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Record W4310870395 · doi:10.18280/ijsdp.170706

The Role of Sustainable Service Quality in Achieving Customer Loyalty in the Residential Housing Industry

2022· article· en· W4310870395 on OpenAlexvenueno aff
Radyan Dananjoyo, Fitra Roman Cahaya, Udin Udin

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessService qualityCustomer satisfactionLoyalty business modelCustomer retentionCustomer advocacyMarketingSustainabilityCustomer delightCustomer equityService (business)

Abstract

fetched live from OpenAlex

The aim of this study is to investigate and observe the effect of (1) sustainable service quality on sustainable construction, reliability, assurance, tangible, empathy, responsiveness, customer satisfaction, customer loyalty; (2) customer satisfaction on customer loyalty; and (3) customer satisfaction in mediating the relationship between sustainable service quality and customer loyalty. The investigation of sustainable service quality employed a survey for homeowner from sixty-three housing developer which listed in Indonesia Stock Exchange. This study will use a quantitative approach, where the research data that has been collected will be processed and examined using structural equation modeling (SEM) based on AMOS version 26. There are 215 homeowners assessed for this study. The results of this study revealed that Sustainable Service Quality elements namely sustainability construction, reliability, assurance, tangible, empathy, responsiveness significantly and directly influence customer satisfaction. In addition, customer satisfaction significantly influences customer loyalty. Finally, the significant of this study is to prove that the implementation of sustainability construction and service quality affecting homeowners’ satisfaction and loyalty.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.277
Teacher spread0.257 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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