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One of the most innovative library services recently introduced by public and academic libraries, the technology of 3D printing, has the potential to be used in multiple educational settings. The purpose of this paper is to examine how this novel library digital service motivates students’ learning, and to investigate managerial issues related to the introduction of 3D printing services at a medium-size urban community college library with restricted funding.

Since Fall 2014, the LaGuardia Library Media Resources Center has been offering a portable consumer-end 3D printer for classroom use. This paper provides historical context for the implementation of 3D printing as a service offered by librarians and discusses how the community college library managed 3D printing services to support class curriculum. At the end of the three-semester-long project students were asked to volunteer to take a survey conducted by the librarian and the class instructor.

The results of the student survey demonstrated that library 3D printing services significantly promoted students’ motivation to learn. The conceptual model of a makerspace should be an essential part of the twenty-first century academic library. To help make that possible this paper examines certain challenges and limitations faced by librarians when introducing 3D printing, including dedicated space management, professional education, and personnel availability.

During the project described students were able to use library services to print out and study complex engineering and biology models in 3D. The proper planning and management of this innovative service allows academic librarians to enhance class curriculum by providing the means of transforming theory into physical reality.

The purpose of this paper is to figure out the paths about transformation of tacit knowledge into explicit knowledge, i.e. tacit knowledge explicating (TKE) in real estate companies, and determine the influencing factors of TKE in Chinese real estate companies to enable enterprises make better use of their knowledge resources.

The study adopted an exploratory design method using thematic analysis and grounded theory, and semi-structured interviews were conducted to collect data. The interviewees consisted of employees in different positions, who come from Chinese real estate companies with different ranking ranges and different knowledge management levels. Data collection was divided into two rounds for the identification of transformation paths and influencing factors.

This study has shown that 11 paths about TKE divided into solidified organization process and construction of organizational infrastructure go into effect within the real estate companies. Factors influencing TKE in real estate companies concern three main categories: organizational distal factors, contextual proximal factors and individual factors, including 21 subordinates in total. Furthermore, correlation between TKE paths and influencing factors is established.

Research results may lack generalizability due to the method adopted. Therefore, researchers are encouraged to verify the outcomes of this research.

This research provides a new idea and solutions for the tacit knowledge management in real estate companies.

To the best of the authors’ knowledge, this study is the first to systematically identify paths and the influencing factors of TKE in real estate companies, contribute to the incipient but growing understanding of achievement of “tacit to explicit” and enrich the corporate tacit knowledge management literature.

The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

After receiving advertising messages, most consumers rarely purchase the advertised products at once, which results in a delay between advertising exposure and its effect. This paper is devoted to exploring the advertising decision and coordination issues for a supply chain system subject to advertising immediate and delayed effects.

By applying the game theory, the differential game models with delay are constructed for the supply chain to examine the equilibrium advertising efforts, brand goodwill and the optimal profits under the different cooperation situations. A class of transfer payment contracts is designed to achieve the best outcome of the supply chain. Illustrative examples are given to demonstrate the effectiveness of addressed results and provide some managerial perspectives.

It can be found that the complete cooperation situation can stimulate the advertising investment, drive the product demand and improve the economic profit. Also, a class of transfer payment contracts is designed in this paper, such that the supply chain can perfectly realize the profit maximization, and each member can achieve the Pareto improvement.

This work does not address the random market environment, which can be filled in the future. Furthermore, this paper has been done in a single supply chain structure. It is an interesting future line of research when taking competitive behavior (e.g. competition among manufacturers, retailers or supply chains) into account.

This study will help managers make advertising strategies, advise an optimal cooperation way and design the coordination contracts to ensure the economic development of the supply chain. These obtained conclusions may provide a valuable decision-support for marketing management.

For a supply chain, the most previous literature about dynamic advertising models focused on a single advertising effect-immediate effect. This work explores advertising strategy with double advertising effects and investigates the coordinating power of new transfer payment contracts.

The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.

Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.

This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.

This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.

This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

Drawing on the control agency theory and the network effect theory, the purpose of this paper is to examine the effect of privacy assurance approaches, network externality and technology complementarity on consumers’ self-disclosure in mobile payment (MP) applications. The authors identify four types of privacy assurance approaches: perceived effectiveness of privacy setting, perceived effectiveness of privacy policy, perceived effectiveness of industry self-regulation and perceived effectiveness of government legislation. The research model considers how these privacy assurance approaches influence privacy concerns and consumers’ self-disclosure in MP applications under boundary conditions of network externality and technology complementarity.

An online survey with 647 sample users was conducted to empirically validate the model. The target respondents were current consumers of a popular MP application. The empirical data were analyzed by a structural equation modeling approach.

The empirical results reveal several major findings. First, privacy assurance approaches can effectively decrease privacy concerns, which ultimately formulates consumers’ self-disclosure in MP applications. Second, network externality and technology complementarity weaken the effect of perceived effectiveness of privacy setting on privacy concerns. Third, network externality and technology complementarity strengthen the relationship between perceived effectiveness of government legislation and privacy concerns, while they have non-significant interaction effect with perceived effectiveness of privacy policy and industry self-regulation on privacy concerns.

MP providers and stakeholders can harness the efficacy of privacy assurance approaches in alleviating privacy concerns and promoting consumers’ self-disclosure in MP applications.

The authors’ work contributes to the information privacy literature by identifying effective privacy assurance approaches in promoting consumers’ self-disclosure in MP applications, and by highlighting boundary conditions of these privacy assurance approaches.

At present, the evaluation methodology on the design innovation of the stadia is not complete. The degree of innovation of the design scheme of the stadia still cannot be quantitatively estimated; this inability makes it difficult to guide the selection of a design scheme. To solve the problem of evaluation on the innovative design of the stadia, improve such design’s evaluation theory, and accurately direct the selection of the design scheme of the stadia, the Delphi method is used to select evaluation indexes. Moreover, analytic hierarchy process (AHP) is applied to determine the index weight in this study; based on this index weight, fuzzy comprehensive evaluation is used to establish the model of the design innovation evaluation of the stadia. The model involves the comprehensive and simple selection of indexes and the high reliability of weight selection; the model can quantitatively calculate the comprehensive index evaluation value of the design innovation of the stadia in a relatively accurate and rapid way. In this study, Hunan People’s Stadium is taken as the example for design innovation evaluation; the procedure of design innovation evaluation is introduced in detail. The design innovation evaluation value of the stadia is 2.977 through analysis; this value indicates an ordinary degree of innovation. The innovation evaluation value of the shape is 3.425; this value shows a relatively high degree of innovation. The innovation evaluation value of the structure is 2.47; this value represents a relatively low degree of innovation. The model in this study is an accurate and prompt model that can conduct a comprehensive evaluation on the design innovation of the stadia based on quantitative calculation. This model directly and comprehensively finds the advantages and disadvantages of a design scheme. Thus, it is quite suitable for the design innovation evaluation of the stadia.

The purpose of this paper is to develop an employee job performance (EJP) scale questionnaire that encompasses the job time, job quality and job quantity dimensions of employee performance.

In this study, the questionnaire questions were first validated using exploratory factor analysis (EFA) and then confirmatory factor analysis (CFA) along the three performance dimensions. The research samples were auto-parts assembly line workers. The factor analysis results confirmed the validity of the questionnaire as a reliable employee performance evaluation tool, as evidenced by the composite reliability (CR) and the average variance extracted (AVE).

The questionnaire questions identified three factors of EJP, namely, job time, job quality and job quantity, with 13 items. The factor analysis results confirmed the validity of the questionnaire as a reliable employee performance evaluation tool, as evidenced by a CR of 0.894 and AVE of 0.739, in addition to χ²=63.340, df=54, p=0.180, goodness of fit index=0.976, adjusted goodness of fit index=0.960, RMSEA=0.021 and root mean square residue=0.014.

The EJP scale questionnaire was examined using only EFA and CFA. These scales are expected to help academics, researchers and practitioners test theories. Because of the features of the sample, the final results should be considered carefully.

Importantly, the factor analysis results suggest that the proposed EJP scale questionnaire can be applied to various industries and settings either as is or with minimal modifications.

The EJP questionnaire is novel and can serve as an excellent EJP tool to measure employees’ behavioral output. The questionnaire can provide empirical data on employee performance output based on the employee’s perspective.

Lattice Boltzmann method (LBM) is employed to explore friction factor of single-phase fluid flow through porous media and the effects of local porous structure including geometry of grains in porous media and specific surface of porous media on two-phase flow dynamic behavior, phase distribution and relative permeability. The paper aims to discuss this issue.

The 3D single-phase LBM model and the 2D multi-component multi-phase Shan-Chen LBM model (S-C model) are developed for fluid flow through porous media. For the solid site, the bounce back scheme is used with non-slip boundary condition.

The predicted friction factor for single-phase fluid flow agrees well with experimental data and the well-known correlation. Compared with porous media with square grains, the two-phase fluids in porous media with circle grains are more connected and continuous, and consequently the relative permeability is higher. As for the factor of specific porous media surface, the relative permeability of wetting fluids varies a little in two systems with different specific surface areas. In addition, the relative permeability of non-wetting fluid decreases with the increasing of specific surface of porous media due to the large flow resistance.

Fluid-fluid interaction and fluid-solid interaction in the SC LBM model are presented, and schemes to obtain immiscible two-phase flow and different contact angles are discussed. Two-off mechanisms acting on the wetting fluids is proposed to illustrate the relative permeability of wetting fluids varies a little in two systems with different specific surface.