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Record W2796136076 · doi:10.30958/ajbe.4.2.1

The Combined Effects of Service Offering and Service Employees on the Perceived Corporate Reputation

2018· article· en· W2796136076 on OpenAlexaff
Nha Nguyen, Gaston LeBlanc

Bibliographic record

VenueAthens Journal of Business & Economics · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsBusinessService (business)ReputationMarketingSociology

Abstract

fetched live from OpenAlex

The present study contributes to the understanding of the role of two major components of the service production and delivery system in reinforcing the perceived corporate reputation. Specifically, the purpose of this study is to assess the combined effects of service offering and service employees on customers' perception of corporate reputation. A hierarchical multiple regression with interaction analysis was performed on data collected from customers of a banking institution to assess the main effect of service offering and service employees, as well as their interactive effect on customers' perception of corporate reputation. A significant interaction between service offering and service employees in their influence on corporate reputation was found. This results suggest that service employees intervene as a moderator variable in the relationship between service offering and corporate reputation. Furthermore, service organizations should focus on the crucial role of the service offering during the service encounter and recognize the importance of service employees in such way to reinforce customers' perception of corporate reputation. The study has limited generalization given the convenience sample and the great variety of service industries. The efficacy of the direct measures and the hierarchical multiple regression must be considered. It would be helpful to realize similar studies in other service settings and to explore the exact nature of the interaction between service offering and service employees.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.221
Teacher spread0.188 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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