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Organization development is often mourned as stagnant or perhaps dead, but most of these declarations seem to be insular, being supported primarily by anecdotal or survey research among organization development scholars and practitioners. This exploratory study seeks a more objective understanding of the state of organization development by examining big data from the social media platform Twitter. Drawn from over 5.7 million tweets extracted through Twitter's Application Program Interface (API) during 2 months in 2018, this research approaches the state of organization development through a quantitative, abductive study utilizing social network analyses. Organization development is examined through its characteristics as a social network on Twitter and how it relates to and interacts with other familial networks from management and organization studies. Findings show that organization development is relatively inactive as a social network on Twitter, as compared to other familial networks, and the relationships between the organization development network and these familial networks tend to be ones of inequality. Organization development references familial networks much more than any of the familial networks reference organization development. This inequality in social media presence is particularly surprising since several of these familial networks were founded from the field and principles of organization development. We locate organization development's generalist status, as compared to familial networks' specialist status, as generating this interaction disparity drawing on recent research that suggests specialized fields fare better in times of rapid change compared to generalist fields. We discuss the potential for greater specialization of organization development with a reemphasis on its process philosophy and focus.

Network analysis involves the examination of the structure and patterning of relationships among a set of persons or agencies by taking into account the relations or ties that exist, as well as those that do not, among various groups (Knoke & Kuklinski, 1982). For the evaluation project, the structure of relationships within the county behavioral health service network, were examined over the course of three years. The method applied interorganizational theory and a network analytic approach, based on the works of Heflinger and Northrup (Heflinger, 1996; Heflinger & Northrup, 1998, 2000a), Morrissey and colleagues (Morrissey et al., 1994, 1998; Morrissey, 1992; Morrissey, Tausig, & Lindsey, 1985), Van de Ven (1976), and Bolland and Wilson (1991a, 1991b, 1994).

Purpose: The study’s objective is to look at the link between money laundering and economic and financial performance, emphasising the effectiveness of the literature and possible later research directions using science mapping, which allows for scientific knowledge analysis.

Need for the Study: This study is related to a better understanding of the field’s historical evolution in terms of publications.

Methodology: This study used bibliometric approaches to analyse a sample of 660 studies from the Web of Science between 1994 and 2022, concentrating on keywords, author, paper, journal, and subject analysis. This study focused on performance analysis and scientific mapping of articles using the R package.

Findings: The empirical results indicated that the research field’s primary issues include corporate governance, fraud, machine learning, fraud detection, financial fraud, financial statement, corruption, earnings management, ethics, governance, financial reporting, bankruptcy, internal control, or performance. M. S. Beasly, D. B. Farber, E. M. Fich, R. Romano, and A. Shivdasani are the most well-known authors on the issue of money laundering and financial and economic performance. At the same time, the most typical journals are the Journal of Business Ethics, Journal of Money Laundering Control, Accounting Review, Journal of Financial Economics, and Journal of Corporate Finance.

Practical Implications: This study will act as a guide for researchers of various fields to evaluate the development of scientific publications in a particular theme over time, especially for those who are in the field of money laundering and financial performance.

Blockmodeling is viewed often as a data reduction method. However, this is a simplistic view of the class of methods designed to uncover social structures, identify subgroups, and reveal emergent roles. Worse, this view misses the richness of the method as a tool for uncovering novel human resource management (HRM) insights. Here, we provide a brief overview of some essentials of blockmodeling and discuss research questions that can be addressed using this approach in applied HRM settings. Finally, we offer an empirical example to illustrate blockmodeling and the types of information that can be gleaned from its implementation.

Challenge-based learning (CBL) has gained acceptance as a contemporary and progressive teaching pedagogy that provides a holistic and inclusive experience to learners in higher education (HE) institutions. However, its lack of appeal to non-STEM subjects and the need for further development, particularly concerning improved approaches, have been recognized. It seems that CBL runs the risk of becoming a portmanteau pedagogy that blends aspects of problem-based learning, project-based learning, and situated learning, as opposed to its development as an effective pedagogy tool. This points to a lack of a formal implementation framework, code of practice, and standard procedures for its delivery. We argue that blending a design thinking (DT) pedagogy with CBL can potentially provide the stability that CBL currently lacks. At the same time, it also presents a more inclusive proposition to potential non-STEM audiences. Thus, in this chapter, we seek to interrogate the intersectionality between CBL and DT literature in the context of HE teaching and learning with a view of establishing CBL as a pedagogy in its own right. We attempt to achieve this by systematically analyzing the separate literature to reveal the synergies and common touchpoints.

The adverse effect of plastic pollution on the tourism industry is one of the emerging research areas in the field of sustainable tourism over the past years. However, there is a lack of discussion on this issue in the academic platforms. Considering this, in this chapter, a scientometric analysis of 110 academic publications from the Web of Science (WoS) between 1999 and 2021 related to plastic pollution and tourism is presented. A bibliometric analysis using CiteSpace was utilised to analyse the data to present the keyword timezone, collaboration network and co-citation network. The analysis shows that the publications in this field have increased rapidly, and it has become an emerging and interdisciplinary research topic. Scholars from Australia, Spain, Brazil and China have published most on the topic. The Chinese Academy of Science with four articles is the largest contributor in this field among other institutions. Moreover, citizen science is found to be a new keyword coming up in recent years, and human health is one of the major concerns. The findings from this study provide valuable insights for the academicians and policy makers in understanding the issue.

While Chapter 3 highlights contextual conditions that foster the formation of policy networks, Chapter 5 highlights contextual conditions that impede the formation of policy networks. The overarching question of this chapter is the following: What are the factors that hinder cross-sectoral arrangements from becoming collaborative in complex policy settings such as low- and middle-income countries with a tradition in centralized policy making? In an attempt to address this question, this study provides a detailed assessment of all cross-sectoral arrangements within the Turkish health sector focusing on the post-2003 period. Within this framework, three types of networks will be examined including (a) consultative networks where stakeholders come together primarily to legitimize certain government policies within national level health policy platforms; (b) cluster networks where stakeholder interaction falls short of becoming collaborative due to diverging interests and persistent competition at the regional level; and (c) patronage networks where governmental actors and a selected number of NGOs linked by clientelism serve as a bridge between the ruling party and its constituency at the community level. Unlike policy networks, these actor constellations observed at different levels of governance do not serve the purpose of policy collaboration. On the contrary, they have the potential to trigger politicization, fragmentation, and even polarization at the social level, especially through the distribution of selective benefits. Ultimately, this chapter aims to rise to the challenge of policy collaboration by assessing the impediments to network collaboration based on insights from the Turkish case.

This chapter discusses the empirical application of a class of strategic network formation models, using the approach to identification introduced by de Paula, Richards-Shubik, and Tamer (2018). The author emphasizes the interplay between model specification and computational complexity, and suggests tactics to make empirically realistic models become tractable. Two detailed examples, on friendship networks and coauthorship networks, are used to illustrate these issues and to demonstrate the performance of the approach with both simulation and empirical evidence. Also, the author presents extensions to the estimation method, which expand the potential range of applications, and which provide statistical inference with minimal computational burden.

Each of the four objectives can be applied within the military training environment. Military training often requires that soldiers achieve specific levels of performance or proficiency in each phase of training. For example, training courses impose entrance and graduation criteria, and awards are given for excellence in military performance. Frequently, training devices, training media, and training evaluators or observers also directly support the need to diagnose performance strengths and weaknesses. Training measures may be used as indices of performance, and to indicate the need for additional or remedial training.