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The purpose of this paper is to investigate the relationships among corporate social responsibility (CSR), analyst forecast accuracy and firms’ earnings management behavior using US-based firms.

The authors use the Kinder, Lydenberg, Domini (KLD) database to construct CSR performance scores and divide all firms into ten groups from high to low as a proxy for CSR performance. The authors obtained an initial sample of 33,364 firm-year observations from 1991 to 2012. Filtering for records which exist in the KLD, Compustat, and Center for Research in Security Prices databases lefts a total of 16,807 firm-year observations and CSR evaluation reports for 5,896 firms.

The authors find that high CSR-score firms have lower rates of analyst forecast error than their low CSR-score counterparts, suggesting that CSR performance is a useful means of forecasting earnings. Furthermore, firms with better CSR performance have significantly lower accrual-based earnings management behavior. However, the level of the manipulation behavior of real earnings management (REM) activities increased significantly in better CSR firms, suggesting that high CSR-score firms substituted REM methods for accrual-based methods. REM methods are consistent with the stipulations of the Sarbanes-Oxley Act and allow high CSR-score firms to better manipulate earnings behavior. These results hold after the authors control for various factors related to firm financial characteristics.

Overall, the findings have important implications for investors and regulators to more easily assess firms’ earnings manipulation behavior and earnings stability under CSR performance and financial information in financial markets.

Blockchain is one of the most significant emerging technologies that is set to transform many aspects of industry and society. However, it has several major technical, social, legal, environmental and ethical complexities that offer significant challenges for mainstream use within the public sector. The coronavirus disease 2019 (COVID-19) pandemic has compelled many public sector employees to work remotely, highlighting a number of challenges to blockchain adoption within the Indian context signifying the pertinence of this research topic in the post-pandemic era. This study offers insight to researchers and policymakers alike on how such challenges are interdependent within this important subject.

We explored 16 unique sets of challenges selected from the literature and gathered data from nine experts from government settings, healthcare and education sectors and academia who have significant knowledge and experience of blockchain implementation and use in their respective organisations. The implementation of Interpretive Structural Modelling (ISM) and Matriced' Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) provided a precise set of driving, linkage and dependent challenges that were used to formulate the framework.

The developed ISM framework is split into six different levels. The results suggest that the bottom level consists of challenges such as “Lack of standards (C9)” and “Lack of validation (C10)” form the foundation of the hierarchical structure of blockchain adoption. However, the topmost level consists of a highly dependent challenge termed “adoption of blockchain in the public sector (C16)”. The research filters the selected set of five challenges to develop a parsimonious model and formulated six propositions to examine the impact of “lack of standard (C9)”, “lack of validation (C10)” on “security issues (C3)” and “privacy concerns (C2)”, which eventually determine individuals' “reluctance to use blockchain technology (C12)”.

This research fills a key gap in exiting research by exploring the key challenges in blockchain adoption within the public sector by developing a valuable framework to model this important topic. To the best of our knowledge, this is the first paper to address these challenges and develop a parsimonious model for challenges of blockchain adoption in the public sector settings.

The main objective of this paper is to justify the implementation of blockchain (BC) over the traditional method deployed in the supply chain (SC) after using the fuzzy–analytic network process (fuzzy-ANP) application. Over the past two decades, the overall product cost is affected by the SC at a global level. Organizations are working on their existing SC for improving their performance. BC technology is a newly emerging technology and magnetizes the attention of researchers and industrialists. This technology is still at the initial stage, and only little investigation is available in the literature and it has not been much investigated by researchers.

Literature and expert opinion interpretation in BC characteristics are further analyzed and modeled using fuzzy–interpretive structural modeling (fuzzy-ISM), fuzzy-MICMAC and fuzzy-ANP. The combined approach of both fuzzy-ISM and fuzzy-MICMAC is applied to identify the common drivers to integrate the BC technology in the light of efficient supply chain management (SCM).

Comparative analysis between traditional and BC-based supply chain (BCSC) using fuzzy-ANP is carried out, considering the common driving characteristics. The proposed integrated (combined) approach of fuzzy-ISM, fuzzy-MICMAC and Fuzzy-ANP found that integration of BC with SCM is better prioritized than traditional supply chain management (TSCM). The findings in the article endorse that the TSCM can be made efficient by integrating the BC technology considering five most driving characteristics, namely, data safety and decentralization, accessibility, documentation, data management and quality.

The current proposed research work identifies 12 characteristics after studying numerous literature reviews and having a discussion with SC experts with knowledge of BC. The integrated approach of fuzzy-ISM and fuzzy-MICMAC is implemented here. After that, fuzzy-ANP is used to give ranking among BCSCM and TSCM. The study carried out in this article motivates industries to implement BC in their SC system. It will reduce the transaction cost, documentation work, save time and eliminate human error at the national and international levels. The common characteristics identified in this proposed work would help in managerial decisions for the adoption of BC to ensure that the system becomes more transparent, easily traceable and finally improve the performance.

Refugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.

A spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.

The derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.

Some parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.

The model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.

The study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

The objective of this article is to carry out the driving power and dependency analysis of green human resource management (GHRM) indicators of the automotive service sector to identify the most significant ones.

The GHRM indicators were identified through exhaustive literature search and validated through the semi-structured interview with 15 domain experts. The ‘Total Interpretive Structural Modelling (TISM)’ approach was applied for exploring the contextual relationship between the indicators and simultaneously developing their structural hierarchy. The MICMAC analysis was used for categorising the indicators based on their ability to influence the other ones.

In the present study, indicators namely ‘Green organisational culture and adoption of green strategy (C5)’ and ‘Green training and development (C1)’ were found to be the significant ones, whereas ‘Green employee relations and union-management (C10)’ was found to be highly dependent on the rest of the indicators.

The proposed model has been developed in the Indian context and is limited to the automotive sector. However, the same model may apply to other domains of different economies by carrying out slight modifications to the same. Also, the inputs taken from the experts of the case sector could be biased. For the HR professionals, the present study helps to identify the key indicators which need to be considered for enlightening the environmental performance of the service organisation.

This research adds a significant assessment to the current knowledge base by assessing the contextual relationship between the indicators of GHRM as none of the past studies focused on the same by using the TISM method in the Indian service sectors context.

Contract incompleteness with deficiency, inconsistency, defectiveness, and ambiguity in contract clauses, which can cause misunderstandings and misinterpretations, may result with disputes in projects. This study aims to investigate contract incompleteness factors with a hybrid fuzzy multi-criteria decision approach.

Contract incompleteness factors were ranked by fuzzy VIKOR (Visekriterijumska Optimizacija I Kompromisno Resenje) method, and the most significant factors were subjected to fuzzy decision-making trial and evaluation laboratory (DEMATEL) to examine their causal relationships. The study is not limited to ranking the identified factors solely, since their cause-effect interactions are also essential for proper risk management in construction projects.

Hybrid use of multi-criteria analysis reveals that ambiguity in enforceability including excessive demands and significant amendments in the scope of works are the top two causal contract incompleteness factors, while lack of implementation details and focus of focal point, and insufficient supporting and technical documents are the most affected ones.

Contractual causes of disputes due to contract incompleteness factors other than requirements of the contracts have been rarely investigated in the literature. The research is one of the first studies in the literature investigating the causal relationship among factors in construction contracts, which might lead to project disputes. Findings are expected to improve contract drafting, eventually contributing to effective risk management in construction projects.

Third-party logistics (3PL) plays a main role in supply chain management and, as a result, has experienced remarkable growth. The demand for 3PL providers has become a main approach for companies to offer better customer service, reduce costs, and gain competitive advantage. This paper identifies important criteria for 3PL provider selection and evaluation, and the purpose of this paper is to select 3PL providers from the viewpoint of firms which were already outsourcing their logistics services.

This study utilized the grey decision-making trial and evaluation laboratory (DEMATEL) method to develop 3PL provider selection criteria. Because human judgments are vague and complicated to depict by accurate numerical values, the grey system theory is used to handle this problem.

The findings revealed the structure and interrelationships between criteria and identified the main criteria for 3PL provider selection. The most important criteria for 3PL provider selection are on time delivery performance, technological capability, financial stability, human resource policies, service quality, and customer service, respectively.

The paper’s results help managers of automotive industries, particularly in developing countries, to outsource logistics activities to 3PL providers effectively and to create a significant competitive advantage.

The main contributions of this paper are twofold. First, this paper proposes an integrated grey DEMATEL method to consider interdependent relationships among the 3PL provider selection criteria. Second, this study is one of the first studies to consider 3PL provider selection in a developing country like Iran.

Increasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.

First, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.

The paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.

The presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.

The paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.

The purpose of this paper is threefold: first, to present a structural competency model; second, to remark new criteria for personnel selection in Industry 4.0 environment; and third, to contribute to the operations management literature by focusing on recruitment process in Industry 4.0 environment and supporting human resources activities with Industry 4.0 related criteria and point out a new research field in Industry 4.0.

Fuzzy DEMATEL has been used in the implementation. The study is conducted in a high-tech firm, which has started to modify its processes according to Industry 4.0, and introduces a new specific department that is responsible of this transformation. In total, 11 personnel selection criteria were presented and then assessed by experts through a fuzzy linguistic scale. Both importance order and causal relation between criteria are presented at the end of the study.

According to the results, the most important criteria in the selected firm are the ability of dealing with complexity and problem solving, thinking in overlapping process, and flexibility to adapt new roles and work environments. While cause group includes criteria such as knowledge on IT and production technologies, awareness of IT security and data protection, and ability of fault and error recovery, effect group includes flexibility to adapt new roles and work environments, organizational and processual understanding, and the ability to interact with modern interfaces.

Analytical thinking and system approach are the key topics for new supporting personnel selection criteria, which lead to the need for the skills and qualifications in decision making and process management. Results of the cause group criteria also indicate the importance of technical abilities such as coding, IT security and human-machine interfaces. On the other hand, effect group of the study emphasizes on the flexibility and interdisciplinary working structure that suggests the suitability of matrix organization in the companies which follow the Industry 4.0 trends. Moreover, team work comes forward as another key concept for organizations transforming to Industry 4.0.

The originality of this study appears on modeling of a competency structural model for Workforce 4.0 which is proposed as a road map, including the suggested set of related criteria and the fuzzy MCDM-based methodology for companies which alter their organizations according to Industry 4.0.

The purpose of this paper is to implement the mathematical models to predict concretes physico-mechanical characteristics made with binary and ternary sands using a mixture design method. It is a new technique that optimizes mixtures without being obliged to do a lot of experiments. The goal is to find the law governing the responses depending on mixture composition and capable of taking into account the effect of each parameter separately and in interaction between several parameters on the characteristics studied.

Mixture design method was used for optimizing concretes characteristics and studying the effects of river sand (RS), dune sand (DS) and crushed sand (CS) in combinations of binary system and ternary on workability, the compressive and flexural strengths of concretes at 7 and 28 days. A total of 21 mixtures of concrete were prepared for this investigation. The modeling was carried out by using JMP7 statistical software.

Mixture design method made it possible to obtain, with good precision, the statistical models and the prediction curves of studied responses. The models have relatively good correlation coefficients (R² = 0.70) for all studied responses. The use of binary and ternary mixtures sands improves the workability and their mechanical strengths. The obtained results proved that concrete, based on binary mixture C15, presents the maximum compressive strength (MCS) on 28 day with an improvement of around 20%, compared to reference concrete (C21). For ternary mixtures, MCS on 28 day was obtained for the mixture C10 with an improvement of around 15% compared to C21. Increase in compressive strength during the progress of hydration reactions was accompanied by an increase in the flexural strength, but in different proportions.

The partial incorporation of DS (= 40%) in the concrete formulation can provide a solution for some work in the southern regions of country. In addition, the CS is an interesting alternative source for replacing 60% of RS. The concrete formulation based on local materials is really capable of solving the economic and technical problems encountered in the building field, as well as environmental problems. Local resources therefore constitute an economic, technological and environmental alternative.