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Record W2885837457 · doi:10.5267/j.msl.2018.7.005

Impact of service quality and satisfaction on employee loyalty: An empirical investigation in Indian SMEs

2018· article· en· W2885837457 on OpenAlexvenueno aff
Surjit Kumar Gandhi, Anish Sachdeva, Ajay Gupta

Bibliographic record

VenueManagement Science Letters · 2018
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessService qualityLoyaltyMarketingCustomer satisfactionQuality (philosophy)Service (business)Business administrationLoyalty business modelJob satisfactionPsychologySocial psychology

Abstract

fetched live from OpenAlex

This study uses a hybrid scale to identify the factors contributing to internal and external service quality at employer-employee interface in the SMEs of emerging economies like India. 144 shop floor workers and executives working in different SMEs situated in northern India participated in an interview schedule to rate the quality of services being offered to (and delivered by) the employees in such units on 1-5 Likert scale. Application of factor analysis followed by Structural Equation Modelling developed a model showing how organization's HR practices influences employee service quality which consequently leads to Satisfaction and Loyalty which are the established indicators of competitive advantage for such firms. The model is empirically validated using model fit indices and is found satisfactory. This paper thus proposes an empirical framework for the measurement of employee service quality in a relatively less explored sector. This study finds support for strengthening relationships with employees to achieve a culture of achievement in SMEs. The two scales proposed in this study can be used as benchmarks by SME practitioners for evaluation of services being offered to (and delivered by) their employees. The methodology used may be applied in more such settings for evolving a generic and tailor-made scale.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.037
GPT teacher head0.327
Teacher spread0.290 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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