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

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes

Editorial

It is tradition that the Electronic Journal of Information Systems Evaluation (EJISE) publish a special issue containing the full versions of the best papers that were presented in a preliminary version during the 8th European Conference on Information Management and Evaluation (ECIME 2014). The faculty of Economics and Business Administration of the Ghent University was host for this successful conference on 11-12th of September 2014. ECIME 2014 received a submission of 86 abstracts and after the double-blind peer review process, thirty one academic research papers, nine PhD research papers, one master research paper and four work-in-progress papers were accepted and selected for presentation. ECIME 2014 hosted academics from twenty-two nationalities, amongst them: Australia, Belgium, Bosnia and Herzegovina, Brazil, Finland, France, Greece, Ireland, Lebanon, Lithuania, Macedonia (FYROM), Norway, Portugal, Romania, Russia, South Africa, South Korea, Spain, Sweden, The Netherlands, Turkey and the UK. From the thirty-one academic papers presented during the conference nine papers were selected for inclusion in this special issue of EJISE. The selected papers represent empirical work as well as theoretical research on the broad topic of management and evaluation of information systems. The papers show a wide variety of perspectives to deal with the problem

The Fuzzy-Neuro Classifier for Decision Support

This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

In the era of big data explosion, movie recommendation system is widely used as an information tool by human to support decision making. Two common issues found in the machine learning movie recommendation system still undeniable cold start and data sparsity. In resolving or reducing the issues, a research study is conducted with the objectives to analyze, study, and test a decision-making algorithm that can solve cold start problem in a movie recommendation system with precise parameter. The proposed of demographics filtering technique with k-means clustering method using machine learning approached were conducted in this study. The research findings shall present the effects of the proposed demographic filtering for movie recommendations. Demographic filtering can group users into clusters based on parameter like gender, age group and occupation. An effective clustering process based on user demographic information can bring a productive user categorization or grouping. Based on the clusters, ideally, users in same cluster will enjoy the recommended movie that come from a similar genre. This research paper shall contribute demographic filtering studies as an alternative solution for the future work of technical development

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Harnessing Intellectual Resources in a Collaborative Context to Create Value

The value of electronic collaboration has arisen as successful organisations recognize that they need to convert their intellectual resources into customized services. The shift from personal computing to interpersonal or collaborative computing has given rise to ways of working that may bring about better and more effective use of intellectual resources. Current efforts in managing knowledge have concentrated on producing; sharing and storing knowledge while business problems require the combined use of these intellectual resources to enable organisations to provide innovative and customized services. In this chapter the collaborative context is developed using a model for electronic collaboration through the use of which organisations may mobilse collaborative technologies and intellectual resources towards achieving joint effect.electronic collaboration;value creation;collaborative computing;knowledge management and intellectual resources

BIBLIOMETRIJSKA ANALIZA UMJETNE INTELIGENCIJE U POSLOVNOJ EKONOMIJI

Invention of artificial intelligence (AI) is certainly one of the most promising technological advancements in modern economy. General AI reaching singularity makes one imagine its disruptive influence. Once invented it is supposed to surpass all human cognitive capabilities. Nevertheless, narrow AI has already been widely applied encompassing many technologies. This paper aims to explore the research area of artificial intelligence with the emphasis on the business economics field. Data has been derived from the records extracted from the Web of Science which is one of the most relevant databases of scientific publications. Total number of extracted records published in the period from 1963-2019 was 1369. Results provide systemic overview of the most influential authors, seminal papers and the most important sources for AI publication. Additionally, using MCA (multiple correspondence analysis) results display the intellectual map of the research field.Otkriće umjetne inteligencije zasigurno predstavlja jednu od najvažniji tehnoloških inovacija moderne ekonomije. Opća umjetna inteligencija koja može dosegnuti singularitet ima potencijal kreirati novu tehnološku arenu. Jednom otkrivena smatra se da će nadmašiti sve ljudske kognitivne sposobnosti. Nadalje, specifična umjetna inteligencija već je otkrivena i primijenjena u brojnim sustavima. Ovaj rad nastoji istražiti područje umjetne inteligencije s naglaskom primjene u ekonomiji. Podaci su derivirani na osnovi zapisa Web of Science baze jednog od najrelevantnijih izvora znanstvenih radova. Ukupan broj ekstrahiranih zapisa u periodu 1963-2019 bio je 1369. Rezultati čine sustavan pregled najutjecajnijih autora, radova te izvora publikacija. Dodatno, koristeći MCA kreirana je intelektualna mapa istraživačkog područja