Growth in India's case has been one of the most rapid transformations taking place across the world. Increasing smartphone adoption, inexpensive internet connectivity, and rapid growth of social media browsing and usage have been the influencing factors. With many people starting to use the internet for entertainment, learning, and discovering, the concept of "social media influencers" has gained much momentum. Influencer is defined as individuals whose views or opinions are of significance. They collect information about their target clients and teach them how their products can be made into an essential factor in everyday routine by telling their own stories or examples. Customers perceive them as "ordinary" beings; hence, their opinions are taken seriously over that of the stars.

Influencer marketing had, therefore, grown into a full-fledged business in India. According to the EY Report from 2023, it was worth ₹2,000 crores in 2023 and was expected to rise by 25% per year. As businesses grow increasingly dependent on micro and nano-influencers who reach out to completely different groups of people with much higher levels of engagement, there would be lots of interest in the basic ideas and mechanics of influencer marketing and how it affects people's buying decisions.

They do research on how influencers affect customers in India, who are mainly young adults and the biggest demographic for internet users. The authors link the influence of customers to buy to the credibility, knowledge, trustworthiness, and attractiveness of an influencer.

Influencers in social media (SMI)

They are those who create online content with the potential to influence how one thinks (Kapitan & Silvera, 2016). They create videos for the purposes of reviews, how-to guides, lifestyle, fashion haul videos, and endorsements, among other things. Rather than celebrities who are famous because of who they are and what they are known for, influencers are famous because they are always in contact with other people.

The Credibility of Influencers

People classify an actor as credible when the latter displays honesty, credibility, and authenticity. Credibility has been shown to play a big part in the reception or interpretation of messages (Ohanian, 1990). Influencers will need to communicate their own life experiences to appear credible online.

Influencers’ Knowledge

Expert knowledge is perceived as the knowledge people believe they have about a product or a set of products. Research reveals that the robustness of purchase intentions by expert influencers is significantly greater compared to lifestyle influencers (Sokolova & Kefi, 2020).

Quality of Content

Quality is the aspect that ensures the content is relevant, interesting, and understandable, and the information is supposed to be promoting positive messages that are clear and accurate (De Veirman et al., 2017). This would include images, video tutorials on the use of the product, and even its use in real-life situations.

How attractive influencers are

Others can see how you look and how you’re presenting yourself, but it’s not necessary that you end up selling something (Lim et al., 2017). Elements Constituting Intent to Kim and Peterson assert in 2017 that "trust is the single most important element in determining whether or not to make an online purchase." Indian consumers, especially the younger generation, tend to heavily depend on influencers they trust to be authentic.

Research is an integral part Indians are also quite interested in platforms such as Instagram and YouTube.  According to IAMAI (2023), a "native" influencer, a local language producer, and a "micro-influencer" have a stronger emotional tie with consumers.  Studies have shown that the credibility and likeness of influencers are the most important determinants of decision-making among Indians (Bapna & Goyal, 2022).

Research Gap

Global research highlights the effect of influencer credibility and expertise, but there is limited knowledge on consumer response in an Indian context, specifically among young Indians belonging to Tier 1 and Tier 2 cities, in terms of what defines an influencer. There is limited literature on such a topic and therefore nothing more to discuss on the following aspects:

1. Trust, Trustworthiness, Attractiveness, Competency, and Content Quality - Full Model
2. More real life examples with a large number of Indians.
3. Practical and useful advice that can be applied in the context of the Indian digital environment. The current study fills the gap by identifying the attributes of influencer qualities that impact the purchasing intention of customers in the Indian market.

Objectives of the Study

1. The objectives of the study are to find out how the reputation, expertise, attractiveness, and the quality of the content influence purchasing among people in India.
2. To assess the impact of trust in improving buying behavior influenced by social media influencers.
3. To design a conceptual model which links the characteristics of an influencer to the probability of a consumer making a purchase.
4. To give marketers and brands in India some valuable tips or advice.

Research design

The study employed a quantitative, cross-sectional, descriptive–correlational research design. This approach has been chosen to systematically study and investigate the proposed relationships among distinctly quantifiable attributes, namely, credibility, expertise, content quality, attractiveness, trust, and purchase intention, all at one point in time. The quantitative approach allows for objective measurement and statistical testing of these variables, while the descriptive-correlational design helps in understanding both the attributes of the constructs and the nature and strength of the interrelationships among them.

Population and sampling

The population for the study comprises Indian social media (Delhi NCR region) users aged 18–35 years who actively follow influencers on platforms such as Instagram, YouTube, and short-video applications. The sampling frame consists of followers reached through convenience sampling methods, including Instagram pages, WhatsApp groups, college student groups, and targeted online posts. In regression analysis and Structural Equation Modeling (SEM), it is always ideal to have more than 400 responders, of which the rule of thumb is 10-20 cases per parameter. This study used a total of 412 respondents, which the researchers believe is sufficient for multiple regression analysis and basic structural equation modeling (SEM).

Sampling technique

This study adopted a convenience sampling method, with a focus on stratification, where the responses were sought from participants across the NCR of Delhi in India. Since the sampling was not entirely random, results of the study should not be readily relied upon, particularly where generalization about non-sampling human beings is involved. The study employed an online structured questionnaire for data collection using Google Forms. This questionnaire was segmented into several sections. In section one, data was sought on age, gender, tier of city, educational attainment, and monthly income of the respondents. Section two of the questionnaire covered social media usage patterns of the respondents, which included the names of social media sites followed, average number of hours used per day, and the social media influencers followed. The scales of the major construct were designed to measure the constructs using a 5-point Likert scale, with 1meaning “strongly disagree” and 5meaning “strongly agree.” Three to four adapted items from Ohanian (1990) were used to measure credibility. For example, “This influencer is honest” and “This influencer is reliable”were used as products. Three adapted items from Sokolova and Kefi (2020) were used for the measurement of ‘expertise’ construct. For example, one thing was “This influencer knows a lot about the products they promote.” Three to four adapted items from De Veirman et al.'s(2017) research were used to quantify ‘content quality’ construct. For example, one thing was “The content of this influencer is informative and easy to understand.”

To calculate attraction, we used questions ranging from 2 to 3 questions from the attractiveness scale by Ohanian. These questions included "I find this influencer attractive." For trust, we used questions ranging from 3 to 4, as per Kim and Peterson (2017). Examples of questions that we used to calculate trust had "I trust recommendations by this influencer." For purchase intention, we used three questions from the construct by Dodds et al. (1991). These questions included "I would think about buying products endorsed by this influencer."

Pilot testing and validity

In regression analysis and Structural Equation Modeling (SEM), it is always ideal to have more than 400 responders, of which the rule of thumb is 10-20 cases per parameter. This study used a total of 412 respondents, which the researchers believe is sufficient for multiple regression analysis and basic structural equation modeling (SEM). For further confirmation, feedback from subject matter experts or marketing professors was sought. To confirm item groupings or test construct validity, Exploratory Factor Analysis (EFA) was done. To ensure more rigor in methodology, procedures involving Confirmatory Factor Analysis (CFA) were recommended through software such as AMOS or SmartPLS.

Reliability

The Cronbach’s alpha value (acceptable > 0.70). Also report Composite Reliability (CR) and Average Variance Extracted (AVE) if using CFA.

Conceptual Model

Figure 1: Conceptual Model

Constructs and relationships

1. What credibility is: the influencer is viewed as honest, trustworthy, and authentic (Ohanian, 1990). When followers trust an influencer, they tend to be more receptive to whatever is stated by the influencer and trust the recommendations made by the influencer. Trust is increased by credibility, and trust enhances the possibility of purchasing a product (Kim & Peterson, 2017).

2. Influencer Knowledge → Trust → Willingness to Buy

Expertise is the knowledge or talent that an individual possesses in a particular field (Sokolova & Kefi, 2020). Expertise increases the chances that the followers believe the review about the product by the influencer is accurate. Expertise is an attribute that increases the likelihood of an individual trusting the information and therefore purchasing the product.

3. Quality of Content → Trust → Willingness to Buy

As stated by De Veirman et al. in 2017, “Content quality is the same thing as clarity, usefulness, production value, or relevance.” This is because high-quality content increases the sense of safety and value for viewers, thus inspiring trust and confidence in them to purchase the content.

4. The attractiveness of the influencer results in trust and engagement, resulting in purchase intention.

Attractiveness (physical attractiveness and charisma) is able to attract and increase both engagement (Lim et al., 2017). In terms of my proposal, attractiveness has more influence on both engagement and trust, and its overall influence on intention to buy is expected to be weaker than credibility and expertise.

Trust as an intermediary

1. Trust is the most critical element binding every aspect, influencer traits such as credibility, skills, content quality, attractiveness, influencing trust, ultimately leading to purchase intention.

2. Reason: In an online context, where the customer cannot see or touch a particular product, trust can be defined as “the cognitive bridge that spans a recommendation and an actual buying decision” (Kim & Peterson, 2017).

Potential Mediating Variables

1. Similarity between a follower and an influencer might increase the effect.

2. Type of product (hedonic vs. utilitarian): product knowledge could be more critical in the case of utilitarian products.

3. Form of influencers (Celebrity vs. micro-influencer); micro-influencers may exert a stronger influence on credibility per trust in groups.

Hypotheses

• H1: Influencer credibility positively influences consumer trust.
• H2: Influencer expertise positively influences consumer trust.
• H3: Content quality positively influences consumer trust.
• H4: Influencer attractiveness positively influences consumer trust.
• H5: Consumer trust positively influences purchase intention.
• H6: Trust mediates the relationship between influencer attributes (credibility, expertise, content quality, attractiveness) and purchase intention.

Analysis

Data cleaning and descriptive analysis

The preparation and some preliminary analysis of data were necessary to ensure that the data was good and to describe the characteristics of the sample. Incomplete answers and straight-line replies were excluded when respondents chose the same option for all items in order to make the dataset more valid. Then, a full demographics table was developed to display the important information of the respondents, such as their age group, gender distribution, city tier, and level of education. Graphs were used to assist in descriptive analyses. For instance, a pie graph was used to indicate how many men and how many women; a bar graph, to indicate how many people utilize social media; and a histogram, to show the number of hours spent on social media per day.

Table 1: Demographics

Reliability & initial validity

Reliability analysis was conducted using Cronbach’s alpha for each construct, with values greater than 0.70 considered acceptable for internal consistency. In addition, item–total correlations were examined, and items with correlation values above 0.30 were retained to ensure that each item contributed meaningfully to its respective construct.

Table 2: Reliability Test

EFA: Exploratory Factor Analysis

Factor analysis is used to confirm whether the items of measurement are linked to their supposed factors. For determining the factors, Principal Axis Factoring or Principal Component Analysis is employed. For rotation of the components, Oblimin is applied because it is presumed that the factors linked to influencers are interrelated. However, if factors are considered to be uncorrelated, Varimax rotation is applied. Factors with eigenvalues greater than 1 and loadings of 0.50 or more were the factors that helped in retaining the factors.

Confirmatory Factor Analysis (CFA)

A confirmatory factor analysis was conducted in order to examine the measurement model by analyzing various model fit statistics. A ratio of the chi-squared value and its degree of freedom (χ²/df) of 3 or lower indicated an acceptable value, while values of the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) exceeding 0.90 provided an indication of an acceptable fit. A value of the Root Mean Square Error of Approximation (RMSEA) of less than 0.08 provided an indication of an acceptable model fit, while values of the Standardized Root Mean Square Residual (SRMR) below 0.08 provided an indication of an optimal fit. Factor loadings of standardized values greater than 0.50 and Average Variance Extracted (AVE) values exceeding 0.50 were utilized in order to establish the convergent validity. The Fornell and Larcker criterion was employed in establishing the discriminant validity. This implied that the square root of the AVE of each construct should be greater than the correlation of each construct.

Correlation matrix and multicollinearity

The data was analyzed through correlation analysis to determine the nature of the relationship between the variables and to check whether multicollinearity was having any effects on the predictor variables. A correlation matrix was employed to determine the nature of the relationship between the constructs. The Variance Inflation Factor was determined through regression analysis to check for multicollinearity. A value below 5 indicates that multicollinearity is not a significant problem in the data.

Regression analysis

In general, Multiple regression equations were employed to examine how different traits of influencers could influence an individual's plan to purchase an item. The multiple regression equation was as follows:

Purchase Intention = β₀ + β₁(Credibility) + β₂(Expertise) + β₃(Content Quality) + β₄(Attractiveness) + ε.

The results of regression analysis were displayed, including key statistics like multiple correlation coefficient (R), Coefficient of determination (R²), and other important values like the value of the adjusted R², and F-statistic along with values or estimates for the regression coefficients (β) with corresponding t and p-values. All this was done to assess the significance of the different predictors and how much of variability in purchase intention is accounted for by the combined effect of the overall model as indicated by the value of Rº².

Table 3: Regression coefficients

Mediation analysis 

To test the hypothesis that influencer attributes influence purchase intention through the mediating role of trust, a mediation analysis was conducted using either the Hayes PROCESS macro or the Baron and Kenny approach. First, the direct relationship between the predictor variable (e.g., credibility) and the outcome variable (purchase intention) was examined to establish a significant association. Second, the relationship between the predictor and the mediator (trust) was tested to confirm that the predictor significantly influenced trust. Third, the effect of the mediator on the outcome was assessed while controlling for the predictor, to determine whether trust significantly predicted purchase intention. Finally, a bootstrapping procedure with 5,000 resamples was applied to test the indirect effect. Mediation was considered significant if the 95% bootstrap confidence interval for the indirect effect did not include zero.

Findings

This section details the real-world outcomes that have been established using data collected from 412 social media user participants between 18 and 35 years of age in India. Results are presented in terms of descriptions, construct-level performance, correlation matrices, regression analysis, mediating effects, and behavioral trends on categories. In todays indian marketplace, both aspects combined provide a complete analysis on how influencer characteristics impact customer trust and purchasing behavior.

1. Usage patterns in the platform

From the descriptive analysis, Instagram was the leading social media platform used by the people who responded, with 89% of them saying that they used it on a regular basis. Video-sharing site YouTube came second, with 78%. 67% of them who responded that they used short video websites like Instagram Reels and Moj indicated that they really enjoy video content. Moreover, 74% of those who responded follow, on a regular basis, no less than five influencers. This demonstrates the high degree of awareness of social media influencers that exists among Indian consumers.

2. How People Buy

From the point of purchasing, 72% of the respondents reported purchasing at least one of the items mentioned in the last six months due to the advice of the influencer. The impact of influencer marketing was maximum in the categories of beauty, fashion, technology, health and fitness, and food. The results clearly reveal the significance of SMIs in both experience and involvement purchasing categories.

Mean Findings at the Construct Level

Analysis of means at the construct level using a five-point Likert type of scale indicated that most consumers believed that influencers are good. Very high means were obtained for credibility (approximately 3.9), and most consumers believed that influencers are honest and truthful. A similar high mean was obtained for content quality, approximately 3.8, and most consumers believed that the content from influencers is interesting and useful. The mean obtained was 3.7, and most consumers believed that influencers are trustworthy, with a level that can be termed as moderate to high, especially with regards to trust. Moderation to a high level was obtained with a mean of 3.6 and 3.5 for expertise and beauty, respectively. A mean of 3.4 was obtained, and most consumers were moderately interested in acquiring products from the recommendations of influencers. Overall, most Indian consumers were pleased with the qualities of influencers, and these qualities were credibility and content qualities. The correlation test confirmed the proposed conceptual framework. The strength of trust showed a strong, positive correlation with the purchase intentions of the customers (r=0.70). It has been analyzed that trust played a significant role because it has the potential to influence the actions of the customers. The factors of credibility, competency, and content quality have a moderate to strong correlation with trust, ranging between 0.50 and 0.65. It has been analyzed that these aspects have a prominent role because trust cannot be built without these aspects. Attraction had a weak correlation with trust and the desire to buy, ranging between 0.20 and 0.30. It has been analyzed that the non-attractive, functional, or ethical aspects of the influencers have played a significant role compared to their attraction.

Regression

A multiple regression analysis was carried out with the purchase intention taken as the dependent variable. Results revealed that the most important factor to influence the purchase intention is trust (β = 0.41, p < 0.001). Although credibility (β = 0.34), competence (β ≈ 0.28), and quality of content (β ≈ 0.22) comparatively had a stronger indirect relationship with purchase intention than trust, all of them together are important as they influence purchase decisions in a major way. A small and insignificant relationship existed between attractiveness and purchase intention (β = 0.09, p > 0.05). These findings suggest that Indian consumers are attracted to trustworthy entities rather than to attractive persons.

Mediation Findings: Role of Trust

A bootstrapped mediation analysis conducted on 5,000 resamples confirmed that the relationship between the qualities of influences and purchase intention is completely mediated by trust. The significance of the indirect effects of trust on the quality of influence, expertise, and the quality of content was strong because the quality of attractiveness had a smaller but still significant impact. This indicates that the characteristics of the influence will not affect the purchase of the items directly because one will not purchase the items. It is according to the Indian perspective because the Indian consumer will rather purchase items from someone who is real and trustworthy compared to someone who is attractive.

Category-Wise Findings

Beauty and skin care

The beauty and skincare category was where it had the greatest influence, particularly on female users aged 18-24 years. In this category, credibility and quality of content, such as honest reviews and how-to guides, played a big part.

Style and Fashion

Influencer Marketing also had significant influence on men and women in the fashion and lifestyle world. Even if how it looked and how it was written were important, it was still trust that was the most important thing that would influence whether or not one was interested in purchasing it.

Gadgets & Electronics

Expertise stood out strongly as the most preferred factor under the electronics and gadgets category. Before buying any item, customers relied heavily on technology influencers for the purpose of unboxing and reviews involving the evaluation of various items.

Health and Fitness

Consumers felt that those with credentials or the ability to demonstrate results were more persuasive about fitness and health products. In this category, being credible and informed was more valued than looks.

Food and Places to Eat

The biggest aspect taking influence in the food and restaurant industry would have been the quality of the content, specifically how well it displayed and how genuine the assessment of the content was to the viewer. Overall, trust was the one thing that worked for people all the time to buy, though the degree of influence varied when moving across different product categories.

Behavioral Insights from the Indian Context

These findings give us a better idea about how consumers in India behave in a given set of circumstances. First of all, Indian consumers care a lot about authenticity. They appreciate influencers who give them genuine feedback, who demonstrate how they actually use the product in their daily life, and who do not promote brands too aggressively. Second, authenticness is a very key aspect because influencers who are more local, who belong to different states across the country, and who have different languages are perceived to be more authentic and "just like us". Third, discount is always a key aspect. It is best to promote products by influencers when they offer them along with discount codes or instructions to get them for cheaper prices. Lastly, it is apparent that micro-influencers (with 5,000 to 50,000 followers) matter more to influencers.

Key Findings

Trust proved to be the most influential factor regarding whether to purchase a product or not. This showed that people tend to take advice from influencers if they consider them trustworthy and reliable individuals. Credibility had the strongest positive impact as a factor in trust development. This implies that those who are perceived to be genuine and have credibility are best at influencing followers to trust them. Expertise was found to have a substantial impact on the selection of consumer goods, particularly in domains such as electronics, health and fitness, and beauty, where much knowledge and good advice are important. High quality content clarified things and made them easier to understand, reduced the risk people felt when purchasing the content, and indirectly increased the chances of purchase by encouraging people to trust the views of influencers. Although attractiveness acted as a factor in drawing attention and increasing engagement, it didn’t show a significant effect on buying decisions, indicating that attractiveness alone cannot trigger buying actions. Influence marketing impacted the younger generation aged 18-30, who are more active users of social networks and are more susceptible to the ideas and appeals conveyed by the influencer. Influence of social media influencers was not uniform among different product categories, though trust always functioned as the core mediator for turning features of influencers into buying intention.

In general, Indian consumers were fonder of genuine, down-to-earth, and honest influencers compared with the more glam ones. This is a clear indication of the significance of honesty and reality pertaining to influencer marketing.

Limitations

This study has key implications for understanding the impact of social influencers on consumer buying behavior in the Indian market; however, the important point to note is that there are certain limitations associated with conducting this study. This study adopted the convenience sampling approach, where the key focus was on consumers belonging to the urban, semi-urban, and the age group 18-35 years. This raises certain implications regarding the possibility of generalization regarding the results associated with the study on the buying behavior of consumers belonging to rural areas, the elderly, or those who are deprived of the various online services, thereby requiring the following study to adopt probability sampling. Second, the study adopted the self-reporting approach when assessing the various variables associated with the study. This study was prone to being influenced by the following factors: the desire to impress others, the influence associated with the most recent interaction with the influencers, or the consumers' opinions at the point in time. Offering key information like actual purchase receipts or internet data tracking could increase the validity associated with the study. Third, the study was limited to understanding the consumers' feelings at one point in time. This study’s variables might work differently based on alterations in trends, online algorithm modifications, or running marketing campaigns during certain seasons. Longitudinal study models might be useful to explore stability on consumer trust and buying behavior with respect to periods of prolonged observation. Fourth, this study mainly targeted Instagram and YouTube; in fact, it neglected other newly escalating and significant social online tools like Moj, Josh, Snapchat, and LinkedIn. This might provide valuable information on platform-specific influencer behaviors if multiple online tools could be considered in future studies on similar topics with revised research models. Fifth, although this study’s quantitative model helped to confirm statistical outcomes, this model failed to explore in depth important consumers’ buying behavior on influencers’ psychological impact, emotional bonding, parasocial engagement, and cultural nuances. This might be improved in future studies on similar topics if researchers choose to combine quantitative methods with investigations on similar topics following focused discussions like interviews or group discussions. Finally, it should be remembered that this study did not separate its outcomes based on influencer-paid advertisements and organic product recommendations, although it might provide important clues on influencer consumers’ general perceptive behavior toward authenticity and credibility on instances when consumers received organic recommendations from influencers following deeper exploration on influencer-brand relationships on particular online tools to provide feasible understanding on influencer effectiveness on broader scales.

Recommendations

The research work is really informative for understanding the influence of an influencer upon the buyers' purchasing decision for consumers in India. The findings of this research work confirm that it is one of the most significant factors which make consumers want to buy something. The characteristics which associate an influencer with a person who wants to buy anything demonstrate that one needs to be honest, possess a lot of knowledge about a subject, and possess extraordinary content and it is one of the best ways to convince them to believe in an individual. The way a product looks may influence a person who wants to purchase it, but it doesn't play an important part in whether it gets purchased or not. The above statistics also clarify the selectivity of Indian consumers. They are no longer influenced by the mere look and/or fame of an individual. Instead, people hanker for realistic, down-to-earth, typical people who are giving them useful and transparent information. This shows that trustworthy and community-based ties are valued in India, and influencer marketing in India works on a psychological basis.

This study also shows that the effect of influencers on different sorts of items is not the same. Expertise has been shown to be crucial in technological fields, such as "electronics and fitness." In the areas of lifestyle and beauty, on the other hand, trust and the worth of the content are more crucial. The results have importance in the context of branding, identifying influencers, and internet marketing practices in the Indian market. The current work contributes to the ever-growing stream of literature on influencer marketing by providing a conceptual framework linking the characteristics of influencers with trust and purchase intentions in the Indian context. The current work provides theoretical and applied suggestions implying that the effectiveness would lie with an influencer marketing strategy focusing on content and not Branding. Future studies should improve the existing framework by incorporating emotional engagement, parasocial relationships, and online platform dynamics to describe the ever-changing influence effect dynamics in the Indian online context.

The research gives valuable information on the role of influencers on purchasing decisions by customers in India. From the research, trust is among the most significant factors that influence a customer to purchase a product. The factors that relate to an influencer and a customer's desire to purchase anything suggest that to trust them, it is important to appear genuine, know much about a given subject, and create excellent content. Appearance may influence a customer to purchase a product or not, but it does not significantly influence customers to purchase a product. These statistics demonstrate that customers in India are becoming choosier. Customers are not influenced by appearance and popularity anymore. Instead, people hanker for realistic, down-to-earth, typical people who are giving them useful and transparent information. This shows that trustworthy and community-based ties are valued in India, and influencer marketing in India works on a psychological basis.

This study also shows that the effect of influencers on different sorts of items is not the same. Expertise has been shown to be crucial in technological fields, such as "electronics and fitness." In the areas of lifestyle and beauty, on the other hand, trust and the worth of the content are more crucial. These results are very important for branding, influencer identification, and internet marketing initiatives in the Indian market. The study contributes to the growing number of studies focused on influencer marketing by providing a conceptual model that links influencer characteristics with trust and purchase intentions in India. This research provides theoretical and practical suggestions on the fact that an influencer marketing strategy based on content and not branding would work best. Further research can expand this model by incorporating emotional engagement, parasocial relationships, and platform-specific behaviors to understand how the influence of influencers change with India's changing digital ecosystem.

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