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This case study, within the context of boundaryless and protean career development frameworks, investigates linguistic profiling and how code-switching is used to mitigate its impact on Black leaders during their careers. The experiences of Black women coaches and the coaching support they offered Black women clients in code-switching, leadership and career advancement are described. The value of leadership coaching when used to navigate these career progression challenges is emphasized.

The study employed a multiple-case study approach of two Black women leadership coaches.

The findings of this study illustrate the understanding of code-switching and the coaching techniques employed by two Black women leadership coaches. Sage focused on educational strategies, offering historical contexts and resources, while Khadijah leaned on empathy-driven methods, using storytelling to evoke reflection. Both coaches emphasized creating safe spaces for open dialog, encouraged clients to reconsider their actions and values regarding code-switching challenges and sought to prompt clients towards authenticity while navigating career spaces effectively.

Additional strategies for coach practitioners include cultivating trust and a safe environment; active listening; challenging biases and assumptions; contextual understanding; empowering authentic self-expression; fostering skill development; challenging stereotypes; promoting autonomy and flexibility and adopting cross-cultural sensitivity, humility and competence. These practical coaching strategies bridge the gap in career development research by demonstrating how race-conscious strategies can promote workplace inclusivity and promulgate career development.

The study underscores the problem of linguistic profiling, the complexity of code-switching and implications for Black women navigating their career journey within professional spaces. It highlights the significance and value of tailored leadership coaching strategies to promote career advancement. This study addresses the gap in career development research related to linguistic profiling avoidance strategies for workplace inclusivity.

National surveys reveal that sports fans exhibit greater support for athletes, sports teams and leagues endorsing social justice initiatives compared to the general population, highlighting the potential of sports for positive social impact. This study investigates whether such responses are influenced by systematic biases.

Replicating a Nielsen national survey, two experiments explore whether biases affect support for athletes' participation in the Black Lives Matter (BLM) movement. The study also examines partisan motivated reasoning as a factor driving sports fans' support for BLM.

While avid fans display stronger endorsement of BLM compared to causal/non-sports fans, evidence suggests that systematic biases distort these responses. When sport identity becomes salient, reported support for the BLM movement becomes inflated.

Researchers often employ self-report surveys to gauge audience perceptions of athlete activism or cause-related initiatives, particularly when assessing their impact. This study's findings indicate that this context is susceptible to SDB.

The study underscores the role of systematic biases in self-report surveys, particularly in socially desirable contexts. People tend to over-report “positive behavior,” leading survey participants to respond more favorably to questions that are socially desirable. Therefore, interpreting survey results with caution becomes essential when the research context is deemed socially (un)desirable. It is crucial for researchers to apply appropriate measures to identify and mitigate systematic response biases. The authors recommend that researchers adopt both procedural and statistical remedies to detect and reduce social desirability biases.

This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.

A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.

The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.

This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.

The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy.

Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations.

Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.

While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

Given the historical legacy of policing Black bodies, this research focuses on the structures of anti-Blackness within school policing and the strategies students of Color activists use as they work to defund or abolish police departments in the Los Angeles Unified School District (LAUSD).

Specifically, this article looks to Twitter as a counter-storytelling space for students of Color activists to organize and build movements to end anti-Black school policing. Through the frameworks of critical race theory (CRT) and Black critical theory (BlackCrit), this research applies inductive coding to analyze 42 Twitter posts from three students of Color-led organizations based in Los Angeles.

This document analysis presents four themes, which describe four dominant strategies students of Color activists use in their campaigns to defund or abolish school police in the LAUSD: (1) centering Blackness and Black student experiences, (2) making demands for the elimination of funding and support for school police, (3) calling for a shift in funding to support Black students and (4) employing multiple tactics concurrently.

These findings demonstrate the importance of developing and centering a critical understanding of anti-Blackness to achieve racial and educational justice within social movements.

Moreover, the demands of students of Color activists reflect visions of public schools free from anti-Black school policing.

This article explores how firms' unresponsiveness to Black customer feedback influences Black (vs. White) customers' perceived firm-based discrimination and brand engagement.

Two experimental studies (Study 1(N1) = 254) and Study 1(N2) = 484) are conducted to test the modeled relationships. The data are analyzed using ANOVA, PROCESS Model 4 and PROCESS Model 7.

The findings suggest that though perceived discrimination remains modest in all conditions, Black (vs. White) respondents report higher perceived discrimination when the firm fails to respond to a Black customer's negative or neutral (but not positive) brand-related feedback on social media. The results also indicate that Black (vs. White) customers exhibit lower engagement through perceived discrimination in the case of the firm's unresponsiveness to a Black customer's negative and neutral (but not positive) brand-related feedback regardless of the manager's race.

Prior research on intercultural service encounters and ethnic differences in consumer engagement on social media are combined to examine the relationship between customer race and perceived discrimination based on the firm's unresponsiveness to customers' social media posts.

Manipulations were created based on a fictitious e-tailer. Thus, it is recommend that future researchers examine the extent to which the findings hold for existing (r)etailers. In addition, future studies using secondary data could provide additional evidence for the findings.

Managerial attention is accentuated among customer feedback responsiveness, engagement and perceived firm discrimination. Managers are encouraged to adopt communication strategies that complement the firm's strategy and social media presence.

The current study evaluated (1) characteristics of the community leadership development program associated with successful participant recruitment, (2) active ingredients that promoted fellow engagement and program completion and (3) how the program addressed blackness and racism.

Individual interviews were conducted with a representative subset of former program fellows.

Results indicated that offering training in small cohorts and matching fellows with individual mentors promoted program interest. Program strengths and unique ingredients included that the program was primarily led by people from the Black community, program malleability, and that the program was a partnership between fellows and leadership. Additionally, the program was responsive to fellows’ needs such as by adding a self-care component. Fellows also noted dedicated space and time to discuss race and racism. Results offer a unique theoretical perspective to guide leadership development away from the uniform or standardized approach and toward one that fosters diversity and equity in leadership.

Altogether, this work demonstrates how leadership development programs can be participant-informed and adapted to participants’ social and cultural needs.

Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.

Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.

The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.

This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.

The purpose of this study is to provide for critical literature on the legal aspects of corporate governance and their application in Mauritius. The drawbacks of having the principles in the form of a non-binding code are discussed, and a case is made to consider their enshrinement in laws such as the Companies Act 2001 to render them legally enforceable for the good health of companies in Mauritius.

A doctrinal legal methodology has been adopted to assess the effectiveness of the principles of the 2016 Code of Corporate Governance of Mauritius. Legislations, legal texts, case law and regulations are used to conduct this assessment. In addition, a black-letter approach is taken while discussing the enshrinement of the principles in the Companies Act 2001 of Mauritius. The doctrinal methodology is further supported by a qualitative analysis of the principles of corporate governance based on existing legal literature, which emphasises their relevance and importance.

The principles of the 2016 Code of Corporate Governance are no doubt a progress over the former 2004 Code in various aspects, aligning the Code with the requirements of the OECD. However, there are still certain loopholes that have been highlighted. In addition, the extent to which these principles are reflected in the Companies Act, which is the primary legislation for companies, has been found to be lacking and inadequate.

This paper is, to the best of the author’s knowledge, the first legal literature concerning the Mauritian legal framework on corporate governance. This is relevant because the country has recently experienced corporate collapses, which could arguably have been avoided with the application of the principles of corporate governance. As such, the paper will present a case study that can be used as a reference for future research on the enforceability and justiciability of these principles.

This paper aims to explore how one elementary school administrative team responded to their changing student populations to include Latin(x) within their black community. The responses included looping practices, relationship building with families and culturally relevant pedagogies. In particular, this paper considers how the three aspects of the change worked together toward the goal of providing its students with quality educational opportunities and enhancements.

The research presented here is part of a longitudinal (four-year) qualitative study where ethnographic approaches to data collection were adopted.

The four-year immersion in the values of culturally relevant pedagogy created a reciprocal growth in understanding among the teachers and the students of the black and Latin(x) populations, sustained the overarching ideas of deep family connections and contributed to asset-driven curriculum.

A national trend shows rapid changing demographics where Latin(x) families are moving into black neighborhoods and schools. This change in schools’ student populations finds educators facing new challenges in addressing the educational and cultural needs of two minoritized populations. This research adds to the existing scholarship by documenting how one school shifts their learning atmosphere to deeply engage students in culturally relevant pedagogies.