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The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic.

The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model.

The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model.

At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

Thus, this study aims to determine the status-quo of research on the role of IT in M&A from 2010 to 2022 by providing a summative meta-analysis of this phenomenon.

This study presents a meta-analysis of mergers and acquisitions (M&A) research in information systems (IS), aimed at accounting for themes in M&A literature over the past 13 years, research methodology, research frameworks, level of analysis and geographical distribution. A total of 47 articles from 24 peer review articles and 23 conference publications were analyzed from 2010 to 2022.

Findings of the study suggest that M&A research in IS emphasizes IS integration at the expense of other under-explored dimensions such as M&A context, stakeholder involvement and within-firm conditions. Although studies on M&A have increased over the past 10 years, a significant number of studies have not been underpinned by models and theories. Also, a large number of studies adopted the qualitative approach as research methodology compared to quantitative, design science and mixed methods.

This study contributes to the literature on M&A in IS by proposing an M&A in IS research framework that bridges the gap between existing and future studies on M&A in IS research by shedding more light into well research areas and opportunities for further studies.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

This study examines whether financial development affects entrepreneurship, and how financial openness moderate this relationship.

The study employs panel data consisting of 781 country-year observations of 48 countries of Asia for the period 2001–2018.

The study provides empirical support for the eclectic theory of entrepreneurship in Asian countries. The findings of the study indicate that effective allocation of resources and ease of transactions increases the entrepreneurial activities in the country. Additionally, the less stringent regulations, allowing for the cross border transactions, increase the funds availability to the entrepreneurs which in turn increase innovation and establishment of new businesses.

The study only considered the moderating influence of financial openness on the nexus. Other indicators such as governance quality and political stability could also have significant impact on entrepreneurship. Further, our study was based on countries belonging to Asian continent. Since Asian continent has culture distinguished from other regions, therefore, the results cannot be generalized to the other continents.

The study’s results provide insight to policymakers and regulators that in order to increase the entrepreneurial activities, the financial sector improvement is of paramount importance. The regulators should focus on well-functioning financial system and availability of capital to improve the investor's confidence and boost economic activities.

The study provides novel evidence on the effects of financial development on entrepreneurship and moderating influence of financial openness in the context of the entire Asian region, which is yet an unexplored area. This paper offers a fresh contribution in this area.

Skilled migrants' careers are heterogeneous, with existing theories capturing only some of their diversity and dynamic development over time and circumstance. This paper aims to draw out the multilevel (macro, meso and micro levels) influences impacting skilled migrants' careers by using the lens of the intelligent career framework. Furthermore, structuration theory captures the agency of skilled migrants facing different social structures at and across levels and explains the idiosyncratic nature of skilled migrants' careers.

Following an abductive approach, this paper examines the career influences for a sample of 41 skilled migrants in three different host countries. Individual career stories were collected through qualitative interviews. Important career influences from these narratives are categorised across the intelligent career competencies (knowing why, how and whom) at the macro, meso and micro levels.

Findings illustrate the lived reality for skilled migrants of these interrelated multilevel career influences and go some way in elucidating the heterogeneity of skilled migrants' careers and outcomes. The interplay of individual agency in responding to both facilitating and challenging social structures across the multilevels further explains the idiosyncratic nature of skilled migrants' careers and how/whether they achieve satisfying career outcomes. Some potential policy implications and options arising from these findings are suggested.

By considering multilevel themes that influence skilled migrants' career capital, the authors were able to better explain the complex, relational and idiosyncratic shaping of their individual careers. As such, the framework informs and guides individuals, practitioners and organisations seeking to facilitate skilled migrants' careers.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

Researchers have tried analysing how the organizations’ practices of doing good can help improve their employees’ satisfaction over the past couple of decades. Employee satisfaction has a complicated relationship with a company’s corporate social responsibility (CSR) activities. Subsequently, the purpose of this paper is to conduct a bibliometric analysis and a literature review to trace the links between CSR and employee satisfaction, summarize and analyse the advances in this field, the knowledge gaps, publishing trends and further directions for future research.

The authors conducted a bibliometric analysis followed by a literature review of papers indexed in the Scopus database and published between 2000 and 2022. A total of 233 papers were identified, while 152 of them met the inclusion criteria for the analysis.

The subsequent analysis sheds light on the overlaps and connections between the two phenomena in human resource management (HRM). The authors outline potential avenues for future research and practical insight into how to leverage CSR activities for increasing work satisfaction.

By detailing the different ways CSR and employee satisfaction impact one another, analysing their relations and other supporting constructs, the authors contribute to the academic discourse by synthesizing prevailing literature and introducing practical guides for human resource (HR) professionals, managers and executives to manage turbulent surroundings more effectively, considering the major disruptions post-COVID-19 and the adoption of advanced technologies.

This study uses a multi-level framework to systematically summarize and synthesize the empirical literature on determinants of sustainability disclosure.

This review study is based on 159 empirical studies examining determinants of sustainability disclosure and published in Charted Association of Business Schools (CABS) ranked journals over the last 40 years.

Companies are experiencing multi-level pressures for sustainability disclosure. Macro-level variables include political, legal, social-cultural and international pressures. Meso-level factors include customers' concerns, shareholders’ and investors' demands, industry-level variables and media coverage. Micro-level factors include the firm-level governance mechanisms, executives' reporting attitude and role of sustainability promoting institutions. Unlike in developed markets, companies in developing markets feel minimal public pressure for sustainability disclosure but rather are influenced by international NGOs, the media and international buyers. Multi-level and multitude of pressures for sustainability disclosure explains the widely observed differences between studies.

This research presents the most extensive systematic review of the extant sustainability disclosure literature and is the first study to group determinants into micro-, meso- and macro-level components using multi-level analysis.