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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 study aims to compare linear programming and stable marriage approaches to the personnel assignment problem under conditions of uncertainty. Robust solutions should exhibit reduced variability of solutions in the presence of one or more additional constraints or problem perturbations added to some baseline problems.

Several variations of each approach are compared with respect to solution speed, solution quality as measured by officer-to-assignment preferences and solution robustness as measured by the number of assignment changes required after inducing a set of representative perturbations or constraints to an assignment instance. These side constraints represent the realistic assignment categorical priorities and limitations encountered by army assignment managers who solve this problem semiannually, and thus the synthetic instances considered herein emulate typical problem instances.

The results provide insight regarding the trade-offs between traditional optimization and heuristic-based solution approaches.

The results indicate the viability of using the stable marriage algorithm for talent management via the talent marketplace currently used by both the U.S. Army and U.S. Air Force for personnel assignments.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

The semantic and structural heterogeneity of large Extensible Markup Language (XML) digital libraries emphasizes the need of supporting approximate queries, i.e. queries where the matching conditions are relaxed so as to retrieve results that possibly partially satisfy the user's requests. The paper aims to propose a flexible query answering framework which efficiently supports complex approximate queries on XML data.

To reduce the number of relaxations applicable to a query, the paper relies on the specification of user preferences about the types of approximations allowed. A specifically devised index structure which efficiently supports both semantic and structural approximations, according to the specified user preferences, is proposed. Also, a ranking model to quantify approximations in the results is presented.

Personalized queries, on one hand, effectively narrow the space of query reformulations, on the other hand, enhance the user query capabilities with a great deal of flexibility and control over requests. As to the quality of results, the retrieval process considerably benefits because of the presence of user preferences in the queries. Experiments demonstrate the effectiveness and the efficiency of the proposal, as well as its scalability.

Future developments concern the evaluation of the effectiveness of personalization on queries through additional examinations of the effects of the variability of parameters expressing user preferences.

The paper is intended for the research community and proposes a novel query model which incorporates user preferences about query relaxations on large heterogeneous XML data collections.

This paper aims to argue that, traditionally, service recovery attempts have paid little attention to customer preferences. Despite attempts to recover the customer, firms generally do not know if the recovery solution is what the customer expects. Hence, the paper seeks to examine whether customer recovery preferences influence customers' evaluation of the recovery attempt in terms of recovery satisfaction and repurchase intentions.

First, a two‐stage qualitative study was conducted. Then the research model was tested empirically on a sample of 431 consumers using a multivariate analysis.

The findings support the argument that customers have distinct recovery preferences. Moreover, customers are satisfied with the service recovery solution only when it matches the most demanding recovery preference. Customers' recovery preferences have a significant impact on their satisfaction with recovery and their repurchase intentions.

First, the model developed is tested on a cross‐sectional sample. Second, the measure of recovery satisfaction and repurchase intentions used here was relatively simple. Third, the study relies on repurchase intentions instead of actual behavioural data.

This research indicates that customers have a preference for how service recovery should be undertaken. Given these distinct recovery preferences, different recovery solutions should be applied to address each preference appropriately.

It is widely accepted in the service recovery literature that customers' perceptions of a service recovery attempt are often different to those of the service provider. However, this research suggests that customer recovery preferences need to be carefully considered given their effect on customer satisfaction and repurchase intentions.

The purpose of this paper seeks to develop a motivation-based complementary framework for temporally dynamic user preferences to facilitate optimal timing in web personalisation. It also aims to highlight the benefits of considering user motivation when addressing issues in temporal dynamics.

Through theory, a complementary framework and propositions for motivation-based temporal dynamics for further testing are created. The framework is validated by feeding back findings, whereas some of the propositions are validated through an experiment.

The suggested framework distinguishes two ways (identifying/learning and shifting) of using a motive-based approach to temporal dynamics in web personalisation. The suggested outcomes include enhanced timing in matching current preferences and improved conversion. Validation measures predominantly support both the framework and the tested propositions. The theoretical basis for the approach paves a path towards refined psychological user models; however, currently on a complementary level.

While the framework is validated through feeding back findings, and some of the propositions are validated through basic experimentation, further empirical testing is required.

A generalised approach for complementing personalisation procedures with motivation-based temporal dynamics is offered, with implications for both user modelling and preference matching.

This paper offers novel insights to web personalisation by considering the in-depth effects of user motivation.

The reasons for writing the paper are flexibility of information under multi‐agent approach.

The main method used for the research is preferences in environment and matching of information.

The benefits of hierarchy flow from the fact that it attenuates opportunism and attenuates the problem stemming from bounded rationality. The preference of an agent over sets of possible future choices can be represented using subjective states. This is sufficient to give a representation in which the agent has a coherent subjective state space, and shows that the size of the subjective state space depends on the individual circumstances.

The original value of the paper is a measure of the agent's uncertainty about future contingents. Changes in the surplus yield valuable information about the comparative static of matching patterns across environments in equilibrium.

This paper aims to introduce the concept of a framework of cyber-security controls that are adaptable to different types of organisations and different types of employees. One of these adaptive controls, namely, the mode of training provided, is then empirically tested for its effectiveness.

In total, 1,048 working Australian adults completed the human aspects of the information security questionnaire (HAIS-Q) to determine their individual information security awareness (ISA). This included questions relating to the various modes of cyber-security training they had received and how often it was provided. Also, a set of questions called the cyber-security learning-styles inventory was used to identify their preferred learning styles for training.

The extent to which the training that an individual received matched their learning preferences was positively associated with their information security awareness (ISA) level. However, the frequency of such training did not directly predict ISA levels.

Further research should examine the influence of matching cyber-security learning styles to training packages more directly by conducting a controlled trial where the training packages provided differ only in the mode of learning. Further research should also investigate how individual tailoring of aspects of an adaptive control framework (ACF), other than training, may improve ISA.

If cyber-security training is adapted to the preferred learning styles of individuals, their level of ISA will improve, and therefore, their non-malicious behaviour, whilst using a digital device to do their work, will be safer.

A review of the literature confirmed that ACFs for cyber-security does exist, but only in terms of hardware and software controls. There is no evidence of any literature on frameworks that include controls that are adaptable to human factors within the context of information security. In addition, this is the first study to show that ISA is improved when cyber-security training is provided in line with an individual’s preferred learning style. Similar improvement was not evident when the training frequency was increased suggesting real-world improvements in ISA may be possible without increasing training budgets but by simply matching individuals to their desired mode of training.