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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 purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively.

A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed.

The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities.

Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved.

To the best of the author’s knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

This research paper explores how human resource management can embrace new perspectives to create smart and happy workplaces post-pandemic. Analysis of smart human resources and human resource analytics findings revealed opportunities to connect these areas and to use data-driven talent practices to optimize organizational and individual outcomes. The key results show that aligning smart technologies with competency development, while applying analytics ethically to elevate engagement, can transform competitive advantages. Major managerial insights from the paper include adopting smart tools to actively empower employees, and developing analytics measuring the impact of smart practices on happiness.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

In this chapter, we discuss talent development in the context of higher education. After discussing the advantages and disadvantages of inclusive and exclusive approaches to talent development, we present empirical data that detail how the participants of a focus group study perceive talent development in higher education. Our data show the importance of a contextualized reading of talent development as the competitive context in academia hinders an inclusive focus on talent development. This context results in a performance-centred, instead of a development-centred approach to talent management, where outperforming others in narrowly defined areas (e.g. publication record) is the main goal. We show that in such a context the development of competitive talent is rewarded, and the development of communal talent is not. The focus on performance instead of (inclusive) development becomes more pronounced when employees move through their career and is believed to have several negative consequences. Mostly women perceived that such a non-inclusive approach to talent development hinders the development and deployment of their talents and obstructs their career progression.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

This strategic commentary aims to examine the benefits and drawbacks of rigid frameworks versus flexible approaches to measuring employee engagement, arguing for a hybrid model that incorporates the best of both to better correspond with organisational subtleties and strategic goals.

This study compares the standardised, benchmarking capabilities of inflexible frameworks such as Gallup’s Q12 to the adaptability and customisation potential of flexible approaches. It emphasises the creation and implementation of a hybrid methodology that preserves the integrity of engagement measurement while also incorporating organisational-specific insights.

Despite their different benefits, rigid frameworks may neglect distinct organisational cultures, whereas completely flexible techniques may suffer with measuring consistency. A hybrid model, which combines core standardised questions and unique items, provides a balanced solution for improving the relevance, actionability and reliability of engagement data across dynamic organisational landscapes.

The discussion culminates with the proposal of a hybrid measurement strategy as a strategic innovation in human resource management. By combining scientific rigour and contextual sensitivity, this model provides a nuanced roadmap for organisations looking to thoroughly understand and effectively negotiate the complexity of employee engagement in an evolving work environment.

Introduction: Sustainable human resource management (SHRM) as a practice is nowadays seen as an essential factor contributing to an individual’s and organisation’s growth. At the organisational level, the people concerned face many pressures to inculcate SHRM practices from various authorities and stakeholders.

Purpose: This chapter explains SHRM as a concept through an extensive literature review along with the evolutionary stages and multi-lateral perspectives of SHRM. The factors affecting this concept are Economic, Social, and Environmental; its driving forces like Employees, Government and Market Pressure; Employee Outcomes, namely Employee retention, satisfaction, motivation and Employee Presence; Organisational outcomes – Business level, Workers’ satisfaction, improved environmental outcomes better correlations, etc.; and value created by SHRM in terms of both employee and organisation are thereby explained.

Methodology: A specific procedure has been employed since the chapter has been based on literature review. The process of systematic literature review has been followed, which lays down the process followed by the authors – right from the Scope Formulation to the Illustration of Conceptual Framework.

Findings: A conceptual model is represented as a basis of the literature review, which the organisation can use and apply to develop SHRM practices, and finally, the precise effects of the research findings are suggested alongside ideas for future research.

Organisational strategy becomes reality by connecting organisation’s resources and capabilities in daily operations, and physical workspace is one of the environments in which this takes place. This study aims to explore to what extent factors required for successful strategy implementation are considered when designing, using and managing workspaces of knowledge-intensive organisations.

For the study, managers in 25 large and medium-sized knowledge-intensive organisations were interviewed. The semi-structured interviews focused on organisation’s strategy, strategy implementation practices and workspace design and management. To form a comprehensive framework of strategy implementation success factors for the study, the factors of 11 frameworks were analysed, grouped and renamed.

Current workspace design, usage and management mainly support human-related strategy implementation factors. However, both organisation- and human-related factors are needed for the strategy implementation to be successful. Therefore, the organisations studied may have unused potential in their workspaces to ensure strategy-aligned operations and behaviour.

Due to the potential imbalance between organisation- and human-related strategy implementation factors, a more holistic, organisational-level approach to workspace design, usage and management is recommended to ensure the success of strategy implementation.

Workspaces have extensively been studied from individual strategy implementation factors’ as well as employees’ perspectives. Prior to this work, there are only few studies exploring workspace in the holistic, strategy implementation context.