Use of web mining in studying innovation.
Use of web mining in studying innovation.
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DOI：
10.1007/s11192-014-1434-0
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发表时间：
2015
期刊：
影响因子：
3.9
通讯作者：
Shapira P
中科院分区：
文献类型：
--
作者：
Gök A;Waterworth A;Shapira P
随着企业在其网站上扩展并发布越来越多关于其业务活动的信息，网站数据有望成为调查创新的宝贵来源。本文探讨了网络挖掘作为创新研究的一种研究方法的实用性和有效性。我们使用Web挖掘来探索296家英国绿色产品中小企业的研发活动。我们发现，与其他传统的不引人注目的研究方法（如专利和出版物分析）相比，网站数据提供了额外的见解。我们研究企业创新Web挖掘的优势和局限性，在广泛的数据质量方面，包括准确性，完整性，货币，数量，灵活性和可访问性。我们观察到，在我们的样本报告中，更多的公司在其网站上进行研发活动，而不是只看传统的数据来源。传统方法通过出版物和专利提供有关研发和发明早期阶段的信息，而网络挖掘提供的见解在创新过程中更为下游。处理网站数据不像其他数据源那么容易，在执行搜索策略时需要小心。网站信息也是自我报告的，公司在网站上发布（或不发布）有关其活动的信息的动机可能各不相同。尽管如此，我们发现，网络挖掘是一个重要的和有用的补充，目前的方法，以及提供新的见解不容易从其他不显眼的来源。
As enterprises expand and post increasing information about their business activities on their websites, website data promises to be a valuable source for investigating innovation. This article examines the practicalities and effectiveness of web mining as a research method for innovation studies. We use web mining to explore the R&D activities of 296 UK-based green goods small and mid-size enterprises. We find that website data offers additional insights when compared with other traditional unobtrusive research methods, such as patent and publication analysis. We examine the strengths and limitations of enterprise innovation web mining in terms of a wide range of data quality dimensions, including accuracy, completeness, currency, quantity, flexibility and accessibility. We observe that far more companies in our sample report undertaking R&D activities on their web sites than would be suggested by looking only at conventional data sources. While traditional methods offer information about the early phases of R&D and invention through publications and patents, web mining offers insights that are more downstream in the innovation process. Handling website data is not as easy as alternative data sources, and care needs to be taken in executing search strategies. Website information is also self-reported and companies may vary in their motivations for posting (or not posting) information about their activities on websites. Nonetheless, we find that web mining is a significant and useful complement to current methods, as well as offering novel insights not easily obtained from other unobtrusive sources.