The Role of Virtual Community Participation and Engagement in Building Brand Trust: Evidence from Pakistan Business Schools

Abstract

The purpose of this study is to examine the role of virtual community participation and engagement in the brand decision by building community and brand trust. In this study, two theories (Social Capital Theory and Theory of Collective Action) were tested. Both theories were linked based on existing literature, and empirical evidence was obtained through testing hypotheses. Students from five prestigious Pakistani business schools provided a total of 849 replies. The measurement model and structural model were both analyzed using the partial least square (PLS) method. The findings show that virtual community engagement and participation have a positive and significant impact on virtual community trust. Although virtual community engagement has a positive effect on brand trust, we found no evidence of a link between virtual community participation and brand trust. Later, these trusts positively influence brand choice. This study has several managerial implications. The suggested model of this study helps managers to get customers' insight, choose the right target market, and set integrated marketing communication strategies besides social media marketing strategies. The study represents a new perspective of consumer behavior that helps to understand how a customer behaves in virtual communities to trust and take the final decision to purchase.

1. Introduction

The relative functions of virtual interfaces and community forums have risen to unprecedented heights in the recent epoch. As a result, marketers systematically use digital media to develop virtual communities to boost target audiences’ involvement with branding operations (Yao et al., 2021). Members of virtual communities have varying levels of access to social activities and events that occur at regular intervals. The community interfaces ensure that target consumers may provide valuable input to such exercises and, as a result, form an emotional bond with the brand (Hur et al., 2011).

Many networking platforms are also becoming increasingly ideal for developing online communities to strengthen user relationships. Social media has recently allowed the construction and efficient exchange of Internet based apps among brand communities on technological foundations (Yao et al., 2021). Customer participation and engagement influence trust, which narrows the buying options and, as a result, the frequency of purchases.

Earlier research shows that participation in virtual communities intrigues a consumer’s intention from rationality towards hedonic aspects while making a purchase decision. A positive experience of participation in a virtual community would motivate a consumer as well as influence a brand choice holistically (Casaló et al., 2007; Kamboj & Rahman, 2017). Previous studies have also shown that the experience of participating in a virtual community creates different values and perspectives towards a brand. The values are influenced by results of Word of Mouth (WOM) and exposures acquired on virtual communities. A strong set of values and perspectives would create a favorable brand choice among customers with utmost consistency (Brodie et al., 2013).

Past literature demonstrates community engagement as an intrinsic motivation of an average consumer to interact with the brand and make brand-influenced choices (Vohra & Bhardwaj, 2019; Akrout & Nagy, 2018; Kang et al., 2016). Popular virtual brand communities created by brands like Ducati and Volkswagen are found highly engaged and enthusiastic, resulting in higher consumer retention due to evident choices. Prior research on virtual communities has focused on community participation and engagement, which affect the social norms, confidence, and hedonic concerns of a consumer, shaping trust tendencies towards a brand collectively (Yao et al., 2021). Also, research on the virtual community focused on community participation and engagement, which impact brand loyalty and behavioral outcomes in a systematic manner. Experiences on community forums would influence the confidence and transform into community trust ultimately (Xi & Hamari, 2020).

Several studies have looked at brand trust as an emotive commitment that predicts a likely brand choice during the purchase process. Unless the affective commitment or brand trust is altered by an adverse factor, a consumer with high brand trust is immune to switching to competitors while making purchase decisions (Zheng et al., 2015; Fogel & Adnan, 2019; Agmeka et al., 2019; McClure & Seock, 2020). Although these researches have provided valuable insights the role of virtual community participation and engagement is not clear in the perspective of brand choice.

Customers’ interest in virtual communities has risen dramatically as a result of the digital era of advertising and marketing (Yao et al., 2021). Only a few empirical studies have been undertaken on virtual communities, particularly in the context of community and brand trust (Adnan, 2019; McClure & Seock, 2020). Virtual communities have already been identified as a predictor of brand loyalty by studies (Xi & Hamari, 2020). Brand communities will henceforth be managed only for the purpose of increasing brand loyalty. Though many virtual communities assist customers in making brand decisions, there is a lack of appreciation for the role of virtual community participation and engagement in brand choice by fostering community and brand trust.

This research adds to the existing body of knowledge in two ways. It first proposed a model that demonstrates the impact of virtual community participation and engagement on brand choice by increasing community and brand trust. This study shows how community participation and engagement build a strong community and brand trust, which is subsequently turned into a brand choice. Second, it extends the application of Social Capital Theory (SCT) and Theory of Collective Action (TCA).

Furthermore, in the context of virtual communities, this study successfully connects both ideas. This study creates a conceptual framework that demonstrates how virtual community participation and engagement can influence customers’ brand choices, thereby ensuring community and brand trust indirectly. Customers frequently choose one brand over another based on its online presence or reviews. Business schools are also essential in Pakistan, where the financial industry is developing and macroeconomic fundamentals have an impact on the economy (AsadUllah, 2017, 2021a, 2021b; Alshammari et al., 2020).

2. Literature Review and Hypotheses

2.1. Definitions and Dimensions

2.1.1. Brand Choice (BCH)

Brand choice is the ultimate objective of each brand while offering products and services to the consumer. In general, brand choice is referred to the preference of customers based on the equality in price and availability of a product or service (Govender, 2017). However, the scope of the concept has been expanded with the passage of span in the academic literature. Brand choice is defined as an indicator of loyalty among the target audience regardless of the scenario.

2.1.2. Brand Trust (BT)

Through various offers, appeals, and emotional exchanges made over a period of time, brand trust plays a critical part in retaining customers (Hegner & Jevons, 2016; Chang et al., 2019). To build such trust among the consumer base, brands invest extensively in content and product or service quality, which serves as a competitive advantage over the course of partnerships.

2.1.3. Virtual Community Trust (VCT)

Community trust is a metric used to assess the trustworthiness, honesty, and quality of material produced by brands across various platforms. Because content or communications create a consumer base and reliability through regular contacts, the variable is considered a nurturing aspect for the consumer (Casaló et al., 2007; Ruan & Durresi, 2016).

2.1.4. Virtual Community Engagement (VCE)

The exchange of meaningful dialogues between brands and potential customers is referred to as virtual community engagement (Hollebeek et al., 2017). It’s a two-way channel that encourages both sides to exchange their thoughts, opinions, and aspirations, which are reflected in the product or service given in the later phases of the trade life cycle (Kang et al., 2016).

2.1.5. Virtual Community Participation (CP)

The inclination of the intended audience to become involved in a particular forum is referred to as community participation. For a brand, the excellence or influence of an activity is measured through community participation (Kamboj & Rahman, 2017).

2.2. Theoretical Framework and its Contribution

A consistent and valid outcome, a strong research methodology, and a robust theoretical framework always play an important role in the reliability of discoveries in social science research. Two different theories i.e. Social capital theory and the Theory of collective action, have been used in this study. Both theories have been used in different researches previously separately. But in the current study, these theories have been connected based on the available literature review. We will place variables in both theories, make hypotheses and get the empirical evidence from testing.

Social capital is “the networks of relationships among people who live and work in a particular society, enabling that society to function effectively. It’s a set of shared values that allows individuals to work together in a group to effectively achieve a common purpose (Häuberer, 2011). It’s a virtually undetectable source that may be inserted into any social group or network. Structured and relational capital are two elements of social capital. Several earlier research has looked into these dimensions. Social participation and interaction are represented by structural capital. It also signifies the robust interaction among the members of a social group or network (Chang & Hsu, 2016). That is why community participation and engagement can be included in structural capital. Relational capital is strongly linked to structural capital. Individual behavior in any social network or group is represented by relational capital, which describes how an individual’s actions affect the other members of the group. Because of this, we can convert community and brand trust into structural capital. Relational capital includes engagement and trust, which is why we can readily link structural and relational capital to the theory of collective action.

The theory of collective action denotes such action that has been taken collectively or influenced by others in a social network or group. According to this, the affiliates of a social network or group cannot take decisions independently. In this scenario, we can put brand choice here easily.

2.3. Hypotheses Development

2.3.1. Virtual Community Engagement and Virtual Community Trust (H1)

Once interactions are done through social networking sites, community participation has an impact on a customer’s cognitive aspects. Systematically, community participation improves a consumer’s virtual community trust and, as a result, brand loyalty. The quality relationship built by a brand through word-of-mouth (WOM) and hedonic motives on the fan page result in virtual community engagement and community trust (Akrout & Nagy, 2018).

All of a brand’s relationship-building activities over time are built based on trust. Community trust is defined as a consumer’s psychological reliance on a brand’s claims and attractions, which determines the depth of virtual community participation over time (Vohra & Bhardwaj, 2019). Community participation is a crucial component of a consumer-brand relationship built on trust. Reliable brand contacts with social network sites or digital fan base forums would enhance the consumer’s level of trust, leading to frequent and positive replies to the propositions (Liu et al., 2018). Based on the above literature, we led to propose the following hypothesis:

H1: Virtual Community Engagement positively impacts Virtual Community Trust.

2.3.2. Virtual Community Engagement and Brand Trust (H2)

Throughout the academic literature, virtual community engagement is considered as a metric to optimize brand trust among the consumer significantly. According to Fogel and Adnan (2019), Word of Mouth (WOM) in virtual communities has a remarkable role in developing trust towards a brand. Virtual communities engage the consumer with different activities while simultaneously sharing among the social circle.

Furthermore, WOM has also been discussed as a consumer-to-consumer (C2C) interaction that virtual communities offer. Customers share positive and negative aspects of a brand openly and transparently through social network sites that intrigue other viewers to rely on the propositions offered by a brand. As a result, the relationship between virtual community engagement and brand trust is found directly associated theoretically and conceptually with the consumer (Hollebeek et al., 2017). The social sharing framework or virtual engagement develops gratifications towards a brand unprecedentedly. Brands would gain a strategic advantage in the form of trust if virtual community engagements are administered effectively (Hollebeek & Macky, 2019). Thus, based on the above literature, we directed to develop the following hypothesis:

H2: Virtual Community Engagement positively impacts Brand Trust.

2.3.3. Virtual Community Participation and Virtual Community Trust (H3)

Individuals develop the desire to participate in virtual communities once community trust has been established (Pedeliento et al., 2020). Individuals acquire trust in virtual fan forums after a healthy WOM is common, according to the research of digital tourism groups. To increase the likelihood of community engagement, brands must create maximum loyalty through healthy WOM, engaging content, and consumer relevance. Customers’ social interaction habit of participating in community events has a strong relationship to community trust if strategic reliability on a brand image is created over time through relationship marketing and corporate branding activities. Therefore, based on the above literature, we led to formulate the following hypothesis:

H3: Virtual Community Participation positively impact Virtual Community Trust.

2.3.4. Virtual Community Participation and Brand Trust (H4)

Building brand trust through community participation is a cost-effective approach for the brands, particularly in the dynamic business environment. Community participation is encouraged by posting frequent content on virtual communities and triggering an appealing trend to capture the attention of a relevant audience (Kamboj & Rahman, 2017). Systematically, brand trust is created once participants interact with the content more frequently and develop a reliable perception of the messages. Based on the above literature, we headed to posit the following hypothesis:

H4: Virtual Community Participation positively impacts Brand Trust.

2.3.5. Virtual Community Trust and Brand Choice (H5)

Community trust is considered the foundational pathway to brand loyalty or brand choice. The audience would build unique brand identity through interactive association, which would eventually become a popular choice among customers. In other words, involvement in digital forums fosters trust among virtual communities, which influences customer brand preference over time (Coelho et al., 2018). Consumers’ long-term brand loyalty is influenced by their level of trust. While conducting activities to strengthen customer choice, the tendency of trust serves as both a mediator and a moderator for brands. Precisely, community trust has a direct and positive influence on the brand choices made by the consumers. Significantly, it is seen as the underlying exchange of credibility between customers and brands. Therefore, based on the above literature, we directed to form the following hypothesis:

H5: Virtual Community Trust positively impacts Brand Choice.

2.3.6. Brand Trust and Brand Choice (H6)

Brand trust can be defined as the willingness of an average consumer to rely on the value propositions offered by a brand, which becomes an ultimate choice (McClure & Seock, 2020). Brands having a positive association and relevant propositions would develop a sustainable and reliable relationship with the consumer, which increases the probability of purchase decision in the journey evidently (Agmeka et al., 2019).

Chae et al. (2020) identified the link between brand trust and brand choice as the authenticity of a brand’s commercial success. The willingness of a consumer to pay a premium price for a product or service given by a brand is used to determine the probability of this relationship. In terms of the knowledge and worthiness of a product or service supplied to a consumer, brand trust is a credibility element. Higher credibility would positively affect customer brand choice and demonstrate a strong purchasing intention (Chang et al., 2019). Thus, based on the above literature, we led to postulate the following hypothesis:

H6: Virtual Community Trust positively impacts Brand Choice.

Based on the above all discussion, the conceptual framework of our study is developed as Figure 1.

Figure 1: A Proposed Conceptual Framework

3. Research Methodology

3.1. Research Design and Data Collection

A deductive approach has opted for this study. Established theories i.e. social capital theory and theory of collective action, have been used for hypotheses development. For collecting data survey method has been chosen. As a result, this is quantitative research with a limitation on primary data collection, which is why a questionnaire is a research tool used to collect data and gain insight for the study.

There were 61.34 million Internet users in Pakistan, of which 46 million are social media users till January 2021. We didn’t find any specific number of virtual community or brand community members in Pakistan. The population of this study is all those students participating in any virtual or brand community. Non-probability convenient sampling technique has been used. It is recommended that a sample size of 1000 to 2500 be enough for a nationwide research and 200 to 1000 sample sizes for a district or regional-wise research. For structural equation modeling (SEM) sample size of 100 to 200 would be suitable. There are 5 variables in this research so, we should have a minimum sample size of 150 but to take a broad view of the research, the sample would increase to 849.

Data has been collected from the students of five renowned business schools. The motive for gathering data from business schools lonely is that they have a better understanding of business or marketing conceptions. It can help us determine better insights from a chosen sample. First, we took the consent of participants through email, 1152 participants agreed to participate, who were already participating in any virtual community. The questionnaire was developed on Google Form and link shared through email to undergraduate to Ph.D. level students. Only 872 people out of 1152 responded to the email and completed the questionnaire. After removing the 23 incomplete responses, the total number of responses was reduced to 849. 411 were male and 438 were female, 424 were under the age of 20, and 425 were over the age of 20, 710 were undergraduate students, 129 were MS students, and 10 were Ph.D. students, respectively.

3.2. Measures and Data Analysis Tools

The structural relationship between exogenous and endogenous latent variables was examined using Structural Equation Modelling (SEM), a multivariate statistical analysis (Blanthorne et al., 2006). Structural Equation Modelling (SEM) has two techniques or approaches to analyze the data; Component-based modeling i.e. Partial Least Square (PLS) and covariance-based modeling i.e. CB-SEM. We adopted the Partial Least Square (PLS) technique. The rationale to choose this technique was twofold; first, this technique was used broadly in behavioral studies; second, this technique is more relevant to the current study. Smart PLS package was used to analyze the structural and measurement modeling.

All the constructs that have been used in this study are adopted from prior established research. The measurements of all items are shown in Table 2. The questionnaire comprises 5 constructs i.e., Virtual Community Engagement, Virtual Community Participation, Virtual Community Trust, Brand Trust, and Brand Choice. Responses are accordingly 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree.

4. Results

SEM (structural equation modeling) is a multivariate statistical approach that examines the structural relationships between variables (Tabachnick et al., 2007). There are two techniques for structural equation modeling (SEM), a one-step approach and a two-step approach. In the one-step procedure, a researcher assesses the router model’s measurement. The structural or inner model, on the other hand, is evaluated in the 2^nd stage.

4.1. Examining the Measurement Model

The first part is examining the measurement or inner model by calculating Construct Convergent Validity, Confirmatory Factor Analysis (CFA), and Discriminant Validity through Heterotrait-Monotrait Ratio (HTMT). Convergent Validity checks that all constructs of a single study explain the same concept or idea and confirm the same theory (Fornell & Larcker, 1981). Convergent Validity can be confirmed through Composite Reliability (CR) and Average Variance Extracted (AVE). The threshold for Composite Reliability (CR) would be 0.7 while for Average Variance Extracted (AVE), it should be equal to or greater than 0.5. The value of standardized loading should be equal to or greater than 0.7 in Confirmatory Factor Analysis (CFA) or Indicator Reliability to check the causal link between the latent variable and observable variable. Discriminant Validity ensures that each of the constructs used in the study is different and unique. It can be checked using the Heterotrait-Monotrait Ratio (HTMT), which ensures discriminant validity by taking the value from less than one of all relationships.

4.2. Convergent Validity

Table 1 shows that the lowest value of composite reliability is 0.79 (CR > 0.7). While the lowest value of AVE is 0.53 (AVE > 0.5) and meets the criteria. So, we can confidently confirm the convergent validity of all constructs.

Table 1: Convergent Validity

4.3. Measurement Model and Confirmatory Factor Analysis

The value of standardized loading for all items is equal to or greater than 0.7. It confirms the underline relations of Observed Variable (OB) and Latent Variable (LV) (Table 2).

Table 2: Confirmatory Factor Analysis (CFA)

4.4. Discriminant Validity (HTMT)

The value of the Heterotrait-Monotrait Ratio (HTMT) for all unique or different constructs is less than one, which shows that all constructs in the study are unique and different (Table 3).

Table 3: Discriminant Validity

4.5. Examining the Structural Model

The second part of examining if the model is structural inner by testing the hypotheses and evaluating path coefficient (β), determination of coefficient ), and effect size. Fit indices for model fit would be determined by SRMR and NFI. The threshold value for SRMR is less than 0.08, while the value of NFI should be greater than 0.9.

The results in Table 4 divulge that virtual community engagement has a direct positive impact on virtual community trust (β = 0.47, p = 0.00), and brand trust (β = 0.46, p = 0.00). Thus, H1 and H2 are supported. Virtual community participation has a direct positive impact on virtual community trust (β = 0.24, p = 0.00), therefore H3 is supported. But empirical results show that there is no direct positive impact of virtual community participation on brand trust ((β = 0.11, p = 0.19), thus H4 is not supported. Virtual community trust has a direct positive impact on brand choice (β = 0.16, p = 0.00), and brand trust has a direct positive impact on brand choice (β = 0.47, p = 0.00). Therefore, H5 and H6 are also supported.

Table 4: Hypotheses Results

Note: ***p < 0.001, **p < 0.01, and *p < 0.05. VCE: Virtual Community Engagement; VCP: Virtual Community Participation; VCT: Virtual Community Trust; BT: Brand Trust; and BCH: Brand Choice.

The model elucidates that virtual community engagement and virtual community participation explain 45 percent of the variance in virtual community trust. And virtual community engagement and virtual community participation explain 30 percent of the variance in brand trust. While virtual community trust and brand trust explain a 33 percent variance in brand choice.

Table 5: Model Fit Indices

4.6. Model Fit

Standardized Root Mean Square Residual (SRMR) value is 0.032 i.e. less than 0.08 and the value of Non normed Fit Index (NFI) is 0.95 i.e. greater than 0.90. These results indicate a very good model fit. Thus, based on the above results we can easily conclude that both the outer or measurement model and inner or structural model are authenticated. Moreover, we can say that the proposed conceptual model has considerable predictive relevance and descriptive power.

5. Discussion

When people engage in a community on a more personal level, marketers hope to gain more potential customers as a result of these interactions. A brand that seeks to get its customers to engage with them more is more likely to gain community trust. To put it another way, the more customers interact with a brand, the more they trust it. The study reveals that community engagement and participation have a significant positive relationship (H1). The findings are in line with previous research (Akrout & Nagy, 2018; Vohra & Bhardwaj, 2019; Liu et al., 2018). As a result, it can be deduced that brands must communicate successfully with customers because this determines the level of trust that people have in the brand. Customers eventually come to rely on a brand because of the successful messaging it sends out. Hence, the research suggests that community engagement has a significant impact on community trust.

According to previous research and our current findings, there is a clear link between virtual community engagement and brand trust (H2). The findings support earlier research showing there is a significant positive association between virtual community involvement and brand trust, as frequent engagement builds users’ belief in the company’s credibility (Sheng, 2019; Hollebeek et al., 2017; Hollebeek & Macky, 2019). The findings also imply that if consumers do not acquire brand trust, the brand would most likely fail in the near future. Maintaining trust among potential customers is crucial to any brand’s success.

The hypothesis states that significant community engagement leads to powerful feedback and increased consumer trust in a business. Individual engagement, in which they participate to express their opinions and values, leads to the building of communal trust (H3). In the context of past studies, the findings are relevant (Pedeliento et al., 2020; Nohutlu et al., 2021). The findings also imply that sharing trustworthy and honest information fosters community trust.

Our findings support previous research that shows a strong positive association between virtual community engagement and virtual community trust. The data also show that as individuals participate in the community, trust develops among the community members. The hypothesis reveals that effective participation among members is required to achieve community trust; else, engagement will not be as effective as it should be. The study also tries to emphasize the fact that virtual community engagement allows customers to interact with individuals who share their viewpoints on various forums where they may share their experiences and communicate with brands, resulting in increased brand trust (H4). However, the new study’s conclusions are incompatible with existing research material (Kamboj & Rahman, 2017). Previous research has demonstrated a positive direct impact of community engagement in brand trust, however, based on the findings of our study, we are unable to find any link between the two. The reason could be the growing trend of consumer awareness. Until or unless a person feels involved with, a virtual community, he or she does not trust any brand that has been suggested or discussed in that community. Customers will have more trust in brands that engage with the community to increase their interest. By encouraging consumer engagement, the community can establish trust, the brand will be valued, and customers will be more inclined to choose that brand again (H5). Individuals create specific brand recognition through participatory affiliation, which leads to a shopper’s repeated decisions in the final phases of their purchase. The findings of this hypothesis are in line with previous research, and it is observed that community trust has a significant impact on customer brand preference (Coelho et al., 2018).

To summarise, higher brand engagement leads to improved consumer trust, which leads to brand choice as people regularly choose that particular brand over others when making purchases. Previous studies have shown that active engagement and a complete approach to a customer’s pleasure with a brand increases brand loyalty, which leads to increased brand trust. Additionally, positive word of mouth increases the customer’s attitude toward the brand, resulting in the brand becoming the best choice for the customer (H6). In the current study, it was demonstrated that increased community participation and engagement leads to increased brand trust, which in turn leads to increased brand choice (McClure & Seock, 2020; Chae et al., 2020; Chang et al., 2019). As a result, there is a positive correlation between brand trust and brand choice. Many corporations have taken advantage of emotional appeal by targeting different populations in their advertisements and other promotional channels.

6. Theoretical and Managerial Implications

This study makes a two-fold contribution to the theory. First, this study aims to broaden the application of social capital theory to virtual community participation, engagement, and trust, as well as the idea of collective action to brand selection. Second, by uncovering the factors that influence the two phases of the buying process, this study contributes to the research on customer-to-customer interaction in a virtual community environment. The interaction is the first, and the transaction is the second. This study looked into the factors that influence the building of trust in virtual communities. It’s also been discovered that a community’s social capital reinforces trust, which leads to better purchasing intentions.

In addition, the role of emotional attachment to the platform and usage frequency in the development and expansion of trust was discovered in this study.

This study also presented a number of managerial implications, with the primary goal of determining the role of virtual community participation and engagement in brand choice through the development of community and brand trust. A great deal of material has been added to the topic that can assist managers in understanding customer behavior in a virtual community and making the final decision to purchase any brand. This research reveals an individual’s behavior in a virtual community environment, and it proposes how marketers might track their customers or potential customers, receive real-time feedback, and deliver timely information.

The era has ended when a brand used to approach the customers. Customers are now approaching relevant brands over the Internet. And virtual communities are performing well in this regard, thanks to customer-to-customer engagement in virtual communities. Is it now possible for you to respond to a consumer who contacts you through the Internet? Our research not only provides an answer to that issue but also demonstrates how practitioners can use input from various virtual communities to influence customers by offering relevant information. Customers trust these online platforms because they allow users to discuss their ideas or reviews without being interrupted by companies or manufacturers. In addition to social media marketing strategies, our recommended approach helps in gaining customer insight, selecting the appropriate target market, and developing integrated marketing communication strategies.

7. Limitations and Future Research

Certain limitations apply to our research since as opposed to our findings, there are other factors as well that can affect brand trust. Hence, future researchers can investigate this issue further with different variables in different settings. Regarding the model developed in our research, one can argue that it would have a reverse effect instead of the one proposed in the research.

The research was done solely keeping in view Pakistani markets. Hence, owing to the limited sample size and different cultures of every market, there is a high chance that the views of customers regarding brand trust might vary in other cultures. Furthermore, there could be more factors that can influence the choice of a customer. Moreover, we didn’t check any mediating or moderating effect, future researchers can add or subtract some elements or can check the mediating and moderating effect also.