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Emergency remote teaching (ERT) has potential for transforming future instruction and learning across the K-12 educational domain. The study presented here evaluated empirical evidence from peer-reviewed literature pertaining to the challenges and opportunities experienced by teachers and students during the implementation of ERT prompted by the COVID-19 pandemic. To locate relevant reports and research, the authors explored three databases: Web of Science, ScienceDirect and Scopus. Based upon predefined selection criteria, they selected 51 studies for thematic and content analysis. Next, they developed a taxonomy which comprised three categories: (1) K-12 education responses to ERT; (2) educational inequality; and (3) learning outcomes. Using this taxonomy, the authors conducted a deep analysis and critical review to highlight multiple challenges and critical gaps in the literature surrounding ERT in K-12 education settings. Their review reveals innovative strategies for overcoming obstacles to technological readiness, online learning adaptation and teachers' and students' physical and mental health. This knowledge will be valuable to policymakers, researchers, practitioners and educational institutions in reducing the adverse effects of catastrophic situations on childhood education in the future.

Regardless of the prevalence and value of change initiatives in contemporary organizations, these often face resistance by employees. This resistance is the outcome of change recipients' cognitive and behavioral reactions towards change. To better understand the causes and effects of reactions to change, a holistic view of prior research is needed. Accordingly, we provide a systematic literature review on this topic. We categorize extant research into four major and several subcategories: micro and macro reactions. We analyze the essential characteristics of the emerging field of change reactions along research issues and challenges, benefits of (even negative) reactions, managerial implications, and propose future research opportunities., Author(s): Khai Wah Khaw [sup.1] , Alhamzah Alnoor [sup.1] [sup.2] , Hadi AL-Abrrow [sup.3] , Victor Tiberius [sup.4] , Yuvaraj Ganesan [sup.5] , Nadia A. Atshan [sup.2] Author Affiliations: (1) [...]

Due to the outbreak of Covid-19 epidemic, work stress among health sector employees has risen too high. The study aims at determining the effect of the current coronavirus epidemic in the form of stress perceived among the medical workers in Pakistan and to discover the moderating role played by a healthy climate in offsetting it. The data was collected from 255 medical workers through a self-administered online questionnaire. Multiple Hierarchical Regression was used as a tool to test the hypotheses of the study. The results obtained indicate a correlation between the pandemic and the stress caused by it among the health workers, whereas, the role of a wholesome climate in the reduction of stress among them was found lacking. Sub-hypotheses indicate that the healthy environment provided by supervisors is effective in reducing the impact of workers' handling of the Covid-19 epidemic and perceived stress, while the healthy environment provided by hospitals in general or by workgroups fails to cause such positive change. This revelation necessitates the adoption of compulsory precautionary measures on the part of relevant authorities, because increase in stress caused by the pandemic can prove more lethal than the pandemic itself. The threat of the coronavirus pandemic has emerged as a massive socio-economic challenge for the global community, especially for the developing countries like Pakistan which faces serious socio-economic challenges in the current scenario. On account of the similarity of situations, the results obtained through this study can be safely generalized to other developing countries, particularly from the South Asian region., Author(s): Sammar Abbas [sup.1] , Hadi AL-Abrrow [sup.2] , Hasan Oudah Abdullah [sup.3] , Alhamzah Alnoor [sup.4] , Zeeshan Zaib Khattak [sup.1] , Khai Wah Khaw [sup.5] Author Affiliations: (1) [...]

Consumers' willingness to pay a price premium is pivotal for assessing brand value and competitive advantage. Yet, limited and scattered research has focused on how combining brand emotion, strength, and brand loyalty can influence consumers' willingness to accept a price premium. The present study examines the role of brand attachment, brand strength, and brand loyalty in determining consumers' willingness to pay a price premium and explores their interplay using a serial mediation model within a unified framework, specifically focusing on home appliance brands. Data from 323 valid questionnaires collected from Algerian households were analyzed using PLS‐SEM. Results demonstrate that consumers' willingness to pay a price premium is significantly and positively influenced by brand strength, brand attachment, and brand loyalty. Furthermore, the relationship between brand strength and consumers' willingness to pay a price premium is mediated positively by brand attachment and brand loyalty. Grounded on various theories and addressing gaps captured in previous studies, this research is considered pioneering in this field. This study significantly advances our understanding of how brand emotional bonds, brand relationships, and brand strength interplay to influence consumers' willingness to pay a premium. The findings highlight the importance for brand managers to sustain robust brands to stimulate consumers' opening to pay extra, thereby achieving and maintaining long‐term success in a competitive market. [ABSTRACT FROM AUTHOR]

The integration of big data analytics in digital marketing has fundamentally transformed how organizations understand and influence consumer behavior. This systematic review explores the potential of big data to offer deep insights into consumer preferences and behaviors. The current literature on big data and consumer behavior showcases its potential in enhancing marketing and customer experiences. However, significant gaps exist, such as longitudinal studies on how continuous big data–driven personalization affects consumer trust and loyalty over time. Addressing these gaps will advance academic understanding and offer practical insights for optimizing marketing strategies and improving customer experiences ethically. Focusing on these areas will contribute to a holistic view of big data's responsible use in digital marketing. By utilizing large datasets, businesses can now tailor their marketing strategies to individual consumers, enhancing customer satisfaction and engagement. Adopting the PRISMA methodology, this review synthesizes recent literature to evaluate the benefits of big data in digital marketing. The research was conducted through a rigorous five‐stage process, encompassing the identification of key research questions, database searches, and the critical analysis of selected articles: (1) defining the initial topic, (2) developing the appropriate research questions, (3) identifying the necessary keywords, (4) identifying and searching databases, and finally (5) accessing and reading the articles. The databases that were searched were as follows: Scopus, Web of Science, Emerald Insight, Springer Link, and ScienceDirect. The articles that were selected were 19, in a total sum of 265 identified articles. The findings consolidate current knowledge on how big data analytics can optimize marketing strategies and consumer experiences. Ultimately, this review underscores the transformative potential of big data in digital marketing, highlighting its role in enhancing customer insights and driving more effective marketing strategies. [ABSTRACT FROM AUTHOR]

The spread of misinformation on social media is a critical issue. One potential solution to mitigate the spread is user corrections; however, users often refrain due to various concerns. Leveraging the established influence of user interface design (UID) on how user interact with and respond to misinformation, this study investigates how user interface features can be designed to motivate users to challenge misinformation. It is aimed at gaining insights into users' needs and UID requirements that encourage this behaviour. We conducted four codesign sessions with 18 social media users (age range 20–60 years M = 39.1; 10 female and 8 male). We applied the unified theory of acceptance and use of technology (UTAUT) as a theoretical framework and analysed our data based on the core constructs of this framework: performance expectancy, effort expectancy, social influence, and facilitating conditions. Our findings reveal four design considerations: creating secure and supportive environments, facilitating informed discussions through easy confrontation and access to reliable resources, leveraging recognition and social proof, and user support infrastructure. We also identified specific design elements with users, including indirection, semianonymity and privacy, simplicity, one‐click challenging, easy access to reliable sources, recognition, displaying social proof, and platform support. These elements are aimed at reducing social discomfort and making the process of correcting misinformation more approachable for users. Our findings offer actionable insights for social media platform designers to reduce the spread of misinformation by creating environments that encourage constructive dialogues and allow users to challenge misinformation without fear of conflict. [ABSTRACT FROM AUTHOR]

Context: In high‐stakes assessments, such as court cases or managerial evaluations, decision‐makers heavily rely on psychological testing. These assessments often play a crucial role in determining important decisions that affect a person's life and have a significant impact on society. Problem Statement: Research indicates that many psychological assessments are compromised by respondents' deliberate distortions and inaccurate self‐presentations. Among these sources of bias, socially desirable responding (SDR) describes the tendency to provide overly positive self‐descriptions. This positive response bias can invalidate test results and lead to inaccurate assessments. Objectives: The present study is aimed at investigating the utility of mouse‐ and eye‐tracking technologies for detecting SDR in psychological assessments. By integrating these technologies, the study sought to develop more effective methods for identifying when respondents are presenting themselves in a favorable light. Methods: Eighty‐five participants completed the Lie (L) and Correction (K) scales of the Minnesota Multiphasic Personality Inventory‐2 (MMPI‐2) twice: once answering honestly and once presenting themselves in a favorable light, with the order of conditions balanced. Repeated measures univariate analyses were conducted on L and K scale T‐scores, as well as on mouse‐ and eye‐tracking features, to compare the honest and instructed SDR conditions. Additionally, machine learning models were developed to integrate T‐scores, kinematic indicators, and eye movements for predicting SDR. Results: The results showed that participants in the SDR condition recorded significantly higher T‐scores, longer response times, wider mouse trajectories, and avoided looking at the answers they intended to fake, compared to participants in the honest condition. Machine learning algorithms predicted SDR with 70%–78% accuracy. Conclusion: New assessment strategies using mouse‐ and eye‐tracking can help practitioners identify whether data is genuine or fabricated, potentially enhancing decision‐making accuracy. Implications: Combining self‐report measures with implicit data can improve SDR detection, particularly in managerial, organizational, and forensic contexts where precise assessments are crucial. [ABSTRACT FROM AUTHOR]