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Record W2135223759 · doi:10.5539/ijms.v4n2p80

Jordanian Travel Agencies' Websites Assessment: Experts vs Tourists' Perceptions

2012· article· en· W2135223759 on OpenAlexvenueno aff
Husam Ahmad Kokash

Bibliographic record

VenueInternational Journal of Marketing Studies · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsStrengths and weaknessesTourismPerceptionBusinessThe InternetMarketingOrder (exchange)Service (business)Knowledge managementComputer sciencePsychologyWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

By using importance-performance analysis (IPA), this paper examines the perceptions of tourists as websites users, and experts of Internet applications, of various tourism services attribute in the Jordanian travel agencies' websites. IPA is a feasible approach to the assessment of clients' satisfaction which allows for an easy and practical understanding of both the strengths and weaknesses, and for developing areas of a given service. While, IPA Analysis as a practical methodology has been used to determine customers' perceptions and satisfaction in different fields and subjects, to the best of the author knowledge, this is the first time for this methodology has been used to compare the perceptions of tourists and experts of Internet technology in order to assess the effectiveness of travel agencies websites. The main findings determined differences between tourists' perceptions of importance and experts' evaluation of Performance about JTAs websites, majorly in categories of marketing, functionality, interaction and basic information. The study also demonstrates the effectiveness of IPA model as a strategic tool at the marketers' hand, to identify priorities for areas of development and improvement by select how to increase their capacity.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.090
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.338
Teacher spread0.297 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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