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Unveiling the Nexus: Exploring the Collective Social Exchange Dynamics of High-Performance Work Systems in Shaping Organizational Outcomes
Get PDFPurpose:This study aims to explore the nuanced role of organizational “collectives” in transmitting the effect of high-performance work systems (HPWS) on organizations’ market success and workforce retention.Design/methodology/approach:The multi-source data was collected from 113 construction firms operating in Pakistan using a survey questionnaire.Findings:The findings indicate that collective human capital and collective satisfaction of organizations differentially mediate the effect of HPWS on market success and workforce retention. Collective satisfaction mediates the effect of HPWS on both market success and workforce retention, however collective human capital only mediates this relationship for market success of organizations.Practical implications:Organizations should consider prioritizing investment in cognitive and affective development of overall human resources. Knowledge, skills, abilities and emotions of individual employees operate at the collective level so organizations should design HRM practices to manage collective thoughts and interpretations.Originality/value:This is the first study to investigate human capital and satisfaction at the collective organization level to explore collective developmental and motivational paths for HPWS to boost organization strategic outcomes.© 2024, Emerald Publishing Limited. This is an author produced version of a paper published in Journal of Organizational Effectiveness: People and Performance uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it
Adaptive SOC Estimation for Lithium-Ion Batteries Using Cluster-Based Deep Learning Models Across Diverse Temperatures
No full textBig data capability and supply chain performance: the mediating effect of supply chain resilience and the moderating effecting of environmental uncertainty
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The effect of perceived financial risk on purchase intention in Pakistan
No full textPurposeThe purpose is to understand the fundamental mechanism of the consumer decision-making process and how perceived financial risk of search and experience goods influences electronic word-of-mouth adoption (e-WOMA) on social networking sites (SNSs), which will lead to purchase intention.Design/methodology/approachDrawing on information processing theory, the study conceptualizes a moderated mediation model to investigate the underlying influence of perceived financial risk and online social ties on e-WOMA and the subsequent effect on online purchase intention. Survey data from 275 individuals were analyzed through statistical tools using Statistical Package for the Social Sciences (SPSS).FindingsThe results revealed that e-WOMA mediates the effect of perceived financial risk of search and experience goods on online purchase intention. Strength of online social ties on SNSs positively moderates the electric word of mouth adoption for both the experience and search goods.Research limitationsThe limitation of this study was about the researcher's restrictions related to the length of the survey. Moreover, causal explanations can't be deduced as this is a cross-sectional study.Practical implicationsThis research offers insight into the consumers that allow marketers to dive into the target market. Marketers should focus on social ties importance while selling products/services of markets online.Originality/valueThe study is novel in the context of an emerging economy to educate marketers on the product categorization of search goods and experience goods based on financial risk
Deep Learning-Driven Virtual Furniture Replacement Using GANs and Spatial Transformer Networks
No full textThis study proposes a Generative Adversarial Network (GAN)-based method for virtual furniture replacement within indoor scenes. The proposed method addresses the challenge of accurately positioning new furniture in an indoor space by combining image reconstruction with geometric matching through combining spatial transformer networks and GANs. The system leverages deep learning architectures like Mask R-CNN for executing image segmentation and generating masks, and it employs DeepLabv3+, EdgeConnect algorithms, and ST-GAN networks for carrying out virtual furniture replacement. With the proposed system, furniture shoppers can obtain a virtual shopping experience, providing an easier way to understand the aesthetic effects of furniture rearrangement without putting in effort to physically move furniture. The proposed system has practical applications in the furnishing industry and interior design practices, providing a cost-effective and efficient alternative to physical furniture replacement. The results indicate that the proposed method achieves accurate positioning of new furniture in indoor scenes with minimal distortion or displacement. The proposed system is limited to 2D front-view images of furniture and indoor scenes. Future work would involve synthesizing 3D scenes and expanding the system to replace furniture images photographed from different angles. This would enhance the efficiency and practicality of the proposed system for virtual furniture replacement in indoor scenes
Placing a child's or partner's needs above my own: Impacts on a couple's sexual relationship
No full textMachine Learning Algorithm-Based Prediction of Diabetes Among Female Population Using PIMA Dataset
Get PDFBackground: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy. Although they cannot substitute the work of physicians in the prediction and diagnosis of disease, they can be of great help in identifying hidden patterns based on the results and outcome of disease. Methods: In this research, we retrieved the PIMA dataset from the Kaggle repository, the retrieved dataset was further processed for applied PCA, heatmap, and scatter plot for exploratory data analysis (EDA), which helps to find out the relationship between various features in the dataset using visual representation. Four different ML algorithms Random Forest (RF), Decision Tree (DT), Naïve Bayes (NB), and Logistic regression (LR) were implemented on Rattle using Python for the prediction of diabetes among the female population. Results: Results of our study showed that RF performs better in terms of accuracy of 80%, precision of 82%, error rate of 20%, and sensitivity of 88% as compared to other developed models DT, NB, and LR. Conclusions: Diabetes is a common problem prevailing across the globe, ML-based prediction models can help in the prediction of diabetes much earlier before the worsening of the condition
Entrepreneurial opportunities and working conditions of self-employed online freelancers in the platform economy: Lessons from the COVID-19 pandemic
No full textThis paper examines the experiences of self-employed online freelancers working on digital labour platforms during the COVID-19 pandemic. It is based on interviews with freelancers and platform managers and experts in Belgium, France, Italy, the Netherlands and Poland. Their experiences during COVID-19 reveal issues of asymmetric power vis-à-vis platforms. Notably, they reported lack of transparency and certainty in their contracts with platforms, lack of power in negotiating with clients, and limited ability to engage with clients on other platforms. In addition, they often experienced difficulties in accessing government temporary supports for businesses during the pandemic. The paper puts forward policy recommendations to address these issues