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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.

This paper aims to examine how the number of short videos posted and the number of influencers employed, two important strategies in short video marketing, affect consumer behavior and how price discounts moderate the effects of influencer endorsement on consumer browsing and purchasing behavior.

Drawing on the literature on influencer endorsement, this study used an ordinary least square model to empirically examine the two effects of endorsement strategies in increasing product traffic and sales for consumers at a short video app, Douyin (TikTok).

The results show that the number of short video ads produces the classic inverted U-shape for traffic and sales, and both effects were strengthened under a high discount condition. Whereas the number of influencers has a positive effect on traffic but produces an inverted U-shape for sales, both effects were undermined under a high discount condition.

This study is the first to explore the two distinct effects (repetition effect and diffusion effect) of influencer endorsement on browsing and purchasing behavior and theorize about the moderate effects of discounts on these effects.

Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.

First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.

External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.

This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

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 objective of this study is to utilize empirical research and analysis to examine the coupling coordination relationship between new quality productivity and higher vocational education sustainable development.

To this end, an evaluation index system for the new quality productivity and higher vocational education sustainable development was constructed. The panel data of 30 Chinese provinces from 2016 to 2022 were then analyzed using the entropy method, the coupling coordination degree model, the Tobit regression model and Dagum’s Gini coefficient.

The findings indicate that the coupling coordination degree of new quality productivity and higher vocational education sustainable development exhibited an upward trend, though significant regional disparities were observed, with the highest coupling coordination degree recorded in the eastern region and the lowest in the northeastern region.

The study’s findings further suggest that the three factors of technological innovation level, rationalization of industrial structure and advanced industrial structure have a significant positive influence on the coupling coordination degree, while the level of government intervention has a significant negative influence on the Coupling Coordination Degree. The study posits that augmenting policy support, optimizing the government’s role, reinforcing the drive for technological innovation, and enhancing regional cooperation and exchange are imperative to foster high-quality development of the integration of industry and education between new quality productivity and higher vocational education.

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/