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Online environments have evolved from the early-stage technical systems to social platforms with social communication mechanisms resembling the interactions which can be found in the real world. Online marketers are using the close relations between the users of social networks to more easily propagate the marketing contents in their advertising campaigns. Such viral marketing campaigns have proven to provide better results than traditional online marketing, hence the increasing research interest in the topic. While the majority of the up-to-date research focuses on maximizing the global coverage and influence in the complete network, some studies have been conducted in the area of budget-constrained conditions as well as in the area of targeting particular groups of nodes. In this paper, a novel approach to targeting multi-attribute nodes in complex networks is presented, in which an MCDA method with various preference weights for all criteria is used to select the initial seeds to best reach the targeted nodes in the network. The proposed approach shows some symmetric characteristics—while the global coverage in the network is decreased, the coverage amongst the targeted nodes grows.

The analysis of decisions under uncertainty has always been a challenging problem and numerous tools have been proposed to deal with it. Recently, a new extension of the COMET method has been proposed, which is based on hesitant fuzzy sets (HFS). This approach has been presented to handle situations in which experts hesitate during identifying the membership grade for the considered attributes. This technique, as well as its basic version, are entirely free of the rank reversal phenomenon, which allows making more reliable decisions.In this paper, we present a short empirical case study of the electric city buses selection. This problem allows presenting how the decision-making process is working by using the HFS COMET. Afterwards, the paper shows efficiency and advantages of the HFS COMET in accordance to the presented study case. The obtained results show that the HFS COMET is a chance to get more reliable decisions when uncertain data is being used.

Multi-Criteria Decision Analysis (MCDA) methods are widely used in various fields and disciplines. While most of the research has been focused on the development and improvement of new MCDA methods, relatively limited attention has been paid to their appropriate selection for the given decision problem. Their improper application decreases the quality of recommendations, as different MCDA methods deliver inconsistent results. The current paper presents a methodological and practical framework for selecting suitable MCDA methods for a particular decision situation. A set of 56 available MCDA methods was analysed and, based on that, a hierarchical set of methods' characteristics and the rule base were obtained. This analysis, rules and modelling of the uncertainty in the decision problem description allowed to build a framework supporting the selection of a MCDA method for a given decision-making situation. The practical studies indicate consistency between the methods recommended with the proposed approach and those used by the experts in reference cases. The results of the research also showed that the proposed approach can be used as a general framework for selecting an appropriate MCDA method for a given area of decision support, even in cases of data gaps in the decision-making problem description. The proposed framework was implemented within a web platform available for public use at www.mcda.it.

Research related to information diffusion within complex networks tends to focus on the effective ways to maximize its reach and dynamics. Most of the strategies are based on seeding nodes according to their potential role for social influence. The presented study shows how the seeding can be supported by changes in the target users' motivation to spread the content, thus modifying the propagation probabilities. The allocation of propagation probabilities to nodes takes the form of a spraying process following a given probability distribution, projected from the nodes' rankings. The results showed how different spraying strategies affect the results when compared to the commonly used uniform distribution. Apart from the performance analysis, the empirical study shows to which extent the seeding of nodes with high centrality measures can be compensated by seeding the nodes which are ranked lower, but are having higher motivation and propagation probabilities.

The current marketing landscape, apart from conventional approaches, consists of campaigns designed especially for launching information diffusion processes within online networks. Associated research is focused on information propagation models, campaign initialization strategies and factors affecting campaign dynamics. In terms of algorithms and performance evaluation, the final coverage represented by the fraction of activated nodes within a target network is usually used. It is not necessarily consistent with the real marketing campaigns using various characteristics and parameters related to coverage, costs, behavioral patterns and time factors for overall evaluation. This paper presents assumptions for a decision support system for multi-criteria campaign planning and evaluation with inputs from agent-based simulations. The results, which are delivered from a simulation model based on synthetic networks in a form of decision scenarios, are verified within a real network. Last, but not least, the study proposes a multi-objective campaign evaluation framework with several campaign evaluation metrics integrated. The results showed that the recommendations generated with the use of synthetic networks applied to real networks delivered results according to the decision makers' expectation in terms of the used evaluation criteria. Apart from practical applications, the proposed multi-objective approach creates new evaluation possibilities for theoretical studies focused on information spreading processes within complex networks.

In the last decade, the number of practical applications that use eye tracker devices has significantly increased. Nowadays, eye tracking is a very important part of neuromarketing and experimental economics research. This chapter presents the experiment with eye tracking usage that was conducted to check how the contrast of advertisements influences their effectiveness. The main motivation for this research is the fact that it is very difficult to gain consumer’s attention in online systems. The experiment was aimed to detect the relations between the contrast of advertising units, its location within the site, and the level of the recipient’s interest based on absorption and attention. The results of the experiment are presented and discussed.

Since the 1960s, a turning point for civilization related to the increasing role of information and communication technologies (ICT) in creating the modern society can be observed. The sustainable information society (SIS) entails a new phase of information society development, in which the ICT adoption by the society stakeholders is the key enabler of sustainability. One of the most important issues in the debate about the SIS is the means to measure it. In this paper, a methodological framework for creating SIS evaluation indexes is presented. In practical terms, an exemplary adjustable index for evaluating SIS in households is created based on the presented approach. During the empirical research, the presented index is used to evaluate SIS in Polish households. The study showed the usefulness of the proposed approach and confirmed the fact that it can be used to build other SIS indices.

While viral marketing has captured substantial academic and professional interest, the processes that underpin successful viral marketing campaigns remain poorly understood. High competition and pressure for successful campaigns lead to strategies based on persuasion, unsolicited messages, and other techniques that negatively affect brand perception. The need for more sustainable strategies with a limited negative impact on web users is observed. Therefore, the current study examines the effectiveness of viral marketing and a supporting campaign, where the main goal was to increase user engagement and overall campaign performance. Supporting campaigns were evaluated, to determine whether they enhanced viral activity, but without the need for high persuasion or intrusive techniques. Results showed that supporting actions could be integrated with lower performing campaigns to increase their effectiveness. Apart from the main scientific goal that is presented, the study demonstrates how virtual worlds can provide a laboratory-like environment for identifying the processes that underpin viral marketing.

Atmospheric emissions of anthropogenic origin are one of the most important problems in cities. A particularly discrete ecological footprint in urban environment is made by urban freight transport. This problem has become the key challenge for all stakeholder groups involved in freight transport in urban areas. Over the recent years, there has been a growing interest in using alternative fuel vehicles in urban logistics, including those equipped with electric drive systems. This paper presents a multi-criteria analysis (MCA) of selected electric vehicles (EVs) in the context of their application for the purposes of deliveries in cities. In methodological terms, we present a unique MCA-based approach for evaluation of Electric Freight Vehicles (EFVs). A successful attempt is made to build a multistep MCA procedure based on two carefully selected MCDA methods (PROMETHEE II and fuzzy TOPSIS) to handle both certain and uncertain data sets in a single decision process. In practical terms, we successfully demonstrate the usefulness of the proposed approach by creating a set of decision maker’s preference models (based on certain and uncertain data) of carefully selected EFVs and provide sensitivity and robustness analyses of the obtained solutions.

The COMET method is a new multi-criteria decision-making (MCDM) method using the elements of fuzzy sets theory to identify a decision-making model in the space of a problem. This technique is entirely free of the rank reversal phenomenon, which is one of the most critical challenges in the MCDM field. The COMET method was primarily developed for dealing only with real-valued data. However, in many cases it is hard to present precisely the exact values of attributes for decision-making criteria. Therefore, intervals or fuzzy numbers should be used instead of numerical values in complex decision-making problems with data uncertainty.This paper proposes a new direct approach to use uncertain input data by using the COMET method in the decision-making process. We present a procedure to evaluate the preferences for the considered alternatives, which are described by using intervals or fuzzy numbers. A simple numerical example along with short discussion is presented for both cases.