Search results

This chapter explores the impact of equality, diversity, and inclusivity (EDI) on internal change agents’ (ICAs) personal and professional development. We have surveyed 117 ICAs that undergo a four-year digital development programme at Edinburgh Business School (EBS). Our survey design draws from expectancy, surprise, sensemaking, and attribution theories to test four hypotheses using Spearman’s rank. We found that diversity features such as gender, age, sector affiliation, work experience, management responsibility, and programme stage do not strongly impact ICAs’ experience of personal and professional development. Surprisingly, some diversity features had a modest or moderate impact on ICAs’ experience of personal and professional development. This disconfirmed our basic assumption about the effectiveness of inclusivity practices in the digital development programme at EBS. We conclude that future research should further investigate the impact of evaluation on ICAs’ personal and professional development and how we can secure it in a digital Business School context.

The authors herein carry out a literature review of retirement planning and highlights that proper retirement planning starts by looking at the level of income an individual is likely to continue receiving at retirement if they were to take no action, then comparing this to what they would need to lead the lifestyle they desire. The authors review the traditional economic theories that many are accustomed to when interpreting financial matters (i.e., rational behavior) and compares this to the various studies and articles found in literature. The authors then dig into retirement planning in Malta and the behavioral obstacles to proper planning and how these are tackled in different European countries.

The purpose of this chapter is to explain why ethical evaluation of the impact of a merger or acquisition matters, to place ethical evaluation of M&A in the wider context of knowledge of business ethics and corporate governance, and to develop and demonstrate a framework for evaluating the treatment of stakeholders during M&A. This contribution surveys the relevant governance, ethical and M&A literature. A new stakeholder framework is proposed and then applied to an important case study.

We found that M&A has important consequences for a variety of stakeholders; the strategy and finance literature has concentrated on top management and shareholders and neglected advisers, employees, customers, and suppliers. We also found that a stakeholder analysis framework can be adopted to evaluate each merger or takeover.

This chapter establishes a new framework for evaluating M&A beyond the conventional shareholder value approach; however only one case study is analyzed.

Managers and other stakeholders can use the proposed method to determine the likely impact of an M&A upon themselves and others and consequently weigh up the desirability of doing a deal in a wider context than currently.

The consequences for stakeholders following a merger or acquisition are often profound. The key protagonists ought to be more aware of these consequences which can be detrimental to stakeholders and the organization itself. The approach taken in this chapter provides a new method for both academics and practitioners to evaluate the impact of M&A.

Purpose – This chapter examines how sentiment analysis and web-crawling technology can be used to conduct large-scale data analyses of extremist content online.

Methods/approach – The authors describe a customized web-crawler that was developed for the purpose of collecting, classifying, and interpreting extremist content online and on a large scale, followed by an overview of a relatively novel machine learning tool, sentiment analysis, which has sparked the interest of some researchers in the field of terrorism and extremism studies. The authors conclude with a discussion of what they believe is the future applicability of sentiment analysis within the online political violence research domain.

Findings – In order to gain a broader understanding of online extremism, or to improve the means by which researchers and practitioners “search for a needle in a haystack,” the authors recommend that social scientists continue to collaborate with computer scientists, combining sentiment analysis software with other classification tools and research methods, as well as validate sentiment analysis programs and adapt sentiment analysis software to new and evolving radical online spaces.

Originality/value – This chapter provides researchers and practitioners who are faced with new challenges in detecting extremist content online with insights regarding the applicability of a specific set of machine learning techniques and research methods to conduct large-scale data analyses in the field of terrorism and extremism studies.