The purpose of this chapter is to provide an extensive overview of the underlying dynamics and socio-economic effects and consequences of the post-pandemic global tech layoffs while contributing to the literature by bringing forward pressing issues, like ‘backdoor layoffs’, and comprehensive insights while identifying the opportunities and challenges. Due to the multi-faceted nature of the phenomenon, an interdisciplinary approach is being employed, combining the empirical findings from various sources, including interviews, social media, and databases, into theoretical frameworks and real-world case studies. A multi-level analysis of the global tech layoffs is conducted through these case studies. As a result, this study reveals the root causes from the top and uncovers various challenges, like economic downturns, uncertainties, corporate strategies, and unethical practices. Moreover, companies are expected to follow the strategies of successful peers, and in the case of workforce reduction, small and medium enterprises follow different trajectories from each other but homogenous trends within their peer groups. This finding indicates that the global tech layoff has a multi-level nature, and each level has diverse needs and expectations to be addressed. At the social level, this chapter defines backdoor layoffs term and delves into their consequences that can exacerbate inequalities, especially for discrimination groups, and create barriers to re-employment. Addressing unethical backdoor layoff practices and creating a more sustainable tech labor market requires a wider participation of stakeholders. Finally, this chapter sheds light on the gaps in the mass layoffs scholarship, offering theoretical frameworks, definitions, and empirical evidence to expand the literature. <!----> <!----> <!----> <!---->

This protocol outlines the process for using Google Sheets, along with Google Apps Script and Google Colab, to set up and manage a language-localizable Multilingual Customizable Structured Electronic Brainwriting (mCSEB) session, which enables remote collaborative sessions for idea generation and downstream analysis. This technique creates an online platform by automating traditional brainwriting sessions, which involve six participants each coming up with three ideas in five minutes per round and passing their paper round-robin to the next participant, building new ideas upon previous contributions over the course of six rounds, producing 108 ideas in just half an hour. Google Sheets functions as a versatile platform for real-time input collection and documentation while enabling participants to effortlessly browse the spreadsheet and contribute to the idea development process. With this approach, moderators can efficiently guide and document brainstorming sessions, whether they’re in-person or virtual. The (6-)6-3-5 structured electronic brainwriting system is designed to automatically track progress, organize participant contributions, and prepare input for further analysis in Google Colab, which offers access to various statistical and machine learning libraries of the Python language.

Although the accumulated knowledge repository of the Corporate Responsibility (CR) literature continues to expand, organizational- and institutional-level studies dominate the field. This article addresses this gap by introducing a driver-based approach to CR through the moral mapping of CR policies and theory-building case studies in the textile sector. Considering the CR notion’s diverse stakeholder involvement and cross-disciplinary nature, process tracing of diverse actors from the Industrial Revolution is conducted. A reconstituted Kantian method is employed to assess the moral responsibility of individual agents. Employing this technique, the study categorizes diverse policies and strategies based on decision-makers and the most probable ethical thought processes, or “maxims”, at the time of the decision-making. The findings identify gaps in conventional CR practices, giving rise to CSR-washed actions and irresponsible corporate behavior. To tackle these challenges, we introduce the Corporate Local Responsibility (COLOR) model. This model is based on moral mapping by integrating them into its four pillars to minimize the ethical dilemmas of various actors by engaging broader stakeholders and local communities in the decision-making process.