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Article

Assessing Repurchase Intention of Learning Apps during COVID-19

by
Ganesh Dash
1,*,
Debarun Chakraborty
2 and
Faisal Alhathal
1
1
College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia
2
Symbiosis Institute of Business Management, Nagpur, Constituent of Symbiosis International, Deemed University, Pune 440008, India
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(9), 1309; https://doi.org/10.3390/electronics11091309
Submission received: 1 April 2022 / Revised: 14 April 2022 / Accepted: 18 April 2022 / Published: 20 April 2022
(This article belongs to the Special Issue Mobile Learning and Technology Enhanced Learning during COVID-19)
Browse Figures
Versions Notes

Abstract

:
Learning apps are becoming increasingly popular, and consumers have widely recognized their benefits, particularly during COVID-19 and the resultant lockdowns. However, despite the growing popularity of learning apps, little is known about the consumer values that impact repurchase intent. Learning apps must increase client engagement by providing stronger value propositions to overcome this hurdle. The current study proposes the consumption values theory to find this gap, better explaining customer behavior toward learning apps. Data from 429 learning app users are used to test the suggested model. According to the research, all consumption values have a favorable and significant impact on the intention to repurchase learning apps. In addition, the moderating effect of Visibility on intent to use and trust’s mediating role are confirmed. The study’s findings add to our knowledge of consumer behavior and practice.

1. Introduction

The last decade, especially recent years, has seen a massive growth of information technology and other enabled services (IT and ITES, respectively) [1]. Advances in mobile communication technology have given rise to a plethora of useful and innovative mobile applications and a new market segment [2]. The purpose of mobile applications (apps) aims to give consumers a meaningful experience that adheres to the eudemonic concept of well-being [3,4]. With the dynamism of technology, people now have apps that offer us real-time information and free up the user’s time with a simple tap [5,6]. Moreover, apps today make it easier for users to communicate with one another [7,8]. Business, education, lifestyle, travel, shopping, health and fitness, food and drink, news, music, sports, social networking, and other applications cater to a broad spectrum of people, supporting them in completing tasks in a matter of minutes [9,10,11]. Furthermore, the emergence of a complex modern lifestyle, an ever-increasing working population (both male and female), work style, and a competitive environment has increased individual and family concern for education, leading to an increase in the use of learning apps [12]. People’s reliance on learning apps for essential courses has expanded over time as they face significant challenges in their daily lives [13].
It is crucial to understand the mechanics of such technology’s acceptability, especially as society has entered an era of information technology and is moving toward intelligent learning via learning apps [14]. Therefore, it would be good to understand the forces that drive a user’s acceptance and adoption of an information system (apps) [15]. The acceptability of any new technology for a specific purpose can be anticipated using user psychology, the quality of the technology, and the design process, all of which can be regarded as reliable predictors to some extent [16].
This business is very competitive due to the enormous growth potential of learning apps [17]. Learners already have access to a diverse range of learning content in the learning apps, and a few of them are also free [17]. As a result of this trend, the various learning apps demand money to be the app’s subscribers, who have less chance of success in the market than free apps do [18]. As a result, learning apps marketers and app developers must grasp the critical elements that affect the users/learners’ decisions to purchase the paid version of learning apps [19]. Effective and successful apps need to take advantage of the product’s strengths and overcome the drawbacks related to infrastructure while also employing the most appropriate and effective pedagogies [20]. In this context, the success and usefulness of commercial learning apps are primarily unknown [20].
The COVID-19 pandemic has had a huge impact on education systems all over the world [21,22,23]. Researchers found that during the COVID-19 pandemic, university students who used digital learning were frustrated, which led to a lot of psychological stress [24,25,26]. In addition, the fear of losing the school/college year was a big factor [27]. Researchers also looked into how school/college closures during the COVID-19 pandemic affected undergraduate and graduate students at colleges and universities worldwide [28,29]. Studies found that about 70% of students took online courses or used digital learning systems [30,31].
On the other hand, these students still had much anxiety because of bad home learning environments or bad internet connections that made it hard for them to learn [32]. However, the COVID-19 pandemic has refocused people’s attention on how information technology can be used in education [33]. As a result, many online courses and digital learning systems have sprung up and attracted many users [34].
There has been significant research into user adoption of technology [35]. As a result, numerous theoretical models and points of view have emerged, each with its own set of determinants. By better understanding the consumption values of learning apps, trust, and intention to repurchase, this study intends to fill the gap in the literature. The researchers also use the Theory of Consumption Values (TCV) [36] as a theoretical framework to establish a model. In addition, researchers look at moderating and mediating elements that might affect these underlying linkages’ strength to understand better how learning apps are used. Finally, the researchers plan to address three key research questions (RQs):
RQ1: What is the link between consumption values, trust, and repurchase intention?
RQ2: Is there a relationship between consumption values and repurchase intention moderated by Visibility?
RQ3: Does trust play a role in mediating the link between consumption values and repurchase intention?
The current study adds five new contributions to the literature in this context. It is probably the first study to put the Theory of Consumption Values to the test regarding customer decision-making in learning apps. Secondly, the study also looks at the learning app sector, which is primarily untouched in developing countries. Third, Visibility is used as a moderator to test the association between consumption values and repurchase intention. Fourth, the study uses repurchase intention as a construct instead of an overly used construct named purchase intention. Fifth, the study also shows trust’s mediating effect on consumption values and repurchase intention. Overall, the research looks into the popularity of learning apps in depth.

2. Background Literature

2.1. Learning Apps

Learning apps are applications for learning through mobile technologies [37]. Simply expressed, learning apps give learners access to opportunities exclusively available on mobile computing devices [38]. In addition, person-to-person communication via mobile devices gives rise to learning through apps [19]. Therefore, mobile technology is a critical component of mobile learning’s infrastructure [20]. Some studies have classified mobile learning based on the physical dimensions of the gadgets as they are small, portable, and independent [20,37]. However, a different definition of mobile learning focuses on the learners’ mobility [39]. It was further defined as a type of learning that can be formal (in the classroom) or informal (outside the classroom), with the learner having the option of when and what to learn [39]. In conclusion, the initial concepts and ideas in mobile learning show that the main characteristics of mobile learning are learners’ mobility, learning almost anywhere and at any time via mobile devices [40].
Learning apps are the process of transmitting practical skills and understanding of the application and technique of learning through mobile and the internet [41]. Learning apps refer to skills that can be learned or improved via information technology [42]. Whether or not they are connected to the internet, information and communication systems use precise media in the learning process to avoid continuous human engagement [42]. Regardless of advancements in equipment and prospectus, learning apps are commonly defined as technology-assisted out-of-classroom or in-classroom education for comprehension [18]. In addition, learning apps refer to the internet’s use to deliver training courses to students without requiring them to travel to a physical location, reducing the inconveniency of space, interval, and time for students [43].

2.2. Theory of Consumption Values (TCV)

The Theory of Consumption Values (TCV) describes how consumers make decisions about services and products [36]. The TCV explains why a consumer utilizes a product or service [44]. Perceived value is the consumer’s assessment of the cost and benefit differences between two or more competing alternatives [45]. The subject of perceived values has been explored from two angles: one focuses on utility and economic theory, while the other emphasizes the many dimensions of hedonic senses [46]. The second approach has been used in several studies to explain why customers choose items and services based on consumption values [47,48]. The TCV, on the other hand, has been used to describe consumers’ choices of a wide range of products and services in over two hundred studies, to support research involving learners’ watching behavior toward learning content, and to guide research involving learning services, as well as to study the continued use of these services [49]. As a result, the TCV has more explanatory power in learning apps and can be successfully used in this area. The TCV is thus appropriate for our research; as a result, the researchers examined consumers’ repurchase intentions in connection to learning apps using consumption values. Previous literature studies have demonstrated the TCV’s significance in utilizing online services based on consumption values [50]. As a result, the TCV is used in this study to determine whether people intend to repurchase learning apps.

3. Research Model

The research was focused on a model (Figure 1) that integrates TCV with learning applications. Trust was investigated as a mediating variable in this study as previous research has demonstrated that trust impacts consumption values [51]. In this study, the construct Visibility was the moderating effect, whereas age, gender, educational background, family size, and monthly household income served as controls.

3.1. Functional Value

The perceived utility and quality that a consumer obtains from a product or service is functional value [52]. The relationship between trust and functional value has been investigated, with a positive correlation discovered [49]. The functional value of any service is linked mainly to quality and pricing [53]. The literature on consumption values supports the favorable impact of functional value on trust [54]. Hence, the researchers posited,
H1. 
Functional value positively influences trust.
Economic and utilitarian theories underpin the concept of perceived value [55]. The functional value derived by the consumer is determined by their overall judgment of what has been compromised and what has been gained [56]. The price value, a functional value dimension, has a beneficial impact on consumers’ buying intention for online learning content [57]. Furthermore, several other researchers have found that functional value impacts purchasing intent [57,58]. As a result, the researchers hypothesized that functional value would have a favorable impact on repurchase intent on learning apps, leading to the following hypothesis:
H2. 
Functional value positively influences repurchase intention.

3.2. Social Value

Consumers may purchase products and services that influence their social image and confer prestige and social position [36]. The perceived improvement in one’s social image after using any product or service is social value [57]. In online transactions, the consumer’s social worth can instill trust in such systems [59]. Furthermore, social value can boost commitment to online transactions [52]. As a result, the social value created by the use of learning apps can help consumers trust brands, leading us to hypothesize:
H3. 
Social value positively influences trust.
Social value enhances a consumer’s perceived social image, influencing an individual’s decision to participate in activities connected to a specific social group [36,57]. “Social value” is a word linked to “social norms” that aids in persuading customers to buy or adopt a product or service [60]. A person’s intention to view online video content is influenced by their social value [49]. As a result, this phenomenon is expected to drive repurchase intent on learning apps. As a result, researchers came up with the following hypothesis:
H4. 
Social value positively influences repurchase intention.

3.3. Emotional Value

The perceived emotional value received by the consumer is the favorable sensation experienced when using a product or service [36,57]. Contents significantly impact emotional impressions, and consumers are more inclined to show trust when experiencing pleasant feelings [61]. As a result, the quality of video programming encourages customers to have a favorable emotional response, which leads to the creation of trust [62]. As a result, the researchers came up with the following hypothesis:
H5. 
Emotional value positively influences trust.
Because buyers are impulsive, the emotional value may stimulate a buying response [63]. A pleasant, socio-psychological response experienced by a consumer using a product or service is referred to as emotional value [64]. Enjoyment and playfulness can describe the perceived emotional value [36]. Because consuming content is a source of pleasure for many users, the two components of enjoyment and playfulness [57] are inherent to learning apps. In numerous situations, the literature has thoroughly documented the efficacy of emotional value in influencing purchase intention [56]. Hence, the researchers hypothesized:
H6. 
Emotional value positively influences repurchase intention.

3.4. Trust and Repurchase Intention

Trust is a result that is based on a buyer’s perception of a seller’s ability to keep a promise made to them [65]. In foreign transactions, where the buyer and seller never meet, trust is based on the customer’s belief in the vendor’s ability to keep promises made to them [66]. Trust is built between the parties involved in a transaction [67]. The expectation-confirmation model explains why purchasers want to keep using or repurchase something [68]. Repurchase intent is positively influenced by vendor trust [69]. In many circumstances, the existing literature illustrates the positive association between trust and buying intention [65,68,69]. Hence, the researchers posited,
H7. 
Trust positively influences repurchase intention.

3.5. Trust as a Mediator

The role of trust as a mediator between various concepts and purchasing intent has been extensively researched in the past [70]. Client retention and trust have a favorable relationship [70,71]. The importance of trust in subscription-based services cannot be overstated [71]. We believe that trust will mediate between the influencing forces of repurchase intention and consumption values [72]. The study’s relevant consumption values are now explained, and it is mediated with the help of trust. The two key elements that generate trust in online services are perceived integrity and risk [73]. The three aspects of trust in the context of any service through mobile apps are ability, benevolence, and integrity [74]. Hence, the researchers proposed,
H8a. 
Trust mediates the relationship between functional value and repurchase intention.
H8b. 
Trust mediates the relationship between social value and repurchase intention.
H8c. 
Trust mediates the relationship between emotional value and repurchase intention.

3.6. Visibility as a Moderator

“Visibility” refers to how closely a person observes other people adopting an innovation [75]. The level to which a consumer sees others using any service or product and the amount of advertising that particular service or product receives is referred to as Visibility [76]. The more clarity a person can perceive the benefits of implementing a new idea, the greater the chance that they are going to accept it [77]. Researchers have always felt that the construct named Visibility is having an influence on technology adoption [78]. Visibility is linked to purchase intentions in different domains such as e-commerce and mobile services. [79]. In addition, a study also demonstrated how Visibility has influenced the customers to use m-payment services using different mobile apps [80]. It could be because the users of different mobile apps may believe that it can increase their popularity using the mobile apps. It also influences the younger individuals who believe that mobile apps may increase their image in their friend circle [78]. According to the current study, more Visibility improves the market image of learning apps and motivates potential customers to use them more frequently. As a result, the researchers provided the following hypotheses:
H9a. 
The relationship between functional value and repurchase intention is moderated by Visibility.
H9b. 
The relationship between social value and repurchase intention is moderated by Visibility.
H9c. 
The relationship between emotional value and repurchase intention is moderated by Visibility.
H9d. 
The relationship between trust and repurchase intention is moderated by Visibility.

3.7. Demographic Variables as Controls

This study included demographic characteristics as control variables [81,82]. Several prior research studies have discovered a strong correlation between customer behavior and demographic characteristics [83]. According to the existing literature, when employed as controls, demographic factors can successfully explain differences in individual consumer behavior [84]. The researchers looked at how demographic factors, including age, education, gender, income, and household size, influenced the results [82,85,86]. The hypothesized research model is presented in Figure 1

4. Methodology

The researchers tried to find out the answers to the research questions. The first research question finds the association between various consumption values, trust, and repurchase intention. The second research question is the relationship between consumption values and repurchase intention when Visibility works as a moderator. Moreover, the third research question tries to find the mediating link of trust between consumption values and repurchase intention. The researchers sent the structured questionnaire using email and chatting apps for better reach amongst the customers of learning apps. The questions were extracted from the different sources and used in learning apps’ repurchase intention. The survey was sent to 936 people between May and September of 2021. Initially, 434 replies were received, with 429 responses having no missing values. Table 1 shows the demographic characteristics of the respondents. Responses ranged from strongly agree to strongly disagree on a five-point Likert scale, with five indicating strongly agree and 1 indicating strongly disagree. This questionnaire was used to collect feedback and ideas from users of learning applications.
This section explains how a questionnaire and structural equation modeling were used to collect and analyze data (SEM). The researchers utilized SPSS version 28 and AMOS version 26 to analyze the data.

5. Data Analysis

Confirmatory factor analysis and structural equation modeling were used to assess the measurement model. AMOS version 28 was used to conduct the analysis. The data were cleaned, verified, and corrected for missing and inaccurate data and skewness, kurtosis, and multicollinearity [86]. The method produced a 429-response dataset, which was transmitted for further investigation and analysis. According to the findings, the skewness and kurtosis items included in the scale were well within the specified ranges [67], and the data were distributed regularly.
The researchers used Harman’s single component test for common method bias to quantify the study’s constructs for preliminary checks in SPSS; a single factor explained 31.584 percent of the total variance, less than the threshold value of 50%. As a result, there were no difficulties with common method bias in the data [87]. After testing for common method bias, confirmatory factor analysis (CFA) was used to determine the fit indices, validity, and reliability. The factor loadings are shown in Table 2, and it was observed that all the CFA and SEM values were more than 0.7. They also emphasized that the factor loading of each item was more than the desired value of 0.7 and was ready for further investigation [88,89]. The reliability was checked with the help of Cronbach’s alpha values, which are reported in Table 2.
CFA confirmed that the model was fit and all the values of CFA were more than 0.7. The next step was to check the validity of the data. In Table 3, validity and reliability analysis is shown. At first, composite reliability was used to assess the internal consistency between the different constructs used in the study. The composite reliability value for all the study constructs was more than 0.7 (Table 3), which was desirable for the study. The result of the study also confirmed that the data for the study satisfied the convergent and discriminant validity and reliability. The average variance extracted (AVE) value for all the constructs was more than 0.50, and the maximum shared variance value was less than the AVE values. This specifies that CR, AVE, and MSV were all within the specified range [88]. The square roots of the AVEs are written diagonally, and all the values were more significant than the inter-construct correlation values, which further describes the presence of discriminant validity [88,89].
Finally, the researchers assessed the goodness-of-fit criteria values to check how well the model fit (χ2/degrees of freedom = 1.312; TLI = 0.992, CFI = 0.994, and RMSEA = 0.027). A structural model analysis was used to verify the assumptions, revealing excellent model fit indices (χ2/degrees of freedom = 1.312; TLI = 0.992, CFI = 0.994, and RMSEA = 0.027). The results (Table 4, Figure 2) revealed that functional value (H1: ß = 0.324, p < 0.001) and social value (H2: ß = 0.409, p < 0.001) had a substantial relationship with trust; however, emotional value (H3: ß = 0.032, p > 0.05) did not. Functional (H4: ß = 0.354, p < 0.001), social (H5: ß = 0.269, p < 0.001), emotional value (H6: ß = 0.217, p < 0.01), and trust (H7: ß = 0.116, p < 0.05) factors, on the other hand, had a strong relationship with repurchase intention (Table 4, Figure 2).
The constructs were subjected to mediation analysis using SEM. The association between functional, social values, and repurchase intention was partially mediated by trust. Whereas trust does not mediate the association between emotional and repurchase intention, Table 5 illustrates the relationship between the study’s constructs. H8a and H8b were significant, and H8c was found to be nonsignificant.
Further, researchers assessed the moderating effect of Visibility on the relationship between consumption values and repurchase intention. According to the findings, Visibility successfully moderated the association between social values and repurchase intention. Table 6 and Figure 3 both showed the same thing. However, the researchers also discovered that Visibility did not affect the relationship between social value, emotional value, and trust with the repurchase intention. The results of this discovery are shown in Table 6.
According to the findings, age, gender, household size, income, and educational qualification did not have any statistically significant confounding influence on the dependent variables, trust, and repurchase intention. As a result, the control variable had no confounding influence on trust and repurchase intention in this study.

6. Discussion

The primary purpose of this study was to check the effect of consumption values on trust and repurchase intention toward learning apps. In addition, the effect of trust in mediating the relationship between consumption values and repurchase intention was also studied. Therefore, the scope of our study was expanded to include the moderating effects of Visibility on consumption values and repurchase intention. The researchers discovered that the hypotheses H1, H2, H4, H5, and H6 substantially associated consumption values, trust, and repurchase intention. Except for emotional value, this indicates that the consumers’ consumption values lead to trust.
In addition to emotional value, other linked components of consumption values, such as pricing, quality of services, intriguing product displays, and an appealing promotional program contribute to developing confidence in learning applications. A combination of social value, functional value (novelty and curiosity), and emotional value drive the desire to repurchase learning applications [14,94]. Functional values, a logical conclusion, provided the most significant positive value for repurchase intention. At the same time, during COVID-19, the consumers do not have any other options to choose from. The researchers predicted that trust would influence the repurchase intention to use learning applications using H7. The findings of a study that found a link between trust and repurchase intention are supported by this conclusion. The literature on the subject strongly supports this conclusion [15,95]. Disruptive technology, such as greater security and privacy features, propels learning applications forward [96,97,98].
Furthermore, consumers are more prepared to pay a premium for learning applications when they have a sense of trust. It appears to be leading to an increase in the repurchase intention of learning apps. The association between consumption values and repurchase intention has been somewhat mediated by trust. As these hypotheses demonstrate, perceived consumption values for boosting repurchase intention are logically plausible, and trust will improve through mediation. The association between functional value and repurchase intention was moderated by Visibility. According to this study, increased functional values and a high level of Visibility will improve the propensity to repurchase learning apps. During COVID-19, the Visibility was high for mobile apps as the consumers had minimal options. Especially, the schools and colleges were also closed, which gave an increase in the sales of mobile learning apps.

7. Theoretical and Practical Implications, Limitations, and Future Scope

The researchers broadened the research to incorporate other cultures, demographic factors, and an investigation of the moderating influence of Visibility based on recommendations in the recent literature on learning apps. After identifying the research gaps, the study contributes to the theory on four levels. First, the study uses a unique model that integrates trust and Visibility as new constructs yet to be explored in learning app models. Second, the study examines trust’s mediating effect on repurchase intention, which aids in establishing the interplay between consumption values and repurchase intention use via trust mediation. Third, the study tested the moderation effect of Visibility by looking at the impact of consumption values on repurchase intention using a broader and more demographically varied group of respondents. Fourth, by applying consumer value theory to the very relevant situation of learning apps, the current study contributes to the body of knowledge on consumption value theory. This feature could benefit other subscription-based consumer businesses.
Practitioners must understand better how to attract new clients and retain existing ones. Learning applications have a faster growth rate than other subscription-based services in the technology-driven consumer services sector [11]. This study helps to understand better consumer values, which is critical for building trust and promoting client repurchase intent. Learning apps can tailor their services to their users’ most significant values and enhance their present offerings [99] by including consumer value-related benefits. The findings of the impact of trust in enhancing repurchase intention can help marketers focus their efforts on trust-building initiatives so that existing subscribers will keep utilizing the company’s services [100]. According to the study, there is a variation in consumer perceived value and buy intention based on demography. It means that subscription-based service providers can customize their offerings to different age groups and family sizes to provide better value to their customers. Our findings can inform the government’s learning app policies [92]. The company’s consumption figures may be used to form the policy for increasing learning app acceptability and penetration.
While this study’s theoretical and practical contributions have been highlighted, there are several limitations that researchers would like to point out. First, the study was conducted during the COVID-19 outbreak, which led respondents to stay indoors and order more through learning applications. The dramatic surge in learning apps that occurred during the lockdown is unlikely to continue. This limitation can be overcome by more research under normal conditions to support the veracity of the study’s conclusions. Second, because the study was conducted in India, researchers urge that the model be evaluated in various locations and cultures to build views and analyze the support or distinction of our findings.

8. Conclusions

In the context of learning apps, this study will contribute to consumer behavior theory. Because of its connection to technology-based services, this study is significant for the learning app-related sector. Trust was employed to mediate consumption values and repurchase intention in the study, while the TCV was used to evaluate the intention to use learning apps. To answer our first research question, researchers looked into the literature to see what consumption values were connected with learning apps. To answer the second study question, researchers looked at the role of trust in mediating the relationship between consumption values and repurchase intention. The researchers discovered that trust had a partially mediated effect on consumption values and repurchase intention. The third research objective was to see if Visibility influences the relationship between consumption values and repurchase intent. India has seen a shift in learning app choices due to technological advancements in recent years. Apps for learning have become an integral element of today’s educational scene. The researchers analyzed and discovered the variables that can predict consumer behavior when adopting and using learning apps in this study.

Author Contributions

Conceptualization, D.C. and G.D.; methodology, G.D., F.A. and D.C.; software, D.C. and G.D.; validation, D.C. and G.D.; formal analysis, G.D. and D.C.; writing—original draft preparation, D.C. and G.D.; writing—review and editing, G.D., F.A. and D.C.; visualization, D.C. and G.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the second author.

Conflicts of Interest

The authors declare no conflict of interest.

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Electronics 11 01309 g001 550
Figure 1. Hypothesized research model.
Figure 1. Hypothesized research model.
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Figure 2. Results of Hypothesis Testing.
Figure 2. Results of Hypothesis Testing.
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Figure 3. The moderating role of Visibility on the association between Functional Value and Repurchase Intention.
Figure 3. The moderating role of Visibility on the association between Functional Value and Repurchase Intention.
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Table
Table 1. Demographic Profile of the Respondents.
Table 1. Demographic Profile of the Respondents.
Demographic CharacteristicsCategoryFrequencyPercentage
AgeLess than 25 years4911.42%
25–35 years7116.55%
35–45 years10925.41%
45–55 years10123.54%
55 years and above9923.08%
Household sizeonly one member5111.89%
two members5913.75%
three members9221.45%
four members14233.10%
five members and more8519.81%
GenderMale22452.21%
Female20547.79%
Educational QualificationCompleted high school286.53%
pursuing/completed professional degree/vocational school6114.22%
pursuing/completed bachelor’s degree11426.57%
pursuing/completed a master’s degree15335.66%
pursuing/completed doctorate (PhD or equivalent)7317.02%
Monthly incomeLess than INR 40,000399.09%
INR 40,001–60,0007617.72%
INR 60,001–80,00012128.21%
INR 80,001–100,00014734.27%
INR 100,001 and more4610.72%
Table
Table 2. Constructs and Factor Loadings.
Table 2. Constructs and Factor Loadings.
ConstructsItem No.ItemsSourcesCFASEMCronbach’s Alpha
Social Value (SOC)SOC1When I use Learning Apps, I feel more acceptable.[59]0.8190.8190.897
SOC2When I use Learning Apps, I believe it produces a positive impression on others.0.8940.894
SOC3I believe that using Learning Apps will provide me with social approval.0.9020.902
Functional Value (FUN)FUN1Learning apps provide consistently high-quality content.[59,90]0.8710.8710.911
FUN2Learning apps deliver on their content promises.0.8580.858
FUN3Learning apps provide content that is of acceptable quality.0.840.84
FUN4Learning apps provide excellent value for money.0.7910.791
Emotional Value (EMO)EMO1On Learning apps, I enjoy watching video content.[91]0.8960.8960.904
EMO2Learning apps content entices me to watch more.0.8760.876
EMO3Watching content on learning apps gives me pleasure.0.7690.769
Trust (TRU)TRU1Learning Apps are reliable and trustworthy.[92]0.9360.9360.918
TRU2Learning Apps offer high-quality services.0.9230.923
TRU3Customers are taken care of through learning apps.0.910.91
TRU4I believe the learning apps are trustworthy and keep their promises.0.90.9
Repurchase Intention (REP)REP1If at all possible, I would like to keep using learning apps.[93]0.9310.9310.884
REP2In the future, I plan to utilize learning apps frequently.0.7190.719
REP3In the future, I will most likely continue to use learning apps.0.7660.766
Table
Table 3. Validity and Reliability Analysis.
Table 3. Validity and Reliability Analysis.
CRAVEMSVMaxR(H)FUNTRUEMOSOCREP
FUN0.9060.7070.2380.9090.841
TRU0.9550.8420.2230.9560.3740.917
EMO0.8850.7210.2220.8980.4710.2090.849
SOC0.9050.7610.2230.9120.3290.4720.1910.872
REP0.8500.6570.2380.9000.4880.3900.3760.4530.810
Table
Table 4. Results of Hypothesis.
Table 4. Results of Hypothesis.
HypothesisPathEstimatepSupport
H1TRU←FUN0.324<0.001Yes
H2TRU←SOC0.409<0.001Yes
H3TRU←EMO0.032>0.05No
H4REP←FUN0.354<0.001Yes
H5REP←SOC0.269<0.001Yes
H6REP←EMO0.217<0.01Yes
H7REP←TRU0.116<0.05Yes
Table
Table 5. Results of Mediation Analysis.
Table 5. Results of Mediation Analysis.
HypothesisHypothesized RelationshipDirect EffectIndirect EffectResultAccepted/
Rejected
H8aFUN→TRU→REP0.354 ***0.038 *PartialAccepted
H8bSOC→TRU→REP0.269 ***0.047 *PartialAccepted
H8cEMO→TRU→REP0.217 **0.004 n.sNoRejected
* p < 0.05, ** p < 0.01, *** p < 0.001, n.s. = not significant.
Table
Table 6. Results of Moderation Analysis with Visibility as a moderator.
Table 6. Results of Moderation Analysis with Visibility as a moderator.
HypothesisPathβseCRpModeration?
H9aFUN→REP0.1410.0442.8230.006Yes
H9bSOC→REP−0.0230.042−0.5490.583No
H9cEMO→REP−0.0530.043−1.2480.212No
H9dTRU→REP−0.0620.042−1.4710.141No
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Dash, G.; Chakraborty, D.; Alhathal, F. Assessing Repurchase Intention of Learning Apps during COVID-19. Electronics 2022, 11, 1309. https://doi.org/10.3390/electronics11091309

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Dash G, Chakraborty D, Alhathal F. Assessing Repurchase Intention of Learning Apps during COVID-19. Electronics. 2022; 11(9):1309. https://doi.org/10.3390/electronics11091309

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Dash, Ganesh, Debarun Chakraborty, and Faisal Alhathal. 2022. "Assessing Repurchase Intention of Learning Apps during COVID-19" Electronics 11, no. 9: 1309. https://doi.org/10.3390/electronics11091309

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