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Capturing Relatedness: Comprehensive Measures based on Secondary Data
Author(s) -
Nocker Elisabeth,
Bowen Harry P.,
Stadler Christian,
Matzler Kurt
Publication year - 2016
Publication title -
british journal of management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.407
H-Index - 108
eISSN - 1467-8551
pISSN - 1045-3172
DOI - 10.1111/1467-8551.12124
Subject(s) - replicate , diversification (marketing strategy) , construct (python library) , variation (astronomy) , econometrics , entropy (arrow of time) , measure (data warehouse) , survey data collection , business , marketing , computer science , economics , statistics , data mining , mathematics , physics , quantum mechanics , astrophysics , programming language
This paper presents new measures of technological and customer‐side relatedness constructed from widely available secondary data. Relatedness is a concept central to predicting the existence and nature of a relationship between corporate diversification and firm performance. Yet, finding appropriate measures has been an ongoing struggle. The widely used SIC‐based entropy measure has low construct validity, and survey‐based measures are hard to replicate across firms and industries and over time. The measures we develop significantly outperform established measures in explaining variation in firm performance across firms and over time, and both sources of relatedness are found to be independent and significant explanations of firm performance.