Search results

This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

Textile manufacturers worldwide are reformulating their networks, often outsourcing them to maintain a competitive advantage and increase market share. From this perspective, the purpose of this study is support the partnership selection process to develop a sustainable chain that effectively meets customer needs. Brazil has the largest textile and apparel chain in the West and is distinguished by its completeness, from fiber production, spinning, weaving, knitting, finishing and sewing to fashion shows. However, a firm’s relationship, especially in the production stage, is based on informal contracts, which result in a negative operational impact.

A methodological framework was developed based on a stable matching process to determine the optimal supplier network structure. This study presents a model application for the denim apparel chain in northeast Brazil.

In these environments, providing choices and recommending suppliers can be beneficial for effectively attending to demand requests, reducing production costs and improving quality through collaboration with sense relationships in a network. Thus, this study presents a better match from the negotiators’ perspective.

The findings of this research are of primary interest for guiding collaborative network composition in the textile and apparel chain. In particular, apparel domain companies can improve their effectiveness in decision-making by measuring the characteristics and potential of all companies involved in networks.

The uncertainty of consumers' perceived value makes online education enterprises face great challenge in developing the pricing strategy. So the purpose of this paper is to research the pricing strategies of online education products by considering knowledge consumers' characteristics.

Considering consumer matching degree and price comparison, this study establishes the utility functions of consumers in normal sales period and discount selling period. On this basis, the research builds pricing models of the online education enterprise under the strategy of price undertaking and intertemporal pricing strategy. It further discusses the impact of consumer matching degree, consumer price sensitivity and different types of consumers on the product price and profit of online education enterprises, and reveals the optimal pricing strategy of the enterprise.

Consumer matching degree and price sensitivity coefficient have positive effects on product price and enterprise profit, but they have different effects on product demand; there are differences in the perceived value of the three types of consumers, and matching consumers are the optimal consumer group; the intertemporal pricing strategy is better than the strategy of price undertaking only when the price sensitivity coefficient is greater than a critical value.

This study enriches the literature on the pricing model of online education products and owns a practical significance to guide the online enterprise to make marketing strategies to increase profit.

The purpose of this study was to investigate assortative mating processes inside Internet-dating-service settings. Unattached consumers traditionally sought to satisfy their need for love through conventional search processes, including old-fashioned match-making. That was then, this is now; dozens of internet-mediated dating websites promising romantic-love-matches currently operate internationally. These dating services cultivate dating-exchanges by offering new-fashioned match-making processes. Despite these trends, theoretical and practical questions related to how and why dating services marketers might induce superior romantic exchanges between customers by managing assortative mating processes remain unanswered until now.

A survey-based approach was used to test hypotheses. Pretests were conducted to develop reliable measures of assortative mating propensity. Seven subconstructs of assortative mating were identified by analyzing data from a representative sample. The measurement model was validated before hypotheses testing. The focal assortative mating construct was measured formatively; assortative mating subdimensions functioned as indicators. The model was tested by structural equation modeling.

Assortative mating processes facilitated superior preference-selection outcomes for individuals seeking consumer-to-consumer romantic relationships inside internet-mediated service settings. Insights were generated about how and why assortative mating processes exercised positive effects on consumers’ attitudes toward online dating and about how dating services marketers might leverage assortative mating tendencies to benefit consumers.

A novel concept was introduced to the services marketing literature, as were several theoretical implications. The study simultaneously measured consumers’ propensities to engage in assortative mating and captured the effects of various physical/behavioral consumer characteristics. This study develops new and practical insights about how dating service marketers could manage the effects of assortative mating processes.

Central to the fit concept is that congruence between individual and environmental attributes leads to improved outcomes. However, when discussing fit, researchers often describe congruence as alignment between distinctive or unique individual and environmental attributes. We suggest that current approaches to examining fit do not adequately account for this assumption of distinctiveness because they fail to consider normative expectations and preferences. As such, we propose an alternative theoretical and methodological approach to conceptualizing and measuring fit.

We introduce the normative theory of fit, outline how researchers can decompose fit into distinctive and normative components and identify areas for future research.

Management researchers have largely ignored the importance of decomposing fit into distinctive and normative components. This shortcoming necessitates additional research to ensure a more accurate understanding of fit and its relationship with outcomes.

We provide a clarification and critical examination of a pervasive construct in the field of management by introducing the normative theory of fit, identifying areas where researchers can employ this theoretical lens and suggesting a reevaluation of the importance placed on differentiation that is traditionally employed in practice.

The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.

The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.

The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.

The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

This study investigates theoretical relationships among personality characteristics, preferences for different types of rewards and the propensity to choose a job in auditing by management-related higher education students. Specifically, the authors consider motivation, locus of control (internal and external) and self-efficacy (SE) as personality characteristics and financial, extrinsic, support and intrinsic as types of rewards.

Data were collected through a questionnaire targeted at management-related higher education students in Portugal. Partial least squares structural equation modelling was used to analyse the data.

The full sample results show that different types of motivation, locus of control and SE are related to different reward preferences. The authors also find a positive association between a preference for extrinsic rewards and the propensity to choose a job in auditing. Moreover, when the authors consider the role of working experience in the model, the authors find that the reward preferences that drive the choice of an auditing job differ according to that experience.

This study enriches the literature by assessing preferences for different types of rewards, considering multiple personality characteristics and a comprehensive set of rewards. Furthermore, the authors identify the reward preferences that drive the choice of an auditing career. This knowledge empowers auditing firms to devise recruitment strategies that resonate with candidates’ preferences, which boosts the capacity of these companies to attract new auditors.