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The literature currently offers only fragmentary insights into the research on the relationship between employee emotions and human resource management (HRM). Therefore, further research is essential to bridge this knowledge gap. Our study aims to identify the mediating effects of positive employee emotions and exhaustion in the relationship between HRM and employee engagement.

Drawing on the literature review findings, a conceptual model was formulated to illustrate the relationship between HRM, employee emotions and engagement. A confirmatory analysis was conducted using structural equation modelling (SEM CFA) on a sample of 1,000 employees to validate the proposed model. The data were collected in 2021, with a particular emphasis on exploring the indirect influence of HRM on engagement through positive employee emotions and exhaustion.

The quantitative research aimed to test a model depicting the relationship between HRM and employee emotions. The findings indicate the robust effect of HRM on positive employee emotions and exhaustion. The authors observed significant variation in the level of impact depending on the size of the organisation (stronger in large firms) and the sector (stronger in the public sector).

The study bridges the gap in our understanding of the link between HRM and employee emotions. It would be advisable to further explore the specific impact of individual HRM practices on both positive and negative employee emotions. It is worth extending the scope of future research to explore components of the investigated constructs as well as mediators and moderators of the relationship between HRM and employee emotions.

We aimed to analyze the impact of the transition to the hybrid model of teamwork and team dynamics from the perspective of the five key challenges, i.e. communication, coordination, connection, creativity and culture.

To achieve the stated aim, we conducted a literature review and then an exploratory qualitative study. We split the research into phases: December 2021 to January 2022 and July to August 2022. In the first phase, we conducted computer-assisted online interviews (CAWIs) with all members of the remote team and an in-depth interview with the manager. After the transition from remote to hybrid work in February 2022, we returned to the team to conduct in-depth interviews with team leaders and the manager.

We identified key findings, i.e. managerial implications of differences across the 5 Cs (communication, coordination, connection, creativity and culture) noted in the functioning of the analyzed team as the team shifted from fully remote work to the hybrid work model.

We concluded that if people do not spend time together and are not impregnated with the unique culture and values of a given organization, they will not feel a connection to its distinctive ethos and may choose to leave. In the longer-term, the last challenge may be the biggest single opportunity for employees post-pandemic and concurrently the single biggest challenge that organizational leadership will need to address, given that sustainable market success depends on talent.

The results showed that team communication, teamwork coordination, social and emotional connections among team members, nurturing of creativity, as well as of the organizational culture were of high importance to the team in the hybrid work model. Thus, we confirmed the findings of other authors. The study contributes to our understanding of the impact of the hybrid work model on teamwork and team dynamics and provides some guidance on how organizations can mitigate these, in particular through the team manager.

This study aims to examine the relationship between Achievement Motivation (AM), Smart Work (SW), and human resources (HR) performance. It questions how moral global leadership (MGL) could moderate the relationship between AM, SW, and HR performance. A theoretical model was developed and tested on sample data representing 219 employees, educators, and educational staff of Islamic Higher education (IHE). The data were collected through surveys and applied to structural equation modeling using SEM-PLS. This study found that AM and SW significantly affect HR performance. While MGL substantially moderates the relationship between AM, SW, and HR performance. This study contributes to the literature on MGL, AM, and SW in creating HR performance that has yet to be studied so far. This study offers the concept of MGL, which plays a central role in moderating the relationship between AM, SW, and HR performance.

Personality characteristics have a significant impact on the economic segment of women entrepreneurs. Due to gender biases or other factors, women entrepreneurs are fewer in India than in other countries. The purpose of this study is to identify the personality factors and challenges that affect their growth and success.

Logistic regression was used to show the impact of personality characteristics and firm performance and the moderating effect of challenges between personality characteristics and firm performance.

The findings revealed a significant impact of personality factors on firm performance, the absence of moderating effects of challenges and the presence of a mediation effect of enterprise age and enterprise location.

This research will help policymakers adopt policies and plans to reduce obstacles and challenges so that the economic conditions of women’s entrepreneurship can transform.

Women in the 21st century still live in a male-dominated patriarchal society because they face the problem of financial capital.

The results show the impact of personality traits and challenges on the firm performance of women’s entrepreneurship.

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.