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Analysis of institutional authors

Gómez-Talal, ICorresponding AuthorGonzalez-Serrano, LAuthorRojo-Alvarez, JlAuthorTalon-Ballestero, PAuthor

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Avoiding food waste from restaurant tickets: a big data management tool

Publicated to:Journal Of Hospitality And Tourism Technology. 15 (2): 232-253 - 2024-03-05 15(2), DOI: 10.1108/JHTT-01-2023-0012

Authors: Gomez-Talal, Ismael

Affiliations

- Author
Rey Juan Carlos Univ, Dept Signal Theory & Commun & Telemat Syst & Compu, Fuenlabrada Campus, Fuenlabrada, Spain; Rey Juan Carlos Univ, Dept Business & Management, Fuenlabrada Campus - Author

Abstract

Purpose - This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand. Design/methodology/approach - A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers. Findings - The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior. Research limitations/implications - This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications. Originality/value - The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

Keywords

BarriersBehaviorBig dataBootstrapBootstrap resamplingChallengesConfidence-intervalsFood wasteHospitalityHospitality industryReductionSalesSales forecastingTime-seriesTourismUnsupervised learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal Of Hospitality And Tourism Technology due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 19/140, thus managing to position itself as a Q1 (Primer Cuartil), in the category Hospitality, Leisure, Sport & Tourism.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-06-22:

  • WoS: 7
  • Scopus: 11
  • OpenCitations: 3

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-22:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 96 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Gómez Talal, Ismael) and Last Author (Talón Ballestero, María del Pilar).

the author responsible for correspondence tasks has been Gómez Talal, Ismael.