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This chapter examines how citizen journalism affects perceptions of legitimacy among local residents and police officers.

Qualitative data were collected through semi-structured in-depth interviews with residents and police officers.

Local residents are mostly willing to obey police commands, but a lack of trust in the police and fear of retaliation hinder willingness to cooperate with the police. Citizens’ willingness to follow police orders is mostly a way for them to end the encounter as quickly as possible so the contact will not extend for a prolonged period of time and cause even more serious consequences. Citizens have recorded the police in the past when they witnessed officers not following proper procedures. The police view citizens recording them as a form of defiance and while this makes policing challenging, police officers interviewed still hold high levels of self-legitimacy, most likely due to their organizational and occupational culture. Recording the police has emerged as a way for citizens to challenge police authority and legitimacy during encounters.

While recording the police has increased with recent technological advances, little empirical research has examined its impact on policing and police-community relations. This study connects three critical issues in policing – technology, citizen journalism, and police legitimacy – by assessing the impact of recording the police on police legitimacy in the eyes of the public and police officers. Not only does this study fill our gap in knowledge on citizens recording the police, but it also furnishes valuable implications for policy and future study.

This chapter is devoted to sustainable human resource management that leads to sustainable competitiveness. It features the ways human resources can be managed to carry out sustainable goals and the impact of sustainability on employees' attitudes and behaviours. The aim of this study is to explore the complex objectives of sustainability and human resource management and empirically investigate the dynamic relationship between human resources in science and technology and sustainable competitiveness in the case of 35 European countries. Our contribution emphasizes this interrelationship and its causality. For this research, we applied a vector auto-regression (VAR) model, and the Granger causality method to examine the relationship between human resources in science and technology and sustainable competitiveness. A panel data included 314 observations between 2012 and 2021. The panel VAR for analysing the impulse response function was enriched with the 5% and 95%, using Monte Carlo simulations. The research results revealed bidirectional causality in the European countries between human resources in science and technology and sustainable competitiveness. Human resources in science and technology trigger sustainable competitiveness and vice versa. As an element of originality, our study demonstrates that human resources in science and technology contribute to sustainable performance, and, on the other hand, a more competitive and sustainable environment contributes to the development of human resources in science and technology. Thus, the chapter outlines the role of human resources in science and technology with regard to sustainable human resource management (HRM), and how to navigate these objectives so that they can positively influence sustainable competitiveness.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

Even though there is neither case law nor policy negating the concept of the maximisation of shareholders' profits, the ‘schizophrenia’ of the legal conception of the corporation (Allen 1992), and the incertitude that stems from this, justify a new definition of the ‘best interests of the corporation’. Doubt is accentuated by the statutes of American companies, called non-shareholder constituency statutes, which refer to ‘best interests’ in the assessment of corporation director duties. Indeed, nearly half of U.S. states have adopted ‘constituency statutes’ which allow the board of directors to take into account the interests of non-shareholders when making decisions (Mitchell, 1992; Orts, 1992).⁷

Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.