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The transition to digital business systems has revolutionized organizational operations, driven by the integration of advanced technologies such as artificial intelligence (AI). This integration indicates a shift, redefining traditional practices and enhancing efficiency across diverse sectors such as finance, healthcare, and manufacturing. This study explores the impact of AI on auditing through a systematic literature review to develop a conceptual framework for auditing practices. The theoretical implications show the transformative role of AI in redefining auditors' roles, shifting from retrospective examination to proactive real-time monitoring. Moreover, managerial contributions stress the benefits of AI integration, enabling informed decision-making in risk analysis, financial management, and regulatory compliance. Future research should explore AI's influence on auditing efficiency, performance, regulatory challenges, and auditor adaptation. Overall, this study underlines the importance for organizations to embrace AI integration in auditing practices, fostering innovation, competitiveness, and resilience.

Currently, students in basic secondary school in Colombia have a low performance in reading comprehension at the inferential level. This pedagogical proposal aimed to strengthen the level of inferential reading of students who attend elementary school, through the application of a didactic sequence from the cognitive-discursive perspective. Theoretically, the concepts proposed by Cisneros, Olave and Rojas regarding four types of inferences were assumed. Likewise, the theoretical orientations of Van Dijk (1978), Solé (1992) and Martínez (2002) were taken into account to support the cognitive-discursive perspective adopted in the study. Methodologically, the qualitative approach was used with the action research method, and techniques such as observation, questionnaire, field diary and didactic sequence were used. In relation to the results, it is evident that the participants in the closing questionnaire increased significantly compared to the four types of inference evaluated, thus demonstrating greater interpretation when facing the texts.

AbstractDue to the hybrid‐competitive market and the economic crisis, consumers are more ethically concerned about companies failing to adhere to environmental and human rights standards. Thus, companies have implemented corporate social responsibility (CSR) initiatives to improve their ethical performance and establish the groundwork for sustainable growth and competitive advantage. Relationship marketing orientation (RMO) contributes to strengthen customers relationships and value co‐creation. Drawing from the stakeholder theory, this study aims to examine how CSR initiatives, that is, customer‐oriented and employee‐oriented, affect customer participation behavior (CPB) and customer citizenship behavior (CCB) in the banking sector. The main findings highlight a significant correlation between the CSR initiatives and customer value co‐creation behavior. Finally, by triangulating CSR, RMO, and value co‐creation, this study aims to provide more insights on RMO and CSR strategies for academicians and practitioners.

The use of lamps worldwide is rapidly increasing, and the number of broken, burned-out, and defective lamps is very significant, polluting the environment. For this reason, we will address the topic of lamp recycling using magnetic induction technology. This paper presents induction separation by an innovative, improved induction separator design with an important advantage. It extracts aluminum particles from a mixture of waste materials, including non-metallic and non-ferrous particles from broken neon lamps. The induction separator features a single vertical disk rotated by an electric motor. The disk has a series of permanent magnets mounted alternately on its surface. The experimental results of the separation process achieved through an induction separator exhibit a high purity level, boasting a 100% separation efficiency, surpassing the performance of previous separators. These findings substantiate the validity of employing the induction separator to recycle conductive materials.

We introduce the complicated relationship between Shakespeare, performance studies, and fan studies before focusing on antifandom. In conjunction with evolving perspectives on antifan activities in recent years, we identify two broad, sometimes overlapping, categories of Shakespeare antifandom: (1) external antifandom or trolling that challenges the quality of his work or the perception of its cultural and intellectual value; and (2) internal antifandom or gatekeeping couched as a defense of Shakespeare against perceived threats posed by other Shakespeareans. In the process, we historicize Shakespeare antifandom activities and perspectives from Robert Greene to the present in order to answer the question, What does Shakespeare antifandom look like, and what are productive, rather than reductive, ways to engage with it? Over the course of this discussion, we offer the theoretical framework for the collection as a whole before explaining the organizational logic of the volume and overviewing the individual chapters.

This article accounts for a particular kind of politicised anonymity, namely 'disobedient anonymity', that operates as a liberatory response to the longue durée of gender violence. We examine the street performance Un violador en tu camino created by the Chilean feminist theatre collective LASTESIS, to show how disobedient anonymity is an embodied and collective disruption of colonial subjectification and state-sanctioned gender violence. Building on the insights of the Argentinian decolonial feminist scholar Rita Segato's analysis of high-intensity patriarchy as well as discussions of perverse anonymity in critical phenomenology, we examine how disobedient anonymity operates in Un violador as an embodied practice that makes possible a decolonising, feminist liberatory public. We argue that through the deployment of disobedient anonymity, Un violador re-territorialises and re-temporalises colonial fields of sens. In doing so, it institutes a new form of political subjectivity that upends colonial logics.

This article discusses collaborative ethnography as a meaningful source for spatial research, in particular, for participatory methodologies in urban planning and placemaking processes. It investigates the experiences with co-creation and co-research in different research projects to gain insight into the performance of collaborative ethnography as a technique to explore and enrich local knowledge. To better understand the possible causal relationships between the experience gained in the projects and the learnt lessons, we also identify recommendations for improving research methodologies to be applied in placemaking. This article concludes that collaborative ethnography is an effective tool for adding value to spatial co-research and co-creation processes. It opens opportunities for the co-production of space, ideas and knowledge, contributing at the same time to better informed decision-making. It also helps improve ideas and gather insights into the spatial needs of focus groups.

AbstractWe examine how fiscal legibility, the ability of central authorities to observe local conditions for the purposes of taxation, shapes political centralization and state development. When rulers lack information about the periphery, they may benefit from ceding autonomy to tax‐collecting intermediaries to encourage fiscal performance. As information quality improves, rulers become better able to monitor and sanction local officials, allowing them to tighten control over taxation and establish more direct state presence. Centralization, in turn, encourages investment in improving fiscal legibility, leading to long‐term divergence in state development. We study the consequences of a technological innovation that dramatically improved the Spanish Crown's fiscal legibility in colonial Mexico: the discovery of the patio process to refine silver. We show that political centralization differentially accelerated in affected districts and that these areas subsequently saw disproportionate state investment in informational capacity, altering the trajectory of state development.

We investigate whether boards of directors reward and punish chief executive officers (CEOs) based on employee satisfaction ratings. Using data from Glassdoor, we find that CEOs tend to receive larger bonuses when employee satisfaction ratings increase. Similarly, we find a higher rate of CEO dismissal when employees become less satisfied. Further, we investigate three factors that may amplify the role of employee satisfaction ratings in CEO evaluations: the importance of employees to financial performance, the board's commitment to stakeholders, and the need to preserve firm reputation. We find some evidence that each of these three factors strengthens the relationship between employee satisfaction ratings and CEO evaluations. Finally, we exploit the staggered timing of first-time reviews on Glassdoor and use a difference-in-differences design to strengthen our inferences. Collectively, these findings suggest that boards' evaluations of CEO compensation and retention incorporate employee satisfaction ratings.

One of the fundamental objectives of statistics is to achieve accurate predictions. In high-dimensional settings (where the number of variables, p, exceeds the number of observations, n), the performance of ordinary least squares (OLS) is often suboptimal due to its high variance, which leads to lower prediction accuracy. Shrinking the variables is a promising approach, and methods such as ridge regression, elastic net, lasso, and adaptive lasso are well-known techniques for this purpose. While variable shrinkage introduces a small bias, it significantly reduces the variance compared to OLS. The effectiveness of shrinkage methods largely depends on the selection of the tuning parameter. Cross-validation and the Bayesian Information Criterion (BIC) are commonly used for this purpose, and an improved version of BIC has shown impressive results.???????? = (5.6,5.6,5.6,0), ???????? = (3,1.5,0,0,2,0,0,0), ???????? = (0.85,0.85,0.85,0) are the multiple regression models which are compared.

After Lithuania re-gained its independence, linguistic and cultural minority groups residing in the country had the right to be educated in non-Lithuanian-medium schools. Yet, the education of linguistic minority children has been a contentious topic in Lithuania for many years, and there is no conclusive evidence on how minority-language-medium schools in Lithuania perform academically, with national assessment suggesting that they perform worse than Lithuanian-speaking schools, and international assessment showing mixed results. Our research focuses on Russian- and Polish-medium schools and analyses the difference in academic performance among children attending Lithuanian-medium school and minority-language schools, as well as its reasons and social implications. Using the OECD Program for International Student Assessment (PISA) data, we find that minority-language children perform better when attending a school in which the language of instruction is the same as their home language.

This paper explores the idea of professional sport as a form of labour. While research on everything from performance statistics to sports rights and technology had proliferated, what players think about <i>sport as work</i> deserves greater attention. The inequalities and exclusion of sports are evident and we need to understand the drivers of this, but we also need to understand why working in these industries matters to people, how they experience it and what values it has for them. In order to do this, I want to draw on critical cultural labour studies, and the paper argues that applying this lens to work in sport could help develop a notion of what constitutes 'good work' for athletes. The aim of the paper is to both to consider the landscape of sports labour research and to propose a future research agenda.

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method.

Nesta introdução ao dossiê "Atmosferas e Antropologia", revisamos alguns temas centrais ao debate contemporâneo sobre atmosferas na teoria social e apresentamos os artigos que compõem o volume. Nos guiamos por algumas questões basilares. O que são as atmosferas? Apresentamos a fenomenologia das atmosferas de Hermann Schmitz e suas repercussões contemporâneas, destacando como ela fornece novas soluções para os problemas da materialidade, mediação e sentidos. Como as atmosferas são produzidas? Propomos uma tipologia de atmosferas e dialogamos com os conceitos de Stimmung e geradores para analisarmos a sua relação com as técnicas, o habitus e a performance. Como a ubiquidade, visceralidade e indeterminação que caracterizam as atmosferas são incorporadas na modernidade pelo capitalismo e pelas mídias de massa? Por fim, como as atmosferas exercem seu poder? Destacamos como este conceito permite uma redescrição de componentes essenciais da política, como a soberania, a estética e a temporalidade.

Successful disinformation campaigns depend on the availability of fake social media profiles used for coordinated inauthentic behavior with networks of false accounts including bots, trolls, and sockpuppets. This study presents a scalable and unsupervised framework to identify visual elements in user profiles strategically exploited in nearly 60 influence operations, including camera angle, photo composition, gender, and race, but also more context-dependent categories like sensuality and emotion. We leverage Google's Teachable Machine and the DeepFace Library to classify fake user accounts in the Twitter Moderation Research Consortium database, a large repository of social media accounts linked to foreign influence operations. We discuss the performance of these classifiers against manually coded data and their applicability in large-scale data analysis. The proposed framework demonstrates promising results for the identification of fake online profiles used in influence operations and by the cottage industry specialized in crafting desirable online personas.