Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach

Yanni Ping, Chelsey Hill, Yun Zhu, Jorge Fresneda

Research output: Contribution to journalArticlepeer-review

Abstract

Key Opinion Leaders (KOLs) have an undeniable influence on businesses. Many online review communities, such as Yelp, give KOL users prominent status in their communities as cues of source trustworthiness. Using both econometric analysis and machine learning methods, we adopt an antecedents and consequences framework to investigate the drivers of KOL status and their economic impact on businesses. We find that a user’s social activity is more important in determining KOL status than the reviews themselves. On the consequences side, the paper shows that the first KOL review significantly boosts sales, regardless of the actual rating assigned by the KOL. After confirming this sales boost, we use random forest regression to predict sales using KOL review characteristics, including text. It is found that the number of KOL reviews as the most influential feature in predicting sales. This research contributes to the existing literature by adding a more granular, holistic investigation into KOLs in online consumer review communities.

Original languageEnglish (US)
JournalElectronic Commerce Research
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Economics, Econometrics and Finance (miscellaneous)

Keywords

  • Difference-in-difference
  • Key opinion leader
  • Machine learning
  • Online consumer reviews
  • Reviewer certification

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