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The purpose of this paper is to investigate the size effect of market reaction to unexpected earnings based on whispers or unofficial individual earnings forecasts.

Using both univariate and multiple regression analysis, this paper attempts to demonstrate that there is a size effect in the market reaction to unexpected earnings based on whispers. The empirical results are based on 13,795 quarterly earnings whispers over 1997‐2006.

The results show that for both abnormal returns (ARs) and trading volume, the market reaction for big firms is less compared to that of small firms.

Given that information for small firms is less available and transparent than for big firms, this paper provides useful evidence that whispers play a larger role in equity pricing for small firms.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

Power structure research examines core issues in the discipline of sociology; yet this important area of study is declining because of the conceptual, theoretical, and methodological problems. In this paper, I address each of these problems and proposing solutions. I then test the validity of my proposed solutions by conducting empirical analyses examining how big business and labor political action committee (PAC) contributors influence U.S. House decision making. My findings vividly show significant big business influence on House decision making, but negligible labor influence. These findings carry considerable implications for power structure theorizing and research, and provide a solid foundation for future power structure work.

Using the most recent observations (2005‐2011) from a sample of UK listed companies, This paper aims to investigate whether Big 4 audit firms exhibit a “fee premium” and, if this is the case, whether the premium is related to the delivery of a better audit service.

Univariate tests, multivariate regressions and two methodologies that control for self‐selection bias are used to answer the proposed research questions. Data are collected from DataStream.

Findings provide consistent evidence about the existence of an “audit fee premium” charged by Big 4 firms while they do not highlight any significant relationship between audit quality and type of auditor with respect to the audit quality proxies investigated.

Evidence from this paper might signal the need for legislative intervention to improve the competitiveness of the audit market on the basis that its concentrated structure is leading to “excessive” fees for Big 4 clients. Findings might also enhance Big 4 client bargaining power. However, as the paper analyses only one country, generalizability of the results might be a limitation.

This study joins two streams of the extant literature that investigate the existence of a “Big 4 audit fee premium” and different levels of audit quality among Big 4 and non‐Big 4 clients. Evidence supports the concerns raised by the UK House of Lords in 2010 that the concentrated structure of the audit market could be the driver of “excessive” fees for Big 4 clients as it does not find differences in audit quality between Big 4 and non‐Big 4 clients.

This study aims to examine whether the industry characteristics of homogeneity, product competition, high auditor competition and accounting standards complexity are associated with auditor changes.

Logistic regressions test for significance of the industry characteristics on resignations, dismissals and directional changes to and from Big 4 and nonBig 4 auditors after controlling for client, auditor and engagement factors.

The authors report a lower likelihood of auditor resignations with greater accounting standards complexity. The authors also report a greater likelihood of auditor dismissals with greater industry homogeneity, greater product competition and greater auditor competition. Results also show that accounting standards complexity is associated with a lower likelihood of changes from Big to nonBig auditors, and industry homogeneity is associated with a greater likelihood of changes from Big to nonBig. Also, greater auditor competition is associated with a lower likelihood of changes from nonBig to Big auditors.

Prior research has established the importance of industry characteristics to the market for audit services (Cairney and Stewart, 2015; Wang and Chui, 2015; Cahan et al., 2011; Bills et al., 2015). The authors report that industry characteristics also impact auditor changes. Second, previous research has used various methods that indicate general industry effects on changes. The paper contributes to this research by specifying industry characteristics. Limitations include the reliance on the self-reporting in 8-Ks to identify auditors resigning and firms dismissing auditors. Also, the paper relies on proxies for industry characteristics that were developed in prior research.

Regulators have expressed concern over the relatively low rates of auditor changes and the problem of lack of auditor choice. By demonstrating a significant effect of industry characteristics on changes, the authors indicate some levers that may be available to influence rates of auditor changes, especially realignments to nonBig.

This is one of the first studies to examine how specific industry characteristics impact auditor changes. The study may be of interest to academics who are interested in how industry factors influence auditor changes. It may also interest policymakers who could lever the characteristics of industries to address concerns about the low rates of auditor changes.

This paper aims to investigate how narrative is constructed to create connections with fat readers, how books function to envision spaces of fat liberation for young readers and to highlight the incredible importance of providing bigger mirrors (Bishop, 1990) for fat representation in children’s literature.

This paper analyzes and reflects on two texts that contain counternarratives of fatness: The (Other) F Word: A celebration of the fat and fierce edited by Angie Manfredi (2019) and Big by Vashti Harrison (2023) to interrogate how these two narratives intentionally disrupt anti-fat bias.

Body size and fatness are identities that need to be included in diversity efforts within education. Books like The (Other) F Word: A celebration of the fat and fierce (Manfredi, 2019) and Big (Harrison, 2023) offer positive representations of fatness, disrupt biases around body size and provide spaces that allow fat students to find joy, hope, connection and, more than anything, imagine a way toward liberation.

This paper highlights the need to include more narratives of positive fat representation within children’s literature and calls for educators to interrogate their own anti-fat biases and practices.

There is a lack of research on fat representation specifically within children and young adult literature. This paper provides an analysis of two pieces of literature with fat representation that brings attention to the need for this type of future research.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

South African scholars, like most scholars in the developing world, have sold the idea that social constructivism is the gold standard of qualitative management research. In this chapter, we caution against this subordination to unquestioned conventions and offer a process relational ontology as an alternative to social constructivism that is often punted by most qualitative research programmes and textbooks. We also debunk the idea that ‘grounded theory’ exists by delving into epistemology and showing how science is ‘self-correcting’ rather than ‘tabula rasa’. Instead of boxing business ethics knowledge, as has been done by the case study gurus, we encourage business and organisational ethicists to own their indigenous heritage through storytelling science based on the self-correcting method underpinned by Popperian and Peircian epistemological thought. This chapter encourages business management researchers to move towards more profound ethical knowledge by refuting and falsifying false assumptions in each phase of the study, in a sequence of self-correcting storytelling phases. This is what Karl Popper called trial and error, and what C.S. Peirce called self-correcting by the triadic of Abduction–Induction–Deduction. We offer a novel method for accomplishing this aim that we call ‘Conversational Interviews’ that are based on antenarrative storytelling sciences. Our chapter aims to evoking the transformative power of indigenous ontological antenarratives in authentic conversation in order to solve immediate local problems ad fill the many institutional voids that plague the South(ern)-/African context.