Search results

The measurement of women's economic welfare and exploring its underlying factors have been undervalued in the context of Khyber Pakhtunkhwa, Pakistan. This study addressed this gap by focusing on assessing women's subjective economic welfare and its socioeconomic and cultural determinants in the education and health sectors within Mardan, Northern Pakistan.

The study used stratified random sampling techniques for the selection of sample respondents and collected data through a well-structured questionnaire. To measure women’s economic welfare, the study utilizes Lorenz curves, the Gini index, the Sen Social Welfare function and an individual's gross monthly income. Furthermore, the ordinary least squares method was utilized to analyze the determinants of economic welfare.

The findings show greater income inequality and a lower welfare level for women in the education sector compared to the health sector. Likewise, the study identifies several key determinants, such as age, educational qualification, job experience, respect for working women, outside and work-place problems and the suffering of family members of working women for their economic well-being.

This study makes valuable contributions to the literature by focusing on the cultural perspective of Pakhtun women in Mardan and providing a context-specific understanding of subjective economic welfare. Additionally, the authors collected first-hand data, which gave an original outlook on working women's current economic welfare level. Furthermore, this study undertakes a comparative analysis of working women's welfare in the health and education sectors. This comparison offers a more accurate portrayal of the challenges and opportunities specific to these occupations.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0246

The research aims to provide a nuanced understanding of the complex social, psychological and organizational factors that serve as the foundation driving academics' knowledge-sharing behavior (KSB) within an academic enclave.

A cross-sectional research design using the partial least squares structural equation modeling (PLS-SEM) approach was employed to examine the determinants of personality traits among tertiary academics in Malaysia in relation to their KSB. To this end, a self-administered survey was distributed to a sample group of 526 respondents.

It is evident that conscientiousness and agreeableness are the personality traits that play a significant role in promoting KSB among academics in higher learning institutions (HLIs). These personality traits are positively linked with academics' willingness to transfer and receive knowledge. In contrast, the personality trait of openness to experience does not significantly influence KSB.

This study has employed a four-item measurement for evaluating the three distinct personality traits. Despite employing a brief measurement tool, the study has demonstrated significant reliability and validity, particularly in terms of convergent and discriminant validity.

The present study has revealed that conscientiousness in academics is intimately linked with their KSB, which is of paramount importance in the output-based education system. Notably, agreeableness among academics also conveys a positive effect on knowledge sharing (KS) in HLIs, as it cultivates trust and helpfulness among individuals and facilitates the exchange of valuable tacit knowledge.

This research explores the relationship between personality traits and KSB among Malaysian academics in HLIs. The study adopts the theories of planned behavior (TPB) and social capital theory (SCT) as theoretical ground, providing a nuanced understanding of the underlying motivations and mechanisms driving academics' knowledge-sharing behavior within the unique socio-cultural context of Southeast Asia.

Studies show that supervisor incivility can have detrimental consequences for subordinates. However, little is known about the job and personal resources that can reduce the effect of supervisor incivility on subordinates' counterproductive work behavior (CWB). Based on the Job Demand-Resources (JD-R) model, we investigate social job crafting (job resource) and internal locus of control (LOC; personal resource) as buffers on the relationship between supervisor incivility and subordinates' CWB toward the organization.

Two field studies to test our proposed hypotheses were conducted. A two-wave time-lagged design was used and data was collected from 115 supervisors and 318 subordinates from a large electricity provider company (study 1) and 121 employee–coworker dyads from a large insurance company (study 2).

Across the two studies it was found that supervisor incivility positively relates to subordinates' CWB toward the organization. Further, this relationship was weaker for individuals with high internal LOC and those who engaged in social job crafting.

The findings are helpful for HR managers to figure out how to stop supervisor incivility through civility training and motivating employees to social job crafting behavior.

This study implies that social job crafting (job resource) and internal LOC (personal resource) are essential factors that can reduce the effects of supervisor incivility on subordinates' CWB toward the organization.

The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection.

“Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review.

Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news.

The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

This paper aims to examine the effects of oil price uncertainty on corporate investment of Islamic stocks during the COVID-19 pandemic.

The study uses a panel data set that covers 398 listed Islamic stocks from seven major Asia Pacific countries over the period of five years from 2017 to 2021, yielding 1,990 observations. Specifically, this paper investigates the said association by combining the real options theory regarding investment and the panel data-based econometric method that captures the dynamic relationship, the generalized method of moments estimators.

The findings show that the relationship between the oil price volatility and corporate investment of Islamic stocks is significant and nonlinear in nature, suggesting the presence of both the growth options and the waiting options. Overall, the results reveal that corporate investment of Islamic stocks is hindered during the unprecedented corona crash, when oil price increases at exponential rates.

The findings suggest that considering the information caused by unprecedented events like the COVID-19 pandemic is crucial for investment decisions of Islamic stocks. Therefore, policymakers and regulators should incorporate the impact of oil price uncertainties caused by unprecedented events like the COVID-19 pandemic on firm’s investment expansion and diversification strategies.

To the best of the authors’ knowledge, this paper is the first to examine the relationship between the investment of Islamic stocks and the oil price uncertainty under compound options theory in top Asian oil-importing countries.

Variations due to errors and mistakes have caused cost overruns in building projects. Therefore, it is undeniable that the gravity of such variations will be a critical factor in deciding the success of any building project. In addition, the design stage of a building project is considered the most suitable stage to identify and mitigate the causes of potential variations. However, there are no proper mechanisms to minimise the frequency or gravity of variations. Many researchers experienced the promising essence of Poka-Yoke, a mistake-proofing method aimed at increasing efficiency by early detection and eradication of the causes of potential errors. However, less attention has been paid so far to implement Poka-Yoke principles to minimise variations in the building project. Therefore, this study aims to develop a framework to minimise variations in building projects through the integration of the Poka-Yoke principles.

An interpretivism stance is adopted, and a qualitative research approach is used. The data collection technique adopted is semi-structured interviews with ten experts, and the data is analysed using code-based content analysis through NVivo12.

Research findings revealed 23 causes of variations, categorised under client-originated, consultant-originated, contractor-originated and other variations. The identified causes were then mapped with the Poka-Yoke principles to develop the framework. The research findings could prove useful to researchers, academics, government agencies and construction professionals in developing nations that have demographic/cultural and socioeconomic characteristics such as Sri Lanka.

The findings benefitted the Sri Lankan construction sector by minimising the causes of variations. To the best of the authors’ knowledge, this study will be the first of its kind in the Sri Lankan construction industry, leading to a better understanding of the “Poka-Yoke” principle within the building construction context.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.