by Dr Venkataraman Balaji
Vice President, COL
Search is possibly the most widely used function on the Internet. For a typical user, a search engine and the web are often indistinguishable. In our own context, use and adaptation of open educational resources (OER) presuppose the ability to search for or locate OER for which the search engine is a must.
Google is the most widely used search engine with a share of about 91 per cent of global searches as of early 2022. Bing, owned by Microsoft, has a much smaller share. There are many engines based with geographic specificity or serving particular languages. Baidu is a well known example in Chinese, with a near total command over the market. Russia-based Yandex is also used extensively. Qwant, a service operating from France, is estimated to have about 50 million users. Japan and Korea also have search engines in national languages that are extensively used. There are also meta search engines such as Metacrawler or Dogpile that trigger searches in other search engines when users query them.
The essential business model of popular search engines is to gather user’s personal data to generate a revenue stream for the company, while the user is presented with results free of cost. Advertising is the main source of revenue. The results are ‘personalised’ for the user with advertisements inserted in search results. When a browser and a search engine are tightly integrated, the company obtains access to a wider range of personal data, therefore even higher revenue.
Until the advent of General Data Protection Regulation (GDPR) in the European Union (EU) in 2018, users were not given many options to stop involuntary collection of personal data on the web by free services. With GDPR, search service providers and browser designers have been obliged to restrict data collection only to contexts where the user provides informed consent. This can produce the unintended consequence of reducing potential revenues for search engine providers. (Of note, Google’s year-on-year revenue declined by 36 per cent as of October 2022, while the cost of acquiring traffic from users increased by about 10 per cent).
Herein lies the context where alternative search engines are emerging. One example is DuckDuckGo (DDG) — a USA-based search engine that, by default, disavows collection and use of personal data from the user. Thus, explicit consent by the user is necessary for DDG to collect personal data. It is increasing in popularity because its results are presented well with reasonable relevance.
Presearch, a Canada-based search engine, offers another business model. It does not, in turn, collect personal data but encourages users to create accounts for browsing. Personal data, so collected, is used to deliver personalised results. Reviewers have offered good ratings for this service. As a departure from the traditional business model, this service offers cryptocurrencies to the users offering them, in effect, a share of the revenue generated from the deployment of personal data.
While new models may emerge, there is an increase in the probability of some search functions becoming paid services. A number of site-specific search services already require a subscription. Algolia, an efficient engine for site searches, is an example. It is deployed on COL’s website to facilitate ease-of-search without collecting personal data.
Given the changes in regulatory approaches and the reductions in revenues of major search engine providers, advanced personalised services may become paid services in the near term. And as a result, this may have an impact on the ability of individuals and institutions in developing countries to search for and locate OER.
