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Abstract

Based on the information system success and situational strength theories, this study explores the moderating effect of learner quality and instructor quality on the relationship between information quality and student satisfaction with e-learning. Survey data were collected from 333 college students, and the proposed relationships were assessed using the SMARTPLS structural equation modeling tool. The direct relationship between information quality and student satisfaction is significant; however, the moderating effect of learner quality and instructor quality is insignificant. The moderating effects of learner and instructor quality have been less researched in the existing literature. This study contributes to uncovering the complex interactions between these variables and the relationship between information quality and satisfaction.

Keywords

e-learning student satisfaction information quality instructor quality learner quality

Introduction

The COVID-19 pandemic forced education and government institutions to operate remotely by switching to online forms of work and education. Educational institutions were particularly affected, as e-learning systems were not as widespread, and their implementation was limited to fewer higher education institutions. Both learners and instructors shifted from traditional face-to-face teaching methods to fully digitized online instructional modes. Although the changes were slow, the shift was intense for both parties, especially because governments mandated it. Hence, further understanding of the quality and success of e-learning is needed.

An e-learning system is a digital platform that provides all necessary tools and services to enable and enhance online education. The platform seamlessly manages and delivers educational content, effectively facilitates interactions and communication between learners and instructors, and offers robust functions for monitoring and assessing educational progress.

Hence, in e-learning, technological factors (digital platforms) and social factors (instructors and learners) interact. In contemporary teaching strategies, it is crucial to investigate the multiple dimensions of success of learners, instructors, and technology (Al-Fraihat et al., 2020). Student satisfaction (SS) is one of the most important indicators of e-learning success and quality. Existing research has identified several factors that affect SS. For instance, information quality (IQ) refers to the characteristics of the system output that are accurate, up-to-date, and complete (Petter & McLean, 2009). In other words, IQ on e-learning platforms concerns the tools for creating, managing, and delivering content. This ensures that the content is displayed clearly and easily for all learners. IQ is a critical antecedent of SS (Almaiah & Alismaiel, 2019; Cheng, 2020; Machado-da-Silva et al., 2014; Ohliati & Abbas, 2019). This is grounded in DeLone and McLean’s (2003) information system success theory, which states that high-quality information meets learners’ needs and expectations, increasing their satisfaction with the e-learning experience.

Many studies have assessed the impact of IQ on SS in pre-COVID-19 settings (see N. A. Abdallah et al., 2019; Al-Fraihat et al., 2020; Alkhattabi et al., 2010, 2011; Lee et al., 2009). E-learning has been effectively used during and after the COVID-19 pandemic, and existing research has identified several factors that could affect the direct relationship between IQ and SS (Saxena et al., 2021; Tran, 2022). Hence, it is proposed that the relationship between IQ and SS is not simple and that there could be other contributory factors that can affect the relationship (Kuo et al., 2014; Lu & Chiou, 2010; Shehzadi et al., 2020).

Studying moderating effects can help build a comprehensive understanding of the complex relationship between IQ and SS, especially in emergencies such as pandemics. Therefore, the present study considers social factors, such as learner quality (LQ) and instructor quality (InQ), as contributory factors affecting satisfaction and possibly moderating the relationship between IQ and SS. The significance of technology within both traditional and online educational frameworks is undeniable; simultaneously, it is essential to acknowledge that the effectiveness of e-learning tools in digital education is considerably influenced by both educators’ and learners’ traits and behaviors (Baber, 2021).

Assessing the role of LQ and InQ as moderators in the relationship between IQ and SS with e-learning will aid in uncovering the complex interactions between these factors and the relationship between IQ and SS. The moderating effects of LQ and InQ have been less researched in the existing literature. This is one of the first studies to assess both LQ and InQ as possible moderators in the relationship between IQ and SS. Research has been conducted on the role of moderators in the relationship between different antecedents and e-learning use, such as students’ long-term orientation and instructor support (Altalbe, 2021; W. Cidral et al., 2020; Mazlan & Sumarjan, 2022). However, to the best of our knowledge, there is little to no research in peer-reviewed databases that assesses LQ and InQ as possible mediators between IQ and SS.

Understanding what drives the success of e-learning remains a complex issue (James, 2021). Scholarly investigations have consistently acknowledged LQ, such as self-efficacy and learner behavior, as paramount and highly influential parameters for evaluating the attainment of e-learning success (Al-Fraihat et al., 2020). Additionally, multiple studies have suggested that the performance of instructors plays a pivotal role in the successful deployment of e-learning systems. Positive attitudes of instructors toward e-learning, their teaching methods, control in online classes, and enthusiasm for teaching online are all linked to better student satisfaction. InQ is thought to improve the way instructors and learners interact in e-learning, and when it is good, it is likely to lead to improved interaction (Saad & Yamin, 2021; Si, 2022). Subsequently, success in e-learning is a complex combination of key factors, including learners’ interpersonal behaviors, motivation, computer anxiety, self-efficacy, and instructors’ characteristics.

Furthermore, much research on e-learning has been conducted in developed countries (Al-Fraihat et al., 2020), with little in the Middle East/Gulf countries. This could be attributed to the slow shift to e-learning systems before the COVID-19 pandemic. When the COVID-19 pandemic forced a full shift to e-learning, it offered an opportunity to explore the future impact of e-learning in oil-rich countries that have the resources to apply learning systems and the required infrastructure. Accordingly, this research aims to answer the following questions:

Does IQ have an impact on SS?

Do LQ and InQ have an impact on SS?

Does LQ moderate the relationship between IQ and SS?

Does InQ moderate the relationship between IQ and SS?

The theoretical lenses for this study are information system success and situational strength theories. Information system success theory is a comprehensive theory developed by DeLone and McLean (1992) to provide an integrated view of the complex nature of information system success. This theory emphasizes that a system’s success and effectiveness are influenced by how well the system meets the needs and expectations of its users (DeLone & McLean, 1992). Subsequently, the direct relationships between IQ, InQ, LQ, and SS can be explained by considering information system success (DeLone & McLean, 1992). Situational strength theory describes the extent to which a situation informs the appropriate behavior of an individual in a particular context (Mischel, 1977). Strong situations often demand a similar type of response, whereas weak situations encourage individuals to respond naturally, as they react to what is comfortable. Subsequently, this theory can be used to explain the moderating effects of LQ and LnQ on the relationship between IQ and SS.

This study extends the current understanding of e-learning success by incorporating a dual-moderator model that examines the influence of LQ and InQ on the relationship between IQ and SS. This study’s theoretical contribution is the integration of information system success and situational strength theories to comprehensively understand the dynamics influencing student satisfaction in the context of e-learning. This study acknowledges the critical role of IQ in shaping SS by incorporating information system success theory. A novel aspect of this relationship is the recognition of the moderating influence of LQ and InQ. This nuanced approach recognizes that the effectiveness of e-learning depends not only on the quality of the information system but also on learners’ and instructors’ abilities. Situational strength theory is used to explain how differences in learner and instructor characteristics affect the strength of the relationship between IQ and SS. As a result, the study not only adds to our understanding of the factors that influence e-learning satisfaction but also provides a sophisticated framework for future research in the fields of information systems and educational technology. This study provides insights into e-learning dynamics within the Middle East and Gulf region, a rapidly developing educational landscape that has been influenced by significant infrastructure investments. In terms of management and organizations, this study provides a practical framework for managers to understand the complex interplay between technology and human factors in e-learning. This study also provides managers with data-driven insights to make informed decisions regarding e-learning initiatives. These insights can inform the allocation of resources, the adoption of new technologies, and the design of personalized learning experiences.

Literature Review

Information Quality and Student Satisfaction

The information systems success theory developed by DeLone and McLean (1992) describes IQ as a construct of e-learning and identifies IQ as “the degree to which the instructors’ teaching performance is enhanced because of the use of the information acquired from or through such systems” (p.66). Wang and Wang (2009) also define IQ as “the quality of the output from a web-based learning system” (p.767). The information systems success theory defines IQ in the context of e-learning, emphasizes the importance of both the content and form generated by e-learning platforms, and includes specific metrics for evaluation. This includes several variables such as information consistency, scope, relevance, efficiency, currency, completeness, accuracy, and timeliness. According to DeLone and McLean (1992) and Tella (2013), the theory incorporates aspects of course quality and flexibility into its assessment criteria; consequently, IQ in e-learning encompasses not only the characteristics of the information itself but also the quality and adaptability of the courses offered within the educational platform. E-learning systems are attractive to instructors because of the richness of their content, which can be delivered via the Internet (Lee et al., 2009). Compared to traditional learning methods, learning management systems can deliver relevant, accurate, updated, and rich course content that supports instructors’ perceptions of the system as a valuable and beneficial form of instruction (N. A. Abdallah et al., 2019; Lee et al., 2009).

IQ is often seen as a key antecedent of user (student in this research) satisfaction. The success dimension of user satisfaction accounts for the user’s level of satisfaction when using an information system and is considered a pivotal form of measurement (Al-Fraihat et al., 2020). Measuring user satisfaction becomes especially helpful when information system use becomes mandatory and the amount of use is not perceived as an appropriate indicator of the system’s success.

Existing studies have used the information systems success theory to understand the relationship between IQ and e-learning satisfaction. However, a review of these studies has highlighted some inconsistencies in their findings. For instance, the relationship between IQ and SS was not supported by Lwoga (2014), Mtebe and Raphael (2018), or Ghazal et al. (2018). Conversely, other studies found a significant relationship (Alyoussef, 2023; Elneel et al., 2023; Jami Pour et al., 2022; Kim et al., 2023).

Li and Zhu (2022) analyzed the factors affecting college students’ acceptance of and satisfaction with online learning platforms in China. They explored eight dimensions: IQ, system quality, information satisfaction, user attitude, user intention, perceived ease of use, and perceived usefulness. The results revealed various blended and online learning scenarios. For example, the quality of online learning platforms and information affects user satisfaction. Susilowati (2020) analyzed the effects of system, information, and service quality on user satisfaction with e-learning systems during lectures in Malaysia. They found that system, information, and service quality had positive impacts on user satisfaction, both partially and simultaneously, and IQ was the dominant variable affecting user satisfaction with university lecturers’ academic information systems. Another study (Nuryanti et al., 2021) determined and analyzed the effects of system, information, and service quality on user satisfaction with online learning systems in Tangerang and found that system, information, and service quality had a significant effect on user satisfaction with e-learning systems. Subsequently, the better the perception of the system, information, and service quality, the higher the user satisfaction (Nuryanti et al., 2021). Shahzad et al. (2021) also explored service quality, system quality, IQ, user satisfaction, system use, and e-learning portal success and demonstrated that information and system qualities are positively related to user satisfaction, and IQ was also positively related to system use. Achmadi and Siregar (2021) analyzed and explained the influence of system quality, IQ, and service quality on user satisfaction in e-learning systems in a Master of Accounting program. Using the analytical method of generalized structured component analysis (GSCA), they found that system, information, and service qualities significantly influenced user satisfaction with e-learning systems. The results demonstrated that the better the perception of the quality system, quality of information, and quality of service, the greater the increase in user satisfaction (Achmadi & Siregar, 2021).

These and other studies during the COVID-19 pandemic in different places and settings have demonstrated that IQ has a significant effect on SS with e-learning systems (Hapsara et al., 2020; Husin et al., 2022; Hwee Ling & Kan, 2020; Sabirin & Sulistiyarini, 2020). Therefore, we propose the following hypotheses:

H1: There is a significant and positive impact of IQ on SS.

Moderating Role of Instructor Quality

Instructors significantly impact program success in e-learning environments. Many factors can be used to evaluate their effectiveness, including reliability, assurance, empathy, and responsiveness (Alam et al., 2021). In addition, instructor response, timeliness, and attitudes toward e-learning play major roles in user satisfaction (Sun et al., 2008). Research has indicated that the utilization of e-learning systems may be influenced by InQ, which includes positivity, enthusiasm, encouragement from students, participation in interactive communication, and responsiveness. Subsequently, instructors need to cultivate these qualities to ensure that students are motivated and engaged in e-learning (Al-Fraihat et al., 2020).

The instructor’s role in the success of e-learning has received attention from researchers in e-learning literature (Abu Seman et al., 2019; Altalbe, 2021; Mazlan & Sumarjan, 2022). However, their attention has been limited, especially during emergencies. Keržič et al. (2021), in their study of 10,000 higher education students from 10 countries across four continents during the COVID-19 pandemic, demonstrated that the quality of e-learning is mainly influenced by the instructor’s active role in the process of online learning, service quality, and the overall system quality. Abu Seman et al. (2019) assessed the role of InQ in students’ acceptance of and satisfaction with the use of a learning management system. They found that students’ acceptance and satisfaction were significantly affected by InQ, service quality, and course quality.

Saad and Yamin (2021) reviewed the factors influencing e-learning continuance in higher education and found InQ to be one of the main factors. Multiple studies have suggested that instructors’ performance plays a pivotal role in the successful deployment of e-learning systems. An instructor proficient in administering e-learning tasks and promptly addressing students’ concerns is likely to enhance both the quality of learning and learner satisfaction (Saad & Yamin, 2021). Previous studies have indicated that InQ is a major determinant of e-learning success. The positive attitudes of instructors toward e-learning, their teaching methods, control of online classes, and enthusiasm for teaching online are all linked to better student satisfaction (Si, 2022). These qualities are thought to improve the way instructors and learners interact in e-learning, where good InQ is likely to lead to greater interaction (Si, 2022). InQ, which includes attitudes and skills, impacts learner attitudes toward e-learning and can directly and significantly impact learners’ performance (N. Abdallah & Abdallah, 2022).

IQ in e-learning refers to the mechanisms that deliver instructor-provided content and ensure that the system output is accurate, current, complete, relevant, understandable, and accessible. It emphasizes the quality of the content and forms that e-learning produces (N. A. Abdallah et al., 2019). E-learning tools are favored by instructors for the richness of the content they deliver through the Internet, making it a valuable teaching resource compared to traditional methods. (Lee et al., 2009). Recent research has indicated that the quality of information available on e-learning platforms has a significant impact on instructional quality. A recent study conducted by Li and Zhu (2022) established that IQ and system quality are the major factors determining user satisfaction with online learning platforms. Specifically, high-quality information leads to greater satisfaction with the information itself, which affects the perceived ease of use and usefulness of e-learning platforms. This study further revealed that in an online learning environment, the quality of information has a greater impact on user satisfaction than in a blended learning environment. This suggests that learners value the quality of learning resources provided by the platform in fully online contexts. When learners are satisfied with the quality of information, they perceive resources as more helpful and the platform as easier to use. These findings highlight the critical role of IQ in improving the effectiveness and user satisfaction of e-learning platforms, which has a direct impact on the quality of the instruction delivered.

According to situational strength theory, which suggests that context can either strengthen or weaken the impact of individual traits on behavior, InQ is a contextual factor that amplifies the influence of IQ on SS. Effective student engagement and SS are promoted by high InQ, characterized by traits such as responsiveness and a positive attitude toward e-learning (Mayer, 2008; Richardson, 2001).

Some of the studies before the COVID-19 pandemic, where the reliance on e-learning was not high, found a positive relationship between instructor attitude toward e-learning and user satisfaction (W. A. Cidral et al., 2018; Lwoga, 2014; Mtebe & Raphael, 2018; Sun et al., 2008). To the best of our knowledge, no previous study has specifically examined whether InQ moderates the relationship between IQ and SS. Hence, based on these arguments, we propose:

H2: There is a significant and positive impact of LQ on SS.

H4: InQ moderates the relationship between IQ and SS such that improved LQ would strengthen the relationship between IQ and SS.

Moderating Role of Learner Quality

LQ refers to attributes that can affect learning achievements, including existing knowledge, motivation, participation, and the capacity to acquire and retain skills and knowledge (Thindwa, 2015). The learner’s role stands as a vital determinant for the successful implementation of e-learning, with the quality of e-learning services contingent upon various facets of the learner. Scholarly investigations have consistently acknowledged LQ as a paramount and highly influential parameter for evaluating e-learning success. Findings from prior research have unequivocally established the positive influence of LQ on the success and quality of e-learning initiatives (Alam et al., 2021). The integral role of LQ in the effectiveness of e-learning systems has also been substantiated (Alam et al., 2021). E-learning, as a self-directed educational mode, requires learners to possess characteristics such as time management and autonomy. Consequently, several e-learning models have incorporated factors related to LQ within their proposed frameworks (Alenezi, 2022).

Aljuhani et al. (2022) found that service quality, LQ, and intention to use e-learning systems play crucial roles as determining factors in adopting and utilizing e-learning systems. Learners’ skills and knowledge positively influence the development and success of e-learning programs, leading to enhanced learning outcomes, satisfaction, and academic performance (M. F. Teng et al., 2023). Alenezi (2022) found that attitude partially mediates the relationship between service quality and e-learning system effectiveness. In the context of emergencies, where the shift toward online learning has accelerated, the importance of learner satisfaction with e-learning has become evident. School managers and instructors should focus on these factors to promote satisfaction with online learning during challenging times (Ayanwale & Oladele, 2021). Si (2022) explored the determinants of e-learning SS and identified system, learner, instructor, and interaction quality as significant factors contributing to SS.

Furthermore, the quality of the technological, information, learner, and educational systems was found to be essential for achieving perceived utility in e-learning. LQ has been highlighted as a crucial factor determining the effectiveness of e-learning systems (Candra & Jeselin, 2024). James’s (2021) comprehensive literature review demonstrated that success in e-learning is a complex combination of key factors, including learners’ interpersonal behavior, motivation, computer anxiety, self-efficacy, and instructors’ characteristics.

Learners’ characteristics, including computer efficacy and subjective norms, play major roles in their acceptance and use of e-learning. Moreover, computer anxiety is one of the main components limiting learners’ satisfaction with e-learning (N. Abdallah & Abdallah, 2022). Many studies have identified self-efficacy as the main factor in e-learning development. It is the level of user confidence at which a certain task can be successfully completed using a specific system. Thus, the higher their self-efficacy, the more they can achieve certain goals (Widjaja & Widjaja, 2022). Learners’ roles in e-learning success cannot be underestimated, and their success depends largely on attitudes and characteristics (Baber, 2021).

Individual learner characteristics are critical when assessing the viability of e-learning systems, affecting both information utilization and interpretation, and these include cognitive abilities, learning styles, and prior knowledge impact engagement with e-learning. (Sun et al., 2008). Learners with higher self-efficacy and advanced metacognitive skills engage more deeply and are more satisfied with their e-learning resources (Liaw et al., 2007). This body of research emphasizes the variation in SS due to individual differences, implying that IQ does not uniformly impact learners (Liaw et al., 2007; Sun et al., 2008). Davis’s (1989) technology acceptance model (TAM) supports this notion by emphasizing perceived usefulness and ease of use as critical determinants of technology acceptance, which are also subject to variation based on individual learner characteristics.

Situational strength theory (Mischel, 1977) can be applied to examine the influence of LQ on SS. According to this theory, LQ can act as a moderator and enhance the impact of IQ on satisfaction. This means that when there are strong indicators of LQ, such as advanced skills and knowledge, the benefits of high-quality content can be amplified. Consequently, learners with excellent attributes were more likely to derive greater satisfaction from high-quality information. Ultimately, this interaction strengthens the relationship between IQ and SS, and LQ plays a pivotal role in moderating this relationship.

LQ has been explored using many factors, as mentioned earlier, but it has been less examined to moderate the relationship between IQ and SS. Therefore, it remains unclear whether LQ moderates this relationship. Therefore, this study proposes the following hypotheses:

H3: There is a significant and positive impact of LQ on SS.

H5: LQ moderates the relationship between IQ and SS such that improved LQ would strengthen the relationship between IQ and SS.

Research Methods

Data Collection Tool

According to Burkell (2003), a survey could be the most appropriate instrument for obtaining perspectives and information about students’ experiences. Al-Fraihat et al. (2020) designed a questionnaire to test an e-learning model. The constructs from their model were extracted to build the research model. The questionnaire was modified to meet the aims of this study. Afterward, the items were pilot-tested on 80 students from two colleges of the Public Authority for Applied Education and Training in Kuwait. The questionnaire passed the pilot test and was then prepared for distribution to SurveyMonkey. We also ensured that students’ anonymity and personal details were protected and used only for research purposes. Informed consent was obtained from all participants before they completed the questionnaire. Participation was voluntary and participants were informed of their right to withdraw at any time.

Data Collection

The study chose two colleges with the highest student enrollment within the Public Authority for Applied Education and Training, specifically the College of Business Studies and the College of Basic Education. During the COVID-19 pandemic, these colleges switched to e-learning platforms and systems using Microsoft Teams and Moodle. They started training for the full transition in June 2020 and began actual use in August 2020.

The target sample size was 500, which was adequate for structural equation modeling analysis (Hu & Bentler, 1999). We randomly selected professors from both colleges and distributed the questionnaire through their teams on Microsoft Teams, Moodle, and Email. We began collecting data in July 2021, which is nearly a year of experience with e-learning. We used purposive sampling, which means that professors offered the questionnaire only to students engaged in e-learning; students who might have withdrawn from the courses were excluded. The questionnaire was distributed to more than 500 students. There were 355 responses, but the number of complete and adequate responses was 333, indicating an effective response rate of approximately 67%.

Participants

The study included 32.73% male students and 67.27% female students. In terms of their age, 50.75% were 17 to 20 years old, 36.94% were 21 to 23, and 12.31% were 24 and above. More than half the participants had no experience with e-learning before the COVID-19 pandemic, whereas 20.20% had little or limited experience.

Of the students, 90.09% used the e-learning system daily, whereas 6.61% used it more than once per week. Most students used it for educational content (74.47%), attended lectures (83.78%), completed homework and projects (75.68%), and communicated with instructors and peers (66.37%).

Data Analysis

Quantitative data were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3.2.7. PLS-SEM is a data analysis tool used in business and social sciences research because of its ability to handle large, complex models, and non-normal data (Hair et al., 2021b). PLS-SEM involves the assessment of measurement and structural models (Hair et al., 2021a). The measurement model involves examining factor loadings, construct reliability, and construct validity, whereas the structural model assessment weighs the path coefficients and tests their significance.

The PLS-SEM offers several advantages over the covariance-based SEM. It does not assume the normality of data and is more robust to violations of normality, can be used when the objective is to assess variance in outcome variables, and is suitable for studies with small sample sizes (Hair et al., 2021a).

Data Analysis and Results

Measurement Model

The measurement model displays the relationships between the constructs and indicators. Indicators with low factor loadings (<0.60) were removed (Gefen & Straub, 2005). None of the items in the study had factor loadings less than the recommended threshold of 0.70 (Hair et al., 2021a). The first component of the measurement model is the reliability analysis, which includes composite reliability. The desirable cutoff value for Cronbach’s alpha and composite reliability is 0.70 (Ringle et al., 2020). ll the latent constructs of the model had Cronbach’s alpha and composite reliability statistics over 0.70. Hence, construct reliability was established (Table 1).

Table 1.

Outer Loadings, Construct Reliability, and Convergent Validity.

Items	Outer loadings	Cronbach’s alpha	Composite reliability	Average variance extracted (AVE)
Information quality
°IQ1	0.869	0.848	0.908	0.767
°IQ2	0.881
°IQ3	0.877
Instructor quality
°InQ1	0.797	0.676	0.820	0.603
°InQ2	0.800
°InQ3	0.732
Learner quality
°LQ1	0.878	0.857	0.913	0.777
°LQ2	0.876
°LQ3	0.891
Satisfaction
°SAT1	0.875	0.903	0.932	0.775
°SAT2	0.899
°SAT3	0.857
°SAT4	0.890

The second component of the measurement model was construct validity, which included the assessment of convergent and discriminant validity. Convergent validity was established through the Average Variance Extracted (AVE), for which the cutoff criterion was 0.50 (Ringle et al., 2020. The AVE value for all constructs in this study was greater than 0.50. Hence, the constructs possessed convergent validity (see Table 1). Next, the discriminant validity of the constructs was established using the Heterotrait Monotrait (HTMT) ratio. According to Henseler et al. (2015), to examine discriminant validity, the recommended threshold should be less than or equal to 0.90. In this study, all HTMT values were below the threshold value of 0.90. Hence, discriminant validity was confirmed (Table 2).

Table 2.

Heterotrait-Monotrait Ratio.

	Information quality	Instructor quality	Learners quality
Information quality
Instructor quality	0.67
Learners quality	0.614	0.697
Satisfaction	0.673	0.775	0.874

Structural Model

The structural model assessed the significance of the relationships (paths) between the constructs in the proposed model (Figure 1). H1 evaluated whether IQ has a significant positive effect on satisfaction. The results revealed that IQ has a significant impact on satisfaction (β = .185, t = 3.87, p < .0001). Hence, H1 is supported.

Figure 1.

Research model.

H2 evaluated whether InQ has a significant and positive impact on satisfaction. The results revealed that InQ had a significant positive impact on satisfaction (β = .208, t = 4.071, p < .0001). Hence, H2 is supported.

H3 evaluated whether the LQ had a significant and positive impact on their satisfaction. The results revealed that LQ has a significant and positive impact on satisfaction (β = .559, t = 9.313, p < .0001). Hence, H3 is supported.

In addition to the analysis of direct effects, the moderating roles of InQ and LQ were assessed. The moderation analysis results revealed that InQ does not moderate the relationship between IQ and SS (β = −.052, t = 1.136, p > .05). Hence, H4 was not supported. Similarly, the moderation analysis results revealed that LQ does not moderate the relationship between IQ and SS (β = 0.007, t = 0.127, p > .05). Hence, H5 is not supported. The results are summarized in Table 3.

Table 3.

Hypotheses Results.

	Path coefficient	Standard deviation	T statistics	p Values	Results
H1: Information quality → Satisfaction	0.185	0.048	3.87	.000	Supported
H2: Instructor quality → Satisfaction	0.208	0.051	4.071	.000	Supported
H3: Learners’ quality → Satisfaction	0.559	0.06	9.313	.000	Supported
H4: Mod_Instructor Q → Satisfaction	−0.052	0.046	1.136	.128	Not Supported
H5: Mod_Learner Q → Satisfaction	0.007	0.059	0.127	.450	Not Supported

Discussion

This study examined the impact of IQ, LQ, and InQ on SS and the moderating role of InQ and LQ on the relationship between IQ and SS. The results revealed that IQ has a significant positive impact on SS. These results are consistent with those of previous studies conducted during the COVID-19 pandemic, suggesting that students tend to be more satisfied when they receive high-quality information (Al Maqbali, 2021; Achmadi & Siregar, 2021; Li & Zhu, 2022; Nuryanti et al., 2021; Shahzad et al., 2021; Susilowati, 2020). The significance of IQ in improving SS aligns with the information system success theory (DeLone & McLean, 1992). High-quality e-learning information that is accurate, relevant, and understandable positively affects user satisfaction. This finding suggests that when learners perceive information in an e-learning system to be of high quality, their overall satisfaction with the system increases.

The study also found that information and LQ had a significant and positive impact on SS. First, InQ has a significant impact on students in both online and offline learning contexts. Existing research confirms this relationship (see Gopal et al., 2021; Nalintippayawong et al., 2023; Phan et al., 2023; Romeo et al., 2022; C. Teng, 2023). The significance of the relationship can be attributed to the fact that instructors are players who can promote students’ engagement by using different media available in e-learning platforms to encourage students to communicate and engage (Al-Fraihat et al., 2020; Mazlan & Sumarjan, 2022). Instructors are available to answer student queries and provide timely and constructive feedback, fostering a sense of community between students and instructors, which usually contributes to students’ satisfaction by creating an engaging and collaborative learning environment despite the physical distance (Mazlan & Sumarjan, 2022).

Second, extensive academic research has consistently recognized the importance of learner attributes as primary and influential factors in assessing the success of e-learning (Alam et al., 2021; Alenezi, 2022; Aljuhani et al., 2022; Ayanwale & Oladele, 2021; M. F. Teng et al., 2023). Learners’ participation and engagement play crucial roles in determining the effectiveness of e-learning because the quality of e-learning services depends on several aspects related to the learners themselves. The reason for these results may be that the main users and players in e-learning are learners. Their attitudes, engagement, self-motivation, collaboration, and interactions with each other and instructors directly affect the success of e-learning and, in return, their satisfaction (Kumar et al., 2021). The findings of the significant and positive impacts of InQ and LQ on SS align with information system success theory. InQ and LQ are considered dimensions of service quality and user characteristics, respectively. Instructors who possess high-quality teaching abilities and when learners exhibit favorable attributes contribute to user satisfaction, which is consistent with information system success theory.

The results of the moderation analysis revealed that neither InQ nor LQ moderated the relationship between IQ and SS. Previous studies have used moderators to assess the use of e-learning, but the use of e-learning in Kuwait was mandatory. Satisfaction is, therefore, a critical outcome. Both learners and instructors play major roles in the learning process. The lack of a significant moderating role for instructors and LQ can be attributed to the fact that e-learning, when first applied in Kuwait during the COVID-19 pandemic, was not optional. This pushed both instructors and learners to use it effectively because they did not have any other choice; this can be related to the context in which the study was conducted, as well as the characteristics of the sample. Future research with different contexts may yield different results. Another reason may be that the importance of IQ, which may play a dominant role in determining satisfaction (Rachmat et al., 2022), had no other variables that could overcome its effect. Additionally, other variables may act as moderators between IQ and SS, which can reduce the role of InQ and LQ (Abdullah Al Thnayan & Sami Mohammed Husain, 2021)

Furthermore, the lack of support for moderation can be discussed through the lens of situational strength theory, which suggests that other situational factors, such as the design of the e-learning platform or the availability of resources, may have a stronger influence on satisfaction than the individual qualities of instructors and learners. These situational factors can reduce the effects of the proposed moderators on the relationship between IQ and SS.

Practical Implications

Policymakers, system developers, and educational institutions can benefit from this research. System developers can consider adding features that help improve IQ, promote effective instructor support, and cater to diverse learner needs. Providing professional development opportunities for instructors to enhance their online teaching skills, communication abilities, and technological proficiency can improve InQ. Encouraging learners to develop the necessary attributes and skills for an e-learning environment through training and courses can contribute to increased satisfaction.

Given the importance of IQ for SS, educational institutions should invest in systems and processes to ensure the accuracy, relevance, and timeliness of e-learning. Having mechanisms for the easy updating and maintenance of information on e-learning platforms can also help keep the content relevant and engaging. Educational institutions should prioritize professional development programs to improve instructors’ online teaching skills when it comes to InQ. Training in effective online communication, the use of multimedia tools to engage learners, and strategies for providing timely and constructive feedback are some examples. Recognizing the importance of LQ in determining satisfaction, educational institutions should devise strategies to encourage learner engagement, self-motivation, and collaboration. The lack of support for the moderating role of instructors and LQ opens further avenues to suggest that other factors may influence this relationship. Institutional administrations should consider investigating and integrating additional variables that may influence the impact of IQ on satisfaction, such as platform design, user interfaces, and availability of learning resources. In line with situational strength theory, educational administrations should consider the broader context in which e-learning occurs. Factors such as the overall design of the e-learning platform, the availability of support resources, and the specific circumstances under which learning occurs can impact satisfaction. Understanding these factors can guide educational institutions to create stronger and more cohesive e-learning environments.

Theoretical Implications

The results of this research support the information success theory because IQ is identified as an important factor in determining satisfaction. This also demonstrates the strength of the direct impact on IQ and SS, regardless of InQ or LQ. These results also contribute to existing literature by confirming the importance of instructor, learner, and SS in e-learning environments. It also extends the literature by providing valuable insights into the role of InQ and LQ in not moderating the relationship between IQ and SS, and identifying the need for future research to identify additional factors that moderate the relationship between IQ and SS.

Limitations and Future Research Directions

The study was conducted in Kuwait during the COVID-19 pandemic when e-learning was newly introduced to learners and instructors; this might have impacted the study results and limited its generalizability to other populations, such as those who used e-learning before the COVID-19 pandemic. Contextual factors, such as the e-learning platform used, the subject area studied, and the cultural background of the participants, may have influenced the results. Considering other contexts and conducting studies in different cultural settings can help develop a more comprehensive understanding and comparative results. In addition, the study variables were limited, and investigating other potential factors, such as usability, interactivity, or social presence, can help capture a broader understanding of the e-learning experience. This research is cross-sectional and limited to the COVID-19 pandemic; future research might conduct longitudinal studies to examine how satisfaction evolves in e-learning and the factors that contribute to its stability or change. Finally, utilizing a mixed-methods approach in future research can provide a more comprehensive understanding of satisfaction and its underlying factors.

Future research can explore additional variables, employ a mixed-methods research methodology, and consider diverse contexts to enhance the understanding of e-learning satisfaction. These implications emphasize the importance of focusing on information, instructor, and LQ to improve SS in an e-learning environment.

Footnotes

Appendix

Appendix 1.

Microsoft Teams Success Factors Questionnaire.

Microsoft Teams Success Questionnaire
1. Gender ○ Male ○ Female
2. Age ○ 17–20 ○ 21–23 ○ 24-above
3. Academic Status ○ College ○ University
4. Location ○ Assima ○ Hawally ○ Alahmady ○ Mubarak Alkabeer ○ Aljahra ○ Alfarwania
5. What was your Microsoft Teams experience pre-pandemic? ○ No previous experience ○ Some experience (less than a year) ○ Very good experience (1–2 years) ○ Advanced experience (more than 2 years)
6. How frequently do you use the Microsoft Teams? ○ Every day ○ Once a week ○ More than once a week ○ More than once a month
7. What do you use the Microsoft Teams for? Choose all that apply. ○ To access course information ○ To listen to lectures ○ To do my homework and projects ○ To complete exams ○ To communicate with the instructor and other students ○ To view my grades ○ To monitor my attendance
8. What do you think about the Microsoft Teams information quality? ○ Information and resources needed from Microsoft Teams are always accessible. ○ The structure of Microsoft Teams is well organized into logical and understandable components. ○ The content of Microsoft Teams is up to date.
9. What do you think of Microsoft Teams as a learner (Learners’ Quality)? ○ I believe it is good to use Microsoft Teams. ○ I’m not intimidated by using Microsoft Teams. ○ I am able to successfully perform tasks on Microsoft Teams.
10. What do you think about Microsoft Teams instructor quality? ○ I use Microsoft Teams because my professor uses it. ○ I think communicating and interacting with instructors are important and valuable on Microsoft Teams. ○ Generally, my instructors have a positive attitude toward the utilization of Microsoft Teams
11. How satisfied are you with using Microsoft Teams? ○ I am satisfied with the performance of Microsoft Teams. ○ I enjoy using Microsoft Teams in my studies. ○ Microsoft Teams satisfies my educational needs. ○ Overall, I am pleased with the experience of using Microsoft Teams.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Mariam A. Alterkait

Manal Y. Alduaij

Data Availability Statement

The research data is available on request.

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Impact of Information Quality on Satisfaction with E-Learning Platforms: Moderating Role of Instructor and Learner Quality

Abstract

Keywords

Introduction

Literature Review

Information Quality and Student Satisfaction

Moderating Role of Instructor Quality

Moderating Role of Learner Quality

Research Methods

Data Collection Tool

Data Collection

Participants

Data Analysis

Data Analysis and Results

Measurement Model

Structural Model

Discussion

Practical Implications

Theoretical Implications

Limitations and Future Research Directions

Footnotes

Appendix

Declaration of Conflicting Interests

Funding

ORCID iDs

Data Availability Statement

References