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Record W2888243263 · doi:10.1108/ijppm-07-2018-0258

Welcome to the seventh issue of IJPPM this year

2018· article· en· W2888243263 on OpenAlexaboutno aff
Nicky Shaw, Luisa Huaccho Huatuco

Bibliographic record

VenueInternational Journal of Productivity and Performance Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessOperations managementPsychologyEconomics

Abstract

fetched live from OpenAlex

Welcome to the seventh issue of IJPPM this year.We have eight thought-provoking and diverse papers in this issue ranging with studies in Ghana, Iran, India, the USA, Canada and Italy, using a broad variety of methods.Famiyeh and Kwarteng provide insights into the implementation of environmental management practices (EMP) in Ghana.They used a survey questionnaire collected from 219 managers either in manufacturing or extracting industries.Employing factor analysis, the findings supported that regulative and mimetic institutional pressure have a significant influence on EMP whereas normative institutional pressure did not have a significant influence.Salehi, Lari Dashtbayaz and Mohammadi study the relationship between management characteristics and firm innovation in Tehran Stock Exchange-listed companies.Using data from 125 companies, descriptive-correlational design and panel data regression models were applied.They found that managerial entrenchment has a negative effect on innovation, whereas managerial ability could foster innovation; surprisingly neither agency cost nor overconfidence has a significant effect.Adaku, Amoako-Gyampah, Kwasi, Lomotey, Amoatey and Famiyeh provide another study in Ghana, this time with the Pension Trust Company.They used extensive data (old system: 14,400 claims and new system: 41,600 claims) in two types of programmes and compared them statistically.They found that the new system has reduced processing time by 20 per cent.This is quite an improvement.Rai and Agarwal investigate the topic of workplace bullying and its influence on innovative work behaviour.Using data from 835 managerial employees in India, they found that workplace bullying is negatively related to workplace innovation and positively related to neglect.Based on their findings, they provide useful recommendations to managers.Sticking with the workplace, Thomason, Brownlee, Beekman and Rustogi studied individuals' attraction to different performance appraisal types using a five-factor model of personality types and applying a forced distribution ranking system (FDRS).They surveyed 148 students on graduate-level business courses in the USA.Their findings suggest that FDRS are attractive to those with high levels of psychological entitlement and low levels of conscientiousness, which contrasts with the existing research.Ochieng presents a case study of financial performance in the non-profit context of US Triathlon.He shows that financial performance is related to both financial effectiveness and financial efficiency; this is particularly important in contexts where financial resources are unreliable.Marchand and Raymond's article studies performance measurement and management systems as IT artefacts using the Burton-Jones and Grange's (2013) theoretical framework.Their work is applied to 16 SMEs in Canada and findings indicate that transparent interaction enables representational fidelity, which in turn enables informed action.This contributes to both the performance management and IS literatures.Finally, our reflective practice article by Bianchi, Winch and Consenz presents work supporting entrepreneurial capabilities by incorporating individual attributes into organisation routines associated with lean dynamic performance management systems in small and micro enterprises in Italy.Their approach contrasts four organisation types: artisan, new company start-up, established firm and micro-giant company, and utilises system dynamics modelling.As you can see, again another varied and interesting content to our seventh issue.We hope you enjoy!

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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