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This study aims to examine the impact of workplace safety (WPS) on employee retention (ER) in the health-care sector in Azad Jammu and Kashmir (AJ&K), Pakistan. At the same time, a mediation relationship through job satisfaction (JS) and employee loyalty (EL) was also tested.

Structured questionnaires were used to collect the data from 300 doctors, using purposive sampling technique analysed using partial least squares (Smart-PLS 3).

This study’s findings supported all hypotheses, such as WPS has a significant positive relationship with ER. In addition, a mediation relationship between JS and EL was also confirmed. Furthermore, a serial mediation effect of JS and EL between WPS and ER was also confirmed in this study.

This study might not fit organisations from other regions due to regional norms. In the future, this study’s model may be tested on other regions and segments of the health-care sector, such as nurses, management staff and support staff.

The present study is unique because it is based on a newly formulated framework, WPS → JS → EL → ER, under the social exchange theory, which has not been tested before.

In a safe environment, doctors will feel relaxed, stay longer and provide better services; resultantly, patients will get better treatment.

This study tested the sequential mediation effect through JS and EL for the first time in ER, which was missing previously, to the best of the authors’ knowledge. This will add more insights to the safety-retention literature in health-care settings. Furthermore, this study is also the first attempt to explore the relationship between WPS and ER in the health-care sector in AJ&K.

The purpose of this paper is to examine the factors which will contribute to consumers’ purchase intention on skin care products. Four factors, namely, brand awareness, brand association, perceived quality and brand loyalty, were included in this study.

In total, 150 sets of self-administered questionnaires were distributed to students in a local private university in Melaka. Convenience sampling was used and data collected were analysed using SmartPLS to perform the measurement model and structural model.

Findings have showed that there are positive relationships between brand awareness, brand association, perceived quality and brand loyalty and consumers’ purchase intention towards skin care products. Furthermore, it is concluded that perceived quality is the most significant factor in influencing consumers’ purchase intention.

Firms are able to benefit from this study by formulating their brand management tactics referring to the findings to have competitive advantage over their competitors.

This paper aims to explore the drivers behind the accuracy of self-reported home valuations in the Warsaw (Poland) housing market.

In order to achieve the research goal, firstly, unique data on subjective residential property values estimated by their owners were compared with market-justified ones. The latter was calculated using geographically weighted regression, which allowed for taking into account spatially heterogeneous buyers' housing preferences. An ordered logit model was then used to identify the factors influencing the probability of the occurrence of bias towards over or undervaluation.

The results of the study revealed that, on average, homeowners overvalued their properties by only 1.94%, and the fraction of interviewees estimating their properties accurately ranges from 20% to 68%, depending on the size of the margin of error adopted. The drivers of the valuation bias variation were the physical, locational and neighbourhood attributes of the property as well as the personal characteristics of the respondents, for which their age and employment situation played a key role.

In contrast to previous studies, this is the first to examine drivers behind the accuracy of self-reported home valuations in a Central and Eastern Europe country. In addition, this work is the first to consider heterogeneous housing preferences when calculating objective property values.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.