Search results

The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

Considering the resource constraint, this paper aims to study how to make value-added service (VAS) investment strategy considering the negative intra-group network externality on the seller side from the perspective of a two-sided platform.

The authors use the dynamic game theory, optimization, sensitive analysis and numerical study in this research. The authors model their research question from the perspective of the dynamic game theory, and through optimizing the platform’s profit function, the equilibrium results in terms of VAS investing and pricing strategies are derived. To explore the characteristics of the optimal strategies, sensitive analysis is used, and numerical studies are conducted to further illustrate the analytical results.

It is found that the intra-group network externality is not necessarily the determinant for VAS investment strategy, and its overall negative impact can be overtaken by the investment in certain conditions. The optimal VAS investment level decreases in the negative intra-group network externality. Though the VAS investment is on the seller side, it has either positive or negative impact on the pricing for buyers. Moreover, for a stronger intra-group network externality among sellers, the two-sided prices could either increase or decrease.

The authors implicate how the intra-group network externality reduces the investment benefit and impacts the other side users. The limitation of considering the intra-group network externalities on only one side needs further extension.

The authors provide insights for platform operators in how to use recourse to improve users’ utility and how to price the two sides when competition exists on the seller side.

This study specifies the role of negative intra-group network externality in determining the investment and pricing strategy of a two-sided platform in addition to the positive inter-group network externality.

Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability.

Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system.

The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively.

The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

This paper aims to determine a suitable anti-wear and friction-reducing compounding additive for lithium greases (LG) by investigating the effects of three single additives potassium borate (PB), zinc dialkyl dithiophosphate and molybdenum dialkyl dithiophosphate (MoDDP) and two compound additives on the friction, wear and extreme pressure properties of LG.

The effects of the above five additives on the friction, wear and extreme pressure properties of LG were investigated using an SRV-5 friction tester. An X-ray photoelectron spectrometer was used to analyze the various elements presented on the wear surface as well as the types of compounds.

The compound additive suitable for grease consists of PB and MoDDP, which have excellent friction reduction, anti-wear and extreme pressure properties. And a boundary protection film consisting of oxide and MoS₂ is formed on the friction surface, thus improving the friction reduction and anti-wear performance of the grease.

This study can improve the anti-wear and friction-reduction performance of greases, which is of great importance in the field of industrial lubrication. The results of this paper are expected to be useful to researchers and academics of grease.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2022-0350/

The purpose of this paper is to explore the impact of ChatGPT on the development of corporate social responsibility (CSR).

Based on the current applications of ChatGPT in enterprises and existing challenges in CSR, the paper analyzes how the tool promotes corporate sustainable development and what potential risks and challenges are in the practical application.

This paper finds that ChatGPT can promote the development of CSR from four aspects: to support social responsibility activities, to strengthen internal control, to promote external supervision and to convey the notion of sustainability and other corporate philosophies for enterprises.

This paper provides the ground for applying ChatGPT to CSR and includes the potential risks and challenges of the practical applications that enterprises can use for reference.

ChatGPT is becoming increasingly popular and mature, but its applications and effectiveness are less mentioned in CSR. In the future, the research in this area needs to be further advanced.

This paper makes contributions to the link between ChatGPT and CSR. To the best of the authors’ knowledge, this is one of the first studies that explore the applications, impacts, challenges and opportunities of the technology in the area of social responsibility.

The scholarly literature that examines the economic assimilation of migrant families has focussed on the educational and economic achievements of the children of international migrants relative to the children of native born parents. Lower relative incomes of the children of immigrants might be attributable to discrimination, while higher relative incomes could be attributable to ambitious parents who produce more ambitious children. These potential effects have been difficult to disentangle. The purpose of this paper is to control for discrimination by examining internal migration in Honduras, allowing us to isolate evidence for or against the “ambition” effect.

The paper uses the Blinder-Oaxaca decomposition technique to ask if the children of migrants are similar or different than their parents in their attitudes toward work and economic advancement.

This study finds that migrants are relatively hard workers in the sense that they experience relatively high marginal effects on earnings from improved socio-economic characteristics, such as years of schooling. The study also finds that these migrants do not pass on this hard-work ethic to their children, who experience much smaller marginal effects from increased years of schooling and other socio-economic characteristics.

This study demonstrates that the children of migrants do not necessarily inherit the ambitious work ethic characteristic of their migrant parents. This result has important implications for studies that examine the assimilation and economic progress of migrant families, particularly those studies that use second-generation earnings as a measure of assimilation and economic progress.

Confucianism underpins Chinese traditional culture and the values of the Chinese people. The purpose of this study is to examine the relationship between adherence to Confucianism and corporate irregularities.

The authors use the historical numbers of Jinshi (Imperial Scholars) in the Ming and Qing dynasties within 200 km of a company's location to proxy for the influence of Confucianism on the company, presenting strong evidence that Confucianism significantly reduces corporate irregularities.

The authors' findings are robust even when criticized with alternative definitions of Confucianism, sensitivity analysis and instrumental variable regression. The authors also discover that this effect is weaker in state-owned and foreign enterprises and weakened by the influence of Western culture.

This paper brings a new traditional-cultural perspective to the understanding of corporate irregularities and contributes to the literature on culture and finance. This paper also helps the authors understand the “China Puzzle” that is China's rapid economic development under an imperfect legal system.

The link between interlocking directors and mergers and acquisitions (M&A) efficiency has been analyzed in an information asymmetry environment. Despite an abundance of evidence highlighting that interlocking directors do contribute to M&A efficiency in an acquirer-target binary relationship, the target is embedded in a complex network of supplier-customer relationships, which implies that the acquirer needs to consider the value of suppliers, distributors and retailers in the target’s supply chain in improving M&A efficiency. Through the lenses of acquirer-target multivariate relationships, this paper aims to examine how directors with supply chain experience (DSCs) act as heterogeneous network pipes to affect M&A efficiency.

Using a sample of 311 A-share listed firms on the Shanghai and Shenzhen stock exchanges in China during 2011–2020, this paper investigates the relationship between DSCs and M&A efficiency by using ordinary least squares (OLS) regression.

Through empirical research, we verify a negative relationship between DSCs and M&A duration and an inverted U-shaped relationship between both DSCs and M&A performance, revealing the complexity of the relationship between experience and efficiency. Furthermore, drawing on upper echelon theory, the information value of DSCs will be greatly reduced when executives have overconfident psychological characteristics, which are mainly shown to negatively moderate the relationship between DSCs and M&A performance. We also conduct multiple robustness tests and supplemental analyses to illustrate the robustness and boundaries of our findings. Finally, DSCs are likely more important in environments among growth and mature firms as well as high-growth industries.

We break through the assumption that interlocking directors contribute to M&A efficiency in an acquirer-target binary relationship and examine the impact of DSCs on M&A efficiency based on micro-empirical evidence from the value of target-related upstream or downstream industries, which extends the connotation of interlocking directors and enriches the study related to factors influencing M&A efficiency.

Complex system modeling requires not only understanding of modeling framework but also domain knowledge of the system. The purpose of this paper is to present an approach which separates the domain knowledge from the modeling framework with different views.

By establishing the mechanism of association and fusion among the views, the description and characterization of system from different aspect and point of view can form a complete system model. Based on the approach, a modeling and simulation (M&S) platform named SimFaster is developed. Modeling environment and simulation engine are the most important parts of the platform. The modeling environment provides multi‐views and multi‐layers to help the developers to modeling the structure, layers, composition, behavior, and interactions of an application system. The simulation engine provides mechanism of integration and interaction for components and objects, and provides runtime support for the concepts and terms from modeling environment. The simulation engine organizes the objects in the memory of distributed system as reflective object database system, so it is repository centered architecturally.

Based on the approach of multi‐views modeling, the platform is a flexible framework and supports top‐down design, model reuse and interoperation, dynamic refinement of models, corporative design among different users in different stages, and the rebuilt of application rapidly.

This paper deals with high‐level models of the complex systems.

This platform helps to design, modeling, and simulation complex system (especially for weapon combat system). It can participate into all the stages of the development of complex product/system, and can support the validation, refinement, optimization of models, and systems.

This paper presents a multi‐views modeling approach for the modeling of complex system.

This study delineates the effect of cover thickness on reinforced concrete (RC) columns and beams under an elevated fire scenario. Columns and beams are important load-carrying structural members of buildings. Under all circumstances, the columns and beams were set to be free from damage to avoid structural failure. Under the high-temperature scenario, the RC element may fail because of the material deterioration that occurs owing to the thermal effect. This study attempts to determine the optimum cover thickness for beams and columns under extreme loads and fire conditions.

Cover thicknesses of 30, 40, 45, 50, 60 and 70 mm for the columns and 10, 20, 25, 30, 35, 40, 50, 60 and 70 mm for the beams were adopted in this study. Both steady-state and transient-state conditions under thermomechanical analysis were performed using the finite element method to determine the heat transfer through the RC section and to determine the effect of thermal stresses.

The results show that the RC elements have a greater influence on the additional cover thickness at extreme temperatures and higher load ratios than at the service stages. The safe limits of the structural members were obtained under the combined effects of elevated temperatures and structural loads. The results also indicate that the compression members have a better thermal performance than the flexural members.

Numerical investigations concerning the high-temperature behavior of structural elements are useful. The lack of an experimental setup encourages researchers to perform numerical investigations. In this study, the finite element models were validated with existing finite element models and experimental results.

The obtained safe limit for the structural members could help to understand their resistance to fire in a real-time scenario. From the safe limit, a suitable design can be preferred while designing the structural members. This could probably save the structure from collapse.

There is a lack of both numerical and experimental research works. In numerical modeling, the research works found in the literature had difficulties in developing a numerical model that satisfactorily represents the structural members under fire, not being able to adequately understand their behavior at high temperatures. None of them considered the influence of the cover thickness under extreme fire and loading conditions. In this paper, this influence was evaluated and discussed.