Abstract
The internet massification and e-commerce growth that have been driven by “millennials” and the coronavirus pandemic cannot remain indifferent to luxury brands. These brands have had to adapt to e-commerce and develop an online shopping experience which satisfies its customers, so that they repeat purchase. Therefore, the main objective of this research is to understand the main impacts of shopping experience on luxury brand websites on satisfaction and loyalty. A model which analyzes the relationship between the three constructs was developed and information was gathered through an online survey, from which resulted 356 valid answers. Through the analysis of data collected and using a structural equation model, using SmartPLS software, we realized that online shopping experience is positively related to satisfaction. Loyalty, in turn, is positively affected by brand satisfaction. This study makes an important contribution to luxury brands and to people in charge of marketing and online platforms selling luxury goods. It helps brands understand that enhancing online shopping experience can positively impact satisfaction and loyalty levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Companies are increasingly focusing on audiovisual content as part of their strategy, and YouTube being a massive video hosting platform that makes content sharing possible has been the most successful platform for reaching their consumers products and services. Researches have proven that user-generated content impacts brand engagement, loyalty and firm revenue. Therefore, it is necessary to determine what factors stimulate the creation of consumers perceived value from user-generated content, on social media. We analyze the driving factors of user-generated content on YouTube and its influence on consumers perceived value. The sample data consists of 282 YouTube users’ responses collected through an electronic survey. This research contributes toward the digital content marketing literature by complementing existing research exploring consumer behavior on social media, assessing the driving factors of user-generated content and its impacts on customer perceived value. The study findings provide academic contributions and several challenges for firm and user-generated content, on actions they can tackle. Finally, based on the study limitations, we discuss future research in generated content in social media, providing insights for future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Artificial intelligence (AI) has altered our world in numerous ways. Although its application has benefits, the underlying issues surrounding privacy and security in AI need to be understood, not only by the organizations that use it but also by the users that are susceptible to its vulnerabilities. To better understand the impact of privacy and security in AI, this chapter reviews the current literature on artificial intelligence, trustworthiness, and privacy and security concepts and uses bibliometric techniques to understand and identify current trends in the field. Finally, the authors highlight the challenges facing AI and machine learning and discuss the results obtained from the bibliometric analysis, which provides insight into the several implications for managers and contributions to future research and policy. © 2023, IGI Global. All rights reserved.

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

Abstract
Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio-annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which make their real-time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology to identify bird sounds. In this paper, we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.