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

Document Type

Article

Publication Date

9-1-2023

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.

Identifier

85143586499 (Scopus)

Publication Title

Electronic Commerce Research

External Full Text Location

https://doi.org/10.1007/s10660-022-09650-9

e-ISSN

15729362

ISSN

13895753

First Page

1459

Last Page

1484

Issue

3

Volume

23

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