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Artificial Intelligence to Identify Factors Linking with Purchase Intention in Restaurants in Ecuador
Journal
Lecture Notes in Networks and Systems
Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2024)
ISSN
2367-3370
Date Issued
2026
Author(s)
Mikel Ugando Peñate
Ángel Ramón Sabando García
Reinaldo Armas Herrera
Angel Alexander Higuerey Gómez
Elvia Rosalía Inga-Llanez
Pierina D’Elia Di Michele
Byron Vinicio Lima Rojas
Veronica Maria Rojas Montaño
Omar Enrique Ajila Pinzon
Andrés Wladimir Herrera Manosalvas
Abstract
The objective of the research is to determine the binding and determining factors with purchase intention and consumer access to food services in 4 and 5 fork establishments in Quito, Pichincha province, Ecuador employing artificial intelligence.
An updated database of the Tourism Establishment Cadastre of the Ministry of Tourism as of June 2024 was used, including 418 restaurants in this classification.
A structural equation model (SEM), with PLS-SEM approach, is proposed as a methodology, with an exploratory and confirmatory approach, generating coefficients by means of artificial intelligence for the items and constructs and the validation of hypotheses. Data analysis uses SPSS version 25 and the AMOS version 24 interface. Partial results highlight the binding and deterministic factors with purchase intention in restaurants, Staff Service β = 0.207; R2 = 0.348 (0,000),
Psychological β = 0.192; R2 = 0.369 (0,000), Technological β = 0.092; R2 = 0.210 (0,000). Evidence Physical and Social factors had an incidence rate of 95%.
Evidence Physical and Social factors had an incidence rate of 95%. In addition, it has convergent and discriminant validity and excellent critical reliability in terms of purchase intention.
However, it should be noted that the Personal factor, although it presents an important bond with the Social factor is not determinant with a negative correlation and little significant in the consumer's attitude, which contrasts with the existence of a homogeneous bond with the Psychological factor and the Social factor, that are not oriented to its determination. Furthermore, there is no evidence of acceptance the hypothesis H4 (The Personal factor positively influences consumer purchase intention).
An updated database of the Tourism Establishment Cadastre of the Ministry of Tourism as of June 2024 was used, including 418 restaurants in this classification.
A structural equation model (SEM), with PLS-SEM approach, is proposed as a methodology, with an exploratory and confirmatory approach, generating coefficients by means of artificial intelligence for the items and constructs and the validation of hypotheses. Data analysis uses SPSS version 25 and the AMOS version 24 interface. Partial results highlight the binding and deterministic factors with purchase intention in restaurants, Staff Service β = 0.207; R2 = 0.348 (0,000),
Psychological β = 0.192; R2 = 0.369 (0,000), Technological β = 0.092; R2 = 0.210 (0,000). Evidence Physical and Social factors had an incidence rate of 95%.
Evidence Physical and Social factors had an incidence rate of 95%. In addition, it has convergent and discriminant validity and excellent critical reliability in terms of purchase intention.
However, it should be noted that the Personal factor, although it presents an important bond with the Social factor is not determinant with a negative correlation and little significant in the consumer's attitude, which contrasts with the existence of a homogeneous bond with the Psychological factor and the Social factor, that are not oriented to its determination. Furthermore, there is no evidence of acceptance the hypothesis H4 (The Personal factor positively influences consumer purchase intention).