Most business owners assume that higher star ratings are linked to better business outcomes. A peer-reviewed study tested that assumption directly.
Researchers Eddie Inyang and Juliana White surveyed 251 U.S. small-business owners on online reputation management, Google star ratings, and business performance. Notably, Google star ratings alone didn’t predict performance.
What was associated with performance was the practice of ORM. Active reputation management correlated with better business results. Not the stars, but their behind-the-scenes work.
What The Research Found
The study, published 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 correlated 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 would predict business performance on their own, was not supported.
That competitive-intensity finding is worth pausing on. The study treats ORM as a “strategic resource” under Resource-Advantage theory. The argument is that ORM works as an operational capability, not a customer service activity that produces better ratings. The performance gap widens when competition increases. In competitive markets, ORM appears to be moving from a supporting activity to a difference-maker.
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 can’t establish causation.
The pattern raises a question the study doesn’t address. If intense competition boosts ORM’s effect, what happens when the competitive landscape becomes more condensed?
AI Compresses Local Visibility
The study doesn’t examine AI-powered discovery, but its findings on competitive intensity matter since SOCi’s data shows AI systems surface fewer businesses than Google’s local 3-pack.
BrightLocal’s 2026 Local Consumer Review Survey found that 45% of consumers now use ChatGPT or other generative AI tools for local business recommendations. That’s up from 6% the year before. BrightLocal, which sells local SEO tools, has run this survey annually since 2010.
SOCi’s 2026 Local Visibility Index analyzed over 350,000 locations across 2,751 brands. ChatGPT recommended 1.2% brand locations, 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 is roughly 30 times more selective than traditional local search.
The overlap between traditional and AI visibility was less than expected. In retail, SOCi found only 45% overlap between brands top in local search and those recommended by AI platforms. Strong local search rankings didn’t ensure AI visibility.
SOCi’s data showed ChatGPT-recommended locations averaged 4.3-star ratings, indicating reviews matter to AI platforms. However, ratings aren’t the whole story. SOCi views AI visibility as driven by data accuracy, reputation signals, and engagement, not just star ratings.
As Joy Hawkins, owner and founder of Sterling Sky, wrote on LinkedIn:
“Google’s AI-driven local results are showing fewer businesses and, in many cases, fewer ways for customers to contact you.”
The Multi-Location Execution Gap
The Inyang and White study examined small businesses at a single location. ORM gets more challenging when multiplied across many locations.
Birdeye’s 2025 State of Online Reviews report, based on data from more than 150,000 U.S. businesses, found review volume grew 13% year over year. Response rates rose from 63% to 73%. Localogy’s analysis of the report confirmed both figures independently.
The gap between high- and low-performing brands is wide. SOCi’s 2024 LVI data shows 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. Everyone who manages multiple locations understands that engaging with reviews is important. What we’re seeing is a failure to execute.
Robert Barrueco, founder of Webnition, which sells review automation tools, wrote on LinkedIn:
“Responding to reviews across dozens—or hundreds—of locations isn’t just exhausting… It’s almost impossible to do consistently without an automated, branded solution.”
For multi-location teams, this may require an organizational change. ORM can’t rely on scattered logins, inconsistent responses, or each location handling 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” earns its place. Infrastructure is what you build when the load exceeds what any single person or team can handle manually. For multi-location ORM, the load is review volume, response consistency, listing accuracy, and platform coverage across every location simultaneously.
What AI Systems Appear To Evaluate
SOCi’s analysis views AI visibility as distinct from traditional ranking, treating AI platforms as recommenders rather than sorters. The recommendation depends on the system’s confidence in the accuracy and quality of the data.
That’s SOCi’s interpretation, not a confirmed mechanism. But the pattern lines up with what practitioners are seeing.
Justin Silverman, founder and CEO of Merchynt, which sells GBP optimization tools, wrote on LinkedIn, “Your Google Business Profile is no longer just for Google.”
Meg Clarke, founder of Clapping Dog Media, was more specific, saying, “AI favors businesses that show up everywhere with aligned information.”
Review content adds location-specific context a star rating can’t carry alone. Customer reviews mentioning services, locations, or use cases are accessible to systems parsing business info. This text offers context that can improve customer understanding and AI system analysis.
NAP consistency, which SEJ has covered extensively as a key local SEO factor, now has a second audience. If AI cross-references business data, inconsistencies may undermine confidence, as SOCi warns. These discrepancies confuse customers, call into question basic business facts, and potentially affect AI visibility.
Looking Ahead
Star ratings alone didn’t predict small business success in the Inyang and White study. Active reputation management correlated with better performance, especially in competitive markets.
For multi-location brands, reviews matter, but they need systems to manage reputation across all locations and platforms. That’s more effort, but the ongoing work provides a valuable advantage, while overlooking it could lead to less visibility.
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Featured Image: Tetiana Yurchenko/Shutterstock
