Purpose: Smart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.Design/methodology/approach: A game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.Findings: The numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.Research limitations/implications: This work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.Practical implications: By using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.Originality/value: This work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.

The management of costly and limited healthcare resources, such as operating rooms, doctors, nurses, and beds, is challenged by the uncertainty of arrivals and the service time of different types of patients. Therefore, healthcare units encounter unbalanced utilization of such resources and unnecessary long patient waiting times. A system dynamics model simulating a typical medium-sized hospital, where different types of patients are served using the same limited resources, is developed to implement the Theory of Constraints philosophy. The AnyLogic environment is used for execution. The model is used to identify the system bottleneck resource, then to exploit and to subordinate the system around this resource. The number of served patients (throughput) is increased by 6% without any resource elevation. Furthermore, the model is used to determine the proper capacity needed to elevate the bottleneck resource.

Blockchain has completely changed how business is performed today, thus making it one of the most disruptive technologies in recent times. However, it is a challenging task to adopt and implement blockchain technologies in different services and industries. Therefore, this study introduces a framework for investigating critical factors influencing the successful adoption of blockchain technologies in different applications and prioritizes them using the hierarchical Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. First, it provides fourteen critical success factors with the help of the extant literature and further classifies them into three categories: technological, organizational, and environmental. In addition, a set of sixteen key performance indicators (KPI) of successful blockchain adoption is introduced and classified into five categories: overall performance, system robustness, data robustness, accessibility, and overall cost. Then, the fourteen success factors are ranked based on their degree of prominence and relationships. It is concluded that environmental factors are the most critical factors for successful blockchain adoption, and law and policies and competitive pressure are the top two factors needed for blockchain adoption. In the technological context, only blockchain scalability is ranked among the top significant factors for blockchain adoption. On the other hand, adequate resources, top management support, and financial constraints are highly ranked in the organizational context.

With the growing number of connected online users producing a tremendous amount of unstructured short-texts daily, understanding and mining these data becomes very useful for individuals, governments and companies for identifying the public users’ attitudes towards different entities, such as products, services, events, places, organizations and topics. However, analyzing these short-texts using traditional methods becomes a significant challenge due to the shortness and sparsity nature of short-texts. To address such challenges, the literature introduced a broad spectrum of short-texts mining approaches and applications. Hence, this paper provides a comprehensive survey of this spectrum based on a criterion-based research strategy. The different mining techniques and approaches utilized in short-texts were highlighted along with their related issues and challenges. This paper surveyed a total of 1575 research papers published in the refereed conferences and journals in the area of short-texts mining were sur-veyed from 2006 until 2017, from which 187 primary studies were included and analyzed to constitute the source of the present paper. After a careful review of these articles, it is obvious that there are research gaps in other languages than English and Chinese, multi-languages, and in specific domain studies.

We provide two empirical models for calculating the sailing time and berth time of maritime container liner networks to effectively model the ambiguity associated with sea and port contingency for ex-ante decisions of fleet deployment and route planning. The models are based on recorded AIS data of 110 mega vessels including all the operating container mega vessels with a capacity of 16,000 TEU or more during the summer of 2015. The models are able to estimate the sailing time (with R² of 0.974) and the berth time (with R² of 0.895) without knowledge of any operational-level explanatory variables. The models are validated against the published East Asia–North Europe services. Moreover, the study reveals that vessel operators adopt different berthing and sailing strategies even under the same conditions.