilise online learning for a better learning experience (Ng et al., 2023) and as a pivotal measure in the execution of a successful e-learning atmosphere (Al-Adwan et al., 2013). This study aims to address the following research questions: What is the extent of online learning acceptance among undergraduate students in both technical and non-technical programmes? Are there any significant differences in the acceptance of online learning and the factors that influence acceptance among undergraduate students in technical and non-technical fields?

The Unified Theory of Acceptance and Use of Technology (UTAUT) model is utilised as the theoretical framework for this research to comprehend the behaviours of undergraduate students across different areas of study towards online learning. UTAUT posits that the acceptance of technology can be examined through various lenses, such as performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) (Batucan et al., 2022). The study focuses on two distinct groups of students: those pursuing a degree in information technology (IT), who possess a higher level of familiarity with communication and information technology (ICT), and students enrolled in the technology management and business (TM) programme, who are typically exposed to the foundational aspects of IT. The rationale behind selecting these groups lies in their varying levels of awareness and proficiency in digital technology and online learning applications. Understanding the distinctions between the two programmes is crucial for HEIs seeking to create effective online learning environments for specific disciplines, including technical and non-technical programmes. This knowledge allows for meaningful comparisons and informed decision-making (Yusoff et al., 2017). The terms technical and non-technical are used to categorise students based on their respective backgrounds and varying competencies in technical and digital skills. Technical fields prioritise highly skilled and technology-related disciplines such as engineering and IT. The focus is on hands-on, problems-based teaching and learning through experience. In contrast, non-technical fields offer courses related to general skills such as management and business studies, without the need for extensive technical knowledge. The information will play a crucial role in improving the overall preparedness of both the educational institution and its students in relation to online learning.

The subsequent sections of this paper are organised in the following manner: Section 2 of this research paper delves into an exploration of relevant studies and theories that are closely connected to the topic. Section 3 presents the methodology employed in this study, while Section 4 focuses on the discussion of the findings and the implications. Section 5 provides a summary of the research findings, limitations, and suggestions for future research.

2. Online learning and Unified Theory Of Acceptance And Use Of Technology (UTAUT)

Online learning, also known as e-learning, refers to the use of ICT, including the Internet, to facilitate and enhance the learning and teaching process, transcending traditional classroom boundaries (Chugh, 2010). The use of this approach is experiencing a growing global trend as it allows students to extend their reach beyond the limitations of time and distance (Baber, 2021) and supports enhanced technological knowledge, teaching quality, learning outcomes, flexibility, and control (Turnbull et al., 2021). Even before the COVID-19 pandemic, some universities had been implementing hybrid or blended learning, which combines online and traditional face-to-face teaching and learning. However, the acceptance of online learning is uncertain without the right attitude from lecturers and students towards the approach (Bolliger & Martin, 2018).

Several researchers have identified possible disadvantages of online learning, including reduced opportunities for social interaction, a mismatch between students’ expectations and course content, challenges in managing the organisational aspects of virtual learning, and a lack of dynamism in the virtual learning environment (Meşe & Sevilen, 2021; D′souza et al., 2023). Studies have demonstrated that social isolation has a detrimental impact on the psychological well-being (Sa & Serpa, 2020) and motivation (Meşe & Sevilen, 2021) of students. The lack of student motivation can severely weaken the effectiveness of online learning, posing a significant risk to universities’ reputations as respectable providers of higher education services. According to D′Souza et al. (2023), the higher education sector can be conceptualised as a marketplace, where university education is considered a service that can be marketed. Hence, it is imperative to acknowledge the significance of students’ perceptions regarding value and satisfaction, as it substantially influences both student achievement and attrition rates. In this scenario, online learning should be carefully designed with a focus on meeting the needs and preferences of the students, rather than solely prioritising the convenience of the lecturers (L´opez et al., 2023). Furthermore, the design of the learning platform should take into consideration the premise that learners possess self-directed attributes, and the learning materials should be meticulously structured and arranged to facilitate learning, regardless of the presence or absence of a lecturer (Cohen, 2021). Istijanto (2023) establishes that HEIs must acknowledge and understand the motivational factors linked to the adoption of online learning, as well as the factors associated with students’ reluctance to engage in online learning. This can help universities efficiently maximise the favourable elements that stimulate students’ participation in diverse learning approaches while minimising the unfavourable factors that impede their motivation towards online learning (Istijanto, 2023; Mariam et al., 2023).

Researchers have utilised the theories of planned behaviour (TPB) and Unified Theory of adoption and Use of Technology (UTAUT) to analyse the key factors influencing the acceptance of online learning among its users. The UTAUT has become a fundamental theory to explain the user’s intention to use an information system and subsequent behavioural intention towards acceptance of information systems (Batucan et al., 2022). According to TPB, behavioural intention indicates a person’s subjective likelihood of performing the behaviour (Ajzen, 2002). This model asserts that an individual’s inclination to engage in a specific behaviour at a specific time and place is shaped by their behavioural intentions, which are in turn influenced by specific determinants. It has garnered significant recognition as a valuable conceptual framework for comprehending human behaviour in diverse contexts, including the use of information technology (Aloulou, 2016). Meanwhile, the UTAUT framework presents dimensions pertinent to user behaviour in adopting technology. These dimensions include performance expectancy (PE), social influence (SI), effort expectancy (EE), and facilitating conditions (FC).

2.1. Hypotheses development

To date, a considerable number of scholars have utilised the UTAUT framework to examine significant inquiries related to the acceptance of technology, particularly in the context of online learning or e-learning acceptance, with the aim of improving accessibility and user experience. This study employs the UTAUT to understand online learning acceptance through its four contextual variables. Perera and Abeysekera (2022) established that students’ intention to use online learning can be significantly explained by performance expectancy, social influence, effort expectancy, and facilitating conditions.

2.1.1. Online learning acceptance

Current literature has revealed significant information on factors influencing the acceptance of online learning among specific groups of university students, including those studying business, education, and management (Alami & El Idrissi, 2022; Darley, 2021; Mariam et al., 2023). It is shown that the acceptance and anticipation of online learning vary among different groups of people (L´opez et al., 2023; Briggs et al., 2023; Reddy et al., 2021). Studies conducted by Wan et al. (2008) and L´opez et al. (2023) indicate that regular use of information-seeking ICT can enhance online competence, resulting in better learning outcomes and increased satisfaction in online learning. Some people are more accepting of online learning than others due to factors such as personal expectations and different competencies in digital technology (Panigrahi et al., 2018 In this study, we aim to determine if there is a variation in the acceptance of online learning between technical and non-technical students, specifically IT and management students. Thus, the following hypothesis is proposed:

H1:

There is a significant difference in the online learning acceptance between management and IT students.

2.1.2. Performance expectancy (PE)

HEIs around the world have recognised the use of educational technology to enhance student performance. In this setting, how students and educators expect to increase their performance will influence their willingness to employ specific technologies. According to Brown et al. (2010), PE refers to an individual’s belief in the effectiveness of using a system or technology to improve their performance on a particular task or project. People are more willing to use technology if they think it will improve their performance. Several studies that have been done across different groups of individuals have found a significant relationship between PE and the intention to utilise online learning or e-learning (see, for example, Ali et al., 2018; Chua et al., 2018; Perera & Abeysekera, 2022; Weilage & Stumpfegger, 2022; Yeboah & Nyagorme, 2022). Wan et al. (2008) found that learners who lack virtual competence may experience ineffective learning performance. Students are more likely to embrace online learning and expect it to improve their academic performance when they are familiar with the system. Thus, taking into account this context, we test whether there is a difference in the performance expectancy between IT and management students and propose the following hypothesis:

H2_a

There is a significant difference in the performance expectancy between management and IT students.

2.1.3. Effort expectancy (EE)

All levels of education in developed and developing countries have increasingly adopted new digital technology in teaching and learning. The acceptance of this approach is largely due to how technology provides flexibility and simplicity not only in speeding up certain activities or processes but also in how people communicate, gather, and use information (Weilage & Stumpfegger, 2022). The amount of comfort and usefulness that people feel when using a particular technology or information system is referred to as effort expectation (Venkatesh et al., 2003). It is also considered as the perception of the learner who is able to use the information system without any extra effort (Yadav et al., 2016). Chua et al. (2018) and Wong et al. (2015) reported a significant association between EE and technological acceptance and concluded that the ease with which people benefit from utilising technology enhanced their acceptance of it. Considering this backdrop, we examine if there is a difference in effort expectations between IT and management students due to their varied ICT competencies. As such, the following hypothesis is proposed:

H2_b

There is a significant difference in the effort expectancy between management and IT students.

2.1.4. Social influence (SI)

Social influence is defined as an individual’s belief that the new system is important enough that others believe he or she should utilise it (Venkatesh et al., 2012). As a result, social influence becomes one of the most crucial supporting factors in adopting new technologies. Social influence tends to affect the student’s intention to interact with their instructor to use online learning and directly impacts educators’ behaviour and intention to use the technologies (Wut et al., 2022). Furthermore, lecturers also play an important role in technology acceptance based on their capacity to control the implementation of the online class (Lin & Yu, 2023). Prior research by Ali et al. (2018) and Wut et al. (2022), reported that social influence is significant in relation to behavioural intention to use online learning. However, considering the unique social circles for IT and management students, we aim to determine if there are differences in perceived social influence perception among these groups. Hence, the following hypothesis is proposed:

H2_c

There is a significant difference in the perceived social influence between management and IT students.

2.1.5. Facilitating conditions (FC)

Most universities have provided essential ICT tools to facilitate the online teaching and learning process. The degree to which a person feels that organisations and technological infrastructure exist to make the use of a system or technology easier is referred to as the facilitating condition (Venkatesh et al., 2003). Perera and Abeysekera (2022) assert that the inclusion of facilities-related factors, such as the availability of bandwidth and students’ access to laptop computers, significantly contributes to the facilitation and success of online learning. Moreover, Tetteh et al. (2023) argued that various factors, such as lecturers’ attitudes towards students and inadequate administrative support, significantly impacted students’ negative responses to the implementation of online learning. Several researchers have conducted studies examining the relationship between facilitating condition and acceptance of online learning. For instance, the research conducted by Almulla (2022) demonstrated that FCs have a beneficial influence on individuals’ inclination to embrace ICT and engage in online learning. In contrast, Alami and El Idrissi (2022) revealed the absence of a statistically significant association between the FC and students’ attitudes towards online learning. Despite the results, comparative studies between individuals with a technical background and those without a technical background are scarce. Therefore, we investigate whether the facilitating conditions for students studying IT and those studying management differ. Hence, the following hypothesis is proposed:

H2_d

There is a significant difference in the perceived facilitating conditions between management and IT students.

3. Research methodology

3.1. Study participants

The study focuses on undergraduate students from one public university located in the southern region of Malaysia. These students with prior online learning experience are specifically from the IT department within the faculty of computer science and information technology (FCSIT), as well as the department of technology management (production and operations management) in the faculty of technology management and business (FTMB). The total number of undergraduate students from both departments is around 750, with approximately 290 students from the IT department and 460 students from the TM department. According to Krejcie and Morgan’s (1970), a total sample of 254 is needed for that size of population. To achieve this objective, the study aimed to gather at least 100 samples from each programme of study, with a greater emphasis on obtaining a larger proportion from the TM group. In order to achieve this quantity, around 500 sample sizes were allocated for distribution.

3.2. Measurements

The survey consisted of demographic inquiries encompassing the student’s age, faculty or programme, year of study, and gender. Additionally, it included questions designed to assess the acceptance of online learning as well as the four primary technological factors. The questionnaires utilised in this study were derived and adapted from the original UTAUT model (Venkatesh et al., 2003) and previous research in a related domain. This was done to ensure the questionnaire’s validity and appropriateness for assessing the implementation of online learning in the context of HEIs in Malaysia. The UTAUT model was selected as the foundational theory due to its incorporation of essential elements related to the acceptance of online learning. The research employed four items to evaluate the level of acceptance towards online learning (OLA), using a rating scale ranging from 1 (strongly disagree) to 7 (strongly agree). The measurements utilised in this study have been derived from the work of Selim (2007). The four underlying factors, which are performance expectation (PE), effort expectation (EE), social influence (SI), and facilitating condition (FC), consisted of 18 items were assessed using a scale that ranged from 1 (strongly disagree) to 7 (strongly agree). This scale was adapted from previous studies conducted by Venkatesh et al. (2003), and Alami and El Idrissi (2022). All 22 measurement items are provided in the Appendix.

3.3. Data collection procedures

This study utilised a quantitative approach, where the data was obtained through a self-administered closed-questionnaire survey. In order to collect responses, a convenience probability sampling method was employed, which enabled the researcher to easily engage with nearby and accessible respondents. The survey was distributed through a combination of both online survey and in-person data collection. During the initial phase of data collection, target respondents were approached via several student WhatsApp groups from the two departments. The survey informed respondents that their participation in this study was entirely voluntary and assured them that their responses would be kept confidential and anonymous. After several follow-ups, the survey obtained 260 valid replies, resulting in a response rate of 52%.

3.4. Data analysis

We performed descriptive and inferential analyses, specifically using a one-way MANOVA, to investigate the differences in online learning acceptance and the four technological acceptance criteria among IT and TM students. The skewness and kurtosis of each main variable were used to check if the data distribution was normal. The results indicate that the skewness and kurtosis values fall within the allowed range of ± 2 (Hair et al., 2022).

3.5. Reliability and validity assessment

A data reliability analysis was conducted to assess the internal consistency and the degree of association among a set of items. According to Nunnally (1988), a measurement is considered reliable if Cronbach’s alpha value is equal to or greater than 0.7, indicating that it is adequate for retaining all items in the instrument. According to Hair et al. (2015), as the internal consistency of the items increases, Cronbach’s alpha coefficient approaches a value of 1.0. With Cronbach’s alpha coefficients for technological acceptance variables and online learning acceptance exceeding 0.80, we conclude that all of the items demonstrate a high level of reliability.

We also examined the McDonald Omega coefficient (ω) to further confirm the reliability of the items. The results show that McDonald Omega values for the four categories of acceptance factors as well as the online learning acceptance dimension are greater than 0.70, which is considered acceptable for internal consistency. Confirmatory factor analysis with varimax rotation was employed to assess the extent to which the items effectively captured the underlying concepts. The Kaiser-Meyer-Olkin test score for the dimensions of technological acceptance exceeded the threshold of 0.80, suggesting that all variables were deemed suitable for conducting a component analysis. The significance value obtained from Bartlett’s test of sphericity was 0.000. The cumulative variance accounted for by the UTAUT dimensions was found to be 70.59%. All items with eigenvalues exceeding 1.0 were allocated to four distinct sections, each exhibiting factor loadings greater than 0.50.

Table 1 also shows that the average variance extracted (AVE) values of all constructs are greater than 0.50 and the composite reliability (CR) is greater than 0.70, thus exceeding the acceptable thresholds. It suggests that all items are able to explain the variance in the main constructs. A discriminant analysis was conducted to assess the distinctiveness between elements by examining construct cross-loadings based on the Fornell-Larcker criterion (Hair et al., 2015). Table 1 displays a discriminant validity index, which compares the square root of the AVE with the correlation values of other variables. The values in the matrix are displayed in italics and arranged diagonally, representing the square roots of AVE. The off-diagonal entries, on the other hand, represent construct correlations. The square roots of the constructs’ AVEs are consistently higher than all inter-factor correlation values, which are all above 0.70.

Table 1. AVE, CR and discriminant validity

	AVE	CR	PE	EE	SI	FC	OLA
PE	0.522	0.766	0.723
EE	0.615	0.886	.598**	0.784
SI	0.624	0.892	.719**	.643**	0.790
FC	0.502	0.832	.546**	.543**	.609**	0.709
OLA	0.591	0.851	.676**	.556**	.589**	.298**	0.769

4. Data analysis

4.1. Demographic analysis of the respondents

Table 2 presents the demographic distribution of the respondents. The findings indicate that 69% of respondents are identified as male, while the remaining 31% are female. The majority of respondents are under the age of 21, while 33.8% of respondents are between the ages of 22 and 24. Most respondents consist of Year 1 and Year 2 students, accounting for 78.8% of the total sample. Year 3 students make up 12.7% of the respondents, while Year 4 students constitute 8.5% of the sample. Lastly, the results show that TM students account for 61.5% of respondents, while IT students account for 38.5%.

Table 2. Demographic analysis of the respondents

	N	Percentage
Gender
Male	179	68.8
Female	81	31.2
Total	260	100.0
Age
19	49	18.8
20	56	21.5
21	67	25.8
22	30	11.5
23	30	11.5
24	28	10.8
Total	260	100.0
Programme enrolled
TM	160	61.5
IT	100	38.5
Total	260	100.0
Year of study
Year 1	102	39.2
Year 2	103	39.6
Year 3	33	12.7
Year 4	22	8.5
Total	260	100.0

4.2. Descriptive analysis for online learning

Table 3 presents the descriptive analysis of the acceptance of online learning, four technology acceptance factors, and the correlation matrix of the variables. The overall level of online learning acceptance among the respondents is moderate, as shown by a composite mean score of 3.71. Online learning is popular among students for its convenience, time-saving benefits, and cost-effectiveness. However, there are certain challenges associated with online learning that may not be ideal for effective learning for certain students. Therefore, traditional in-person learning methods continue to hold significance in the educational approach at higher education institutions.

Table 3. Descriptive statistics and correlation matrix of the variables

Variable	PE	EE	SI	FC	OLA
1. Performance expectancy (PE)	1.000
2. Effort expectancy (EE)	.598**	1.000
3. Social influence (SI)	.719**	.643**	1.000
4. Facilitating condition (FC)	.546**	.543**	.609**	1.000
5. Online learning acceptance (OLA)	.676**	.556**	.589**	.298**	1.000
Mean	3.67	3.62	3.48	3.92	3.71
Median	3.67	3.80	3.60	3.80	4.00
SD	1.26	1.30	1.38	1.16	1.18
IQR	1.67	1.80	2.20	1.35	1.25
Skewness	−0.01	0.24	0.16	0.10	−0.79
Kurtosis	−0.16	−0.33	−0.28	0.27	−0.31

The results indicate that the composite mean scores for all four technology acceptance factors fall within the range of 3.48 to 3.92. The scores reflect the level of acceptance towards online learning. The FC category exhibits the highest ranking (mean score of 3.92), which indicates a moderate level of support such as Wi-Fi access infrastructure for online learning. However, the lecturer’s enthusiasm for delivering online teaching received the lowest mean score in the FC category. It appears that students feel their lecturer’s commitment to online teaching is somewhat lacking.

Meanwhile, the PE and EE categories have composite mean scores of 3.67 and 3.62, respectively. This suggests that students have moderately positive views on the overall performance and effort associated with online learning. The SI category exhibits a mean score of 3.48, which is the lowest among all factors. The results imply that a significant number of respondents have different opinions about how effective online learning is in promoting communication and interaction with peers and lecturers. The lack of active participation in online learning among students can detract from its effectiveness by limiting opportunities for spontaneous questions and discussions with instructors and peers.

4.3. Test of differences on online learning acceptance between IT and TM students

This section presents the findings on the comparison of online learning acceptance and the perception of UTAUT elements among students from IT and TM programmes. Table 4 displays the composite mean scores for each TM and IT group regarding online learning acceptance and all technological acceptance factors. Additionally, Tables 5 and 6 present the findings of the one-way MANOVA. Based on the results, students from the IT group indicate significantly higher online learning acceptance as compared to the TM group. The mean score for the IT group is 4.04, while the TM group is 3.51. The results suggest that IT students are much more accepting of online learning than TM students. It is evident that students with technical backgrounds are more inclined towards utilising online learning and hold a firm belief in its effectiveness as an educational tool. This finding confirms the first hypothesis, H1, which states that there is a significant difference in the level of online learning acceptance among TM and IT students.

Table 4. Descriptive statistics for online learning acceptance and students’ programmes

Programme		Mean	Std. Deviation	N
OLA	TM	3.51	1.26	160
	IT	4.04	0.94	100
	Total	3.71	1.18	260
PE	TM	3.54	1.31	160
	IT	3.87	1.16	100
	Total	3.67	1.26	260
EE	TM	3.48	1.37	160
	IT	3.84	1.16	100
	Total	3.62	1.30	260
SI	TM	3.35	1.44	160
	IT	3.70	1.25	100
	Total	3.48	1.38	260
FC	TM	3.79	1.21	160
	IT	4.14	1.06	100
	Total	3.92	1.16	260

Table 5. Multivariate test for online learning acceptance and students’ programme

Effect		Value	F	Hypothesis df	Error df	Sig.	Partial Eta Squared
Intercept	Pillai’s Trace	0.95	890.808^b	5	254	0.000	0.946
	Wilks’ Lambda	0.05	890.808^b	5	254	0.000	0.946
	Hotelling’s Trace	17.54	890.808^b	5	254	0.000	0.946
	Roy’s Largest Root	17.54	890.808^b	5	254	0.000	0.946
Programme	Pillai’s Trace	0.06	3.403^b	5	254	0.005	0.063
	Wilks’ Lambda	0.94	3.403^b	5	254	0.005	0.063
	Hotelling’s Trace	0.07	3.403^b	5	254	0.005	0.063
	Roy’s Largest Root	0.07	3.403^b	5	254	0.005	0.063

Table 6. Tests of between-subjects effects

Source		Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared
Corrected Model	OLA	17.327^a	1	17.33	13.07	0.000	0.048
	PE	6.634^b	1	6.63	4.21	0.041	0.016
	EE	8.098^c	1	8.10	4.87	0.028	0.019
	SI	7.474^d	1	7.47	4.00	0.047	0.015
	FC	7.261^e	1	7.26	5.46	0.020	0.021
Intercept	OLA	3507.3	1	3507.27	2645.24	0.000	0.911
	PE	3380.5	1	3380.48	2145.08	0.000	0.893
	EE	3302.1	1	3302.11	1984.26	0.000	0.885
	SI	3053.0	1	3052.98	1633.09	0.000	0.864
	FC	3868.4	1	3868.38	2908.88	0.000	0.919
Programme	OLA	17.33	1	17.33	13.07	0.000	0.048
	PE	6.63	1	6.63	4.21	0.041	0.016
	EE	8.10	1	8.10	4.87	0.028	0.019
	SI	7.47	1	7.47	4.00	0.047	0.015
	FC	7.26	1	7.26	5.46	0.020	0.021
Error	OLA	342.1	258	1.33
	PE	406.6	258	1.58
	EE	429.4	258	1.66
	SI	482.3	258	1.87
	FC	343.1	258	1.33
Total	OLA	3944.8	260
	PE	3911.2	260
	EE	3846.0	260
	SI	3641.3	260
	FC	4355.0	260
Corrected Total	OLA	359.4	259
	PE	413.2	259
	EE	437.4	259
	SI	489.8	259
	FC	350.4	259

Next, the results indicate that students from the IT programme show higher mean scores in all technology acceptance factors n comparison to the TM group. The IT group demonstrates composite mean scores of 3.87, 3.84, 3.70, and 4.14 for PE, EE, SI, and FC, respectively. The TM group has slightly lower composite mean scores, specifically 3.54, 3.48, 3.35, and 3.79, respectively.

A one-way MANOVA was conducted to investigate any statistical differences in online learning acceptance and perceived PE, EE, SI, and FC between the TM and IT courses. Table 5 shows a multivariate test for online learning acceptance, its factors, and students’ programmes. Based on Wilks’ criterion, the results show there is a statistically significant difference in online learning acceptance and its factors among students from two different programmes.: F (5, 254) = 3.403, p < .05; Wilk’s Λ = 0.940, partial η2 = 063. Hence, it can be inferred that the online learning acceptance and its contributing factors were significantly impacted by the specific programme in which individuals were enrolled (p < 0.05).

Next, Table 6 presents the individual differences between IT and TM students on OLA, PE, EE, SI, and FC scores. The results show that there is a significant difference between student programmes on OLA: F = 13.07, p = 0.000, partial η2 =.048. In terms of UTAUT constructs, the results indicate that there is a significant difference between student programmes in PE: F = 4.21, p = 0.041, partial η2 =.016; there is a significant difference between student programmes on EE: F = 4.87, p = 0.0028, partial η2 = 0.019; there is a significant difference between student programmes on SI: F = 4.00, p = 0.047, and partial η2 = 0.015; and there is a significant difference between student programmes on FC: F = 5.46, p = 0.020, and partial η2 = 0.021.

The results suggest that students in the IT field believe that online learning has a greater impact on their academic performance compared to students in management. With respect to EE, the findings suggest that a significant proportion of students in the field of IT hold the belief that online learning applications and technologies are more user-friendly and require less effort in their day-to-day learning activities compared to TM students. The results can be attributed to the advanced level of technological competency and familiarity with online learning among IT students, as they specialise in subjects related to digital and computer technology.

The reported statistically significant difference in SI indicates that IT students are more influenced by their peers and colleagues in terms of technology acceptance and utilisation. The potential reason for this result could be attributed to the active involvement of IT students in collaborative and technology-oriented projects, which consequently results in greater influence from their social networks. In terms of FC, the IT students again displayed a higher mean score compared to the TM group, which implies that they perceive the availability of resources, support, and infrastructure for technology adoption to be more favourable compared to the TM students. The presence of well-established technology-related facilities and initiatives within the IT faculty may contribute to this perception. The results indicate that hypotheses H2a-H2d have been confirmed, suggesting that there exists a statistically significant difference in the perceived PE, EE, SI, and FC between students studying TM and IT.

5. Discussion

This study examines the differences in online learning acceptance among undergraduate students in technical and non-technical programmes, with a specific focus on IT and TM students. The study also examines the differences in acceptance criteria for online learning among these two groups using the UTAUT framework. The key components of the UTAUT model include PE, EE, SI, and FC. According to the findings presented earlier, the level of online learning acceptance among respondents is considered moderate. Online learning can pose different challenges and opportunities that can affect students’ academic performance and acceptance. Issues such as limited student engagement, a lack of competency, and disruptions in the classroom might compromise the effectiveness of online learning (Hollister et al., 2022). This suggests that the traditional method of teaching and learning continues to play a significant role in the educational approach of higher education institutions. The findings of this study align with the assertions made by Ng et al. (2023) and Darley (2021) regarding the importance for educators to enhance their online teaching practices by acquiring relevant skills, promoting class engagement, and effectively managing learning materials online. It also supports a prior study by Mariam et al. (2023), who revealed that the perceived quality of online teaching played a moderating role in the direct associations between students’ initial adoption of blended learning and their subsequent changes in cognitive flexibility, as well as their intention to continue using online and blended learning in the future.

The results also suggest that students from technical-based programmes exhibit a higher level of acceptance of online learning, in contrast to students lacking a similar educational background. Students in technical disciplines also acknowledge the significance of online learning as an effective learning approach. The findings support the research of Wan et al. (2008) and L´opez et al. (2023), who argue that individuals who use information-seeking ICT more frequently can improve their online competence, resulting in better learning outcomes and satisfaction. L´opez et al. (2023) concluded that the acceptance of online education is greatly influenced by digital abilities, which have a substantial and significant effect. As such, the first hypothesis is supported. In terms of online learning acceptance factors, the findings demonstrate that students enrolled in IT programmes exhibited significantly higher mean scores across all variables, including PE, EE, SI, and FC, in comparison to students in TM programme. One possible explanation for the difference may lie in the distinct characteristics of their respective fields. IT students heavily rely on digital technology and communication, and they often have access to superior IT resources, network support, and infrastructure. Therefore, they have a slight edge when it comes to utilising online learning methods compared to TM students.

The one-way MANOVA test supports all the hypotheses proposed that there are significant differences between management and IT students in terms of online learning acceptance, performance expectancy, effort expectancy, social influence, and facilitating conditions. The results are consistent with the claims by Briggs et al. (2023) that when designing content for an online learning platform, it is crucial to consider factors such as the digital literacy of students, their level of engagement, and different design interventions. Furthermore, individual expectancies such as performance expectancy, effort expectancy, social norms, and facilitating conditions should also be considered to achieve optimal learning outcomes (Panigrahi et al., 2018). The results are also consistent with Reddy et al. (2021) that there are disparities in the use and anticipation of online learning among different groups of individuals, particularly those with a digital gap. The findings support the remaining four hypotheses that propose there are significant differences in the perceived PE, EE, SI, and FC between management and IT students. This study’s findings confirm the established body of research on the UTAUT model, which emphasises the importance of four technological acceptance factors in understanding online learning acceptance. These findings support previous studies conducted by Almulla (2022), Yeboah and Nyagorme (2022), and Perera and Abeysekera (2022).

5.1. Implications

From a theoretical perspective, this study adds to the existing empirical evidence on the UTAUT model and addresses a research gap by investigating the variations in online learning acceptance and its related factors among students in technical and non-technical fields. By incorporating four key elements from UTAUT—performance expectancy, effort expectancy, social influence, and facilitating conditions—the findings further support the students’ behaviour towards online learning in various academic fields. As originally suggested by Venkatesh et al. (2003), it is reasonable to regard the UTAUT model as a useful instrument for understanding technological acceptance as well as determining the intention to embrace a technology from different perspectives. The presence of a supportive environment, well-equipped facilities, and an understanding of online learning methods collectively contribute to the increased acceptance of online learning by students. This is apparent in the different levels of acceptance and perceptions among students enrolled in technical and non-technical programmes. This research provides useful insights into the current body of literature and builds a significant foundation for future inquiries. The evidence contributes to the existing knowledge and addresses the research gap in investigating key elements associated with the acceptance of technology. For the methodological implication, this study employed an exploratory, descriptive, and cross-sectional research design, with a particular focus on students enrolled in technical and non-technical fields. The study incorporated samples from two departments for the purpose of comparison that comprise of technical and non-technical programmes offered at one higher learning institution.

The practical implication of this study involves the need to comprehend the underlying issues of online learning implementation and the means to strengthen it through a more structured approach by relevant bodies. This is especially crucial in developing countries that often grapple with a substantial digital divide and a lack of inclusivity. E-learning, or online learning, is one of the most important strategies for fostering the future of education not only in Malaysia but globally. To successfully execute an online learning strategy and improve the educational experience, HEIs should evaluate their approaches to enhance students’ and instructors’ perceptions of online learning value, optimise its advantages, and provide complete assistance to enable seamless operation of online learning in all academic disciplines. The findings provide valuable insights for regulators and policymakers in relevant governmental ministries and institutions to take into account the needs of students and universities, thereby assisting in the implementation of necessary adjustments. We propose that universities and the ministry of higher education to actively promote the implementation of online education in a way that enhances student and instructor acceptance, satisfaction, and long-term dedication.

6. Conclusions, limitations and future research

This study highlights the importance of considering the unique characteristics of students from different disciplines of study when designing an online teaching and learning approach. Gaining insight into the distinct variables that influence students’ online learning acceptance can be beneficial for customising interventions and support mechanisms accordingly. The challenges of online learning are often centred around communication and interactions with peers and lecturers. It is crucial for educators to overcome the challenges by acquiring relevant competencies to enhance their online teaching abilities, encompassing effective classroom management, student involvement, virtual discourse, and utilisation of relevant e-learning resources and applications. It is also imperative for universities to enhance online learning experiences for students who do not possess a background in information technology. This necessitates the development of a customised approach that caters to students’ specific requirements. Such initiative can be accomplished by improving students’ digital literacy, encouraging the use of ICT for academic and project-related tasks, and fostering a comprehensive understanding of the advantages of online learning for students’ academic improvement.

The outcome of this study provides a valuable contribution to the growing field of online learning in developing countries by presenting original empirical findings that explore the different perspectives of students enrolled in higher education institutions. In many developing countries, universities often incorporate teaching methods that closely resemble Western approaches, often without significant modifications. To effectively implement an e-learning strategy and enhance the educational experience, it is essential for the university to possess an in-depth understanding of students’ behaviours within an online learning environment.

Future research may investigate this topic utilising alternative theoretical bases other than UTAUT, such as from the perspectives of socioeconomics, cultural frameworks, and the extended UTAUT model, to gain a thorough knowledge of online learning implementation in HEIs. Furthermore, further inquiry can consider the inclusion of moderating and mediating variables that may aid in understanding the complexities of the influence of contextual variables on online learning acceptance. In terms of limitations, the outcomes of this study are based solely on the viewpoints of students. A comparative analysis of attitudes towards online learning among academic staff and students would yield intriguing insights and further contribute to the existing knowledge in this field. Another limitation is that the study only represents the viewpoints of students from one university. Hence, generalisability should be cautioned.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the Universiti Tun Hussein Onn Malaysia .

Appendix

Table

Code	Descriptions
PE	Performance expectancy
PE1	Using online learning improves my academic performance.
PE2	Online learning is useful for enhancing academic learning.
PE3	Online learning helps me to allocate time more efficiently for other learning activities
EE	Effort expectancy
EE1	Easy to use university online learning system.
EE2	I find it easy to use online platforms to do assignments.
EE3	I find the learning system is easy to use.
EE4	Using online learning improves my self-efficacy
EE5	Online learning helps me do academic tasks in less time.
SI	Social influence
SI1	My lecturers influence me to use online learning as it facilitates communication.
SI2	People around me think I should use online learning to improve knowledge & experience
SI3	My course mates influence my attitude towards online learning.
SI4	Online learning facilitates seamless communication with my classmates.
SI5	I find it convenient to interact with fellow students through online learning
FC	Facilitating condition
FC2	I have technological and financial resources to use online learning
FC3	I have knowledge & technical skills to use online learning
FC4	There is always internet connection for my online learning classes.
FC5	The university support for online learning system is good
FC7	Enthusiasm of the lecturer in teaching online is high
OLA	Online learning acceptance
OLA1	I like the idea of using online learning system
OLA2	I will recommend others to use the online learning
OLA3	I have high intention to continue using online learning
OLA4	Online learning is an effective learning method for me