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Record W4402870041 · doi:10.5267/j.jpm.2024.7.006

Performance measurement: Key performance indicators as drivers in assessing risk and improving value in the services sector

2024· article· en· W4402870041 on OpenAlexvenueno aff
Mohammad Salameh Almasarweh, Hanadi lbrahim AlHassan, Sohail Mustafa, Abd Al-Salam Ahmad Al-Hamad, Maher Nawasra, Ahmad Y. A. Bani Ahmad, Lama Ahmad Alsmad

Bibliographic record

VenueJournal of Project Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsKey (lock)Performance indicatorValue (mathematics)BusinessRisk analysis (engineering)Performance measurementProcess managementComputer scienceStatisticsComputer securityMathematicsMarketing

Abstract

fetched live from OpenAlex

The research investigated the relationship among Key Performance Indicators (KPIs), risk assessment capabilities and value creation in service sector firms. The study also sought to examine the effect of KPI`s components on risk assessment & value capitalisation, and how they either facilitate or hinder implementation, monitoring and continuous improvement processes. In this context, a quantitative cross-sectional research design was applied using an online survey of shared middle and senior managers in service organizations. After filtering, the final version of segmented sample included a total of 215 respondents engaged in different service businesses. The analysis was determined using Partial Least Squares Structural Equation Modeling. The results showed that all components of KPIs have significant positive relationships with risk assessment and value improvement outcomes First, performance drivers were found to be the most significant predictor of both constructs. As such, the results show that both risk assessment and value improvement had a positive effect on implementation/monitoring processes which in turn enabled continuous improvements. Performance measurement, risk management and value creation in service organizations: A performance at-risk-based conceptual model. The results have numerous managerial, practical and policy implications for the service sector. This drives home the necessity of creating integrated KPI systems that include risk assessment and value improvement factors. In building on existing theory, the study is of substantial interest in that it provides empirical evidence for these organizational mechanisms related to service organizations. Resilient Organizations in the Service Sector picture of Resilience across Performance Management with KPIs, Risk Assessment and Value Creation strategies offering a comprehensive foundation for sustainable organizational success.

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)0.010
metaresearch head score (Gemma)0.035
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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
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

Citations5
Published2024
Admission routes1
Has abstractyes

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