Reviews and reputationEvidence check: 3.5/5
Bayerl, A., Schoenmueller, V., Goldenberg, J., & Stahl, F. (2026). Journal of Marketing Research, 63(2), 211–233.
Plain-English take: Weekend reviews averaged about 0.04 stars lower; a negative pattern appeared on 26 of 33 platforms. For your marketing: If review volume matters, test request timing and compare response rate and rating distribution.
Question: Do online reviews differ by day of week, and does the timing of a review request affect the ratings collected?
Design: Multi-platform observational analysis and review-solicitation experiments. Nearly 400 million reviews across 33 platforms, more than 20 million reviewed items and over 60 million users; categories include products, travel, employers, entertainment, and professional services.
Weekend reviews averaged about 0.04 stars lower; a negative pattern appeared on 26 of 33 platforms. The authors identify temporal self-selection as a major mechanism and report that weekend review requests can collect more negative ratings.
Journal of Marketing · 202610.1177/00222437251391808Publisher abstract and article page; access may vary
Reviews and reputationEvidence check: 3.5/5
Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). Journal of Retailing, 90(2), 217–232.
Plain-English take: Review valence and volume were positively related to sales elasticities. For your marketing: Use as evidence that review volume and valence can matter while designing review and reputation programs.
Question: How are review valence and volume related to retail sales, and which settings moderate those relationships?
Design: Meta-analysis of empirical studies. Published studies of online product reviews and retail sales across different products, review sites, reviewer types, and use situations.
Review valence and volume were positively related to sales elasticities. The reported relationships were stronger on third-party sites, when critic reviews were included, and for higher-involvement products.
Journal of Retailing · 201410.1016/j.jretai.2014.04.004Publisher abstract and highlights; full text may require access
Reviews and reputationEvidence check: 2.5/5
Chen, Y., & Xie, J. (2005). Marketing Science, 24(2), 218–240.
Plain-English take: Depending on product and review conditions, advertising can be a better response than price changes. For your marketing: Treat third-party badges as a positioning choice to test, not an automatic conversion lift.
Question: How should firms adjust price and advertising after third-party product reviews or awards?
Design: Analytical model illustrated with industry data. Two product industries used to illustrate the theory; the main contribution is a model rather than a causal field test.
Depending on product and review conditions, advertising can be a better response than price changes. Award-logo advertising can hurt the winning product, and increasing ad spend to publicize a favorable review is not always optimal.
Marketing Science · 200510.1287/mksc.1040.0089Publisher abstract; full text may require access
Reviews and reputationEvidence check: 3.5/5
Li, K., Chen, Y., & Zhang, L. (2020). Journal of Retailing and Consumer Services, 55, 102107.
Plain-English take: Looking across the studies, product-category moderation. For your marketing: This is a second synthesis supporting a context-dependent view of reviews.
Question: Which online-review features relate to product sales, and does product category moderate those relationships?
Design: Meta-analysis. Prior empirical studies across online product-review settings; the abstract does not report a pooled observation count.
The analysis found product-category moderation. Review length and special shipping were the factors reported as not significantly affecting sales; other analyzed review-related factors showed significant relationships.
Journal of Retailing and Consumer Services · 202010.1016/j.jretconser.2020.102107Publisher abstract; full text may require access
Reviews and reputationEvidence check: 3.0/5
Chevalier, J. A., & Mayzlin, D. (2006). Journal of Marketing Research, 43(3), 345–354.
Plain-English take: An improvement in a book's reviews increased its relative sales at that site. For your marketing: This is a foundational example of why negative feedback may carry asymmetric weight and why review text can matter.
Question: Do online consumer reviews affect relative book sales at competing retailers?
Design: Longitudinal observational analysis of review and sales data. Books sold on Amazon.com and Barnesandnoble.com; the publisher abstract does not state the number of titles.
An improvement in a book's reviews increased its relative sales at that site. In most samples, the impact of one-star reviews was greater than that of five-star reviews; review-length patterns suggested readers used review text, not just summary ratings.
Journal of Marketing Research · 200610.1509/jmkr.43.3.345Publisher abstract and open earlier working-paper version
Reviews and reputationEvidence check: 3.5/5
Chen, Y., Wang, Q., & Xie, J. (2011). Journal of Marketing Research, 48(2), 238–254.
Plain-English take: Negative word of mouth was more influential than positive word of mouth. For your marketing: This separates testimonial sentiment from visible adoption signals such as customer counts or use cases.
Question: How do consumer opinions (word of mouth) and visible purchase behavior (observational learning) separately affect product sales?
Design: Three longitudinal quasi-experimental field studies. Products and sales on Amazon.com; specific sample sizes are not listed in the abstract.
Negative word of mouth was more influential than positive word of mouth. Positive purchase-statistic information raised sales, while negative purchase-statistic information had no effect; observational-learning effects increased with review volume.
Journal of Marketing Research · 201110.1509/jmkr.48.2.238Publisher abstract; full text may require access
Reviews and reputationEvidence check: 3.5/5
Bairathi, M., Zhang, X., & Lambrecht, A. (2025). Marketing Science, 44(1), 84–101.
Plain-English take: Endorsements increased search and purchases for endorsed as well as unendorsed services. For your marketing: This shows how a credible third-party signal may lift perceptions of the whole marketplace, not just highlighted listings.
Question: How does a marketplace's endorsement of selected items affect search and purchases for both endorsed and unendorsed offerings?
Design: Field experiment on an online marketplace. Users and services on an online freelance marketplace; the abstract does not provide sample size.
Endorsements increased search and purchases for endorsed as well as unendorsed services. The increase was linked to higher perceived quality of services on the platform and was larger for users with higher purchase propensity.
Marketing Science · 202510.1287/mksc.2022.0226Publisher abstract; full text may require access
Reviews and reputationEvidence check: 3.5/5
Verma, D., Dewani, P. P., Behl, A., Pereira, V., Dwivedi, Y., & Del Giudice, M. (2023). Journal of Business Research, 154, 113292.
Plain-English take: Review- and reviewer-related characteristics were positively associated with online word of mouth credibility, which in turn related to purchase intention, online word of mouth adoption, and product attitude. For your marketing: This may be useful for thinking about trust signals and why a testimonial's source, context, and platform matter.
Question: Which message- and reviewer-related factors shape eWOM credibility, and how does credibility relate to consumer responses?
Design: Meta-analysis. Published empirical studies of electronic word of mouth across cultures and platform types.
Review- and reviewer-related characteristics were positively associated with eWOM credibility, which in turn related to purchase intention, eWOM adoption, and product attitude. Culture, product type, and platform type moderated relationships.
Journal of Business Research · 202310.1016/j.jbusres.2022.08.056Publisher abstract; full text may require access
Reviews and reputationEvidence check: 3.0/5
Yoganarasimhan, H. (2013). Marketing Science, 32(6), 860–891.
Plain-English take: Buyers were forward-looking and placed significant weight on seller reputation. For your marketing: This is a useful model for thinking about how reputation accumulates and affects buyer choice in a digital marketplace.
Question: How do buyers value seller reputations in an online freelance marketplace, accounting for forward-looking behavior and selection?
Design: Dynamic structural analysis using marketplace data. Data from a leading online freelance marketplace; the publisher abstract does not report sample size.
Buyers were forward-looking and placed significant weight on seller reputation. The author finds that ignoring dynamics and selection can bias reputation estimates and uses counterfactuals to infer reputation's dollar value.
Marketing Science · 201310.1287/mksc.2013.0809Publisher abstract and open author-hosted full text
Reviews and reputationEvidence check: 3.5/5
Qiu, K., & Zhang, L. (2024). Data and Information Management, 8(2), 100058.
Plain-English take: All analyzed review antecedents were significantly related to purchase intention; review valence had the largest reported association (r = 0.563). For your marketing: This is a broad synthesis of review signals and trust factors.
Question: How do online-review characteristics relate to purchase intention, and which cultural, product, or study factors moderate those effects?
Design: Meta-analysis. Consumer purchase-intention studies involving online reviews across products, platforms, cultures, and research methods.
All analyzed review antecedents were significantly related to purchase intention; review valence had the largest reported association (r = 0.563). Individualism, product type, and several study characteristics moderated the results.
Data and Information Management · 202410.1016/j.dim.2023.100058Publisher abstract and article page; access may vary
Reviews and reputationEvidence check: 2.5/5
Aron Culotta, Ginger Zhe Jin, Yidan Sun, Liad Wagman (2025). Safety Reviews on Airbnb: An Information Tale. Marketing Science. https://doi.org/10.1287/mksc.2023.0552.
Plain-English take: Safety reviews and personal safety experiences reduced bookings; removing vicinity-safety reviews could increase Airbnb reservation revenue under unchanged beliefs, but highlighting/removing effects reverse under rational updating; review–crime correlation is stronger in low-income/minority neighborhoods. For your marketing: Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Question: How does visibility of vicinity-safety reviews affect bookings, revenue and neighborhood sorting?
Design: Empirical analysis joining Airbnb reviews with official crime data in five cities; counterfactual simulations of review visibility.. Airbnb listings/reviews and official crime data in five major U.S. cities.
Safety reviews and personal safety experiences reduced bookings; removing vicinity-safety reviews could increase Airbnb reservation revenue under unchanged beliefs, but highlighting/removing effects reverse under rational updating; review–crime correlation is stronger in low-income/minority neighborhoods.
Marketing Science · 202510.1287/mksc.2023.0552Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 3.5/5
Gangseog Ryu, Lawrence Feick (2007). A Penny for Your Thoughts: Referral Reward Programs and Referral Likelihood. Journal of Marketing. https://doi.org/10.1509/jmkg.71.1.084.
Plain-English take: Rewards can increase referral likelihood, but their effectiveness depends on the strength of the relationship between referrer and recipient; incentives are less useful for weak ties. For your marketing: Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Question: When and for whom do referral rewards increase the likelihood of making a referral?
Design: Field and laboratory experiments plus survey analyses of referral reward framing and likelihood.. Consumers in referral-program contexts; experiments compare reward/no-reward and relationship-strength conditions.
Rewards can increase referral likelihood, but their effectiveness depends on the strength of the relationship between referrer and recipient; incentives are less useful for weak ties.
Journal of Marketing · 200710.1509/jmkg.71.1.084Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 3/5
Wenjing Duan, Bin Gu, Andrew B. Whinston (2008). Do Online Reviews Matter? An Empirical Investigation of Panel Data. Decision Support Systems. https://doi.org/10.1016/j.dss.2008.04.001.
Plain-English take: Review volume is positively associated with box-office demand, while the relationship between review valence and sales is more nuanced; review activity can be a demand signal as well as influence. For your marketing: Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Question: Do online review volume and ratings affect sales, and how should firms interpret review metrics?
Design: Panel-data econometric study of review volume, ratings, and sales with product/time controls.. Movie-market panel data linking online reviews and movie box-office performance over time.
Review volume is positively associated with box-office demand, while the relationship between review valence and sales is more nuanced; review activity can be a demand signal as well as influence.
Decision Support Systems · 200810.1016/j.dss.2008.04.001Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 4/5
Jaehwuen Jung, Ravi Bapna, Anil Gupta, Sudha Ram (2021). Impact of Incentive Mechanism in Online Referral Programs: Evidence from Randomized Field Experiments. Journal of Management Information Systems. https://doi.org/10.1080/07421222.2021.1870384.
Plain-English take: The incentive mechanism and allocation between referrer and referred customer materially affect online referral performance; incentive design is not one-size-fits-all. For your marketing: Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Question: How does the design/allocation of online referral incentives affect referral outcomes?
Design: Randomized field experiments manipulating online referral incentive mechanisms.. Customers participating in an online referral program; referral initiation and downstream referral outcomes under alternative incentive allocations.
The incentive mechanism and allocation between referrer and referred customer materially affect online referral performance; incentive design is not one-size-fits-all.
Journal of Management Information Systems · 202110.1080/07421222.2021.1870384Publisher abstract/metadata retrieved; full text may require access.
Reviews and reputationEvidence check: 2.5/5
Yong Liu (2006). Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing. https://doi.org/10.1509/jmkg.70.3.74.
Plain-English take: WOM volume is positively related to box office and its effect varies over a movie’s life cycle; WOM is both an outcome of consumption and a predictor of later demand. For your marketing: Use it as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Question: What are the dynamics of movie WOM and how does it affect box-office revenue?
Design: Dynamic panel/time-series analysis of WOM measures and weekly box-office revenue.. Movies released in theaters; online WOM volume/valence and weekly box-office revenue across the movie life cycle.
WOM volume is positively related to box office and its effect varies over a movie’s life cycle; WOM is both an outcome of consumption and a predictor of later demand.
Journal of Marketing · 200610.1509/jmkg.70.3.74Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 3.5/5
Xiaoyue Wu, Liyin Jin, Qian Xu (2021). Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews. Journal of Retailing. https://doi.org/10.1016/j.jretai.2020.05.006.
Plain-English take: Consumers are more willing to accept a reviewer’s recommendation when the reviewer’s historical ratings in a product domain show greater variance; the publisher reports consistent support across five experiments. For your marketing: Use it as a product, platform or behavior mechanism reference rather than a direct forecast for a SaaS funnel.
Question: How does the variance of a reviewer’s historical ratings affect consumers’ acceptance of that reviewer’s online recommendations, and what role does perceived expertise play?
Design: Five experiments testing the effect of historical-rating variance on review persuasiveness, its mechanism, generalizability, and boundary conditions; the publisher page describes perceived expertise as an underlying process.. Consumers/participants evaluating online-review recommendations across product-review contexts in five experiments; the publisher page describes studies testing the effect, mediation by perceived expertise, another product category, and boundary conditions.
Consumers are more willing to accept a reviewer’s recommendation when the reviewer’s historical ratings in a product domain show greater variance; the publisher reports consistent support across five experiments.
Journal of Retailing · 202110.1016/j.jretai.2020.05.006Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 4/5
Liyin Jin, Bingyan Hu, Yanqun He (2014). The Recent versus The Out-Dated: An Experimental Examination of the Time-Variant Effects of Online Consumer Reviews. Journal of Retailing. https://doi.org/10.1016/j.jretai.2014.05.002.
Plain-English take: Recent reviews are more influential for near-future decisions, whereas outdated reviews become relatively more influential for distant-future decisions; temporal/construal fit drives engagement and influence on preferences. For your marketing: Use it as a product, platform or behavior mechanism reference rather than a direct forecast for a SaaS funnel.
Question: When and why do recent versus outdated online consumer reviews shift consumers’ preferences differently?
Design: Four controlled experiments comparing the effects of recent versus outdated online reviews under near- versus distant-future purchase timeframes, including different review valences.. Consumers making purchase decisions after exposure to online reviews posted at different times; four experiments, as reported in the publisher abstract and section headings.
Recent reviews are more influential for near-future decisions, whereas outdated reviews become relatively more influential for distant-future decisions; temporal/construal fit drives engagement and influence on preferences.
Journal of Retailing · 201410.1016/j.jretai.2014.05.002Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 2.5/5
Young Kwark, Gene Moo Lee, Paul A. Pavlou, Liangfei Qiu (2021). On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data. Information Systems Research. https://doi.org/10.1287/isre.2021.0998.
Plain-English take: Mean ratings of substitutive products negatively affect focal-product purchase while ratings of complementary products have a positive effect; spillovers are stronger on mobile, across different brands for substitutive products, among less experienced consumers, and when rating variance is lower. For your marketing: Use it as a product, platform or behavior mechanism reference rather than a direct forecast for a SaaS funnel.
Question: How do online reviews of products viewed alongside a focal product spill over into focal-product purchases, and how do complementarity/substitution, device, brand, experience, and rating variance moderate the effect?
Design: Observational clickstream study of purchases and reviews, supplemented by supervised machine-learning models trained and validated with Amazon Mechanical Turk product-pair labels to identify complementary and substitutive products.. Clickstream data from a large retailer, examining reviews of covisited products and purchases of a focal product; moderators include device (mobile versus PC), brand relationship, consumer experience, and rating variance.
Mean ratings of substitutive products negatively affect focal-product purchase while ratings of complementary products have a positive effect; spillovers are stronger on mobile, across different brands for substitutive products, among less experienced consumers, and when rating variance is lower.
Information Systems Research · 202110.1287/isre.2021.0998Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
Reviews and reputationEvidence check: 3.5/5
Alisha Stein, B. Ramaseshan (2015). Customer Referral Behavior: Do Switchers and Stayers Differ?. Journal of Service Research. https://doi.org/10.1177/1094670514563495.
Plain-English take: Positive changes in service quality and perceived value have stronger effects on referral behavior among switchers than stayers. Thus, investments in quality and value improvements generate higher referral returns among newly acquired customers. For your marketing: Use it as a product, platform or behavior mechanism reference rather than a direct forecast for a SaaS funnel.
Question: Do recently acquired customers (switchers) and long-term customers (stayers) differ in how service quality and perceived value changes affect customer referrals?
Design: Two-wave empirical study testing moderation by customer tenure/acquisition status; it uses actual referral data and examines how changes in perceived service quality and value relate to referral behavior for switchers versus stayers.. 441 customers of an Internet Service Provider, observed in two waves; switchers are recently acquired customers who changed providers, while stayers are longer-term customers.
Positive changes in service quality and perceived value have stronger effects on referral behavior among switchers than stayers. Thus, investments in quality and value improvements generate higher referral returns among newly acquired customers.
Journal of Service Research · 201510.1177/1094670514563495Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.