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Record W4416209172 · doi:10.52783/tangence.17

Perception of Cleanliness & its Role in Customer Loyalty-Evidence from Gurugram Multiplexes.

2025· article· W4416209172 on OpenAlexvenueno aff

Bibliographic record

VenueTangence · 2025
Typearticle
Language
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
Fundersnot available
KeywordsHousekeepingService qualityClothingCustomer satisfactionExhibitionWord of mouthQuality (philosophy)Empirical researchCustomer intelligence

Abstract

fetched live from OpenAlex

The Indian cinema exhibition industry has witnessed remarkable transformation with the advent of multiplexes, fundamentally altering the moviegoing experience. This empirical study investigates customer satisfaction levels regarding housekeeping practices in multiplexes across Gurugram, India. Through a comprehensive analysis of 350 respondents visiting various multiplexes including PVR INOX, Carnival, and other prominent chains, this research examines the correlation between housekeeping quality and customer satisfaction. The study employs both primary and secondary research methodologies, utilizing structured questionnaires and statistical analysis to assess key housekeeping dimensions including cleanliness, maintenance, staff behavior, and overall service quality. Findings reveal that 78% of customers consider cleanliness as the most critical factor influencing their overall cinema experience (1), while 65% directly correlate housekeeping standards with their likelihood to return (2). The research identifies significant gaps between customer expectations and current service delivery, particularly in areas of restroom maintenance, seating cleanliness, and waste management. These insights provide valuable recommendations for multiplex operators to enhance customer satisfaction through improved housekeeping practices, ultimately contributing to increased customer retention and business sustainability in the competitive entertainment industry.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.036
GPT teacher head0.334
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

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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