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In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

There has been an increased interest in the use of semantic description and matching techniques, to support service discovery and to overcome the limitations in the traditional syntactic approaches. However, the existing semantic matching approaches lack certain desirable properties that must be present in an effective solution to support service discovery. The purpose of this paper is to present a solution to facilitate the effective semantic matching of resource requests and advertisements in pervasive environments.

The paper presents a semantic description and matching approach to facilitate resource discovery in pervasive environments; the approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request.

The solution has been evaluated for its effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. The solution was also evaluated for its efficiency/scalability and from the experimental results obtained, it can be observed that for most practical situations, matching time can be considered acceptable for reasonable numbers of advertisements and request sizes.

The proposed approach improves existing semantic matching solutions in several key aspects. Specifically; it presents an effective approximate matching and ranking criterion and incorporates priority consideration in the matching process. As shown in the evaluation experiments, these features significantly improve the effectiveness of semantic matching.

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.

Education depends on high-quality teachers who are committed to professional development and do not get burned out. The purpose of this paper was to investigate how job demands and resources can affect the health and cognitive development of teachers using the Demand-Induced Strain Compensation model.

A cross-sectional sample of 120 teachers in vocational education was used to investigate the proposed relationships and hypotheses with Ordinary Least Squares (OLS) regression method.

In terms of teacher health and development, significant main effects were found for several predictors. Autonomy was significantly and negatively related to emotional exhaustion. Autonomy, emotional supervisor and colleague support were significantly and positively related to teachers’ development. However, little support was found for matching hypotheses, suggesting that matching demands and resources do not offer more explanatory power for occupation outcomes than other types of interaction effects.

More powerful analyses techniques like structural equation modeling could be used in future research with a larger sample size. A second limitation is common method variance.

Schools in vocational education should provide sufficient job resources, such as autonomy and emotional support, but possibly also put a limit on teacher task variety.

Job demands and resources have until now mainly been related to negative outcomes such as poor health and ill-being, while the relationship with learning has also been hypothesized and is therefore meaningful to examine. In addition, it was investigated whether interaction effects of matching demands and resources, better explain these outcomes.

Although service research typically asserts that institutions coordinate actors’ value creation processes, institutions and resources are not necessarily transparent, aligned, or pre-existing. This paper aims to develop a more granular perspective on how actors coordinate for value.

Drawing on the established concepts of signaling and screening theory, this paper adopts a service marketing perspective to explore how independent heterogeneous actors coordinate for value creation at the individual level. Illustrative cases of corporate startup collaborations are presented in support of the proposed conceptual framework.

Actors share and acquire information through signaling and screening activities in a coordinative dialogue with other actors. These resource integration activities (for resource creation and matching) affect actors’ valuations and future actions.

The one-sided explanations of coordination in the existing literature reflect the dominance of the institutional theory. By contrast, the proposed agency-oriented perspective based on the integration of signaling and screening functions offers a more granular conceptualization of the resource integration process. As well as capturing how actors use coordinating dialogue to match resources and institutions, this account also shows that matching is a core element of resource integration rather than an antecedent. The findings indicate paths for future research that focus on the actor.

For parts of the time on a typical construction site concrete pour, the site placing crew is idle waiting for the arrival of the next truckmixer delivery, whereas for other periods, truckmixers are idle on site waiting to be unloaded. Ideally, the work of the crew should be continuous, with successive truckmixers arriving on site just as the preceding truckmixer has been emptied, to provide perfect matching between site and concrete plant resources. However, in reality, sample benchmark data, representing 118 concrete pours of 69 m³ average volume, illustrate that significant wastage occurs of both crew and truckmixer time. The purpose of this paper is to present and explain the characteristics of the wastage pattern observed and provide further understanding of the effects of the factors affecting the productivity of this everyday routine site concreting system.

Analytical algebraic models have been developed applicable to both serial and circulating truckmixer dispatch policies. The models connect crew idle time, truckmixer waiting time, truckmixer round trip time, truckmixer unloading time and truckmixer numbers. The truckmixer dispatch interval is another parameter included in the serial dispatch model. The models illustrate that perfect resource matching cannot be expected in general, such is the sensitivity of the system to the values applying to those parameters. The models are directly derived from theoretical truckmixer and crew placing time-based flow charts, which graphically depict crew and truckmixer idle times as affected by truckmixer emptying times and other relevant parameters.

The models successfully represent the magnitudes of the resource wastage seen in real life but fail to mirror the wastage distribution of crew and truckmixer time for the 118 pour benchmark. When augmented to include the simulation of stochastic activity durations, however, the models produce pour combinations of crew and truckmixer wastage that do mirror those of the benchmark.

The basic contribution of the paper consists of the proposed analytical models themselves, and their augmented versions, which describe the site and truckmixer resource wastage characteristics actually observed in practice. A further contribution is the step this makes towards understanding why such an everyday construction process is so apparently wasteful of resources.

The purpose of this paper is to respond to the call of international marketing professionals for more studies on strategies that firms use in response to the complexities of interacting with other institutions in the emerging markets (EMs) of sub-Saharan Africa. The key research question investigated by employing the exploratory qualitative data gathered is: What strategies and global alliances do small local firms (SLFs) in Nigeria adopt to succeed under complex market conditions?

The methodology employed is exploratory qualitative research. The authors conducted extended interviews to generate rich case study data from the top management of the selected SLFs in Nigeria. The interview data were assessed using open, axial and selective coding to uncover macro-narratives that guide SLFs’ strategies and global alliances.

The macro-narratives derived from the qualitative case analysis reveal a theoretical framework centered on three major elements of competitive strategies in Nigeria: build global capacity and strategic alliances from the get-go; develop local strategic alliances; master matching alliance partners’ needs to create innovative payment plans and, when necessary, shift the transaction cost burden to alliance partners. Matching theory rather than traditional network theories is better at explicating SLFs’ alliances in Nigeria. Implementation of these strategies requires flexible strategic initiatives.

The study adapts institutional interaction theory, network theory, matching alliance perspective, trade credit theories and the literature on small firms’ strategies in EMs to explicate successful small local firm strategies and global alliances under complex market conditions in Nigeria. The recognition that SLFs regularly migrate and shift the burden of transactions’ cost to multiple stakeholders in the supply network by matching customers and supplier needs is important. The discovery of matching theory in explicating SLFs’ global alliances in Nigeria is unique to this study.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

Presents a new model for resource discovery in grids and peer‐to‐peer networks designed to utilise efficiently small numbers of messages for query processing and building of the network. Outlines and evaluates the model through a theoretical comparison with other resource discovery systems and a mathematical analysis of the number of messages utilised in contrast with Chord, a distributed hash table. Shows that through careful setting of parameter values the model is able to provide responses to queries and node addition in fewer messages than Chord. The model is shown to have significant benefits over other peer‐to‐peer networks reviewed. Uses a case study to show the applicability of the model as a methodology for building resource discovery systems in peer‐to‐peer networks using different underlying structures. Shows a promising new method of creating a resource discovery system by building a timeline structure on demand, which will be of interest to both researchers and system implementers in the fields of grid computing, peer‐to‐peer networks and distributed resource discovery in general.

Literacy represents one’s ability to process and produce materials related to a domain. One type of this higher-order, global individual difference variable is consumer financial literacy. It stems from one’s financial information processing capacity, prior financial knowledge, and proficiency in optimizing financial decisions and managing financial resources. The paper aims to discuss these issues.

The research matching perspective theoretically explains findings related to literacy, including those in this special issue. Optimal processing arises as available and required processing resources correspond. Thus, cognitive comprehension and behavioral application/decision-making outcomes following financial marketing communication exposure are optimized when consumer financial literacy matches the level needed for successful processing. Insufficient or excess available resources harm outcomes.

The resource-matching perspective clarifies consumers’ increasing financial difficulties. Consumers limit personal finance efforts because required resources overwhelm limited financial literacy. However, education or experience can expand consumer financial literacy. Alternatively, financial service marketers may accommodate low consumer financial literacy by simplifying financial information presentation. Consumers reward firms that show sensitivity to their domain-specific literacy limitations with stronger loyalty.

Construct definition is vital to advance research. Yet, financial literacy has no generally accepted definition. This paper’s definitions should aid understanding of the psychological underpinnings of financial literacy’s components.

Much has been written about consumers’ inability to manage personal finances. This paper provides a unified, theoretical explanation for consumers increasing financial literacy difficulties and suggests ways that consumers, financial service providers, and public policy makers can overcome these difficulties.