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Predicting green technology innovation in the construction field from a technology convergence perspective: A two-stage predictive approach based on interpretable machine learning

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Are employees willing to voluntarily share knowledge with their higher-ups? The existing studies show that the answer is no—employees are less likely to share knowledge with their higher-ups in the offline setting, corporate wikis, and online discussion groups. We answer the same question in a corporate question-and-answer (Q&A) community and argue that the answer can be yes. A potential-dyads approach and a quasi-natural experiment jointly demonstrate that employees are inclined to answer a question from their higher-ups and even exert more effort in those answers. Using an instrumental-variable design, we show that users who post more answers to higher-ranked individuals and who display greater effort in those answers are more likely to get promoted in subsequent years, meaning that employees do not need to worry about their careers when sharing knowledge with their higher-ups in corporate Q&A communities. Our research, together with research on other contexts, are useful for companies to take the role of the managers into account when considering which type of online community to adopt. Community designers can use our findings to better motivate knowledge sharing by considering users’ different job ranks.
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Technological convergence is the phenomenon whereby a focal technology, originating in one domain of application, end up as a part of new technologies that pertain to distant domains of application. This phenomenon is assuming increasing relevance due to its potential to create new markets and disrupt existing ones. Therefore, by adopting a search and recombination perspective, we seek to understand if and how the technological search breadth (i.e., the degree of different technological domains characterizing the knowledge base of the focal technology) and the geographical search breadth (i.e., the diversity of the knowledge base available for subsequent inventing activities) influence the likelihood and the speed of technological convergence events. We test our hypotheses on a sample of 135,496 European patents; they are applied between 1990 and 2009 and can be classified as key enabling technologies according to the European Commission. Our analysis shows that the likelihood and speed of technological convergence are positively affected by technological search breadth, negatively affected by the geographical search breadth, and positively affected by the interaction between technological and geographical search breadths.
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This article conducts a comparative analysis to investigate the effects of different classification algorithms and structural proximity indexes on the performance of the supervised link prediction approach to anticipating technology convergence at different forecast horizons. For this, we identify relationships between technologies of interest for different time periods and compute 10 structural proximity indexes among unconnected technologies at each period. We develop a set of classification models that identify potential convergence among unconnected technologies where each model is configured differently by a classification algorithm and a combination of the proximity indexes. We compare the performance of the classification models to investigate effective combinations of classification algorithms and proximity indexes at different forecast horizons. The empirical analysis on Wikipedia articles about artificial intelligence technology indicates that random forest outperforms others in short-term forecasting while support vector machine outperforms others in mid-term forecasting. We also identify structural proximity indexes that produce higher performance when combined with the most effective algorithm at each forecast horizon. The results of this article are expected to offer guidelines for choosing classification algorithms and indexes when applying the supervised link prediction approach in anticipating technology convergence.
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To expedite green building technologies (GBTs) adoption among construction enterprises, the Chinese government has launched a series of environmental policies that encourage the establishment of green building alliances. By building an evolutionary game optimization model, this study reveals the game strategy changes of multiple stakeholders and the impact of environmental policies on GBTs adoption among alliance-based construction enterprises. On this basis, the case study and simulation method are used to simulate the impact of implementation strength for environmental policies on the decision-making of stakeholders in alliances. The simulation results show that the strength of different environmental policies should reach a certain threshold (over 0.5). Comparatively speaking, the effect of green subsidy policies is usually superior to the impact of environmental tax policies; and the higher the ratio of market participants in the carbon emission trading, the better and more complete the GBTs adoption in alliances. Moreover, a single policy is more affected by the internal environment in alliances, but policy mixes can be most effective to promote GBTs adoption among construction enterprises. Therefore, the combination of environmental taxes, green subsidies and carbon trading will be the better policy instruments to develop GBTs in green building alliances.
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The innovation and development of emerging technology mostly depend on the way of knowledge convergence defined as the blurring of previously distinct domain-specific knowledge. This paper aims to explore the potential motivation of knowledge convergence and find the law of knowledge convergence, taking the solar energy field as an example. We established Keywords co-occurrence networks of solar energy literature in 2008–2017, and then link prediction is introduced to study the structural mechanism of knowledge convergence. We found that: (1) the common neighbor index better characterizes the knowledge convergence pattern in the knowledge networks among four similarity indicators. (2) The keywords co-occurrence network could effectively mine the structural characteristics of knowledge convergence; (3) the convergence cycle of knowledge in the field of solar energy was about 4 years; (4) keywords with higher betweenness centrality or eigenvector centrality easily generated knowledge convergence; (5) a literature knowledge convergence prediction model is proposed based on these results; and (6) the prediction results showed that scholars should pay attention to six basic issues including energy storage, efficiency, cost, ecological effect, application scenarios, and hybrid photovoltaic systems. This work can provide guidance not only for scholars to grasp the research direction and to generate more innovations but for the government to formulate the policies of government funding.
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The quality of novel technological innovations is extremely variable, and the ability to measure innovation quality is essential to sensible, evidence-based policy. Patents, an often vital precursor to a commercialised innovation, share this heterogeneous quality distribution. A pertinent question then arises: How should we define and measure patent quality? Accepting that different parties have different views of, and different sets of terminologies for discussing this concept, we take a multi-dimensional view of patent quality in this work. We first test the consistency of popular post-grant outcomes that are often used as patent quality measures. Finding these measures to be generally inconsistent, we then use a raft of patent indicators available at the time of grant to dissect the characteristics of different post-grant outcomes. We find broad disagreement in the relative importance of individual characteristics between outcomes and, further, significant variation of the same across technologies within outcomes. We conclude that measurement of patent quality is highly sensitive to both the observable outcome selected and the technology type. Our findings bear concrete implications for scholarly research using patent data and policy discussions about patent quality.