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The rapid growth of emerging market firms (EMFs) has been a topic of interest for the past two decades, especially in China. However, few studies have discussed how and why EMFs can impel the upgrading of their capabilities to quickly win competitive advantages in the global market. In this context, the purpose of this paper is to unravel the implausible upgrading phenomenon from the perspective of technological proximity.

This paper adopts a single case study, specifically that of a leading Chinese e-bike firm, with a special focus on the dynamic nature of the capability upgrading process and underlying mechanisms.

The results show that taking advantage of technological proximity is an important way for EMFs to climb the ladder of capability upgrading. The stage-based process reveals how capability upgrading is achieved through elaborate actions related to technological proximity. Furthermore, this study finds three learning mechanisms behind the technological proximity, which enable firms to successfully upgrade to higher levels of capabilities. In particular, the trigger role played by contextual conditions in guiding firms' capability upgrading is highlighted and characterized.

This study enriches traditional capability upgrading literature from a technological proximity perspective, especially the traditional static upgrading research related to EMFs. The authors also contribute to the conceptualization of technological proximity. However, the research setting is China's e-bike industry; therefore, the study's generalizability to other emerging markets and industries may be limited.

The results show that it is important to recognize the value of the transfer and sharing of technology between proximal industries for local governments. Also, appropriate policies should be developed to break down the technology barriers between these industries. Moreover, rather than catching up with the superior technologies of multinational corporations in advanced countries, focusing on products with high technological proximity in local or regional areas may be more helpful for EMFs' upgrading.

This paper investigates the capability upgrading process and mechanisms in EMFs, particularly with respect to the role played by technological proximity.

The purpose of this paper is to examine stock market reactions and liquidity effects following the first bank loan announcement of zero-leverage firms.

The authors use an event studies methodology in both a univariate and multivariate framework. The authors also use regression analysis.

Using a sample of 96 zero-leverage firms listed on the FTSE 350 index over the time period of 2000–2015, the authors find evidence of a significant and permanent stock price increase as a result of the initial debt announcement. The loan announcement results in a sustained increase in trading volume and liquidity. This improvement continues to persist once the authors control for stock price and trading volume effects in both the short and long run. Furthermore, the authors examine the spread decomposition around the same period, and discover the adverse selection of the bid–ask spread is significantly related to the initial bank loan announcement.

The results can be attributed to the information cost/liquidity hypothesis, suggesting that investors demand a lower premium for trading stocks with more available information.

This is the first paper to look at multiple industries, more than one loan and information asymmetry effects.

The abnormal behaviors of staff at petroleum stations pose significant safety hazards. Addressing the challenges of high parameter counts, lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models, this paper proposes GTB-ResNet, a network designed to detect abnormal behaviors in petroleum station staff.

Firstly, to mitigate the issues of excessive parameters and computational complexity in 3D ResNet, a lightweight residual convolution module called the Ghost residual module (GhostNet) is introduced in the feature extraction network. Ghost convolution replaces standard convolution, reducing model parameters while preserving multi-scale feature extraction capabilities. Secondly, to enhance the model's focus on salient features amidst wide surveillance ranges and small target objects, the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction. Lastly, to address the challenge of short time-series features leading to misjudgments in similar actions, a bidirectional gated recurrent network is added to the feature extraction backbone network. This ensures the extraction of key long time-series features, thereby improving feature extraction accuracy.

The experimental setup encompasses four behavior types: illegal phone answering, smoking, falling (abnormal) and touching the face (normal), comprising a total of 892 videos. Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7% with a model parameter count of 4.46 M and a computational complexity of 3.898 G. This represents a 4.4% improvement over 3D ResNet, with reductions of 90.4% in parameters and 61.5% in computational complexity.

Specifically designed for edge devices in oil stations, the 3D ResNet network is tailored for real-time action prediction. To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices, a lightweight residual module based on ghost convolution is developed. Additionally, to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations, a triple attention mechanism is introduced during feature extraction to enhance focus on salient features. Moreover, to overcome the potential for misjudgments arising from the similarity of actions, a Bi-GRU model is introduced to enhance the extraction of key long-term features.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

This paper aims to explore the effect of fairness concerns on supply chain members' optimal decisions and profits, to compare their profits under different policies, and to investigate the impact of each policy on members, consumers, and the environment with fairness concerns.

Considering government policies and fairness concerns in recycling management, this paper develops five recycling and remanufacturing decision models (anarchy policy model, reward-penalty mechanism model, recycling investment subsidies model, government tax model, and fund subsidy system model). In each model, the manufacturer and the online platform form the Stackelberg game. This research further discusses comprehensive environmental benefits and consumer surplus under five scenarios.

First, the fairness concerns of the online platform inhibit the recovery rate and supply chain members' profit while increasing the platform's utility. Second, fairness concerns increase the profit gap between the manufacturer and online platform, and the higher the degree of fairness concerns, the greater the profit gap; however, the four policies reduce the profit gap. Finally, when there are fairness concerns, environmental taxes damage the interests of supply chain members and consumers, but are most beneficial to the environment; recycling investment subsidies are on the contrary; the fund subsidy system depends on the relative size of the treatment fund and the subsidy fund.

This paper provides useful insights on how to regulate government policy to improve supply chain management with fairness concerns.

This paper is drawn on resource-based theory (RBV), dynamic capability theory (DCV) and situational strength theory (SST). It aims to investigate the relationship of entrepreneurial leadership (EL) on project success (PS) through the mediating role of dynamic capabilities (DCs), big data analytic capability (BDAC) and sustainable resilience (SR). It also explores the moderating effect of knowledge sabotage behaviour (KSB) on the relationship of BDAC and SR with PS.

Data was collected via Questionnaire survey through convenience sampling from the sample of 550 employees working on project in software companies. Of these, 467 response was deemed valid for analysis. The data was analysed using structural equation modelling (SEM) with SMART-PLS tool.

The study revealed a significant impact of EL on PS (p < 0.05). It also confirmed the significant mediating role of BDAC and SR (p < 0.05) in EL and PS relationship. These findings emphasize that adapting an entrepreneurial leadership style provides an environment conducive to achieving project success. Moreover, the presence of DCs like BDAC and SR enhances the organization adaptability, efficiency and firms’ endurance to disruption and strengthens their ability to navigate challenges and drive firm outcomes.

The research provides valuable insight into the role of EL as a contemporary leadership style in project-based firms that are marked by high risk and uncertainty. Also, this research is the first to examine the role of DCs, i.e. BDAC and SR as essential support in the execution of a project. Moreover, the research also highlights the importance of the effective role of DCs in achieving PS by mitigating the moderating influence of KSB. Thus, these DCs are empirically proven to facilitate EL in-driving project success in volatile environment while avoiding counterproductive work behaviour.

This systematic review paper aims to examine extant empirical research involving educational technology during COVID-19 to provide an aggregated analysis of how the pandemic has influenced educational technology research.

Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic review and an integrative review methodology, 50 primary research studies were selected from ten top-ranked educational research journals. These studies were reviewed regarding research purposes, methodologies, instruments, educational level, geographical distribution, and findings of the studies.

The findings reveal four emerging themes: influencing factors, effectiveness, challenges and teachers. The majority of the studies focused on higher education. Quantitative research design based on a questionnaire was the most adopted method of investigation by researchers.

Search parameters focused on the top 10 journals in the field of educational technology. Although this provides a level of quality, it narrowed the search.

For practitioners and researchers, this study provides a summary of the field to better understand what knowledge we have gained on the use of educational technology to enable a more agile, knowledgeable response to education in future emergencies.

This systematic review is unique in examining how the pandemic has influenced educational technology research. It also provides insight into gaps in the research that future researchers can use as a springboard to enable a more knowledge and a more agile approach to future emergencies.

This paper proposes a new intra-layer partition adaptive slicing algorithm for FDM 3D printing, aiming to further improve forming efficiency based on the adaptive slicing algorithm while preserving the surface finish quality of the formed model.

This method initially applies a large layer thickness for primary slicing, then refines layer thickness in layer height ranges with significant cross-sectional contour changes. Refined layers are partitioned: the internal region uses the large layer thickness for efficiency, while the external region uses a smaller layer thickness for surface quality. A thickness ratio and transition zone between regions prevent overlaps and gaps in printing paths.

The experimental results show that, compared to traditional adaptive slicing algorithms, the intra-layer partition adaptive slicing algorithm can effectively improve forming efficiency for most models while ensuring the model’s surface finish, with minimal impact on the bonding strength of the model.

The intra-layer partition adaptive slicing algorithm is a novel algorithm improved upon the traditional adaptive slicing algorithm, enabling models to achieve higher printing efficiency while maintaining the surface finish provided by the conventional adaptive slicing algorithm. This algorithm is of significant importance to vendors and individual users who provide printing services for large-sized fused deposition modeling models, as it can greatly enhance their production efficiency.

In uncertain and emerging conditions, businesses must adopt new strategies to compete and survive the changing and unstable environment. This research seeks to investigate the role of dynamic capabilities in the competitiveness of startups, emphasizing resilience and strategic alignment.

Isfahan Scientific and Research Town has always been a pioneer in the field of science and technology in Iran and is known as the most extensive technology and knowledge-based complex in Iran. The sample size of 300 companies active in the startup field was selected using a simple random sampling method. Questionnaires were collected from the managers of technological startup companies in Isfahan, and the SEM model was used to analyze the data.

This research shows that dynamic capabilities in terms of coordination, flexibility and integration significantly impact competitiveness. Resilience and strategic alignment also increase the organization’s performance and strengthen the organization in gaining a more competitive advantage in the industry.

Finally, dynamic capabilities indirectly affect competitiveness through resilience and strategic alignment. This shows a need for strategic alignment and resilience to change advantage shape in dynamic conditions.