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The purpose of this study is to examine the use of IoT technology (RFID technology, sensor networks, wearable devices and other smart items) in office settings and its respective impact on the optimization of employees’ productivity and workspace effectiveness.

The paper reviews 41 relevant publications reporting IoT use in office settings to identify how this technology has been applied in office settings and what topics are mostly addressed in the literature; how IoT technology improves employees’ productivity; and what the benefits and risks associated with IoT use in the workplace environment are.

Two main areas of application of IoT technology in the workplace environment were identified. The first one concerns the influence of the physical characteristics of workplaces on aspects related to workspace effectiveness. The second one is employee-centered and concerns the use of IoT data to identify employees’ social behavior, physiological data and emotional estates associated with productivity. IoT technology provides real-time data with speedy information retrieval. However, its deployment in office settings is not exempt from risks. Employee workplace surveillance, re-individualization of the IoT data and employee refusal of IoT technology in office settings are the main risks associated with this technology.

This literature review categorizes IoT application in office settings according to two perspectives and highlights employees' attitudes, user-experience of IoT technology and the risks associated with this technology. These results will help researchers and workplace managers interested in the deployment of this technology in the workplace environment.

The research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This study aspires to provide an in-depth literature review and critically assess the knowledge gaps in HR analytics and attritions within organisational performance.

The review analyses the corpus of 196 research articles published in ostensible journals between 2011 and 2023. To identify research gaps and provide valuable insights, this study synthesises relevant studies using School of thought (S), Context (C), Methodology (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) (SCM-TBFO framework). This study employs the R programming language to conduct a systematic literature review in accordance with the “preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines.

The emerging discipline of HR analytics encompasses the potential to manage attrition and drive organisational performance enhancements effectively. The study of SCM-TBFO encompasses a multidimensional approach, incorporating diverse perspectives and analysing its complex aspects compared to various approaches. The School of thought includes the human capital theory, expectancy theory and resource-based view. The varied research contexts entail the USA, United Kingdom, China, France, Italy and India. Further, the methodologies adopted in the studies are artificial neural networking (ANN), regression, structure equation modelling (SEM) case studies and other theoretical studies. HR analytics and attrition triggers are data mining decision systems, forecasting for firm performance and employee satisfaction. The barriers include leadership styles, cultural adaptability and lack of analytic skills, data security and organisational orientation. The facilitators were categorised into data and technology-related facilitators, human resource policies and organisational growth and performance-related facilitators. The study's primary outcomes are technology adoption, effective HR policies, HR strategies, employee satisfaction, career and organisational expansion and growth.

The primary goal of the literature review is to provide a comprehensive overview of the current state of HR analytics and its impact on organisational performance, particularly in relation to attrition. Further, the study suggests that attrition, a critical organisational concern, can be effectively managed by strategically utilising HR analytics and empowering data-driven interventions that optimise performance and enhance overall organisational outcomes.

This study aims to draw lessons on how talent identification becomes a critical factor in the field of talent management (TM).

A simulation approach with three developed scenarios is used in the paper. The first utilised the standard deviation of skewed performance scores, the second applied the standard deviation of normalised data and the third practised a percentile approach. Concerning the normalisation process of employee performance data, the paper proposed a weighted function to address skewness.

The results indicate that the process of identifying talent using a nine-grid box is sensitive to changes in the classification criteria used, indicating a bias in identifying talent. In sum, using a standard deviation approach using transformation data is the most appropriate choice for use in performance data with a skewed distribution.

The Government of West Java Province, Indonesia, can use the simulation results to objectively identify excellent civil servants and develop an appropriate TM strategy. A similar process treatment can be implemented in other organisations that have skew distribution issues.

This paper introduces a weighted function approach to address practical problems in the unsymmetrical distribution of employee performance scores when identifying talent using a TM framework. It shows the application of a unique mathematical technique to solve issues found in the field of human resources management systems.

The purpose of this paper is to critically reflect on the pros and cons of using employee information in big data projects.

The authors reviewed papers in the area of big data that has immediate repercussions for the experiences of employees and employers.

The review of papers to date suggests that big data lessons based on employee data are still a relatively unknown area of employment literature. Particular attention is paid to discussion of employee rights, ethics, expectations and the implications employer conduct has on employment relationships and prospective benefits of big data analytics at work for work.

This viewpoint paper highlights the need for more discussion between employees and employers about the collection, use, storage and ownership of data in the workplace. A number of recommendations are put forward to support future data collection efforts in organisations.

The purpose of this paper is to empirically test the validity of the service-profit chain (SPC) in a restaurant company context to comprehensively explicate the relationship between organizational practices, employee attitudes with customer and financial outcomes.

The method used both questionnaire and company proprietary data to measure the predicted SPC outcomes through structural equation modeling. The research data were obtained from employees, customers and management at five restaurants in one casual theme restaurant chain in Australia.

The findings indicate that revenue may be a more appropriate outcome than profit in the SPC, that context and individual unit circumstances matter and that there may be a time lag between organizational actions, employee behavior, customer satisfaction and financial outcomes.

Because of the nature of field research, there are limitations. As restaurants were added during the study, data per unit were impacted. Moreover, budgetary constraints limited the number of customer surveys. Nonetheless, the data set includes management, customer, employee and proprietary financial measures which are rarely available in the research literature. These data allow a thorough study of the SPC that provides both important findings and a model for future investigations into the SPC.

As the SPC is a widely cited model used to explain the linkages between managerial and organizational actions and financial outcomes as they work through employee interactions with customers, the findings suggest that the chain may have a more direct impact on revenue than profit. Moreover, the data strongly suggest that context matters as the unique context of the restaurants had important influences on financial outcomes. The findings also indicate that a time lag exists between managerial and organizational actions and financial outcomes, suggesting that it can take time for such actions to ripple through the SPC.

Structural equation modeling and standardized measures allowed the authors to overcome prior limitations in SPC research. Moreover, SPC researchers seldom have access to the proprietary data that enabled a test of the entire SPC. Consequently, this study contributes new insights into this classic model’s value in predicting and explaining financial outcomes resulting from the actions of an organization’s leadership influencing employee behavior toward customers in the restaurant industry.

This study examined employees’ experiences of remote work and the impact of remote work on working life.

This was a mixed-methods study undertaken in three Finnish nonprofit firms. In total, 1,154 respondents took part during the first and second COVID-19 waves in Finland.

COVID-19 remote working saved employees’ and businesses’ financial resources; increased job satisfaction, firms’ performances and employee employment opportunities; and positively affected organizations’ trust outcomes. This study found that female team leaders were significantly more likely than male team leaders to trust team members’ data protection abilities. In addition, remote work can reduce climate pollution. Remote work during the COVID-19 pandemic also affected the social lives of the employees and work relationships and led to a reduction in physical activity levels and work overload.

The main theoretical contributions of this study are the identification of remote working characteristics during the COVID-19 pandemic, positive/negative outcomes of remote working, and further forms measurable propositions. This study contributes to the change management literature and opens up new avenues for future research.

This study can help managers to better understand and lead employees at a time when significant numbers continue to work remotely. Continued work in this field is important because organizations are required to be agile in a changing operating work environment. Given that home-based work has become the new normal, organizations may need to update their data protection rules and address organizational trust issues.

Social effects can be seen as an increased understanding of the effects of remote work in the colleague network. Although remote work suits many employees, they also wish to see their colleagues occasionally. Thus, a mix of remote work with some in-office time may be a more attractive option than remote work only.

The integrated multidimensional framework applied in this study is based on research findings. The framework is dynamic and can be further expanded with new findings, serving as a theoretical basis to guide future research.

The purpose of this paper is to investigate employees' and managers' accounts of interactive learning and what might encourage or inhibit emergent learning.

The approach taken was a constructivist/social constructivist ontology, interpretive epistemology and qualitative methodology, using grounded theory method. Data collection included semi‐structured interview, “complete this sentence” and “scenarios” from 51 respondents: 22 managers and 29 employees in four private sector organisations. As respondents' theories emerged, these informed the next round of data collection, this process named “theoretical sampling”. Managers and employees were asked about perceptions of their own role and the other's roles in learning.

Reciprocity and participative learning involving managers and employees emerged. There was dynamism to the data and evidence of both Billett's notion of affordances and Stacey's patterns of local interactions. Employees encouraged learning through peer discussions, and motivation/personal initiative. Managers encouraged learning through have a go coaching, formal training opportunities and working with company structure and resources. The data support the idea of complex and integrated learning.

The data informed both managers and employees in such a way as to highlight the dynamic and complex interactions around learning processes. One practical implication is employee and manager training in emergence and complexity as learning environments. Ideas of complex responses and patterns of local interaction resonated with the data more than particular typologies of learning.

This paper captures insights, especially from employees, into the dialogue and dynamism of their learning opportunities, whilst supporting existing theories. The need for managers to “learn” employees' local interaction patterns emerged as a future research agenda, alongside the need to penetrate the social space of employee learning more deeply.

This study aims to investigate whether big data enabling (BDE) and empowerment-focused human resource management (EHRM) can effectively promote employee intrapreneurship and their effects on platform enterprises’ innovation performance. The paper also examines the contexts under which employee intrapreneurship may affect business performance.

Data were collected from 155 platform enterprises in China in the form of questionnaires. Participants were mainly middle and senior managers with a comprehensive grasp of the enterprises’ information.

The results indicated that BDE, EHRM and their synergy positively influenced employee intrapreneurship, which could potentially extend to enterprise performance. Specifically, employee intrapreneurship played a partial mediating role between BDE, EHRM and performance, and a whole mediating role between synergy and performance. Finally, platform strategic flexibility played a positive moderating role between employee intrapreneurship and performance.

Platform enterprises should focus on the construction and utilization of big data and EHRM to stimulate organizational vitality. They also need to encourage employees to start businesses and build more flexible strategies to adapt to the dynamic economic environment.

This is an empirical study on the effect mechanism of big data and HRM on employee intrapreneurship and platform enterprises’ performance in China. The paper combined big data, HRM and employee intrapreneurship, which broke through the previous research on enterprise entrepreneurship and social entrepreneurship. The findings guide platform enterprises to stimulate organizational vitality and achieve better performance in the digital era.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.