Most business owners assume that higher ratings are linked to better business results. A peer-reviewed study directly tested this hypothesis.
Researchers Eddie Inyang and Juliana White surveyed 251 US small business owners about online reputation managementGoogle star ratings and business performance. Notably, Google star ratings alone do not predict performance.
What was associated with performance was ORM practice. Active reputation management is correlated with better business results. Not the stars, but their work behind the scenes.
What the research found
The studypublished in the Journal of Small Business Strategy, tested six hypotheses regarding ORM and small business performance using partial least squares structural equation modeling.
Five were supported. Customer orientation and Internet self-efficacy positively predicted ORM practices, with Internet self-efficacy having a stronger effect. ORM correlates with better business performance and higher Google ratings, with competitive intensity strengthening these relationships. In more competitive markets, the gap between ORM practitioners and non-practitioners was wider.
The sixth hypothesis, that Google star ratings alone would predict business performance, was not supported.
This observation of competitive intensity deserves attention. The study treats ORM as a “strategic resource” within the framework of resource-benefit theory. The argument is that ORM functions as an operational capability, not a customer service activity that produces better ratings. The performance gap widens when competition intensifies. In competitive markets, ORM seems to shift from a supportive activity to a difference-making activity.
The study included 251 U.S. small business owners across various industries. Performance and star ratings were self-reported, a noted limitation. Because the design is cross-sectional, it cannot establish causality.
This trend raises a question that the study does not answer. If intense competition strengthens the effect of ORM, What happens when the competitive landscape becomes more condensed?
AI compresses local visibility
The study does not examine AI-based discovery, but its findings on competitive intensity are important since SOCi data shows that AI systems turn up fewer businesses than Google’s local three-pack.
BrightLocal’s 2026 Local Consumer Ratings Survey found that 45% of consumers now use ChatGPT or other generative AI tools for local business recommendations. This is up from 6% the previous year. BrightLocal, which sells local SEO tools, has conducted this survey every year since 2010.
SOCi Local Visibility Index 2026 analyzed over 350,000 locations across 2,751 brands. ChatGPT recommended 1.2% branded placements, Gemini 11%, Perplexity 7.4%. The same brands appeared in Google’s local 3-pack 35.9% of the time. SOCi, which offers multi-location marketing software, said this method is about 30 times more selective than traditional local search.
THE overlap between traditional and AI visibility was less than expected. In retail, SOCi found only a 45% overlap between top-performing brands in local search and those recommended by AI platforms. Good local search rankings did not guarantee AI visibility.
Data from SOCi showed that ChatGPT recommended locations had an average rating of 4.3 stars, indicating that reviews are important to AI platforms. However, grades aren’t everything. SOCi views AI visibility as determined by data accuracy, reputation signals, and engagement, not just star ratings.
As Joy Hawkinsowner and founder of Sterling Sky, wrote on LinkedIn:
“Google’s AI-powered local results show fewer businesses and, in many cases, fewer ways for customers to contact you.”
The Multi-Site Execution Gap
Inyang and White’s study looked at small businesses in one location. ORM becomes more difficult when multiplied across many locations.
Birdeye Report on the State of Online Reviews 2025based on data from more than 150,000 U.S. businesses, found that review volume increased 13% year over year. Response rates increased from 63% to 73%. Analysis of the report by Localology independently confirmed both figures.
The gap between high-performing and low-performing brands is wide. SOCi LVI 2024 data shows that low visibility brands responded to 10.9% of reviews in 12 days, while high visibility brands responded in 2.1 days.
It’s not that they don’t understand the importance of responding. Anyone who manages multiple sites understands the importance of participating in reviews. What we’re seeing is a failure of execution.
Robert Barruecofounder of Webnition, which sells review automation tools, wrote on LinkedIn:
“Responding to reviews on dozens, if not hundreds, of sites is not only exhausting… It’s almost impossible to do it consistently without a branded automated solution.
For multi-site teams, this may require organizational change. ORM cannot rely on scattered connections, inconsistent responses or each location handles reviews differently. The research identifies ORM as a capability that requires shared standards, clear ownership, and operational support to ensure consistency.
This is where the word “infrastructure” comes into play. Infrastructure is what you build when the load exceeds what a single person or team can handle manually. For a multi-location ORM, the overhead is revision volumeconsistency of responses, accuracy of listings, and platform coverage across all sites simultaneously.
What AI systems appear to assess
SOCi’s analysis views AI visibility as separate from traditional ranking, treating AI platforms as recommendations rather than sorters. The recommendation depends on the system’s confidence in the accuracy and quality of the data.
This is SOCi’s interpretation, not a confirmed mechanism. But the pattern matches what practitioners see.
Justin Silvermanfounder and CEO of Merchynt, which sells GBP optimization tools, wrote on LinkedIn: “Your Google Business Profile is no longer just for Google. »
Me Clarkefounder of Clapping Dog Media, was more specific, saying: “AI favors companies that appear everywhere with aligned information. »
Review content adds location-specific context that a star rating can’t provide on its own. Customer reviews mentioning services, locations or use cases are accessible to systems analyzing business information. This text provides context that can improve customer understanding and analysis of the AI system.
Consistency of the NAP, which SEJ has widely covered as a key factor in local SEO, now has a second audience. If AI intersects with business data, inconsistencies can undermine trust, as SOCi warns. These discrepancies confuse customers, call into question fundamental business facts, and potentially impact AI visibility.
Looking to the future
According to Inyang and White’s study, star ratings alone do not predict the success of small businesses. Active reputation management is correlated with better performance, especially in competitive markets.
For multi-site brands, reviews are important, but they need systems to manage reputation across sites and platforms. It takes more effort, but the work being done provides a valuable benefit, while neglecting it could lead to lower visibility.
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Featured image: Tetiana Yurchenko/Shutterstock
