Search results

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

The purpose of this study was to identify key areas for benchmarking (BM) towards the improvement of small and medium scale enterprises (SMEs) construction firms in developing countries.

The study employed the use of the quantitative research method in the collection and analysis of primary data collected from field surveys using a piloted close-ended questionnaire created following a review of available literature on BM. Based on 63 solicited views of professionals with SMEs (quantity surveyors, project managers and architects) data collected were statistically analysed using a one-sample t-test.

The findings of the study indicate that the key areas for BM towards the improvement of SME construction firms in developing countries in order of relevance are “Financial Performance”, “Competitiveness”, “Customer Satisfaction”, “Technology Advancement”, “Communication Skills”, “Collaboration”, “Employee Satisfaction” and “Product orientation”.

The study has given more insight into the areas that need more attention for SMEs BM to achieve improvement. It can therefore be suggested firms that adapted identified areas will derive the benefits of BM. It is further opined that more BM education should be provided to various SMEs construction firms to facilitate an effective and efficient BM regime aiming at performance enhancement in project delivery.

This forging research attempts to identify the key areas for BM towards the improvement of SMEs construction firms in developing countries. Although there have been several efforts to create BM tools for the construction industry.

The building and construction industry has a significant potential to reduce adverse climate change effects. There are plans to improve the natural resource use and greenhouse gas emissions caused by the buildings by choosing energy-efficient technologies, renewable energy sources and sustainable architectural and constructional elements. This study systematically reviews the patent data for climate change mitigation technologies related to buildings, aiming to detect their relative importance and evaluate each technology in the Y02B network.

The applied approach covers the process of (1) selecting high-impact technology, (2) collecting patent data from the USPTO database, (3) creating a citation frequency matrix using cooperative patent classification codes, (4) linking high-impact patents with analytical network process method, (5) limiting centrality of identifying core technologies from indicators and (6) creating a technology network map with social network analysis.

The study results show that energy-saving control techniques, energy-efficient lighting devices, end-user electricity consumption, management technologies and systems that convert solar energy into electrical energy are core solutions that reduce the effects of climate change. In addition, solutions that will support core technologies and whose effects are expected to increase in the coming years are energy-efficient heating, ventilating and air conditioning systems, smart grid integration, hybrid renewable energy systems, fuel cells, free cooling and heat recovery units and glazing technologies.

Most of the studies on patent analysis have failed to demonstrate any convincing evidence down to the lowest component groups of an entire technology network. The applied approach considers and evaluates each component included in a technology network from a holistic perspective.

This paper analyzes the effect of the Belt and Road Initiative (BRI) on firm green innovation upgrading using data on Chinese firms between 2009 and 2021.

The author adopts the staggered difference-in-difference (DID) method to estimate regressions, treating the proposal of the BRI in 2013 as a policy shock. Our analysis yields few findings.

The author yields few findings. First, the BRI can significantly promote Chinese firms green innovation upgrading. Specifically, the BRI can promote firm green innovation upgrading by 0.9%. Second, the BRI mainly promotes firms green innovation upgrading by promoting firms to increase green entrepreneurship, cooperative innovation and environmental investment. Finally, the BRI has a greater impact on the green innovation upgrading of firms in the digital industrialization industry rather than digital industry and firms with low pollution emissions rather than firms with high-pollution emissions. This research indicates that the BRI is not only an important platform for sustainable development and also an important opportunity for green entrepreneurship.

First, due to the low quality of data and the lack of detailed information on some firms' patents owned after 2018, fully applying data of all years for regression was not possible. Second, the author did not construct a theoretical model to explore the impact of the BRI on green innovation upgrading of firms from the perspective of outward foreign direct investment (OFDI), which is also the direction of future research. Finally, there are still some unexplored mechanisms of the BRI on firms green innovation upgrading, which should be further explored in the future.

First, from the micro perspective, the author measures the quality of firms' green patents, further measuring the firms' green innovation upgrading. Second, the author discusses the impact of the BRI on firm green innovation upgrading with the method of staggered DID, so that the policy effect of the BRI can be more accurately evaluated. Third, the author comprehensively analyzes the mechanism of cooperative innovation and green infrastructure investment, as well as analyzing the heterogeneity from the perspective of industry digital transformation and firm pollution emissions. Lastly, the author provides specific paths for firms to make high-quality investment from the green BRI construction.

With the deep development of the new technological revolution and industrial transformation, the development, application, expansion and integration of digital technology provide opportunities for transforming the manufacturing industry from traditional manufacturing to intelligent manufacturing. However, little research currently focuses on analyzing the influencing factors of intelligent development in this field. There is a lack of research from the perspective of the digital innovation ecosystem to explore the intrinsic mechanism that drives intelligent development. Therefore, this article starts with high-end equipment manufacturing enterprises as the research subject to explore how their digital innovation ecosystem promotes the effectiveness of enterprise intelligent development, providing theoretical support and policy guidance for enterprises to achieve intelligent development at the current stage.

This article constructs a logical framework for the digital innovation ecosystem using a “three-layer core-periphery” structure, collects data using crawling for subsequent indicator measurement and assessment and uses the fuzzy set Qualitative Comparative Analysis method (fsQCA) to explore how the various components of the digital innovation ecosystem in high-end equipment manufacturing enterprises work together to promote the development of enterprise intelligently.

This article finds that the various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises, through mutual coordination, can help improve the level of enterprise intelligence. Empirical analysis shows four specific configuration implementation paths for the digital innovation ecosystem of high-end equipment manufacturing enterprises to promote intelligent development. The core conditions and their combinations that affect the intelligent development of enterprises differ in each configuration path.

Firstly, this article discusses the practical problems of intelligent transformation and development in the manufacturing industry and focuses on the intelligent development effectiveness of various components of the digital innovation ecosystem of high-end equipment manufacturing enterprises in the context of digitalization. Secondly, this article uses crawling, text sentiment analysis and other methods to creatively collect relevant data to overcome the research dilemma of being limited to theoretical analysis due to the difficulty in obtaining data in this field. At the same time, based on the characteristics of high-end equipment manufacturing enterprises, the “three-layer core-periphery” digital innovation ecosystem framework constructed in this article helps to gain a deep understanding of the development characteristics of the industry's enterprises, provides specific indicator analysis for their intelligent development, opening the “black box” of intelligent development in the industry's enterprises and bridging the gap between theory and practice. Finally, this study uses the fsQCA research method of configuration analysis to explore the complexity of the antecedents and investigate the combined effects of multiple factors on intelligent development, providing new perspectives and rich research results for relevant literature on the intelligent development of high-end equipment manufacturing enterprises.

Delivering housing to resource-constrained communities (RCCs) is a complex process beset with difficulties. The purpose of this study is to use a complexity lens to examine the approach taken by a social enterprise (SE) in Australia to develop and manage a housebuilding supply chain for RCCs.

The research team used a longitudinal case study approach from 2017 to 2022, which used mixed methods to understand the phenomenon and gain an in-depth understanding of the complex issues and problem-solving undertaken by an SE start-up.

Balancing mission logic with commercial viability is challenging for an SE. The supply chain solution that evolved accommodated the particulars of geography and the needs of many stakeholders, including the end-user community and government sponsors. Extensive and time-consuming socialisation and customisation led to a successful technical design and sustainable supply chain operation.

Analysing supply chain intricacies via a complexity framework is valuable for scholars and practitioners, assisting in designing and developing supply chain configurations and understanding their dynamics. Meeting the housing construction needs of RCCs requires the SE to place societal focus at the centre of the supply chain rather than merely being a system output. The developed business model complements the engineering solution to empower a community-led housing construction supply chain.

This longitudinal case study contributes to knowledge by providing rich insights into the roles of SEs and how they develop and operate supply chains to fit with the needs of RCCs. Adding a contextual response dimension to an established complexity framework helped to explain how hybrid organisations balance commercial viability demands with social mission logic by amending traditional supply chain and governance practices. The case provides insights into supply chain configuration, needed changes and potential impacts when an SE as a focal actor inserts into a traditional for-profit construction supply chain.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.

The development of prefabricated buildings has become one of the primary solutions to transform the traditional construction industry around the world. Incentive policy is one of the important driving factors for the development of prefabricated building. The policy system in the field of prefabricated buildings needs to be improved urgently. However, there is still a dearth of research on how incentive policies exert impact on the development of prefabricated buildings. This paper aims to reveal the impact mechanisms of different types of policies on the development system of prefabricated buildings.

This study categorizes prefabricated building policies, constructs a system dynamics model of prefabricated building policies and conducts scenario simulations to examine the impact and sensitivity of different types of policies on the development system of prefabricated buildings.

The results show that compulsory policies play a greater role in the early stage of prefabricated building development and need to be withdrawn at the right time. Preferential and encouraging policies play an incentive role in the middle and later stages of prefabricated building development. Encouraging policies predominate in the later stage of prefabricated building development. Based on the research results, policy recommendations for prefabricated building development are put forward respectively from the government, developers and consumers.

The research results are expected to make up for the lack of clear policies paths in existing research and provide theoretical references for the formulation and optimization of future policies.