Employees' Workplace Deviant Behavior (WDB) is an organizational threat to its sustainability. This study examines the impact of the supervisors' role in improving organizational behavior because of the gap in the body of knowledge indicating the inconsistency, paucity, and uncertainties of relationships between variables when relating to their underpinning theories. The conceptualized model consists of the impact of family supportive supervisor behavior (FSSB) on workers' workplace deviant behavior (WDB) while considering Affective Commitment and Work-Family Supportive Behavior Attribution between the key variables. In terms of methodology, this quantitative study analyzed 321 valid surveys through descriptive and inferential statistics to ascertain if FSSB negatively impacts employees' WDB. As findings and novelty of this study, FSSB is found to negatively affect employees' WDB, while affective commitment mediates between FSSB and employees' WDB. Work-family supportive behavior attribution and personal life attribution of employees moderated the negative relationship between affective commitment and WDB, while work productivity attribution of employees had no significant effect as a moderator. With three (out of four) hypotheses supported by empirical evidence, this research has broadened previous studies of workers' WDB and offers organizations theoretical and practical recommendations for managing employees' WDB. More studies could be conducted in the future to address limitations in this research, examine other related theories in a new context, location, and/or culture, or select other suitable research methods.
Water ecological civilization construction (WECC) is regarded as the core and cornerstone of ecological civilization construction. However, a lot of uncertainty is involved in assessing the WECC level, which presents serious and intricate difficulties for the related multiple- attribute decision-making (MADM) processes. The interval-valued hesitant Fermatean fuzzy set (IVHFFS) is a powerful tool for handling uncertainty in MADM issues. However, in the existing MADM approaches, attribute weight calculation involves high data redundancy and low computational efficiency. The existing aggregation operators ignore the importance of the attributes and their ordered positions. In order to solve these problems, in this paper, we propose a novel MADM model using interval-valued hesitant Fermatean fuzzy (IVHFF) Hamacher aggregation operator (AO) and statistical variance (SV) weight calculation. Firstly, the SV weight calculation method is given under IVHFFSs, aiming to computing objective weights of attributes. This greatly reduces data redundancy and improves the computational complexity. Secondly, we propose some IVHFF Hamacher AOs, such as IVHFF Hamacher (ordered) weighted averaging operator, IVHFF Hamacher (ordered) weighted geometric operator, IVHFF Hamacher hybrid averaging operator and geometric operator which consider the significance of the attributes and their ordered positions. Thirdly, a new MADM model based on the above information AOs and SV weight calculation is proposed. Finally, a comparative study on the real-world application for WECC and randomly generated data sets is also carried out to further demonstrate that our method outperforms the existing methods.