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Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

This study aims to explore children’s cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own device and use social media and YouTube, despite platform age requirements.

Nine focus groups with 34 socioeconomically, racially and ethnically diverse children (8–11 years) were conducted in California. Groups discussed data flows online, digital privacy, algorithms and personalization across platforms.

Children had several misconceptions about privacy risks, privacy policies, what kinds of data are collected about them online and how algorithms work. Older children had more complex and partially accurate theories about how algorithms determine the content they see online, compared to younger children. All children were using YouTube and/or social media despite age gates and children used few strategies to manage the flow of their personal information online.

The paper includes implications for digital and algorithmic literacy efforts, improving the design of privacy consent practices and user controls, and regulation for protecting children’s privacy online.

Research has yet to explore what socioeconomically, racially and ethnically diverse children understand about datafication and algorithms online, especially in middle childhood.

This study underscores the critical importance of knowledge management (KM) in the context of small and medium entrepreneurial firms (SMEFs) that aim to leverage their organisational learning capability (OLC) to enhance their product innovation performance (PIP). Drawing on the foundations of resource-based and contingency theories, this study delves into the impact of OLC on SMEFs' PIP through the intermediary role of KM, focussing on an emerging economy perspective. Additionally, this investigation explores how market dynamism (MDY) moderates the indirect connection between OLC and PIP via KM.

The study involved 262 SMEFs in Ghana, with data analysis conducted using PROCESS macros in SPSS 23.0 and LISREL 8.50.

This study's findings underscore the mediating role of KM in shaping the relationship between OLC and PIP. Furthermore, they reveal that, particularly in high MDY environments, the link between KM and PIP through KM is significantly strengthened.

The study clarifies that responding to MDY's demands is a complementary managerial capability enabling firms to channel their KM activities to improve PIP. Effectively, understanding the relationship between MDY and KM could substantially influence the policies and strategies managers adopt to improve PIP for organisational growth and survival.

This study extends the OLC–PIP research and contributes to the growing literature by offering a strong account of how OLC influences PIP and the prevailing boundary conditions that impact the KM-PIP relationship.

The main purpose of this review is to enhance the understanding of intellectual structure and outlook of management innovation research as an interesting and growing research field.

This systematic literature review examines the question, what is the relationship of management innovation with the performance of companies and with other types of innovation? The work also pursues to summarize theories, contexts, characteristics of the papers and methodologies with the purpose of facilitating further development and opportunities and priorities for future research.

The results suggest that management innovation is an interesting and growing research field; in its relation to different types of innovation and performance, it is a field explored with theoretical approaches, contexts and methodologies that begin to form a consolidated body of knowledge. However, through a critical analysis, this review highlights the gaps in the literature and provides suggestions for future studies to further explore the field. This revision contributes to the literature on management innovation summarizing the findings and contributions of research published in the field and its relationships with innovation and performance. It then identifies three comprehensive research streams, namely, future research on conceptualization, definitions and measurements; research on the level of analysis; and future research on management innovation drivers, antecedents and use as mediator/moderator variables.

Management innovation is an emerging research field that is characterized as a branch of research long ignored by more orthodox lines dedicated to technological innovation and topics in product and service development research.

This study aims to address the question of why managers make different decisions in employee downsizing when their firms face external threats. Our research intends to shed light on whether and how CEOs' cognition (motivational attributes associated with regulatory focus) influences their decision-making and firms’ strategic actions on downsizing under high resource scarcity in the industry environment.

We used a longitudinal panel of 5,544 firm-year observations of US firms from 2003 to 2015 to test our conceptual model. The data was obtained from various sources, including corporate earnings call transcripts and archival databases. We used panel logistic regressions with both fixed and random effects in our research design.

Our results suggest that CEOs' motivational attributes could influence their employee downsizing decisions in response to external threats. We find that CEOs who are more promotion-focused (a stronger drive towards achieving ideals) are less likely to lay off employees during high resource scarcity. Conversely, CEOs with a higher prevention focus (a greater concern for security) do not have a meaningful impact on employee downsizing during periods of external resource scarcity.

Previous research has argued that a significant external threat would diminish individuals' impact on firm strategies and outcomes. Our findings challenge this idea, indicating that CEOs with a stronger drive towards achieving ideals are less inclined to lay off employees when resources are scarce in the environment. This study contributes to behavioral strategy research by providing new insights into how upper echelons’ cognition can influence their decision-making and firms’ employee downsizing.