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Human resource analytics (HRA) is an HR activity that companies and academics increasingly pay attention to. Existing literature conceptualises HRA mostly from an objectivist perspective, which limits understanding of actual HRA activities in the complex organisational environment. This paper therefore draws on the practice-based approach, using a novel framework to conceptualise HRA-as-practice.

The authors conducted a systematic literature review of 100 academic and practitioner-oriented publications to analyse existing HRA literature in relation to practice theory, using the “HRA-as-practice” frame.

The authors identify the main practices involved in HRA, by whom and how these practices are enacted, and reveal three topics in nomological network of HRA-as-practice: HRA technology, HRA outcomes and HRA hindrances and facilitators, which the authors suggest might actualize enactment of HRA practices.

The authors offer HR function and HR professionals a basic ground to evaluate HRA as a highly contextual activity that can potentially generate business value and increase HR impact when seen as a complex interaction between HRA practices, HRA practitioners and HRA praxis. The findings also help HR practitioners understand multiple factors that influence the practice of HRA.

This systematic review differs from the previous reviews in two ways. First, it analyses both academic and practitioner-oriented publications. Second, it provides a novel theoretical contribution by conceptualising HRA-as-practice and comprehensively compiling scattered topics and themes related to HRA.

This paper aims to address a gap in investigating specific impacts of climate change on mental health in the Pacific region, a region prone to extreme events. This paper reports on a study on the connections between climate change, public health, extreme weather and climate events (EWEs), livelihoods and mental health, focusing on the Pacific region Islands countries.

This paper deploys two main methods. The first is a bibliometric analysis to understand the state of the literature. For example, the input data for term co-occurrence analysis using VOSviewer is bibliometric data of publications downloaded from Scopus. The second method describes case studies, which outline some of the EWEs the region has faced, which have also impacted mental health.

The results suggest that the increased frequency of EWEs in the region contributes to a greater incidence of mental health problems. These, in turn, are associated with a relatively low level of resilience and greater vulnerability. The findings illustrate the need for improvements in the public health systems of Pacific nations so that they are in a better position to cope with the pressures posed by a changing environment.

This paper contributes to the current literature by identifying the links between climate change, extreme events, environmental health and mental health consequences in the Pacific Region. It calls for greater awareness of the subject matter of mental health among public health professionals so that they may be better able to recognise the symptoms and relate them to their climate-related causes and co-determinant factors.

The purpose of this paper was identification and empirical assessment of the differentiation of consumers' attitudes in Ukraine and Poland to Russian brands and other brands offered on the Russian market after Russia’s aggression against Ukraine on February 24, 2022.

The main research methods include a systematic literature review and the authors' own surveys conducted in November 2022. The research sample comprised 950 consumers – 67% of them were Poles, 30% – Ukrainians and 3% from other countries.

A respondents' country (Poland and Ukraine) does not impact attitudes to brands offered on the Russian market after Russia's invasion of Ukraine on February 24, 2022. Moreover, it does not affect and differentiate emotional engagement in the conflict and assistance to war victims. Cluster analysis resulted in identifying two groups on the basis of consumers' declared emotional reactions to the war. The first group was smaller (N = 353, 37.2%), referred to as “indifferent consumers”, and was characterized by a greater inclination to purchase brands offered in Russia. The other cluster, referred to as “sensitive consumers” (N = 597, 62.8%), comprises those engaged in offering assistance to war victims, showing strong emotions in connection with the aggression and military activities and characterized by a clearly negative attitude to Russian and other offered brands and an inclination to boycott these brands.

A short time horizon, the study confined to two countries, difficulties in reaching Ukrainian respondents due to power failures in Ukraine in the period of conducting the survey (November 2022), a non-representative research sample – overrepresentation of people aged 18–25 years.

The research study contributes to the knowledge about consumer brand attitudes and preferences under unique social, economic and market conditions. These conditions were created by Russia's invasion of Ukraine in 2022, as well as the international and global character of the war in Ukraine. The significant implications of the study refer to brand communication policies and companies' CSR-related declarations. A number of consumers' errors were recorded, resulting from wrong brand retrieval, which were rectified at a later stage as a result of international restrictions imposed on Russia, harsh media criticism and social international embargoes imposed on brands offered in Russia. The marketing communication of contemporary global brands should give consideration to the informative function of CSR activities, and the communication process should be continuous. Critical attitudes and an inclination to boycott brands point to the possible consequences faced by inconsistent and ethically doubtful brand policies. This implication is clearly confirmed by the results of the study.

The authors also wish to highlight the implications for practice and society. As mentioned earlier, Polish consumers involved in providing aid to victims of the war also expressed their opposition to the war by boycotting Russian products and international brands remaining in Russia. Popularization of the research results obtained by the authors can be a form of sensitizing the public to the need for long-term relief, awakening global awareness of the essence and importance of sanctions imposed on Russia, as well as the possibility of expressing opposition through individual purchasing decisions and boycotting brands still present in Russia.

The study allowed for identifying consumers' differentiated brand attitudes in two countries: a country inflicted by war (Ukraine) and a front-line country, strongly supporting Ukraine (Poland). The research contributes to consumer behavior theories and studies of consumer attitudes and preferences from the perspective of international corporations' CSR activities under the unique conditions of war. Also, it contributes to the knowledge of the mechanism of forming attitudes to Russian and international brands offered in Russia among CEE consumers.

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.

DEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.

 The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.

Many of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.