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Record W4408363697 · doi:10.53894/ijirss.v8i2.5286

Time management for leaders and impact on productivity: A review study

2025· review· en· W4408363697 on OpenAlexaff
Rajesh Ranjan, Rashmi Singh, Jaswinder Kumar, Saumya Tripathi

Bibliographic record

VenueInternational Journal of Innovative Research and Scientific Studies · 2025
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsSt. Clair College
Fundersnot available
KeywordsProductivityDelegationCreativityTime managementCriticismMiddle managementProcess (computing)Action (physics)Knowledge managementPublic relationsBusinessProcess managementPsychologySociologyComputer sciencePolitical scienceManagementEconomicsSocial psychology

Abstract

fetched live from OpenAlex

Increased productivity, action, and results are oriented towards the objectives of the organization and rely heavily on effective time management, which is among the critical attributes of a leader. This article argues that time management should be treated as a key skill among leaders, with a focus on the need for reserving time for what matters, effective delegation, and resolution of overstrain. This article addresses multiple streams that present the phenomenon of time management as a problem of leadership, productivity, and emotional intelligence in a remote and hybrid era. How time management is perceived in different cultures and time management in a globalized world are examined as well. Additionally, this study illustrates a positive relationship between efficient time organization and the performance of the organization, including creativity and motivation among employees. With the criticism of the literature already available, the possibilities for the practical application of such strategies as planning a management process and consideration of the trends are presented in a more general way. This study emphasizes the changing landscape of leadership output while shedding light on how time management skills can be aligned with today’s organizations.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.241
GPT teacher head0.513
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes1
Has abstractyes

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