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Using the Analytical Hierarchy Process, this study aims to identify organizational and market factors that prevent hoteliers from adopting greener management systems. This study first constructs a list of critical factors based on expert judgments reported in the literature and the responses from a select group of experienced managers. Then, the list is further refined by six hotel managers who consolidate the factors, which results in four categories explained by 20 items. Subsequently, this study draws from surveys on an expert panel consisting of 20 study subjects who are familiar with hotel operations. The survey results show that hoteliers’ abilities to create a greener image are the most important factor influencing the managers’ decisions to adopt environmentally friendly management schemes. Nevertheless, their motivation of adopting a green strategy seems to be associated with a marketing strategy, instead of generic environmental stewardship.

Social movements are made up of organized groups and individuals working together to accomplish shared objectives. Under what circumstances do active groups build and break their coalitions? Five conditions have been identified in the literature as influencing coalition formation: common identity, resources, organizational structure, historical connection, and institutional setting. Whereas coalition dynamics within a movement wave are best understood in terms of institutional opportunities and threats, further research is needed to determine how and to what extent these contextual elements influence coalitions. This chapter examines how threats posed by indiscriminate and selective repression affect the shape and structure of interorganizational coalitions during the 2019 Anti-Extradition Law Amendment Bill (Anti-ELAB) protests in Hong Kong. The analysis relies on an original political event dataset and an organization-event network dataset. These datasets were produced utilizing syntactic event coding techniques based on Telegram posts, which Hong Kong protesters used to distribute information, plan future actions, and crowdsource news. Furthermore, Telegram provides detailed information about state activities, event-level coalitions, and violent groups, which is difficult to access from other sources. This study investigates the coalition networks across the movement's four stages, each of which was marked by a particular type and degree of repression. The findings indicate that indiscriminate and selective repression have varied effects on coalition networks. A wide coalition disintegrates as a result of indiscriminate repression. Selective repression, however, leads to the formation of coalitions around activist groups targeted by repression.

This study examined social media, tax morale, and tax compliance behaviour. Partial Least Square-Structural Equation Modelling (PLS-SEM) was utilized to investigate the quantitative data gathered from 592 salaried and self-employed taxpayers in East Malaysia, comprising two regions – Sabah and Sarawak. The results showed that social media had no significant effect on tax compliance. There was, however, a significant and negative relationship between social media and tax morale. A significant and positive effect of tax morale on tax compliance was also discovered. The bootstrapping technique indicated that tax morale mediates the association between social media and tax compliance. This research is among the earliest in a developing country to investigate the effect of social media in enhancing tax compliance, thus, contributing to the tax literature with a broader focus.

The purpose of this paper is to introduce an integrated approach using failure modes and effects analysis (FMEA), multiple-criteria decision making (MCDM), mathematical modeling and quality function deployment (QFD) techniques, for risk assessment and service quality enhancement in coronary artery bypass grafting (CABG) as a treatment for cardiovascular diseases (CVDs).

First, the disruptions in the CABG process are identified and prioritized following FMEA instructions, using two MCDM techniques, called analytic hierarchy process (AHP) and TOPSIS. Consequently, several corrective activities are identified and weighted on the basis of QFD. Finally, a mathematical model is established to determine the most cost-effective activities for implementation. The approach is developed in a fuzzy environment to reflect the uncertainty and ambiguity of human reasoning.

Regarding the CABG process disruption, a total of 30 failure modes in four main categories were identified and prioritized. Moreover, eight corrective activities were devised and ranked according to their impact on the failure modes. Finally, considering a limited amount of budget, a sensitivity analysis on the mathematical model’s objective function indicated that using 30 percent of the total budget, required to implement all corrective activities, was enough to cover more than 70 percent of the effects of corrective activities on the failure modes.

This paper contributes to the quality risk assessment knowledge by introducing an integrated approach to evaluate and improve healthcare services quality. Also, the case study conducted on the CABG process has not been done by other related studies in the literature.

This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors.

Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment.

Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods.

This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.

This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.

The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.

The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.

Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

This research has three objectives: one is to develop measuring criteria for ascertaining performance of customs clearance firms, another is to test reliability and validity of the factors, and the third is to analyze the relationship between customer service and firm performance. This research gathered the data from customs clearance firms. Reliability and validity concerned with the collected data are tested by exploratory factor analysis and confirmatory factor analysis and the relationship between variables is tested by analyzing structural equation modeling. The results are as follows. There are no problems in reliability and validity. According to the result of the analysis, customer service is divided into customer focus, customer needs, customer response and flexibility and performance is classified into customer performance and financial performance. The result of empirical tests is as follows. Customer focus has a positive effect on customer performance and financial performance. Flexibility has a positive effect on both types of performance. This means that firms which have discriminative services and a high level of flexibility through collaboration with customers can achieve high levels of customer performance and financial performance.

Focusing on the individual unit of analysis, we explore how workplace identification can explain why individuals engage in unethical behavior that benefits an organization (unethical pro-organizational behavior; UPB). Social identity theory (SIT) stipulates that we want membership within high status organizations and, at extreme levels, may put the organization’s needs above all else. In taking a holistic approach to identification, we investigated how a strong occupational identification can mitigate this desire to unethically help an organization; occupations are a separate identity source and contain codes of conduct that guide ethical behavior. Utilizing a sample of 236 accountants and financial professionals, results indicated that organizational identification and occupational identification alone did not significantly predict UPB, however, the interaction of these identities did. More specifically, organizational identification significantly positively predicted UPB only when occupational identification was extremely low in strength. This effect was found after controlling for relevant personality and cognitive mechanisms related to unethical behavior. Implications for a multidimensional identification view of unethical behavior are discussed.

Past research has primarily focused on the negative impact of workplace mistreatment or aggression on the individuals involved, workgroups, and organizations. Certain circumstances, however, create paradoxical effects in which mistreatment positively relates to desirable workplace outcomes and characteristics at the individual and/or organizational level. Reviewing the theoretical and empirical evidence of beneficial outcomes provides researchers and practitioners with a more comprehensive understanding of the progression of workplace mistreatment, allowing them to target specific mechanisms to mitigate detrimental effects and potentially discover important avenues that lead to desired outcomes. A qualitative review of 13 articles demonstrated that different forms of aggression such as bullying, abusive supervision, incivility, and ostracism have positive relationships with paradoxical outcomes and characteristics such as resilience, prosocial behaviors, socially desirable behaviors, job performance, job satisfaction, and creativity. The authors caution against leveraging mistreatment as a method for producing these desired outcomes; instead, the authors encourage researchers and practitioners to utilize the information to further their understanding of the nomological network of workplace mistreatment and its underlying mechanisms, such as cognitive reappraisal and social learning.