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Record W4200382563 · doi:10.5539/ibr.v15n1p98

Motivational Strategies of Retail Company during Work from Home

2021· article· en· W4200382563 on OpenAlexvenueno aff
Maribeth Padura, Manuel Martín Hernández, Nickie Boy A. Manalo

Bibliographic record

VenueInternational Business Research · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)Task (project management)Marital statusMarketingPower (physics)DemographicsWork (physics)PsychologyJob satisfactionEmployee motivationBusinessSocial psychologyPublic relationsManagementEconomicsSociologyPolitical science

Abstract

fetched live from OpenAlex

According to the management scholars Richard Kreitchner and Carlene Cassidy, “the term motivation, refers to a physiological process that gives behavior purpose and direction”. It is thought that if an employee is motivated, they will be usually content with his employment, and since they will be happy, they will be able to provide their best effort and contribution to the task at hand. However, there are numerous sorts of motivation for everyone, and it is unlikely that every individual in the firm or in that particular area will be motivated in the same way. Employee Motivation Is defined as the Power That Propels Employees toward Achieving the Organization's Unique Goals and objectives. As the covid-19 epidemic affects our country, the goal of this study is to examine motivating elements that might influence employee performance while working from home. The respondents were chosen using standard random sampling procedures. The data was collected from 46 administrative personnel. The researcher studied several motivating elements and investigated how employee demographics such as age, gender, position, department, marital status, and housing situation may affect their degree of job satisfaction in this research study.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.316
Teacher spread0.254 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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