What Makes the Difference? Employee Social Media Brand Engagement: An Abstract

Through the employee lens of business-to-business (B2B) firms, we explore word use through brand engagement and social media interaction to understand what makes the difference of those employees who rate their employer brands highly on social media, and those who do not. This content becomes a valuable source of information for marketing decision makers, as well as an interesting and rich new source of data for B2B marketing scholars.

Fortunately, the recent past has not only seen a significant rise in the amount of unstructured textual data available to researchers, but also a noteworthy increase in the number and sophistication of tools available to perform textual content analysis using computers. One of the major computerized text analysis tools in use today is LIWC (Linguistic Inquiry and Word Count), the development of which is described by Pennebaker and his colleagues (Pennebaker et al. 2001). We conducted a textual content analysis of social media job evaluation site glassdoor.​com using the LIWC software package to analyze 30 of the top 200 B2B brands listed on Brandwatch using four variables, namely, analytical thinking, clout, authenticity, and emotional tone.

The results show that employees who rate their employer’s brand low use significantly more words, are significantly less analytic, and write with significantly more clout because they focus more on others than themselves. Employees who rate their employer’s brand highly, write with significantly more authenticity, exhibit a significantly higher tone, and display far more positive emotions in their reviews.

Brand engagement drives brand equity. Brand equity is not only a significant indicator of marketing effectiveness, it is also a fundamental driver of firm value. This research demonstrates that B2B brand managers and B2B branding scholars should treat social media data disseminated by individual stakeholders, like the variables used in this study (tone, word count, and frequency), as an opportunity to tap a rich source of data with powerful automated text analysis tools to better understand and manage brand insight, brand engagement, and brand equity now and over time.