Review Article: 2022 Vol: 26 Issue: 5
Consumer Trust In user Generated Brand Recommendations on Social Networking Sites
Minimol M C, Rajagiri College of Social sciences
Citation Information: Minimol, M.C (2022). Consumer trust in user generated brand Recommendations on social networking sites. Academy of Marketing Studies Journal, 26(5), 1-11.
Abstract
The proposed study provides an insight into the consumer trust social networking sites, ultimately on user generated brand recommendations, and also investigating the role of ad-scepticism. The work contributes to a better understanding of trust development in social networking sites. Specifically, the study reveals that not all dimensions of trustworthiness are equal. The individual user characteristic varies according to the person. The major finding is that, high degrees of trust toward user generated brand recommendations can be generated on the basis of high trust toward the social networking sites and ad-scepticism. Consumer trust the user generated brand recommendations based on the individual’s trust in the particular social networking platform, and the level of ad-scepticism
Keywords
Consumer Trust, User Generated Brand Recommendations, ad-Scepticism, Social Networking Sites.
Introduction
Before the advent of social media, brands spoke for themselves. Today’s consumers are immune to the traditional form of marketing where the advertisers talk “at” audiences. The social media channels are on the rise which influence the online brand engagement. The photos and videos posted by product users serve as authentic visual reviews and is trusted by peers, making it a visual marketing platform. User-generated content, or UGC for short, is any content that has been created and published by unpaid contributors. UGC can be content of any type, including blogs, website pages, images, social media posts, and testimonials. (Source: What is User Generated Content (and Why You Should Be Using it, Jose Angelo, TINT digital publications, 2017). When it comes to making a buying decision we are more likely to trust the opinions of our friends, colleagues, or family members who have used the products. Besides providing social proof of authenticity, there are a few other benefits the brands get from user generate content like, Boost Social Media Reach and Growth, Angelo (2016) Gain Audience Insights etc.76% of individuals surveyed said that they trusted the content shared by "average" people more than by brands, and nearly 100% of consumers trust recommendations from others. User generated contents can generate more engagement on Instagram meaning more comments and likes on posts which is critically important to brand’s success on the platform. A lot of global brands are sharing Instagram content reposted, or "regrammed" from fans and users. The brands can use user generated contents to showcase an unexpected or unique aspect of your brand. Give people a reason to get involved in the company’s campaigns is better than Instagram. Whether it's an awareness campaign or a donation drive like Aerie, customers want to buy from companies itself. User Generated Content turns a loyal enthusiastic consumer to an automatic campaigner of brands in order to build engagements among various communities for their products or services.
Review of Literature
Akbar & Ozgul (2018) aimed to analyse the influence of social media on brand awareness of young cosumers. Authors commented that the peculiarity in the structure of social networking websites allows direct communication among the company and its customer. Hence it is being used increasingly by several agencies for advertising and marketing functions. The study was made on 100 students of Dokuz Eylul University, Turkey. Result of regression analysis shows that social media marketing has a significant role in creating brand awareness among customers. Especially facebook contributes to 34 % total share for making the same. Also proposed few techniques such as incorporation of more number of samples, other forms of social media to improve the result of the study. Goh et al. (2013) tried to examine the impact of community contents such as User Generated Content (UGC)) and marketer-generated content (MGC) on consumers’ apparel purchase behaviour through embedded information and persuasion. Also focussed to quantify the effectiveness of retailer’s marketing activities and customer’s online Word of Mouth (WOM) on social media. There are many platforms such as facebook, Instagram are available, with attracted extensive information sharing in the form of reviews from consumers. However, these platforms do not currently provide much access to marketer’s proactive engagements. The study was carried out by collecting data from one of the brand community on Facebook. Indeed, their study suggests that these platforms can actually do better by enabling marketer’s engagements. Nam & Kannan (2014) commented that wealthy associative statistics on social tagging presents entrepreneur’s new possibilities to deduce logo associative networks and they have a look at further deals with how the statistics contained in social tags can act as a proxy degree for logo overall performance.
Moorman et al. (1993) describes a comprehensive idea of trust in market research relationships. This concept specializes in the factors that determine customers accept as true with in their researchers, along with character, interpersonal, organizational, inter organizational/interdepartmental, and project elements. Chari et al. (2016) stated that excessive ranges of believe in user generated content material are associated with excessive ranges of believe towards facebook friends and offer guide for the moderating position of advert-scepticism. Benevolence and integrity are observed to be important/core conditions for the improvement in the directions of Facebook friends. Rachna (2017) analysed the impact of UGC on brand equity constructs and have implications for brand managers and media planners for administering the user-generated content on social media, and also for various researchers and academicians towards examining the effects of such social interactions on brand elements. Bahtar & Muda (2016) stated that social media being a virtual community linking people around the globe and being the platform for UGC, the element of how UGC influences purchasing and reviews is very critical. Mira Mayrhofer et al. (2020) commented that persuasive user-generated messages on social media not only triggers user’s persuasion knowledge but can also have persuasive effects without creating awareness for the persuasive potential.
Objectives of the Study
• To find out the relationship between trust in social networking sites and its determinants
• To find out the relationship between trust in social networking sites and trust in user generated brand
Recommendations.
• To find out the effect of ad-scepticism on the relationship between trust in social networking sites and trust
In user generated brand recommendations.
H1. There is significant positive relationship between Benevolence and trust in social networking sites
H2. There is significant positive relationship between integrity and trust in social networking sites
H3: There is significant negative relationship between ability and trust in social networking sites
H4: There is significant positive relationship between propensity to trust and trust in social networking sites
H5: There is significant positive relationship between individual user characteristic and trust in social Networking sites
H6: There is significant positive relationship between trust in social networking sites and trust in user generated brand recommendations
H7: There is significant positive relationship between ad-scepticism and trust in social networking sites
H8: Ad-scepticism moderates the relationship between trust in social networking sites and trust in user Generated brand recommendations.
H9: Ad-scepticism mediates the relationship between trust in social networking sites and trust in user Generated brand recommendations.
Variables and Their Definitions
The independent variables used in this study are benevolence, integrity, ability, propensity to trust and individual user characteristic. The individual user characteristic used in the study is the consumer’s willingness to rely on user generated brand recommendations. The dependent variables are trust in social networking sites, ad - scepticism and trust in user generated brand recommendations. Trust in social networking sites is measured in this study as the consumers trust in their instagram friends. Benevolence is the kindness that a customer possesses while recommending a brand to another customer. Integrity is the measure of fairness that a customer possesses while recommending a brand to another customer. Ability is defined as the skill that a customer possesses while recommending a brand to another customer. Propensity to trust is described as the trust that a customer possesses while recommending a brand to another customer. Individual user characteristics are personality, demographics, and use behaviour of a customer who recommends a brand to another customer. Trust towards Instagram followers is the trust that a customer possesses while a brand is recommended by another customer who is a follower of him/her on Instagram. Ad-scepticism is the tendency toward disbelief of advertising claims that a brand is demanding. Trust in user generated brand recommendations is the trust towards the user generated brand recommendations on social networking sites.
Conceptual Model
The study adopted the conceptual model developed by Simos Chari in 2016, to explore the relationship between trust in social networking sites and trust in user generated brand recommendations. The model is given as Figure 1.
Figure 1 Relationship Between Trust in Social Networking Sites
Research Design and Methodology
The study intended to explore the trust of customers in user generated brand recommendations and social networking sites. Thus, the population for this study is taken as the customers using social networking sites. The research was conducted among a sample of customers who are using social networking sites in Kerala. Descriptive research design was adopted for the study as it describes the relationship between the variables. The primary data were collected through questionnaire using Google forms. The sampling technique used was convenience sampling. A sample size of 281 is selected for the analysis. Structural Equation Modelling (SEM) was used for analysing the data Table 1.
| Table 1 Reliability Test | ||
| Variable | Cronbach's Alpha | No. of Items |
| Benevolence | 0.911 | 3 |
| Integrity | 0.822 | 4 |
| Ability | 0.911 | 6 |
| Propensity to Trust | 0.837 | 5 |
| Individual user characteristics | 0.937 | 4 |
| Trust in social networking sites | 0.828 | 2 |
| Ad - Scepticism | 0.929 | 9 |
| UGBR Trust | 0.910 | 3 |
Results and Discussion
Reliability is checked based on the value of Cronbach’s Alpha. A Cronbach’s Alpha value of 0.70 is recommended as the standard. The variable Ad-scepticism has recorded highest Cronbach’s Alpha value of 0.929, which means that it is highly reliable. From table 1 it is clear that all the variables are reliable for conducting the study Table 2.
| Table 2 Descriptive Statistics | |||||
| Variables | N | Minimum | Maximum | Mean | Std. Deviation |
| Benevolence | 281 | 1.00 | 7.00 | 2.6987 | 1.82731 |
| Integrity | 281 | 1.00 | 7.00 | 3.4564 | 1.38168 1.36889 |
| Ability | 281 | 1.00 | 7.00 | 3.8642 | |
| Propensity to trust | 281 | 1.00 | 7.00 | 4.3089 | 1.36631 |
| UGBR Trust | 281 | 1.00 | 7.00 | 3.7616 | 1.54403 |
| Ad - scepticism | 281 | 1.00 | 7.00 | 3.6659 | 1.40806 |
| Trust social networking sites | 281 | 1.00 | 7.00 | 4.1744 | 1.48191 |
| Valid N | 281 | ||||
Mean value of all the variables lies above 3.5 except for benevolence. This indicates that most of the data is centred above the average value and they are positively skewed. The standard deviations are low. 1.82 being the highest value and 1.366 being the lowest value, which shows that the data is not wide spread. More of the data is clustered around the mean value Table 3.
| Table 3 Correlation Between Variables | |||||||
| Correlation | |||||||
| Benevolence Sig. (2 tailed) |
1 | .699 .000 |
.602 .000 |
.361 .000 |
.566 .000 |
.605 .000 |
.520 .000 |
| Integrity Sig. (2 tailed) |
.699 .000 |
1 | .761 .000 |
.371 .000 |
.597 .000 |
.535 .000 |
.489 .000 |
| Ability Sig. (2 tailed) |
.602 .000 |
.761 .000 |
1 | .462 .000 |
.645 .000 |
.538 .000 |
.445 .000 |
| Propensity to trust Sig. (2 tailed) |
.361 .000 |
.371 .000 |
.462 .000 |
1 | .572 .000 |
.499 .000 |
.499 .000 |
| UGBR Trust Sig. (2 tailed) |
.566 .000 |
.597 .000 |
.645 .000 |
.572 .000 |
1 | .602 .000 |
.508 .000 |
| Ad-scepticism Sig. (2 tailed) |
.605 .000 |
.535 .000 |
.538 .000 |
.499 .000 |
.602 .000 |
1 | .623 .000 |
| Trust in SNS Sig. (2 tailed) |
.520 .000 |
.489 .000 |
.445 .000 |
.499 .000 |
.508 .000 |
.623 .000 |
1 |
| Valid N = 281 | |||||||
The variables benevolence and integrity show a moderate positive correlation between each other with a correlation coefficient of 0.699. The variable propensity to trust shows lowest correlation with benevolence. The variables integrity and ability show high positive correlation between each other with the reported correlation coefficient 0.761. The coefficient of correlation between the variables integrity and propensity to trust is calculated as 0.371. Hence propensity to trust has least correlation with integrity.
Integrity is found to be highly correlated with ability. The coefficient of correlation between trust in user generated brand recommendations and ability is only 0.445. The variables propensity to trust and trust in user generated brand recommendations show a moderate positive correlation between each other with a correlation coefficient of 0.572. The variable benevolence shows lowest correlation with propensity to trust. Among the variables under study, ability has recorded highest correlation with Trust in user generated brand recommendations with coefficient of correlation 0.645. The coefficient of correlation between trust in social networking sites and trust in user generated brand recommendations is calculated as 0.508. The variables ad-scepticism and trust in social networking sites show a moderate positive correlation between each other with a reported correlation coefficient of 0.623. The variables propensity to trust shows only low correlation with ad-scepticism. The variables trust in social networking sites and ad-scepticism are moderately correlated with each other. There exists a low positive correlation between ability and trust in social networking sites.
Assessment of the Measurement Model
Measurement model represents the model constructs and indicator variables and the interrelationships in the model. To proceed with a structural model, it is essential to establish the validity of the measurement model Paswan (2009). A measurement model is assessed by evaluating the goodness of fit indices, construct validity and reliability of the proposed model. The commonly used fit indices are goodness of fit index, adjusted goodness of fit index, normed fit index, incremental fit index, relative fit index, Tucker-Lewis index, comparative fit index and Root Mean Square Error of Approximation (RMSEA). Goodness Bagozzi (1991) of different fit indices are evaluated based on the sample size, data type and acceptable scores of such indices, with respect to the model proposed Hu & Bentler (1999), Mac-Callum et al. (2006). This study repored most of the goodness of fit indices obtained in the model fit summary output of AMOS MacCallum et al. (1996) Table 4.
| Table 4 Model Fit Indices | ||||
| Model | CMIN/DF | GFI | AGFI | RMSEA |
| Default model | 3.873 | 0.987 | 0.88 | 0.07 |
A Goodness of Fit Index (GFI) value of 0.987 indicates that the model is appropriate. The GFI value increases and decreases according to the number of parameters. The Adjusted Goodness of Fit Index (AGFI) value of 0.88 is also appropriate. Here the sample size of 281 has increased the AGFI value. CMIN is the relative chi-square. It is the chi-square fit index divided by the degrees of freedom and represents the minimum value of the discrepancy. In the case of maximum likelihood estimation, CMIN contains the chi-square statistic. The chi-square statistic is an overall measure of how many of the implied moments and sample moments differ. The more the implied and sample moments differ, the bigger the chi-square statistic, and the stronger the evidence against the null hypothesis. The value should be between 1 and 5. Here the CMIN value is 3.83, which is an acceptable fit. The Root Mean Square Error of Approximation (RMSEA) tells us how well the model, with unknown but optimally chosen parameter estimates would fit the population’s covariance matrix. In a well-fitting model the lower limit is close to 0 while the upper limit should be less than 0.08. Here the value is 0.07 which is close to the upper limit. So it can be accepted Table 5.
| Table 5 SEM Baseline Comparisons | |||||
| Baseline Comparisons | |||||
| Model | NFI | RFI | IFI | TLI | CFI |
| Delta1 | rho1 | Delta2 | rho2 | ||
| Default model | 0.989 | 0.921 | 0.992 | 0.94 | 0.991 |
Incremental fit indices, also known as comparative are a group of indices that do not use the chi-square in its raw form but compare the chi-square value to a baseline model. Normal Fit Index (NFI) was developed as an alternative for comparative fit index and hence are not expected to have the chi-square assumptions. NFI reflect the proportion by which the current model improves the fit compared to the null model. Recommended NFI value is greater than 0.90 indicating a good fit. The NFI value of this study is reported to be greater than 0.90 which is appropriate. The Relative Fit Index (RFI) is obtained from the normed fit index. A reported value of RFI close to 1 indicates a very good fit. Reported value of relative fit index in the current model is 0.921 which is appropriate.
The Incremental Fit Index (IFI) values that exceed 0.90 are regarded as acceptable, although this index can exceed 1. IFI of this study is found to be 0.992 which is acceptable. The Tucker-Lewis Index (TLI) is also called non-normed fit index, and is similar to normed fit index, except in penalizing for model complexity. Tucker-Lewis index close to 1 represents a good fit. RFI values lower than 0.90, signifies the need to respecify the model. The RFI value reported in this study is 0.94 and therefore considered to be acceptable. Finally, the Comparative Fit Index (CFI) compare the current model of interest with some alternative, such as the null or independence model, that assumes the latent variables in the model are uncorrelated. Values that approach 1 indicate acceptable fit. Here we’ve a value 0.991 which is really close to the approachable mark and hence indicates a good fit.
Validity of the measurement model is established through construct validity, convergent validity and discriminant validity. Construct validity is the extent to which the contents of the items is consistent with construct definition, while, convergent validity addresses the extent to which indicators of a specific construct ‘converge’ or share a high proportion of variance in common. Convergent validity is measured by factor loadings, Discriminant validity is the extent to which a construct is truly different from other constructs and is measured using Average Variance Extracted (AVE), (Fornell & Larcker, 1981) criterion and composite reliability. As rule of thumb, factor loadings should be 0.5 or higher and ideally 0.7 or higher Garson (2011). AVE estimates the quantum of variance captured by a construct in relation to the variance due to random measurement error. AVE varies from 0 to 1, and it represents the ratio of the total variance that is due to the latent variable. According to Bagozi (1991), a variance extracted of greater than 0.50 indicates that the validity of both the construct and the individual variables is high. In the proposed model, all the factor loadings were above 0.70, hence, it can be concluded that the model possess convergent validity. The AVE values ranges between 0.614 and 0.682 and hence, the convergent validity is established in the model. Fornell & Larcker criterion of all the constructs are higher than its corresponding correlation with all other constructs, so there exists discriminant validity. HTMT criterion values are within the range of 0.486 to 0.532, therefore, discriminant validity is acceptable.
Assessment of the Structural Model
The final model along with the structural path coefficients is shown below Figure 2:
Figure 2 Structural Model
Results of structural equation modeling supported both the hypotheses proposed in the study. The same has been reflected in the Table 6.
| Table 6 Results of Hypothesis Testing | |||||
| Results of Hypothesis Testing | |||||
| Dependent Variable |
Direction | Independent Variable |
Regression Estimate |
P Value | Result |
| Trust in social networking sites | <--- | Benevolence | 0.222 | 0.001 | Supported |
| Trust in social networking sites | <--- | Integrity | 0.2 | 0.02 | Supported |
| Trust in social networking sites | <--- | Ability | -0.041 | 0.611 | Not supported |
| Trust in social networking sites | <--- | Propensity to trust | 0.364 | 0.001 | Supported |
| Trust in social networking sites | <--- | Individual Characteristic |
0.369 | 0.008 | Supported |
| UGBR Trust | <--- | Trust in social networking sites | 0.458 | 0.001 | Supported |
| Trust in social networking sites | <--- | Ad-scepticism | 0.329 | 0.001 | Supported |
All the four variables, benevolence, integrity, propensity to trust and individual characteristic were found to have a positive and significant impact on consumers trust in social networking sites. Ability is not significantly contributing to trust in social networking sites. Ad-scepticism is found to have a significant and positive impact on trust in social networking sites. Trust in social networking sites positively influences trust in user generated brand recommendations. Therefore, the results supported all the hypotheses formulated. Which shows that trust in medium and ad scepticism have a positive impact on user generated brand recommendations in social networking sites?
Moderation Analysis
A moderator is a variable that specifies conditions underneath which a given predictor is associated with an outcome. The moderator explains when a dependent variable and independent variable are related. Moderation implied an interaction impact, wherein introducing a moderating variable modifications the path or importance of the relationship among two variables Table 7.
| Table 7 Moderation Testing | ||||||
| Dependent Variable | Direction | Independent Variable |
Beta Value | S.E. | C.R. | P value |
| UGBR Trust |
<--- | Ad- Skepticism * Trust Followers |
.329 | .040 | 8.283 | *** |
To know whether ad-scepticism moderates the relationship between trusts in social networking sits and trust in user generated brand recommendations, moderation analysis was done. The result suggests that the Variable Ad - scepticism is acting as a moderator in the path between Trust in Instagram Followers and UGBR Trust (P Value is less than 0.055).
Mediation Analysis
Mediation analysis is used to understand the effect of the mediator variable on the relationship between the independent and dependent variable. It reveals whether the relationship strengthens or deteriorates due to the presence of the mediator variable. Sobel test is used in this to check whether there is reduction in the effect of the independent variable, trust in social networking sites on the dependent variable, trust in user generated brand recommendations Table 8.
| Table 8 Direct and Indirect Effects | ||||
| Path | Path Coefficient | T- statistic | Std. error | P value |
| Direct Effect Trust in social networking sites àTrust in user generated brand recommendations |
0.222 | 1.6841 | 0.0603 | 0.0921 |
| Indirect Effect Trust in social networking sites à Ad-scepticism *Ad-scepticism à Trust in user generated brand recommendations |
0.458 | 2.1267 | 0.0231 | 0.000 |
The results are evaluated based on the direct effects and indirect effects. It is seen that the path coefficient of the specific indirect effect (effect of Trust in social networking sites on trust in user generated brand recommendations, through the mediator ad-scepticism) is 0.458. The path coefficient is found to be statistically significant (t=2.1267, p-value<0.001). Hence it can be concluded that ad-scepticism has a strong mediation effect on the relation between Trust in social networking sites and trust in user generated brand recommendations.
Findings and Discussion
Findings support the suggested relationship between trust in social networking sites and trust toward user generated brand recommendations. Trust in user generated brand recommendations can be better understood in relation to an individual’s trust in his/her social media friends/followers who contribute such recommendations. The findings show that: High degrees of trust toward user generated brand recommendations can be generated with high trust in social networking sites. The current study focused on a sample of respondents from Kerala. So future studies can focus on a wider geographical area to confirm the results. The variable trust in social media sites is operationalized in this study as the respondents trust in their instagram followers. Future studies can focus on other social media platforms also to get a better perspective. Culture can also play an important role in developing trust towards user generated brand recommendations. However this study has not attempted to explore this aspect. The brands should acknowledge that consumers’ trust in user generated brand recommendations is affected by their trust in social networking sites, which in turn is influenced to a greater extent by benevolence, integrity, propensity to trust and individual user characteristics and should attempt to build on these factors so that they can engage consumers to generate user generated content on social media. Brands should be aware that ad-scepticism mediates the relationship between trust in social networking sites and trust in user generated brand recommendations. Social networking sites are not bound to a specific location and the behavior of the users changes rapidly when the geography changes. Thus, future research may include a wide sample size that can accommodate different people from different locations. Which will definitely give a precise idea about how the firms/brands should improve their products and services based on the trust generated by the people on their products and services.
Conclusion
This research provides insights into the consumer trust formation process toward social networking sites, and ultimately towards user generated brand recommendations through ad-scepticism. The study contributes to a better understanding of trust development in social networking sites. Specifically, findings of this study reveal that not all dimensions of trustworthiness are equal in generating trust towards social networking sites and user generated brand recommendations. On Instagram, more emphasis is given on the trustee’s benevolence, integrity, propensity to trust and individual user characteristics respectively and less importance is given to ability. Therefore, the trust is high when the friends/followers possess good behaviour in social networking sites. User generated brand recommendations is a new form of user testimonials that directly influences the buying behavior of the people. Social networking sites play a key role in directing the people to make decisions on purchasing products or services. Thus, we can conclude that consumer trust the user generated brand recommendations based on the trust in the particular social networking site and the level of ad-scepticism.
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Received: 16-Jun-2022, Manuscript No. AMSJ-22-12187; Editor assigned: 18-Jun-2022, PreQC No. AMSJ-22-12187(PQ); Reviewed: 03-Jul-2022, QC No. AMSJ-22-12187; Revised: 23-Jul-2022, Manuscript No. AMSJ-22-12187(R); Published: 26-Jul-2022