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Record W4414438535 · doi:10.38104/vadyba.2025.2.02

EMPLOYEE ENGAGEMENT ACROSS BORDERS: A GALLUP-BASED ANALYSIS OF GLOBAL WORKFORCE

2025· article· en· W4414438535 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee engagementEmployee resource groupsEmployee researchLoyaltyWorkforceWork engagementAntecedent (behavioral psychology)Human resource managementJob satisfactionMeaning (existential)

Abstract

fetched live from OpenAlex

Employee engagement, which concept has garnered substantial scholarly attention in recent decades and has been defined in various ways, with no single leading definition, can be basically described as an employees' emotional and cognitive connection to their work, workplace and its goals. Engaged employees fulfills their job responsibilities and actively participates in the development of the organization, feels a sense of belonging and sees meaning in their work, positively influencing organizational performance. Nowadays labor market, where uncertainty, remote work and generational diversity and competition between companies and organizations are increasing, employee engagement plays a vital role in the successful operation of organizations. Literature highlights employee engagement as one of the most important factors that positively impacts employees' work performance. The benefits from engaged employees are discovered in multiple levels - individual, organizational, customer. At the individual level, employee engagement is associated with improved work performance, as mentioned before, higher employee productivity, loyalty and retention, also employee innovative behavior, initiative and creativity. At the organizational level, engagement contributes to enhanced organizational performance, operational effectiveness and innovation. At the customer level, employee engagement drives better customer experience and satisfaction. Recent research indicates that employee engagement is not only an outcome influencing various factors and performance indicators, but also a construct shaped by multiple antecedent factors that serve to foster and sustain it human resource management practices, job satisfaction, work environment and also individual state of mind as mindfulness. The empirical part of the article about global employee engagement trends is developed by secondary data from Gallup's State of the Global Workplace report (2025). Analyzed data indicates a positive trend with periodic fluctuations during the period from 2009 to 2024, but last year has been decline in employee engagement metrics, highlighting challenges in organizational human resource management practices. Globally, only 21% of employees are engaged. The situation is particularly critical among managers, where engagement is declining, with young managers (under 35) and female managers experiencing the greatest decline. A strong trend towards a higher proportion of not engaged employees has persisted and prevailed in the analyzed period, while the engaged and actively disengaged has been in similar rates. Regional analysis reveals pronounced disparities in employee engagement levels. Within the European region, employee engagement remains at its lowest (13%), accompanied by elevated stress levels and diminished emotional well-being among employees. The data of Europe show a paradox: economically stronger countries demonstrate lower levels of employee engagement, while less developed countries or countries undergoing economic change show relatively higher levels of employee engagement. From the perspective of economic analysis in region of Europe, this distribution confirms that employee engagement does not directly depend on a country's gross domestic product or level of welfare. Conversely, the highest levels of employee engagement are observed in the US, Canada and Latin America and the Caribbean (31%), and this proportion remains relatively low in absolute terms.

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)0.002
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 score0.561
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.383
Teacher spread0.360 · 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
Published2025
Admission routes1
Has abstractyes

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