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Naila BIBI,Bilal Bin SAEED,Muhammad Asim AFRIDI 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.5
Job love is an emerging phenomenon, which is the utmost approach to fulfilling employees’ and organizations’ mutual interests, especially performance. The current study aims to define and extend the existing proposed construct of “loving one’s job” as job love. It provides a novel theoretical multi-level framework of job love, contextual factors, and performance principled on the attraction-selection-attrition framework and social exchange theory through an integrated approach. This study collected cross-sectional data through a questionnaire from 332 nurses across eight tertiary hospitals in Khyber Pakhtunkhwa, Pakistan. The findings are based on the structural equation modeling technique (SEM) at multi-levels. The results show significant relationships between job love, contextual factors, and performance at the individual and organization levels. There are some insignificant relationships between the variables at the cross-level. Job love plays a key role for both employees and organizations. It facilitates the individuals in the recruitment process to select the job they love, be a good fit, and stay committed to that particular job and organization. This phenomenon allows people to pursue their common interests. Job love assists firms in developing human resource capacity utilization plans that satisfy the needed requirements.
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Faiz Muhammad Khan,Naila Bibi,Saleem Abdullah,Azmat Ullah 한국지능시스템학회 2023 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.23 No.3
One of the notable advantages of the complex fuzzy set is its ability to incorporate not only satisfaction and dissatisfaction but also the absence of vague information in two-dimensional scenarios. By combining a fuzzy rough set with a complex fuzzy set, this study aims to provide a powerful and versatile tool for multi-criteria group decision-making (MCGDM) in complex and uncertain situations. This approach, based on EDAS (evaluation based ondistance from average solution) method allows decision-makers to consider multiple criteria, account for uncertainty and vagueness, and make informed choices based on a wider range of factors. The main goal of this study is to introduce complex fuzzy (CF) rough averaging aggregation and geometric aggregation operators and embed these operators in EDAS to obtain remarkable results in MCGDM. Furthermore, we propose the CF rough weighted averaging (CFRWA), CF rough ordered weighted averaging (CFROWA), and CF rough hybrid averaging (CFRHA) aggregation operators. Additionally, we present the concepts of CF rough weighted geometric (CFRWG), CF rough ordered weighted geometric (CFROWG), and CF rough hybrid geometric (CFRHG) aggregation operators. A new score function is defined for the proposed method. The basic and useful aspects of the explored operators were discussed in detail. Next, a stepwise algorithm of the CFR-EDAS method is demonstrated to utilize the proposed approach. Moreover, a real-life numerical problem is presented for the developed model. Finally, a comparison of the explored method with various existing methods is discussed, demonstrating that the exploring model is more effective and advantageous than existing approaches.
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