What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

An Intelligent Customer Relationship Management (I-CRM) Framework and its Analytical Approaches to the Logistics Industry

This thesis develops a new Intelligent Customer Relationship Management (i-CRM) framework, incorporating an i-CRM analytical methodology including text-mining, type mapping, liner, non-liner and neuron-fuzzy approaches to handle customer complaints, identify key customers in the context of business values, define problem significance and issues impact factors, coupled with i-CRM recommendations to help organizations to achieve customer satisfaction through transformation of the customer complaints to organizational opportunities and business development strategies

Cross-border E-commerce Risk Evaluation from Sellers’ Perspective

While the development of cross-border e-commerce is flourishing, the associated risk factors should not be underestimated. In this paper, we analyze risk in cross-border e-commerce from the external environment and platform construction to establish a cross-border e-commerce risk evaluation index system from sellers\u27 perspective. Subsequently, the weights of the indicators are calculated on account of entropy weight method, and combined with fuzzy comprehensive evaluation method to construct a cross-border e-commerce risk evaluation model. The results show that the weight of platform risk is more than environmental risk, and the risk level of cross-border e-commerce from sellers\u27 perspective is low. Finally, we attempt to propose some strategies to prevent risks, hoping to provide some theoretical insights into the issue of risk prevention in cross-border e-commerce

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem

A decision support framework for sustainable supply chain management

Sustainable Supply Chain Management has become a topic of increased importance within the research domain. There is a greater need than ever before for companies to be able to assess and make informed decisions about their sustainability in the Supply Chains. There is a proliferation of research about its understanding and how to implement it in practice. This is mainly since sustainability has been assessed from various disciplines, organizational industries and organizational functional silos . There is a lack of comprehension, unified definition and appropriate implementation of Sustainable Supply Chain Management (SSCM), leading to failure in decision making for sustainability implementation within supply chains. The proposed research identifies the research gaps through the novel application of Systematic Literature Network Analysis (SLNA) to SSCM literature. In doing so, methods including Systematic Literature Review (SLR), Citation Network Analysis (CNA) and Citation Network Mapping of literature have been used to identify definitions, KPIs, barriers and drivers of SSCM from the literature. Furthermore, a combination of methods from Text Mining and Content Analysis has been used to identify KPIs, barriers and drivers from sustainability reports of top global manufacturing companies, to better understand the practices of organizations for SSCM. The consolidation of the findings from literature and practice led to the development of an SSCM Performance Evaluation Framework built on multiple methods. A 4-level hierarchical model has been developed by classifying the identified KPIs into Economic, Environment and Social as well as considering the key decision areas including tactical, strategic and operational. Furthermore, a rigorous data collection process was conducted among supply chain and sustainability managers from top global manufacturing firms and leading academicians in the field, assessing the identified SSCM KPIs. The collected data were analyzed through novel application of hybrid Multi-Criteria Decision Analysis (MCDA) methods, which includes Values Focused Thinking (VFT), Fuzzy Analytical Hierarchical Process (FAHP), Fuzzy Technique of Order Preference by Similarity to Ideal Solution (FTOPSIS) and Total Interpretive Structural Modelling (TISM), for prioritizing and modelling of interdependencies, interactions and weightages among SSCM KPIs. The results obtained were subsequently used to develop a Decision Support System (DSS) that allows managers to evaluate their sustainability by identifying problem areas and yielding guidance on the KPIS and most important areas to focus on for SSCM implementation. The application of DSS has been demonstrated in the context of a case company. From a theoretical development point of view, a Tree perspective framework contributing to the ecological Theory of Sustainability has been proposed through the identification of the most influential organizational theories, and how they interrelate with each other. Overall, the proposed research provides a holistic perspective of SSCM that incorporates the various aspects of organizations, relevant organizational theories and perspectives of academics and practitioners together. The proposed DSS may act as a guiding tool for managers and practitioners for SSCM implementation in companies

LEVERAGING TEXT MINING FOR THE DESIGN OF A LEGAL KNOWLEDGE MANAGEMENT SYSTEM

In today’s globalized world, companies are faced with numerous and continuously changing legal requirements. To ensure that these companies are compliant with legal regulations, law and consulting firms use open legal data published by governments worldwide. With this data pool growing rapidly, the complexity of legal research is strongly increasing. Despite this fact, only few research papers consider the application of information systems in the legal domain. Against this backdrop, we pro-pose a knowledge management (KM) system that aims at supporting legal research processes. To this end, we leverage the potentials of text mining techniques to extract valuable information from legal documents. This information is stored in a graph database, which enables us to capture the relation-ships between these documents and users of the system. These relationships and the information from the documents are then fed into a recommendation system which aims at facilitating knowledge transfer within companies. The prototypical implementation of the proposed KM system is based on 20,000 legal documents and is currently evaluated in cooperation with a Big 4 accounting company