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Advances in artificial intelligence have ushered in new opportunities for marketers in the domain of content generation. We discuss approaches that have emerged to generate text and image content. Drawing on the customer equity framework, we then discuss the potential applications of automated content generation for customer acquisition, relationship development, and customer retention. We conclude by discussing important considerations that businesses must make prior to adopting automated content generation.

An emerging line of research examining the role of numerological superstition in the capital market shows that it has significant impact on investor behavior (Bhattacharya, Kuo, Lin, & Zhao, 2018; Hirshleifer, Jian, & Zhang 2018). However, to the authors’ best knowledge, there is a dearth of evidence on whether numerological superstition affects corporate behavior. This study fills this void by examining the association between investors’ numerological superstition and earnings management using Chinese data.

Chinese culture views 6 and 8 as lucky numbers. Using Chinese publicly traded firms, the authors examine the relation between investors’ numerological superstition and corporate financial reporting behavior.

The results suggest that firms reporting lucky earnings-per-share (EPS) numbers ending with 6 or 8 are more likely to engage in earnings management. These firms also raise more capital through seasoned equity offerings in the following year; however, they do not have more capital investments. Instead, their controlling shareholders siphon a significant amount of capital through related party transactions. Overall, the findings suggest that managers collude with controlling shareholders to manage earnings by exploiting the superstitious beliefs of minority shareholders.

To the authors’ best knowledge, there is a dearth of evidence on whether numerological superstition affects corporate behavior. This study fills this void by examining the association between investors’ numerological superstition and earnings management using Chinese data.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

This paper aims to explore the commodity transshipment planning among customers, which is commonly observed in production/sales enterprises to save the operational costs.

A mixed integer programming (MIP) model is built and five types of valid inequalities for tightening the solution space are derived. An improved variable neighborhood search (IVNS) algorithm is presented combining the developed multistart initial solution strategy and modified neighborhood local search procedure.

Experimental results demonstrate that: with less decision variables considered, the proposed model can solve more instances compared to the existing model in previous literature. The valid inequalities utilized to tighten the searching space can efficiently help the model to obtain optimal solutions or high-quality lower bounds. The improved algorithm is efficient to obtain optimal or near-optimal solutions and superior to the compared algorithm in terms of solution quality, computational time and robustness.

This research not only can help reduce operational costs and improve logistics efficiency for relevant enterprises, but also can provide guidance for constructing the decision support system of logistics intelligent scheduling platform to cater for centralized management and control.

This paper develops a more compact model and some stronger valid inequalities. Moreover, the proposed algorithm is easy to implement and performs well.

This paper aims to theoretically investigate a knowledge management model from the combined perspective of knowledge acquisition and knowledge application and its effect on organizational performance.

This study reviews prior research on knowledge acquisition and knowledge application, puts forward the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application” and more importantly proposes an integrated model by combining these two dimensions. Four case examples of enterprises are subsequently described and analyzed to illustrate the sources of knowledge acquisition, the objects of knowledge application and their influences on organizational performance.

Four knowledge management modes and their impacts are confirmed in this study. Specifically, the organization of the turbojet engine mode (high extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve good performance. The pipeline mode (high extensiveness of knowledge acquisition and low concentration of knowledge application) is the second, which has limited influence on good organizational performance. Organizations with the flashlight mode (low extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve limited performance under the appropriate environment. The candle mode (low extensiveness of knowledge acquisition and low concentration of knowledge application) is the worst, performance of which is poor due to the break of the knowledge chain.

This paper holds that organizations should actively use the turbojet engine mode, adopt the pipeline mode and the flashlight mode cautiously, and avoid falling into the candle mode.

To the best of the authors’ knowledge, this study is among the first to propose the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application,” and provides a combined model for analyzing differences in organizational performance from the perspective of knowledge.

The purpose of this paper is to explore recent national human resource development (NHRD) practices in China through a literature review focusing on programs and activities that represent the roles and interactions among the government, industry and universities.

To effectively consolidate previous work and conceptualize the recent development of the NHRD practices in China, a semi-narrative literature review was used to explore and analyze NHRD-related functions and activities.

Findings from the literature review showed that although the central government still plays a predominant role in China, universities and corporations are increasingly playing a critical role in developing an innovative and skilled workforce. At the regional level, NHRD initiatives in China have been increasingly undertaken by universities, industry and government–industry–university collaborations. The authors also found a disparity between developed and underdeveloped regions in terms of NHRD in China.

This study used the triple helix model as a framework that provides an insightful lens for researchers to examine how various social entities interact with each other and jointly contribute to NHRD. Further case studies are needed to generate evidence-based knowledge to the NHRD literature.

A more systematic NHRD leadership structure at both the national and local level is desired to unleash the potential of bottom-up development and active government–industry–university collaboration. To counter regional divergence in NHRD in China, intra- and cross-regional collaborations are helpful in improving resources distribution and workforce development.

Based on open system theory, this study focused on programs and activities that represent the roles and interactions among the government, industry and university in Chinese NHRD through the lens of the triple helix model. In addition, this study offers a conceptual model of Chinese NHRD to help scholars and practitioners understand the transitional efforts in NHRD.

The purpose of this paper is twofold: to identify, evaluate and structure the research that focusses on omni-channel retailing from the perspective of logistics and supply chain management; and to reveal the intellectual foundation of omni-channel retailing research.

The paper applies a multi-method approach by conducting a content-analysis-based literature review of 70 academic papers. Based on the reference lists of these papers, the authors performed a citation and co-citation analysis based on the 34 most frequently cited papers. This analysis included multidimensional scaling, a cluster analysis and factor analysis.

The study reveals the limited consideration of logistics and supply chain management literature in the foundation of the omni-channel retailing research. Further, the authors see a dominance of empirical research as compared to conceptual and analytical research. Overall, there is a focus on the Western retail context in this research field. The intellectual foundation is embedded in the marketing discipline and can be characterised as lacking a robust theoretical foundation.

The contribution of this research is identifying, evaluating and structuring the literature of omni-channel research and providing an overview of the state of the art of this research area considering its interdisciplinary nature. This paper thus supports researchers looking to holistically comprehend, prioritise and use the underpinning literature central to the phenomena of omni-channel retailing. For practitioners and academics alike, the findings can trigger and support future research and an evolving understanding of omni-channel retailing.

The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.

The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.

According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.

By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.