Influencer Marketing and Consumer Decision-Making in Online Retail: A Social Media Perspective

In the digital age, social media has transformed from a communication tool into a powerful marketing platform that significantly influences consumer behavior. Platforms like Instagram, Facebook, YouTube, and Twitter have become essential for brands seeking to connect with target audiences, especially in the online retail sector. The proliferation of user-generated content and influencer marketing has led to an increase in the weight that customers give to online reviews, recommendations, and social media trends when making purchases.

This paradigm shift in marketing strategy has prompted businesses to adopt new methods for engaging consumers, building brand loyalty, and driving online sales. The growing dependency on social media platforms has also changed the traditional buyer journey, where information, trust, and influence are shaped by digital interactions rather than face-to-face engagement or conventional advertising.

This study aims to quantify the effect of SMM on online consumers' ultimate purchase decisions as influenced by advertisements, recommendations from influencers, and interactive brand content. The study purposes to investigate the behavior of active online shoppers to determine if social media participation have a direct impact on purchase intent or if it is more of a tool for awareness and engagement. In today's cutthroat e-commerce landscape, this research is critical for marketers looking to maximize the effectiveness of their digital strategy.

• To research how social media marketing affects online retailer.
• To examine how social media is affecting consumer purchasing decisions.

H01: Social media has no significant impact on online retailers.

H11: Social media has a significant impact on online retailers.

H02: There is no association between the effect of social media & buying behaviour of customers.

H12: There is an association between the effect of social media & buying behaviour of customers.

Research in social sciences seeks knowledge. Scientific & methodical inquiry of particular issues for the purpose of gaining information is what is known as research. Research is the process of carefully seeking out information in order to determine anything. As a rule, research focuses on solving problems. In order to get reliable and verifiable results, it relies on an honest and thorough process of identifying problems, collecting data, and analyzing it. The amount of knowledge that this adds is substantial. By employing a research design and a variety of statistical techniques, a study can empirically prove any theoretical assumptions through research methodology. This allows for thorough analysis and comprehension of the topic being studied.

Due to the necessity of the study in analyzing the significance of social media promotion for online retailers, and the facts utilized to assess and explore the data, this research study has been examined using methods of both exploratory and descriptive research design. In the modern day, it has revolutionized communication by making it easy to build many platforms for communication. The most genuine method, social media has altered people's daily routines.

Primary Data

An online survey-based questionnaire was used to gather primary data. Users who are actively engaged in platform-based online purchasing were the target audience for this poll. Using a method of purposeful sampling, we will only contact people who are active online consumers and who utilize social media.

Secondary Data

The secondary data was culled from a variety of sources, including scholarly journals, online books, periodicals, newspapers, and databases like ResearchGate, Google Scholar, and JSTOR. To provide the groundwork for the study and identify any gaps in the current literature, a comprehensive literature review was the first step in the secondary research process.

QUESTIONNAIRE

The questionnaire assisted as the primary data collection instrument for this study and was developed with careful attention to clarity, relevance, and research alignment. It was designed to collect quantifiable data on social media usage, consumer perceptions, and demographic influences on online shopping behavior.

The collected data were systematically analyzed using a range of statistical tools and techniques. We employed Microsoft Excel and other statistical software to generate pie charts, graphical representations, and perform both descriptive and inferential analyses. The hypotheses were tested using chi-square tests after frequency tables with percentages were produced.

Primary data were obtained through a structured survey questionnaire designed to assess the impact of online media on customer buying behavior. The responses were coded, scored, and compiled into a master datasheet for further analysis.

·                    Descriptive Analysis: A comprehensive examination of the data was conducted, including the generation of summary statistics and graphical illustrations. Relevant tables and visual aids are included to present the findings clearly and concisely.

·                    Inferential Analysis: Statistical tests, particularly the chi-square test, were utilized to evaluate the hypotheses and validate the research objectives. This analytical approach provided insights into the relationships and significance levels among the studied variables.

Descriptive Analysis of Respondents' Demographics

1. Gender

The survey included respondents of all genders to ensure inclusivity and represent a balanced viewpoint. Male and female participants constituted the majority, while a small percentage identified as others, highlighting the diverse nature of the sample.

Table 1: Distribution of Respondents According to Gender

Figure 1: Gender distribution

The gender distribution among the 591 respondents reflects a fairly balanced representation. Males constitute the slight majority at 50.76%, closely followed by females at 46.87%. A small yet inclusive segment of the population, 2.37%, identified as ‘Others.’ This distribution underscores the study’s commitment to gender diversity and inclusivity, ensuring that perspectives from all gender identities are considered in analyzing consumer behavior.

2. Age 

Respondents were categorized into distinct age groups ranging from 15 to above 30 years. This segmentation helped in understanding generational preferences and behavioral patterns related to online media influence and retail decisions.

Table 2: Distribution of Respondents According to Age

Figure 2: Age distribution

The majority of respondents (38.07% of the total) are in the 20–25 age groups, according to the distribution of ages. This is followed by 25–30 years at 30.79%, indicating that the majority of participants are young adults likely to be active digital users. The 15–20 years group accounts for 12.35%, while those aged 30 years and above form 18.79%. The dominance of youth in the sample provides a valuable lens into the behavior of digitally-savvy consumers who are typically more influenced by online platforms.

3. Marital Status

The sample included individuals who were single, married, and others (such as divorced or widowed). Analyzing marital status helps in evaluating how family responsibilities and lifestyle impact online shopping behavior.

Table 3: Distribution of Respondents According to Marital Status

Figure 3: Martial status distribution

A significant majority of the respondents (60.07%) are single, suggesting a strong representation of individuals with fewer familial obligations, potentially leading to more autonomous and impulsive online purchase behaviors. 36.89% are married, indicating the presence of more responsible spending patterns. The 3.05% identifying as Other (divorced, widowed, etc.) add depth to the demographic diversity, allowing the study to analyze how different family structures affect consumer decisions.

4. Income

Income brackets ranged from below ₹1 lakh to above ₹15 lakh annually. This classification was essential to assess spending power and affordability, which are key factors in understanding consumer behavior in online retail.

Table 4: Annual Income of Respondents

Figure 4: Yearly Income wise distribution

The income distribution shows that the highest proportion of respondents (34.86%) fall into the ₹1–₹5 lakh annual income range, followed by 24.54% in the ₹5–₹10 lakh bracket. About 19.97% of respondents earn below ₹1 lakh, and 13.03% fall in the ₹10–₹15 lakh category. A smaller group (7.61%) earns above ₹15 lakh annually. This distribution suggests that a majority of the respondents belong to low- to middle-income brackets, which is critical for analyzing price sensitivity and purchasing behavior in online retail.

5. Education 

Respondents had varying educational backgrounds, from school-level to professional and postgraduate qualifications. Education level plays a crucial role in digital literacy and influences responsiveness to social media marketing.

Table 5: Educational Qualification of Respondents

Figure 5: Educational wise distribution

The survey reflects a highly educated sample, with 41.47% holding undergraduate degrees and 29.27% having postgraduate qualifications. Participants with professional course certifications constitute 13.88%, while those with school-level education form 9.31%. Additionally, 6.09% fall under the ‘Other’ category. The high level of educational attainment among respondents suggests strong digital literacy, making them more likely to engage with online content and marketing campaigns.

6. Occupation

The occupational profile included students, employees, business owners, housewives, and others. This variety allowed a comprehensive view of how professional roles and time availability affect online engagement and purchase behavior.

Table 6: Distribution of Respondents according to Occupational

Figure 6: Occupational wise distribution

Occupationally, students represent the largest group at 33.84%, followed by employees at 27.59% and business professionals at 16.92%. Job seekers comprise 9.31%, while housewives account for 7.61% and others for 4.74%. The significant proportion of students and working professionals indicates a sample that is both digitally active and economically aware, providing key insights into consumer behavior in the digital marketplace.

Table 7: Chi-Square Test Association between Time Spent on Social Networking and Web-Based Product Research for In-Store Purchase

Take note that the projected count is less than 5 in 0 cells (0.0%). The count must be at least 29.92.

The p-value, or Pearson Chi-Square Asymptotic Significance, is 0.536, which is greater than the 0.05 level of significance, according to the interpretation. Hence, we accept the null hypothesis and draw the conclusion that there is no significant correlation between the amount of time respondents spend on online networking sites and their usage of the internet to research products with the intention of purchasing them from a store.

This implies that the time spent online does not meaningfully influence a consumer's research-based purchasing behavior. Additionally, when cross-comparing related variables (e.g., ad interaction, feedback sharing), similar independence is observed in subsequent tests.

Table 8: Chi-square Analysis of Social media and Online Product Research for Store Buying

Table 9: Chi-Square Test – Brand Page Visits vs. In-Store Purchase Intent via Web Search

Interpretation: Asymptotic significance is reached when the Pearson Chi-Square value is more than 0.05, which is 0.127. Hence, we accept the null hypothesis, which states that there is no statistically significant correlation between researching products online and visiting retail brand accounts/pages on social media with the aim of making a purchase in-store. That is to say, it appears that the actions taken to research products for in-store purchase and the number of times people visit brand pages on social media are unrelated.

Table 10: Chi-Square Test Ad Clicks on Social Media vs. Web Research for In-Store Purchase

Interpretation: With a result of 0.114, the Pearson Chi-Square test yields a p-value higher than the significance level of 0.05. So, we'll go ahead and accept the null hypothesis and say that there isn't a correlation between social media display ad clicks and online product research leading up to in-store purchases.

This implies that engaging with social media advertisements (click behavior) does not significantly influence offline-oriented product research behavior.

Table 11: Chi-Square Test Online Ad Influence vs. Web Research for In-Store Purchase

The Pearson Chi-Square value is 6.876 with 4 degrees of freedom and a p-value of 0.143, which is greater than the standard significance level of 0.05. This indicates that there is no statistically significant association between consumers being influenced by online advertisements and their tendency to research products online before purchasing them in-store.

Furthermore, the assumptions for the Chi-Square test are satisfied, as all expected cell counts are greater than 5, with a minimum of 21.17. This supports the reliability of the test outcome.

Table 12: Chi-Square Analysis Retail Brand Page Visits on Social Media vs. In-Store Buying Intent via Web Search

The Pearson Chi-Square value is 3.633 with 4 degrees of freedom and a p-value of 0.458, which is well above the 0.05 threshold. This result indicates that there is no statistically significant relationship between visiting retail brand pages on social media and the intention to purchase products in-store after researching them online.

Additionally, the test meets validity criteria, with no expected cell count below 5, ensuring reliable test conditions.

The act of visiting retail brand pages on social media appears to be independent of consumers' in-store purchase decisions following web-based product research.

Table 13: Chi-Square Test Sharing E-Retail Links vs. Web Research for In-Store Purchase

Noted that the projected count is less than 5 in 0 cells (0.0%). A count of at least 23.01 is anticipated.

Interpretation: Since the p-value is higher than the generally accepted significance level of 0.05, the absence of a statistically significant link between the categorical variables under examination is shown by the Pearson Chi-Square value of 3.441 and p-value of 0.487. A p-value of 0.487 is likewise produced by the Likelihood Ratio, further confirming that there is no significant link. Consequently, we can't rule out the possibility that the observed distribution of values is just as random as it may be. In addition, the chi-square test's assumption is satisfied; the statistical inference can be trusted because no cell has an expected count lower than 5, and the lowest is 23.01.

Table 14: Chi-Square Test Seeking Feedback via Social Media vs. Web Research for In-Store Purchase

Interpretation: We can accept the null hypothesis that social media advertising increases product questions and feedback, and that there is no relationship between employing the internet to search for products with the intention to buy, since the asymptotic value of 0.640 is larger than 0.05.

Figure 7: Respondents’ Usage Percentage across Various Online Media Platforms

In this research, we looked at how social media marketing affects online shoppers' final purchasing decisions. The findings revealed that while social media platforms significantly enhance brand visibility, user engagement, and product awareness, they do not have a statistically significant impact on offline purchase intentions. Chi-square analyses showed no strong association between variables such as ad clicks, brand page visits, or feedback seeking and actual consumer purchasing behavior in physical stores. Despite high digital literacy and active social media usage among respondents, the transition from online engagement to offline buying decisions remains limited. This indicates a gap between awareness and action, suggesting that social media primarily serves as an engagement tool rather than a direct driver of in-store purchases. Marketers should thus refine their digital strategies to bridge this gap, focusing on more personalized, action-oriented campaigns to convert engagement into actual sales.