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Record W3089009994 · doi:10.3390/jrfm13100223

Impact of Value Co-Creation on International Customer Satisfaction in the Airsoft Industry: Does Country of Origin Matter?

2020· article· en· W3089009994 on OpenAlexvenueno aff
Gabriela Menet, Marek Szarucki

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer satisfactionValue propositionBusinessMarketingValue (mathematics)PerceptionCountry of originCustomer valueQuality (philosophy)Customer delightCustomer retentionService qualityEconomicsPsychologyService (business)MicroeconomicsMathematics

Abstract

fetched live from OpenAlex

The paper’s objective is to investigate the impact of value proposition co-creation on international customer satisfaction in the airsoft industry. This empirical paper aims at answering a question “Which factors influence satisfaction of the international customers involved in the process of value co-creation in the airsoft industry” and sets a hypothesis that value co-creators’ country of origin has a positive impact on customers’ satisfaction. A case study approach of an entrepreneurial company (GATE) was supplemented with data collected via a survey (n = 176), where consumers’ perception of the firm’s value proposition and its influence on their satisfaction were investigated. The study contributes to the value creation theory by identifying the main factors influencing customer satisfaction in the airsoft industry and verifying whether the co-creators’ origin affects the factors’ ratings. The results indicate that the most crucial factors influencing international customer satisfaction in this industry are quality level and product functionality and that the country of origin of customers has no significant impact on international customer satisfaction.

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 score0.030
Threshold uncertainty score0.265

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 communication0.0000.000
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 head0.012
GPT teacher head0.281
Teacher spread0.268 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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