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Record W2621753428

The Effects of Social Media on Young Professionals’ Work Productivity: A Case on Ghana

2017· article· en· W2621753428 on OpenAlexaff
Ganga Bhavani, Christian Tabi Amponsah

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYorkville University
Fundersnot available
KeywordsGratificationSocial mediaProductivityBusinessCompetitive advantageWork (physics)Public relationsPsychologyMarketingSocial psychologyPolitical scienceEngineeringEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

The study surveyed 488 young professionals to examine the relationships between social media and productivity in the workplace. Drawing on Users and Gratification theory, random samples were taken from the study group between the ages of 18-35 years. The results indicate that there is a positive relationship between productivity at the workplace and social media. However, the impact appears to be related to certain specific uses. Networking, finding information as well as knowledge sharing and exchanging appear to have a higher impact on the professional enhancement of the young professionals. Also the results show that more females (64%) than male (36%) are influenced by social media sites in the professional discharge of their duties. In addition, respondents indicated that social media is a catalyst for the enhancement of their professional development. The study concludes that organizations should take advantage of the strengths in social media use and develop appropriate policies at workplace that will govern the use of social media sites to gain competitive advantage.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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