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This chapter aims to investigate sentiments of blue-collar workers, specifically couriers, during Covid-19 pandemic. Data are collected from the online review posting platform, where employees and previous employees of a Turkish delivery company posted their comments on the company as an employer. The results reveal that there are mainly four topics on which the reviewers discuss: work environment, compensation, salary and working hours, and lay-off procedures. The analyses indicate that the most discussed topic is compensation, while the most negative sentiments appear to be on lay-off procedures.

As one of the most influential theories in strategic management, Hambrick and Mason’s Upper Echelons Theory has yielded significant conceptual and empirical advancements linking executive characteristics and perceptions to decision-making. Specifically, work on this theory consistently shows that CEOs’ decisions are biased by personal characteristics to the benefit and detriment of firms. While this stream of research links executive decision processes to outcomes such as executive dismissals, analyst evaluations, and press coverage, surprisingly little is understood about if and whether the information CEOs convey is subject to the same filtering process by a firm’s key evaluators. Thus, in this chapter, we aim to extend Upper Echelons Theory by positing that a double filtering process occurs whereby the cognitive aids CEOs use can be informed by not only their cognitive base and values but also the characteristics and priorities of those who evaluate the nonverbal and verbal signals they send. To do so, we build on recent conceptual and empirical advancements to make a case for the decision-making biases and tendencies that influence signal interpretation by three key evaluator groups internal and external to the firm: boards of directors, financial analysts, and the media. We conclude by considering the implications of evaluators’ information filtering and how this more holistic view of Upper Echelons decision-making can enable executive teams to be strategic with the cognitive aids they use to influence evaluations.

Purpose: This chapter explores the two major schemes applicable to skill development in India: Skill Acquisition and Knowledge Awareness for Livelihood Promotion (SANKALP) and Pradhan Mantri Kaushal Vikas Yojana (PMKVY).

Need for the Study: The primary objective of this research is to check the role of these schemes in enhancing the skills of socio-economically stressed community members for their livelihoods. The secondary aim is to analyse the outcomes of these schemes through a qualitative inquiry.

Methodology: A survey was conducted, and the data was collected from trainees of the skill development programmes. Based on the responses, a qualitative content analysis was performed, which showed that most trainees have the thirst and urge to enhance their life skills for a minimalistic livelihood.

Findings: The study concluded that though there are many schemes, only PMKVY is active. They focus on more than just youth communities. Instead, they consider individuals in different age categories.

Practical Implications: The Government of India (GOI) is progressing towards a healthy economy to compete with other countries. For this mission to be achieved, skill and labour development is paramount. Appropriate training must be provided and administrated through government schemes.

This chapter critically examines whether it may be possible to create an AI-based authentic leader, questioning the inherent contradiction between artificial and authentic. The authors pose central research questions: Does the application of AI – even just as a powerful resource – challenge the tenets of authentic leadership? What are the possibilities and limitations of the concept of authenticity in AI-based management systems? Moreover, with the help of three vignettes illustrating practical applications of AI-based systems in leadership and management tasks, the authors illustrate how technology may be used to either control or empower workers and leaders. The authors call for research to assess whether the search for authenticity in AI-based leadership could lead anywhere, warning that it could entrap us in unresolvable existential and conceptual ambiguity, ultimately diverting our focus from the essence of leadership altogether.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.