Abstract
The recent pandemic crisis has greatly impacted startups, and some changes are expected to be long-lasting. Small businesses usually have fewer resources and are more vulnerable to losing customers and investors, especially during crises. This study investigates how startups’ business processes were affected and how entrepreneurs managed this sudden change brought by the COVID-19 outbreak. Data were analyzed using qualitative research methods through in-depth interviews with the co-founders of eighteen startups. Results show that the three core business processes affected by the COVID-19 crisis were marketing and sales, logistics and operations, and organizational support. The way to succeed is to be flexible, agile, and adaptable, with technological knowledge focusing on digital channels to find novel opportunities and innovate. Additionally, resilience, self-improvement, education, technology readiness and adoption, close relationship with customers and other stakeholders, and incubation experience seem to shield startups against pandemic crisis outbreaks.

Abstract
Bin picking based on deep learning techniques is a promising approach that can solve several analytical methods problems. These systems can provide accurate solutions to bin picking in cluttered environments, where the scenario is always changing. This article proposes a robust and accurate system for segmenting bin picking objects, employing an easy configuration procedure to adjust the framework according to a specific object. The framework is implemented in Robot Operating System (ROS) and is divided into a detection and segmentation system. The detection system employs Mask R-CNN instance neural network to identify several objects from two dimensions (2D) grayscale images. The segmentation system relies on the point cloud library (PCL), manipulating 3D point cloud data according to the detection results to select particular points of the original point cloud, generating a partial point cloud result. Furthermore, to complete the bin picking system a pose estimation approach based on matching algorithms is employed, such as Iterative Closest Point (ICP). The system was evaluated for two types of objects, knee tube, and triangular wall support, in cluttered environments. It displayed an average precision of 79% for both models, an average recall of 92%, and an average IOU of 89%. As exhibited throughout the article, this system demonstrates high accuracy in cluttered environments with several occlusions for different types of objects.

Abstract
Serious games are increasingly present in higher education and many researchers are reflecting on how to use them in the development and training of new skills. However, an unexplored area is the analysis of the impact that serious games have on students’ academic performance in an entrepreneurship course. In this sense, this study simultaneously seeks to explore the impact of the use of a serious game, titled FLIGBY, on the development of hard and soft skills through the use of a mixed methods approach, in which quantitative and qualitative methods are combined by adopting the convergent parallel design model. The findings did not allow us to establish a correlation between the parameters assessed in the FLIGBY and the students’ academic performance. However, it was possible to identify several benefits in the development of soft skills with potential impact on the students’ academic and professional careers.

Abstract
Th chapter presents an empirical study on a Brazilian cyber-activism movement on Instagram associated with the hashtag #CorpoLivre (#FreeBody in Portuguese). This movement, which was established in 2018, has published more than 3,000 posts and has over 400,000 followers, disseminates anti-fatphobia and real body discourses, and promotes a positive relationship between women and their bodies beyond traditional beauty standards. The study analyses the posts made by the feminist movement on Instagram in December 2022, with a sample size of 101 posts. The study adopted the framework developed by Khurana and Knight for the analysis, which enables the classification of the sample posts in terms of message appeal, orientation, engagement, popularity, and image characteristics. This framework was used to examine the relationship between content characteristics and engagement. Additionally, the study includes a content analysis of the posts' comments, specifically evaluating the valence (positive, negative, or neutral) to assess the effectiveness of the characteristics of the posts. © 2023, IGI Global. All rights reserved.

Abstract

Abstract
Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.