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Record W2016376271 · doi:10.5430/ijba.v3n6p1

Basic Assumptions of Service Employees: Influence on Job Performance and Market-oriented Behaviors

2012· article· en· W2016376271 on OpenAlexvenueno aff
Olga Gjerald, Torvald Øgaard

Bibliographic record

VenueInternational Journal of Business Administration · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)MarketingBusinessCustomer serviceService (business)Control (management)Sample (material)Job performancePsychologyComputer scienceJob satisfactionSocial psychology

Abstract

fetched live from OpenAlex

Understanding what drives frontline service employees in their interactions with guests and customers is a focal question in service management. Employees’ basic assumptions may determine employee performance and work behavior in several domains. This study investigates the relation of service employees’ basic assumptions about customers and co-workers with subjective job performance and market-oriented behaviors of service employees. The study sample consists of 241 hotel employees. The analyses revealed that basic assumption about co-worker competence associated positively with customer intelligence generation, intelligence dissemination, and responsiveness. The basic assumption about customer control related positively with perceived customer-related performance but negatively with customer intelligence generation and responsiveness. The results of the study suggest that basic assumptions about co-workers can predict customer-related behavior; therefore, researchers should consider that the basic assumptions about customers significantly influence job performance towards customers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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
Published2012
Admission routes1
Has abstractyes

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