How Organizational Characteristics Influence the Choice of Performance Measures: A Large-Scale Empirical Study

Despite the growing emphasis on performance measurement, empirical evidence linking the selection of performance measures to distinct organizational characteristics remains limited. Moreover, practical guidance on selecting appropriate performance measures for different organizational contexts is often ambiguous or absent. This study addresses these gaps through a large-scale empirical analysis of 372 organizations across diverse industry sectors. It provides validation for the hypothesized contingency effects of various organizational characteristics—such as size, structure, and global exposure—on performance measurement choices. The findings support our contention that no single performance measure is universally optimal; rather, the suitability of specific measures depends on the organization’s contextual attributes. By integrating both operational and relational performance dimensions, the study offers a comprehensive framework for understanding how organizational contingencies shape measurement practices. The results yield actionable implications for managers, enabling them to align their performance measurement systems with organizational characteristics, thereby enhancing resource allocation, strategic decision-making, and their ability to compete.

Authors

Institutions

Publication Details

Journal
International Journal of Global Business and Competitiveness
Published
2025-12-01
DOI
https://doi.org/10.1007/s42943-025-00139-4
Primary Topic
Accounting and Organizational Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How Organizational Characteristics Influence the Choice of Performance Measures: A Large-Scale Empirical Study

Ravi Kathuria, Lorenzo Lucianetti
International Journal of Global Business and Competitiveness
Accounting and Organizational Management
article

How Organizational Characteristics Influence the Choice of Performance Measures: A Large-Scale Empirical Study

Ravi Kathuria, Lorenzo Lucianetti
article en

Abstract

Despite the growing emphasis on performance measurement, empirical evidence linking the selection of performance measures to distinct organizational characteristics remains limited. Moreover, practical guidance on selecting appropriate performance measures for different organizational contexts is often ambiguous or absent. This study addresses these gaps through a large-scale empirical analysis of 372 organizations across diverse industry sectors. It provides validation for the hypothesized contingency effects of various organizational characteristics—such as size, structure, and global exposure—on performance measurement choices. The findings support our contention that no single performance measure is universally optimal; rather, the suitability of specific measures depends on the organization’s contextual attributes. By integrating both operational and relational performance dimensions, the study offers a comprehensive framework for understanding how organizational contingencies shape measurement practices. The results yield actionable implications for managers, enabling them to align their performance measurement systems with organizational characteristics, thereby enhancing resource allocation, strategic decision-making, and their ability to compete.

International Journal of Global Business and CompetitivenessVol. 20(S1)
Chapman University (US), University of Chieti-Pescara (IT)
Openalex Percentile: Top 43%
Accounting and Organizational Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.