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This study aims to share the development of new conceptual tools, which merge theories of critical whiteness studies (CWS), epistemic injustice and abolitionist teaching, applying them to the discourse of pre- and in-service teachers across the predominantly white institutions (PWIs) as they discuss antiracist teaching through the book Stamped and a series of online discussions.

This qualitative, collaborative practitioner inquiry derived data from video-recorded, online discussions, interviews and weekly research meetings. Critical discourse analysis revealed theoretical gaps and prompted the integration of additional theories, resulting in new conceptual tools, which are applied here to both “in the moment” exchanges between participants and individuals’ reflections in interviews.

Applying new conceptual tools to discussions of whiteness and race revealed how epistemic harm, microresistance and epistemic justice emerge in talk along with the importance of cultivating critical vigilance among antiracist educators.

This study elucidates how merging the conceptual frameworks of CWS, epistemic injustice and abolitionist teaching provides new tools for interrogating antiracism relative to whiteness in participants’ and researchers’ experiences. It challenges teacher educators, particularly at PWIs, to recognize how epistemic harm may be inflicted on students of color when centering whiteness in teacher education.

To present a cross-case analysis of two pre-service teachers who studied their own teaching using video within a teacher inquiry project (TIP) – a teacher education pedagogy we are calling video-mediated teacher inquiry.

Activity theory is used to examine how inquiry groups collaboratively used video to mediate shifts in goals and tool use for the two pre-service teachers presented in the study. This chapter addresses the question of how video-mediated teacher inquiry supports the appropriation of teaching tools (i.e., classroom discussion) in a teacher education program.

The findings indicate that shifts in goals and tool use made during the TIP suggest greater appropriation of the pedagogical tool of classroom discussion. We also consider how these shifts may be bound by the inquiry project.

The use of video cases of teachers’ own teaching is an emergent pedagogy that combines elements of both case study methods and practitioner inquiry. We argue that this pedagogy supports tool appropriation among pre-service teachers in ways that may help them develop as reflective practitioners.

Scant attention has been paid to the influence of professors on science, technology, engineering, and mathematics (STEM) students' learning and lives at the tertiary level. To fill this void, this chapter examines the influence of professors on students' entering and remaining in the STEM disciplines and pursuing STEM careers within the context of six funded STEM grants in the southern United States. We examine professor–student interactions using the students' storied experiences as the fodder for our narrative inquiry. We present narrative exemplars from which the following themes emerged: (1) agency as a student and agency as a human being, (2) development of students' multilayered identities, and (3) professors' engagement of themselves in their interactions with students. A discussion of learner-centeredness and professors' professional development in higher education concludes this study of professors' influence on students' learning and intended careers.

How do we measure the success of a hotel business? What factors determine performances? This paper seeks to explore the responses which researchers and practitioners have given to these questions in the last 20 years.

The paper is based on the analysis of 152 contributions and uses the balanced scorecard as a model to rationalize the main streams of research.

The analysis of literature shows the gradually assumed importance of the balanced scorecard as a satisfactory performance measurement system. The findings related to the determinants of results are instead highly complex and far‐reaching. The determining factors are generally looked for within the enterprise. Four main functional research fields have been identified (strategy, production, marketing and organization) and for each one main research goals, findings and open questions are defined. The horizontal axis of the balanced scorecard (customer perspective, strategy and process perspective) is the area of greatest research (over half of the papers). This evidence appears in line with the structural features of the hotel business and with the importance held, respectively, by customer relations and the protection of the efficiency of management processes.

The paper shows the main weaknesses and strengths in previous research design in terms of: dependent and independent variables, sample and data sources. At theoretical level, the current research is strongly based on six countries (69 percent of the sample). Given the profound diversity of national contexts, researchers focusing on internal determinants should use external control variables more extensively. Furthermore, some recent subfields appear very fragmented especially in terms of independent variables used.

The paper identifies research streams and gaps in the field of hotel performance.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.