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Aside from grappling with technological advancements, enterprises in the industrial internet era are embracing business model innovation to align with the evolution of the industrial internet. However, a gap persists in the existing research regarding the strategies and methods available to small and medium-sized enterprises (SMEs) for executing business model innovation. Therefore, this paper aims to explore the connotation, characteristics and logic of business model innovation for SMEs in the industrial internet era.

To explore the business model innovation logic of small and medium-sized enterprises in the era of industrial internet, the paper adopts a longitudinal single-case study approach, with PAYA, a medium-sized enterprise in the electromechanical industry, serving as the subject of research. It systematically analyzes PAYA’s business model innovation, centering on four key elements of the business model: value proposition, value creation, value delivery and value capture.

The study proposes two types of business model innovation, namely, “Migration” and “Expansion”, and explains the logic of business model innovation for SMEs in the industrial internet era: faced with a rapidly changing market environment, entrepreneurs put forward the value proposition through the insight of the market environment, then enterprises conduct technological innovation to support the value creation by their own unique experience and knowledge, and then improve the legitimacy of the market by expanding the influence of market acceptance of the new business model to promote the value delivery, and finally capture the economic value and ecological value.

The types and logic of business model innovation proposed in this paper contribute to supplementing and developing the theory of business model innovation and meanwhile have important reference value for SMEs in the industrial internet era.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

This study investigates firms' innovation behaviour under environmental change. Therefore, it examines the effect of trade disputes on corporate technological innovation and how product market competition moderates this relationship.

This research tests the hypotheses using the fixed effects model based on panel data of publicly listed enterprises in China from 2007–2020.

The empirical results validate the positive association between trade disputes and corporate research and development (R&D) intensity as well as the U-shaped relationship between trade disputes and radical innovation. Additionally, the moderating effect of product market competition is verified: a concentrated market with less competition flattens the U-shaped curve of radical innovation induced by trade disputes; as the market becomes more concentrated and less competitive, the U-shaped relationship eventually turns into an inverted U.

First, this study contributes to the corporate innovation and trade dispute literature by expanding the environmental antecedents of technological innovation and the firm-level consequences of trade disputes. Second, this study enriches the theoretical framework of the environment–innovation link through an integrated perspective of contingency theory and dynamic capabilities view. Third, instead of the traditional linear mindset which had led to contradictory results, this study explores a curvilinear effect in the environment–innovation relationship.

Salt rock from salt lakes can serve as a cost-effective material for subgrade filling, as demonstrated in projects like the Qarhan Salt Lake section of the Qinghai-Tibet Railway and the Qarhan Salt Lake section of the G215 Highway. This state-of-the-art paper aims to summarize the engineering properties of salt rock filling and present the advances of its utilization.

This paper collects and analyzes laboratory and field data of salt rock filling from previous studies to present a comprehensive analysis of the engineering properties and utilization of salt rock fillings.

Salt rock primarily contains minerals such as halite and glauberite, which contribute to its unique phase-changing behavior under varying environmental conditions, impacting its mechanical properties. Salt rock filling shrinks when in contact with vapor or unsaturated brine and expands under cooling or evaporation. Its use is particularly recommended for arid regions, with specific restrictions depending on the structure type. This paper discusses suggested countermeasures to mitigate these issues, as well as key quality acceptance indices for salt rock filling compaction. Moisture content after air-drying is recommended as a crucial parameter for construction quality control.

This review aims to support future research and engineering practices in salt rock subgrade applications.

Innovation in AI technology has transformed the global economic landscape, thereby becoming a focal point for academic research. A review of extant literature reveals a preponderant focus on the application aspects of AI technology, underscoring the necessity for a more nuanced examination. However, the innovation of AI technology is led by managers who are likely influenced by cognitive biases.

This study combines supervised machine learning models in the AI field and patent abstracts to accurately identify AI technology innovation. It also considers a vital type of cognitive bias, namely overconfidence, to provide novel insights into the literature on AI technology innovation.

Find that overconfident CEOs are more likely to promote AI technology innovation than non-overconfident CEOs. Moreover, consistent with the BTOF prediction, the positive impact of CEO overconfidence on corporate AI technology innovation is strengthened by negative performance feedback, but the above relationship has been weakened by positive performance feedback.

This study does not posit that all cognitive biases invariably contribute positively to a firm’s AI technology innovation. Instead, it advocates for a nuanced understanding of the role of manager-specific cognitive biases in influencing such innovation, taking into account the particular characteristics of these biases. Future researchers could consider key decision-makers behavioral attributes and evaluate the influence of other cognitive biases such as escalation commitment, status quo bias and narcissism.

Executives must comprehend how their inherent beliefs influence their interpretations and reactions to financial outcomes. Given an external environment with uncertainties and crises, organizations must revise the conventional perception of CEO overconfidence, recognizing its positive impact on risk mitigation, adversity response and AI technological innovation. The selection or replacement of overconfident managers should be contingent on the organization’s developmental stage, performance status and strategic requirements, complemented by suitable disciplinary and incentive systems.

This research indicates that when a CEO decides to adopt AI technology innovation in response to negative performance feedback, such a decision might be significantly influenced by personal beliefs rather than an objective assessment of the firm’s strategic predicament. Directors and investors find this perspective enlightening. This awareness can foster support for the firm’s activities in AI technology innovation, concentrate on emerging technologies and market opportunities and augment its strategic trajectory by enhancing the firm’s orientation toward technological innovation.

This study aims to examine how specific regulatory indicators – such as regulatory quality, information and communications technology regulatory environment, regulation of emerging technologies, e-commerce legislation and privacy protection by law content – affect the economic outcomes, quality of life and sustainable development goals associated with future technologies, including artificial intelligence, robotics, big data analytics, cloud computing and app- and web-enabled markets.

Using Bayesian Belief Network models and Network Readiness Index 2023 data from 134 countries, this study explores the relationships between regulatory factors and various socioeconomic outcomes.

Regulatory quality and e-commerce legislation emerge as central determinants, directly or indirectly impacting economic development, societal well-being and sustainability objectives. Notably, regulatory quality is identified as a pivotal factor across all outcomes, emphasizing the critical role of effective regulatory frameworks in fostering positive outcomes.

The study relies on cross-sectional data, which restricts causal inference, and focuses on national-level data, potentially overlooking subnational variations. In addition, the use of secondary data sources introduces possible measurement errors and biases. Despite these constraints, the study offers valuable insights into regulatory strategies and their role in advancing economic and social outcomes.

The study highlights the importance of tailoring regulatory interventions to address specific needs and challenges faced by countries at different stages of development. The findings provide valuable insights for policymakers, regulatory authorities and stakeholders seeking to navigate the regulatory challenges and opportunities inherent in the era of rapid technological advancement. The study contributes to advancing the understanding of the complex interplay between regulation, technology and development outcomes in the contemporary global landscape.

Lack of access to finance is a major constraint to the growth of small and medium-sized enterprises (SMEs) and entrepreneurship in developing countries. The recent proliferation of mobile phone services, access to the internet and emerging technologies has led to a surge in the use of FinTech in Africa and is transforming the financial sector. This paper aims to examine whether FinTech developments heterogeneously contribute to the growth of digital finance for SMEs and entrepreneurship in 47 African countries from 2013 to 2020.

The paper uses a novel method of moments quantile regression, which deals with heterogeneity and endogeneity in diverse conditions for asymmetric and nonlinear models.

The empirical results reveal that the rise of FinTech companies offering services in Africa heterogeneously increases digital finance for SMEs and entrepreneurship in their different stages of growth. FinTech developments have a strong and positive impact in countries with higher levels of digital finance than those with lower levels. FinTech developments and digital finance positively and significantly influence entrepreneurship in Africa, particularly in the nascent and transitional development stages of entrepreneurship. Institutional quality has a considerable positive moderating effect when used as a control rather than an interaction variable.

The results suggest the need to promote FinTech developments in Africa: to provide a wide range of alternative digital finance schemes to SMEs and to promote entrepreneurship, especially in countries where entrepreneurship is in the nascent and transitional development stages. The results also underscore the need to promote FinTech development through supportive regulations and institutional quality to reduce risks related to FinTech and digital financing schemes.

To the best of the authors’ knowledge, this paper is one of the first attempts to account for the often overlooked heterogeneity effects and show that the influence of FinTech developments is not homogenous across the varying development stages of digital finance and entrepreneurship.

Megaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.

The study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.

It was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.

The contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.