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Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

This study aims to investigate the antecedents of corporate social responsibility (CSR) from the perspective of competitive dynamics and proposes a correlation of CSR between competing firms because rival firms’ engagement in CSR induces the focal firm’s catch-up to keep pace with them.

Using a sample of Chinese listed companies through the lens of firm dyads, and drawing on the awareness-motivation-capability (AMC) framework, a set of contingencies of firms’ competitive catch-up in CSR are examined, including the visibility of its competitors, the interdependence between the focal firm and its competitors and the focal firm’s resource slack.

The empirical results reveal that a focal firm’s CSR is in a positive relationship with that of its competitors, which is strengthened by the visibility of its competitors, the interdependence between the focal firm and its competitors, and is affected by the focal firm’s resource slack.

These findings uncover the interplay of CSR among competitors, enriching our understanding of its antecedents by extending the AMC framework to the CSR context.

The survival of companies today hinges on their adaptability and flexibility, with digital transformation (DT) and organizational slack (OS) playing crucial roles. Despite their recognized importance, these factors are often studied separately. This study aims to explore how OS facilitates DT and evaluate their synergies and trade-offs to improve performance.

Using data from the European Manufacturing Survey, structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA), we investigate causal relationships and possible combinations between different dimensions of OS and DT that contribute to business performance.

We confirmed the positive effect of OS and DT on business performance, highlighting the importance of organizational over technological factors. While not definitively establishing OS as a precursor to DT, our findings underscore the need for human and operational slack to improve performance, especially in less technology-intensive contexts.

Our findings evidence that decision-makers should integrate OS with DT initiatives to improve the firm’s competitiveness. However, it is worth noting that while OS seems essential in low-tech shopfloors, its importance is lower in high-tech environments. Furthermore, within the possible combinations, managers should promote operational slack and digitalization, as it seems fundamental to improve business performance.

This article contributes to the management field in three ways. First, it clarifies controversies by providing evidence of the positive roles of DT and OS as drivers of competitiveness for manufacturing firms. Second, we verify that OS is not directly linked to DT, challenging existing assumptions. Third, it investigates the combinations of OS and DT that drive business performance improvement, emphasizing their synergies and trade-offs.

Enhancing total factor productivity through digital transformation is a crucial pathway for the high-quality development of manufacturing enterprises. This research aims to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity in the context of digital transformation.

Using the data from 536 Chinese listed manufacturing enterprises from 2018 to 2021, this research divides digital transformation into two dimensions (i.e. digital transformation breadth and digital transformation depth) and examines their impacts on total factor productivity as well as the mediation effects of innovation capability and reconfiguration capacity.

It is found that digital transformation breadth, digital transformation depth and their interaction can positively affect manufacturing enterprises’ total factor productivity. The innovation capability and reconfiguration capacity of manufacturing enterprises act as mediators between digital transformation breadth and total factor productivity, as well as between digital transformation depth and total factor productivity.

This study is one of the first attempts to investigate the impact mechanisms of manufacturing enterprises’ total factor productivity from the perspective of digital transformation breadth and depth.

This study investigates how perceived political corruption, a generally overlooked corruption type, relates to firms' new product development (NPD) through perceived regulatory obstacles. It also examines firms' perceptions of business association support in this relationship, considering these associations' potential support for NPD.

This study conducted an empirical analysis of 1,663 firms in Turkey, a country noted for a history of legislative corruption, and in which there are strong business associations. Drawing the data from the World Bank's 2019 Enterprise Surveys Dataset, this study tested the hypotheses via the two-stage factor score regression method.

This study finds that perceived political corruption significantly relates to NPD negatively through perceived regulatory obstacles. It also finds that the perceived support of business associations to NPD is significantly greater when firms perceive regulatory obstacles but only slight political corruption.

As far as political corruption is concerned, this study reveals that corruption can also be the cause of regulatory obstacles, expanding the common view of corruption as a means of overcoming regulatory obstacles to NPD. In addition, it introduces the role of business associations in this relationship by revealing their support to NPD for different levels of perceived political corruption and regulatory obstacles.

The quality of the unstructured mesh has a considerable impact on the stability and accuracy of aerodynamic simulation in computational fluid dynamics (CFD). Typically, engineers spend a significant portion of their time on mesh quality evaluation to ensure a valid, high-quality mesh. The extensive manual interaction and a priori knowledge required to undertake an accurate and timely evaluation process have become a bottleneck in the idealized efficient CFD workflow. This paper aims to introduce a neural network-based quality evaluation approach for unstructured meshes to enable higher efficiency and the level of automation.

The paper investigates the capability of deep neural networks for the quality evaluation of unstructured meshes. For training the network, we build a training dataset for mesh quality learning algorithms. The dataset contains a rich variety of unstructured aircraft meshes with different mesh sizes, densities, cell distribution, growth ratios and cell numbers to ensure its diversity and availability. We also design a neural network, AircraftNet, to learn the effect of mesh quality on the convergent properties of the numerical solutions. The proposed network directly manipulates raw point data in mesh source files rather than passing it to an intermediate data representation. During training, AircraftNet extracts non-linear quality features from high-dimensional data spaces and then automatically predicts the overall quality of the input unstructured mesh.

The paper provides a series of experimental results on GPUs. It shows that AircraftNet is able to effectively analyze the quality-related features like mesh density and distribution from the extracted features and achieve high prediction accuracy on the proposed dataset with even a small number of training runs.

Because of the limited training dataset, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper publishes a benchmarking dataset for mesh quality learning algorithms and designs a novel neural network approach for unstructured mesh quality evaluation.

The purpose of this paper is to conduct a thorough study of the current research status and trends of Artificial Intelligence-Generated Content (AIGC), which is thriving and exerting significant influences on society, the economy and technology. This study will encompass both the ample opportunities and the array of emerging risks and challenges associated with AIGC. Furthermore, this paper seeks to propose practical optimization strategies to facilitate its continued development.

A total of 12,702 documents in Scopus, CNKI and Altmetric.com databases are analyzed in this paper. The visualization tools of CiteSpace and Netdraw are used to systematically analyze AIGC from macro, meso and micro perspectives based on bibliometric indicators. The analysis is combined with altmetrics indicators to identify hotspots in AIGC-related research and predict future development trends. Finally, substantive optimization suggestions for the development of AIGC are put forward.

Research has found that firstly, both domestic and international research in the field of AIGC is actively catching up with the pace of the era. Institutions and authors recognize the indispensability of collaboration, leading to the emergence of interdisciplinary cooperation trends. Secondly, at the thematic research level, both domestic and international studies cover the overall trends of AIGC, including technology, applications, challenges and coping strategies. However, international scholars tend to focus more on technological breakthroughs, while domestic researchers emphasize the formulation of national laws and regulations. Finally, through the analysis of hotspots and trends, it is predicted that future research will focus more on addressing the legal issues regarding the originality of AIGC, enhancing its interactivity, optimizing its applications, emphasizing ethical considerations and efficiently addressing major social issues such as pandemics using AIGC technology. Corresponding optimization strategies are proposed to ensure the development of AIGC is consistent with global consensus and values, creating a beneficial environment for its sustainable development.

Firstly, this paper integrates data from three databases and uses multiple software tools to conduct a comparative analysis of the research trends in AIGC from both domestic and international perspectives. Secondly, this paper not only analyzes the academic influence of AIGC-related research through bibliometric indicators but also combines altmetrics indicators to explore the public’s attitude toward AIGC. This method systematically reveals the overall development trends, leading institutions and researchers, thematic research areas, hotspot evolution and future development trends of AIGC-related research, providing theoretical references for subsequent scholars in AIGC research. Additionally, this paper helps governments, institutions and organizations to precisely and wisely formulate policies and investments, as well as to effectively comprehend the development trends of AIGC and promote the advancement of AIGC technology and applications.

The current study is designed to investigate the factors that foster the framing of destination competitiveness and establish the factors that drive the contribution of tourism innovations to economic growth in smart tourism destinations.

A four-year panel data were extracted from the World Economic Forum's travel and tourism competitiveness index and data were analysed using Poisson Pseudo Maximum Likelihood regression model.

The findings demonstrate that both the enabling environment and airport infrastructure significantly affect tourism's impact on the economy of the selected smart European tourism destinations. Conversely, human resources and general infrastructure display a negative correlation with tourism's contribution to the economy. However, no data in the sample support the idea that tourism policies, government prioritization or readiness of tourism information and communication technologies impact tourism's contribution to the economy. Additionally, the marginal effects indicate that improving the enabling environment and airport infrastructure can generate additional benefits for the economy through tourism.

The uniqueness of this study is the integration of smart tourism destinations with the measure of destination competitiveness to provide an empirical bridge that links tourism competitiveness to economic growth.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

This study reveals the green building development path and analyzes the optimal government subsidy equilibrium through evolutionary game theory and numerical simulation. This was done to explore the feasible measures and optimal incentives to achieve higher levels of green building in China.

First, the practice of green building in China was analyzed, and the specific influencing factors and incentive measures for green building development were extracted. Second, China-specific evolutionary game models were constructed between developers and homebuyers under the market regulation and government incentive mechanism scenarios, and the evolutionary paths were analyzed. Finally, real-case numerical simulations were conducted, subsidy impacts were mainly analyzed and optimal subsidy equilibriums were solved.

(1) Simultaneously subsidizing developers and homebuyers proved to be the most effective measure to promote the sustainability of green buildings. (2) The sensitivity of developers and homebuyers to subsidies varied across scenarios, and the optimal subsidy level diminished marginally as building greenness and public awareness increased. (3) The optimal subsidy level for developers was intricately tied to the building greenness benchmark. A higher benchmark intensified the developer’s responsiveness to losses, at which point increasing subsidies were justified. Conversely, a reduction in subsidy might have been appropriate when the benchmark was set at a lower level.

The expeditious advancement of green buildings holds paramount importance for the high-quality development of the construction industry. Nevertheless, the pace of green building expansion in China has experienced a recent deceleration. Drawing insights from the practices of green building in China, the exploration of viable strategies and the determination of optimal government subsidies stand as imperative initiatives. These endeavors aim to propel the acceleration of green building proliferation and materialize high-quality development at the earliest juncture possible.

The model is grounded in China’s green building practices, which makes the conclusions drawn more specific. Furthermore, research results provide practical references for governments to formulate green building incentive policies.