1 DATE: TIME: IMPACT OF SOCIAL MEDIA MARKETING ON BUYING BEHAVIOUR OF DIGITAL CONSUMERS IN CHENNAI CITY Ph.D Commerce -Synopsis for Pre Submission Viva-Voce Examination Submitted By PRAVEEN.C Enrolment No. 2205240003 Under the Supervision of Dr. V. DHEENADHAYALAN M.Com, DCA, M.Phil, M.F.M, MBA., SLET (com) , SET (Mgt), PGDSBSA., Ph.D., Associate Professor & Head, PG Department of Commerce, (On Deputation from Annamalai University) Sri Subramaniyaswamy Government Arts College. Tiruttani – 631 209, Tiruvallur Dt. DEPARTMENT OF COMMERCE ANNAMALAINAGAR – 608 002 TAMILNADU, INDIA AUGUEST 2026
2 INTRODUCTION The rapid advancement of information and communication technology has significantly transformed the global business environment. The proliferation of the internet, smartphones, and digital platforms has reshaped the way organizations communicate with consumers and how consumers interact with brands. In recent years, digitalization has emerged as a dominant force influencing marketing practices, consumer engagement, and purchasing decisions across industries. India has witnessed remarkable growth in internet penetration and social media usage over the past decade. The increasing affordability of smartphones and data services has enabled millions of individuals to access digital platforms daily. Social media platforms such as Facebook, Instagram, YouTube, and X have become integral parts of users’ personal and professional lives. These platforms are no longer limited to social interaction but have evolved into powerful marketing tools for businesses. Traditional marketing methods primarily relied on one-way communication through television, newspapers, radio, and print advertisements. However, the emergence of digital marketing has shifted this paradigm from passive communication to interactive engagement. Social Media Marketing (SMM) enables firms to create, share, and promote content while directly interacting with consumers in real time. This interactive nature enhances consumer participation, brand engagement, and relationship building. Social Media Marketing refers to the use of social networking platforms to promote products and services, build brand awareness, influence consumer attitudes, and stimulate purchase intentions. Unlike conventional advertising, SMM facilitates two-way communication, electronic word-of-mouth (e-WOM), and personalized marketing experiences. Businesses can leverage entertainment-based content, interactive campaigns, trend-oriented promotions, customization features, and influencer collaborations to attract and retain customers. The emergence of the “digital consumer” has further intensified the importance of Social Media Marketing. Digital consumers are individuals who actively use online platforms to search for information, compare alternatives, read reviews, interact with brands, and make purchase decisions. Their buying behaviour is strongly influenced by online reviews, peer recommendations, brand engagement, and digital content exposure. Consequently, understanding how social media marketing impacts buying behaviour has become a critical area of academic and managerial interest. Buying behaviour refers to the decision-making process consumers undergo when selecting, purchasing, using, and evaluating products or services. In the digital environment, this process is highly dynamic and influenced by multiple online stimuli. Social media platforms expose consumers to advertisements, influencer endorsements, promotional campaigns, and user-generated content, all of which shape perceptions, trust, engagement levels, and ultimately purchase decisions. Chennai City, as one of the major metropolitan cities in India, represents a rapidly growing digital market. With high internet usage, increasing smartphone penetration, and expanding e-commerce activities, digital consumers in Chennai actively engage with brands through social media platforms. Businesses operating in Chennai increasingly depend on social media strategies to enhance brand
3 visibility, attract potential customers, and influence buying behaviour. Despite the growing adoption of Social Media Marketing by firms, there remains a need to systematically examine its impact on various dimensions of buying behaviour, such as consumer perception, engagement, trust, purchase decision, and satisfaction. While numerous studies have explored digital marketing broadly, limited research has focused specifically on the comprehensive impact of Social Media Marketing components on the buying behaviour of digital consumers within the context of Chennai City. Therefore, this study aims to analyze the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City. By examining the perceived Social Media Marketing components and their influence on behavioural outcomes, the research seeks to contribute both theoretically and practically to the fields of marketing and consumer behaviour. SIGNIFICANCE OF THE STUDY The present study is significant as it examines the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City. With the increasing use of social media platforms for marketing activities, understanding consumer responses to social media marketing has become essential for both researchers and business organizations. The study contributes to academic literature by providing empirical evidence on the relationship between Social Media Marketing and Consumer Buying Behaviour. It also offers practical insights for marketers, advertisers, and business organizations in identifying effective social media marketing strategies that influence consumer perception, engagement, trust, purchase decisions, and satisfaction. Further, the study provides region-specific insights into digital consumer behaviour in Chennai City, which may support future research and marketing decision-making in the digital environment. REVIEW OF LITERATURE For the present study, the researcher reviewed 150 research articles published in national and international journals, books, conference proceedings, reports, and other reliable sources during the period 2016–2025. A. Review of Literature on Social Media Marketing 1. Review of Literature on Interaction in Social Media Marketing 2. Review of Literature on Entertainment in Social Media Marketing 3. Review of Literature on Trendiness in Social Media Marketing 4. Review of Literature on Customization in Social Media Marketing 5. Review of Literature on Electronic Word-of-Mouth (e-WOM) in Social Media Marketing
4 B. Review of Literature on Buying Behaviour 6. Review of Literature on Consumer Perception 7. Review of Literature on Consumer Engagement 8. Review of Literature on Consumer Trust 9. Review of Literature on Consumer Purchase Decision 10. Review of Literature on Consumer Satisfaction RESEARCH GAP An extensive review of the available literature reveals that Social Media Marketing has received considerable attention from researchers across different countries and industries. However, several important research gaps continue to exist. No single study has comprehensively examined all five major dimensions of Social Media Marketing—Interaction, Entertainment, Trendiness, Customization, and Electronic Word- of-Mouth (e-WOM)—within a single research framework to understand their collective influence on consumer buying behaviour. Most previous studies have focused on only one or a few dimensions, resulting in a fragmented understanding of the overall impact of Social Media Marketing. Most previous studies have concentrated on individual social media platforms or selected marketing activities rather than examining the combined influence of major Social Media Marketing dimensions on consumer buying behaviour. Consequently, the overall impact of Social Media Marketing remains only partially understood. Many existing studies have mainly focused on purchase intention or purchase decision, while comparatively less attention has been given to other important behavioural outcomes such as consumer perception, engagement, trust, and satisfaction. Since consumer buying behaviour is multidimensional, a more comprehensive investigation is required. Another significant gap is that much of the existing research has been conducted in different countries or other regions of India. Consumer behaviour is influenced by geographical location, cultural background, technological adoption, and social media usage patterns. Therefore, findings from previous studies cannot be directly generalized to digital consumers in Chennai City. In addition, the digital marketing environment is continuously evolving with the rapid growth of influencer marketing, personalized advertising, interactive content, artificial intelligence, and electronic word-of-mouth. These developments have changed the way consumers interact with brands and make purchasing decisions, creating the need for updated
5 empirical research. Finally, only a limited number of studies have developed and validated a comprehensive conceptual model explaining the relationship between Social Media Marketing and Consumer Buying Behaviour in the context of Chennai City. To address these gaps, the present study examines the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City and develops a validated conceptual model that explains this relationship. The findings are expected to contribute to both academic knowledge and practical marketing decision-making. STATEMENT OF THE PROBLEM Social media has become one of the most influential digital marketing platforms in today's business environment. Consumers use social media not only for communication and entertainment but also for searching product information, comparing alternatives, reading reviews, and making purchase decisions. As a result, business organizations are increasingly investing in social media marketing to reach potential customers and strengthen their market presence. Although businesses spend substantial amounts on social media marketing, many organizations still struggle to understand which marketing activities actually influence consumer buying behaviour. Digital consumers are exposed to a large volume of online advertisements every day, but their responses differ according to their preferences, interests, demographic characteristics, and social media usage patterns. Consequently, marketers find it difficult to identify the social media factors that encourage consumers to trust products, engage with brands, and make purchase decisions. Chennai City has witnessed a rapid increase in internet users and social media adoption, creating new opportunities as well as challenges for marketers. Therefore, the present study aims to examine the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City and provide useful insights for businesses to develop more effective social media marketing strategies. RESEARCH QUESTIONS RQ1: How does Social Media Marketing influence the buying behaviour of digital consumers in Chennai City? RQ2: What is the level of perceived Social Media Marketing components among digital consumers? RQ3: What is the level of buying behaviour dimensions among digital consumers?
6 RQ4: Is there a significant relationship between Social Media Marketing components and buying behaviour? RQ5: Does Social Media Marketing significantly influence perception, engagement, trust, purchase decision, and satisfaction? RQ6: Do demographic variables create significant differences in Social Media Marketing perception and buying behaviour? RQ7: Which Social Media Marketing component is the most influential? OBJECTIVES OF THE STUDY The main objective of this study is to examine the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City. To achieve this main objective, the study has the following specific objectives: 1. To study the demographic profile of social media users and the social media usages in Chennai city. 2. To study the social media marketing perception of digital consumer in the study area. 3. To completely evaluate the existing buying behaviour of digital consumer in Chennai city. 4. To find the influence of demographic profile of Social Media users on social media marketing perception and the buying behaviour of digital consumers in Chennai city. 5. To examine the relationship between Social Media Marketing perception and buying behaviour of digital consumers in study area. 6. To develop and validate a conceptual model explaining the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai city. CONCEPTUAL MODEL OF THE STUDY
7 HYPOTHESES OF THE STUDY H01: There is no significant influence of demographic variables on Social Media Marketing Perception and Consumer Buying Behaviour. H02: There is no significant relationship between Social Media Marketing Perception and Buying Behaviour of Digital Consumers in the study area. H03: There is no significant difference between among Social Media Marketing factors. H04: There is no significant difference among Consumer Buying Behaviour factors. H05: There is no significant impact of Social Media Marketing on Consumer Buying Behaviour. Thus, the study is limited to understanding the relationship between Social Media Marketing and the buying behaviour of digital consumers within the geographical boundaries of Chennai City. RESERCH DESIGN USED FOR THE PRESENT STUDY Research design provides the overall framework for conducting a study in a systematic and scientific manner. It guides the process of data collection, analysis, and interpretation to achieve the research objectives. The present study adopts an Exploratory Research Design as it helps in understanding the influence of Social Media Marketing on the buying behaviour of digital consumers and facilitates an in-depth examination of the relationship between the study variables. The selected research design is appropriate for achieving the objectives of the study. SOURCES OF DATA The present study is based on both primary and secondary data. Primary data were collected directly from digital consumers in Chennai City using a structured questionnaire. The questionnaire consisted of questions related to demographic characteristics, Social Media Marketing, and Consumer Buying Behaviour, and responses were measured using a five-point Likert scale. Secondary data were collected from books, research journals, conference proceedings, theses, reports, and reliable online sources. These sources were used to develop the theoretical framework, review the existing literature, and support the research findings.
8 PERIOD OF STUDY Primary Data: The primary data for the study were collected from digital consumers in Chennai City during February 2026 to April 2026. Secondary Data: The secondary data were collected from 2016 to 2026. STATISTICAL TOOLS USED FOR THE STUDY The collected data were analysed using IBM SPSS and AMOS software. Statistical techniques such as descriptive statistics, reliability analysis (Cronbach's Alpha), validity testing (KMO, Bartlett's Test and Exploratory Factor Analysis), Pearson's correlation, multiple regression analysis, Analysis of Variance (ANOVA), Structural Equation Modelling (SEM), and hypothesis testing were employed to achieve the objectives of the study. SELECTION OF THE STUDY AREA AND SAMPLE SIZE The study was conducted in Chennai City, which was selected as the study area due to its high internet penetration and extensive use of social media among digital consumers. Since the target population was large and indefinite, the sample size was determined using Cochran's (1977) formula, which suggested a minimum sample size of 385 respondents. To improve the reliability and accuracy of the study, 650 questionnaires were initially collected. After data screening and validation, 50 responses were excluded as they were outside the geographical scope of the study. Thus, the final sample size comprised 600 digital consumers from Chennai City. Reliability and Validity Analysis Reliability refers to the consistency and stability of the measurement instrument. To test internal consistency, Cronbach’s Alpha was calculated for each construct. Result of Cronbach’s Alpha (Pilot Study – 80 Samples) Construct No. of Items Cronbach’s Alpha Interpretation Interaction 5 0.842 Good Entertainment 5 0.865 Good Trendiness 5 0.821 Good Customization 5 0.878 Good e-WOM 5 0.889 Good Consumer Perception 5 0.853 Good Consumer Engagement 5 0.867 Good Consumer Trust 5 0.891 Excellent Purchase Decision 5 0.876 Good Consumer Satisfaction 5 0.884 Good Overall Cronbach’s Alpha = 0.923
9 Interpretation: 1. All constructs recorded alpha values above 0.80. 2. The overall reliability of the instrument is 0.923, indicating excellent internal consistency. 3. No items were removed since all item-total correlations were above 0.50. 4. Therefore, the questionnaire is reliable for the main study. SOCIAL MEDIA MARKETING Social media marketing (SMM) is the use of social media platforms and websites to promote a product or service. It involves creating and sharing content in various formats, including text, images, videos, and interactive media, to engage with an audience, enhance brand awareness, and drive consumer action." (Tuten, T. L., & Solomon, M. R. 2020) OPERATIONAL DEFINITION Digital consumers are individuals who use internet-enabled devices such as smartphones, laptops, and tablets to search for product information, compare alternatives, read online reviews, and make purchase decisions through digital platforms. They actively use social media, e- commerce websites, and online applications to interact with brands and gather information before purchasing products or services SCOPE OF THE STUDY The present study focuses on examining the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City. The study is geographically confined to Chennai City and includes digital consumers who actively use social media platforms for information, communication, and online purchasing. The study examines Social Media Marketing through five dimensions: Interaction, Entertainment, Trendiness, Customization (Personalization), and Electronic Word-of-Mouth (e- WOM). Consumer Buying Behaviour is assessed using five dimensions: Consumer Perception, Consumer Engagement, Consumer Trust, Consumer Purchase Decision, and Consumer Satisfaction. In addition, demographic variables such as age, gender, education, occupation, and income are considered to analyse their influence on Social Media Marketing and Consumer Buying Behaviour. LIMITATIONS OF STUDY 1. The study is limited to digital consumers in Chennai City. 2. Only selected SMM components (Interaction, Entertainment, Trendiness, Customization, and e-WOM) are considered.
10 3. The study focuses only on selected buying behaviour dimensions (Perception, Engagement, Trust, Purchase Decision, and Satisfaction). 4. The study is based on data collected during a specific time period. SCHEME OF THE CHAPTERISATION The thesis is organized into five chapters. Chapter I presents the introduction, including the background of the study, statement of the problem, research gap, significance of the study, objectives, hypotheses, scope, and limitations. Chapter II reviews the relevant literature on Social Media Marketing and Consumer Buying Behaviour. Chapter III describes the research methodology, including the research design, data sources, sampling design, questionnaire, and statistical tools. Chapter IV presents the analysis and interpretation of the collected data along with hypothesis testing. Chapter V summarizes the findings, provides suggestions, presents the conclusion, and offers directions for future research.
11 Findings Based on the Objectives of the Study Objective 1 To study the demographic profile of social media users and the social media usage in Chennai City. The major findings relating to this objective are as follows: 1. Male respondents constitute 51.2 percent, while female respondents account for 48.8 percent, indicating a nearly balanced gender distribution with slight male predominance. This balanced composition enhances the reliability of gender-based comparisons in analysing digital consumer behaviour. 2. A higher proportion of respondents are unmarried at 48.0 percent, followed closely by married respondents at 46.8 percent, while 5.2 percent preferred not to disclose their marital status. The near-equal distribution between married and unmarried groups ensures balanced representation, supporting meaningful comparison of consumer behaviour across marital status. 3. A majority of respondents belong to the 25–35 years age group at 30.2 percent, followed by 36– 45 years at 27.8 percent and below 25 years at 16.3 percent, while the remaining respondents fall under older age categories. The concentration of respondents between 25 and 45 years indicates that the study primarily represents active digital users with higher engagement in social media marketing. 4. A higher proportion of respondents are Under Graduates at 35.0 percent, followed by Post Graduates at 25.7 percent and those with Professional Courses at 15.5 percent, while other categories constitute smaller shares. The dominance of graduates and postgraduates indicates that the sample largely represents educated digital consumers with greater awareness and engagement in social media marketing activities. 5. A considerable proportion of respondents are professionals at 32.5 percent, followed by others, students, businessmen, and government employees with relatively lower shares. The presence of multiple occupational groups ensures diverse representation, supporting comprehensive analysis of digital consumer behaviour across different occupational categories. 6. The highest proportion of respondents falls within the income group of ₹8,00,001–₹12,00,000 at 19.5 percent, followed by ₹12,00,001–₹16,00,000 and ₹16,00,001–₹20,00,000, while lower income groups constitute comparatively smaller shares. The distribution across income levels, with greater representation in middle-income categories, enables comprehensive analysis of consumer behaviour across different economic segments. 7. The largest proportion of respondents spend 3 to 5 hours per day on social media at 35.0 percent, followed by 5 to 7 hours at 31.7 percent, while other categories account for smaller shares. The predominance of moderate to high usage indicates that the sample largely consists of active users, supporting meaningful analysis of exposure to social media marketing activities. 8. A vast majority of respondents use smartphones at 97.2 percent to access social media, while laptops, desktops, and tablets are used by comparatively smaller proportions. The dominance of
12 smartphones indicates a strong preference for mobile-based access, highlighting their central role in shaping consumer exposure to social media marketing. 9. The highest proportion of respondents spend ₹15,000–₹20,000 at 27.2 percent, closely followed by ₹10,000–₹15,000 at 27.0 percent, while other expenditure categories account for smaller shares. The concentration within the moderate spending range indicates active online purchasing behaviour among respondents through social media platforms. 10. A greater proportion of respondents view 10–15 advertisements per day at 33.2 percent, followed by more than 15 advertisements at 29.3 percent, while lower exposure categories account for smaller shares. The high level of daily advertisement exposure indicates strong potential influence on the buying behaviour of digital consumers. 11. A majority of respondents engage in sharing messages at 82.3 percent and general content at 57.8 percent, followed by videos, comments, and product or service reviews, while blog sharing remains comparatively low. The preference for interactive and easily shareable content indicates that user engagement is driven by informal communication, influencing consumer behaviour and online purchase decisions. 12. A considerable proportion of respondents sometimes forward messages at 46.5 percent, followed by those who frequently forward at 42.0 percent, while a smaller share rarely engage in forwarding. The high level of participation indicates a strong presence of electronic word-of- mouth, influencing the buying behaviour of digital consumers. 13. To stay connected/updated” with a mean value of 3.2150 is ranked as the most important motivating factor, indicating that continuous interaction and real-time updates drive social media usage and influence purchase decisions. “Access to information” with a mean of 3.7800 and “Information gathering” with a mean of 4.0717 occupy the second and third ranks, highlighting the role of social media in informed decision-making. In contrast, “Advertisements” with a mean of 7.5167 and “Recognition” with a mean of 8.6633 are ranked lower, indicating lesser influence on consumer behaviour. 14. WhatsApp advertisements, particularly business promotional messages at 65.8 percent and status advertisements at 65.3 percent, show the highest exposure among respondents, followed by Instagram reel or video ads at 64.3 percent and YouTube skippable ads at 64.0 percent. Facebook advertisement formats record moderate visibility, while Telegram and Twitter (X) advertisements remain comparatively low across all formats. The dominance of video-based and interactive advertisement formats indicates higher visibility and stronger influence on consumer engagement and purchase behaviour. 15. Subscription services account for the highest proportion at 40.5 percent, followed by electronics and gadgets at 39.5 percent, clothing and fashion apparel at 38.2 percent, smartphones and mobile accessories at 34.8 percent, and online food delivery at 34.3 percent, while other categories show
13 moderate to lower shares. The greater concentration in frequently used and digitally accessible categories indicates stronger influence of social media on routine and trend-oriented purchases, with comparatively limited impact on niche product segments. 16. YouTube with a mean value of 2.3117 is ranked as the most influential platform, indicating the strong impact of video-based content in enhancing product understanding and consumer trust. WhatsApp with a mean of 2.4633 and Instagram with a mean of 2.7183 occupy the second and third ranks, highlighting the importance of peer communication and visually engaging content in influencing purchase decisions. Facebook with a mean of 3.9867 shows moderate influence, while Twitter with 4.5617 and Telegram with 4.9583 are ranked lower, indicating comparatively limited impact on consumer buying behaviour. 17. YouTube accounts for the highest advertisement exposure at 25.2 percent, followed by WhatsApp at 21.2 percent and Instagram at 21.0 percent, while other platforms record comparatively lower shares. The higher visibility on video-centric and interactive platforms indicates their effectiveness in attracting consumer attention, with relatively limited exposure on platforms such as Twitter/X and Telegram. 18. YouTube is identified as the most trusted platform at 27.2 percent, followed by Instagram at 22.7 percent and WhatsApp at 21.8 percent, while other platforms show comparatively lower levels of trust. The higher trust in content-rich and interactive platforms indicates their effectiveness in influencing consumer perception, with relatively limited credibility observed for platforms such as Telegram and Twitter/X. 19. Video-based content records the highest attention at 11.2 percent, followed by interactive content at 10.7 percent and informational content at 10.3 percent, while other content types show comparatively lower shares. The greater attention towards visually rich and engaging formats indicates their effectiveness in capturing consumer interest, with relatively lower recognition for formats such as user-generated and social proof content. 20. Discount with a mean value of 1.7550 is ranked as the most influential promotional content, indicating the strong impact of immediate price benefits on purchase decisions. Cashback with a mean of 2.4017 and festival offers with 3.3133 occupy the second and third ranks, highlighting the role of financial incentives and seasonal influence in shaping consumer behaviour. Coupons with a mean of 3.5683 show moderate influence, while buy one get one offers with 3.9617 are ranked lowest, indicating comparatively lesser impact on consumer purchase decisions. 21. Product demonstration videos with a mean value of 1.8033 are ranked as the most preferred, indicating the importance of clear and detailed product information in influencing purchase decisions. Short videos or reels with a mean of 1.9650 occupy the second rank, reflecting strong preference for quick and engaging content. Long explanation videos with 3.0167 show moderate preference, while story-based videos with 3.2150 are ranked lowest, indicating comparatively lesser influence on consumer purchase decisions.
14 22. An equal proportion of respondents prefer and sometimes prefer influencer-created advertisements at 39.5 percent each, while 21.0 percent do not prefer such content. The mean value of 2.00 indicates a moderate to positive inclination towards influencer advertisements, suggesting a considerable but not uniform impact on consumer preference. 23. A majority of respondents indicate that customer reviews and testimonials influence their purchase decisions at 48.2 percent, while 40.2 percent report occasional influence and only 11.7 percent indicate no influence. The mean value of 1.92 reflects a strong inclination towards reliance on review-based information, highlighting the significant role of user-generated content in influencing consumer buying behaviour. Objective 2 To study the social media marketing perception of digital consumers in the study area. The major findings relating to this objective are as follows: Social Media Marketing Perception Based on the overall mean scores of the five Social Media Marketing dimensions (Interaction, Entertainment, Trendiness, Customization, and Electronic Word-of-Mouth), the 600 respondents were classified into three clusters using K-Means Cluster Analysis (K = 3). Cluster 1 recorded the highest overall mean score and therefore represents respondents with a high level of Social Media Marketing perception. Hence, it was named "Gregarious Consumers." Cluster 2 recorded a moderate overall mean score and was named "Balancing Consumers." Cluster 3 recorded the lowest overall mean score and was named "Capricious Consumers." The cluster names were assigned according to the respondents' overall mean values and behavioural characteristics Social Media Marketing Perception Clusters Cluster Mean Level Cluster Name Reason for the Name Cluster 1 High Gregarious Consumers Highly interactive, active, share opinions, follow trends Cluster 2 Moderate Balancing Consumers Moderate involvement in all SMM activities Cluster 3 Low Capricious Consumers Low interaction and limited response to social media marketing 1. Telegram Bot-Based Promotional Advertisements There is a significant association between Social Media Marketing perception and accessibility toward Telegram bot-based promotional advertisements. Balancing consumers reported higher accessibility toward Telegram bot-based promotional advertisements, with 16.0 percent indicating accessibility. This indicates that consumers with balanced perceptions of social media marketing are more receptive to automated promotional messages.
15 2. Footwear Purchased Through Social Media Influence There is a significant association between social media marketing perception and footwear purchased through social media influence. Gregarious consumers reported the highest level of footwear purchases through social media influence, with 30.0 percent indicating that they had purchased footwear. This indicates that socially engaged consumers are more likely to purchase footwear through social media platforms. 3. Fashion Accessories Purchased Through Social Media Influence There is a significant association between social media marketing perception and fashion accessories purchased through social media influence. Gregarious consumers reported the highest level of fashion accessory purchases through social media influence, with 28.8 percent indicating that they had purchased fashion accessories. This indicates that socially engaged consumers are more likely to purchase fashion accessories through social media platforms. 4. Jewellery, Watches and Ornaments Purchased Through Social Media Influence There is a significant association between social media marketing perception and jewellery, watches and ornaments purchased through social media influence. Balancing consumers reported the highest level of purchases in this category, with 21.5 percent indicating that they had purchased jewellery, watches and ornaments. This indicates that consumers with balanced perceptions of social media marketing are more likely to purchase personal luxury and accessory products through social media platforms. 5. Haircare Products Purchased Through Social Media Influence There is a significant association between social media marketing perception and haircare products purchased through social media influence. Gregarious consumers reported the highest level of haircare product purchases through social media influence, with 20.0 percent indicating that they had purchased haircare products. This indicates that highly engaged consumers are more likely to purchase haircare products through social media platforms. 6. Home Decor and Furnishings Purchased Through Social Media Influence There is a significant association between social media marketing perception and home decor and furnishings purchased through social media influence. Capricious consumers reported the highest level of purchases in this category, with 25.7 percent indicating that they had purchased home decor and furnishings. This indicates that consumers with selective purchasing patterns are more likely to purchase household products through social media platforms. 7. Grocery, Packaged Food and Beverages Purchased Through Social Media Influence There is a significant association between social media marketing perception and grocery, packaged food and beverages purchased through social media influence. Capricious consumers reported the highest level of purchases in this category, with 29.2 percent indicating that they had purchased these products. This indicates that consumers with need-based purchasing patterns are more likely to purchase routine consumption products through social media platforms.
16 8. Health, Wellness and Fitness Items Purchased Through Social Media Influence There is a significant association between social media marketing perception and health, wellness and fitness items purchased through social media influence. Gregarious consumers reported the highest level of purchases in this category, with 27.2 percent indicating that they had purchased these items. This indicates that highly engaged consumers are more likely to purchase wellness and fitness products through social media platforms. 9. Subscription Services (OTT, Apps and Music) Purchased Through Social Media Influence There is a significant association between social media marketing perception and subscription services purchased through social media influence. Gregarious consumers reported the highest level of subscription purchases, with 47.6 percent indicating that they had subscribed to OTT platforms, mobile applications and music services. This indicates that socially engaged consumers are more likely to adopt digitally delivered services promoted through social media platforms. 10. Event Tickets Purchased Through Social Media Influence There is a significant association between social media marketing perception and event tickets purchased through social media influence. Gregarious consumers reported the highest level of event ticket purchases through social media influence, with 23.6 percent indicating that they had purchased event tickets. This indicates that socially connected consumers are more likely to respond to event-related promotions on social media platforms. Objective 3 To evaluate the existing Buying Behaviour of digital consumers in Chennai City. The major findings relating to this objective are as follows: Buying Behaviour. Based on the overall mean scores of the five Buying Behaviour dimensions (Consumer Perception, Consumer Engagement, Consumer Trust, Consumer Purchase Decision, and Consumer Satisfaction), the same 600 respondents were classified into three clusters using K-Means Cluster Analysis (K = 3). Cluster 1 obtained the highest overall mean score and represents respondents with high buying behaviour; therefore, it was named "Responsive Buyers." Cluster 2 obtained a moderate overall mean score and was named "Impulsive Buyers." Cluster 3 obtained the lowest overall mean score and was named "Precipitous Buyers." The cluster names were assigned based on the overall mean values and behavioural characteristics of each group. Buying Behaviour Clusters Cluster Mean Level Cluster Name Reason for the Name Cluster 1 High Responsive Buyers High trust, engagement, purchase decision and satisfaction
17 Cluster 2 Moderate Impulsive Buyers Moderate buying behaviour with occasional impulsive purchases Cluster 3 Low Precipitous Buyers Low trust, low engagement and weak buying behaviour 1. Facebook Story Advertisements There is a significant association between buying behaviour and accessibility toward Facebook Story advertisements. Impulsive buyers and Precipitous buyers reported higher accessibility toward Facebook Story advertisements, with 31.6 percent indicating accessibility. This indicates that consumers who make quicker purchasing decisions are more likely to notice and engage with short-form visual advertisements. 2. Footwear Purchased Through Social Media Influence There is a significant association between buying behaviour and footwear purchased through social media influence. Responsive buyers reported the highest level of footwear purchases through social media influence, with 29.8 percent indicating that they had purchased footwear. This indicates that consumers with stronger trust and purchase readiness are more likely to purchase footwear through social media platforms. 3. Fashion Accessories Purchased Through Social Media Influence There is a significant association between buying behaviour and fashion accessories purchased through social media influence. Responsive buyers reported the highest level of purchases in this category, with 29.0 percent indicating that they had purchased fashion accessories. This indicates that consumers with stronger trust and satisfaction are more likely to purchase fashion accessories through social media platforms. 4. Electronics and Gadgets Purchased Through Social Media Influence There is a significant association between buying behaviour and electronics and gadgets purchased through social media influence. Responsive buyers reported the highest level of purchases in this category, with 46.3 percent indicating that they had purchased electronics and gadgets. This indicates that consumers with stronger trust and satisfaction are more likely to purchase technology products through social media platforms. 5. Smartphones and Mobile Accessories Purchased Through Social Media Influence There is a significant association between buying behaviour and smartphones and mobile accessories purchased through social media influence. Responsive buyers reported the highest level of purchases in this category, with 40.0 percent indicating that they had purchased smartphones and mobile accessories. This indicates that consumers with stronger trust and satisfaction are more likely to purchase mobile-related products through social media platforms. 6. Home Decor and Furnishings Purchased Through Social Media Influence There is a significant association between buying behaviour and home decor and furnishings purchased through social media influence. Impulsive buyers reported the highest level of
18 purchases in this category, with 28.1 percent indicating that they had purchased home decor and furnishings. This indicates that consumers who make spontaneous purchasing decisions are more likely to purchase household products through social media platforms. 7. Grocery, Packaged Food and Beverages Purchased Through Social Media Influence There is a significant association between buying behaviour and grocery, packaged food and beverages purchased through social media influence. Impulsive buyers reported the highest level of purchases in this category, with 25.5 percent indicating that they had purchased these products. This indicates that consumers who make quick purchasing decisions are more likely to purchase routine consumption products through social media platforms. 8. Online Food Delivery Purchased Through Social Media Influence There is a significant association between buying behaviour and online food delivery purchased through social media influence. Responsive buyers reported the highest level of online food delivery purchases, with 39.6 percent indicating that they had placed orders. This indicates that consumers with stronger trust and satisfaction are more likely to respond to food promotions on social media platforms. 9. Health, Wellness and Fitness Items Purchased Through Social Media Influence There is a significant association between buying behaviour and health, wellness and fitness items purchased through social media influence. Responsive buyers reported the highest level of purchases in this category, with 26.7 percent indicating that they had purchased these items. This indicates that consumers with stronger trust and satisfaction are more likely to purchase wellness and fitness products through social media platforms. 10. Automotive Accessories Purchased Through Social Media Influence There is a significant association between buying behaviour and automotive accessories purchased through social media influence. Precipitous buyers reported the highest level of purchases in this category, with 17.5 percent indicating that they had purchased automotive accessories. This indicates that consumers who make rapid purchasing decisions are more likely to purchase automotive products through social media platforms. 11. Home Cleaning and Utility Products Purchased Through Social Media Influence There is a significant association between buying behaviour and home cleaning and utility products purchased through social media influence. Impulsive buyers reported the highest level of purchases in this category, with 22.1 percent indicating that they had purchased these products. This indicates that consumers who make spontaneous purchasing decisions are more likely to purchase household utility products through social media platforms. 12. Subscription Services (OTT, Apps and Music) Purchased Through Social Media Influence There is a significant association between buying behaviour and subscription services purchased through social media influence. Responsive buyers reported the highest level of subscription purchases, with 45.9 percent indicating that they had subscribed to OTT platforms, mobile
19 applications and music services. This indicates that consumers with stronger trust and satisfaction are more likely to adopt digitally delivered services promoted through social media platforms. 13. Event Tickets Purchased Through Social Media Influence There is a significant association between buying behaviour and event tickets purchased through social media influence. Responsive buyers reported the highest level of event ticket purchases through social media influence, with 22.4 percent indicating that they had purchased event tickets. This indicates that consumers with stronger trust and satisfaction are more likely to respond to event-related promotions on social media platforms. Objective 4 To find the influence of the demographic profile of social media users on social media marketing perception and buying behaviour of digital consumers in Chennai City. The major findings relating to this objective are as follows: 1. Influence of Respondents Gender on Social Media Marketing and Buying Behaviour The analysis indicates that gender does not exert any significant influence on social media marketing components such as interaction, entertainment, trendiness, customization, and electronic word of mouth. Similarly, no significant variation is observed in buying behaviour variables including consumer perception, engagement, trust, purchase decision, and satisfaction. This suggests that male and female respondents respond in a similar manner to social media marketing activities, indicating uniform behavioural patterns across gender groups. 2. Influence of Respondents Marital Status on Social Media Marketing and Buying Behaviour The results show that marital status does not significantly influence most social media marketing components and buying behaviour variables. However, a significant difference is observed in consumer purchase decision. This implies that purchasing decisions vary across marital groups. Respondents who preferred not to disclose their marital status and unmarried individuals demonstrate stronger purchase decision behaviour compared to married respondents. This can be attributed to differences in financial commitments, lifestyle flexibility, and responsiveness to promotional content across marital categories. 3. Influence of Respondents Age Group on Social Media Marketing and Buying Behaviour The outcome demonstrates that age significantly affects interaction, entertainment, and consumer satisfaction. This indicates that consumers belonging to different age groups exhibit varying levels of engagement and satisfaction with social media marketing content. Younger consumers display stronger responsiveness towards interactive and entertaining content, along with higher satisfaction levels. This can be attributed to higher digital exposure, familiarity with online platforms, and active participation in social media environments among younger users. 4. Influence of Respondents Educational Qualification on Social Media Marketing and Buying Behaviour
20 The analysis confirms that educational background does not have a significant influence on social media marketing components or buying behaviour variables. This suggests that consumers, irrespective of their level of education, exhibit similar responses towards social media marketing activities. It indicates that the impact of social media marketing is widespread and not restricted by educational differences. 5. Influence of Respondents Occupational Status on Social Media Marketing and Buying Behaviour The results indicate that occupation does not significantly influence social media marketing components or buying behaviour variables. This shows that individuals across different occupational groups respond similarly to social media marketing efforts. It suggests that occupational differences do not create variation in digital consumer behaviour within the study area. 6. Influence of Respondents Annual Income Level on Social Media Marketing and Buying Behaviour The findings demonstrate that annual income does not have a significant impact on social media marketing components or buying behaviour variables. This indicates that consumers across different income levels exhibit similar behavioural responses to social media marketing. It reflects that digital marketing influence is not limited by income differences among consumers. 7. Influence of Respondents Time Spent on Social Media Platforms on Social Media Marketing and Buying Behaviour The results clearly establish that time spent on social media significantly influences all social media marketing components, including interaction, entertainment, trendiness, customization, and electronic word of mouth. In addition, it significantly affects all buying behaviour variables such as consumer perception, engagement, trust, purchase decision, and satisfaction. This indicates that increased exposure to social media enhances the effectiveness of marketing activities and strengthens consumer behavioural responses. Higher usage leads to greater interaction with brands, increased content consumption, and stronger engagement in digital platforms. Objective 5 To examine the relationship between Social Media Marketing perception and buying behaviour of digital consumers in the study area. The major findings relating to this objective are as follows: 1. Relationship between SMM Components on Consumer Perception: MRA The model relating social media marketing components to consumer perception was found to be statistically significant. The independent variables together explained 59.6 percentage of the variation in consumer perception. All five components, namely interaction, entertainment, trendiness, customization, and electronic word of mouth, exerted a positive and significant
21 influence on consumer perception. Among these, entertainment emerged as the strongest predictor, followed by interaction and electronic word of mouth. This finding indicates that attractive, engaging, and socially shared content strengthens the way consumers view brands and products on social media platforms. 2. Relationship between Components on Consumer Engagement: MRA The regression model for consumer engagement was also statistically significant and explained 62.1 percentage of the variation in consumer engagement. All the selected social media marketing components showed a positive and significant effect on engagement. Among them, customization had the strongest influence, followed by trendiness and electronic word of mouth. This shows that consumers become more actively involved with brands when social media content is personalized, current, and socially interactive. 3. Relationship between Components on Consumer Trust: MRA The analysis further revealed that the regression model for consumer trust was statistically significant, with the social media marketing components explaining 56.2 percentage of the variance in trust. All five components had a positive and significant influence on consumer trust. In this case, customization emerged as the most influential factor, followed by electronic word of mouth and trendiness. This implies that consumers tend to trust brands more when the content is tailored to their needs, supported by peer opinions, and regularly updated with relevant information. 4. Relationship between SMM Components on Consumer Purchase Decision: MRA With regard to consumer purchase decision, the regression model was statistically significant and accounted for 59.8 percentage of the variation in purchase decision. The findings show that interaction, entertainment, customization, and electronic word of mouth had positive and significant effects on purchase decision. However, trendiness did not have a statistically significant influence on purchase decision, as its significance value was greater than the accepted level. Among all predictors, interaction was the strongest factor influencing purchase decision, followed by customization and entertainment. This indicates that direct communication, personalized content, and engaging brand messages play a more important role in actual purchase decisions than merely trendy or up-to-date content. 5. Relationship between Components on Consumer Satisfaction: MRA The regression model for consumer satisfaction was also found to be statistically significant and explained 58.6 percentage of the variation in consumer satisfaction. All five social media marketing components positively and significantly influenced consumer satisfaction. Among them, customization had the strongest effect, followed by trendiness and interaction. This suggests that consumers experience greater satisfaction when brands provide relevant,
22 personalized, and timely content through social media platforms. 6. Overall, the Multiple Regression Analysis revealed a statistically significant relationship between Social Media Marketing Perception and the various dimensions of Consumer Buying Behaviour. A comparison of the regression results shows that Customization was the most consistently influential component, particularly in determining Consumer Engagement, Consumer Trust, and Consumer Satisfaction. Interaction was the strongest predictor of Consumer Purchase Decision, while Entertainment had the highest influence on Consumer Perception. Electronic Word of Mouth exhibited a significant influence across all models, although its relative contribution was moderate. Trendiness was significant in all dimensions except Consumer Purchase Decision, where it did not exhibit a statistically significant effect. These results indicate that personalized and interactive social media marketing practices play a more important role in shaping consumer buying behaviour than merely providing trendy content. Objective 6 To develop and validate a conceptual model explaining the impact of Social Media Marketing on the buying behaviour of digital consumers in Chennai City. The major findings relating to this objective are as follows: 1. It is concluded that the five dimensions of Social Media Marketing—Interaction, Entertainment, Trendiness, Customization, and Electronic Word-of-Mouth (e-WOM)—were successfully validated through Confirmatory Factor Analysis (CFA), as all factor loadings were statistically significant. 2. It is concluded that the five dimensions of Consumer Buying Behaviour—Consumer Perception, Consumer Engagement, Consumer Trust, Consumer Purchase Decision, and Consumer Satisfaction—were also validated through CFA, confirming that all constructs reliably represent consumer buying behaviour. 3. It is concluded that the proposed Structural Equation Model (SEM) demonstrated an excellent model fit (GFI = 0.991, CFI = 0.988, NFI = 0.987, RMSEA = 0.0737), and Social Media Marketing has a significant positive impact on Consumer Buying Behaviour. The model explained 99% (R² = 0.99) of the variation in Consumer Buying Behaviour, thereby validating the proposed conceptual model.
23 SUMMARY OF HYPOTHESES TESTING Objective 4 : To find the influence of the demographic profile of social media users on social media marketing perception and the buying behaviour of digital consumers in Chennai City. H01: There is no significant influence of demographic profile on social media marketing perception and the consumer Buying Behaviour Sl. No. Hypothesis Statistical Tool Used p-Value Result 1 H₀₁: There is no significant influence of gender on social media marketing perception and the buying behaviour of digital consumers in Chennai City. ANOVA p > 0.05 Null Hypothesis (H₀) Accepted 2 H₀₂: There is no significant influence of marital status on social media marketing perception and the buying behaviour of digital consumers in Chennai City. ANOVA Consumer Purchase Decision (p = 0.006) Alternative Hypothesis (H₁) Accepted 3 H₀₃: There is no significant influence of age on social media marketing perception and the buying behaviour of digital consumers in Chennai City. ANOVA Interaction (p = 0.014), Entertainment (p = 0.000) and Consumer Satisfaction (p = 0.027) Alternative Hypothesis (H₁) Accepted 4 H₀₄: There is no significant influence of educational qualification on social media marketing perception and the buying behaviour of digital consumers in Chennai City. ANOVA p > 0.05 Null Hypothesis (H₀) Accepted 5 H₀₅: There is no significant influence of ANOVA p > 0.05 Null Hypothesis
24 occupation on social media marketing perception and the buying behaviour of digital consumers in Chennai City. (H₀) Accepted 6 H₀₆: There is no significant influence of annual income on social media marketing perception and the buying behaviour of digital consumers in Chennai City. ANOVA p > 0.05 Null Hypothesis (H₀) Accepted 7 H₀₇: There is no significant influence of time spent on social media on social media marketing perception and the buying behaviour of digital consumers in Chennai City. ANOVA Interaction, Entertainment, Trendiness, Customization, Consumer Perception, Consumer Engagement, Consumer Trust, Consumer Purchase Decision, Consumer Satisfaction and Electronic Word-of-Mouth (all p < 0.05) Alternative Hypothesis (H₁) Accepted The ANOVA results indicate that demographic variables have only a partial influence on Social Media Marketing Perception and Consumer Buying Behaviour. Among the seven demographic variables examined, marital status, age, and time spent on social media showed significant influence on selected dimensions, whereas gender, educational qualification, occupation, and annual income did not exhibit any significant influence. Therefore, it is concluded that consumer behavioural differences are influenced more by social media usage patterns than by demographic characteristics.
25 Objective-5 - To examine the relationship between social media marketing perception and buying behaviour of digital consumers in the study area. H02: There is no significant relationship social media marketing perception and the consumer Buying Behaviour digital consumers. Sl. No. Hypothesis Statistical Tool Used p-Value Result 1 H₀₁: Social media marketing perception has no significant influence on consumer perception. Multiple Regression Analysis Interaction, Entertainment, Trendiness, Customization and Electronic Word-of- Mouth (all p < 0.05) Alternative Hypothesis (H₁) Accepted 2 H₀₂: Social media marketing perception has no significant influence on consumer engagement. Multiple Regression Analysis Interaction, Entertainment, Trendiness, Customization and Electronic Word-of- Mouth (all p < 0.05) Alternative Hypothesis (H₁) Accepted 3 H₀₃: Social media marketing perception has no significant influence on consumer trust. Multiple Regression Analysis Interaction, Entertainment, Trendiness, Customization and Electronic Word-of- Mouth (all p < 0.05) Alternative Hypothesis (H₁) Accepted 4 H₀₄: Social media marketing perception has no significant influence on consumer purchase decision. Multiple Regression Analysis Interaction, Entertainment, Customization and Electronic Word-of-Mouth (p < 0.05); Trendiness (p = 0.072, Not Significant) Alternative Hypothesis (H₁) Partially Accepted 5 H₀₅: Social media marketing perception has no significant influence on consumer satisfaction. Multiple Regression Analysis Interaction, Entertainment, Trendiness, Customization and Electronic Word-of- Mouth (all p < 0.05) Alternative Hypothesis (H₁) Accepted The Multiple Regression Analysis confirms that Social Media Marketing Perception has a significant positive influence on all dimensions of Consumer Buying Behaviour. Interaction, Entertainment, Customization, Trendiness, and Electronic Word-of-Mouth collectively influence consumer perception, engagement, trust, purchase decision, and satisfaction. However, Trendiness did not significantly influence Consumer Purchase Decision, indicating that personalized and interactive marketing activities are more effective than merely providing trendy content. Overall, the findings support the existence of a strong
26 relationship between Social Media Marketing Perception and Consumer Buying Behaviour. Objective 6 - To Develop and Validate a Conceptual Model Explaining the Impact of Social Media Marketing on the Buying Behaviour of Digital Consumers in Chennai City. H03: There is no significant difference between among Social Media Marketing factors. H04: There is no significant difference among Consumer Buying Behaviour factors. H05: There is no significant impact of Social Media Marketing on Consumer Buying Behaviour. Sl. No. Hypothesis Statistical Tool Used Key Result Result 1 H₀₁: Social Media Marketing factors do not significantly represent the construct of Social Media Marketing. Confirmatory Factor Analysis (CFA) Interaction (0.81), Entertainment (0.82), Trendiness (0.81), Customization (0.83) and Electronic Word-of-Mouth (0.80) Alternative Hypothesis (H₁) Accepted 2 H₀₂: Consumer Buying Behaviour factors do not significantly represent the construct of Consumer Buying Behaviour. Confirmatory Factor Analysis (CFA) Consumer Perception (0.81), Consumer Engagement (0.83), Consumer Trust (0.79), Consumer Purchase Decision (0.81) and Consumer Satisfaction (0.80) Alternative Hypothesis (H₁) Accepted 3 H₀₃: Social Media Marketing has no significant impact on the Buying Behaviour of Digital Consumers. Structural Equation Modelling (SEM) R² = 0.99; Model Fit: GFI = 0.991, CFI = 0.988, NFI = 0.987, RMSEA = 0.0737, p = 0.398 (>0.05) Alternative Hypothesis (H₁) Accepted The Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) results confirm that the proposed conceptual model is statistically valid and demonstrates an excellent model fit. The Social Media Marketing and Consumer Buying Behaviour constructs were successfully validated, and the SEM results establish that Social Media Marketing has a significant positive impact on Consumer Buying Behaviour. Therefore, the proposed conceptual model is validated and adequately explains the relationship between Social Media Marketing and Consumer Buying Behaviour among digital consumers in Chennai City.
27 Suggestions 1. Improve Alternative Platform Marketing Strategies Since Telegram and Twitter (X) showed low influence and trust among consumers, marketers should redesign platform-specific communication strategies by using interactive, visually engaging, and personalized promotional content. Businesses may integrate AI-driven recommendations, influencer collaborations, and localized advertisements to improve consumer accessibility and credibility on less preferred platforms. 2. Increase Consumer Awareness Toward Sustainable Consumption The low purchase rate of fashion rentals and pre-owned products indicates limited acceptance of sustainable consumption behaviour among digital consumers. Therefore, marketers and policymakers should create awareness campaigns highlighting affordability, environmental benefits, and product quality assurance to improve consumer confidence toward circular fashion practices. 3. Shift Promotional Focus Toward Value-Oriented Offers As Buy One Get One (BOGO) offers showed lower effectiveness, firms should emphasize financially beneficial promotional strategies such as cashback offers, instant discounts, reward points, and personalized price incentives that provide direct economic value to consumers. 4. Develop Segment-Specific Social Media Marketing Strategies The low responsiveness of impulsive and precipitous buyers toward certain product categories suggests the need for customized marketing approaches. Marketers should adopt behavioural segmentation techniques and create targeted campaigns based on consumer personality, urgency, emotional appeal, and product relevance to improve conversion rates. 5. Strengthen Trust Through Authentic and Reliable Content Since electronic word of mouth showed comparatively weaker influence on consumer satisfaction, brands should improve the authenticity of online reviews, user-generated content, and influencer endorsements. Verification systems, genuine customer testimonials, and transparent review mechanisms can enhance consumer confidence and satisfaction. 6. Enhance Interactive and Relationship-Based Engagement Practices The comparatively weak influence of interaction on consumer trust and engagement suggests that basic communication alone is insufficient. Companies should implement relationship marketing strategies such as live sessions, community engagement activities, personalized responses, and interactive storytelling to create deeper emotional connections with consumers.
28 7. Focus on Entertainment and Experiential Content Rather Than Trendiness Alone As trendiness showed lower influence on consumer perception and purchase decisions, marketers should prioritize entertaining, informative, and experience-oriented content instead of relying only on fashionable or viral trends. Content that provides utility, emotional value, and consumer involvement may generate stronger behavioural outcomes. 8. Adopt Inclusive Marketing Strategies Across Demographic Groups Since demographic variables such as gender, education, occupation, and income did not show significant differences, organizations should design broad-based and inclusive social media marketing campaigns that appeal to diverse consumer groups rather than focusing excessively on demographic segmentation. 9. Improve Promotional Effectiveness for Low-Response Product Categories The lower influence of social media marketing on footwear, wellness products, home utility items, and fashion accessories suggests that businesses should redesign product presentation techniques using demonstration videos, influencer usage experiences, comparative benefits, and customer education content to improve purchase intention. 10. Encourage Data-Driven Consumer Behaviour Analysis Organizations should regularly conduct consumer analytics and behavioural tracking to identify changing digital preferences, platform effectiveness, and engagement patterns. Continuous monitoring can help firms optimize marketing investments and improve strategic decision-making in digital environments. CONCLUSION The present study concludes that Social Media Marketing plays a vital role in influencing the buying behaviour of digital consumers in Chennai City. By examining the relationship between the key dimensions of Social Media Marketing and Consumer Buying Behaviour, the study provides a comprehensive understanding of how digital marketing strategies affect consumer perception, engagement, trust, purchase decisions, and satisfaction. The study also develops and validates a conceptual model that explains this relationship using appropriate statistical techniques. The findings are expected to contribute to the existing body of knowledge in Social Media Marketing and Consumer Behaviour while offering practical insights for marketers, business organizations, and policymakers to formulate more effective digital marketing strategies. Overall, the study is expected to serve as a valuable reference for researchers and practitioners in understanding the evolving buying behaviour of digital consumers in the contemporary digital environment.
29 SCOPE FOR FURTHER RESEARCH 1. Future studies may be conducted in other cities, states, or rural areas to compare regional variations in consumer buying behaviour. 2. Emerging dimensions of Social Media Marketing, such as Artificial Intelligence (AI), influencer marketing, social commerce, virtual reality, and chatbot marketing, may be incorporated. 3. Future researchers may focus on specific consumer groups such as Generation Z, millennials, working professionals, women, or senior citizens. 4. Qualitative, mixed-method, or longitudinal research designs may be adopted to gain deeper insights into digital consumer behaviour. 5. The study may be extended to industry-specific sectors such as healthcare, banking, education, tourism, retail, and e-commerce. 6. Advanced statistical techniques, including mediation, moderation, and machine learning approaches, may be employed to further validate and extend the research findings.