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The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.

First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.

The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.

Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

The purpose of this paper is to propose a subcontractor selection model to fully consider the impact of construction enterprise demands on subcontractor selection. The objectives are to understand the translating process of specific enterprise demands to the evaluating criteria and the weight calculation process.

A three-stage model of subcontractor selection was designed based on quality function deployment (QFD), analytic hierarchy process (AHP) and improved grey correlation analysis (IGCA). In the proposed model, specific enterprise demands are translated by the QFD method, and the weights of the criteria are determined by the IGCA. The AHP is used to quantify the exporters' experience and construct the judgment matrix, which is used as inputting of the grey correlation analysis.

The proposed model provides a feasible process for subcontractor selection by fully considering the actual requirements of the project. By combining the company requirements and project requirement to put forward the requirements of the target subcontractor, the selection process ensures that the selected subcontractor and the project have a higher “fit”

Few researches on construction subcontractor selection have taken into account the “voice” of the company's stakeholders. Therefore, this paper designs a three-stage construction subcontractor selection model by introducing QFD to achieve the transmission of “voice” in the subcontractor selection process, so as to take full consideration of the project objectives and the needs of the company's stakeholders. Meanwhile, in order to decrease the subjective of weight calculation, this paper designs an AHP-IGCA allocation method to determine the weight of relevant indicators. By integrating the proposed weight calculation method and QFD method, the subcontractor selection results become more reasonable and objective.

The purpose of this paper is to examine if personal identification could explicate the black box between participative leadership and employee ambidexterity. Also, the authors aim to explore how and why the top-down effects of higher-level leadership styles affect lower-level outcomes.

The authors collected multilevel and multisource data from top manager teams, and unit managers and employees of research and development, marketing and sales, and operations from Taiwanese technology firms.

The results revealed that individual-level personal identification partially mediated the relationship between firm-level participative leadership and individual-level employee ambidexterity, and individual-level coworker social support moderated the effect of firm-level participative leadership on individual-level employee ambidexterity through individual-level personal identification.

This paper demonstrated the importance of participative leadership and personal identification. It contributed to profound comprehension for potential mechanisms of individual-level personal identification and an enhancer of individual-level coworker social support why and how affects firm-level participative leadership on individual-level employee ambidexterity.

This study examines whether insider share ownership and personal share collateral affect corporate payout decisions.

This study estimates logit, Tobit and ordinary least squares regression models to explore how insider ownership is related to share repurchase probability, completion rates and the long-term performance following the repurchase announcements and how insider share collateral affects the above associations.

The results show that insider share ownership is negatively associated with the probability of announcing share repurchases and repurchase completion rates and is positively associated with the firm's post-announcement performance. This study further explores the incentive of insiders with high share collateral announcing share repurchases under a threat of margin call. For firms with a high percentage of insider share collateral, the results show that insider share ownership is associated with higher repurchase probability but is associated with lower repurchase completion rates and poorer post-announcement performance.

This study clarifies the interrelationships between insider ownership, insider share collateral and decisions in share repurchases and subsequent performance. This study provides evidence for both the convergence of interest and the entrenchment theories.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

This study aims to propose a new non-isolated Multi-Input Zeta-SEPIC (MIZS) dc–dc converter for renewable energy sources integration with different voltage levels (low-voltage source, high-voltage source). The chosen configuration of the converter is capable of performing bucking as well as boosting operations in various modes of operation.

Parameters of the selected MIZS converter are designed using the time-domain analysis. The selected converter belongs to the sixth-order family with two switches and six energy storage elements. State-space model of the converter is developed for each mode of operation, and using these individual state-space models, an average state-space model of the converter useful to carry out detailed analysis for different operating conditions is developed. Analysis related to operational stability of the converter is also carried out using Participation Factor (PaF)-based Eigen value analysis.

Using the PaF-based Eigen analysis, participation of the various state variables in different Eigen modes and vice versa is carried out. Performance of the converter for different parameter variations in the allowable range is determined and the same has been used to find the operational stability of the converter under different modes of operation. The selected converter has low inductor ripple currents and output voltage ripples when delivering the power to load.

Because operational stability of the converter under various operating conditions is one of the key performance indicators for selecting a particular type of converter, PaF-based Eigen value analysis has been carried out using the average state-space model developed for the selected MIZS converter. Operational stability analysis of the converter is carried out for parameter variations also. In addition, participation of the various states in each Eigen mode and vice versa have been analyzed for designed parameter values and also variation within the specified range of variations.

Digital transformation and innovation-driven development have become an international consensus. The purpose of this paper is to examine the effects of relationships, mechanisms and economic consequences between digital transformation and enterprise innovation quality in order to provide a benchmark for developing countries to implement digital transformation strategies and innovation-driven strategies and provide a major support for economic recovery in the post-coronavirus disease 2019 (COVID-19) era.

Using microdata from A-share listed enterprises in Shanghai and Shenzhen from 2010 to 2021, this study examines the relationship between digital transformation and enterprise innovation quality and further reveals the internal logic and economic consequences of digital transformation to improve enterprise innovation quality through the mediating effect and moderating effect models.

The results demonstrate that digital transformation is beneficial for improving enterprise innovation quality. The heterogeneity test demonstrates that digital transformation has a larger effect on improving enterprise innovation quality in non-state-owned enterprises and eastern enterprises in China. The mechanism test demonstrates that digital transformation can improve enterprise innovation quality by improving internal control quality and analyst attention. Furthermore, with the increase in enterprise innovation inputs, digital transformation plays a significantly stronger role in improving enterprise innovation quality. The extended analysis demonstrates that digital transformation can significantly improve enterprise financial performance by improving innovation quality.

First, the construction of the core explanatory variable digital transformation index in this study is based on the Python data analysis software, which calculates the frequency of digital transformation in the text of the business situation analysis portion of the annual report of the listed companies and then obtains the degree of digital transformation of the company in this year. There may be some deviation from the degree of digital transformation in the actual production and operation of enterprises. Second, in addition to internal control quality and analyst attention, are there other mediating mechanisms for the impact of digital transformation on the quality of enterprise innovation? Third, whether the moderating effect of innovation input on digital transformation and innovation quality is related to human capital factors of the research and development (R&D) team, such as the technical background of R&D personnel, etc.

This study enriches the relevant theories of digital transformation and broadens the research boundaries of digital transformation and enterprise innovation. This study's result provides an empirical basis for enterprises to improve enterprise innovation quality and financial performance from the perspective of digital transformation at the micro level and points out specific practical directions, combining theory with practice.

The purpose of this paper is to present a systematic review and analysis of existing research articles on new product development (NPD) published in the 12-year period starting from 1998 to 2009.

To explore the articles related to NPD, four key words namely new product, product design, product development and product innovation were used in combination of title or abstract or keyword of the articles through several knowledge databases. The abstracts of journal papers were read and a decision as to whether article belongs to any NPD research issue or not was made. In total, 1,127 research articles were categorized systematically and then analyzed on various principal NPD information groups.

Analysis of selected articles led to a certain level of dispersion in the publication of NPD research in different journals. It is found that more attention needs to be on knowledge and creativity management, communication and information transfer in any NPD process.

By observing extended literature from authors reviewing articles from various journals, growth in research, and variety of topics covered in NPD, a broad systematic multi journal review of NPD literature is clearly overdue. The authors have developed a comprehensive listing of publications on NPD where they have classified the surveyed papers according to various principal NPD information groups like: published year, NPD research stream, type of organization studied (industrial/consumer/service), level of innovation (high/moderate/low), NPD focus on frameworks, performance perspective (success, failure or both), NPD research design (conceptual/empirical and qualitative/quantitative) and NPD relevant best practice element. Based on the classification scheme, the issues were analyzed from the system's perspective and their implications to NPD research.