Predictive Analytics: Digital Metrics for Estimating Knowledge Assets

Digitization, big data, and follow-on metrics have burgeoned over the last couple of decades. While the knowledge management (KM) community has embraced some of the advances in data-driven decision-making in business, there remain new applications that are relatively unexplored. One of these applications is the use of digital metrics to estimate knowledge holdings or intellectual capital (IC). In a discipline still at the mercy of troublesome metrics, new opportunities to better measure IC would fill a large gap in the existing body of knowledge. Previous work, across several industries and well-known brands, established digital media variables of interest such as volume of mentions, variability of mentions, influencer quality, brand sentiment, and some more platform-specific (X, Facebook, etc.) measures (Erickson, 2023; Erickson & Rothberg, 2023; Erickson, Schmidt & Rothberg, 2020). These indicators were compared with brand equity from a separate source and methodology, establishing apparent links. In short, certain digital media variables seemed related to higher brand equity, a proxy for the intellectual capital component of relational capital. More recent research explored the statistical link between digital metrics and brand equity (relational capital) (Erickson & Rothberg, 2024). On a very small sample, key indicators were shown to predict brand equity values at a very high correlation using different approaches (regression, neural network). This study continues that work, with a larger set of firms. The firms are drawn from two industries, autos and information technology, both of which have a number of companies with available brand equity estimates. Moreover, the study adds price/book value ratios, calculated on an annual basis (like brand equity) but which can also be easily estimated for shorter time periods by altering share price. As a result, in estimating correlation, periodic digital media independent variables can be compared not only with annual brand equity metrics but with matching periodic price/book ratios. Price/book is an effective proxy for not just relational capital but all intellectual capital of the firm, providing a different approach from a new perspective.

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Publication Details

Journal
European Conference on Knowledge Management
Published
2025-08-29
DOI
https://doi.org/10.34190/eckm.26.1.3691
Primary Topic
Big Data and Business Intelligence
Type
article
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article

Predictive Analytics: Digital Metrics for Estimating Knowledge Assets

Scott Erickson, Helen N. Rothberg
European Conference on Knowledge Management
Big Data and Business Intelligence
article

Predictive Analytics: Digital Metrics for Estimating Knowledge Assets

Scott Erickson, Helen N. Rothberg
article en

Abstract

Digitization, big data, and follow-on metrics have burgeoned over the last couple of decades. While the knowledge management (KM) community has embraced some of the advances in data-driven decision-making in business, there remain new applications that are relatively unexplored. One of these applications is the use of digital metrics to estimate knowledge holdings or intellectual capital (IC). In a discipline still at the mercy of troublesome metrics, new opportunities to better measure IC would fill a large gap in the existing body of knowledge. Previous work, across several industries and well-known brands, established digital media variables of interest such as volume of mentions, variability of mentions, influencer quality, brand sentiment, and some more platform-specific (X, Facebook, etc.) measures (Erickson, 2023; Erickson & Rothberg, 2023; Erickson, Schmidt & Rothberg, 2020). These indicators were compared with brand equity from a separate source and methodology, establishing apparent links. In short, certain digital media variables seemed related to higher brand equity, a proxy for the intellectual capital component of relational capital. More recent research explored the statistical link between digital metrics and brand equity (relational capital) (Erickson & Rothberg, 2024). On a very small sample, key indicators were shown to predict brand equity values at a very high correlation using different approaches (regression, neural network). This study continues that work, with a larger set of firms. The firms are drawn from two industries, autos and information technology, both of which have a number of companies with available brand equity estimates. Moreover, the study adds price/book value ratios, calculated on an annual basis (like brand equity) but which can also be easily estimated for shorter time periods by altering share price. As a result, in estimating correlation, periodic digital media independent variables can be compared not only with annual brand equity metrics but with matching periodic price/book ratios. Price/book is an effective proxy for not just relational capital but all intellectual capital of the firm, providing a different approach from a new perspective.

European Conference on Knowledge ManagementVol. 26(1)
Ithaca College (US)
Openalex Percentile: Top 62%
Big Data and Business Intelligence
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