The primary objective of this investigation is to explore the key factors within a large corporation that facilitate the transition from a conventional hierarchical organizational structure to a more agile one. Although there are studies in the literature on the different agile organizational structures, there are no clear guidelines for a large company to transform to an agile framework. This study employs contingency theory as its framework and specifically focuses on the financial sector to identify both opportunities and challenges encountered during this transformation process. Qualitative research methods were employed, involving an analysis of five case studies in which managers served as respondents. This research contributes to the identification of a model as a guideline for the transformation of the organizational structure towards agility that can provide valuable information for companies undergoing such a transformation. The managers surveyed provided their experience and the competencies of their profiles were validated, in addition to the application of the Delphi method to obtain more objective information. The findings underscore that large companies should refrain from adopting a completely flat hierarchical structure. Moreover, there exists a set of strategic-level elements that serve as a guiding framework for the transformation process. Furthermore, it is essential to acknowledge that the transformation process itself does not follow a one-size-fits-all approach; rather, it is contingent upon the unique context of each individual case, and it is also a cultural challenge.

The pursuit of a sustainable world has today compelled both companies and individuals to scrutinize the environmental impact of goods and services. As a result, the field of green marketing, encompassing topics like sustainability, ecologism, and social impact, has gained significance. This study aims to explore the attributes influencing consumers’ decisions to purchase green products. The research builds upon an extensive literature review conducted using databases such as Scopus and Web of Science. The resulting model integrates the variables linked to green buying behaviour. Empirical analysis utilizing partial least squares (PLS) methodology validates multiple hypotheses, including those concerning personality traits, altruistic attitude, environmental commitment, and the influence of social factors. This study also highlights the intricate relationship between environmental awareness, positive attitudes towards green products, and perceptions of greenwashing. In conclusion, this research contributes to a comprehensive understanding of the factors guiding consumers towards sustainable purchasing choices, shedding light on the intricate interplay of attitudes, influences, and perceptions in the realm of green consumption.

Neural network methods are widely used in business problems for prediction, clustering, and risk management to improving customer satisfaction and business outcome. The ability of a neural network to learn complex nonlinear relationship is due to its architecture that uses weight parameters to transform input data within the hidden layers. Such methods perform well in many situations where the ordering of inputs is simple. However, for a complex reordering of a decision-maker, the process is not enough to get an optimal prediction result. Moreover, existing machine learning algorithms cannot reduce computational complexity by reducing data size without losing any information. This paper proposes an induced ordered weighted averaging (IOWA) operator for the artificial neural network IOWA-ANN. The operator reorders the data according to the order-inducing variable. The proposed sorting mechanism in the neural network can handle a complex nonlinear relationship of a dataset, which results in reduced computational complexities. The proposed approach deals with the complexity of the neuron, collects the data and allows a degree of customisation of the structure. The application further extended to IGOWA and Quasi-IOWA operators. We present a numerical example in a financial decision-making process to demonstrate the approach's effectiveness in handling complex situations. This paper opens a new research area for various complex nonlinear predictions where the dataset is big enough, such as cloud QoS and IoT sensors data. The approach can be used with different machine learning, neural networks or hybrid fuzzy neural methods with other extensions of the OWA operator.

Social relationships have been and are the basis for achieving objectives of all kinds, whether altruistic or lucrative. Among the aspects that make up non-verbal communication are physical appearance in general, clothing, and, in particular, colour combinations. In this article, we analyse whether colour combinations can be established in individuals’ clothing that maximise their chances of success for a specifically established social objective. To measure this objective, we use multivalent logics, which are characterised by their great flexibility and adaptability. Within the framework of fuzzy logic, we extract evaluations for various colours based on the judgements of experts, provided by recognised authors in the literature, and compare these with the results obtained in a survey conducted by the authors. For the purposes of contrast, we employ two instruments with accredited validity: Similarity by Direct Computation (SDC) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS multicriteria method).

As the world is fighting against the continuous spread of the COVID-Sars-2 virus and the consequences the pandemic has brought about on economic, political and societal levels, the emergence of the vaccination programs seems to be the biggest hope for a quick return to, popularly called, the “new normal”.