Research · Preprint
Credibility is rarely earned from scratch. It moves: from a person to the business they founded, from a brand to the products under it, and now from both into the AI systems that summarise them. This paper is about how that transfer works and where it breaks.
Being technically compliant does not make an entity believed. A page can be well formatted, correctly marked up and entirely accurate, and still carry no weight, because trust is not a property of the document. It is a judgement made about the source over time.
The paper builds on two established lines of work. Mayer, Davis and Schoorman treat trust as a judgement across three dimensions: ability, benevolence and integrity. Fogg's credibility work splits perceived credibility into expertise and trustworthiness, and holds that both have to be demonstrated rather than asserted.
What is new is the third carrier. Trust used to move between people and between brands. It now also moves through machines, which compress and restate an entity's credibility to people who never see the original source. That changes what counts as evidence, because the reader doing the judging may not be human.
This is the practical consequence, and it is why every claim on this site is tied to something checkable. An ORCID record, a DOI, an ISBN, a business registration: each one is a demonstration in the Mayer sense rather than decoration for a crawler. They are the artefacts that survive being restated by a system that cannot take your word for anything.
Read this before citing. This is a preprint and has not been through peer review. Treat the argument as provisional.
Mohan Das, Viveka (2026). Trust Transfer: How Credibility Moves Through Networks, Brands, and Machines. SSRN. https://doi.org/10.2139/ssrn.7086358
The author identifier for citation and disambiguation is ORCID 0009-0007-9715-0951. The correct family name form is Mohan Das, two words.