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This paper aims to review the topic of “luxury fashion consumption”, a field of recent interest for academics and practitioners. However, a literature review that can map the existing knowledge and aggregate it into relevant topics and offers a research agenda for future research is still lacking.

This paper uses a systematic review and a text mining approach to analyse 73 articles on luxury fashion consumption aiming to clarify, rationalise and critically interpret the literature on luxury fashion consumption; identify the core topic, create an integrative framework of core constructs; and offer research gaps and suggest a research agenda for future studies.

From this analysis, eight major research topics are found and analysed (brand desire, authenticity, luxury markets, value perceptions, luxury retail experience, luxury brands communication, responsible consumption and sustainability and status signalling). Based on these topics and following the TCM framework, this review offers directions for future research.

This research offers a text-mining review of luxury fashion consumption to help scholars and managers further develop this field, as there is no comprehensive review on the topic exploring the themes, theories, constructs and methods used in prior studies.

Este artículo pretende revisar el “consumo de moda de lujo”, un tema de reciente interés para académicos y profesionales. Sin embargo, sigue faltando una revisión de la literatura que pueda ordenar el conocimiento existente y aglutinarlo en temas relevantes y que ofrezca una agenda de investigación futura.

Este trabajo emplea una revisión sistémica de la literatura y la minería de textos para analizar 73 artículos sobre el consumo de moda de lujo con el objetivo de (i) aclarar, racionalizar e interpretar críticamente la literatura sobre el consumo de moda de lujo, (ii) identificar el tema central, crear un marco integrador de constructos clave y (iii) presentar las lagunas de la investigación y sugerir una agenda de investigación para futuros estudios.

A partir de este análisis, se identifican y analizan ocho temas principales de investigación (el deseo de marca, la autenticidad, los mercados de lujo, las percepciones de valor, la experiencia de la venta al por menor de lujo, la comunicación de las marcas de lujo, el consumo responsable y la sostenibilidad, y la señalización de estatus). Sobre la base de estos temas y siguiendo el marco del TCM, esta revisión propone líneas para futuras investigaciones.

Esta investigación ofrece una revisión de la minería de textos sobre el consumo de moda de lujo para ayudar a los académicos y gestores a seguir desarrollando este campo, ya que no existe una revisión exhaustiva sobre el tema que explore los conceptos, teorías, constructos y métodos utilizados en estudios previos.

Revisión de la literatura

本文旨在回顾 “奢侈时尚消费”, 这是学术界和从业人员最近关注的一个话题。然而, 目前仍然未能将现有知识分类并为未来研究提供议程的文献回顾。

本文使用系统的文献综述和文本挖掘, 分析了73篇关于奢侈时尚消费的文章。此文目的是：（1）批判性地解释关于奢侈时尚消费的文献; （2）确定中心主题, 建立综合框架; （3）提出研究缺憾, 为未来的研究提出议程。

从这个分析中, 我们发现并分析了八个主要的研究主题（品牌欲望、真实性、奢侈品市场、价值认知、奢侈品零售体验、奢侈品品牌传播、负责任的消费和可持续性、以及地位信号）。基于这些主题并遵循TCM框架, 本评论提出了未来研究的方向。

目前还没有关于该主题的全面文献回顾, 以探索以前研究中使用的概念、理论、构造和方法。本研究对奢侈时尚消费的文本挖掘进行了回顾, 以帮助学者和管理者进一步发展该领域。

文献评论

The authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).

The authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.

In this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.

It is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.

ACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.

The main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.

The construction sector has significantly evolved in recent decades, in parallel with a huge increase in the amount of data generated and exchanged in any construction project. These data need to be managed in order to complete a successful project in terms of quality, cost and schedule in the the context of a safe project environment while appropriately organising many construction documents.

However, the origin of these data is very diverse, mainly due to the sector’s characteristics. Moreover, these data are affected by uncertainty, complexity and diversity due to the imprecise nature of the many factors involved in construction projects. As a result, construction project data are associated with large, irregular and scattered datasets.

The objective of this chapter is to introduce an approach based on a fuzzy multi-dimensional model and on line analytical processing (OLAP) operations in order to manage construction data and support the decision-making process based on previous experiences. On one hand, the proposal allows for the integration of data in a common repository which is accessible to users along the whole project’s life cycle. On the other hand, it allows for the establishment of more flexible structures for representing the data of the main tasks in the construction project management domain. The incorporation of this fuzzy framework allows for the management of imprecision in construction data and provides easy and intuitive access to users so that they can make more reliable decisions.

The scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.

This article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.

The authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.

Cultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.

The method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.

This study aims to develop a holistic and dynamic model that governs the various relationships among the critical factors of crisis management from a stakeholder perspective in the context of China’s COVID-19 epidemic outbreak.

Data were collected from 731 textual sources, and the text mining technique identified the themes of a holistic crisis management model. Then, content analysis was applied to reveal in-depth insights into the themes.

From a stakeholder perspective, the model comprises six elements: political environment, economic environment, technology, social environment, health and science and international relationships, which relate significantly to four procedural actions: prevention, response, recovery and adaptation. The overlapping stages and situational dynamic mechanisms of the process are another two new major findings of this study; learning and preparing are threaded throughout the whole dynamic process.

Hospitality stakeholders are advised to collaborate under the guidance of the dynamic crisis management model and adopt high-technology tools for the industry’s recovery management. Developing a new business model and marketing strategy is a useful approach to face similar crisis management challenges in the future.

This paper fills an existing research gap by presenting a health-related crisis management model that can be used to evaluate the dynamic process of collaborations among stakeholders in coping with external challenges forced upon the hospitality industry.

利益相关者视角下的危机管理动态模型：以covid-19在中国的发展为例

本研究以中国新冠肺炎疫情为背景, 从利益相关者的角度构建危机管理关键因素之间各种关系的整体动态模型。

本研究从731个文本来源收集定量数据, 并通过文本挖掘技术识别危机管理模型的构成要素,并运用内容分析对构成要素进行深入剖析。

从利益相关者的角度来看, 该模型包括六个要素:政治环境、经济环境、技术、社会环境, 健康科学和国际关系。这些要素与预防、反馈、恢复和适应这四项运行程序密切相关。本研究的两大发现是防疫机制的动态机制和重叠特性, 学习和预备贯穿于疫情防控的整个动态过程。

酒店利益相关者应在动态危机管理模型的指导下进行合作, 并运用高科技进行行业复苏管理。此外, 开发新的商业模式和营销策略也是面对新的外部环境的有效途径。

本文通过提出一个健康相关的危机管理模型来填补现有研究的空白, 该模型可用于评估酒店行业外部环境中利益相关者之间的动态合作过程。

En este estudio se recogieron datos cuantitativos de 731 fuentes textuales, se identificaron los elementos constitutivos del modelo de gestión de crisis a través de la minería textual y se analizaron en profundidad los elementos constitutivos mediante el análisis de contenido.

este estudio utiliza una combinación de minería de texto y análisis de contenido para explorar el manejo de crisis del brote de covid-19 en el sector turístico y hotelero de China. En primer lugar, se recogieron datos cuantitativos de 731 fuentes textuales y se identificaron los elementos constitutivos del modelo de gestión de crisis a través de la minería textual. En segundo lugar, se utiliza el análisis de contenido para analizar en profundidad los elementos constitutivos y, sobre la base de UN análisis de datos exhaustivo y una revisión de la literatura, se llega a UN marco teórico para el manejo de crisis.

Desde el punto de vista de las partes interesadas, el modelo consta de seis elementos: el entorno político, el entorno económico, el entorno tecnológico, el entorno social, la ciencia de la salud y las relaciones internacionales. Estos elementos están estrechamente relacionados con los cuatro procedimientos operativos: prevención, retroalimentación, recuperación y adaptación. Los dos principales hallazgos de este estudio son los mecanismos dinámicos y las características de superposición de los mecanismos de prevención de epidemias, y el aprendizaje y la preparación a lo largo de todo el proceso dinámico de prevención y control de brotes.

las partes interesadas del sector hotelero deben colaborar bajo la guía de UN modelo dinámico de gestión de crisis y utilizar la alta tecnología para la gestión de la recuperación del sector. Además, el desarrollo de nuevos modelos de negocio y estrategias de marketing es también una forma eficaz de afrontar el nuevo entorno externo.

este artículo colma las lagunas de la investigación existente proponiendo UN modelo de gestión de crisis relacionadas con la salud que pueda ser utilizado para evaluar procesos dinámicos de colaboración entre los interesados en el entorno externo del sector hotelero.

Although there has been a significant amount of research on Smart Tourism, the articles have not yet been combined into a thorough literature review that can examine research streams and the scope of future research. The purpose of this study is to examine the literature on the impact of deploying the Internet of Things (IoT) in tourism sector development to attract more visitors using a text mining technique and citation based bibliometric analysis for the first time.

This study uses R programming to do a full-text analysis of 36 publications on IoT in tourism and visualization of similarities viewer software to conduct a bibliometric citation analysis of 469 papers from the Scopus database. Aside from that, the documents were subjected to a longitudinal study using Excel and word frequency using a trending topic using the R-tool.

Results from the bibliometric study revealed the networks that exist in the literature of Tourism Management. With the use of log-likelihood, the findings from text mining identified nine theme models on the basis of relevancy, which is presented alongside an overview of the existing papers and a list of the primary authors with posterior probability using latent Dirichlet allocation.

This study examines tourism literature in which IoT plays a significant role. To the best of the authors’ knowledge, this study is the first to combine text mining with a bibliometric review. It significantly analyzes and discusses the impact of technology in the tourism sector development on attracting tourists while presenting the most important and frequently discussed topics and research in these writings. These findings provide researchers, tourism managers and technology professionals with a complete understanding of e-tourism and to provide smart devices to attract tourists.

Aunque ha habido un número importante de estudios sobre el turismo inteligente, todavía no se dispone de una revisión bibliográfica exhaustiva que permita examinar las corrientes de investigación y las sugerencias de investigación futuras. Este estudio examina la literatura sobre el impacto del Internet de las cosas en el desarrollo del sector turístico para atraer más visitantes utilizando una técnica de minería de textos y un análisis bibliométrico basado en citas.

Este estudio utiliza la programación R para hacer un análisis de texto completo de 36 publicaciones sobre IoT en el turismo y el software de visualización de similitudes (VOS) para realizar un análisis bibliométrico de citas de 469 documentos de la base de datos Scopus. Además, los documentos fueron sometidos a un estudio longitudinal mediante Excel y a la frecuencia de palabras mediante un tema de tendencia utilizando la herramienta R.

Los resultados del estudio bibliométrico revelaron las redes existentes en la literatura de la Gestión Turística. Con el uso de la log-verosimilitud, los resultados de la minería de textos identificaron nueve modelos temáticos sobre la base de la relevancia, que se presentan junto con una visión general de los documentos existentes y una lista de los autores principales con probabilidad posterior utilizando la asignación latente de dirichlets.

Este estudio examina la literatura sobre turismo en la que la IoT desempeña un papel importante. Este estudio es el primero que combina la minería de textos con una revisión bibliométrica. Analiza y discute de forma significativa el impacto de la tecnología en el desarrollo del sector turístico para atraer a los turistas, a la vez que presenta los temas e investigaciones más importantes y más frecuentemente discutidos en estos escritos. Estos resultados proporcionan a los investigadores, gestores turísticos y profesionales de la tecnología una comprensión integral del turismo electrónico y los dispositivos inteligentes para atraer a los turistas.

虽然已经有大量关于智慧旅游的研究, 但这些文章尚未整合成一个全面的文献综述, 可以检阅目前的研究流和未来研究的范畴。本研究首次使用文本挖掘技术和基于引文的文献计量分析, 来研究有关在旅游业发展中部署物联网对吸引更多游客的影响的文献。

本研究使用R编程对36篇关于旅游业物联网的文章进行全文分析, 并使用相似性可视化（VOS）查看器软件对Scopus数据库中的469篇论文进行文献计量引文分析。除此之外, 还利用Excel对这些文献进行了纵向研究, 并使用R工具对趋势主题进行了词频分析。

文献计量研究的结果揭示了旅游管理文献中现有的网络。通过使用对数似然, 文本挖掘的结果根据相关性确定了9个主题模型, 这些模型与现有论文的概述和主要作者名单在使用潜在狄里奇分配（LDA）的后验概率一起呈现。

本研究对旅游物联网相关文献进行了分析研究, 它首次将文本挖掘与文献计量学审查相结合。这项研究着重分析和讨论了技术在旅游行业发展中对吸引游客的影响, 同时介绍了这些文章中最重要和经常讨论的主题和研究。这些发现为研究人员、旅游管理者和技术专家提供了对科技与旅游的全面了解, 并提供关于智能设备来吸引游客的建议。

The prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.

Machine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.

About 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.

This predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.

The study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

The purpose of this paper is to propose a combination of forecasting methods that enables a holistic understanding of a future situation, given certain influencing variables by a combination of real data and expert knowledge.

The proposal combines two well-known methods: first, system archetypes that correspond to generic structures, allowing us to handle model management issues, and second, system dynamics that offers technical support on a computational level to assess different scenarios or problem solutions.

The case study considers the situation of the mining industry in Chile and its related variables, including four different scenarios. Based on the proposed methodology, the results indicate that: first, the price of copper is paramount for the industry and its effects are not limited to company profits; second, a long period of downfall in copper prices could halt exploration and development projects.

Systemic archetypes are still a subject of research and their application in different fields of knowledge continues to increase to improve this simulation approach.

The case study illustrates the combination of a Vester matrix and initial system archetype models that are enriched using the system dynamics approach. Indeed, the case study aims to understand the consequences of different scenarios based on the problem-driven approach provided by Vester.

The goal of prospective studies of large-scale and complex situations is to model the real situation to obtain solutions that may enhance social welfare.

The proposed methodology contributes to the existing literature by integrating techniques such as the Vester matrix, system archetype modelling and system dynamics simulation, all of which were proposed previously in the literature as independent techniques.

Este artículo propone una combinación de métodos de pronósticos que permite una comprensión holística de una situación futura, dadas ciertas variables de influencia mediante una combinación de datos reales y conocimiento de expertos.

La propuesta combina dos métodos conocidos: (i) arquetipos de sistemas que corresponden a estructuras genéricas, lo que nos permite manejar los modelos, y (ii) la dinámica de sistemas que ofrece soporte técnico a nivel computacional para evaluar diferentes escenarios o soluciones de problemas.

El caso de estudio considera la situación de la industria minera en Chile y sus variables relacionadas, incluidos cuatro escenarios diferentes. Según la metodología propuesta, los resultados indican que i) el precio del cobre es primordial para la industria y sus efectos no se limitan a las ganancias de la empresa; ii) un largo período de caída en los precios del cobre podría detener los proyectos de exploración y desarrollo.

Los arquetipos sistémicos siguen siendo un tema de investigación y su aplicación en diferentes campos del conocimiento continúa aumentando para mejorar este enfoque de simulación.

El estudio de caso ilustra la combinación de una matriz de Vester y los modelos de arquetipos del sistema inicial que se enriquecen utilizando el enfoque de dinámica de sistemas. De hecho, el caso de estudio apunta a comprender las consecuencias de diferentes escenarios basados en el enfoque orientado a los problemas proporcionado por Vester.

El objetivo de los estudios prospectivos para situaciones de gran escala y complejas es modelar la situación real para obtener soluciones que puedan mejorar el bienestar social.

La metodología propuesta contribuye a la literatura existente mediante la integración de técnicas como la matriz de Vester, el modelado de arquetipos del sistema y la simulación de dinámica de sistemas, todo lo cual se propuso anteriormente en la literatura como técnicas independientes.

This research aims to analyse the variables related to the purchase intention of COVID-19 rapid tests in Monterrey, Mexico's metropolitan area.

The chosen method was probit regression. The results show that purchase intention depends on the consumer's perceived value and the perception of having a potential contagion and/or presenting symptoms related to the virus. Regarding limitations, the sampling method used in this investigation is a nonprobabilistic convenience approach delivered through a digital platform, which may not be the first option in other contexts.

The findings indicate that the probability of the purchase intention of rapid COVID tests increases when consumers perceive symptoms of the disease and when they have higher education or are female rather than concerning price or income, as suggested by classical demand theory.

Probabilistic sampling was impossible due to the difficulty of collecting surveys during the COVID-19 pandemic. Instead, a nonprobabilistic sample of a representative random selection of different zip codes from the responses received was considered.

The originality of the paper is its contribution to consumer behaviour during the COVID-19 pandemic in a Latin American context.

One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data.

This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ + PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique.

These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower’s self evaluation about her/his creditworthiness.

In spite of the fact that conducted tests showed no evidence of dependence between results and the initial size of the LVQ network, it is considered desirable to repeat the measurements using an LVQ network of minimum size and a version of variable population PSO to adequately explore the solution space in the future.

In the past decades, there has been an increase in consumer credit. Retail banking is a growing industry. Not only has there been a boom in credit card memberships, specially in emerging economies, but also an increase in small consumption credits. For example, it is very common in emerging economies that families buy home appliances on installments. In those countries, the association of a home appliance shop with a financial institution is usual, to provide customers with quick-decision credit line facilities. The existence of such a financial instrument aids to boost sales. This association generates conflict of interests. On one hand, the home appliance shop wants to sell products to all customers. Therefore, it is in its best interest to promote a generous credit policy. On the other hand, the financial institution wants to maximize the revenue from credits, leading to a strict surveillance of loan losses. Having a fair and transparent credit-granting policy favors a good business relationship between home appliances shops and financial institutions. One way of developing such a policy is to construct objective rules to decide to grant or deny a credit application.

Better credit decision rules generate enhanced risk sharing. In addition, it improves transparency in credit acceptance decisions, giving less room to arbitrary decisions.

This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.