Project _3025 - Research into Potentially Harmful Online Architecture
key details
| Value | £50,000 |
|---|---|
| Status | complete |
| Category (CPV) | 73110000 |
| Deadline | 1 Dec 2023 |
| Contract start | 15 Jan 2024 |
| Contract end | 30 Apr 2024 |
| Procedure | selective |
| SME suitable | Yes |
| OCID | ocds-b5fd17-1fc3795d-20db-4749-8f13-b9647cc4455c |
Award
| Supplier | Value | Date | Status |
|---|---|---|---|
| Alma Economics Ltd | £49,500 | 8 Jan 2024 | active |
description
Research into Potentially Harmful Online Architecture
The Digital Markets, Competition and Consumers (DMCC) Bill is currently passing through the Houses of Parliament. The Bill aims to boost competition in digital markets and increase consumer choice whilst protecting consumers and businesses from unfair and illegal practices. To maximise the effectiveness of the Bill, the government is looking to extend the evidence base on the potential consumer harm that arises from certain practices used in digital markets in the UK. This Contract is an opportunity to contribute to this evidence base by undertaking a research project that will look to test the potential consumer harm arising from the use of 'default's on e-retail websites.
Defaults cover any situation where an e-retailer pre-chooses an option to present to consumers. In general, defaults can range from the automatic settings that websites and apps use when consumers visit the website or app to the products, add-ons, brands or subscription renewal options that are pre-selected when presented to them whilst navigating the website or app .
This Contract shall focus on the use of defaults that most directly relate to the quantity, quality and price of products and services that consumers may purchase on an website or app. It therefore shall focus on how products, product options, add-ons and brands are pre-selected and presented to consumers
notice history
1 notice published against this procurement.
| Published | Type | Regime | Notice |
|---|---|---|---|
| 17 Jan 2024 | Award (award) | · | 33f6738d-c0c0-4c8d-b1cd-03b9cfbffcfd-714701 |
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source
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