| Megha Bharti Research Scholar (Commerce) Govt. Thakur Ranmat Singh College Awadhesh Pratap Singh University Rewa (Madhya Pradesh) | Dr. Rameshwar Prasad Gupta (Professor & HOD) Govt Girls Degree College Rewa (Madhya Pradesh) |
Abstract
This study investigates consumer attitudes and behaviors toward social influence and online reviews in e-commerce. Using a descriptive research design with a quantitative approach, 400 respondents were selected through systematic sampling, representing diverse demographics of gender, age, occupation, income, and marital status. Primary data were collected via a structured five-point Likert scale questionnaire covering five key dimensions. Statistical analyses, including descriptive statistics, ANOVA, and t-tests, were performed using SPSS at a 95% confidence level. Findings highlight the significant impact of social influence and online reviews on online consumer behavior.
Keywords- E-commerce, Social Influence, Online Reviews, Consumer Behavior, Quantitative Research, SPSS.
Introduction:
The respondents’ attitudes toward social influence and online reviews play a crucial role in shaping their buying decisions. Since physical inspection is not possible, online reviews and recommendations from peers often act as important substitutes, boosting trust in both product quality and seller credibility (Chevalier & Mayzlin, 2006). Consumers frequently depend on ratings, user-generated content, and feedback from social media before making a purchase, particularly for products that require more careful consideration (Filieri, 2015). A favorable view of these social signals suggests that respondents appreciate collective experiences, seeing them as vital for minimizing perceived risks and increasing confidence in online shopping (Park & Lee, 2008).
In the contemporary digital marketplace, consumers no longer solely depend on conventional advertising; instead, they actively
seek information incorporating evaluations and experiences from their peers, notably through
online reviews (Ahn & Lee, 2024). This shift reflects a broader psychological phenomenon of social
proof, where individuals are influenced by the actions and opinions of others, especially when faced with uncertainty in purchase decisions (Salihu, 2023). This dynamic transforms online reviews from mere feedback mechanisms into powerful informational cues that significantly shape consumer attitudes and subsequent buying behaviors (Robbiano & Cerruti, 2024). The proliferation of e-commerce platforms has amplified the accessibility and influence of these digital word-of-mouth recommendations, making them a pivotal resource for consumers seeking insights and advice on products and services (Katyal & Sehgal, 2024). This reliance on collective opinion is underscored by statistics showing that a substantial majority of online buyers consult reviews before making a purchase, indicating their critical role in the pre-purchase information-gathering process (Malik et al., 2023). This behavior is consistent with Social Influence Theory, which suggests that individuals’ attitudes can be altered by external inputs like information or communication, leading to attitude change through processes such as compliance, identification, and internalization (Rashad et al., 2025). Furthermore, social commerce platforms leverage constructs like ratings, reviews, and recommendations, integrating social media technologies to facilitate peer-to-peer communication and influence purchasing decisions (Malik et al., 2023).
Literature Review
The literature clearly establishes that social influence plays a decisive role in shaping consumer attitudes and purchase decisions in online shopping environments. Due to the inherent uncertainty and perceived risk associated with e-commerce, consumers heavily rely on electronic word-of-mouth (eWOM), customer reviews, ratings, and peer recommendations to reduce information asymmetry and build trust (Bhatnagar et al., 2000; Cheung & Thadani, 2012). Social Proof Theory explains this dependence, suggesting that individuals look to others’ behavior as a guide in uncertain situations, making reviews and ratings powerful quality signals (Cialdini, 2001; Cheung et al., 2009).
Empirical studies demonstrate that both review valence and volume significantly influence purchase intentions, with negative reviews often exerting a stronger impact due to negativity bias and loss aversion (Chevalier & Mayzlin, 2006; Baumeister et al., 2001; Kahneman & Tversky, 1979). Review credibility and quality further moderate this effect, as detailed and balanced reviews are perceived as more helpful and trustworthy (Mudambi & Schuff, 2010).
Social media has amplified social influence through social commerce, influencer marketing, and user-generated content, integrating product discovery with social interaction (Hajli, 2014; Liang & Turban, 2011; Lou & Yuan, 2019). Demographic factors such as gender, income, occupation, and marital status influence reliance on reviews, while age shows mixed effects (Awad & Ragowsky, 2008; Hernández et al., 2011; Davis, 1976). However, the growing prevalence of fake reviews poses credibility challenges, necessitating stronger verification mechanisms and regulatory oversight (Luca & Zervas, 2016; Adelani et al., 2020). Overall, social influence remains a central driver of online consumer behavior, with significant theoretical, managerial, and policy implications.
Research Objectives
Primary Objective
To examine the influence of social factors, customer reviews, and ratings on consumer purchase decisions in online shopping environments.
Secondary Objectives
- To assess the extent to which customer reviews and ratings influence online purchase decisions among consumers.
- To evaluate consumer confidence levels associated with products having high ratings and positive reviews.
- To determine the role of personal recommendations from friends and family in shaping online shopping behavior.
- To analyze the impact of social media platforms on online shopping decisions and product purchases.
- To examine how consumers weigh both positive and negative reviews in their purchase decision-making process.
- To investigate whether demographic factors (age, gender, occupation, monthly income, and marital status) significantly influence consumer attitudes toward social influence and reviews in online shopping.
Research Methodology
The study employed a descriptive research design with a quantitative approach to investigate consumer attitudes and behavioral responses toward social influence and online reviews in e-commerce. This design enabled systematic analysis of existing perceptions and behaviors without manipulating the environment, while the quantitative approach allowed objective measurement and hypothesis testing. A sample of 400 respondents was selected through systematic sampling, ensuring demographic representation across gender, age, occupation, income, and marital status. Primary data were collected via a structured five-point Likert scale questionnaire, measuring five key dimensions of social influence and online reviews. Data analysis involved descriptive statistics (mean, standard deviation, frequencies, percentages) to summarize demographic and response patterns, one-way ANOVA to examine group differences by age, occupation, and income, and independent t-tests to compare gender and marital status differences, with Levene’s test for variance equality. Analyses were conducted in SPSS at a 95% confidence level (Kothari, 2004; Field, 2013; Pallant, 2016; Hair et al., 2010).
The study tested five hypotheses.
H06a:
There is no significant difference in the attitude of respondents towards social influence and online reviews in online shopping with respect to age.
H06b:
There is no significant difference in the attitude of respondents towards social influence and online reviews in online shopping with respect to gender.
H06c:
There is no significant difference in the attitude of respondents towards social influence and online reviews in online shopping with respect to occupation.
H06d:
There is no significant difference in the attitude of respondents towards social influence and online reviews in online shopping with respect to monthly income.
H06e:
There is no significant difference in the attitude of respondents towards social influence and online reviews in online shopping with respect to marital status.
A. Frequency Social Influence and Reviews in Online Shopping
Table: Overall Influence of Social Factors on Online Purchase Decisions (N = 400)
| Dimension of Social Influence | Disagree (%) | Neutral (%) | Agree (%) | Strongly Agree (%) |
| Influence of customer reviews and ratings | 26.1 | 12.5 | 30.8 | 30.8 |
| Confidence due to high ratings and positive reviews | 25.1 | 24.0 | 19.0 | 32.0 |
| Seeking recommendations from friends/family | 24.8 | 24.8 | 18.5 | 32.0 |
| Influence of social media platforms | 24.5 | 24.8 | 24.5 | 26.3 |
| Consideration of both positive and negative reviews | 32.0 | 19.0 | 23.5 | 25.5 |
The combined frequency results clearly indicate that social influence plays a dominant role in online shopping behaviour. A strong majority of respondents acknowledge the impact of customer reviews and ratings, with over 61 per cent either agreeing or strongly agreeing that such feedback influences their purchase decisions. Similarly, more than half of the respondents feel confident purchasing products with high ratings and positive reviews, highlighting the importance of trust and perceived quality in online environments.
Personal recommendations from friends and family also remain influential, reinforcing the continuing relevance of traditional word-of-mouth even in digital contexts. Social media platforms demonstrate a moderate but growing influence, with just over half of the respondents acknowledging their role in shaping purchase decisions. However, the findings also reveal a review engagement gap, as a notable proportion of respondents do not consistently consider both positive and negative reviews, suggesting variations in digital literacy and depth of information processing. Overall, the frequency analysis confirms that peer-generated content significantly shapes consumer confidence, trust, and decision-making in e-commerce.
B. Combined Hypothesis Testing Summary Table
Table: Summary of Hypothesis Testing Results on Social Influence and Reviews
| Hypothesis | Demographic Variable | Statistical Test | Test Value | Sig. (p-value) | Decision |
| H06a | Age | One-way ANOVA | F = 0.066 | 0.992 | Accepted |
| H06b | Gender | Independent t-test | t = -3.323 | 0.001 | Rejected |
| H06c | Occupation | One-way ANOVA | F = 4.607 | 0.001 | Rejected |
| H06d | Monthly Income | One-way ANOVA | F = 15.307 | 0.000 | Rejected |
| H06e | Marital Status | Independent t-test | t = -4.318 | 0.000 | Rejected |
Explanation of Hypothesis Results
The hypothesis testing results reveal important demographic patterns in attitudes towards social influence and online reviews. The analysis shows no significant difference across age groups, as confirmed by the ANOVA results (p > 0.05). This suggests that reliance on social influence in online shopping is consistent across generations and reflects a general behavioural tendency rather than an age-specific trait.
In contrast, gender exhibits a significant effect, with male respondents demonstrating higher susceptibility to social influence and online reviews than female respondents. Occupational differences are also statistically significant, with employed individuals—particularly those in the service sector—and self-employed respondents showing stronger reliance on reviews and social cues compared to students and homemakers.
Monthly income emerges as one of the strongest differentiating factors. Respondents in the ₹25,000–₹50,000 income group exhibit the highest dependence on social influence, indicating greater perceived purchase risk and a stronger need for social validation. Finally, marital status significantly influences attitudes, with unmarried respondents relying more on social influence and reviews than married respondents, possibly due to higher digital engagement and independent decision-making.
Overall, the hypothesis testing confirms that while age does not shape social influence behaviour, gender, occupation, income, and marital status significantly affect how consumers interpret and rely on online reviews and social cues, underscoring the need for targeted and segmented e-commerce strategies.
Findings
The descriptive results show that social impact strongly influences internet shopping. 61.6 percent of respondents strongly agreed or agreed that customer evaluations and ratings influence their online buying selections. This supports Cheung and Thadani (2012), who argue that peer-generated information and social proof are crucial in e-commerce. 26.1 percent acknowledged minimal influence of reviews, while 12.5% were neutral, demonstrating that not all consumers rely on online comments.
When buying products with good ratings and reviews, 51% of respondents felt more confident. Almost 25% disagreed and 25% were neutral. This pattern supports Park et al. (2007) that positive ratings boost trust and perceived quality, especially in the absence of physical inspection, yet some consumers remain cautious. Personal recommendations remain relevant, as 50.5% of respondents asked friends and family before buying online. Chu and Kim (2011) noted that conventional word-of-mouth and digital reviews remain powerful.
Social media influence was acknowledged by 50.8% of respondents, demonstrating that influencer content and social media platforms influence purchase decisions. Hajli (2014) notes that different customer demographics respond differently to social media marketing, since many respondents disagreed or were ambivalent. In addition, 49% of respondents considered both positive and negative evaluations before buying, suggesting a balanced approach. According to Zhao et al. (2013), roughly one-third did not actively examine mixed reviews due to digital literacy or information processing problems.
Hypothesis testing clarifies demographic differences in social influence. Analysis of variance showed no significant variation between age groups, showing that opinions about social impact and online reviews remained stable from 21 to 70. Mean scores were moderate to favourable regardless of age, demonstrating social aspects influence online shopping behaviour universally. Gender-based research showed that male respondents relied more on social influence and reviews than female respondents. Pallant (2016) found men more susceptible to online decision-making situations.
Service workers had the highest mean scores for social influence, followed by self-employed people, pensioners, students, and homemakers. According to Hair et al. (2010), job exposure, financial stability, and digital engagement affect online review response. In keeping with Kothari (2004), respondents in the 25,000–50,000 income category showed the largest dependency on social influence, indicating a greater need for social validation to reduce perceived purchase risk. Marital status study showed that unmarried respondents relied more on social influence and reviews than married respondents. Unmarried people may rely more on external validation because they make purchasing decisions alone rather than with their families (Pallant, 2016).
Conclusions
The study concludes that social proof is a dominant driver of online shopping behaviour. Peer feedback influences the purchase decisions of more than 60 per cent of consumers, highlighting the critical role of customer reviews, ratings, and social recommendations in digital marketplaces. This finding strongly supports Cialdini’s (2001) social proof theory in the context of e-commerce and underscores the importance of peer-generated content in reducing perceived risk and uncertainty associated with online transactions.
The analysis further reveals that the influence of social factors and online reviews is consistent across all age groups. Unlike typical patterns observed in technology adoption, reliance on peer feedback does not vary significantly with age, suggesting that social influence in online shopping reflects a fundamental behavioural tendency rather than a generational characteristic.
Demographic segmentation emerges as a key determinant in understanding variations in consumer responses to social influence. Although age does not significantly affect attitudes, factors such as gender, occupation, income, and marital status play a decisive role in shaping how consumers interpret and rely on reviews and recommendations. This indicates that marketers should adopt targeted and differentiated strategies rather than uniform communication approaches.
The findings indicate that male consumers are more responsive to social influence and online reviews than female consumers, challenging conventional assumptions about gender and social susceptibility in purchasing behaviour. Differences in information processing styles, risk perception, or engagement with digital content may account for this variation. Income and occupation also significantly affect reliance on social validation, with employed individuals—particularly in the service sector—and lower-middle income consumers (₹25,000–₹50,000) showing greater dependence on reviews to mitigate perceived purchase risk.
Marital status further influences online decision-making, as unmarried consumers rely more heavily on external social cues, possibly due to greater social media engagement or the absence of spousal consultation. Additionally, a notable gap exists in review engagement, as a substantial proportion of consumers do not critically evaluate reviews, potentially increasing vulnerability to misinformation. Finally, the study highlights the growing, though not yet dominant, role of social media in shaping online purchase decisions, indicating an evolving integration of social platforms and e-commerce environments.
Suggestions and Recommendations
The study highlights the critical role of trust and social influence in shaping online consumer behaviour, with strong implications for e-commerce companies, consumers, and policymakers. Since a large proportion of consumers rely on reviews and ratings, online retailers must prioritize robust and verified review systems that ensure authenticity and prevent manipulation. Transparent encouragement of post-purchase reviews can further enhance credibility. The findings also underline the importance of demographic-sensitive marketing strategies. Men are more influenced by social proof, while women respond better to detailed information and personalized recommendations. Occupational, income, and marital differences further necessitate customized approaches, with lower-middle income and unmarried consumers seeking greater social validation, and higher-income or married consumers valuing expertise, exclusivity, and family-oriented benefits.
Effective handling of negative reviews is equally important, as consumers consider both positive and negative feedback. Professionally addressing complaints can strengthen brand trust and authenticity. The growing influence of social media also calls for greater integration of social commerce features, influencer marketing, and user-generated content. From the consumer perspective, the study emphasizes the need for critical review literacy to distinguish genuine feedback from fake reviews and balance social influence with personal needs. Policymakers must strengthen regulations against fake reviews and promote digital literacy to protect consumer interests. Finally, the study suggests future research on evolving social influence, product categories, cultural differences, and review authenticity, while encouraging firms to adopt both short-term and long-term strategic initiatives to leverage social proof sustainably.
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