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Task recommendation is an important way for workers and requesters to get better outcomes in shorter time in crowdsourcing. This paper aims to propose an approach based on 2-tuple fuzzy linguistic method to recommend tasks to the workers who would be capable of completing and accept them.

In this paper, worker’s capability-to-complete (CTC) and possibility-to-accept (PTA) for a task needs to be recommended are proposed, measured and aggregated to determine worker’s priority for task recommendation. Therein, the similarity between the recommended task and its similar tasks and worker’s performance on these similar tasks are computed and aggregated to determine worker’s CTC quantitatively. In addition, two factors of worker’s active degree and worker’s preferences to a task category are presented to reflect and determine worker’s PTA. In the process of measuring them, 2-tuple fuzzy linguistic method is used to represent, process and aggregate vague and imprecise information.

To demonstrate the implementation process and performance of the proposed approach, an illustrative example is conducted on Taskcn, a widely used Chinese online crowdsourcing market. The experimental results show that the proposed approach outperformed the self-selection approach, especially for complex or creative tasks. Moreover, comparing with task recommendation considering worker’s CTC solely, the proposed approach would be better in terms of workers’ response rate. Additionally, the use of linguistic terms and fuzzy linguistic method facilitates the expression of vague and subjective information and makes recommendation process more practical.

In the study, the authors capture alternative workers, collect workers’ behaviors and compute workers’ CTC and PTA manually. However, as the number of tasks and alternative workers grow, the issue, i.e. how to conveniently collect workers’ behaviors and determine their CTC and PTA, becomes conspicuous and needs to be studied further.

The proposed approach provides an alternative way to perform tasks posted in crowdsourcing platforms. It can assist workers to contribute to right tasks, and requesters to get outcomes with high quality more efficiently.

This study proposes an approach to task recommendation in crowdsourcing that integrates workers’ CTC and PTA for the recommended tasks and can deal with vague and imprecise information.

Taking 30 provincial samples from 2001 to 2017 in mainland China as the research objects, this paper aims to evaluate the impact and effects of foreign direct investment (FDI) on the urban–rural income gap and reveals heterogeneity across regions.

Firstly, the Theil index is used to measure the income gap between 30 provinces in mainland China from 2001 to 2017, then the spatial econometric model is used to empirically test the impact of foreign direct investment on China’s urban–rural income gap and its heterogeneity across regions. Finally, a robustness test is performed.

The results show that there is a significant inverted U-shaped relationship between FDI and the urban–rural income gap in China. That is, FDI expands the urban–rural income gap in the short term and helps to converge it in the long term. In the eastern region, FDI has a convergence effect on the urban–rural income gap in the short term, which increases the long term. However, in the central and western regions, the relationship between FDI and urban–rural income gap has a weak inverted U shape.

By assessing the impact of FDI on the urban–rural income gap, this work provides decision-making support for China and other developing countries to improve investment policies and income distribution policies.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

This study explores the factors that characterize the logistics service quality (LSQ) of cross-border e-commerce and identifies the different relationships between these factors with respect to customer satisfaction.

The study applied a two-stage mixed-methods design. The first stage (Stage 1) was a qualitative study of 3,000 reviews from the Amazon China e-commerce platform. The second stage (Stage 2) included a quantitative study that analyzed survey data from 590 Chinese cross-border e-commerce customers using the Kano model.

Stage 1 involved developing a conceptual framework for the LSQ of cross-border e-commerce, including six dimensions: timeliness, safety, reliability, economy, personnel contact quality and information quality. In Stage 2, the study found that only reliability and personnel contact quality indicators are linearly related to customer satisfaction. Timeliness and the safety of packaging greatly contribute to customer satisfaction, but do not cause dissatisfaction when unfulfilled. Economics and information quality indicators, and the safety of goods, are basic requirements that tend to provoke customer dissatisfaction when unmet, but do not increase customer satisfaction when they are met.

This study is one of the first to construct a conceptual model of LSQ that applies to cross-border e-commerce and to identify the instrumental nature of various LSQ attributes and their impact on improved customer satisfaction.

The paper proposes a two-level closed-loop supply chain (CLSC) dynamic competitive model based on different competitive cooperation situations, and explores the impact of competitive cooperation methods on the pricing strategies, recycling and remanufacturing strategies and competitive model selection strategies of supply chain firms.

This paper establishes a CLSC game consisting of a manufacturer and two retailers. Firstly, five CLSC models are established in both horizontal and vertical dimensions, each of which competes with one another. Secondly, the recycling and remanufacturing pricing strategies are analyzed under different competition or cooperation models. Finally, the results are verified through numerical analysis.

The overall profitability of the CLSC is highest when the manufacturer–retailer partnership alliance is in place. The relationship between retailers and manufacturers is also found to be the best way to achieve overall optimization of the CLSC.

The paper investigates the relationship between the competitive partnership and the total profit of the CLSC, taking into account how to optimize the overall benefit, and focusing on how to optimize the individual interests of each participating enterprise. The results can provide basis and guidance for managers' pricing decision and competition cooperation.

The vulnerability of multinational corporation (MNC) knowledge network is one of the major causes for the failure and even the death of MNCs in the fierce global market competition. Employee turnover and knowledge loss are the triggers for the MNC knowledge network vulnerability and a matter of serious concern in the evolution and development of MNC knowledge network. The purpose of this work is to propose a valid and quantitative measurement method to investigate the influence of employee loss and knowledge loss on the vulnerability of MNC knowledge network.

MNC knowledge network is inherently a heterogeneous network where there are mainly two types of units: employees and their knowledge. Therefore, this paper establishes a weighted super-network model for MNC knowledge network to depict its heterogeneous composition. On the basis of the weighted MNC knowledge super-network, the static and dynamic vulnerability measurement methods are further proposed to investigate and evaluate MNC knowledge network vulnerability.

A real case is given to illustrate the applicability of the proposed weighted MNC knowledge super-network model and the network vulnerability measurement methods. The results show the super-network model proposed in this paper can effectively embody the complex features of MNC knowledge network, and the vulnerability measurement methods can effectively investigate the influence of employee loss and knowledge loss on network vulnerability.

From the perspective of super-network, researchers and practitioners can get a more systematic and deeper understanding of the MNC knowledge network and its human and knowledge resource constitute which are vital for the evolution and development of MNC. Moreover, the MNC knowledge network vulnerability measurement methods can effectively measure and analyze the influence of resource loss on network vulnerability, which can provide a helpful decision support for monitoring and managing of MNC knowledge network vulnerability to reduce its adverse effects.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration.

First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection.

A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance.

Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

The purpose of this paper is to propose a valid and quantitative measurement method of knowledge diffusion efficiency for the knowledge collaboration networks (KCNs).

This paper builds a weighted KCN model with the node and edge weights. Based on the weighted KCN, the factors of knowledge diffusion efficiency are proposed and analyzed. Then, the knowledge transfer effect between two nodes is proposed and measured by comprehensively integrating the above factors. Furthermore, the main metric of efficiency of knowledge diffusion is proposed by modifying Latora and Marchiori’s model of efficiency of network.

A case is studied to illustrate the applicability of the proposed weighted network model and the knowledge diffusion efficiency measurement method. The results show the methods proposed in this paper can better measure and analyze the knowledge diffusion efficiency of KCNs than the traditional un-weighted methods and the subjective evaluation methods.

The real KCNs are always weighted networks. The weighted model of KCN can better reflect the real networks than the un-weighted model. Based on the weighted networks, the measurement methods proposed in this paper can more efficiently and accurately measure and evaluate the knowledge diffusion efficiency than the traditional methods. This study can help researchers to better understand knowledge diffusion theoretically, and provide managers with a decision support for knowledge management in practice.