Trust Rhythm in Human and AI Collaboration for Digital Marketing in Nonprofit and Service Organizations

Abstract

This paper builds a conceptual model that explains how artificial intelligence changes the temporal structure of trust in a brand and defines the threshold beyond which automation erodes trust instead of supporting it. The study applies framework synthesis to peer-reviewed literature published from 2018 onward, supplemented by four earlier works whose definitions and instruments later studies reuse. Antecedents of trust are coded into four operational domains, namely transparency, authenticity, timeliness and human accountability, and converted into six propositions with explicit falsification conditions. A simulation on synthetic parameters tests the internal consistency of the model. Trust under machine mediation depends on three parameters of communication rhythm: frequency, amplitude and predictability. Artificial intelligence raises frequency and predictability because it removes the dependence of regular communication on staff availability. It lowers amplitude because content produced in volume without organizational substance carries no verifiable information. The simulated trust index reaches an interior maximum, after which further automation pushes it below its unautomated level. Disclosure of machine use is not the decisive variable. The decisive variable is whether the audience can read human authorship behind the message. The paper contributes a matrix for allocating marketing tasks between people and machines, indicators for monitoring communication rhythm, and a disclosure rule that reports the configuration of human control rather than the bare fact of technology use. The model is theoretical, its evidence base is concentrated in commercial settings, and the nonprofit case is therefore a boundary condition awaiting longitudinal and experimental testing.