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The paper aims to understand how state and non-state domestic terrorism impacts MNEs in foreign markets. Despite the burgeoning literature on terrorism within international business (IB), most research has focused on international terrorism, or terrorism generally. Consequently, there has been limited research examining how domestic or local based terrorism impacts foreign firms.

This is a conceptual paper.

Domestic terrorism is the most common form of terrorism in the world today and involves the state and non-state actors. Non-state domestic terrorism can be low intensity or high intensity. High intensity non-state-domestic terrorism typically involves regular and protracted political violence, along with inter-communal violence. This can expose MNEs to considerable operational, governance and legitimacy pressures.

The paper contributes to the gap in IB terrorism research with regards domestic or local based terrorism. Drawing on IB theory and critical terrorism research, the paper addresses the nature and impact of domestic terrorism within IB. The authors’ paper shows the operational, governance and legitimacy pressures of both state and non-state domestic terrorism for MNEs in host markets. While most IB scholars consider the threat of non-state terrorism for international firms, this study shows how domestic state terrorism benefits and constrains foreign firms.

This study aims to identify the political alignment and political activity of the 11 Presidents of Britain’s most important scientific organisation, the Royal Society of London, in its early years 1662–1703, to determine whether or not the institution was politically aligned.

There is almost no information addressing the political alignment of the Royal Society or its Presidents available in the institution’s archives, or in the writings of historians specialising in its administration. Even reliable biographical sources, such as the Oxford Dictionary of National Biography provide very limited information. However, as 10 Presidents were elected Member of Parliament (MP), The History of Parliament: British Political, Social and Local History provides a wealth of accurate, in-depth data, revealing the alignment of both.

All Presidents held senior government offices, the first was a Royalist aristocrat; of the remaining 10, 8 were Royalist or Tory MPs, 2 of whom were falsely imprisoned by the House of Commons, 2 were Whig MPs, while 4 were elevated to the Lords. The institution was Royalist aligned 1662–1680, Tory aligned 1680–1695 and Whig aligned 1695–1703, which reflects changes in Parliament and State.

This study establishes that the early Royal Society was not an apolitical institution and that the political alignment of Presidents and institution continued in later eras. Furthermore, it demonstrates how the election or appointment of an organisation’s most senior officer can be used to signal its political alignment with government and other organisations to serve various ends.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

This paper aims to examine whether the audit committee moderates the relationship between audit quality and market reactions.

Using fixed effects and the GMM model for robustness, the study used 472 publicly listed firms on South Africa’s Johannesburg stock exchange spanning a period of six years from 2014 to 2019.

Results obtained show that audit quality impacts market reactions through share price and adjusted market returns. And, that the audit committee moderates the relationship between audit quality and market reactions in South Africa’s publicly listed firms. An effective audit committee is expected to play a crucial role in overseeing the audit process, ensuring the independence of auditors and promoting transparency and accountability which in turn impacts asset prices.

The study implies that governments and regulatory bodies in other developing economies could strengthen regulations about companies’ Acts, how firms regulate themselves and more so audit committees. Firms can also strive to make sure that audit committees are staffed with experts to promote higher audit quality and investor attention to get access to the much-alluded capital.

To the best of the authors’ knowledge, the study adds value by being the first to explore the subject matter of the importance of audit committees in defining audit quality and market reactions in publicly listed firms. The research adds to the body of knowledge on corporate governance and audit quality. It provides a case study specific to the South African context, contributing to the global literature on these topics.

This study aims to examine the role of hope and hate in political leaders’ messages in influencing liberals versus conservatives’ social-distancing behavior during the COVID-19 pandemic. Given the increasing political partisanship across the world today, using the appropriate message framing has important implications for social and public policy.

The authors use two Natural Language Processing (NLP) methods – a pretrained package (HateSonar) and a classifier built to implement our supervised neural network-based model architecture using RoBERTa – to analyze 61,466 tweets by each US state’s governor and two senators with the goal of examining the association between message factors invoking hate and hope and increased or decreased social distancing from March to May 2020. The authors examine individuals’ social-distancing behaviors (the amount of nonessential driving undertaken) using data from 3,047 US counties between March 13 and May 31, 2020, as reported by Google COVID-19 Community Mobility Reports and the New York Times repository of COVID-19 data.

The results show that for conservative state leaders, the use of hate increases nonessential driving of state residents. However, when these leaders use hope in their speech, nonessential driving of state residents decreases. For liberal state leaders, the use of hate displays a directionally different result as compared to their conservative counterparts.

Amid the emergence of new analytic techniques and novel data sources, the findings demonstrate that the use of global positioning systems data and social media analysis can provide valuable and precise insights into individual behavior. They also contribute to the literature on political ideology and emotion by demonstrating the use of specific emotion appeals in targeting specific consumer segments based on their political ideology.

The findings have significant implications for policymakers and public health officials regarding the importance of considering partisanship when developing and implementing public health policies. As partisanship continues to increase, applying the appropriate emotion appeal in messages will become increasingly crucial. The findings can help marketers and policymakers develop more effective social marketing campaigns by tailoring specific appeals given the political identity of the consumer.

Using Neural NLP methods, this study identifies the specific factors linking social media messaging from political leaders and increased compliance with health directives in a partisan population.

American Community colleges are vital to the country's economic mobility and are leaders in developing and facilitating career, technical, and workforce education. This study explored employer perspectives on employees' common and specialized skills across industries in the United States.

Employers were profiled for one year using a case study and content analysis method. They submitted performance records for at least three employees who graduated from community college career, technical, or workforce programs.

Data revealed that overall, employees were successful at work, but employers focused on employability skills across disciplines (common skills); specialized skills employers focused on were associated with business operations and processes and seemed to be something other than industry-specific technical knowledge.

This article and the research it refers to constitute original work that has not been reproduced or published. The value of this article is premised on new longitudinal data, which could be used to improve and progress institutional CTE and WD programs.

In February 2018, Jerome Powell had taken over as chair of the FOMC. At first glance, the macroeconomic conditions inherited by Powell appeared favorable for continued stability: unemployment and inflation were low, and the economy had been steadily growing for nearly a decade. Yet despite the appearance of stability, the economy faced significant risks that required the Federal Reserve's attention. Was an uptick in inflation imminent, and if so, should Powell raise rates to limit any inflationary pressure? Or was the economy still operating below capacity, and if so, should the Federal Reserve take a more accommodative stance? To gain perspective, Powell needed to look back at the past fifty years of monetary policy in the United States.