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This study aims to examine how external and internal conditions drive the impact of circular economy mechanism by decomposing into three policy networks in terms of reduce, reuse and recycle, to better understand the contingency model of climate change and effect of firm size on subsequent performance.

Drawing on circular economy network and resource-based view (RBV)-network-resilience strategy framework, a pooled longitudinal cross-sectional data model is developed using a sample of 4,050 Taiwanese manufacturing multinational corporations (MNCs) making foreign direct investment between 2013 and 2018. Structural equation modeling analysis is used to comprehensively examine and investigate each circular economy policy network in the context of climate change and firm size. Post hoc multigroup analysis (MGA) is also conducted.

MGA shows that the reduce policy network is positively and negatively related to manufacturing know-how and production size, respectively. The impact of reuse policy network can enhance the competence of large firms. The recycle policy network is more prominent in terms of competence enhancement of climate change.

MNCs are seeking to build circular economy policy networks to a greater extent, given climate change pressure and guidelines.

This study adds to the circular economy and RBV-network-related literature on climate change and interactions to enhance performance, echoing the recent call on the sustainability of the circular economy of MNCs.

This paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution.

LRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW.

LONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs.

The original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.

Pharmaceutical donations are a practical approach to increase medicine availability during disasters such as disease outbreaks. However, often donated pharmaceuticals are inappropriate and unsuitable. This convergence of inappropriate pharmaceuticals is a severe operational challenge and results in environmental hazards. This study explores the pharmaceutical supply chains (PSCs) during a disease outbreak to relieve the negative impact of the material convergence problem (MCP).

This study adopts a situation-actors-process learning-action-performance (SAP-LAP) linkage framework to understand the PSC dynamics. The problem-solving component of the SAP-LAP analysis provides the strategies catering to MCP. The findings from the SAP-LAP helped to develop the causal loop diagram (CLD). This study conducts several experiments on the proposed strategies by integrating CLD into a stock and flow diagram. Later, a disease outbreak case study accessed the pharmaceutical donations effect on PSC performance.

The study synthesises and evaluates propositions and strategies to incorporate circular economy (CE) principles in PSC. This study proposed two strategies; one to sort and supply and the other to sort, supply and resell. The reuse policy improves humanitarian organisations' finances in the simulation study. This study verified the operational improvement of PSC by reducing the transport and storage burden due to MCP.

This study comprehensively approaches the issue of drug donation and uniquely produced several propositions for incorporating a CE perspective in PSC. The study also proposed a unique simulation approach to model the donation arrivals in response to a disease outbreak using susceptible, exposed, infectious and recovered modelling.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This study aims to present and empirically examines an expanded service model that incorporates green hotel practices together with a multidimensional/higher-order measurement model of service quality, as well as perceived value and satisfaction, to examine the relationships among these variables and hotel consumers’ loyalty/behavioral intentions (BI).

The model was examined using partial least squares structural equation modeling (PLS-SEM) using data gathered in August 2018 from 200 surveys completed by UK subjects who stayed at upscale European hotels.

The results of PLS-SEM found that hotel service quality has a direct and positive effect on perceived value, satisfaction and BI. There is also an indirect effect of service quality on BI through perceived value and satisfaction, while green practices only had a direct effect on perceived value, not satisfaction or BI.

This study offers new insights into the network of causal relationships among determinants of hotel consumers’ BI. The results offer hotel operators a better understanding of specific green practices and service quality attributes they can use to more favorably influence consumers’ intentions to revisit the property and recommend them through positive word-of-mouth.

This research is particularly relevant in today’s reality characterized by travelers’ growing concern for green issues and business’ responsibilities toward the environment. Moreover, unlike previous studies, this study assumes a multidimensional scheme for service quality, further enhancing the understanding of hotel consumers’ BI relationships.

The Java programming language supports the concept of downloadable executable content; a key technology in a wide range of emerging applications including collaborative systems, electronic commerce, and Web information services. Java enables the execution of a program, on almost any modern computer regardless of hardware configuration and operating system. Safe‐Tcl was proposed as an executable content type of MIME and thus as the standard language for executable contents within e‐mail messages. However, the ability to download, integrate, and execute code from a remote computer, provided by both Java and Safe‐Tcl, introduces serious security risks since it enables a malicious remote program to obtain unauthorised access to the downloading system’s resources. In this paper, the two proposed security models are described in detail and the efficiency and flexibility of current implementations are evaluated in a comparative manner. Finally, upcoming extensions are discussed.

The purpose of this study is to assess if the mediating effect of green innovation capability (GIC) in the relationship between green market orientation (GMO) and new product success (NPS) was conditional on the moderating effects of green knowledge acquisition (GKA) and green brand positioning (GBP).

The analysis was based on primary data gathered using a structured questionnaire, which was developed on a five-point Likert scale of 1-Strongly disagree to 5-Strongly agree. There were 259 manufacturing firms engaged in the study, with data analyzed using PROCESS macro (v.3.4) for SPSS (v.23).

The research revealed that GMO had no direct effect on NPS among manufacturing firms, the relationship was rather mediated by GIC of the firms. The effect of GMO on GIC was moderated by GKA, whereas the effect of GIC on NPS was moderated by GBP. Overall, the mediating effect of GIC in the relationship between GMO and NPS was conditional on the moderating effects of GKA and GBP.

The study focused on only knowledge acquisition (green), without recourse to assimilation, transformation and exploitation. These may, however, be very important in explaining the role of knowledge in green innovation.

Green market-oriented manufacturing firms must seek to also make investments in GIC to transform those concepts into successful innovative products.

Despite the increasing number of studies on GMO, very limited concentration has been paid to how firms could leverage on the potentials of GMO to enhance the success of new products introduced into the market. This study did not just establish the effect of GMO on the success of new products but also identified some intervening variables in this relationship.

With the dramatic transformation of China’s industrial landscape, since the late 1990s, adaptive reuse of industrial heritage for cultural purposes has become a widely occurring phenomenon in major Chinese cities. The existing literature mainly focusses on specific cases, yet sees heritage conservation similarly at both national and regional scale and rarely identifies the main factors behind the production of China’s industrial-heritage reuse. The purpose of this paper is to examine the differences in heritage reuse outcomes among three Chinese mega-cities and explore the driving factors influencing the differences.

This paper compares selected industrial-heritage cultural precincts in Beijing, Shanghai and Chongqing, and explores the local intervening factors influencing differences in their reuse patterns, including the history of industrial development, the availability of the nineteenth and/or twentieth century industrial buildings, the existence of cultural capital and the prevalence of supportive regional government policy.

The industrial-heritage reuse in the three cities is highly regional. In Beijing, the adaptation of industrial heritage has resulted from the activities of large-scale artist communities and the local government’s promotion of the city’s cultural influence; while in Shanghai, successful and more commercially oriented “sea culture” artists, private developers in creative industries and the “creative industry cluster” policy make important contributions. Chongqing in contrast, is still at the early stage of heritage conservation, as demonstrated by its adaptive reuse outcomes. Considering its less-developed local cultural economy, Chongqing needs to adopt a broader range of development strategies.

The paper contributes to knowledge by revealing that the production of industrial-heritage cultural precincts in Chinese mega-cities is influenced by regional level factors, including the types of industrial heritage, the spontaneous participation of artist communities and the encouragement of cultural policy.

This paper aims to provide a systematic framework for organizations to analyze their knowledge reuse processes, and balance codification and personalization within their knowledge strategy according to cost/benefit analysis.

This paper divides knowledge reuse process into a sequence of five stages, and accordingly analyzes costs/benefits under codification and personalization strategies. Markov decision process, a mathematical framework for multi-stage decision-making, is employed to optimize a mixed strategy for knowledge reuse processes within an organization.

Organizations need to consider factors such as the number of reusable knowledge items, reuse patterns, and intra-organizational interest alignment which are critical to determine their optimal mix between codification and personalization. Companies should determine a knowledge strategy based on their knowledge reuse contexts instead of following success cases blindly.

This paper presents an illustrative example to show how this framework might be applied by an organization. However, the validity and reliability of strategic decision-making also depends on the accuracy of the model's parameter values. Firms can adopt many methods as surveys, Delphi method, to determine the parameter values.

The proposed framework offers an opportunity for firms to gain insights by setting the model's parameters to their own reuse contexts/characteristics and conducting what-if analysis.

This paper proposes a formal framework for analyzing knowledge reuse processes and offers organizations guidelines about decision-making of knowledge strategies.

The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers.

This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2.

This study identifies earth and environmental scientists’ information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information.

While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields.

The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data.

By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse.

While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record.