Skip to content

Toward an integrated framework of media dependency: a theoretical review of uses–dependency and new media dependency models

Sep 2026 · Frontiers in Communication · Vol 11 · 45 references

Abstract

This theoretical review brings together and explores the evolution and real-world applications of three key frameworks in media dependency theory: the classic Uses and Dependency Model, adaptations for Internet use, and the New Media Uses and Dependency Effect Model. Over the past fifty years, scholars have expanded dependency theory from its original focus on the effects of mass media in traditional broadcast systems to address the increasingly interactive, accessible, and habitual nature of digital media. The foundational model explains how media influence arises from purposeful dependency relationships that connect audiences, media, and society, with audience goals centering on understanding, guidance, and entertainment. As digital technology transformed the media landscape, newer models adapted these principles to capture the unique features of online platforms, such as interactivity, the breadth of information, and constant accessibility. These adaptations shifted attention from broad media systems to individual platforms and their specific qualities. Drawing on a transparent, systematically documented search of communication and interdisciplinary databases covering 1976 through 2026, the review synthesizes theoretical, empirical, and critical scholarship on media dependency and connects it to contemporary developments in platformization, algorithmic curation, recommender systems, AI-mediated communication, and platform governance. The latest model goes further by considering how routine media habits shape dependency, outlining pathways through which everyday use leads to changes in thoughts, feelings, and behaviors. Throughout these developments, all three frameworks maintain a focus on how people rely on media to achieve their goals, while incorporating new factors like platform features, perceived usefulness, and habitual patterns to improve how we measure and test media effects today. Beyond synthesis, the review advances an original theoretical contribution: the Integrated Media Dependency Framework (IMDF), a multi-level conceptual model—presented in a figure and articulated through seven testable propositions—that unites the goal structure of the foundational model, the platform-level specificity of Internet adaptations, the habit-mediation logic of the New Media model, and a newly theorized algorithmic-agent layer capturing dependency on curation systems and generative AI. The framework is explicitly positioned relative to Communication Infrastructure Theory, Media Repertoire Theory, and Affordance Theory. Its distinct contribution is stated explicitly: an adaptive algorithmic-agent stratum of dependency targets, a recursive behavioral–algorithmic feedback loop, a reconceptualization of dependency as jointly constituted by intensity and repertoire distribution, and the treatment of platform governance as a testable macro-level moderator. This review also highlights challenges in current research, identifies contradictory findings and unresolved debates, suggests areas for future study, and discusses how these models can be integrated with other communication theories to better understand media's role in the digital age.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

GPT-Lab Oct 12, 2026

AI Has Grown Up. Have We?

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.