Propuesta de un método para evaluar los factores de imitación de marca

Brand imitation is an approach for new brands to be successful in the market; on the other hand, it can be destructive for developed brands by incurring heavy financial losses. Many studies have investigated imitation and its effective factors. The present paper studies effective factors of imitation and also ranks them through expert judgments. We use rough numbers properties to rank the factors. In so doing, three groups of experts, based in Iran, were asked to rank the factors that affect brand imitation. The ranking process was implemented by Rough-TOPSIS method. Also, the authors apply Fuzzy-TOPSIS method and findings were compared. This study recognizes important factors that affect brand imitation and rank them according to the significance level. Results emphasize that legislation is the most important factor that can prevent brand imitation and counterfeit. This ranking helps companies to improve specifications in order to obtain security for their brands.La imitación de marca es un enfoque para que las nuevas marcas tengan éxito en el mercado; por otro lado, puede ser destructiva para las marcas desarrolladas al incurrir en grandes pérdidas financieras. Muchos estudios han investigado la imitación y sus factores de efectividad. El presente artículo estudia los factores de efectividad de la imitación y también los clasifica a través de juicios de expertos. Usamos propiedades de números aproximados para clasificar los factores. Al hacerlo, se pidió a tres grupos de expertos, radicados en Irán, que clasificaran los factores que afectan la imitación de la marca. El proceso de clasificación fue implementado a través del método Rough-TOPSIS. Además, los autores aplican el método Fuzzy-TOPSIS y se compararon los resultados. Este estudio reconoce los factores importantes que afectan la imitación de marca y los clasifica según el nivel de significación. Los resultados enfatizan que la legislación es el factor más importante que puede prevenir la imitación de marca y la falsificación. Esta clasificación ayuda a las empresas a mejorar las especificaciones con el fin de obtener seguridad para sus marcas

Criteria Uncertainty in Multiple Criteria Decision Analysis of Sustainable Manufacturing Systems

Multiple Criteria Decision Analysis (MCDA) is a discipline used by decision makers to evaluate conflicting features when choosing among alternatives. MCDA methods are applied in the field of sustainable manufacturing to weigh the importance of traditional criteria when compared to sustainability indicators. However, a recurring issue in MCDA is the uncertainty in the assessments of alternatives. In this project, a novel framework to deal with uncertainty in MCDA has been developed. It uses scenario planning to get optimistic and pessimistic assessments for the different alternatives. Then, assigning probabilities to the scenarios and applying COPRAS-N, an introduced modification of COPRAS-G, 11 weighted scenarios are calculated. Finally, the relative significance and ranking of each alternative are graphed according to the weighted scenarios so that their evolution and the different situations are represented. With the presented approach, internal and external uncertainties can be dealt with at the same time. The final decision is made by analysing the graphics and results and, if necessary, looking at the concepts of expected scenario and average performance introduced in this project. The framework has been applied to 3 case studies with a focus on sustainability found in the literature. The results show that providing a final ranking of alternatives without considering other likely scenarios may lead to wrong decisions. In fact, in Case study 1, the choice of the best alternative would have changed if the developed framework had been applied. Representing all the scenarios has proved to ensure the final decision and enable to evaluate all the possible outcomes, solving in this way the uncertainty.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEW AND DEVELOPMENT OF A RESEARCH AGENDA

The potential created by ongoing developments in data and analytics permeates a multitude of research areas, such as the field of Service Innovation. In this paper, we conduct a Systematic Literature Review (SLR) to investigate the integration of data and analytics as an analytical unit into the field of Service Innovation – referred to as Data-Driven Service Innovation (DDSI). Overall, the SLR reveals three main research perspectives that span the research field of Data-Driven Service Innovation: Explorative DDSI, validative DDSI, and generative DDSI. This integrated theoretical framework describes the distinct operant roles of data analytics for Service Innovation, and thus contributes to the body of knowledge in the field of DDSI by providing three unified lenses, which researchers can use to describe and locate their existing and future research endeavors in this ample field. Building up on the insights from the SLR, a research agenda is proposed in order to trigger and guide further discussions and future research surrounding DDSI. Ultimately, this paper aims at contributing to the body of knowledge of Service Innovation in general and Data-Driven Service Innovation in particular by presenting a three-dimensional research space model structuring DDSI towards its advancement

A New Uncertainty Evaluation Method and Its Application in Evaluating Software Quality

Uncertainty theory is a branch of axiomatic mathematics dealing with experts’ belief degree. Considering the uncertainty with experts’ belief degree in the evaluation system and the different roles which different indices play in evaluating the overall goal with a hierarchical structure, a new comprehensive evaluation method is constructed based on uncertainty theory. First, index scores and weights of indices are described by uncertain variables and evaluation grades are described by uncertain sets. Second, weights of indices with respect to the overall goal are introduced. Third, a new uncertainty comprehensive evaluation method is constructed and proved to be a generalization of the weighted average method. Finally, an application is developed in evaluating software quality, which shows the effectiveness of the new method

A Rough VIKOR-Based QFD for Prioritizing Design Attributes of Product-Related Service

Many manufacturers today are striving to offer high value-added product-related services (PRS) due to increasing competition and environmental pressure. PRS can reduce the negative impact on the environment, because it extends the life of products and minimizes the cost. Product and service planning has been considered as the critical factor to the success of PRS. Quality function deployment (QFD) has been recognized as an efficient planning tool which can convert customer needs (CNs) into design attributes of PRS involving product attributes (PAs) and service attributes (SAs). However, the subjective and vague information in the design of PRS with QFD may lead to inaccurate priority of PAs and SAs. To solve this problem, a novel rough VIKOR- (VIseKriterijumska Optimizaciji I Kompromisno Resenje-) based QFD is proposed. The proposed approach integrates the strength of rough number (RN) in manipulating vague concepts with less a priori information and the merit of VIKOR in structuring framework of compromise decision-making. Finally, an application in compressor-based service design is presented to illustrate the potential of the proposed method

Identifying and Prioritizing Successful e-Business Models in Iranian Dot-Coms by Using Machine Learning Techniques

Today, business, economics and society has been transformed by information technology. Many traditional ways of earning money have evolved and new methods and values have come to the fore. In this regard, Study of the e-business system in today's complex and turbulent world is essential. Despite the fact that some businesses succeed in their field of work, there are many businesses that fail, because of selecting inappropriate business model. So, this study which has been done to identify successful e-business models using machine learning techniques. Quantitative survey was used to doing research. 105 businesses with a eTrust were selected to find the best successful electronic business model. The instrument used to collect data was a questionnaire. Analyzing collected data shoes the best business model for the success of businesses in Iran, are e-shop model and the advertising. The results of the k-means algorithm and ID3 show that of the 12 criteria considered in choosing the best model for success, two criteria; including the development of IT tools and company strategy have the most important role for the success of trusted businesses

Hybrid rough set and data envelopment analysis approach to technology prioritisation

The complexity and speed of change in technological systems pose new challenges to technology management. Particular attention should be given to the issue of modelling the uncertainty of assessments and creating rules for determining the weights of the technology assessment criteria. The article aims to present a comprehensive hybrid technology prioritisation model based on the Data Envelopment Analysis and the concept of Rough Sets. The technology prioritisation process that uses the proposed model includes three consecutive stages: (i) the formulation of technology assessment matrix, (ii) the removal of the criteria redundancy based on indiscernibility relation defined in the Rough Set Theory, (iii) the development of rough variables and prioritisation using the DEA super-efficiency model. The combination of DEA and RS is a unique proposal to classify and rank objects based on the tabular representation of their conditional attributes under circumstances of uncertainty. Application of the developed hybrid model to the real data of the technology foresight project “NT FOR Podlaskie 2020” positively verified the assumed effects of its use. The obtained results allow a more objective and rational justification of the chosen technology, simplification of interpretation and better authentication of results from the perspective of decision-makers. First published online 8 May 202

Evaluation of new service concepts using rough set theory and group analytic hierarchy process

One of the most crucial stages in the new service development (NSD) process is concept selection, which is characterized by newly generated alternatives and vaguely defined concept evaluation criteria. Although a number of factors have been found to be influential, a lacuna remains as to how to make a strategic decision based on influential factors. This study proposes a systematic approach to evaluation of new service concepts (NSCs) by integrating the merit of group analytic hierarchy process (AHP) in modeling multi-criteria decision making (MCDM) problems and the strength of rough set theory (RST) in handling subjectivity in concept evaluation. The suggested approach is designed to be executed in four discrete stages. First of all, a hierarchical AHP model for the evaluation of NSCs is constructed in terms of strategy, finance, market, technology, and implementation. Second, pairwise comparisons are made among criteria and sub-criteria, and preferences to NSCs with respect to the sub-criteria are obtained by domain experts. Third, the individual judgments obtained at the preceding stage are aggregated into group judgments. Finally, the NSCs are prioritized based on risk propensity of decision makers. A case study of the video game service is presented to illustrate the suggested approach. We believe that our method can promote consensus building on the promising NSCs.close

Exploring New Service Portfolio Management

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