Treating Reviews as Business Infrastructure Drives Results

Most business owners assume a direct link between higher star ratings and stronger financial results. A recent peer-reviewed study put that assumption to the test, and the findings challenge conventional wisdom.

Researchers Eddie Inyang and Juliana White surveyed 251 U. S. small-business owners, examining their online reputation management (ORM) practices, Google star ratings, and overall business performance. The key takeaway? Star ratings alone did not predict success. What did correlate with better outcomes was the active, behind-the-scenes work of managing reputation.

What The Research Revealed

Published in the Journal of Small Business Strategy, the study tested six hypotheses using partial least squares structural equation modeling. Five were confirmed. A customer orientation and Internet self-efficacy both positively predicted ORM practices, with the latter having a stronger influence. ORM itself was linked to improved business performance and higher Google ratings, and this connection was amplified in more competitive markets. In crowded spaces, the performance gap between businesses that actively managed reputation and those that did not widened significantly.

The sixth hypothesis,that Google star ratings alone would predict business performance,was not supported.

The competitive intensity finding deserves attention. The study frames ORM as a strategic resource under Resource-Advantage theory, treating it as an operational capability rather than a simple customer service task that generates better ratings. When competition heats up, ORM shifts from a supporting activity to a true differentiator.

The study included 251 U. S. small business owners across multiple industries. Performance and star ratings were self-reported, a noted limitation, and the cross-sectional design cannot establish causation. Still, the pattern raises an interesting question: if intense competition boosts ORM’s impact, what happens when the competitive landscape becomes even more concentrated?

AI Compresses Local Visibility

While the study didn’t examine AI-powered discovery, its findings on competitive intensity are timely. SOCi’s data shows that AI systems surface far 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, up from just 6% the year before. SOCi’s 2026 Local Visibility Index analyzed over 350,000 locations across 2,751 brands. ChatGPT recommended only 1.2% of brand locations, Gemini 11%, and Perplexity 7.4%. By contrast, the same brands appeared in Google’s local 3-pack 35.9% of the time. SOCi estimates this makes AI roughly 30 times more selective than traditional local search.

The overlap between traditional and AI visibility was surprisingly low. In retail, SOCi found only 45% overlap between brands that ranked highly in local search and those recommended by AI platforms. Strong local search rankings did not guarantee AI visibility.

SOCi’s data also showed ChatGPT-recommended locations averaged 4.3-star ratings, indicating that reviews matter to AI platforms. But ratings aren’t the whole story. SOCi views AI visibility as driven by data accuracy, reputation signals, and engagement, not just star counts.

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 focused on single-location small businesses. ORM becomes exponentially harder when managing dozens or hundreds of 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%. But the gap between high- and low-performing brands is stark. SOCi’s 2024 LVI data shows low-visibility brands responded to just 10.9% of reviews, taking an average of 12 days, while high-visibility brands responded in 2.1 days.

It’s not a lack of understanding. Everyone managing multiple locations knows that engaging with reviews is critical. The problem is a failure to execute.

Robert Barrueco, founder of Webnition, 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 demands an organizational shift. ORM cannot rely on scattered logins, inconsistent responses, or each location handling reviews its own way. 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” fits. Infrastructure is what you build when the load exceeds what any single person or team can handle manually. For multi-location ORM, the load includes review volume, response consistency, listing accuracy, and platform coverage across every location simultaneously.

What AI Systems Appear To Evaluate

SOCi’s analysis treats AI visibility as distinct from traditional ranking. AI platforms act as recommenders rather than sorters, and their recommendations depend 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 aligns with what practitioners are observing.

Justin Silverman, founder and CEO of Merchynt, wrote on LinkedIn: “Your Google Business Profile is no longer just for Google.” Meg Clarke, founder of Clapping Dog Media, was more specific: “AI favors businesses that show up everywhere with aligned information.”

Review content adds location-specific context that a star rating alone cannot carry. Customer reviews mentioning services, locations, or use cases are accessible to systems parsing business info. This text provides context that can improve both 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 did not 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 requires more effort, but the ongoing work provides a valuable advantage. Overlooking it could lead to less visibility in an increasingly AI-driven discovery landscape.