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This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

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.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

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.

The purpose of this study is to examine the influence of stakeholder integration and project complexity on information technology (IT) projects in Kenya. The following research question guided our inquiry: what is the influence of project complexity and stakeholder integration on the performance of IT projects in Kenya?

To advance the current understanding of the effect of stakeholder integration and project complexity on IT projects, multiple regressions were used to predict how project complexity and stakeholder integration influence project performance. Both government-funded and privately funded IT projects from a developing country were examined.

The study found that any project’s complexity and stakeholder integration levels offer a distinctive contribution to its success. Theoretically, the study contributes to linkages between stakeholder integration and project complexity concerning IT project performance. Through the adoption of actionable research and theoretical elaboration, we have shown that the successful execution of IT projects is driven by the successful integration of stakeholders and monitoring the level of complexity at each phase of the project.

The findings of this study add to the burgeoning literature on the performance of IT projects and come with several managerial implications as well. It brings to the fore the concept of stakeholder integration as an essential element of project success. The findings suggest that the inclusion of stakeholder integration into corporate decisions, strategies and policies can be an asset to the production of sustainable competitive advantages needed during the implementation of IT projects in government entities and organisations. As shown in this study, all the above require a collaborative platform allowing for data sharing among diverse stakeholders to ameliorate distrust or lack of information.

The rise of the mobile Internet has accumulated much text information in various online financial forums. Such information often contains the emotional attitudes of investors toward financial technology (fintech) platforms, so extracting the sentimental tendency information has great practical value for the development of fintech platforms. Based on the investor sentiment theory, the paper aims to analyze the relevant social media data and test the influence path of online news evaluation on the stock price fluctuation of fintech platforms.

Taking Oriental Fortune as the research object, this paper selects multiple variables such as stock bar popularity, snowball popularity, news popularity and news sentiment scores collected by UQER and combines the sentiment scores of single daily news into a daily sentiment score. Based on the period from November 1, 2019 to March 31, 2020, during the emergence of the coronavirus disease 2019 (COVID-19) pandemic as the background, the authors conduct the Granger causality test based on the vector autoregressive (VAR) model and analyze the relevant evaluation of Oriental Fortune through the empirical model.

The authors' results show that different online evaluations impact the rise and fall of stock prices differently, while news popularity has the most significant impact. Besides, news sentiment scores on share price fluctuation have a relatively substantial influence. These findings indicate that the authoritative news evaluation can strongly guide investors to make relevant investment behavior operations in the information dissemination process, significantly affecting stock prices.

The research findings of this paper have good inspiration and reference values for investors and financial regulators.

The aim was to investigate the effect of normal load on the tribological performance of laser cladded FeCoCrMoSi amorphous coating, which might choose the appropriate normal load for the friction reduction and wear resistance.

A FeCoCrMoSi amorphous coating was prepared on 45 steel using laser cladding, and the tribological performance of obtained coating under the different normal loads was investigated using a ball-on-disk tribometer.

The FeCoCrMoSi amorphous coating is composed of M₂₃C₆, Co₆Mo₆C₂ and amorphous phases, where the M₂₃C₆ hard phase enhances the coating hardness to increase the wear resistance and the Co₆Mo₆C₂ with the vein shape forms the strong mechanical interlock to play the role of friction reduction. The average coefficients of friction of containing amorphous FeCoCrMoSi coating under the normal loads of 3, 4 and 5 N are 0.68, 0.65 and 0.53, respectively, and the corresponding wear rates are 17.7, 23.9 and 21.9 µm3•N⁻¹•mm⁻¹, respectively, showing that the appropriate normal load is beneficial for improving its friction reduction and wear resistance. The wear mechanism is composed of adhesive wear, abrasive wear and oxidative wear, which is attributed to the high hardness of amorphous coating by the amorphous phase.

The FeCoCrMoSi amorphous coating was first applied for the improvement of 45 steel, and the effect of normal load on its tribological performance was investigated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0304/

Based on the perspective of knowledge management, this study aims to discuss how to build cross-city emergency management collaboration mechanism in major emergencies and explore the important role of knowledge management in emergency management collaboration.

Based on the theoretical analysis of knowledge management and the typical case study of cross-city emergency management collaborative rescue, this study provides an in-depth analysis of how these cities achieve high emergency management performance through multidimensional and multilevel knowledge collaboration, thus revealing the mechanism of knowledge transfer, integration and sharing in achieving high emergency management performance.

Through analyzing typical cases, this study finds that building a smooth mechanism for multichannel emergency rescue information can promote the diversification of knowledge transfer methods, building a platform-based integration mechanism for emergency rescue information can enhance knowledge integration capabilities and building a linkage mechanism for emergency rescue materials between cities can promote knowledge-sharing level, thereby improving emergency management performance level.

This study has great significance for how to build cross-city emergency management collaboration mechanism in the digital era. In the future, the authors need to further discuss the following two aspects in depth: research on the impact of cross-city emergency management collaboration mechanism on improving the knowledge management capabilities of government emergency management departments; and research on the impact mechanism of knowledge management capabilities on city resilience.

Through case analysis of cross-city emergency management collaborative rescue for major emergencies in China in recent years, this study proposes three specific strategies for cross-city emergency management (smooth, integration and linkage mechanisms) and reveals that these three strategies are essentially aimed at improving the government’s knowledge management level.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

The impact of climate change (CC) has become the most defining issue of the present times. From an increase in temperature to food and water scarcity, the adverse effect of CC is faced globally by all countries on an unprecedented level. Environmentally polluting industries are considered one of the primary contributors to the problem of CC. This chapter aims to provide an understanding of the emerging global threat of CC and various strategies that environmentally polluting firms could undertake to tackle the climate change crisis.

Based on the review of the literature, this chapter presents deliberation on the climate change crisis and various strategies for environmentally polluting industries to tackle CC issues.

This chapter presents a bird's eye view of the CC issue and various strategies that could be undertaken by environmentally polluting industries to address the CC crisis.

This chapter should be very useful for policymakers, practitioners and corporations to understand the issue of climate change and global warming and its implications. Further, it will also help polluting companies to reduce greenhouse gas (GHG) emissions and address the adverse impact of their business activities.

Considering the dearth of sector-specific studies based on climate change, this chapter is one of the few studies that explore CC strategies in the context of polluting industries.