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The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

The article analyzes the challenges Slovak businesses and organizations are facing in relation to the Covid-19 pandemic and the resulting human resource management solutions they apply.

The authors present a part of their research conducted in six Central Eastern European (CEE) countries. Based on timely data, the article presents socioeconomic characteristics of Slovakia and the results of the research “HRM Challenges in Times of the Covid-19 Crisis” conducted among employers in Slovakia. The survey method encompassed 247 Slovakian organizations.

The year 2020 was an extraordinary one for businesses, governments, and individuals alike. According to the results of this research, Slovak organizations were not prepared for the Covid-19 crisis. Respondents who had a pandemic action plan at the time of the breakout were hardly found. Measures taken to preserve human health directly impacted the way how work is done and organized. This poses significant challenges for both employers and employees. In general, small organizations used cost and staff reduction tools. On the other hand, some of the respondents, mainly large organizations have been using future-oriented solutions, e.g. organizational development.

In this research the authors analyzed the experiences of a Central European country, namely Slovakia. However, the experiences gained and collected here can be useful in the international arena as well.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

This research studies the behavior choice of key actors in the construction supply chain and constructs a model which includes the benefit function of the government, contractors and owners, aiming at improving the coverage of green buildings.

In this paper, tripartite dynamic game is studied and simulated based on duplicate dynamic equation. The tripartite game under government intervention is rarely considered, and government punishment measures are seldom introduced into the research.

According to the simulation results, the practical insights in line with the development of green supply chain are put forward. Rewards and punishments affect the development of the supply chain. New technologies and new materials accelerate the development of green supply chain and then improve the coverage of green buildings.

This paper constructs a dynamic model based on complete information rationality, which is difficult to realize in practice, for information is incomplete and human rationality is limited.

In fact, the government has not issued a punishment document to introduce a new variable adjustment model.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

This article advocates that privacy literacy research and praxis mobilize people toward changing the technological and social conditions that discipline subjects toward advancing institutional, rather than community, goals.

This article analyzes theory and prior work on datafication, privacy, data literacy, privacy literacy and critical literacy to provide a vision for future privacy literacy research and praxis.

This article (1) explains why privacy is a valuable rallying point around which people can resist datafication, (2) locates privacy literacy within data literacy, (3) identifies three ways that current research and praxis have conceptualized privacy literacy (i.e. as knowledge, as a process of critical thinking and as a practice of enacting information flows) and offers a shared purpose to animate privacy literacy research and praxis toward social change and (4) explains how critical literacy can help privacy literacy scholars and practitioners orient their research and praxis toward changing the conditions that create privacy concerns.

This article uniquely synthesizes existing scholarship on data literacy, privacy literacy and critical literacy to provide a vision for how privacy literacy research and praxis can go beyond improving individual understanding and toward enacting social change.

Task conflict and relationship conflict are common in organizations. This paper aims to present a unique case of the use of the targeted conflict-resolution technique. The revival of positive group dynamics is aptly shown.

This descriptive case study is developed as a practice insight to showcase how a peculiar case of misunderstanding is resolved in the most unconventional way through the intervention of a mediator who unearths the real cause of contention. The mediator works through logic and emotion to remove negativity. Narration, a necessary component of the case study approach, peeps into the research subject involving flashbacks, flash forward, backstories and foreshadowing. The mediator uses reframing as a tool very efficiently, encouraging the people in conflict to understand the nothingness in their cold war and eventually prompting them to collaborate and compromise.

The shifts in communication dynamics post-mediator’s intervention are subtle and full of wisdom, encouraging introspection and constructive interaction, eventually bridging the differences. The possibility of achieving a state of homeostasis in the future magnifies. The belief in the power of affirmation and manifestation is validated. The heavy, difficult, hardened negativity loses ground and gets transformed.

Conversation/prayers at the deepest level in several meetings are the communication tools that have immense social relevance in the Indian context.

A unique combination of intermediation encompassing written communication and energy transformation is adopted to resolve ongoing conflict by stroking the positive psychology of the partakers. To some, the method may appear to have a spiritual connotation.

This literature review paper aims to discuss the impacts and measures taken on Covid-19 with respect to architecture, built form and its allied fields along with observations from around the world with the end results of its implementation.

Various research journals from Science Direct and Taylor and Francis among others were referred and reviewed to learn more on Covid-19 along with its impacts on people and their built environment. Keywords that aligned with the topic of interest like pandemic, Covid-19, coronavirus, urban spaces, built environment and built-up space were identified. Research papers that aligned with the scope of the paper and its keywords were found. An excel sheet with the basic data of these papers was written down. The essence of each of these articles were understood and linked with the topic in hand. A review of all these papers was written coherently to the topic. Future scope in this field of study was also identified.

It discusses the various measures taken around the world in built-up spaces along with the various measures given by researchers, architects and urban planners to mitigate and reduce the transmission of Covid-19 in the built environment and urban spaces. The discussed measures along with observations from around the world have also been discussed with its end results. Discussions and conclusions on these design strategies and recommendations are made with the anticipation of a paradigm shift due to post-pandemic changes. Current scenarios of public spaces in densely populated countries are discussed. Future scope in this ongoing field of study includes measures that can be taken in specific to some particular built-up typologies like markets, theaters, parks, bazaars, etc.

The limitation of this study is the restricting of the study of Covid-19 to only architectural, urban and public realm scales. The study does not facilitate the involvement of other fields and their influence with Covid-19.

This paper has been completely written by the author and the co-authors and has not been copied from any other sources.

Virtual reality (VR) technologies have been gaining popularity in training and development in many fields to promote embodied training. However, its adoption in management has been slow and rigorous empirical research to understand its impact on learning and retention is scarce. Thus, this paper aims to examine the benefits of VR technologies for management training.

Through a longitudinal experiment comparing VR platforms and a traditional video platform, this study examines two as yet unexplored benefits of VR technologies vis-à-vis management training – the cognitive outcome and affective reaction of the training experience over time.

This study finds that, for cognitive outcomes, immediate gains are similar across video and VR platforms, but subsequent knowledge retention is significantly higher for VR platforms. In terms of affective reaction, VR platforms generate significantly more enjoyment, which carries over to two weeks later, and is partially associated with higher knowledge retention.

This study has implications for management and human resource trainers and system designers interested in integrating VR for training and development purposes.

This study makes a unique contribution by unpacking the long-term benefits of an embodied training system, as well as identify a possible link between cognitive outcomes and affective reaction.

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.