Abstract
E-commerce penetration rates are distant among those groups of individuals with the lowest and the highest levels of online shopping adoption. This is an indicator of digital-divide, having negative effects in terms of untapped opportunities for people, companies, and the whole economy. Key socioeconomic and demographic determinants of adoption of e-commerce are explored, analyzing a dataset of 174,776 observations for the period 2008–2017 in Spain. The empirical analysis is based on a standard neoclassical utility maximization framework. Linear probability model, logistic regression, and Heckman’s sample selection correction model have been used. The results suggest that e-commerce adoption is positively related with being male, having higher levels of education, income and digital skills, being Spanish, and being employed; while being female, older, and belonging to a household of two or more members have negative effects. An interaction between digital skills and age has been introduced in the model, where high digital skills seem to have a positive influence, partly counteracting the lower odds for some age groups. Policy recommendations related to demand and supply measures are suggested to foster the adoption of e-commerce.
This research is funded by the Autonomous Community of Madrid—Spain. Project: Finance, Innovation and Strategies: Economic Analysis of Business Productivity and its Determinants (PRODECON-CM HM S2015/HUM-3491), 2016–2018.
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Notes
- 1.
Fixed broadband, mobile broadband, and prices.
- 2.
Internet use, basic and advanced digital skills.
- 3.
Citizens’ use of content, communication, and online transactions.
- 4.
Business digitization and e-commerce.
- 5.
eGovernment and eHealth.
- 6.
EU-28: Member States of the European Union.
- 7.
Having Spanish nationality or not.
- 8.
For comparability with the data published by EUROSTAT, INES’s ICT survey results are referred to households with at least one person between 16 and 74 years old.
- 9.
DESI’s medium-performing countries are Spain, Austria, Lithuania, Germany, Slovenia, Portugal, Czech Republic, France, and Latvia.
- 10.
Small increases of penetration rates are observed in 2018. Households with broadband connection reached up to 86.1%, matching internet use. E-commerce adoption has also increased up to 43.5%.
- 11.
All comparisons assessed in absolute values show greater distances. On the other hand, relative distances (the difference between the lowest and the highest penetration rate, as a percentage of the highest penetration rate) show a rapid growth in e-commerce adoption, but slow narrowing of the digital-divide.
- 12.
From 2008 to 2017 used questionnaires have included between 50 and 70 questions, many of them with several possible answers, meaning more than 200 variables, depending on the composition of the questionnaires for each year.
- 13.
Digital Skills is a self-elaborated index, based on the answers where the respondent declares whether he or she used specific internet services and/or performed specific computer and internet-related tasks.
- 14.
All three models are estimated using Stata 15.
- 15.
Base categories for each variable have been set as follow: Gender, Male; Age, 16–24 years old; Education, Primary or less; Digital Skills, Low; Habitat, lower than 20,000 inhabitants; Household Members, one member; Nationality, Foreigner; Employment Situation, Employed; Income, Low; Year, 2008; and Autonomous Community, Andalucía. The interaction term “Digital Skills × Age” uses the same base categories already fixed for interacted explanatory variables.
- 16.
Complete estimates for the interactive terms are show in the Appendix, Table 4.5.
- 17.
If it is needed to be analyzed the other way around: Interaction term allows us to infer how the effect of Digital Skills on the e-commerce adoption depends on the magnitude of Age.
- 18.
- 19.
Stata–Heckman selection model: “Heckman” command and “maximum likelihood estimates” option (which allows to include the sample elevation factor for all observations, as well as compute robust standard errors.).
- 20.
Estimation results of first stage of HSM: Appendix, Table 4.7.
- 21.
- 22.
A pure play e-commerce is an e-commerce business that only sell through the internet.
- 23.
A good example are the physical stores of the third largest Spanish e-commerce firm, Pcomponentes.com, where customers and prospective customers get advice and guide for later purchasing online.
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An earlier version of this paper was presented at the 29th ITS European Conference, Trento—Italy, 2018.
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Appendix
Appendix
See Tables 4.5, 4.6 and 4.7 and Figs. 4.7 and 4.8.
(Source Authors’ creation from estimation results, Table 4.2)
Odds ratios of e-commerce. Pool (2008–2017)
(Source Authors’ creation from estimation results, Table 4.2)
Odds of e-commerce. Pool (2008–2017)
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Valarezo, Á., López, R., Pérez Amaral, T. (2020). Adoption of E-Commerce by Individuals and Digital-Divide. In: Alleman, J., Rappoport, P., Hamoudia, M. (eds) Applied Economics in the Digital Era. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40601-1_4
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