Context
This was an academic study conducted during my senior year psychology research course, with a focus on UX methods within social psychology. The study was the central point of the course and took around two months. The topic I chose to investigate was the impact of social proof on user experience in e-commerce environments.
What is Social Proof?
According to the Nielsen Norman Group,
Social proof is a psychological phenomenon where people reference the behavior of others to guide their own behavior. This tendency is driven by our natural desire to behave “correctly” under most circumstances—whether making a purchase, deciding where to dine, determining where we should go, what we say, who we say it to, and so on.
In effect, the social proof concept may be viewed in a similar light to groupthink. This can be observed in many contexts and situations, such as the popularity of a person, product or trend factoring into its continued success. This seems relatively innocuous, but the application of social proofs in a digital or consumer environment may be more layered; for example, can social proofs portray a misleading view of a product, person, object or institution? What is the correlation between social proofs and the level of satisfaction people possess after making their choice? The goal of this study was to discover more in regards to how social proof factors into UX design and how it can be used to affect user behavior.
Background: Positive vs. Negative Interaction
In a 2018 study, Hilverda et al. hypothesized that positive comments tend to set up positive perceptions, feelings and behavior in regards to online food shopping compared to negative reviews. The results did show positive correlations between positive comments and consumer consumption of organic foods on Facebook product pages where customers could interact and leave feedback.
Background: Emotion Affects Decisions
Furthermore, in a 2013 study conducted by Genschow et al., users identified positive stimuli in regards to consumer choice faster than negative stimuli, helping to reinforce that positive user reviews may have an effect on buyer decision making (pg. 298). These social psychology implications show that positive stimuli do correlate with faster-acting decision making compared to an absence of stimuli.
Social proof is often stratified based on personal connections and items of interest; David Teodorescu, UX Collective
Background: Certainty of Emotions
In perhaps the most relevant of studies concerning social proofs and online buying, a study by Yujie Li divided customer reviews into certain positive, uncertain positive, certain negative and uncertain negative categories; the reviews with certain emotions, both positive and negative (such as happiness and anger) were given more attention in regards to decision-making influences compared to those uncertain emotions (2019, pp. 1-2).
Setting up the study
This research study used the presence of customer reviews as the experimental group, and the lack of customer reviews as the control group; both groups featured product images and descriptions. The study’s goals included distributing surveys containing materials such as product images, descriptions and customer reviews divided into groups online for participants to fill out. The independent variable was the presence or absence of customer reviews when the participant fills out their survey, while the dependent variables were the likelihood to purchase the product and the satisfaction in the choice to purchase that product. These were measured via Likert scales.
Hypothesis: The primary hypothesis was that the presence of customer reviews in the experimental group will lead to a higher likelihood to purchase the product, as opposed to the control group that does not feature reviews. The second hypothesis was that the presence of customer reviews in the experimental group will lead to a higher level of confidence with the decision to purchase that product, as opposed to the control group that does not feature reviews. These hypotheses were based on the results from the 2008 study of Hilverda et al., where positive comments resulted in positive feelings towards a product.
Left image: control group settings, right image: experimental group settings
Method & Participants
The amount of participants in the experiment numbered around 44 subjects. These participants were primarily recruited through online convenience, with subjects from social media such as WhatsApp, Snapchat and Instagram as well as my workplace and academic environments. The population was asked to provide characteristics such as age and gender, with 57% of the population being male and 43% of the population being female, and mostly consisted of college students, college workers and young adults, with a variety of ages ranging from 19 to 27 (M = 21.29, SD = 1.84). This provided an accurate and representative demographic for which the study was laid upon.
Due to the younger, more online-acquainted demographics of the population, it is safe to assume they would mostly be familiar with e-commerce and online shopping. It is likely they would have familiarity with Amazon. Additionally, due to the impact of the COVID-19 virus, it is likely they would have familiarity with the product being displayed in the survey, a set of face masks for everyday wear. Due to the online nature of the study and the quick and informal process of providing data, no compensation was provided to the subjects.
Measure & Procedure
The questionnaires used for this study were hosted on Google Forms. The primary measure used in this study was the Likert scale that measured the variables Likelihood to Purchase and Confidence in Purchase, which ranged from 1 to 10, 1 being the lowest level of a certain quality and 10 being the highest. There were five total items in the control group and six total items in the experimental group. In the control group, these consisted of a text input question asking the participant their age; a multiple choice question asking the participant their gender; an image of the face mask product on Amazon; a 1-10 Likert scale question asking “How likely would you be to purchase the above product, on a scale from 1 to 10 (1 being extremely unlikely, and 10 being extremely likely)?”; and a 1-10 Likert scale question asking “How confident would you be with your choice to purchase the above product, on a scale from 1 to 10 (1 being extremely unconfident, and 10 being extremely confident)?”. In the experimental group, after the initial product image, an image of three user reviews from the product page of the item were shown.
My operational definitions were as follows; the independent variable will be the presence or absence of customer reviews when the participant fills out their survey, while the dependent variables will be likelihood to purchase the product and the confidence in the choice to purchase that product. These are measured via Likert scales. A high likelihood to purchase the product would be if the rating is above 5 on the Likert scale, with the same concept for confidence in purchase choice. If the experimental group shows higher levels of product purchase likelihood and confidence in product purchasing, it may provide support for the hypothesis that the presence of user reviews upholds a type of social proof in regards to the product.
Design
The design of this study was between-groups experimental, as there is a control group and an experimental group where it is hypothesized that changes to certain variables may manifest with the addition of factors to the experimental group.
Results
Left image: control group responses, right image: experimental group responses
The participants in this study rated their likelihood to purchase a face mask product on Amazon and their perceived confidence in the choice to purchase the product, both on a 10-point Likert scale. Participants were randomly assigned to either the control or experimental groups. The main variables of the study were the likelihood to purchase and the confidence of purchase.
An independent samples t-test was conducted to examine the difference between the control and experimental groups on each dependent variable (likelihood to purchase and confidence to purchase). Results from the t-test found a marginal significant difference between the control group (M = 6.95, SD = 1.8) and experimental group (M = 8, SD = 1.5) on the likelihood of purchasing the product, t(44) = -1.85, p = .07 . The confidence interval of the mean differences at 95% ranges from -2.18 to 0.09. Thus, the experimental group reported a marginally greater likelihood of purchasing the product compared to the control group.
Results from the t-test found a significant difference between the control group (M = 7.24, SD = .6) and experimental group (M = 8.35, SD = 1.2) on the confidence to purchase the product, t(44) = -2.62, p = .01. The confidence interval of the mean differences at 95% ranges from -1.96 to -0.25. Thus, the experimental group reported greater confidence in purchasing the product compared to the control group.
To summarize these results, it suggests that while online customer reviews may only play a marginally significant role in improving the likelihood to purchase a product, they may be significant in regards to the confidence of the user toward buying the product.
Discussion
Let’s quickly restate the hypotheses. The primary hypothesis was that the presence of online customer reviews will increase the likelihood to purchase an item on an online shopping platform. The secondary hypothesis was that the presence of online customer reviews will increase the user’s confidence in purchasing an item on an online shopping platform.
Looking at the findings displayed in the results, it can be seen that the likelihood to purchase hypothesis was marginally supported, and may require further research to fully explore. As for the confidence in purchase hypothesis, we can conclude that the hypothesis was supported by the findings as there was a significant difference displayed in the t-test results.
The findings in this study should indicate that the presence of positive user reviews on ecommerce platforms may be influential in affecting customers’ purchase decisions and their confidence in the product. Placement of positive reviews on product pages generally may be beneficial for customers. However, the placement of neutral or negative reviews was not tested and may show different results and applicability that may change the applicability of showing only positive ratings.
Conclusion: Previous & Future Research
The marginal difference displayed in likelihood to purchase and the significant difference displayed in confidence of purchase may correlate with prior findings that social factors impact social influence and group buying (Chen & Lu, 2015). Furthermore, these findings also investigated the premise of conformity among individuals purchasing items, as they describe how the behavior of conformity impacts the purchase behaviors of others. Their findings reinforced how conformity and social proofs do matter significantly in online community marketplaces (Chen & Lu, 2015). Another study measuring the effects of social proof across collectivist (Polish) and individualistic influenced (American) cultures and found both the principles of social proof and commitment/consistency help to determine decision making in both societies and would support the notion that showing social proof in regards to a task or choice may help to influence participants to make a certain choice in a given situation (Cialdini et al., 1999). One study based around the perceived usefulness of user reviews based on emotions expressed in those reviews found that in the positive emotion condition a higher emotional certainty led to a higher perceived usefulness of reviews; this draws parallels and support to the findings in this study, where positive reviews tended to increase, either marginally or significantly, the likelihood to purchase and the confidence in the purchase of products by users (Li, 2019). Overall, previous research, while not extensive, does show a trend of support for the findings discovered in this study.
Future research may be best centered on studying the effects of how non-positive reviews may interact with social proofing on online e-commerce platforms. For example, studying the effects of negative reviews with social proofs on e-commerce platforms may be interesting to examine if they adversely impact sales in a fashion similar to how positive reviews may significantly boost sales. Studying neutral reviews, or a mix of positive and negative reviews, may reveal more conclusions about the topic and its interactions with social proofs and social conformity. Other areas of future research may warrant investigation as well, such as studying the effects of in-person social proofs done at a retail store versus user reviews left on the retailer’s product page; anonymous versus named reviews and their impacts on user compliance and social proof; and star-based review systems versus text-only systems. Investigating reactions correlating with social proof theory in physical versus online settings would be of great interest to marketers and retailers who wish to use social proof as a way to elicit customer purchase decisions. It may also be interesting to see how social proof in regards to COVID-19 health products may have played out, as masks are a near-universally used item for public use during the ongoing COVID-19 pandemic.
In conclusion, the effects of social proof through positive user reviews on e-commerce marketplaces shows that user likelihood to purchase is marginally significant and user confidence in purchase is significant. This carries potential for further research in the realms of how social proof and online shopping work together and how negative reviews may conflict with these conclusions.
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