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Abstract The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Abstract Bioremediation techniques, which harness the metabolic activities of microorganisms, offer sustainable and environmentally friendly approaches to contaminated soil remediation. These methods involve the introduction of specialized microbial consortiums to facilitate the degradation of pollutants, contribute to soil restoration, and mitigate environmental hazards. When selecting the most effective bioremediation technique for soil decontamination, precise and dependable decision-making methods are critical. This research endeavors to tackle the aforementioned concern by utilizing the tool of aggregation operators in the framework of the Linguistic Intuitionistic Fuzzy (LIF) environment. Linguistic Intuitionistic Fuzzy Sets (LIFSs) provide a robust framework for representing and managing uncertainties associated with linguistic expressions and intuitionistic assessments. Aggregation operators enrich the decision-making process by efficiently handling the intrinsic uncertainties, preferences, and priorities of MADM problems; as a consequence, the decisions produced are more reliable and precise. In this research, we utilize this concept to devise innovative aggregation operators, namely the linguistic intuitionistic fuzzy Dombi weighted averaging operator (LIFDWA) and the linguistic intuitionistic fuzzy Dombi weighted geometric operator (LIFDWG). We also demonstrate the critical structural properties of these operators. Additionally, we formulate novel score and accuracy functions for multiple attribute decision-making (MADM) problems within LIF knowledge. Furthermore, we develop an algorithm to confront the complexities associated with ambiguous data in solving decision-making problems in the LIF Dombi aggregation environment. To underscore the efficacy and superiority of our proposed methodologies, we adeptly apply these techniques to address the MADM problem concerning the optimal selection of a bioremediation technique for soil decontamination. Moreover, we present a comparative evaluation to delineate the authenticity and practical applicability of the recently introduced approaches relative to previously formulated techniques.

Research on employee harassment, in the form of workplace bullying, has increased over the past decade. However, there is little research on the prevalence and impact of cyberbullying, a type of cyber-related violence in the workplace. Thus, it would be interesting to examine the impact of cyberbullying on interpersonal deviance through the serial mediating effect of emotional exhaustion and anger. Drawing from the conservation of the resource theory and the affective event theory, this proposed study clarifies the mediating effects of emotional exhaustion and anger. The time lag approach was used to collect the data from the sample of 385 employees in the telecommunication sector of Pakistan. By employing SPSS and PLS, bootstrapping was performed to conduct the mediation analysis. Findings indicated that workplace cyberbullying increased interpersonal deviance by enhancing emotional exhaustion and anger. The current research contributes to the literature by considering the behavioral outcomes of workplace cyberbullying with the practical implications for human resource practitioners.

This study investigates when leaders can promote knowledge sharing by leading ethically? Drawing on social learning theory, we hypothesise that ethical leaders can induce knowledge sharing via role modelling. Moreover, social exchange and social capital theories explain that organisational citizenship behaviour (OCB) and normative commitment (NC) can boost knowledge sharing among employees through dual mediating effects. We collected two waves data from 506 manager-employee dyads in the banking sector of Pakistan. In addition, structural equation modelling was used to test the model, and bootstrapping was applied to examine the mediation effect. Results show that ethical leadership is positively related to employee knowledge sharing and this relationship is significantly mediated by OCB and NC parallelly. Both are equally important in accounting for the impact of ethical leadership on employee's knowledge sharing. Our research suggests that ethical leaders can promote knowledge sharing by increasing OCB and engendering NC. [ABSTRACT FROM AUTHOR]