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Record W2185136803

THE IMPACT OF STRATEGIC PLANNING AND THE BALANCED SCORECARD METHODOLOGY ON MIDDLE MANAGERS' PERFORMANCE IN THE PUBLIC SECTOR

2012· article· en· W2185136803 on OpenAlexaboutno aff
Jean-Charles Marin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsBalanced scorecardStrategic planningBusinessStrategic controlProcess managementStrategic financial managementStrategy mapStrategic managementMiddle managementStrategic thinkingPerformance measurementPublic sectorPerformance managementKnowledge managementMarketingComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

The aim of this research is to study the impact of two strategic management systems, strategic planning and the Balanced Scorecard, on the performance and managerial competencies of middle managers working in the public sector. The two strategic management systems are both integrated in a business planning process and the organisation in question is the Defence Department of Canada. The research, supported by a questionnaire, identifies that strategic planning and the Balanced Scorecard are well implemented in the Canadian Defence Sector. Secondly, we discovered that the middle managers working for the Defence Department have the perception that the use of the two strategic management systems has a positive impact on their general performance and also on their managerial competencies. More precisely, we discovered that the competencies of managing change and organisational awareness are the most impacted by the use of the two strategic management systems. Finally, there is a correlation between the two strategic management systems. The more time a managers spends doing strategic planning, the more they are inclined to spend time doing balanced scorecard activities and believe that their performance is increased.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.288
Teacher spread0.162 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations13
Published2012
Admission routes1
Has abstractyes

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