Abstract
This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.

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Identifying stress in people is not a trivial or straightforward task, as several factors are involved in detecting the presence or absence of stress. The problem of detect stress has attracted much attention in the last decade and is mainly addressed with physiological signals and in a controlled ambience with specific tasks. However, the widespread use of video cameras permitted the creation of a new non-invasive data collection techniques. The goal of this work is to provide an alternative way to detect stress in the workplace without the need of specific laboratory conditions. For that, a stress detection model based on images analysed with deep learning neural networks was developed. The trained model achieved a F1 = 79.9% on a binary dataset, of stress/non-stress, with an imbalanced ratio of 0.49. This model can be used in a non-invasive application to detect stress and provide recommendations to the collaborators in the workplace in order to help them to control their stress condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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The assertive online strategies of Portuguese luxury fashion brands/designers (LFBDs) are essential for managers to succeed in the digital medium and establish long-lasting relationships with clients. This paper explores the challenges in digitizing all the dis-tinctive elements of a luxury fashion brand: the product characteristics, the luxury atmosphere, the personalization of the service, and the services provided. Fourteen Portuguese brands/designers were selected for content analysis. According to Hansen's framework, 43% of Portuguese LFBDs fail to share information about their com-panies on their websites, and only 43% include promotions. Only 36% have a com-munity outside the websites, compared to 93% of the international luxury fashion brands. Moreover, Portuguese websites lack 360 & DEG; technology and augmented reality. Considering the Digital Visual Merchandising - MVD Grid, Portuguese LFBDs do not allow multiple zooms and 2D F/T cursor change, while international brands feature these tools. Regarding the Social Media Performance Analysis Grid, the global aver-age engagement rate of Portuguese LFBDs on Facebook is 0.19% and on Instagram - 0.89%. According to Social Media Content Analysis Grid, Portuguese brands bet a lot on sharing content in story format, mainly on Instagram; however, Facebook has a higher network value. Therefore, the findings show that Portuguese LFBDs should be present and market their products online. This online presence must be supported by a multi-channel strategy while maintaining luxury product characteristics and elements of differentiation.

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The main aim of the paper was to estimate the impact of older employees aged 55-74 on productivity compared with other age groups. The research (foundation of the paper) was conducted in 72 countries in the period 2000-2021. Countries were divided into three groups composed of 24 economies according to their GDP pc in 2021, i.e. the lowest GDP pc, the middle GDP pc, and the highest GDP pc countries. The study covered the five age and sex groups of employees: 25-34, 35-44, 45-54, 55-64, and 65-74. The Productive Capacities Index (PCI) built by UNCTAD and its selected categories were assumed to be dependent variables. The research results obtained for three groups of countries indicate that older employees had stronger positive impact on improving PCI than younger employees, especially on human capital development and private sector productivity. Our outcomes also suggest that older employees are better integrated than younger age groups with ICT tools that increase work productivity. To sum up, we can state that older employees can be a key factor in economic development due to their knowledge and experience, provided that others are willing to learn from them and they receive relevant organizational support.