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Record W1555613376 · doi:10.1108/jsma-06-2014-0048

Strategy execution: five drivers of performance

2015· article· en· W1555613376 on OpenAlexaff
Vincent Sabourin

Bibliographic record

VenueJournal of strategy and management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAction (physics)Sample (material)Dimension (graph theory)Process managementPerformance managementWork (physics)Order (exchange)Computer scienceKnowledge managementBusinessMarketingEngineering

Abstract

fetched live from OpenAlex

Purpose – What are the strategies of managers to implement their strategy? What are the strategies to execute organizational objectives? The purpose of this paper is to approach what the authors call the drivers of performance that is the driving forces which impact the performance of a manager in executing his/her objectives. What are the strategies, which you as a manager have to have in order to execute your objectives and to obtain better results with your respective department? The authors discuss the five drivers of performance, that of rules, emotions, initiatives, immediate action and integrity. The research findings are presented with a discussion on the usefulness of the drivers. Design/methodology/approach – A survey questionnaire was administered to a population of 484, with a study sample of 180 managers to better understand the underlying drivers of performance in strategy execution. The authors used primarily components analysis to examine the relationship between drivers of performance identified in previous research. Findings – The study found four drivers the performance and management practices of managers. The following driver were found; driver of emotions, (getting a commitment for your objectives), the dimension of taking initiatives (translating the objectives into concrete projects/empowerment), the driver of rules (clarifying and aligning the objectives) and driver of immediate action (taking valued added action and facing emergencies in the execution). Research limitations/implications – The paper found that the fundamental of strategic management such as management leadership in performance and strategy execution could be organized according to four drivers. Additional work will be necessary to generalize the findings to other type of management programs which are related to performance effectiveness. Originality/value – The study sought to contribute a new management direction by identifying four drivers gathering the strategic platforms that managers could employ to organize their performance and strategy execution.

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)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0020.004
Scholarly communication0.0100.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.238
Teacher spread0.200 · 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 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

Citations14
Published2015
Admission routes1
Has abstractyes

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