Who Developed Social Media Engagement Theory

Understanding Social Media Engagement Theory and Its Roots

Social media engagement theory isn't something one person woke up and invented. It's more accurate to say it accumulated over twenty years from people working in communication studies, marketing, and organizational behavior. If you're looking for a single name, you're going to be disappointed. The framework developed organically as researchers tried to figure out why people interact the way they do online. The most commonly cited foundational work comes from Kaplan and Haenlein, whose 2010 paper "Users of the world, unite! The challenges and opportunities of Social Media" laid out early structure around what engagement actually means in digital environments. But they weren't working in a vacuum. Kaplan is at Boston University and Michael Haenlein was at Essec Business School at the time, and their work synthesized a lot of prior research rather than creating something entirely new. Earlier contributors worth noting include Katz and Blumler from the 1970s with uses and gratifications theory, which basically asks what people get out of media consumption rather than what media does to them. That framework got recycled and adapted for social media by researchers like Davenport and Beck in 2001 with their work on attention economy, and then later by others studying electronic word-of-mouth, or eWOM. Bhattacharya and Sen did work on consumer-brand relationships that influenced how people thought about engagement as something deeper than likes and shares.

More recently, Hollebeek, Glynn, and Bryde have been referenced heavily in academic circles for their work on brand engagement. Hollebeek specifically developed what they called the BE-V model, breaking engagement into cognitive, emotional, and behavioral dimensions. That model gets cited a lot in marketing journals, though honestly the practical application in real campaigns is often thinner than the paper makes it seem. There's also significant contribution from the computer-mediated communication side. Researchers like Walther studied parasocial interaction and social presence in online environments back in the late 90s and early 2000s. His work on hyperpersonal communication helps explain why people form attachments to brands and creators they've never met in person. That insight underpins a lot of modern engagement strategy even if most practitioners don't know where it came from.

How the Theory Actually Works in Practice

The core idea is straightforward enough. Social media engagement theory proposes that engagement happens at three levels: cognitive, which is how much people think about a brand or content; emotional, which is how they feel about it; and behavioral, which is what they actually do. Clicking, commenting, sharing, creating user-generated content, even just lurking counts as a form of behavioral engagement depending on how generous you want to be with the definition. What most people miss is that these dimensions don't always move together. You can have high cognitive and emotional engagement with very low behavioral output. That's the classic silent majority problem. I spent months trying to optimize for comments and shares on a B2B SaaS client's LinkedIn presence and kept getting frustrated by low numbers despite having solid content. The breakthrough came when I stopped chasing behavioral metrics and focused on the cognitive layer instead. We shifted from promotional posts to genuinely useful industry analysis, and engagement quality improved dramatically even if raw numbers didn't explode. People were thinking about the brand more, which eventually translated into pipeline, just not on the timeline anyone expected. Here's a specific edge case I ran into that the theory doesn't really prepare you for. A client in the fintech space noticed their engagement rates plummeted after switching from short-form video to long-form educational posts. On paper, the longer content should have driven higher cognitive engagement. What actually happened was a platform algorithm shift. LinkedIn's distribution logic at the time favored quick consumption signals, and our content wasn't generating them fast enough. The theory assumes engagement is about the relationship between user and brand. It doesn't fully account for the fact that a platform's engagement distribution mechanism acts as a gatekeeper. My workaround was to keep the educational content but add a hook in the first three lines that forced a click-through or pause, which signaled to the algorithm that the content deserved further distribution. Not ideal, but it worked within the constraints we had.

Common Misinterpretations and Where the Theory Breaks Down

One major pitfall is conflating engagement with positivity. The theory treats all engagement as valuable, but negative engagement, arguments in the comments, brand bashing, that still registers as behavioral engagement. I've seen clients celebrate high comment counts only to discover the comments were almost entirely negative. That's engagement by the book, terrible for business. You need sentiment analysis paired with engagement measurement, and most tools don't do that automatically. Another thing the original framework doesn't handle well is cross-platform variation. What counts as engagement on Reddit is completely different from engagement on Instagram or TikTok. The behavioral dimension means something different depending on the platform's affordances. A upvote on Reddit carries different weight than a like on Instagram. Comparing engagement rates across platforms without accounting for these structural differences is meaningless, and I see it done constantly in agency reports. The theory also struggles with automation and bot engagement. As soon as you introduce engagement rings, fake accounts, or overly optimized posting schedules, the data becomes noisy. During one audit I found that roughly 18 percent of a client's "engagement" came from accounts that had posted zero original content and followed thousands of accounts in the same niche. Pure bot activity inflating the numbers. No version of social media engagement theory from the 2010s anticipated how bad this would get. The workaround is basic filtering, but even that misses sophisticated human-like bots that are becoming harder to detect.

There's also the problem of temporal decay. Engagement theory often treats a like today the same as a like from last month in terms of what it represents about brand relationship. But the reality is engagement is highly ephemeral. Most social media interactions have a half-life measured in hours, not weeks. The theories that talk about lasting brand-consumer relationships are more rooted in traditional brand equity literature than in social media engagement frameworks. If you're building a strategy on engagement alone, you're building on sand. Combine it with retention metrics and lifetime value modeling, or you'll optimize for noise. For anyone trying to apply this practically, start with the Hollebeek framework if you need something structured, but treat it as a starting point not a gospel. The people behind it know the model has gaps, and so should you. Pair it with platform-specific analytics, regular sentiment checks, and a clear understanding of what behavioral engagement actually means for your specific business objectives. Engagement for brand awareness looks different than engagement for customer support or sales conversion. The theory gives you vocabulary. It doesn't tell you which dimension to prioritize for your situation.