Monitoring E-commerce Adoption from Online Data

[EN] 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 classification model that includes dimensionality reduction techniques. The system is evaluated with a data set consisting of 426 corporate websites of firms based in France and Spain. The system successfully classified most of the firms into those that adopted e-commerce and those that did not, reaching a classification accuracy of 90.6%. This demonstrates the feasibility of monitoring e-commerce adoption from online data. Moreover, the proposed system represents a cost-effective alternative to surveys as method for collecting e-commerce information from companies, and is capable of providing more frequent information than surveys and avoids the non-response errors. This is the first research work to design and evaluate an intelligent system to automatically detect e-commerce engagement from online data. This proposal opens up the opportunity to monitor e-commerce adoption at a large scale, with highly granular information that otherwise would require every firm to complete a survey. In addition, it makes it possible to track the evolution of this activity in real time, so that governments and institutions could make informed decisions earlier.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness with Grant TIN2013-43913-R, and by the Spanish Ministry of Education with Grant FPU14/02386.Blazquez, D.; Domenech, J.; Gil, JA.; Pont Sanjuan, A. (2018). 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Automatic detection of e-commerce availability from web data

Resumen de la ponencia[EN] In the transition to the digital economy, the implementation of e-commerce strategies contributes to foster economic growth and obtain competitive advantages. Indeed, national and supranational statistics offices monitor the adoption of e-commerce solutions by conducting periodic surveys to businesses. However, the information about e-commerce adoption is often available online in each company corporate website, which is usually public and suitable for being automatically retrieved and processed.In this context, this work proposes and develops an intelligent system for automatically detecting and monitoring e-commerce availability by analyzing data retrieved from corporate websites. This system combines web scraping techniques with some learning methods for Big Data, and has been evaluated with a data set consisting of 426 corporate websites of manufacturing firms based in France and Spain.Results show that the proposed model reaches a classification precision of about 85% in the test set. A more detailed analysis evidences that websites with e-commerce tend to include some specific keywords and have a private area. Our proposal opens up the opportunity to monitor e-commerce adoption at a large scale, with highly granular information that otherwise would have required every firm to complete a survey.Blázquez Soriano, MD.; Domenech, J.; Gil, JA.; Pont Sanjuan, A. (2016). Automatic detection of e-commerce availability from web data. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 121-121. https://doi.org/10.4995/CARMA2016.2016.3603OCS12112

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No abstract available

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