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Record W1986742525 · doi:10.1108/17410401111167780

When is a balanced scorecard a balanced scorecard?

2011· article· en· W1986742525 on OpenAlexaffabout
Marvin J. Soderberg, Suresh Kalagnanam, Norman T. Sheehan, Ganesh Vaidyanathan

Bibliographic record

VenueInternational Journal of Productivity and Performance Management · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversity of SaskatchewanWorkers Compensation Board of Alberta
Fundersnot available
KeywordsBalanced scorecardPerformance measurementProcess managementConstruct (python library)Computer scienceTaxonomy (biology)CategorizationKnowledge managementBusinessMarketing

Abstract

fetched live from OpenAlex

Purpose The Balanced Scorecard (BSC) is widely applied as a performance measurement and strategy implementation tool by organizations. Research has revealed that the term “balanced scorecard” may be understood differently by managers both within as well as across organizations implying that the performance measurement systems implemented in organizations may not be similar to the construct envisioned by Kaplan and Norton. Using Kaplan and Norton's Balanced Scorecard construct as a basis, the paper aims to develop and test a five‐level taxonomy to classify firms' performance measurement systems. Design/methodology/approach A Balanced Scorecard taxonomy is validated using a large sample of professional accountants working in Canadian organizations. Findings The five‐level taxonomy is used to categorize the performance measurement systems of 149 organizations. It is found that 111 organizations' (74.5 percent) performance measurement systems met the criteria to be classified as a Basic Level 1 BSC, while 61 (40.9 percent) organizations have structurally complete Level 3 BSCs, and 36 (24.2 percent) organizations have fully developed Level 5 BSCs. The paper also discusses differences between Level 1 and Level 5 BSC organizations. Research limitations/implications While many researchers assume that organizations' performance measurement systems are similar in implementation level and use, the paper demonstrates that organizations are at different levels of BSC implementation and use, a factor that should be taken into consideration when designing empirical studies to test the efficacy of Kaplan and Norton's BSC. Practical implications The five‐level BSC taxonomy scheme provides managers working with Kaplan and Norton's BSC with a tool to plan their implementation steps and then benchmark their progress towards implementing a fully developed Level 5 BSC. Originality/value In developing and empirically validating a BSC taxonomy, the paper builds on and extends previous research on BSC implementation and its potential implications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.156
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.156
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.019
Science and technology studies0.0030.009
Scholarly communication0.0170.016
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.210
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations81
Published2011
Admission routes2
Has abstractyes

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