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The purpose of this paper is to test a multilevel framework to further explicate how team leaders’ authentic leadership is related to their followers’ individual creativity.

This study is based on a questionnaire survey/analysis of analyses of multisource and lagged data from 63 team leaders and 428 followers in Taiwan.

The findings demonstrated that leader-member exchange (LMX) and team psychological safe climate mediated the positive relationship of authentic leadership on followers thriving at work. Furthermore, employee thriving at work sequentially mediated the positive relationship between authentic leadership and employee creativity. The author also found that indirect relationship of LMX with employee creativity through thriving at work was stronger when authentic leadership was higher.

The authors contribute to the existing understanding that authentic leadership relates to individual creativity through three multilevel mechanisms: leaders modeling their authenticity to develop and maintain their dyad-level exchange relationships with their followers (LMX), motivating the team, captured by team-level psychological safe climate and its members, reflected by employee-level thriving at work, and facilitating the relationship between LMX and employee thriving at work.

The purpose of this paper is to develop an information system which is based on the Internet of things (IoT) and used to support the communication and coordination in a cooperative robot team.

The architecture of the IoT applications for decision-making activities in a complex system is elaborated, the focus lies on the effective implementation of system interactions at the device-level. A case study is provided to verify system performances.

The IoT concept has been introduced in an information system of a football robot team to support the coordination among team players. Various sensors are used to collect data from IoT, and data are processed for the controls of robotic players to achieve the better performance at the system level. The field test has shown the feasibility and effectiveness.

To investigate how IoT can be utilized in an information system for making complex decisions effectively, the authors use the decision-support system for a football robot team to illustrate the approaches in developing data acquisition infrastructure, processing and utilizing real-time data for the communication and coordination of robot players in a dynamic competing environment. While the presented work has shown the feasibility of an IoT-based information system, more work are needed to integrate advanced sensors within the IoT and develop more intelligent algorithms to replace manually remote control for the operations of robot players.

The proposed system is specifically for a football robot team; however, the associated approaches are applicable to any decentralized system for developing an information system to support IoT-based communication and coordination within the system in the real-time mode.

The exploration of IoT applications is still at its early stage, existing relevant work is mostly limited to the development of system architecture, sensor networks, and communication protocols. In this paper, the methods on how to use massive real-time data for decision-making of a decentralized team have been investigated, and the proposed system has its theoretical significance to developing other decentralized wireless sensor networks and decision-making systems.

Purpose: There have been continuous developments in the production industry to meet the increasing customer demand from the past to the present. At this point, supply chain management (SCM) systems emerge as an important topic. SCM is a set of systems that manages the entire process from the production of a product to its delivery to the end user. Industry 4.0 aims to improve the production industry by increasing the quality, efficiency, and performance of the production process. Therefore, in this chapter, the authors highlight the challenges, benefits, and future trends of the combination of Industry 4.0 and SCM systems.

Methodology: In this chapter, the integration of Industry 4.0 and SCM systems was investigated. For this purpose, the Industry 4.0 position of the countries and the current status of SCM systems have been examined. In addition, the key technologies in the Industry 4.0 transformation, the possible problems encountered in the transformation, the deficiencies encountered in SCM systems, and how these deficiencies can be solved with Industry 4.0 were investigated.

Findings: The results of this study show that companies that use an SCM system can separate themselves from their competitors by using Industry 4.0 technologies.

Significance: This can allow them to achieve their strategic goals and to ensure the maintenance of their competitive advantage.

With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

The purpose of this paper is to identify the relationships between process modeling and Industry 4.0, the strategic themes and the most used process modeling language in smart factories. The study also presents the growth of the field of study worldwide, the perspectives, main challenges, trends and suggestions for future works.

To do this, a science mapping was performed using the software SciMAT, supported by VOS viewer.

The results show that the Business Process Model and Notation (BPMN), Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing. The authors also highlighted the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories.

It was possible to identify the most used process modeling languages in smart environments and understand how these languages assist control and manage smart processes. Besides, the authors highlighted challenges, new perspectives and the need for future works in the field.

Product development process (PDP) in the context of Industry 4.0 may present several marketing implications. To understand these implications, the purpose of this study is to identify the benefits and difficulties of Industry 4.0 related to the PDP and its impact in marketing strategies and operations.

The methodology used to perform this research was a systematic literature review. For this, five steps were followed, namely, research question formulation; studies location; studies selection and evaluation; analysis and synthesis; and reporting and use research results.

The systematic literature review considering PDP in Industry 4.0 context resulted in 28 benefits and 14 difficulties, in a total of 53 articles. From the analysis of these benefits and difficulties, several implications for marketing were identified, namely, better understand customer preferences; greater agility in marketing decision-making; better align marketing, product development and operations processes issues; better understand product/service lifecycle; analyze possibilities of new ways of distribution and communication channels; better define the value of products and services and location requirements.

The findings presented here can be used both by market professionals, interested in the subject and by researchers for future studies. The better understanding of PDP in the context of Industry 4.0 can enhance marketing strategies for market professionals and provide insights for researchers. No similar studies were found in the literature.

The food supply chain (FSC) challenges coupled with global disruptions, such as the recent coronavirus disease 2019 (COVID-19) pandemic outbreak, exacerbate its vulnerability. The Internet of things (IoT) is one of the disruptive technologies being adopted in food supply chain management (FSCM). This study aims to address the challenges of IoT adoption in the FSC by systematically analyzing the prior pertinent literature.

A structured literature review was used to collate a list of peer-reviewed and relevant publications. A total of 72 out of 210 articles were selected for the final evaluation.

The literature review findings suggest five themes: technical, financial, social, operational, educational and governmental related challenges. A total of 15 challenges were devised from the review related literature of IoT adoption. The study concludes with future research recommendations for scholars and practical implications for practitioners.

While this study focuses on the overall FSC, further research should address other domains in the FSC such as cold supply chain, agriculture and perishable food to gain a better contextual understanding of the specific case.

The topic of IoT adoption in the FSCM is still considered emerging. Therefore, the present work contributes to the limited studies and documentation on the level of IoT implementation in the FSCM. This study should help organizations to assimilate how to adopt and manage the IoT application by addressing the factors and challenges presented in this research.

The purpose of this paper is to introduce a quantitative method for assisting contractors to select appropriate projects for bidding by considering multiple attributes and integrating decision group member opinions.

The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is used to help contractors make decision on project selection and the linguistic terms are defined for representing the triangular fuzzy numbers for ratings of alternatives and weights of criteria.

The selection of appropriate projects for bidding is a multiple attribute group decision‐making exercise. In a real decision process, there are many uncertainties and ambiguities, and time limitations mean that decision makers cannot always make precise judgments. The numerical example demonstrates that the fuzzy TOPSIS approach can be used to simulate the decision process in project selection, and the results provide contractors with valuable insight into the project selection problem.

Selecting appropriate projects for bidding is to use a contractor's limited resources more efficiently and increase the probability of winning contracts. Therefore, there is a need for a quantitative method to help contractors make better decision on project selection. That leads to the formulation of this paper. The fuzzy TOPSIS method can assist contractors to make better decisions in bidding.