The scarce yet exceptionally productive star inventors are the pivotal human capital of enterprises. Managers emphasize team-level contributions of star inventors over their individual excellence. However, the impact of star inventors on team innovation remains contested with empirical evidence reporting both positive and negative effects. By theoretically analyzing the advantages and limitations of star inventors at different innovation phases, this study endeavors to reconcile the ongoing debate by investigating the dual effects of star inventors on the novelty and impact of team innovation output. Furthermore, by incorporating technological turbulence and internal network cohesion as moderators, this research demonstrates how external and internal contextual factors shape star inventors’ influence. The findings reveal that star inventors enhance the technological impact of team innovation but hinder its novelty. Technological turbulence weakens their positive effect on impact while exacerbating their negative effect on novelty. Conversely, internal network cohesion amplifies their beneficial influence on impact and mitigates their adverse effect on novelty. By integrating psychological, knowledge management, and social network theories, this study advances the understanding of star inventors’ dual effects and their boundary conditions.

E-commerce platforms increasingly deploy green labeling programs to signal environmental performance, yet they face a critical strategic choice: should they adopt a selective strategy that certifies only high-greenness products, or an inclusive strategy that extends certification to low-greenness alternatives? To address this, we develop a game-theoretic model of a platform and two competing suppliers with heterogeneous greenness levels. We find that the Selective strategy induces a polarization effect that benefits the high-greenness product and expands total sustainable consumption. In contrast, the Inclusive strategy primarily benefits the low-greenness product under partial market coverage, but renders equilibrium outcomes inelastic to label utility once full coverage is achieved. Comparing firm preferences, the low-greenness firm always favors the Inclusive strategy, whereas the high-greenness firm prefers the Selective strategy unless label dilution is mild and label utility falls within an intermediate range. From an environmental perspective, the Inclusive strategy maximizes total green demand only when label dilution is below a critical threshold. Ultimately, the platform’s optimal strategy exhibits a non-monotonic dependence on label dilution and utility. The Selective strategy dominates under severe dilution or extreme label utilities, while the Inclusive strategy emerges as optimal under moderate-to-mild dilution and intermediate utility. However, as the ratio of baseline greenness to the greenness gap increases, the optimality region for the Inclusive strategy vanishes, establishing the Selective strategy as the dominant equilibrium.

When a seller sells its product through both direct and platform channels, it is faced with the decision of selecting a supporting service for each channel-either the platform service or the third-party service. In this article, we investigate whether the platform owner allows the seller on it to use the third-party service and further explores which service portfolio should be adopted by the seller. Our results show that the platform never allows the seller to use third-party service if the platform service has a quality advantage or only a slight quality disadvantage. Interestingly, such platform behavior does not necessarily lead to lower consumer surplus or diminished social welfare. In addition, the seller's choice of service portfolio depends on the quality difference between platform and third-party services. When the platform service has a significant quality disadvantage, the seller may adopt either the PT (platform service on the platform and third-party service in the direct channel) or the TT (third-party service in both channels) portfolio. Conversely, if the disadvantage is relatively low or if the platform service has a quality advantage, the seller chooses either the PT or the PP (platform service in both channels) portfolio. Moreover, the seller may opt for a service portfolio that maximizes its profit at the expense of total consumer demand. Notably, service differentiation in the channels (PT or TP) consistently results in higher commission price charged by the platform and greater total consumer demand.

Celebrity avatars—digital instantiations of real-world public figures—are increasingly used in brand endorsements, yet firms lack evidence-based guidance on visual style. Drawing on five scenario-based experiments, this study compares two dominant visual styles: stylized avatars (characterized by artistic, exaggerated features) and photorealistic avatars (closely mimicking the celebrity's physical likeness). The findings reveal a general “stylized advantage”: stylized celebrity avatars elicit more favorable brand attitudes than their photorealistic counterparts (Study 1). This effect is driven by a reduction in perceived commercial intention—a motive inference that mitigates consumer defensiveness and enhances trust in the endorsement (Study 2). However, the stylized advantage attenuates for utilitarian products (Study 3) and reverses for low-entertainment celebrities (Study 4) and serious brands (Study 5), where photorealistic executions better signal credibility, professionalism, and seriousness. Collectively, the findings show that visual style matters, explain why it matters, and delineate when it matters. This study offers a contingent, mechanism-based framework to guide style selection and provide actionable implications for optimizing celebrity-avatar endorsements.

A paradox derived from the knowledge-based view argues that knowledge commonality, which facilitates communication and subsequent knowledge integration, induces knowledge redundancy causing no gain from identical knowledge. For this reason, we build a conceptual link from knowledge commonality to knowledge creation through search behaviour, i.e. search scope and search depth. Doing so sheds light on both the bright and dark sides of knowledge commonality in recombinant search. We further explore the role of team experience in search scope and its moderating effect on the relationship between scope and knowledge creation. The analysis of US patent data shows that while knowledge commonality has a negative significant effect on search scope consequently increases the knowledge creation, it also has a strong positive effect on search depth that increases the knowledge creation. Team experience enhances search scope but negatively moderates the relationship between search scope and knowledge creation.

The high logistics costs of biomass feedstock and the involvement of other firms that use biomass and independent suppliers make feedstock acquisition increasingly difficult for biomass power plants. Governments provide feed-in tariffs (FiT) for biomass power plants to help reduce the negative impacts of high raw material costs. Using a game-theoretic approach, we study the optimal government FiT for biomass power plants and explore the effects of biomass feedstock competition and the presence of independent biomass feedstock suppliers on FiT strategy. Our results show that governments should subsidize biomass power plants whose competitiveness exceeds a certain threshold. The entry of a feedstock competitor into the biomass supply chain raises this threshold, but the involvement of an independent biomass supplier will not. However, the involvement of an independent biomass supplier reduces the efficiency of the FiT. In addition, FiT for biomass power plants should not be provided if government funds are below a certain threshold, as it does not increase social welfare. By comparing FiT with an alternative use of government funds, the technology subsidy, we find that the technology subsidy should be adopted instead of FiT if the government funds are below a threshold.

The outbreak of COVID-19 brings almost the biggest explosions of scientific literature ever. Facing such volume literature, it is hard for researches to find desired citation when carrying out COVID-19 related research, especially for junior researchers. This paper presents a novel neural network based method, called citation relational BERT with heterogeneous deep graph convolutional network (CRB-HDGCN), for COVID-19 inline citation recommendation task. The CRB-HDGCN contains two main stages. The first stage is to enhance the representation learning of BERT model for COVID-19 inline citation recommendation task through CRB. To achieve the above goal, an augmented citation sentence corpus, which replaces the citation placeholder with the title of the cited papers, is used to lightly retrain BERT model. In addition, we extract three types of sentence pair according citation relation, and establish sentence prediction tasks to further fine-tune the BERT model. The second stage is to learn effective dense vector of nodes among COVID-19 bibliographic graph through HDGCN. The HDGCN contains four layers which are essentially all sub neural networks. The first layer is initial embedding layer which generates initial input vectors with fixed size through CRB and a multilayer perceptron. The second layer is a heterogeneous graph convolutional layer. In this layer, we expand traditional homogeneous graph convolutional network into heterogeneous by subtly adding heterogeneous nodes and relations. The third layer is a deep attention layer. This layer uses trainable project vectors to reweight the node importance simultaneously according to both node types and convolution layers, which further promotes the performance of learnt node vectors. The last decoder layer recovers the graph structure and let the whole network trainable. The recommendation is finally achieved by integrating the high performance heterogeneous vectors learnt from CRB-HDGCN with the query vectors. We conduct experiments on the CORD-19 and LitCovid datasets. The results show that compared with the second best method CO-Search, CRB-HDGCN improves MAP, MRR, P@100 and R@100 with 21.8%, 22.7%, 37.6% and 21.2% on CORD-19, and 29.1%, 25.9%, 15.3% and 11.3% on LitCovid, respectively.

The linear ordering problem (LOP) is an NP-hard combinatorial optimization problem with wide applications. As the problem is computationally intractable, the only practical alternative is to develop efficient heuristics to solve it. Here we present four new properties of block insertion for the LOP, and show that changing the node order in one sub-problem does not affect the objective values of the remaining sub-problems. Based on these properties, we then propose three local search schemes for solving the LOP. Our experimental results show that the block insert with the first strategy often outperforms other local search schemes within the same computational time. To further improve the performance of this local search scheme, we incorporate it into the iterated local search and genetic algorithm frameworks, and develop the block-insertion-based iterated local search (ILSb) and memetic algorithm (MA(b)), respectively. The computational results show that both the ILSb and MA(b) outperform the state-of-the-art meta-heuristics. Moreover, with appropriate parameter settings, the MA(b) frequently outperforms the ILSb. Finally, we design a parallel computing framework, which divides the LOP problem into independent subproblems that are solved in parallel by exact methods. This parallel framework can further improve the solutions derived by MAb or other heuristics. (C) 2019 Elsevier Ltd. All rights reserved.

The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We find that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten treatment time is markedly reduced and the importance of contact quarantine and social distancing increases.

科学分析互联网行业的技术发展历程及现状不仅能够为互联网技术的自身发展提供坚实的理论基础,同时对关联行业的技术发展和经济决策同样具有十分重要的借鉴意义.百度、阿里巴巴、腾讯(BAT)作为互联网行业无可争议的三巨头,其专利技术和资本存量占据整个市场的绝大部分,因此其技术发展历程具有很强的代表性,但现有研究鲜有针对其专利创新路径、知识图谱以及内外部合作网络的研究和探讨.通过收集处理中国国家知识产权局提供的关于三家企业的专利申请和专利授权数据,详细分析了三家企业的专利持有量、知识深度和广度以及企业内外部合作网络的形态和结构,以期揭示互联网技术的先进发展模式和未来趋势,为相关企业的发展和决策提供依据.

PurposeThis study aims to clarify the effect of team effort allocation between knowledge exploration and exploitation on the generation of extremely good or poor innovations. The influence of previous collaborative experience among team members on the effect of team effort allocation is also investigated to understand the relationship between team members’ collaboration networks and knowledge learning.Design/methodology/approachThis study uses data of all patents granted by the US Patent and Trademark Office between 1984 and 2010. The inventors involved in a patent are regarded as members of the focal team. Logistic regression is used to analyze the data.FindingsAllocating greater effort to exploration than to exploitation is beneficial to achieving breakthrough innovations despite the risk of generating particularly poor innovations. This benefit increases with collaborative experience among team members. Placing an equal emphasis on knowledge exploration and exploitation is not particularly effective in achieving breakthrough innovations; it is, however, the best strategy for avoiding particularly poor innovations.Originality/valueThis research not only provides valuable insights for research on innovation and knowledge management by studying the team effort allocation strategy used to achieve breakthroughs and avoid particularly poor innovations but also represents an advancement in bridging two streams of research – knowledge learning and social networks – by highlighting the influence of the team members’ collaborative networks on the effect of team effort allocation between knowledge exploration and exploitation.

The literature on innovation have made great effort on understanding the connotation and the contingent effect of structural hole.However,the contingent effect of structure hole on innovation performance of inventors possessing differ-ent level of knowledge heterogeneity and embedness centrality are lacking in theoretical and empirical studies.Understand-ing the contingent mechanism of structural hole is critical for inventors to adj ust their network strategy properly and pro-mote efficiency of taking advantage of structure hole.This study uses Chinese patent data of 5 7 enterprises in electronic in-dustry to construct collaboration network and test hypothesis.On the basis of analyzing the effect of knowledge heteroge-neity and coreness in whole network on innovative performance,the moderating effect of both variables on the relationship between structure hole and innovative performance are investigated.The significant results show that knowledge heteroge-neity and coreness in core/periphery network both have positive influence to the effect of structure hole on innovative per-formance.It suggests that the relationship between the own advantages and external resource advantages resulting from structural hole are not substitutive for each other.On the contrary,the improvement of the own advantages is the premise of utilizing structural hole efficiently.Moreover,integrating structural holes lying in core network is more beneficial.The theoretical significance and practical advice are given in the end.