
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12008-7
10.1016/j.heliyon.2024.e35977
e35977
Research Article
Polytechnic students’ perceived satisfaction of using technology in the learning process: The context of Bangladesh TVET
Sadam Nsangou Youmo Souleman
Al Mamun Md Abdullah a.mamun@iut-dhaka.edu
⁎
Department of Technical and Vocational Education, Islamic University of Technology, Board Bazar, Gazipur, 1704, Bangladesh
⁎ Corresponding author. a.mamun@iut-dhaka.edu
08 8 2024
30 8 2024
08 8 2024
10 16 e359777 2 2023
30 7 2024
7 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Technology integration is becoming pervasive in the polytechnic institutes of Bangladesh. Many institutions opted for a blended learning approach to deliver their education. This approach drives the transformation of education towards more digitalization and obliges students to thrive in using technology in the learning process. However, many students lack technical competency and more importantly are deprived of having modern devices in their households. In particular, how students perceive technology and how it affects their learning experience and satisfaction is crucial for effective learning. This area of research is still unexplored in the context of TVET Bangladesh. This study attempts to investigate what are the factors that affect the students’ perceived satisfaction with the usage of technology and to what extent students are satisfied with the way technology is being used in their learning process. A quantitative research method using an online survey was conducted across Bangladesh and a total of 847 polytechnic students from 16 polytechnic institutes were recruited through non-probability convenience sampling technique. A structural equation modelling (SEM) and independent multivariate analysis of variance (MANOVA) were employed for the data analysis. The results revealed that self-efficacy positively influenced attitude, and attitude positively influenced social interaction. In contrast, attitude negatively influenced perceived satisfaction. This study can help educators implement strategies such as providing scaffolding, promoting self-directed learning, and commemorating student successes to build self-efficacy and a positive attitude towards technology acceptance.

Keywords

Polytechnic student
technology use
Satisfaction
Self-efficacy
Social interaction
Social cognitive theory
Bangladesh
TVET
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pmc1 Introduction

The issue of effective integration of modern technology into teaching-learning becomes critical as the educational landscape dramatically shifted in the post-pandemic context and accelerated the transformation of digitalization process [1]. As technology offers endless opportunities for students with rich multimedia resources, educators attempt to embrace technology in teaching and learning to make lessons more exciting and motivating for the students [2]. However, research shows that technology leads to effective learning only if it is associated with effective integration and efficient use [3].

Farjon et al. [4] claimed that technology integration is more than just merging technology into teaching. For teachers, technology is an enabler that provides a flexible design of lessons to meet individual needs and keep track of the students' progress [5]. However, Taber [6] warned that teachers do not get tempted by the novelty of technology but need to develop pedagogical skills and be familiar with the characteristics and capabilities of technology for its effective integration. Teachers also need to be skillful and well-prepared regarding their technical, pedagogical, and content knowledge [7]. Numerous studies indicate that proper use of technology can motivate technology-savvy students, maintain students’ interest in completing the assigned tasks, and allow teachers to provide students with enriching learning experiences [2].

However, the successful implementation of technology integration depends on several learner related factors such as students' perceptions about technology use, readiness to incorporate technology, and self-efficacy. Many studies examined technology acceptance and explored students’ satisfaction and intention to use them in future [8,9]. For example, the results of a recent review of empirical research about the use of digital technology stress that technology use itself does not live up to its perceived potential to transform the learning experience [10]. Specifically, in the context of Technical and Vocation Education and Training (TVET) Bangladesh, there is a dearth of research focusing on student satisfaction with technology use. Thus, it is important to further investigate how modern technologies can be integrated into the teaching and learning process to influence student learning and satisfaction.

In the literature, student satisfaction is found to be an important indicator of the quality and services of higher education institutions. Satisfaction is defined as the perceived value and appreciation of the learning experiences that a student receives in the educational institutions [11]. It is the combined effect of students' learning experiences with the services and facilities provided by the institutions. Research found that satisfaction in online environment can be influenced by the content and design of the courses, flexibility to participate, and interaction with the peers and teachers [12,13]. Thus, understanding how students perceive and evaluate their learning experiences with technology and how these experiences affect their learning outcomes are important determinants of their satisfaction.

In the context of higher education, TVET plays a key role in producing skilled graduates. TVET institutions prepare students by providing training, practical skills, and knowledge for specific jobs and occupations. In this regard, technology can play a key role in enhancing TVET education quality and accessibility. However, technology-mediated environments in the TVET sector pose some challenges and require careful design and implementation of the curriculum to ensure their effectiveness and suitability for different skills and practical-training oriented courses.

A plethora of research has been conducted in different countries to understand the role of technology integration in the context of TVET. Research shows that TVET can benefit from technology integration by increasing access, quality and relevance of learning opportunities for diverse groups of learners [14]. In the Malaysian context, the use of innovative teaching increased students' understanding and satisfaction of learning the TVET subjects [15]. In a quasi-experimental study conducted in China on vocational college students, researchers found positive relations among virtual reality technology, learning satisfaction and learning outcome [16]. Njenga et al. [17] examined students' perceptions of technology use such as e-learning readiness in Kenya's TVET institutions. The study surveyed 400 students from four public TVET institutions and found that students had positive perceptions of e-learning readiness. However, the study also identified some challenges such as a lack of adequate equipment, internet access, training, and guidance which is a common issue prevalent in developing countries. Technology integration also poses challenges for TVET students as social media appears as the key distractor for their learning [18].

Research showed that student satisfaction can affect student motivation, engagement, retention, and achievement [13]. In Germany, Vladova et al. [12] conducted a longitudinal study on students' acceptance of technology in their learning. They compared students from two different disciplines: information systems and music and arts. They found that students' satisfaction was influenced by perceived usefulness, perceived ease of use, time flexibility, learning flexibility and social isolation. Similarly, a study conducted in Saudi Arabia found that students' satisfaction and acceptance of e-learning were influenced by perceived usefulness, ease of use, social influence, facilitating conditions, and hedonic motivation [19]. Also, students were generally found to be satisfied with technology-assisted learning but had some concerns about technical issues, instructor support and feedback [20].

In brief, these studies suggest that student satisfaction with technology-mediated environments in TVET contexts depends on various factors such as availability of equipment, internet access, training, guidance, quality, and relevance of learning opportunities. Noticeably, perceived usefulness, perceived ease of use, time flexibility, learning flexibility, social isolation, social influence, facilitating conditions, hedonic motivation etc. Are the crucial factors to influence students’ satisfaction. These studies also indicate that student satisfaction may vary across different disciplines, countries, and situations. Some of the challenges include technical issues, lack of skills or training, less relevant content, lack of interaction or support from instructors, cultural barriers, resistance to change and so on [21]. Therefore, it is essential to consider the specific needs and preferences of different TVET learners and contexts when designing and implementing technology mediated teaching strategies.

In the context of Bangladesh, the use of technology in the polytechnic institutes is gradually becoming more pervasive. Chowdhury [22] recognises the capability of technology in upgrading accessibility, cost-effectiveness and more broadly the education standard in Bangladesh. Mallick et al. [23] viewed technology as one of the key reasons for the learners' creativity and performances. Students demonstrated improved performances and attainments when technology is integrated effectively into the polytechnic institutes [24] and they can work more productively compared to the traditional classroom settings [5]. In contrast, technology integration does not necessarily promote effective learning experiences due to the existence of multidimensional problems in the polytechnic institutes of Bangladesh [25]. Notably, the low technical competency of teachers and insufficient support from the polytechnic institutions hindered the better learning experiences of the students [26,27]. Also, most of the students and teachers lack motivation and competency in utilizing modern technology in the teaching-learning process [25,28,29]. A recent study revealed that the attitude of students towards technology remains an important issue for the effective utilization of technology in the learning process [30]. Because student attitudes are seen as a critical aspect of the effective use of technology, it can influence their satisfaction. Furthermore, polytechnic students in Bangladesh are mostly from low socio-economic backgrounds and they lack the available modern technologies in their households to access education digitally [31]. Similarly, in the higher education context of Bangladesh, several studies attempt to evaluate students' satisfaction with technology use. For example, students’ positive attitude towards technology is found to be the key factor in the effective use of technology [32].

However, there are several problems and challenges that have emerged for the successful integration of technology into the teaching and learning process. Unreliable networks for accessing the Internet, excessive cost of Internet connections, and the generally poor socio-economic background of the students were found to be key factors that hindered the effective learning experiences with technology [33]. Students' lack of readiness was another important factor obstructing students' use of technology [34]. Students' lack of readiness often creates stress while learning in a technology-enhanced environment [35]. The situational factors describing the contextual dynamics emerging from modern technology also negatively influence students' online readiness and satisfaction [36]. Institutions are required to provide the necessary support for the students to adjust to such contextual dynamics. However, weak facilitating conditions of the institutions adversely affect student readiness and their satisfaction with technology use [32]. In addition, it has been argued that modern technology should be consistent with the existing values, culture, experiences, and needs of students [37]. Thus, the compatibility of educational technology in the context of Bangladesh also plays a vital role in influencing student satisfaction. A recent study found that students frequently assume that technological compatibility affects their satisfaction because of their negative past experiences with technology use [38]. Estriegana et al. [16] argued that perceived satisfaction can exert the most influence on students’ appreciation of the role of technology in their study.

With increased reliance on technology in the higher education sector, it is evident from the above discussion that students’ perceived satisfaction with technology use has been the subject of numerous recent studies [39,40]. Nonetheless, there is a dearth of studies on students' perceived satisfaction with technology use in the higher educational context of Bangladesh. In particular, the lack of research in the TVET and polytechnic education sector necessitates a rigorous effort by the research community to comprehend the dynamics of students' satisfaction with technology use. The study needs to address the issue of perceived satisfaction among TVET students because it directly impacts their engagement, motivation, and ultimately their learning outcomes. If students are not satisfied, it can lead to disengagement, dropout, or even negative outcomes in their careers. While students may have other choices, understanding their satisfaction levels helps institutions improve their programs to better meet their needs and overall expectations. Thus, more concentrated research is necessary to comprehend how satisfied students are with the way technology is integrated into their learning. In fact, this topic of research has been largely overlooked in the context of Bangladesh polytechnic institutes. To address this research gap, the current study attempts to investigate the following research questions.RQ1 What factors affect Bangladeshi polytechnic students' perceived satisfaction with the usage of technology during the learning process?

RQ2 To what extent are the polytechnic students of Bangladesh satisfied with the way technology is being used to support their learning?

RQ3 Do demographic variables impact polytechnic students' satisfaction with technology use?

2 Theoretical framework and hypothesis development

Based on the social cognitive theory (SCT) [41] and technology acceptance model (TAM) [42], we conceptualised a research framework to measure polytechnic students’ perceived satisfaction. Combining social cognitive theory (SCT) and the technology acceptance model (TAM) offers a comprehensive framework to measure polytechnic students' perceived satisfaction. SCT emphasizes the interplay between personal factors, environmental influences, and behaviour, which aligns with understanding how students' perceptions of technology acceptance and their social environment influence their satisfaction. TAM, on the other hand, focuses on how users perceive and adopt technology, providing insights into students' attitudes and intentions towards technology use, which directly impact satisfaction. Integrating both theories allows for a holistic examination of factors influencing satisfaction, encompassing personal beliefs, social influences, and technological factors.

Social cognitive theory (SCT) explains the impact of individual experiences, the actions of others, and environmental factors on individual behaviours. The main premise of social cognitive theory is that learning takes place in a social environment by observing other people [43]. Therefore, the theory explains human behaviour with the reciprocal interactions among the person, behaviour, and environment [41]. The technology acceptance model (TAM) is a theoretical model that structurally shows how users come to accept and use technology. The basic model of TAM focuses on the two main constructs of users' acceptance of technologies-perceived ease of use and perceived usefulness. TAM integrates the attitude of the learner as an important construct influenced by perceived ease of use and perceived usefulness. Recently Estriegana et al. [44] proposed an extended version of technology acceptance model (TAM) in which they showed that perceived satisfaction is a key factor that exerts the most influence on students’ appreciation of the role of technology in their study.

Satisfaction is the consequence of behaviours determined by the individuals' constructs and environmental factors. While current literature shows several factors influencing online learners' satisfaction, personal and environmental variables are found to be the key components impacting learners' satisfaction in an online context [45]. There is a growing body of literature that shows how self-efficacy as a key personal construct relates to satisfaction while using a particular technology (e.g., computer, the Internet) [46]. Self-efficacy refers to the beliefs that individuals have about their capabilities to plan and perform certain actions that will result in specific outcomes [47]. Schunk [43] points out that self-efficacy beliefs are an individual's knowledge about his capacity to accomplish certain tasks. Therefore, self-efficacy plays a central role in behavioural performance. Self-efficacy beliefs are crucial to social cognitive theory because they influence people's motivation to undertake and sustain goal-directed behaviours.

Value is another significant personal construct in social cognitive theory that is recognized as an important motivational process to initiate learners' behaviour [43]. Value refers to the learner's perceptions and beliefs about the importance of the task. Value can be investigated in terms of the beliefs or attitudes of the students [48]. In this study, we conceptualised value as the student attitude about online learning. Besides self-efficacy and attitude, researchers have attempted to explain learners' satisfaction in online learning settings through several other personal factors such as computer or technological anxiety [49], perceived ease of use and perceived usefulness of the online learning system [50].

In an online learning environment, environmental variables often refer to the online community, e.g., instructor, peers, and the technology they use. The literature reveals that the factors relating to technology, instructors, and peers influence student satisfaction in their learning experiences [[51], [52], [53]]. Thus, this study considers learners' social interaction within the premise of the online community as the environmental factors that influence their satisfaction. Based on the discussion made above, this study conceptualised a research framework as shown in Fig. 1 to investigate students’ perceived satisfaction with the use of technology.Fig. 1 Research framework to investigate students' perceived satisfaction with technology use.

Fig. 1

Perceived ease of use (PEU) refers to the degree to which a person believes that using a particular system would be free from effort [42]. Users' PEU of technology influences their acceptance and attitude towards the use of technology [29]. Thus, PEU of technology enables polytechnic students to realize the easiness of the technology and feel comfortable in their studies. Similarly, perceived usefulness (PU) refers to the degree to which a person believes that using a particular system would enhance his or her job performance [42]. A student's PU of technology is found to be a significant determinant of attitude (AT) towards technology use [54]. Other recent literature also confirmed that perceived usefulness and perceived ease of use have a significant effect on students' attitudes toward technologies [55]. Moreover, some studies found that PU and PEU positively impacted students' attitudes toward learning technologies [56]. In the context of Bangladesh, Khan et al. [26] suggest that students' positive attitude toward technology is developed when they are sufficiently comfortable with technology and have knowledge about its usage. Based on the above-mentioned research this study proposes the following hypothesis-H1 Perceived ease of use (PEU) has a positive impact on attitude (AT)

H2 Perceived usefulness (PU) has a positive impact on attitude (AT)

In a technology-mediated environment, anxiety (AX) is characterized as a fear of using technology [57]. Liaw and Huang [58] indicated that the performance of students with a high degree of technology anxiety might be poorer than those with little or no computer anxiety. Sun et al. [53] confirmed that the higher the computer anxiety, the lower the level of learning satisfaction. They further argue that anxiety is a negative predictor influencing perceived satisfaction. Thus, the following hypothesis is made.

H3 Anxiety (AX) has a negative impact on attitude (AT)

Within SCT, self-efficacy (SE) is considered an essential individual trait to understand students' perceived satisfaction [46,59]. Self-efficacy is referred to as the set of beliefs that an individual has about his aptitudes and capacities to initiate and carry out an action [60]. Yalcin [46] concluded that a learner who has self-efficacy beliefs for learning in face-to-face settings may not have sufficient confidence for learning through technology environments. More recently, Pan [61] indicated that students’ self-efficacy affected their attitude toward technology-based learning. In a similar vein, other researchers found that there is a significant relationship between self-efficacy and student perceived satisfaction (PS), specifically, self-efficacy is a significant predictor of student satisfaction within technology-mediated learning settings [33,62]. The consideration of the aforementioned findings helps to formulate the following hypothesis-

H4 Polytechnic students' self-efficacy (SE) influences their attitude (AT)

H5 Polytechnic students' self-efficacy (SE) influences their perceived satisfaction (PS)

Social Interaction (SI) is regarded as one of the essential constructs of environmental factors in both traditional and technology-mediated environments [63]. Research suggests that the opportunity to interact with others (i.e., peers and teachers) in an online learning environment increases student learning outcomes and perceived satisfaction [64]. Students who do not interact adequately with their instructors feel that they learn less and are less satisfied with their learning environment [65]. DeBourgh and Gregory [66] reported earlier that the opportunity to interact and collaborate with peers during the learning process is linked with students' satisfaction. Students who use available technologies to interact with each other during their studies can perceive greater satisfaction. In this study, the emphasis is given to polytechnic students' interaction with peers and their teachers. Thus, the following hypothesis is made.

H6 Social interaction (SI) has a positive impact on students' perceived satisfaction (PS)

Also, social interaction has an impact on student attitude [63]. However, a recent study considered the reverse relationship i.e., student attitude has an impact on social interaction and pointed out the significant effect of students’ attitude on social interaction [67]. A few research studies emphasized this reverse effect of attitude on social interaction. This study attempts to investigate this relationship further. Thus, attitude is theorized as an important determinant in measuring social interaction and perceived satisfaction.

H7 Attitude (AT) has a positive impact on social interaction (SI)

H8 Attitude (AT) has a positive impact on perceived satisfaction (PS)

Finally, this study investigates the mediation effect of attitude and social interaction between the relationship of self-efficacy and perceived satisfaction, and the relationship between attitude and perceived satisfaction, respectively.

H9 Attitude mediates the relationship between self-efficacy and perceived satisfaction.

H10 Social interaction mediates the relationship between attitude and perceived satisfaction.

3 Methodology

This study utilised a quantitative research design to investigate students’ perceived satisfaction with the use of technology. As survey questionnaires are a valid and reliable way to assess the perceptions of the research participants [68], an online survey has been used to collect the data. This study used a survey research method to test causal relationships among the variables [69]. Research shows that the findings from the survey are generally reliable and generalizable [70].

To answer the first and second research questions, this study used structural equation modelling (SEM). The objective of using SEM is to analyse the cause-and-effect relationships among the variables [71]. In answering the third research question, this study employed MANOVA (Multivariate Analysis of Variance). In this study, MANOVA has been used to assess the influence of demographic variables on students’ perceived satisfaction. Researchers used MANOVA to reduce the likelihood of Type I error [72]. It can increase the statistical power of the analysis. Both SEM and MANOVA can provide rigor and validity in the data analysis and increase the explanatory power and generalizability of the results [71,72].

The Committee for Advanced Studies and Research (CASR) at the Islamic University of Technology has granted ethical permission for this work (Ref. No. REASP/CASR/47/11/Proc/002). All individuals taking part in the study gave their informed consent.

3.1 Instrument development and data collection

In this study, the survey instrument has been developed based on the previously validated instruments reported in the literature. There are a total of 30 items in the questionnaire of which three (3) items were adopted from Yuen et al. [73] to measure perceived usefulness (PU); six (6) items have been drawn from Yuen et al. [73] and Sun et al. [53] to measure perceived ease of use (PEU). The attitude (AT) construct consisted of three (3) items which were adopted from Sun et al. [53]. The anxiety (AX) construct is measured by 6 items of which four (4) items were adopted from Sun et al. [53] and two (2) items were newly created by the researchers. To measure self-efficacy (SE), four (4) items were drawn and modified from Sun et al. [53]. Social interaction (SI) has four (4) items of which two (2) items were adopted from Sun et al. [53], and two (2) items were from Yuen et al. [73]. Finally, perceived satisfaction (PS) with technology use was measured using four (4) items which were adopted from Liaw [74]. All the items were measured on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7).

The survey questionnaire was prepared using Google form and the link of the survey was sent to the participants through email and other social media platforms. The survey ran for approximately four months to collect the data.

This study utilised several steps to reduce the measurement error of the online survey. First, the questionnaire was reviewed and modified based on the feedback from a small group of students. Second, experts' opinions were sought to improve the content validity of the survey. Finally, to improve the reliability of the questionnaire items, a pilot test was run with a random sample of thirty-seven (37) polytechnic students. Data collected from the pilot test were then analysed using the statistical package for the social sciences (SPSS) software to assess the internal consistency of the measurement items. The pilot test results ascertained a good reliability score (α > 0.75) for all the measurement items of the survey instrument.

3.2 Population and sample

All public polytechnic students of Bangladesh were considered as the population for this study. Bangladesh has 49 public polytechnic institutes in 8 divisions of the country. We purposively chose at least one polytechnic institute from each of the 8 divisions. In so doing, a convenience sampling technique has been used to select the respondents from different academic levels and disciplines. Convenience sampling is a non-probability sampling technique where participants are selected due to their convenience of accessibility and their proximity to the researcher. To minimize the inherent drawback of convenience sampling and to strengthen the representativeness of the sample, we have collected data from a large group of participants from different polytechnic institutes. For example, the survey link was sent to 1002 respondents, from which we received 959 completed questionnaire forms. A larger sample size can increase the statistical power and effect of the findings [75]. A larger sample size can also reduce the sampling error. The diversity of the participants in this study can also increase the external validity and applicability of the findings [75]. In conclusion, this study utilised a large dataset with diverse and representative samples of the participants.

The dataset was then checked and examined for missing data, incomplete responses, disengaged participants, and outliers which finally produced 847 valid responses (95.7 % of the total participants) for further study. For this investigation, the final sample size of 847 is appropriate based on Yamane's [76] recommendation for a large population. With p = 0.05 assumed and a 95 % confidence level, Yamane offers a formula for calculating sample sizes. According to this suggestion, a sample size of 385 can be used for a population that is larger than 10,000. The sample size in the current study is 847, which is significantly larger than what is advised.

4 Data analysis and results

Prior to data analysis, the researchers screened and scrutinized the dataset to ensure data accuracy. This study employed covariance-based SEM because the primary interest of this study lies in understanding the complex relationships among variables related to polytechnic students' use of technology within a structural model. The study used covariance-based SEM over the other type, which is partial least squares (PLS) SEM, to estimate the extent to which the model fits the observed data. Also, covariance-based SEM is more suitable when the data meet the assumptions of multivariate normality, and the sample size is relatively large. In contrast, PLS-SEM is often chosen when the focus is on prediction rather than testing a specific theoretical model, or when the data violate the assumptions of multivariate normality or have small sample sizes. However, since the current study aimed at confirming a theoretical model and had a sufficiently large sample size, covariance-based SEM was considered more appropriate. We also conduct MANOVA analysis to understand the impact of demographic variables on polytechnic students’ satisfaction with technology use.

In the current study, RQ1 and RQ2 are addressed by using SEM to examine complex relationships among multiple variables simultaneously related to polytechnic students' satisfaction. By analysing the relationships among observed and latent variables on students' satisfaction, it helps to understand the underlying structure of the data and test hypotheses about causal relationships. On the other hand, MANOVA is used to address the third research question (RQ3) of the study to understand the impact of demographic variables on polytechnic students’ satisfaction with technology use. It allows us to assess whether there are significant differences between groups across multiple dependent variables. All the statistical analyses, e.g., SEM and MANOVA were conducted using the statistical package for the social sciences (SPSS) v25 and analysis of moment structures (AMOS) software.

4.1 Assessing the measurement model

The measurement model was evaluated through reliability, convergent validity, and discriminant validity. In the evaluation process, the maximum likelihood technique was used to estimate the parameters as it is appropriate for testing and developing theories [77].

4.1.1 Reliability, convergence, and discriminant validity

Reliability and convergence validity are assessed through internal consistency, average variance extracted (AVE) and composite reliability (CR) of the items. To improve the measurement model, some items (i.e., AX2, AX3, and AX6) have been deleted during the analysis.

Internal consistency shows the extent to which a group of items measures the same construct and shows how they vary together or are intercorrelated to each other. For measuring internal consistency and the reliability of the constructs, Cronbach alpha (CA) and CR have been calculated. The recommended value for both CA and CR should be above 0.70 [78]. As revealed in Table 1, all the constructs satisfy the recommended conditions of CA and CR. AVE indicates the convergence validity of the model and provides the amount of variance captured by the indicators of a latent construct relative to the amount of variance captured due to the measurement error [79]. AVE value less than 0.5 reflects the fact that the variance, due to measurement error, is greater than the variance captured by the respective construct [80]. As revealed in Table 1, only the AVE value of anxiety is less than 0.5. While AVE greater than 0.5 or higher is commonly used as a guideline, several researchers argue that even the value above 0.4 can be acceptable to establish convergent validity, especially when dealing with complex constructions of the model [81]. Also, the anxiety construct exhibited good composite reliability and Cronbach alpha suggesting acceptable levels of convergent validity, even though the AVE is slightly below 0.5 [82,83]. Finally, researchers argue that when the items are theoretically linked to the construct, it strengthens the case for convergent validity [84].Table 1 Reliability, convergence, and discriminant validity with the correlation matrices.

Table 1Constructs	CA	CR	AVE	PU	PEU	AX	AT	SE	SI	PS	
Perceived usefulness (PU)	0.795	0.80	0.56	1							
Perceived ease of use (PEU)	0.865	0.86	0.52	0.986	1						
Anxiety (AX)	0.700	0.64	0.40	−0.122	−0.119	1					
Attitude (AT)	0.804	0.80	0.58	0.928	0.884	−0.056	1				
Self-efficacy (SE)	0.813	0.82	0.53	0.84	0.851	−0.061	0.923	1			
Social interaction (SI)	0.824	0.82	0.54	0.771	0.767	−0.059	0.860	0.915	1		
Perceived Satisfaction (PS)	0.821	0.83	0.55	0.758	0.778	−0.058	0.866	0.941	0.920	1	

Discriminant validity explains how two constructs are statistically distinct from each other by analysing the correlation of one latent variable with another one. The value of correlation among the latent variable that is higher than 0.70 needs to go through further investigation [46]. The correlation matrices between the constructs show 15 bivariable correlations with values above 0.70 (bold numbers in Table 1).

The validation of the distinctiveness between two constructs, when the bivariable correlations are found above 0.70, can be done through a chi-square (χ2) test [46,85]. Anderson and Gerbing [86] suggest that for such a case chi-square test is then run for every possible pairing of constructs in a study. If a chi-square difference value is statistically significant, then a two-factor solution provides a better fit to the data, and discriminant validity between two factors is supported [85].

As revealed in Table 2, the chi-square goodness of fit for the final model was found to be statistically significant χ2(303)=816.8,P<0.01). Table 2 further shows statistically significant differences between every two constructs of the 15 bivariate correlations that have higher correlations shown in Table 1. Therefore, χdiff2 tests revealed the distinctiveness among the constructs and showed strong evidence of the discriminant validity of the measurement model.Table 2 Distinctiveness among the constructs χ2 test.

Table 2Final and bivariable models	χ2 (df)	χdiff2 (df)	p-value	Decision	
Final model	816.8(303)				
AT - PU	928 (309)	111.2 (6)	0.000a	Significant	
AT - PEU	943.6(309)	126.8 (6)	0.000a	Significant	
PU - SE	1088.2(309)	271.4 (6)	0.000a	Significant	
PEU - SE	1144.7(309)	327.9 (6)	0.000a	Significant	
SE - AT	890.7(309)	73.9 (6)	0.000a	Significant	
SI - PU	1236.9(309)	420.1 (6)	0.000a	Significant	
SI - PEU	1283.9(309)	467.1 (6)	0.000a	Significant	
SI - AT	980.7(309)	163.9 (6)	0.000a	Significant	
SI - SE	880.8(309)	64.00 (6)	0.000a	Significant	
PS - PU	1254(309)	437.2 (6)	0.000a	Significant	
PS - PEU	1342(309)	525.2 (6)	0.000a	Significant	
PS - AT	995.7(309)	178.9 (6)	0.000a	Significant	
PS - SE	865.7(309)	48.9 (6)	0.000a	Significant	
PS - SI	864.9(309)	48.1 (6)	0.000a	Significant	
a Significant at p < 0.001.

4.2 Assessing the structural model

As indicated by Anderson and Gerbing [86], the second step of structural equation modelling (SEM) is to assess the structural model. The model fit was evaluated as per the fit indices and their cut-off values proposed by Rainer and Miller [87]. These indices consist of χ2 value, the comparative fit index (CFI), the root mean squared residual (RMSR), the root mean square error of approximation (RMSEA), the normed fit index (NFI), the non-normed fit index or Tucker Lewis index (NNFI or TLI) and the parsimonious fit indices (PNFI). Table 3 shows that the structural model satisfies all the fit indices within the recommended levels.Table 3 Model fit of the proposed structural model.

Table 3Fit indices	Cut-off value	Structural model	
χ2/df	<3.00	862.603/310 = 2.783	
CFI	>0.90	0.955	
RMSR	<0.10	0.033	
RMSEA	<0.08	0.046	
NFI	>0.90	0.932	
TLI	>0.90	0.949	
PNFI	>0.60	0.823	

4.2.1 Path analysis

Table 4 shows the standardized (β) coefficients for the direct effects, the probability (p) and the error variances (S.E.) of the endogenous variables.Table 4 Research hypothesis results.

Table 4Hypotheses	Paths	S.E.	β-value	p-value	Decision	
H1	PEU → AT	1.199	−1.274	0.303	Not supported	
H2	PU → AT	1.265	1.526	0.210	Not supported	
H3	AX → AT	0.042	0.022	0.622	Not supported	
H4	SE → AT	0.111	0.771	0.000b	Supported	
H5	SE → PS	0.252	1.008	0.000b	Supported	
H6	SI → PS	0.087	0.530	0.000b	Supported	
H7	AT → SI	0.045	0.892	0.000b	Supported	
H8	AT → PS	0.241	−0.552	0.030a	supported	
a Significant at p < 0.05.

b Significant at p < 0.001.

As revealed, self-efficacy positively influenced attitude (β = 0.771, p < 0.001) and attitude positively influenced social interaction (β = 0.892, p < 00.001). In contrast, attitude negatively influenced perceived satisfaction (β = −0.552, p < 00.01). Also, perceived satisfaction was positively influenced by self-efficacy (β = 1.008, p < 00.001) and social interaction (β = 0.53, p < 00.001). These results support hypothesis H4, H5, H6, H7, and H8. Therefore, it highlights the essential character of social interaction (environmental factor), self-efficacy and attitude (personal factor) on polytechnic students' perceived satisfaction in learning through technology.

4.2.2 Mediation analysis

A mediator is a variable that conveys the influence of the predictor variable on the criterion variable or dependent variable. As revealed in Table 5, The proposed model displays the significant indirect effect of AT on the relationship between SE and PS (β = −0.486, p < 0.05). Similarly, SI has a significant indirect effect on the relationship between AT and PS (β = 0.496, p < 0.05).Table 5 Parameter estimates for mediation effect.

Table 5Hypothesis	Mediation Paths	Sobel ratio	SE	β-value	p-value	Mediation effect	
H9	SE→AT→PS	−2.3	0.212	−0.486	0.021a	Partial	
H10	AT→SI→PS	6.14	0.08	0.496	0.034a	Partial	
a Significant at p < 0.05.

As both self-efficacy and attitude directly influence perceived satisfaction (see Table 4), the mediation effect of attitude and social interaction is considered partial to influence their relationships.

4.3 Impact of demographic variables on perceived satisfaction

Preliminary analysis of the demographic data suggests that male participants (62.3 %) numbered almost twice the female participants (37.7 %). The majority (56.6 %) of the participants live at home and only 11.2 % of participants live in the institutional halls. The heavy users of technology are 8.6 % who spent daily more than 5 h with the technology for learning purposes. Most of the students use technology for 1–2 h (42.10 %). Also, we noted that 68.4 % of participants used only mobile data and only 17.6 % of participants used broadband to access the Internet connection during their study. Table 6 illustrates the participants’ statistics in terms of gender, living place, hours of using technology and the type of internet connection they used.Table 6 Participants’ characteristics (Gender, living place, hours of using technology and type of internet connection).

Table 6Demographic variables (N = 847)	Variants	Frequency	Percentage	
Gender	Male	528	62.3	
Female	319	37.7	
Place of living during the study	Home	479	56.6	
Institutional Halls	95	11.2	
Rental houses	273	32.2	
Hours of using technology for study	Less than 1 h	107	12.6	
1–2 h	357	42.1	
2–5 h	310	36.6	
Above 5 h	73	8.6	
Type of internet connection	Broadband	149	17.6	
Mobile data	579	68.4	
Both	111	13.1	

Finally, we conducted a multivariate analysis of variance (MANOVA) suggested by Hinto et al. [88] to assess the moderating effect of gender, living place, hours of using technology and type of internet connection on student satisfaction. We assessed the MANOVA assumptions on normality, linearity, and homogeneity of variances of the data [89] and observed no major violations. The summary of the MANOVA results is shown in Table 7.Table 7 Summary of the effect of demography data on AT, SE, SI and PS.

Table 7Demographic variables	Dependent variables	
	AT	SE	SI	PS	
Gender	not supported	not supported	supported	supported	
Living Place	not supported	not supported	supported	supported	
Hours Spent with Technology	supported	supported	supported	supported	
Type of internet connection	supported	supported	supported	supported	

MANOVA test indicates the significant effect of gender on the combined effect of AT, SE, SI, and PS (F (4,842) = 2.904, p < 00.05; Wilks's lambda = 0.986). Further assessment between subjects indicates the non-significant effect of gender on AT (F (1,845) = 2.975; p > 00.05) and SE (F (1,845) = 2.492; p > 00.05). In contrast, gender shows a significant effect on SI (F (1,845) = 8.515; p < 00.05) and PS (F (1,845) = 7.887; p < 00.05).

Similarly, the effects of living place are statistically significant (F (7,838) = 3.083, p < 00.05; Wilks's lambda = 0.971), on the combined effect of AT, SE, SI and PS. Further assessment between subjects indicates students' place of living had a significant effect on SI (F (2,844) = 4.083, p > 0.05), and PS (F (2,844) = 6.805, p > 0.05). The effect of living place on AT (F (2,844) = 2.707, p > 0.05), and SE (F (2,844) = 2.198, p > 0.05) were found non-significant.

The effects of hours spent with technology on the combined effect of AT, SE, SI and PS was found significant (F (7, 837) = 3.689, p < 00.05; Wilks's lambda = 0.935). Additionally, the effects of daily time spent with technology on AT (F (3, 843) = 4.844, p < 0.05), SE (F (3, 843) = 9.398, p < 0.05), SI (F (3, 843) = 9.521, p < 0.05) and PS (F (3, 843) = 10.121, p < 0.05) were found significant too.

Finally, the effects of the type of internet connection were also found significant on the combined effect AT, SE, SI and PS (F (7, 837) = 3.616, p < 00.05; Wilks's lambda = 0.915). The evidence of the effect of the type of internet connection on AT (F (3, 843) = 7.357, p < 0.05), SE (F (3, 843) = 5.920, p < 0.05), SI (F (3, 843) = 7.152, p < 0.05) and PS (F (3, 843) = 4.968, p < 0.05) were found significant.

5 Discussion

Current trends show increased penetration of technology use in higher education. Technology has become the key instrument to provide educators with the ability to create an independent, personalized, interactive and often low-cost learning environment [90]. However, efficient integration of technologies in higher education does not always lead to achieving the desired learning outcomes. Particularly students' acceptance, appreciation, and satisfaction with technology usage in the learning process is a longstanding issue that exists in educational settings [46,91]. This study attempted to address this problem in the context of Bangladeshi polytechnic institutes. We have investigated several factors that can influence polytechnic students’ perceived satisfaction.

The findings show that social interaction is a crucial component in influencing the perceived satisfaction of polytechnic students about the use of technology. As revealed, polytechnic students' interaction with peers and teachers through technology increases their perceived satisfaction towards technology use. This finding is in line with other studies which confirmed the positive effect of social interaction on students' perceived satisfaction [92]. In fact, a recent study strongly argued for improving student interaction with their teachers and peers during online instruction to facilitate learning performance and satisfaction [93]. Sun et al. [94] found that online peer feedback and teacher guidance enhanced students' learning motivation and satisfaction in a blended learning environment. Also, students perceived social presence and teacher immediacy were positively related to their satisfaction and learning outcomes in an online course [95]. Several studies found that online collaborative learning activities enhanced students’ satisfaction and motivation in a blended learning environment, specifically online interaction treatments had positive effects on student satisfaction and learning outcomes [96]. These studies suggest that technology-mediated interaction can foster a sense of community and support among students and teachers, which can improve their attitudes and performance in technology-enhanced learning contexts.

However, the above findings also contradict other literature that suggests that technology-mediated interaction may have negative impacts on students' satisfaction and engagement. For instance, a study by Kirschner and Karpinski [97] argued that social media use may reduce students’ academic performance and satisfaction due to increased distractions and multitasking. Moreover, a survey by Chen and Jang [98] revealed that students who preferred face-to-face interaction over online interaction were less satisfied with their online learning experiences. Therefore, the finding of the research needs to be interpreted with caution and contextualized within the specific characteristics of the polytechnic students and the technology tools they used.

The current study also revealed that learners' self-efficacy is an important determinant of perceived satisfaction in a technology-mediated environment. In fact, self-efficacy is found to be a significant predictor of student satisfaction, confidence, readiness, and ability to perform learning tasks by using modern technology [36]. Research showed that much of human motivation is cognitively generated [99]. Therefore, students' confidence in their abilities to cope with any discomfort situation when using technology would impact their motivation, behaviour, and reactions to a situation. Thus, students’ self-efficacy highlights the fact that their confidence and abilities to perform the learning task using technology led to greater satisfaction. On the other hand, some studies have found limited or no significant relationship between self-efficacy and satisfaction in technology-mediated learning environments. For instance, Liaw and Huang [58] found no significant relationship between self-efficacy in computer use and perceived satisfaction with online learning environments. Similarly, there is a report of a weak positive correlation between self-efficacy and satisfaction [100].

The results of this study further suggest that the polytechnic students' attitude towards technology has a significant negative effect on perceived satisfaction. A previous study indicated attitude as a powerful predictor of satisfaction [58]. Our result reported a negative effect of attitude on polytechnic students' perceived satisfaction. According to this finding, polytechnic students’ perceived satisfaction was greater when their attitude toward technology was lower. On the surface, it looks contradictory to the findings of other studies where a positive relationship between attitude and perceived satisfaction has been reported [101]. In essence, the observed variables in the current study that predict attitude have mostly described the level of difficulty that students face during technology use. Thus, students who agreed with this statement, i.e., they agreed that using technology is difficult, most likely demonstrated lower perceived satisfaction. This is why a negative correlation is observed between attitude and satisfaction.

Previous studies indicated the significant impact of social interaction on attitude [63]. Few studies also emphasized the reverse effect of attitude on social interaction. Considering this relationship, a study found students’ attitude towards technology has a significant effect on social interaction [67]. Our study also found a significant positive effect of attitude on social interaction. Polytechnic students with a positive feeling and attitude towards technology use showed increased social interaction with their instructors and classmates.

Further, the effect of anxiety on student attitude was found non-significant. However, prior studies have shown that technology anxiety causes negative attitudes [102]. Also, the effect of PU and PEU was found non-significant on attitude. Several research studies support the argument that PU and PEU have a positive direct effect on attitude [42,103]. However, it is noteworthy to indicate the weak effect of PU and PEU on attitude reported by these research studies. Further, these weak effects of PEU and PU on attitude have also resulted in an exclusion of attitude construct [104].

Demographic parameters such as gender, living place, hours of using technology and type of internet connection are the other key factors in understanding and appreciating the theoretical constructs that support student learning through technology [105]. The results of this study pointed out the significant effect of study time and type of internet connection on AT, SE, SI, and PS. Thus, polytechnic students’ attitudes, the way they interact through technology (self-efficacy) and their feelings of satisfaction are influenced by the amount of time they study through technology and the nature of the internet connection they use. Additionally, the effect of gender and living place has a significant impact on SI and PS which supports the findings of recent studies [106]. Research showed that women are more likely to use social media and online communication platforms to connect with friends and family [107]. Similarly, women utilize technology more for relationship building and emotional support, while men tend to use it for information gathering and entertainment [108]. Also, research suggests that individuals living in urban areas may engage in more online social interaction than those in rural locations. For example, Rainie and Wellman [109] found that students who lived in city areas had larger online networks and utilize technology more frequently for social connection compared to those who lived in rural areas. This is because students in city areas have greater access to technology and internet infrastructure. In terms of perceived satisfaction, other studies showed that female students are more likely to experience negative emotions (such as anxiety) related to technology use compared to male students [110]. Also, students living in villages may experience less satisfaction due to issues and technical challenges they face while using the technology and the Internet [111].

However, gender differences do not always play a significant role in technology use. A study found that the frequency of online social interaction was more influenced by age and personality than by gender [112]. Research also suggests that factors beyond living place, such as socioeconomic status, age, and cultural background, also play a significant role in shaping social interaction and technology use. Individuals within lower socioeconomic brackets may have limited access to technology, regardless of their living location [113]. Cultural norms and values can also influence how individuals utilize technology for social interaction [114].

In conclusion, though the findings of this study are greatly in line with the recent research findings, several studies have found otherwise contrasting results as discussed above. The inconsistencies in findings may be also stem from the differences in how perceived ease of use, perceived usefulness, social interaction, attitude, anxiety, self-efficacy, and satisfaction are measured across studies. Differences in methodology, sampling procedures, and measurement tools used across studies can also lead to inconsistent findings. Also, learners' prior experiences with technology, their learning styles, and their self-regulatory skills can play a role in how students use technology which affects their satisfaction.

On a theoretical premise, the findings support the social cognitive theory of learning, which posits that learners construct knowledge through social interaction and collaboration with others [115]. Previous studies have found that students' interaction with peers and teachers through technology can enhance their motivation, engagement, and learning outcomes [116]. In contrast, the findings contradict the transactional distance theory, which suggests that technology-mediated learning can create a psychological and communicational gap between learners and others, resulting in lower satisfaction and achievement [117]. Some studies have also reported that students’ interaction with peers and teachers through technology can cause challenges such as technical difficulties, lack of social presence, and information overload [118].

6 Conclusion

This study investigated polytechnic students’ perceived satisfaction while studying through technology with a focus on the structural relationships among the factors that affect polytechnic students' perceived satisfaction with technology use. The structural equation modelling procedure was used to propose the structural model that explains polytechnic students' perceived satisfaction and how it is influenced by other factors. Additionally, independent multiple analysis of variance (MANOVA) helped to appreciate the significance of the effect of some demographic data on student satisfaction. This study contributes significantly to our understanding of the complex interplay between perceived usefulness, perceived ease of use, anxiety, self-efficacy, attitude, social interaction, and perceived satisfaction within a technology-mediated environment. It reveals several key findings that can be crucial for developing blended and online learning environments.

One key finding is that self-efficacy, not perceived usefulness, or ease of use, plays a crucial role in shaping positive attitudes towards technology. Among other important findings, attitude significantly influences social interaction, suggesting its importance in promoting online engagement; and both social interaction and self-efficacy positively influence perceived satisfaction, highlighting their crucial roles in enhancing users' experience. Among the demographic variables, gender significantly affects social interaction and perceived satisfaction, indicating potential gender-specific technology use patterns; living place emerges as a factor influencing social interaction and perceived satisfaction, requiring further investigation; study time significantly affects all constructs except perceived usefulness and ease of use, suggesting the dynamic nature of technology use over time; and finally, type of internet connection significantly influences perceived satisfaction, emphasizing the importance of infrastructure for optimal user experience.

Overall, this study challenges the traditional TAM model by highlighting the primacy of self-efficacy over perceived usefulness and ease of use in shaping positive attitudes towards technology. Furthermore, it emphasizes the critical role of social interaction and self-efficacy and thus the crucial role of social cognitive theory of learning in enhancing perceived satisfaction. The findings of this study have both practical and theoretical implications for educational institutions as well as for the policymakers and educators considering the rapid change of the educational environment in the post-pandemic situation.

6.1 Practical implications

By highlighting self-efficacy's crucial role in shaping positive technology attitudes, the study encourages a shift in educational focus. In fact, the significance of self-efficacy to self-regulate students' learning through technology is consistently emphasized by educators [119,120]. Educators can implement strategies that build self-efficacy, such as providing scaffolding, promoting self-directed learning, and celebrating student successes. Policymakers can support initiatives that train teachers in self-efficacy building techniques and provide resources for self-directed learning.

Recognizing the positive influence of social interaction on technology use and satisfaction suggests the need for fostering online communities for student learning. Educators can create opportunities for collaborative learning, online discussions, and peer-to-peer support within technology-mediated environments. Policymakers can encourage the development of online platforms specifically designed to promote social interaction and knowledge sharing among students.

The results of the study clearly showed that students’ living place and gender are important factors to take into account when integrating technology into the curricula and teaching. Differentiated instruction and scaffolding support need to be adopted by educational institutions to meet the needs of each student. Policymakers and curriculum developers can play an important role in this regard. Also, educational institutions need to ensure equal access to computers and high-speed internet networks for all students. Finally, the findings of this study can apply to other countries with similar educational backgrounds and contexts. The empirical findings of this study would help policymakers rethink the integration of technology into the curricula and provide effective learning experiences for their TVET students.

In conclusion, the study's results can be used by policymakers and leaders in education to help them make well-informed decisions about curriculum reformation, professional development initiatives, and technology adaptations that will improve learning outcomes and satisfaction. In particular, this research offers critical information to academics, educators, and policymakers who want to enhance user experiences and engagement in technology-mediated environments.

6.2 Theoretical implications

The study provides a conceptual framework with different constructs related to students’ satisfaction and contributes to the current body of the literature by offering insights into the potential research problem. The findings of this study highlight the primacy of self-efficacy in shaping positive attitudes towards technology. This suggests that self-efficacy can be integrated as a central factor within the TAM model, alongside perceived usefulness, and perceived ease of use. Thus, the proposed model from the current study informs the development of more robust theoretical frameworks for understanding technology integration in educational settings.

The role of social interaction in increasing students’ perceived satisfaction is another important contribution of this study which is in line with the principles of Social Cognitive Theory (SCT). This study argued that learning within technology-mediated environments can be significantly influenced by social interaction and peer support, along with individual self-efficacy beliefs.

Finally, in the context of Bangladesh, the current study addresses the gap that exists in the current literature regarding students’ perceived satisfaction with technology use. This study extends the works of Al Mamun [28] and Islam and Habiba [121] by offering an observation of the structural relationships among several learning constructs related to the perceived satisfaction of polytechnic students. The results of this study pave the way for further research and the development of more effective strategies to optimize technology integration and enhance learning experiences in the digital age.

6.3 Limitations and future research

Although the data support the proposed model, this study has some limitations to consider when interpreting the findings. For example, this study used the convenience sampling method to collect the data. In convenience sampling, participants are not chosen randomly, which leads to potential sampling bias. This indicates that the sample may not be truly representative of the population, and the findings may not apply to other groups. However, the large sample size of the current study minimizes this limitation and helps to increase the generalizability of the findings.

The average variance extracted for one component i.e., anxiety, is less than the threshold value of 0.50. The construct was kept in the model because it was an individual-based construct and prior research investigations discovered a strong mediation effect of attitude on the links between anxiety and satisfaction.

This study found non-significant effects of perceived usefulness, perceived ease of use and anxiety on students' attitudes. Future research may investigate the nature of the relationship between these constructs and perceived satisfaction with a different sample size. Also, future research studies can consider a longitudinal survey approach to provide further insights into how students’ perceived satisfaction changed over time.

The study's findings capture a snapshot of user experience at a specific point in time. Future research could adopt longitudinal designs to track technology use and satisfaction over extended periods. This could be particularly useful to get insights into the long-term effects of technology integration and the potential impact of it on student satisfaction over time.

Finally, future studies can include more variables such as polytechnic students’ expectations, social desirability, study habits, digital well-being, etc. to further understand the complex dynamics and relationships of different constructs related to learning satisfaction.

Overall, the current study provides a comprehensive understanding of student satisfaction with technology use, but further research is necessary to address the limitations mentioned in the above discussion. By conducting rigorous and diverse research, we can continue to refine our understanding of technology integration and develop effective strategies to create engaging and rewarding learning experiences for all.

Funding information

This work is supported by the Islamic University of Technology under the grant of OIC scholarhip CASR/47/11/Proc/002 .

Data availability statement

The data associated with this study has not been deposited into any publicly available repository. The data will be made available on request.

Ethics declaration

This study was reviewed and approved by the Committee for Advanced Studies and Research (CASR), Islamic University of Technology, with the approval number- REASP/CASR/47/11/Proc/002. All participants provided informed consent to participate in the study.

CRediT authorship contribution statement

Nsangou Youmo Souleman Sadam: Writing – original draft, Software, Investigation, Funding acquisition, Formal analysis, Conceptualization. Md Abdullah Al Mamun: Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e35977.
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