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The purpose of this paper is to propose a model of relationships for conflict-ridden destinations that include variables concerning the dispute and their effect on key constructs that shape visitation decisions.

The theoretical model is examined for two conflict-ridden Eastern Mediterranean destinations, Israel and Turkey, which suffer from ongoing armed conflicts, using two samples of potential tourists residing in the USA (n = 1,581) and India (n = 1,383).

The relationships are stable for both destinations and cultural contexts. Animosity is a strong factor in tourists’ decisions, whereas perceived risk has a relatively insignificant impact. Knowledge of the conflict is also found to influence decisions about visiting conflict-ridden destinations.

The study investigates the role of variables related to the conflict as antecedents of animosity and perceived risk, thus contributing to the understanding concerning decisions to visit conflict-ridden destinations. The model is generalized for varied destinations and cultures.

该研究提出了一个针对有冲突目的地的关系模型, 其中包括与冲突有关的因素以及对旅游访问决策的关键概念的影响。

使用基于美国（n = 1,581 ）和印度（n = 1,383）的潜在游客样本, 本文的理论模型检验了两个东地中海目的地, 以色列和土耳其, 该目的地遭受了持续的武装冲突。

获得的关系在两个目的地和文化背景下都是稳定的。敌意是影响游客决策的重要因素, 然而风险感知的影响相对较小。研究还发现了对冲突的认知会影响有关访问目的地的决定。

该研究调查了与冲突相关的因素作为敌意和感知风险的前因变量, 从而有助于我们理解关于访问有冲突的目的地的决策, 该模型适用于不同的目的地和文化。

La investigación propone un modelo de relaciones para destinos en conflicto que incluye variables relacionadas con el conflicto y su efecto en conceptos clave para las decisiones de visita del turista.

Se examina el modelo teórico para dos destinos del Mediterráneo oriental, que sufren conflictos armados en curso, Israel y Turquía, utilizando dos muestras de turistas potenciales que residen en los Estados Unidos (n = 1.581) y la India (n = 1.383).

Las relaciones obtenidas son estables tanto para los destinos como para los distintos contextos culturales. La animosidad es un factor importante en las decisiones de los turistas, mientras que el riesgo percibido tiene un impacto relativamente insignificante. También se ha encontrado que el conocimiento del conflicto influye en las decisiones de visita al destino en conflicto.

El estudio investiga el papel de las variables relacionadas con el conflicto como antecedentes de la animosidad y el riesgo percibido, contribuyendo así a nuestra comprensión sobre las decisiones de visitar destinos en conflicto. El modelo es generalizable a distintos destinos y culturas.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.