Data Treatment, Consumer Profiling, and Privacy: Toward a Unified Conceptual Framework

Nasser Bouchareb; Ismail Morad; Yacine Herizi

doi:10.2478/joim-2026-0007

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Data Treatment, Consumer Profiling, and Privacy: Toward a Unified Conceptual Framework

Journal of Intercultural Management

Volume 18 (2026): Issue 2 (June 2026)

By: Nasser Bouchareb, Ismail Morad and Yacine Herizi

Open Access

|Sep 2026

Abstract

Objective

To develop a unified conceptual framework explaining how AI-driven data treatment (collecting, integrating, transforming, and inferring from consumer traces) produces consumer profiles, and how those profiles translate into distinct privacy impacts, while showing where governance controls can intervene.

Methodology

A conceptual, theory-building approach that synthesizes insights across marketing, information systems, and privacy/AI governance literature to model privacy risk as a mechanism chain: data treatment dimensions, profiling intensity, privacy impacts, with governance operating as a moderating layer across the chain.

Findings

The paper identifies a small set of structural dimensions that largely determine privacy risk: the extent to which data are linkable across contexts, the depth of inference, the persistence of representations over time, and the degree of coupling to consequential actions. These dimensions shape profiling intensity (granularity, profile stability, automation, actionability) and generate multiple harm pathways– informational, autonomy-related, distributive/fairness-related, and security-related – whose severity increases when systems are opaque and hard to contest.

Value Added

The paper offers a parsimonious vocabulary that connects upstream data practices to downstream harms via profiling intensity, helping researchers and practitioners move beyond narrow “consent/anonymization” framings and better diagnose why privacy harm can occur even when data are legally obtained or “de-identified”.

Recommendations

Firms should limit high-risk profiling by reducing inference depth, cross-context linkage, and persistence, and by making consequential profiles transparent and contestable; regulators should focus on enforceable rules for transparency/contestability and restrict high-impact profiling; researchers should operationalize key constructs (e.g., inference depth, persistence, actionability) and test the framework empirically across sectors.

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Articles in this issue

DOI: https://doi.org/10.2478/joim-2026-0007 | Journal eISSN: 2543-831X (formerly 2080-0150) | Journal ISSN: 2080-0150

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Language: English

Page range: 66 - 90

Published on: Sep 9, 2026

Published by: SAN University

In partnership with: Paradigm Publishing Services

Keywords:

contextual integrity,

algorithmic decision-making,

digital marketing

JEL:

M31,

L86,

D83

Related subjects:

Business and economics,

Business management,

Management, organization, corporate governance

© 2026 Nasser Bouchareb, Ismail Morad, Yacine Herizi, published by SAN University
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.

Volume 18 (2026): Issue 2 (June 2026)