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Record W7127805288

THE DETERMINANTS OF INNOVATIVE BEHAVIOUR IN THE CANADIAN PUBLIC SECTOR

2025· article· en· W7127805288 on OpenAlexaboutno aff
Mohamed Sherief

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPublic sectorAltruism (biology)Organisation climateEmpirical researchSurvey data collectionPublic policy
DOInot available

Abstract

fetched live from OpenAlex

This dissertation investigates the factors that influence creative behaviour among public servants in Canada, with the aim of contributing to the broader understanding of public sector innovation. As governments contend with increasingly complex policy and operational challenges including technological disruption, climate change, and evolving citizen expectations, there is always an interest in understanding how creativity within public institutions can be fostered. In response, this study examines how individual characteristics, leadership behaviours, and organizational climate conditions may shape the potential for public servants to engage in creative practices. The dissertation is informed by the interactionist model of creativity which holds that creativity arises from the interaction of individual, group, and organizational factors. Adopting a three-paper model, the dissertation is structured around three central research questions: (1) What motivates Canadian public servants to be creative in the workplace? (2) How do leaders influence public servants’ creative behaviours? (3) How does the organizational climate influence public servants’ creative behaviours? Each question is addressed through a separate empirical chapter drawing on quantitative data collected from 286 public servants through a web-based survey administered between February and June 2023. The first study focuses on individual-level motivational factors, specifically altruism, risk-taking, job satisfaction, and job performance, and their relationship with creative behaviour. Using stepwise ordinal regression analysis, the findings suggest that risk-taking, job satisfaction, and job performance are positively associated with creative behaviour. Altruism shows a weaker, statistically inconsistent association, indicating that its influence on creative behaviour is limited in this context. The second study investigates leadership practices including participative decision-making, information sharing, leading by example, and the use of humour. The results indicate that participative leadership and information sharing are most consistently associated with employee creativity. Humour and leading by example were not demonstrably influential in this study. The third study explores organizational climate variables such as autonomy, meaningfulness of work, internal communication, and organizational vision. Among these, autonomy, a sense of meaningful work and internal communication are most strongly correlated with creative behaviour, reinforcing the importance of workplace conditions that empower employees and align their efforts with broader public service goals. This dissertation offers an initial contribution to the public administration literature by examining motivational and organizational factors that may underpin creative behaviour in Canadian government workplaces, providing empirical considerations and broad guidance for developing environments that support creativity as a pathway to public value creation. While the findings offer preliminary insights for public sector managers and policymakers, limitations (including the use of convenience sampling and self-reported measures) highlight the need for more targeted, and multi-method research.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.053
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.243
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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