Search results

The purpose of this study is to address the critical need for patent value evaluation within patent management, particularly in the context of the digital economy. Recognizing the importance of utilizing historical data, this research aims to uncover effective methodologies that enhance the appraisal of patent value, which is vital for informed decision-making in the management of scientific and technological advancements.

This study introduces a comprehensive evaluation model by analyzing various factors that influence patent value. An index system is constructed that integrates technical, economic and legal aspects to facilitate a nuanced assessment of patents. The methodological core of this research is the development of an XGBoost patent value appraisal model, which incorporates Bayesian optimization to refine the evaluation process. The model’s validity is tested through empirical analysis of patents in the rapidly evolving sector of cloud computing.

The empirical results demonstrate that the XGBoost model, strengthened by Bayesian optimization, outperforms traditional categorization techniques. The proposed model shows superior performance in terms of accuracy, precision, recall rate and operational feasibility. These findings indicate a significant improvement in the precision of patent potential and value assessments, leading to more reliable and actionable insights for patent management.

This study introduces a novel patent evaluation model that combines XGBoost with Bayesian optimization. XGBoost enhances performance by integrating weak learners, ideal for complex, nonlinear problems like patent valuation. Bayesian optimization refines hyperparameters efficiently using prior distributions and known results. Its practical implications for patent management and technology exploration are substantial, offering a new tool for strategic decision-making.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

Social media provides a convenient way to popularise first aid knowledge amongst the general public. So far, little is known about the factors influencing individuals’ adoption of first aid knowledge on social media. Drawing on the information adoption model (IAM), this study investigates the joint effects of cognitive factors (e.g. perceived information usefulness (PIU)), affective factors (e.g. arousal (AR)) and social factors (e.g. descriptive norms (DN)) on first aid knowledge adoption (KA) and examines their antecedent cues from the perspective of information characteristics.

The data were collected from 375 social media users, and the structural equation model was adopted to analyse the results.

The results indicate that PIU, AR and DN all have positive direct effects on first aid KA. Additionally, the study highlights the positive synergistic effect of AR and PIU. Furthermore, the study suggests that AR is determined by message vividness (MV) and emotional tone (ET), whilst DN are determined by peer endorsement (PEE) and expert endorsement (EXE).

Our research is groundbreaking as it delves into the adoption of first aid knowledge through social media, thus pushing the boundaries of existing information adoption literature. Additionally, our study enhances the IAM by incorporating emotional and social elements and provides valuable insights for promoting the spread of first aid knowledge via social media.

This paper investigates the usage and trust of Chinese social media in the travel planning process (pre-trip, during-trip and post-trip) of Chinese tourists.

Through a combination of structured online survey (n = 406) and follow-up interviews, the research identifies the diversification of the demand-and-supply patterns of social media users in China, as well as the allocation of functions of social media as tools before, during and after travel.

Social media users are diverse in terms of their adoption of social media, use behaviour and scope; the levels of trust and influence; and their ultimate travel decisions and actions. Correlations between the level of trust, influence of social media and the intended changes in travel decisions are observed. Destination marketers and tourism industries should observe and adapt to the needs of social media users and potential tourist markets by understanding more about user segmentation between platforms or apps and conducting marketing campaigns on social media platforms to attract a higher number of visitors.

This paper demonstrated the case of social media usage in mainland China, which has been regarded as one of the fastest growing and influential tourist-generating markets and social media expansions in the world. This study further addressed the knowledge gap by correlating social media usage and travel planning process of Chinese tourists. The research findings suggested diversification of the demand-and-supply pattern of social media users in China, as well as the use of social media as tools before, during and after travel. Users were diversified in terms of their adoption of social media, use behaviour, scope, the levels of trust, influence and the ultimate travel decisions.

Destination marketing organizations should note that some overseas social media platforms that are not accessible in China like TripAdvisor, Yelp, Facebook and Instagram are still valued by some Chinese tourists, especially during-trip period in journeys to Western countries. Some tactics for specific user segments should be carefully observed. When promoting specific tourism products to Chinese tourists, it is necessary to understand the user segmentation between platforms or apps.

Social media is a powerful tool for tourism development and sustainability in creating smart tourists and destinations worldwide. In China, the use of social media has stimulated the development of both information and communication technology and tourism.

Circular economy (CE) is a structured model of production and consumption involving sharing, leasing, reusing, recycling, repairing and refurbishing existing products or materials sustainably. Despite the numerous benefits of CE adoption, the construction and demolition (C&D) sector still struggles to comprehensively understand, integrate and adopt this approach. This study provides a comprehensive analysis of CE within the C&D sector and proposes a structured conceptual framework for an effective construction and demolition waste management (CDWM) program.

A systematic literature review (SLR) was conducted using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) framework to search for articles across three databases: Scopus, Web of Science and EBSCO. EndNote software and Excel spreadsheets were used to analyze and synthesize the articles up to 2024. A total of 102 articles were included in the study. Various key facets of the CE in CDWM, including barriers and mitigation actions, enablers, tools and techniques, benefits, strategies and frameworks, have been thoroughly reviewed and analyzed for the C&D sector to understand their nature and effectively adopt the CE approach in CDWM operations.

The findings provide a comprehensive analysis of different facets of CE in CDWM and a structured conceptual framework for the effective adoption of CE. This will contribute to improving the management of CDWM in the C&D sector. The outcomes offer a comprehensive knowledge base of CE in CDWM to managers, planners, decision-makers, stakeholders and researchers, enabling effective deployment.

This study offers a substantial knowledge base to researchers by examining various key facets of CE in CDWM, aiding further exploration of research in the same domain. Additionally, it assists C&D managers, planners, stakeholders and decision-makers by furnishing a structured conceptual framework of CE, thereby enhancing effective implementation. Furthermore, this study supports society by providing a pathway to improve C&D waste circularity through the execution of CE.

This study is the first to comprehensively review the various facets of CE from a CDWM perspective and to propose a structured conceptual framework for the effective adoption of CE in the C&D sector. Additionally, it not only advances theoretical knowledge of CE adoption in the CDWM field but also provides practical guidance to stakeholders on how to implement a comprehensive CE approach to enhance C&D waste circularity.

This study aims to examine conditions in both inter- and intra-alliance contexts within an oligopolistic alliance industry operating across multiple markets. It focuses on how a focal firm’s optimal performance depends on nuanced evaluations of the trade-offs associated with coopetitive synergy, and on decisions about whether to collaborate or compete with its members.

The authors analyze the six leading global container shipping firms within two major alliances (The Grand Alliance and the New World Alliance) from 2003 to 2010, gathering 7,825 news articles from the Cyber Shipping Guide, a comprehensive global container shipping business database in Japan.

The findings reveal the following: (1) the focal firm cooperating with members of a rival alliance decreases the level of inter-alliance competition. (2) The focal firm cooperating with members of a rival alliance increases the level of intra-alliance competition. (3) Increased inter-alliance competition negatively impacts the performance of the focal firm. (4) Increased intra-alliance competition negatively impacts the performance of the focal firm.

Global container shipping firms should make optimal decisions about which firms to cooperate with, focusing on those that contribute to the focal firm’s overall synergies and thus performance.

This study contributes to the literature on coopetition in strategic alliances by extending the concept of dynamic coopetition to include strategic alliance groupings, and by examining how focal firm members cooperate in both inter- and intra-alliance contexts.

This study aims to present state-of-the-art research on circular economy (CE) implementation in construction and demolition waste management (CDWM) within the construction sector.

A mixed-method (scientometric and critical analysis) review strategy was adopted, involving scientometric and critical analysis to uncover the evolutionary progress within the research area, investigate key research themes in the field, and explore ten issues of CE in CDWM. Moreover, avenues for future research are provided for researchers, practitioners, decision-makers, and planners to bring innovative and new knowledge to this field.

A total of 212 articles were analyzed, and scientometric analysis was performed. The critical analysis findings reveal extensive use of surveys, interviews, case studies, or mixed-method approaches as study methodologies. Furthermore, there is limited focus on the application of modern technologies, modeling approaches, decision support systems, and monitoring and traceability tools of CE in the CDWM field. Additionally, no structured framework to implement CE in CDWM areas has been found, as existing frameworks are based on traditional linear models. Moreover, none of the studies discuss readiness factors, knowledge management systems, performance measurement systems, and life cycle assessment indicators.

The outcomes of this study can be utilized by construction and demolition sector managers, researchers, practitioners, decision-makers, and policymakers to comprehend the state-of-the-art, explore current research topics, and gain detailed insights into future research areas. Additionally, the study offers suggestions on addressing these areas effectively.

This study employs a universal approach to provide the current research progress and holistic knowledge about various important issues of CE in CDWM, offering opportunities for future research directions in the area.

This study focuses on the relationship between top management teams' (TMTs) digital experience and company innovation efficiency. In addition, this study examines the mechanism role of digital transformation processes, including basic capability, chief information officer (CIO) appointments, process management and business management, on the relationship between TMTs' digital experience and innovation efficiency.

Based on the sample of China's A-share listed companies from 2011 to 2021, this article employs data envelopment analysis (DEA) and PSM-DID models to examine the effect of TMTs' digital experience on company innovation efficiency.

The TMTs' digital experience was positively correlated with company innovation efficiency. Artificial intelligence and digital platform are two key technologies that TMTs' digital experience effects company innovation efficiency. The financial ability of companies is a key factor to ensure the effectiveness of these two technologies. Digital foundation only played an important role in the early stages. In addition, TMTs' digital experience is more focused on technological management, not process management and business management. We also found that the CIO did not play a positive role on digital innovation. Finally, within the manufacturing industry and non-high-tech companies, the TMTs' digital experience has proven to be notably more effective in enhancing innovation efficiency.

This study advises that companies in the initial stages of digital transformation should give precedence to recruiting managers with digital experience into TMT, as their role is pivotal in propelling digital transformation in innovation. Futhermore, it is suggested that in the selection of TMT members, firms could consider dual appointments, such as CIO combined with other roles. Finally, this study recommends that TMTs with a digital background should pursue deeper technological competencies before embarking on corresponding business and process managements.

First, this study constructs a theoretical framework for examining how TMTs' digital experience influences innovation efficiency. Second, this study embedded the digital technology into the company innovation input index, elucidating how TMTs' digital experience directly enhances innovation efficiency. Finally, this study reveals how basic capability, CIO appointments, process management and business management affect the relationship between TMT digital experience and innovation efficiency.

Construction waste reduction (CWR) plays a vital role in achieving sustainability in construction. A good CWR practice can result in optimizing material usage, conserving natural resources, limiting environmental pollution, protecting the environment and enhancing human health. In this regard, the purpose of the current study is to identify the most relevant organizational policies that aid in waste reduction and concurrently explores the congruent measures to be adopted during the construction process in the Indian high-rise building sector.

The research findings were obtained through a mixed- method approach. Content analysis was used to identify waste reduction measures (variables) targeting on the two domains of construction – “waste-efficient execution” and “waste – mitigating organizational policies.” Furthermore, the authors explored and documented the key measures from the identified waste reduction measures using the constraint value of the relative importance index. As the next step, the study listed the theoretical hypothesis based on expert interviews and tested the theory through confirmatory factor analysis.

The results revealed that “waste sensitive construction techniques and strategies” as the most significant category under the domain “Execution” with a path coefficient of 0.85. Concurrently, the study has also determined that “control procedures for budget, quality and resources” as the most effective organizational approach in reducing construction waste in the Indian building industry, with a path coefficient of 0.83.

The current research is context-sensitive to the Indian construction sector. It presents the stakeholder’s perspective on construction waste reduction and the relevant measures to be implemented to reduce construction waste in high-rise building projects in India. It can also act as a concordance for decision-makers to further focus on CWR management and aid in formulating policies suitable for the Indian context.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.