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Record W2151881854 · doi:10.5267/j.msl.2013.05.027

A study on the effects of remote working on quality of services: A SERVQUAL survey on central office of Tehran municipality

2013· article· en· W2151881854 on OpenAlexvenueno aff
Hassan Darvish, Fariba Hedayati Shirsavar

Bibliographic record

VenueManagement Science Letters · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSERVQUALWork (physics)Quality (philosophy)ProductivityBusinessWorking environmentQuality of working lifeJob satisfactionPsychologyMarketingService qualityService (business)EngineeringEconomic growth

Abstract

fetched live from OpenAlex

During the past few years, there have been tremendous efforts on developing remote working among women in an attempt to help females take care of their family related responsibilities. In this paper, we study the impact of remote working on quality of services in central office of Tehran municipality of Iran. The proposed study designs a standard questionnaire to survey remote working and using an existing standard SERVQUAL questionnaire measure the level of quality of work because of remote working. The survey indicates that women who participated in remote working program were satisfied from this program in terms of personal, social as well as organizational productivity. Remote workers were highly satisfied from financial advantage of this program. Managers were, however, highly satisfied from the results of their female's remote working. In our survey, remote contract workers were more satisfied than remote formal workers were. The surveyed people believed organizational structure was the most important challenge for remote working followed by economical, personal and social issues. The results of SERVQUAL also indicate there were some meaningful relationship between remote working and quality of work.

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)0.008
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 score0.071
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.065
GPT teacher head0.332
Teacher spread0.268 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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