Abstract:The purpose of this paper is to propose an intelligent system to automatically monitor the firms' engagement in e-commerce by analyzing online data retrieved from their corporate websites. The design of the proposed system combines web content mining and scraping techniques with learning methods for Big Data. Corporate websites are scraped to extract more than 150 features related to the e-commerce adoption, such as the presence of some keywords or a private area. Then, these features are taken as input by a c… Show more
“…Now the government is advancing in enhancing education attainment levels. Government investment in vocational programmers and universities will improve the labor market over the long term [35] . The second response was 60.7 % that digital literacy and language barriers are other challenges that the government is facing on e-commerce adoption in Ethiopia.…”
Section: Data Analysis and Resultsmentioning
confidence: 99%
“…The concept of sharing the performance of their website with rivals does not appeal to website owners. While several tools exist to predict the visitor numbers a website receives, only a few software solutions exist to evaluate the conversion rate of any website [46] . Fig.…”
“…Now the government is advancing in enhancing education attainment levels. Government investment in vocational programmers and universities will improve the labor market over the long term [35] . The second response was 60.7 % that digital literacy and language barriers are other challenges that the government is facing on e-commerce adoption in Ethiopia.…”
Section: Data Analysis and Resultsmentioning
confidence: 99%
“…The concept of sharing the performance of their website with rivals does not appeal to website owners. While several tools exist to predict the visitor numbers a website receives, only a few software solutions exist to evaluate the conversion rate of any website [46] . Fig.…”
“…The complete list of the keywords we used to describe each function can be found in Appendix A. Similar methods for classifying documents were successfully validated in previous works [13,45,46].…”
Public policies have encouraged the proliferation of technology platforms that support the transition towards sustainable agriculture and the development of innovations in the food system. Provided the difficulty associated with assessing the outputs and outcomes of technology platforms, this work proposes a practical assessment method based on the retrieval and analysis of online documents related to the technology platforms. Concretely, the method consists of applying web scraping techniques to retrieve documents related to a technology platform from the Internet and then applying web data-mining techniques to automatically classify these documents into the functions that the platform should fulfill, which are described from the viewpoint of co-evolution of innovation. Data are automatically processed to obtain a variety of metrics, which are applied to measure the impact of European Technology Platforms (ETPs) on promoting an organic food paradigm. This method provides time-series data that helps to follow the evolution of the different functions of the platform and to describe its lifecycle. It has been applied to one platform taken as a case study, TP Organics, which represents a key platform for stakeholders that promote organic farming and agroecology as core components of an ambitious program for sustainable agriculture. The obtained online-based measures have been proven to assess the global evolution of the platform, its dissemination through the European Union (EU) Member States, and the evolution of the different functions expected to be fulfilled by it regarding the diffusion and promotion of innovations in organic agriculture.
“…In contrast with our fully data-driven work on firm level record linkage, our work on web-based e-commerce detection uses manually selected features based on expert knowledge, because it is easier to understand and implement. Moreover, we show that the accuracy of our approach is high, ruling out the need to implement highly advanced, data-driven web-based e-commerce detection algorithms that are currently cutting edge (Blazquez et al, 2018). We do use a (pretrained) machine learning model to find the website of a company on the basis of the legal company name.…”
Summary
The digital economy is a highly relevant item on the European Union's policy agenda. We focus on cross‐border Internet purchases, as part of the digital economy, the total value of which cannot be accurately estimated by using existing consumer survey approaches. In fact, they lead to a serious underestimation. To obtain an accurate estimate, we propose a three‐step data‐driven approach based on supply‐side data. For the first step, we develop a data‐driven generic method for firm level probabilistic record linkage of tax data and business registers. In the second step, we use machine learning to identify webshops based on website data. Then, in the third step, we implement recently developed bias correction techniques that have hitherto been overlooked by the machine learning community. Subsequently, we claim that our three‐step approach can be applied to any European Union member state, leading to more accurate estimates of cross‐border Internet purchases than those obtained by currently existing approaches. To justify the claim, we apply our approach to the Netherlands for the year 2016 and find an estimate that is six times as high as current estimates, having a standard deviation of 8%. Hence, we may conclude that our new approach deserves more investigation and applications.
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