‘Smart building’ technology network analysis: applying core–periphery structure analysis

Madani, Farshad; Daim, Tuğrul U.; Weng, Calvin S.

doi:10.1080/17509653.2015.1100525

Cited by 16 publications

(10 citation statements)

References 49 publications

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“…However, this approach presents several limitations. First, patents focus on technological solutions (Madani, Daim, and Weng 2017), providing a limited snapshot of possible avenues for value creation. Although it is possible to identify both drawbacks and advantages of technologies or products using patents (Chiarello, Fantoni, and Bonaccorsi 2017), it is highly unlikely to find problems and solutions related to policy and legal issues, consumer interactions, skills and knowledge requirements, or new business models.…”

Section: Problems In Patents and Papersmentioning

confidence: 99%

“…Previous approaches to this issue have mainly followed two avenues. On the one hand, quantitative patent analyses (Madani, Daim, and Weng 2017) or text-mining in patents (Chiarello, Fantoni, and Bonaccorsi 2017) have been used to map competitor's research agendas, identify core and peripheral patents, and detect unsolved technical limits. On the other hand, literature reviews of scientific papers provide a broader overview of potential problems at a given moment in time, but require skilled personnel, are time-consuming and often lack in reproducibility (Snyder 2019).…”

Section: Introductionmentioning

confidence: 99%

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Value creation in emerging technologies through text mining: the case of blockchain

Chiarello

Belingheri

Bonaccorsi

et al. 2021

Technology Analysis & Strategic Management

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As technology progresses, organisations must understand where to direct their value-creating efforts to achieve or sustain competitive advantage. This is even more true in the case of emerging technologies, where innovative activities often focus on achieving a technology promise while overlooking a set of technological, operational, organisational and user-related problems that must be overcome before the technology can fulfil this promise. Through an innovative application of textmining, this paper develops a practical methodology to identify a range of problems related to a technological field in an unsupervised manner, that may benefit firms, researchers and policymakers. We apply the methodology to the field of blockchain and compare it to traditional literature reviews.

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Section: Problems In Patents and Papersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Value creation in emerging technologies through text mining: the case of blockchain

Chiarello

Belingheri

Bonaccorsi

et al. 2021

Technology Analysis & Strategic Management

View full text Add to dashboard Cite

show abstract

“…Published papers have also been specifically used as intelligence units (Garces et al , 2017; Cho and Daim, 2016). Patents have been one of the most preferred intelligence unit in the literature (Pereira et al , 2019; Daim et al , 2020; Li et al , 2019; Lin et al , 2019; Madani et al , 2017, 2018; Daim et al , 2018 and Gibson et al , 2017).…”

Section: Mapping Knowledge To Identify Future Innovation Trajectories: Cases In Emerging Technological Innovationsmentioning

confidence: 99%

Guest editorial

Daim

Dabić

Garces

2021

JKM

Self Cite

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Despite widespread academic literature on creativity, future innovation trajectories and knowledge management, these separate areas have rarely been examined together. Studies from different theoretical fields assess innovation and knowledge management as a dynamic process that is established over time. The assessment and evaluation of technologies is particularly critical for technology-driven organizations. Nowadays, this is even more important, as virtually no organizations are exempt from this challenge. Over the past decade, we have seen an exponential increase in studies seeking to develop methods and tools with which to address this challenge as organizations have heterogeneous innovative behaviors and drivers of apiece trajectory. Therefore, the exchange of knowledge is crucial to practical technological applications and implementations in the field of knowledge management. This special issue, titled "Mapping Knowledge to Identify Future Innovation Trajectories: Cases in Emerging Technological Innovations," aims to fill these gaps in scholarly literature, focusing on different kinds of knowledge management and identifying future innovation trajectories that may support the field's development. This research establishes the links forged between knowledge management, innovation and technology and goes on to forecast technologies; identify technologies, actors and opportunities; visualize research fields and technological landscapes; explain the dynamic interactions between different patterns; and identify the flow of technology and the key elements of a successful technological roadmap. As a result of COVID-19, many companies have been forced to rapidly prioritize new systems and embrace new challenges, making swift decisions, improving employee efficiency and risking the continuity of their business.Prior research leveraged many different indicators to chart out the future innovation trajectories. We see some of these studies in the form of literature review (Fakhar Manesh

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“…Against this contextual backdrop, we develop a near-miss classifier based on a CNN, associate the classified results, and visualize them with a network diagram [ 12 ] and a Sankey diagram so that safety managers can easily find the key points of massive near misses [ 13 ]. First, to structure text data, a CNN-based classifier that incorporates a deep learning method is developed to generate structured classification results of near-miss information within safety records.…”

Section: Introductionmentioning

confidence: 99%

Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between “locations” and “types” by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the “location ⟶ type” strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites.

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‘Smart building’ technology network analysis: applying core–periphery structure analysis

Cited by 16 publications

References 49 publications

Value creation in emerging technologies through text mining: the case of blockchain

Value creation in emerging technologies through text mining: the case of blockchain

Guest editorial

Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification

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