Antecedents of Perceived Service Quality of Online Education During a Pandemic: Configuration Analysis Based on Fuzzy-Set Qualitative Comparative Analysis

Abstract

Background

During COVID-19 lockdown worldwide, classroom education continues remotely through online. The question remains, comparing with the face-to-face education, does online education has a similar satisfaction level among the students? There are only a few studies that examine the perceived service quality of online education.

Objective

The study aims to analyze the factors of perceived service quality of online education during a pandemic.

Research Design

A structured questionnaire elicits information from 147 students from different study backgrounds of various universities worldwide. The fuzzy-set qualitative comparative analysis (fsQCA) is used for data analysis and model design. Research constructs evaluation for reliability and internal consistency are subsequently performed. A snowball random sampling method is applied for data collection.

Results

Findings from the fsQCA analysis identify four core factors that underpin student satisfaction through positive perceived service quality of online education. Alternative paths are determined based on gender, students’ current education status, and their loyalty toward online education. We also introduce two topologies of perceived quality regarding online education and student satisfaction.

Originality

Because of the primary nature of the data, this is firsthand experience gathered from different universities around the world who have willingly or unwillingly experienced online learning during the pandemic. The fsQCA technique for examining perceived service quality of online education.

Conclusions

The findings contain a number of contributions, illustrating different topologies of the student from different backgrounds and their intention, satisfaction and loyalty towards e-learning, and identifying causal factors that influence willingness to recommend online education.

Keywords

perceived service quality online education fuzzy-set qualitative comparative analysis pandemic

Introduction

Perceived service quality implies consumers’ perceptions of a profession’s general differentiation or dominance (Berry et al., 1988). Customers assess service excellence based on how much pleasure they have derived from service. On the other hand, quality online learning incorporates research-based best instructional techniques used in an online context. The international community is experiencing difficult times as a result of the coronavirus pandemic (COVID-19). The deteriorating economic and social chaos is visible in the medical sector and all aspects of society. Consequently, the World Health Organization (2020) classified COVID-19 as a pandemic, elevating it to the highest level of danger for infectious illnesses.

COVID-19 has stimulated many corporations to modify their systems and strategies and adopt new technologies. Most corporations did not have enough time to think about incorporating and adapting these new strategies and associated technology into their existing settings and operations (Carroll and Conboy, 2020). Universities all across the world were no different. COVID-19 has a huge impact on students, instructors, and educational institutions all across the world, much as many other parts of daily life. (Mailizar et al., 2020). Its worldwide expansion has resulted in the entire suspension of public school and university classes, causing institutions to modify their original instructional goals (Toquero, 2020). It is not the first time that traditional educational activities have been interrupted because of an infectious disease. Viruses such as SARS (2004) and H1N1 (2009) have already affected existing educational initiatives in a number of countries. To address the dysfunction produced by COVID-19 and restore standard academic order, several countries have decided to encourage online education using technologies such as Zoom and Google Meet. (La Rotta et al., 2020)

Today, online education is an essential teaching method. Student satisfaction is found to be highly connected to student-perceived service quality (P. S. H. Tan et al., 2021). Prior to COVID-19, education technology has already advanced significantly; global education technology investment totaled $18.46 billion in 2019, and the online education sector is expected to reach $350 billion by 2025. The use of language-related apps, virtual tutors, video conferencing tools, and online learning software has exploded since the COVID-19 crisis, which is unsurprising given this increase. (World Economic Forum, 2020).

COVID-19 has created a new form of non-face-to-face online learning. First, owing to the unexpected nature and rapid spread of the outbreak, online education systems were developed in a short period without sufficient national review (Kéri, 2021). As part of a broader cultural agreement, most people unconsciously accepted online education systems. Despite the tremendous global expansion, online education has only served as a foundational infrastructure to supplement traditional classroom instruction (Mishra et al., 2020), Because there had been few previous situations of a large-scale online curriculum being employed in response to emergencies such as a pandemic, no institutions were fully prepared to switch to online education. (Park and Lee, 2021). As a consequence, short-term decisions and practices were implemented in all sectors of education.

Following several modifications to the opening of educational institutions in compliance with the government’s strict social distancing policies, the Ministry of Education of various nations eventually consented to allow online education, ultimately resulting in the full adoption of online classes (Tripathi and Mani, 2022). Before this, universities, which have a more significant proportion of international students than primary, middle, and high schools, had already begun using online classrooms. (Keržič et al., 2021). Educational institutions in affected regions are looking for temporary solutions to keep teaching going, but it is crucial to remember that the quality of learning is directly proportional to the level of digital access and efficiency. When it comes to student motivation, satisfaction, and interaction, the online learning environment is vastly different from the typical classroom setting. Depending on the circumstances of the educators, each institution can choose one of these methods or combine two or more to deliver courses. As an outcome, teaching methods have become more complex than ever, meaning class quality and satisfaction might vary greatly depending on the instructor (Ramírez-Hurtado et al., 2021). Because of the unprecedented expansion of COVID-19, online classes began early in the first semester. According to (Asarbakhsh and Sandars, 2013), system failure during teaching, disconnection of video and voice systems, and technological challenges all have an influence on educational satisfaction.

COVID-19’s impacts on the educational environment have become explicit, and the world of education has reached a tipping point in response to these developments in the post-COVID period. Ha (2020) suggests that schools’ online platforms be quickly developed, while (Toquero, 2020) claims academic institutions must update their curriculum and utilize creative teaching methods, highlighting the importance of technology in education. Furthermore, although students were unable to receive help from peers in classrooms and labs, or even access facilities such as libraries and gymnasiums (Patricia Aguilera-Hermida, 2020, p. 19), online education has aided in the prevention of COVID-19 spread and allowed school curricula to be taught (Mishra et al., 2020). According to UNESCO, 186 countries had imposed nationwide closures by the end of April 2020, affecting 73.8% of all enrolled students (UNESCO, 2020). Even if the only means to limit the development of the COVID-19 by interrupting the transmission chain are lockdown and social separation, the closure of educational facilities has impacted a huge number of students.

Fuzzy-set qualitative comparative analysis (fsQCA) is one of the first tools for examining causal relationships (Rihoux and Ragin, 2009), offering multiple solutions that can explain the same outcome, demonstrating how perceived service quality of online education among university students is defined by its antecedents in this study. Fuzzy-set qualitative comparative analysis gives a more in-depth interpretation of the data and should be seen as an alternate and supplementary way to traditional variance-based approaches (Pappas et al., 2019). The findings of fsQCA provide diverse, unique, and equally effective combinations of the core teaching process, reliability, convenience, and IT system structure, which explain students’ satisfaction toward online education.

This article contributes to the literature in three ways. First, we conducted a demographic study utilizing a questionnaire survey to gather students’ opinions. Second, using the perspectives of teaching technique, reliability, convenience, and IT infrastructure, we investigate multiple topologies of students’ satisfaction with online education. The influence of their combined effects on the intention to continue online study is then evaluated. Finally, we apply fsQCA for the first time to investigate the causal relationship between student satisfaction with online education. The outcomes of this study suggest that none of the research constructs are required or sufficient to understand students’ happiness with online learning; rather, their interactions lead to a willingness or unwillingness to recommend e-learning. Identifying relationships between the structures stated above can aid faculty and practitioners in identifying patterns that inspire student intents, providing better academic content, and refining the online learning method.

The remainder of this paper is organized as follows: Background and Hypotheses Development presents a theoretical framework for online learning during a pandemic, and an explanation of the conceptual model and research hypotheses, as well as the teaching technique, dependability, convenience, and IT system structure. Methodology goes through the study methods in depth, as well as the implementation of fsQCA and how it works. The configurational analysis results using fsQCA are presented in Analysis together with the important facts discussed in the discussion. All of the important findings and discussions are included in Findings and Discussion. Conclusions and Implications concludes with a discussion of the findings and their implications, and also limitations and future research prospects.

Background and Hypotheses Development

Perceived service quality of online education refers to students’ cognitive appraisal of the learning service across episodes compared with stated or implicit comparison standards. Because of the pandemic, the availability of education programs in the online medium has expanded dramatically in recent years. Hence, institutions enable students to adjust to the online learning environment until the university resumes face-to-face sessions. This fact has forced the evaluation of the quality of services like educational, institutional, and information technology systems, among others, whose circumstances and features differ from those of the conventional modality. These services eventually have some detrimental or good impact on students’ satisfaction, loyalty, and willingness to continue online education.

Teaching Service (Core service) and Perceived Quality

The term “teaching service” refers to a specific collection of service features that are only relevant to teaching. For instance, teachers’ knowledge, insight, and instructional strategies, the feedback they provide to students on the various activities they participate in, and their quickness and efficiency in answering student questions.

H1. The better is teaching service, the higher is perceived service quality.

Convenience and Perceived Service Quality

Researchers and managers have noticed a significant growth in tourist demand for convenience and are considering this issue due to numerous socio-cultural and economic variables (Joseph et al., 1999), (Mohd Kassim and Souiden, 2007). In practice, as part of a tactical change toward more effective customer management, corporations invest more in delivering convenience (Chang and Polonsky, 2012) Scholars are also profoundly focused on grasping the effects of convenience, given its distinctive features (Kuo and Wu, 2012) (Chang and Polonsky, 2012). Customers’ impressions of service convenience are heavily influenced by the effort and time necessary to utilize or procure the service (Roy et al., 2016) (Kuo and Wu, 2012). According to Berry et al. (1988), convenience enables a firm to improve both its level of customer service and overall effectiveness. Customers are more likely to acquire the organization’s goods/services if they perceive convenience throughout the purchasing process, whereas post-purchase convenience promotes customer satisfaction (Farquhar and Rowley, 2009). The convenience post-purchase highlights how consumers succeed in addressing their concerns after acquiring a product/service, focusing on a store’s return and exchange procedures (Colwell et al., 2008). Several studies have been performed to study the effect of service convenience on perceived service quality. (Akdere et al., 2018). These considerations result in the following hypothesis:

H2. The greater is convenience; the greater is perceived service quality.

Reliability and Perceived Service Quality

Reliability refers to the consistency of scores across replications. Item, form, raters, or occasions are examples of sources of measurement error and the basis for imitations in education. When the consequences of test use are high stakes, reliability is regarded as becoming increasingly important. Recognizing reliability and validity permits educators to make decisions that enhance their students’ academic and social life since these principles inform educators how to test the conceptual goals their school or district has specified. Several experts feel that the amount and validity of available information are essential factors in assuring the quality of online services. (Commas, n.d.)

H3. Reliability has a significant impact on perceived service quality.

IT System and Perceived Service Quality

IT systems refer to a safe and sustainable information system for employees and customers and adequate internet quality and up-to-date webpage and social media presentations (Celik & Yildiz, 2017) (Demir et al., 2020) that provide sufficient information about the organization. Furthermore, consistency by these services is an essential factor in improving innovativeness (Kleis and Ramirez, 2012). Furthermore, in the educational concept, IT readiness is vital for training students and instructors to adapt to the University’s new System. Some of the most important influencing factors on students’ and lecturers’ perceptions are providing a strong internet connection, appropriately presenting live and offline sessions, and sharing class materials on time and correctly (Hossain et al., 2020). According to the researchers, technological adequacy and readiness significantly impact the productivity or disadvantage of the processes (An Updated and Streamlined Technology Readiness Index: TRI 2.0—A. Parasuraman, Charles L. Colby, 2015, n.d., p. 0) (Huynh et al., 2020). Furthermore, (Irfan et al., 2018) stated that adequate resources in IT systems increase the success of universities in adapting to conversion.

H4. A better IT system is required to have a positive influence on perceived service quality.

Perceived Service Quality and Satisfaction

Customer satisfaction refers to the perception formed by visitors who have observed positive organizational effectiveness and have had their expectations fulfilled (Mukhopadhyay and Gupta, 2014). The quality of service offered by service providers determines the amount of customer satisfaction (Tam, 2004). Several academics feel that customer satisfaction is tied to good emotions experienced when utilizing the product/service (Yang and Peterson, 2004). It encompasses visitors’ happiness, relaxation, and delight after purchasing and consuming any service or product (Suh and Yi, 2006). In fact, the greater the intensity of satisfaction, the higher the quality of the product/service (Suh and Yi, 2006). Hence, managers must understand consumer desires to produce services and products to please customers (El-Adly, 2019). Finally, from this point of view, satisfaction may be described as an organization’s capacity to satisfy the demands and expectations of its consumers (Mohd Paiz et al., 2020); (Huynh et al., 2020). Based on these arguments, the following hypothesis is proposed:

H5. The higher the perceived service quality, the greater is the level of satisfaction.

Satisfaction, loyalty and willingness to recommend

Loyalty is described as “a firmly held commitment to persistently patronize a favored service in the future, resulting in a recurrent same-brand purchase, regardless contextual considerations that may trigger switching behavior.” Customer loyalty, according to this concept, contains an attitudinal dimension as well as a cognitive approach. (Russell-Bennett and Bove, 2002); (Baldinger and Rubinson, 1996); (Hennig-thurau et al., 2001); (Koslowsky, 2000); (Gruber et al., 2010). Loyalty, on the other hand, “may not be a suitable outcome in the setting of higher education; rather, the behavioral intention may play an important role” (Sultan and Wong, 2013); (Sultan and Yin Wong, 2014). Behavioral intentions, according to the idea of reasoned action, are highly dependable predictors of subsequent behavior (LaCaille, 2013); (Anderson, 1980).

Finally, the purpose of this research is to demonstrate that the willingness to recommend the service is impacted, specifically by loyalty and the student’s level of satisfaction.

H6: Students’ level of satisfaction positively influences their intention to continue (willingness to continue studying).

H7: Students’ intention to continue has favorable influence on their loyalty.

H8a: Students’ level of satisfaction has a positive influence on their willingness to recommend their course.

H8b: Students’ intention to continue and loyalty influences their willingness to promote their courses in a favorable and substantial way.

Based on the above hypothesis, a conceptual model is shown in Figure 1.

Figure 1.

Conceptual model: Perceived service quality measurement at online education.

Methodology

The Qualitative Comparative Analysis and Fuzzy-Set Qualitative Comparative Analysis

The qualitative comparative analysis (QCA) is an established research method for testing theoretical hypotheses and producing theoretical insights. Qualitative comparative analysis is based on set-theoretic Boolean algebra logic. It follows the case comparison standards commonly used in qualitative research (Ragin, 2014). Using this methodological technique to explain empirically observed occurrences, a researcher can iterate between data, analysis, and theory (Furnari et al., 2021) (Sukhov et al., 2020). The QCA logic facilitates the need to understand a specific outcome in its factual context (Ragin, 2014). In other words, it supports researchers in comparing examples and studying the effect of multiple elements on the outcome by examining their relevance to the development. Qualitative comparative analysis also contributes to a greater configurational viewpoint by visualizing how multiple circumstances may work as recipes for a certain result. (Furnari et al., 2021) (Sihvonen and Pajunen, 2019)

As defined by, the QCA approach is based on three principles of causal complexity (Misangyi et al., 2017). First, conjunctural causation says that an event may occur due to a complex mix of circumstances. The reason for this principle is based on the idea of social processes in which several occurrences (different criteria that must be satisfied) must occur concurrently for the desired outcome to occur (Sihvonen and Pajunen, 2019). Conjunctural causation is the concept that a result happens when a number of conditions intersect, and it may be stated using the logical “AND” in set-theoretic operations. Second, equifinality indicates that different circumstances might lead to the same conclusion in different ways. This concept highlights the possibility of several explanations for the outcome (Schneider and Wagemann, 2012). By evaluating the logical “OR” between distinct configurations that result in the same conclusion, QCA emphasizes the need to know numerous alternative conditions connected to the intended goal (Furnari et al., 2021). Finally, causal asymmetry suggests that a condition may be linked to a result in one situation but not in another. The factors contributing to the absence of a phenomenon are the inverse of those contributing to its occurrence (A. Woodside et al., 2013). As a result, causal asymmetry is related to the idea of logical “NOT,” which states that a condition does not have to be linearly related to the outcome.

Qualitative comparative analysis is often carried out in one of two approaches. Using either a crisp set qualitative comparative analysis (csQCA) or a fuzzy-set qualitative comparative analysis (fsQCA). CsQCA aims to integrate qualitative and quantitative research approaches for analysis. Fuzzy-set qualitative comparative analysis, on the other hand, can manage both continuous data and partial identity in a given set (Mumu et al., 2022). The responses cannot be utilized directly in the fsQCA because the researcher must describe the elements and structures as theoretical ideas and give set membership in qualitative titles and thresholds that make logical sense concerning hypotheses. So, fsQCA is a case-oriented method that is not expressly geared at generality. (Ragin, 2014).

The fsQCA has various advantages as it employs both qualitative and quantitative assessments to identify the level to which a case belongs to a set (Rihoux and Ragin, 2009)

Data analysis using fsQCA suggests combinations of autonomous elements that also include scenarios that traditional variance-based approaches do not notice since they only record significant impacts (A. G. Woodside, 2014). Fuzzy-set qualitative comparative analysis gives for increased flexibility in data analysis. Fuzzy-set qualitative comparative analysis splits the sample into numerous subgroups, allowing it to analyze multiple criteria combinations. Every setup handles just a piece of the example, and an aberration will be available in just a fraction of the potential solutions. As such, certain solutions are likely to explain considerable sections of the sample based on a variance-based analysis, but others are likely to describe tiny aspects of the sample since they involve cases that are typically recognized as exceptions. Because it is not subject to exceptions, fsQCA is more flexible than variance-based techniques because the representativeness of a sample does not affect all solutions (Pappas et al., 2017). Thus, intentional decisions must be made, and these decisions must be documented to check the study’s effectiveness and allow for dependability during the analysis.

Research Design

This cross-country research strategy uses a survey to obtain information from a specified sample and investigate the correlations of relevant factors in the study. The survey’s target audience consists of undergraduate and postgraduate students from universities around the world. They have shared their opinion in the questionnaire survey about continuing their education in online since last year because of the COVID-19 pandemic.

Data Collection and Survey Instrument

Analyses of how students are pleased with online courses are carried out, as recommended by earlier research. To evaluate the quality of online learning services as multidimensional components, we selected to adapt measurement scales published by (Alzahrani and Seth, 2021) (Tharanikaran et al., 2017) (Stodnick and Rogers, 2008) (G. J. Udo et al., 2011). The selection criteria for these tools are based on past research on service quality in diverse situations and online customer service quality. These elements are then tailored to the online learning environment. As a consequence, the authors developed a 35-item questionnaire to assess the quality of online learning services. Furthermore, the survey included two questions to evaluate overall service quality and student satisfaction with online learning. The questions on this preliminary scale are then reviewed qualitatively with a group of students who have at least one and a half years of an online learning experience. Based on the feedback and reviews, three things are eliminated. The following entries are morphologically adjusted for completeness and correctness. The recalibrated scale is then returned to the group for assessment to determine that the updated preliminary scale had content validity. The final questionnaire consisted of 32 items, 17 of which assessed students’ perceptions of the quality of online learning services based on their previous one and a half years of e-learning experience. Similarly, four questions rated overall e-learning service quality, three evaluated overall student happiness, three measured online education dependability, and the remaining five examined institutional and teaching service. The questionnaire used a five-point Likert scale, with 1 indicating “strongly disagree,” and 5 indicating “strongly agree.” Respondents are asked to choose a scale depending on their understanding and knowledge of each item in the questionnaire.

Sample and Data Analysis

The sample consists of students from various universities in the United States, Canada, Australia, Germany, the United Kingdom, Bangladesh, and other countries. The convenience sample was rendered appropriate for the study. Students taking online classes offered by universities for the past year and a half during the pandemic filled out the questionnaire. We specifically asked students to complete a questionnaire through email. There were 153 questionnaires received. In the end, 147 responses are considered for final analysis, 6 of which were invalid due to infidelity and incomplete information. Descriptive stats, reliability analysis, factor analysis, and regression are statistical techniques used to validate hypotheses and conclude research findings. Factors were filtered using a reliability test and factor analysis.

Analysis

Demographic Properties

Table 1 displays demographic data from students during the COVID-19 outbreak, including age, gender, program, education completed before the pandemic, and residence location/area (rural vs urban).

Table 1.

Demographic breakdown.

Items	Number	Percentage (%)
Gender
Female	89	60.54
Male	58	39.46
Age
18–22	48	32.65
23–27	73	49.66
33 and above	26	17.68
Enrolled degree program
Under graduate program	79	53.74
Graduate program	37	25.17
PhD program	16	10.89
Others	15	10.24
Online education completed before pandemic
Not at all	17	11.56
Less than 10%	59	40.13
Less than 50%	46	31.3
More than 50%	25	17.006
Residential location amid COVID-19
Rural area	44	29.93
Urban area	103	70.06

This research uses the fuzzy-set Qualitative Comparative Analysis (fsQCA), which is known for incorporating fuzzy sets with QCA (Rihoux and Ragin, 2009). Fuzzy-set qualitative comparative analysis applies a specific configuration of causal antecedents to estimate the result variable (Abbady et al., 2019). Due to the limits of conventional regression-based techniques, such structural analysis and the fsQCA provide advantages (Griffiths, 2000) (Pappas et al., 2017). Classical techniques, such as correlation and regression analysis, are based on the “ceteris paribus assumption,” which states that just the impact of a predictor variable on the result variable is considered, and everything else remains constant. These assumptions might lead to questionable study results. Correlation and regression-based analysis cannot establish whether situations a variable have more (or less) influence on the result since they focus on the net effect without accounting for the relevance of other factors. Traditional methods, in other words, cannot detect both synergy and equifinality. (Kaya et al., 2020) (A. G. Woodside, 2014).

Fuzzy-set qualitative comparative analysis is intended to assist in testing hypotheses such as causal asymmetry, synergistic effects, and equifinality. When causal variables (e.g., present or absent circumstances) are organized or interact to predict a higher degree of a result than the net influence of all features (as separate items) driving the outcome, complementarity occurs. As an outcome, equifinality occurs when two or more paths (a combination of causal factors) predict the same quantity of an effect (Fiss, 2011). These fsQCA routes may have both required and sufficient conditions, which appear on a solution as a present or inverted (i.e., not present/absent). Both essential and sufficient variables can exist as core conditions (leading to a strong causal link with the result) or ancillary factors (Rihoux and Ragin, 2009) (Fiss, 2011).

According to (Pappas et al., 2019), the fsQCA primarily provides two sorts of setups. These are implemented employing both the needed and sufficient conditions to give alternative solutions describing the same outcome dependent on the presence, absence, or do not care condition (i.e. either present or absent) of the configurations. The essential and adequate needs are crucial because they assist distinguish between the core and periphery situations. The core ones are the essential substantial elements for the outcome, while the periphery ones are the weak components required for the end result. (Fiss, 2011)

An analysis of necessity is performed for both the presence and absence of the condition to evaluate if any causal conditions are required for the availability of purpose towards perceived service quality (Table 2). According to (Ragin, 2006) necessity shows that a condition is a “superset of the result,” which means that the output’s fuzzy-set membership score is smaller than the causal conditions’ fuzzy-set membership score for each occurrence in the given data (Pappas et al., 2019). Likewise, (Schneider and Wagemann, 2010) contended that for a condition to be necessary, its consistency must always be more than 0.9. (Ragin, 2006) stated consistency as the proportion of occurrences in a sample that contains a causal configuration that shows the focused result. In fsQCA software 3.0, the specialized function is used to study the implications of necessity, producing both the consistency and coverage results for each causal condition, as well as their negated values. Because all consistencies are between 0.5 and 0.9, we may continue with our data calibration analysis. We provide an analysis of the necessary conditions below:

Table 2.

Necessity analysis of the research constructs (Outcome variable: PSQ).

Constructs	Consistency	Coverage
Rel	0.671287	0.887435
∼Rel	0.441584	0.802158
TPr	0.898020	0.878025
∼TPr	0.256436	0.902439
ITS	0.876238	0.860895
∼ITS	0.234654	0.811644
Cvn	0.895050	0.880234
∼Cvn	0.234654	0.808874

Data Calibration

In this stage, the variables must be calibrated into fuzzy-sets by grading their values from 0 to 1. The value 1 represented complete set membership, whereas the value of 0 signaled full non-set membership. For data calibration in QCA, researchers mostly use two approaches. The first is the direct method, in which three thresholds must be defined: complete membership, full non-membership, and the crossover point, which represents the level at which a case belongs to a set the second approach is the indirect method, in which measurements must be rescaled based the researcher’s extensive understanding of the instances. Although either approach can be used depending on the research hypotheses and the nature of the examples, we used the direct method in this study. The three criteria are based on the study questionnaire’s 5-point Likert scale. (Rihoux and Ragin, 2009) Recommendations carried out the calibration method, with 1 denoting complete membership, 0.50 denoting the crossover point, and 0 representing full non-membership. Finally, all of the data were calibrated using a logistic function to ensure that they fit inside the three criteria (Pappas et al., 2019).

The calibration method outlined below was applied in this study to transform the scale into continuous fuzzy-sets (direct method) in the fsQCA software 3.0:

Calibrate (x, n1, n2, n3), where x is the converted research construct, n1 is the whole membership range set to 4, n2 is the crossover point set to 3, and n3 is the full non-membership set to 2.

The computational formula for the constructs is given below:

Compute: PSQ = calibrate (PerceicedServiceQuality, 4, 3, 2)

Compute: Rel. = calibrate (Reliability, 4, 3, 2)

Compute: TPr. = calibrate (TeachingProcess, 4, 3, 2)

Compute: ITS. = calibrate (ITSystem, 4, 3, 2)

Compute: Cvn. = calibrate (Convenience, 4, 3, 2)

Generating the truth table

The fsQCA software 3.1 is used to construct a truth table from the freshly calculated calibrated data. The truth table creates 2n rows; where n is the number of conditions, and each row of the truth table displays every feasible combination for the conditions specified. For example, if there are three requirements, the truth table will present eight logical options from which to choose. After creating the truth table, we must sort it by adjusting the frequency and consistency cut-off values (Rihoux and Ragin, 2009). The truth table is presented in Table 3.

Table 3.

Truth table.

Rel	TPr	ITS	Cvn	Number	Raw consist.	PRI consist.	SYM consist
1	1	1	1	20	0.941842	0.922747	0.959821
0	1	1	1	7	0.954315	0.931298	0.931298
1	0	1	1	2	0.952879	0.876712	1
0	1	0	0	1	0.894737	0.714286	0.833333
0	1	1	0	1	0.910891	0.783133	0.783133
1	1	0	1	1	0.945455	0.878379	0.878379
0	0	0	0	0	—	—	—
1	0	0	0	0	—	—	—
1	1	0	0	0	—	—	—
0	0	1	0	0	—	—	—
1	0	1	0	0	—	—	—
1	1	1	0	0	—	—	—
0	0	0	1	0	—	—	—
1	0	0	1	0	—	—	—
0	1	0	1	0	—	—	—
0	0	1	1	0	—	—	—

To get a minimal number of experimental observations, a frequency cut-off point is required. For small and medium-sized samples (less than 150 cases), the frequency cut-off value is 1, however for large-scale samples (more than 150 cases), the cut-off point should be set > 1. (Rihoux and Ragin, 2009). As the number of cases here is 66, the frequency cut-off point is set at “1". (Dul, 2016) also mentioned in his article that a low consistency threshold might lead to errors, allowing for false-positive scenarios. Consequently, a relatively high consistency criterion of >0.80, which is a standard metric for fuzzy-set analysis, is established. Table 4 displays the set-theoretic consistency values for each configuration and the overall result, with all values exceeding the threshold (>0.8). (Pappas et al., 2019). The consistency of the configuration checks how well a subset connection has been approximated, whereas the coverage evaluates the empirical importance of a dependable subset (Rihoux and Ragin, 2009).

Table 4.

Truth table after logical minimization.

Rel	TPr	ITS	Cvn	Number	PSQ	Cases	Raw consist.	PRI consist.	SYM consist
0	1	1	1	7	1	5,14,22,27,49,56,66	0.954315	0.931298	0.931298
1	0	1	1	2	1	11,15	0.952879	0.876712	1
1	1	0	1	1	1	13	0.945455	0.878379	0.878379
1	1	1	1	20	1	1,2,6,8,9,10,17,25 ,34,35,36,41,43,46,48,51, 54,62,63,64	0.941842	0.922747	0.959821
0	1	1	0	1	1	52	0.910891	0.783133	0.783133
0	1	0	0	1	1	21	0.894737	0.714286	0.833333

Findings and Discussion

Fuzzy-set qualitative comparative analysis:

Our fsQCA analysis indicates five paths to perceived service quality in online education. Table 5 summarizes the results: the black circles (□) symbolize the presence of a condition, the crosses (X) indicate low presence, and the blank areas represent a “do not care” situation (i.e. the condition may be either present or absent).

Table 5.

Configurations leading to perceived service quality of online education.

Paths	Rel	TPr	ITS	Cvn	Raw Coverage	Consistency	Solution coverage	Solution consistency
∼RelTPr∼Cvn	X	●	—	X	0.20198	0.85	—	—
RelTPrCvn	●	●	—	●	0.60495	0.931402	—	—
RelITSCvn	●	—	●	●	0.623762	0.919708	0.893069	0.892186
∼RelTPrITS	X	●	●	—	0.391089	0.916473	—	—
TPrITSCvn	—	●	●	●	0.790099	0.93662	—	—

Table 5 shows that five causal configurations are empirically important with an overall solution (consistency=0.892, 186) and (coverage=0.893, 069). The configurations are combinations of appropriate circumstances, and no single condition is sufficient to account for the perceived service quality of online education. According to, coverage ratings may be used to evaluate the practical importance of a collection of causal factors. Empirical significance refers to the extent to which a specific causal condition can explain an observable result.

Solution 1: This represents a small portion of male students belonging to the undergraduate program of some local universities. This pathway 1 shows that low reliability and convenience, along with a better teaching process and absence of an IT system, is enough to achieve the perceived service quality of virtual learning (Consistency=0.85) and (coverage=0.20,198). This outcome indicates that the teaching method is the only vital factor for students’ satisfaction, whereas reliability and convenience are minor. According to these students development of the teaching method is likely to predict high positive relation to the perceived service quality of online education.

Solution 2: This represents a significant portion of the sample where both the male and female students are belonging to undergraduate and graduate programs. The results show that a well-established teaching process, high reliability, high convenience, and absence of IT system are sufficient for achieving high perceived online learning service quality (Consistency=0.931,402) and (coverage=0.60,495). This could be interpreted as the presence of well teaching method, reliability towards the institution, and academic convenience are likely to predict high positive relation to the perceived service quality of online education. But these students do not feel IT system quality is an essential variable for online learning.

Solution 3: This represents a special group of male students pursuing PhDs and some students of different international universities who are in their graduate programs. The solutions depict that high reliability, high convenience, stable IT system, and absence of teaching process can be a sufficient configuration for achieving high virtual learning perceived quality (Consistency=0.919,708) and (coverage=0.623,762). It might be used to mean the presence of reliability, convenience, and up-to-date infrastructure system are creating better e-learning opportunities and finally it will result in students’ satisfaction.

Solution 4: This portrays a small part of the sample where both the male and female students belong to graduate programs such as MS/MBA/MA. The results show low reliability, a good teaching process, an adequate IT system, and the absence of convenience are sufficient for achieving students’ satisfaction in online learning (Consistency=0.916,473) and (coverage=0.391,089). This denotes that established IT infrastructure and helpful teaching procedures are important factors for online education where reliability can be taken as a minor factor according to these students.

Solution 5: This portrays all the male and female students who are in an undergraduate program. This pathway shows that a good teaching process, the well-established infrastructure of the IT system, high convenience, and absence of reliability is a sufficient configuration for achieving higher perceived service quality of e-learning.(Consistency=0.93,662) and (coverage=0.790,099). This might be viewed as follows: these particular students do not think reliability is a vital issue in e-learning. But the presence of friendly teaching methods, the sufficiency of IT systems, and virtual convenience create learning opportunities and are likely to adopt high positive relation to the perceived service quality of online education.

Based on the solutions stated above, we can determine that the findings of this fsQCA analysis strongly support hypotheses 1, 3, and 4 as friendly teaching methods, high convenience, and a well-established IT infrastructure are the key factors of students’ satisfaction in online education. Similarly, the analysis rejects hypothesis 2 as low reliability is significant for the majority of the solutions. These conditions eventually lead to student loyalty, intent to continue, and willingness to recommend. A visual presentation is presented in Figure 2.

Based on the foregoing arguments and practical solutions, we identify two topologies of university students’ satisfaction and their corresponding paths to online education during the pandemic as presented in Table 6. These topologies are students’ cognitive-affective satisfaction and emotional-behavioral satisfaction.

Here, the outcome and the corresponding variables are represented in XY plots in Figure 3 to determine when the specific variables are high, low, absence, or no relation to lead the outcome in specific number of cases with different control variables like gender, student, and other professions.

Figure 2.

Visual presentation of the configurational pathways to perceived quality of e-learning (lined boxes explain full membership, dotted boxes explain both presence and absence of the variables, and absence of the variables, and no bix explains full non-membership).

Table 6.

Topologies of perceived quality of online education and students’ satisfaction.

Label	Definition	Key insights
Cognitive-affective satisfaction (S1, S4 and S5)	1. Better teaching process with low reliability and convenience. 2. Helpful teaching process and IT system with low reliability 3. Friendly teaching method, stable IT system along with high convenience	1. Students find the teaching process much different in online compared to traditional/physical methods. Therefore a friendly teaching environment should be ensured before everything to facilitate online education. 2. Stable IT infrastructure should be ensured, and faculty members should provide interactive and interesting content to make the teaching method enjoyable. 3. Universities should provide necessary conveniences to the students besides ensuring well-established IT system and teaching process.
Behavioral-emotional satisfaction (S2 and S3)	1. High reliability, convenience along with better teaching process 2. High reliability, convenience along with up-to-date IT system	1. Besides suitable teaching procedures, both students’ and teachers’ reliability should be ensured towards e-learning and they should be provided essential support to facilitate online education. 2. Awareness among students should be created by ensuring reliability and loyalty. The technical infrastructure of the institution should be stable, up-to-date, and reliable as well.

Figure 3.

XY Plot.

Conclusions and Implications

Using fsQCA analysis, this study empirically investigates for the first time the combination of circumstances that are adequate to explain university students’ satisfaction with online learning during COVID-19. We discovered five paths and two groups of students from the fsQCA study that describe their satisfaction toward online education. These findings contribute to empirical and practical knowledge of the pathways through which we can detect students’ loyalty and intent to continue their studies online.

The information obtained (by fsQCA) reveals significant factors impacting students’ satisfaction with online classes and technical suitability. According to the theoretical framework employed in this study, the educational content and teaching methods used in online courses substantially influence students’ motivation to take online courses. To adopt virtual learning services, university students must be satisfied with their education and the valuable info they receive through online classes. Furthermore, the long-term objective should be developed to increase education quality by applying innovative instructional approaches rather than copying current information in an online format.

This paper’s implications are dual. First, a theoretical model of satisfaction with and accepting the purpose of online education was offered as society transitioned from the “offline contact” period to the “online contact” era. This can help to encourage participation in new educational services in these changing times when a wide range of resources and services can be recognized. Based on this, universities may improve their competitiveness by delivering relevant, easy-to-use educational services to their students. Besides, diverse educational programs should ensure university students’ ongoing academic satisfaction and perceived quality. Universities themselves rather than individual professors should address these issues. While university teachers are permitted to modify their teaching techniques, educational quality should be enhanced at the university level. Despite the ambiguity around when and what changes will be implemented due to COVID-19, universities’ academic quality and satisfaction should stay unaltered. The results of the statistical analysis revealed four primary characteristics used to assess the quality of online learning services: Based on the data, the first variable is the teaching approach, which has the most significant coefficient beta and has the most favorable effect on how learners view the quality of online education. One of the current study’s key contributions is using the notion of online learning service quality. It demonstrates that during COVID-19, students are provided personalized attention and are driven to learn online.

Second, the IT System is mainly concerned with the online system’s website material, which must be well-organized, easy to discover, and correct. Students may struggle to navigate the pages if this is not done, and they will not comprehend. The lecturer’s work also includes safeguarding students’ privacy and sensitive data. As a result, universities must continually enhance their security systems through complicated algorithms for students to have confidence in the online learning system in general and the security system in particular. Third, the variable, convenience, is essential so that the students can realize study easily. Besides, convenience ensures students’ engagement during online classes. The response will also increase by providing convenience. Fourth, the reliability factor addresses the resources that must be used in the course’s classes. Teachers who were formerly traditional offline educators have transitioned to online education. The content of online learning must be well-organized, instructive, and practical. Furthermore, these learning resources must be selected based on the needs of the students and should give some obstacles to inspire learners to continue studying. Meeting students’ search process, registration, and online course preparation needs and expectations should be done quickly, comprehensively, and efficiently.

The findings of the FsQCA study would provide educators with a firsthand understanding of how to make a fresh start in maintaining students’ loyalty in online learning behavior considering the changing conditions in the pandemic. Moreover, this research expands on previous research on student behavior and perceived service quality from the COVID-19 perspective. Previous research, such as (Asogwa et al., 2014); (Gorgodze et al., 2019); (Stodnick and Rogers, 2008); (K. C. Tan and Kek, 2004); (G. Udo et al., 2010), emphasized the satisfaction of consumers when there were no public health crises, such as COVID-19. When comparing the current study to existing studies, previous findings investigated the factors that impact student satisfaction in the traditional schooling system. On the other hand, the current research was conducted during the shutdown period to identify the major factors influencing students’ satisfaction with online courses. The results of statistical research revealed that four primary components assess the perceived service quality of e-learning services: teaching method, convenience, IT System, and reliability. These parameters also have characteristics specific to the online learning environment during this outbreak.

This work adds to the education and information literature by employing QCA and a demographic model. Second, our findings confirmed the stress-reduction potential of learning possibilities, in line with the results of a rare study (by fsQCA) on perceived online learning service quality Previous papers use Servqual approach and other approaches like CSFS, Instructional method and so on. Our findings, in particular, suggest that learning opportunities contribute to individuals’ ability to regulate and improve their state successfully. In contrast, the absence, loss, or prospective loss of highly valued resources may be fundamental concerns (Hobfoll, 2002). As a result, in addition to traditional courses and competencies like reading, numeracy, and critical thinking, new knowledge, and perspectives (e.g. computer information processing skills and financial understanding) must be taught.

Individualized, specifically targeted education and the development of student’s capacity to solve problems beyond the knowledge taught in class are essential to achieve this. Universities must provide extensive online learning in order to provide students with vital subjects and more profound understanding while maintaining their current personnel. Most significantly, adaptive learning (i.e. intelligent systems defined by measurement, data collection, analysis, and autonomous responses) would open up a new vision of professor–learner interactions, perhaps improving satisfaction with online classes. Because the context of students’ existing education level has been highlighted as an incumbent element, the fresh insights from this study might motivate future studies to analyze the configuration of conditions for different groups of individuals in terms of age or profession.

Limitations and Directions for Further Research

This research expands on previous research on student behavior and perceived service quality from the COVID-19 perspective. This research contributes to the education and information literature by employing QCA. Because the context of students’ existing education level has been highlighted as an important element, the fresh insights from this study might motivate future studies to analyze the configuration of conditions for different groups of individuals in terms of age or occupation.

Despite the contributions, this study has a few limitations for which follow-up studies are suggested. First, this review focused on online educational services in the form of courses; however, in the ever-changing online education market, online classes differ across multiple sectors (humanities, sciences, arts, and physical education), and so do class features and instructional material. Second, this study concentrated on university education and students who use internet technology more regularly and learn to use it more quickly than other groups. To acquire a more comprehensive understanding of the types of educational services required in the “online contact” period, it is vital to include all individuals participating in primary education, which would provide high reliability. In addition, there is no sample of non-respondents in the research. Non-responders might have a somewhat different profile. The profile obtained here may differ significantly depending on the sort of student. Again, certain survey items may have influenced respondents to rank quality primarily on experience rather than evaluating the overall significance of online course parts. “I felt at ease engaging in course talks” might be rephrased as “comfort in participating in course discussions.”

We considered differences across subgroups (for example, majors) to be modest and statistically inconsequential. A community college’s emphasis and order may differ from a research-oriented university. Furthermore, the QCA analysis was the leading one used in the study. Though QCA analysis adds a new perspective to e-learning research, future studies should utilize more quantitative approaches to investigate this issue. Hence, the study is confined to components recognized from previous research, and future research can explore the configuration of additional circumstances not reflected in this study. Finally, this study obtains data from students using survey questionnaires. Therefore, future research should use longitudinal or panel data to increase the outcome’s reliability and validity.

Footnotes

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 iD

Md Abul Kalam Azad

Author Biographies

Tanjim Istiaque Chowdhury is an undergraduate at the Islamic University of Technology under the program BBA in Technology Management. Islamic University of Technology (IUT), a subsidiary organ of Organisation of Islamic Cooperation (OIC), Gazipur, Bangladesh. She is an assistant researcher whose primary fields of research interests are panel data, fsQCA, Text mining, human behaviour, and MCDM.

Md Rakibul Hoque is an Associate Professor of Management Information Systems at University of Dhaka, Bangladesh. He is also Visiting Scholar, School of Business, Emporia State University, USA. His research interests include technology adoption, Big Data, e-Health and ICT4D. He has published number of research articles in peer-reviewed academicjournals, and has presented papers in international conferences. He is the member of Association for Information Systems (AIS), UNESCO Open Educational Resources Community, Asia eHealth Information Network, Information Systems Audit and Control Association (ISACA), IEEE andInternet Society.

Peter Wanke has received his Ph.D. in Production Engineering from COPPE/ Universidade Federal do Rio de Janeiro (UFRJ) and was a visiting scholar at the Ohio State University. He is currently director of the Business Analytics and Economics Research Unit and Associate Professor at COPPEAD Graduate Business School. Prof. Wanke has over than 150 papers published in relevant international impact-factor, peer reviewed journals.

Mohammad Zahir Raihan has been working as an Associate Professor of Finance at the School of Business, Bangladesh Open University. He also served as a guest faculty at the University of Malaysia Perlis (UniMAP), Islamic University Technology (IUT), and AhsanUllah University of Science and Technology (AUST) in Bangladesh. He teaches at both undergraduate and postgraduate levels. He obtained his Ph.D. from the University of Malaysia Perlis, Malaysia. Mr. Raihan received his BBA, MBA, and MPhil degrees from the University of Chittagong in Bangladesh.

Md Abul Kalam Azad currently working at the Department of Business and Technology Management (BTM), Islamic University of Technology, Bangladesh. His research areas are optimization and risk management. He has published more than 50 high impact papers in relevant fields. He received his PhD from University of Malaya, Malaysia and MSc from University of Bedfordshire, UK. He completed his BBA and MBA from International Islamic University Chittagong, Bangladesh.

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