Search Results (8)

In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company’s stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented. Full article

In today’s dynamic business landscape, companies need to enhance problem-solving efficiency to produce products or services to meet evolving customer needs. Fostering creative talent and utilizing Group Decision Support Systems (GDSS) have become crucial in the management of human resources and marketing. While empirical studies demonstrate the positive impact of GDSS on learning, a gap exists in understanding its correlation with outcomes like enhanced creativity. Thus, qualitative research is needed to uncover the cognitive processes for favorable results with the adoption of GDSS. We explored the relationship between Project-Based Learning (PBL), GDSS deployment, and outcomes in a cross-organizational study. Results highlighted PBL activities facilitated by GDSS and the key benefits of digital learning, and managerial and academic considerations were proposed. Full article

The pandemic period has made remote work a reality in many organizations. Despite the possible negative aspects of this form of work, many employers and employees appreciate its flexibility and effectiveness. Therefore, employers are looking for the most optimal tools to support this form of work. However, this may be difficult due to their complexity, different functionality, or different conditions of the company’s operations. Decisions on the choice of a given solution are usually made in a group of decision makers. Often their subjective assessments differ from each other, making it even more difficult to make a decision. The aim of this article is to propose a methodological solution supporting the assessment of the most popular teleconferencing systems and generating their ranking. The feature of this solutions is the combination of two important methodological aspects facilitating the selection process. The first one concerns the possibility of taking into account quantitative and qualitative criteria expressed linguistically and of an uncertain nature in the assessment (NEAT F-PROMETHEE method). The second one is related to the possibility of taking into account the assessments of many experts, including the consensus study between them (PROSA GDSS method). The use of these combined methods to assess teleconferencing platforms made it possible to create their ranking and indicate the solution that best meets the adopted criteria (based on experts’ opinions). The Microsoft Teams system turned out to be this solution, whose functionality, usability, multi-platform aspect and other elements turned out to be crucial in the context of the overall assessment. The results obtained may be a guideline for managers and decision makers facing the choice of a tool supporting remote work. Full article

Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. 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) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used. Full article

In Poland, there is a high ratio of private transport and unfavorable patterns of daily commuting. These patterns can be changed by introducing comfortable and eco-friendly vehicles, such as e-scooters and e-bikes. At the same time, the development of the e-micromobility-based vehicle sharing services market is developing. The aim of the article is to analyze selected e-scooters available on the Polish market and to identify the most useful vehicles from two opposing perspectives, i.e., the potential customer and owner of the vehicle sharing system. The PROSA GDSS (PROMETHEE for Sustainability Assessment—Group Decision Support System) method and the graphical representation of GAIA (Geometrical Analysis for Interactive Assistance) were used to search for a compromise and balance between the needs of the indicated stakeholders. The results of the methods used were compared with the results of the PROMETHEE GDSS method, which does not take into account the balance between the stakeholders and allows for a strong compensation of the assessments of decision makers. The conducted research allowed indicating the optimal e-scooter to meet the needs of both decision makers, and it is the JEEP 2xe Urban Camou. Both the sensitivity analysis and the solution obtained with the use of the PROMETHEE GDSS method confirmed that it is the optimal alternative, the least sensitive to changes in criteria weights and changes in the decision makers’ compensation coefficients. Full article

In this study, we first analyze the usability of recycling products, and use the fuzzy set method to determine the main impact on recycling items and their corresponding weights by using the Analytic Hierarchy Process (AHP) to identify various impact recycling levels. The Group Decision Supporting System (GDSS) determines the test standards for the recycling rating. It provides a convenient way for recyclers or manufacturers to classify their own products and use fuzzy numbers to select a set of test standards. It can deduce the recovery rate and remanufacturing rate of different recycling processing levels through the Markov chain model to find out the inventory model and total cost. In the numerical analysis, we found that a recycling rate of more than 90% is probably a necessary decision. Since the processing cost of the 100% recovery rate is doubled, the inventory level and total cost will increase with it. Therefore, this study was combined with the reverse logistics method to find the appropriate decision-making strategy and plan, such as the optimal inventory level and recovery rate. Full article

Tourist destinations are increasingly coming across multiple social, economic, cultural, and environmental challenges corroborated with a great urgency for sustainable development. In this context, the European Commission has launched in 2013 a unitary system of indicators for sustainable management of tourism destinations, called The European Tourism Indicators System (ETIS). More specifically, ETIS was configured initially with a total of 27 core indicators and 40 additional (optional) indicators. The purpose of this paper is to present the difficulties and challenges encountered in the application of ETIS, having as a case study, the county of Braşov, located almost entirely in the Romanian Carpathians. In this regard, a testing technique has been designed through an innovative Group Decision Support System (GDSS) that is applied to our destination. The results show that the selection of ETIS indicators is a flexible process that must be adequate with the particularities of each destination taking into account both the needs of the stakeholders, the information that is useful to them, and the existence and periodicity of the data available. Also, in some cases, when the available indicators are insufficient, additional indicators have to be introduced, and they must be subsequently adapted to the needs and specifics of the destination. Full article

After an emergency event (EE) happens, emergency decision making (EDM) is a common and effective way to deal with the emergency situation, which plays an important role in mitigating its level of harm. In the real world, it is a big challenge for an individual emergency manager (EM) to make a proper and comprehensive decision for coping with an EE. Consequently, many practical EDM problems drive group emergency decision making (GEDM) problems whose main limitations are related to the lack of flexibility in knowledge elicitation, disagreements in the group and the consideration of experts’ psychological behavior in the decision process. Hence, this paper proposes a novel GEDM approach that allows more flexibility for preference elicitation under uncertainty, provides a consensus process to avoid disagreements and considers experts’ psychological behavior by using the fuzzy TODIM method based on prospect theory. Eventually, a group decision support system (GDSS) is developed to support the whole GEDM process defined in the proposed method demonstrating its novelty, validity and feasibility. Full article