Firms that combine AI-driven predictive marketing with consent, explainability and bias-audit routines enjoy greater customer trust, innovation readiness and competitive advantage; however, perceived manipulative personalization and concentrated data power erode these gains.

The Impact of AI-Driven Predictive Marketing on Ethical Perceptions and Strategic Business Outcomes

Rianda Ridho Hafizh Thaha, Abdul Razak Munir, Thenmozly Pandurengan · February 21, 2026 · Hasanuddin Economics and Business Review

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Stronger AI-driven predictive-marketing capability is associated with more favorable ethical perceptions, which in turn link to higher brand trust, innovation readiness, competitive advantage, and self-reported performance, with governance practices strengthening and perceived manipulation/market concentration weakening these relationships.

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AI-driven predictive marketing promises superior targeting, personalization, and decision speed, yet its strategic payoffs depend on how customers and managers judge the ethics of its use. This study examines whether and how capability in AI-powered predictive marketing improves strategic business outcomes by shaping ethical perceptions in privacy and consent, transparency and explainability, and fairness and non-discrimination. Drawing on Resource-Advantage theory, we propose and test a model in firms from Makassar, Indonesia, spanning creative industries, financial services, food and beverage, and technology. Using Partial Least Squares Structural Equation Modeling with higher-order constructs, we assess direct, indirect, and conditional effects, including mediation by governance quality and moderation by perceived manipulation and perceived market concentration or data dominance. The estimates show that stronger AI-PM capability is associated with more favorable ethical perceptions, and these perceptions relate positively to brand trust and credibility, innovation readiness, competitive advantage, and performance. Governance practices, consent management, bias audits across pre-, in-, and post-processing, and explainability routines, act as the primary mechanism strengthening ethical perceptions and outcomes. Conversely, perceived manipulative design weakens capability–outcome links, and perceptions of market concentration reduce the ethical appraisal of personalization efforts. The findings position ethics-by-design as a market-based resource that renders data and algorithmic investments more legitimate and defensible over time. Managerially, firms should pair analytics stacks with governance stacks and invest in complementary IT and organizational readiness, while policymakers can enhance contestability and transparency to preserve choice and fairness in data-intensive markets.

Summary

Main Finding

AI-driven predictive marketing (AI-PM) capability improves firm strategic outcomes (brand trust/credibility, innovation readiness, competitive advantage, performance) largely by shaping favourable ethical perceptions (privacy & consent, transparency & explainability, fairness & non-discrimination). Governance quality (consent management, bias audits across pre/in/post processing, explainability routines) is the primary mechanism linking capability to ethical perceptions and outcomes. Perceived manipulative design weakens these benefits, and perceived market concentration/data dominance undermines ethical appraisals of personalization. Ethics-by-design functions as a market-based resource that legitimizes data/algorithm investments.

Key Points

Theoretical framing: Resource-Advantage (R-A) theory — ethics and governance operate as market resources that sustain competitive advantage by preserving legitimacy and trust.
Ethical perceptions are multi-dimensional: privacy & consent; transparency & explainability; fairness & non-discrimination (plus perceived non-manipulation as a boundary concern).
Governance quality (organizational routines for consent, transparency, bias mitigation, oversight) mediates the effect of AI-PM capability on ethical perceptions.
AI-PM capability → stronger governance → more favourable ethical perceptions → better strategic outcomes (H1–H7 tested).
Moderation effects:
- Perceived manipulative design attenuates the capability → outcome linkage.
- Perceived market concentration / data dominance weakens the capability → ethical perceptions link.
Managerial takeaways: pair analytics stacks with governance stacks; invest in complementary IT and organizational readiness; operationalize bias audits and explainability routines.
Policy takeaway: preserve contestability and transparency in data-intensive markets to protect fairness and choice, especially in emerging-market contexts.

Data & Methods

Setting: Firms in Makassar, Indonesia, across creative industries, financial services, food & beverage, and technology (SME and platform ecosystem).
Empirical approach: Partial Least Squares Structural Equation Modeling (PLS-SEM) using higher-order constructs.
Effects estimated: direct, indirect (mediation by governance quality), and conditional (moderation by perceived manipulation and perceived market concentration/data dominance).
Constructs/measures: AI-PM capability (micro-segmentation, personalization, CLV/prediction, adaptive optimisation); governance quality (consent management, audits, oversight, training); ethical perceptions (privacy/consent, transparency/explainability, fairness/non-discrimination, non-manipulation); strategic outcomes (brand trust/credibility, innovation capability/readiness, competitive advantage, performance).
Note: the paper focuses on managerially interpretable operational practices (consent routines, bias-mitigation across pre/in/post-processing, explainability routines) rather than purely prescriptive technical fixes.

Implications for AI Economics

Ethics-as-resource: Ethical governance and legitimacy are economic resources that affect the returns to data and algorithm investments; firms should internalize governance costs as part of capability valuation because they influence market acceptance, regulatory exposure, and durable advantage.
Investment and capability strategy: Firms that underinvest in governance face both reputational risk and lower conversion of AI-PM capability into measurable performance; cost–benefit analyses of analytics projects should include governance/legitimacy effects and potential dilution from perceived manipulation.
Market structure and competition: Data/network concentration not only affects competition directly (scale advantages, entry barriers) but also depresses ethical perceptions of personalization—potentially reducing consumer welfare and increasing political/regulatory risk. Policies that improve contestability and transparency can preserve the social value of personalization and reduce negative externalities.
Trade-offs for efficiency vs. explainability: The paper empirically underscores the practical trade-offs managers face (accuracy vs explainability, automation vs oversight). Economic models of AI adoption should incorporate these trade-offs and their feedback into demand, trust, and long-run performance.
Policy design: Regulatory interventions (consent standards, mandated disclosures, auditability, gatekeeper rules) can materially change firms’ returns to AI-PM by shifting how ethical perceptions mediate market responses; in emerging markets, enhancing institutional capacity and contestability can be especially impactful.
Research suggestions for AI economics: incorporate governance and ethical-perception channels into models of AI-driven productivity and competition; quantify how governance investments alter the effective depreciation or durability of data/algorithmic assets in firm-level production functions.

Assessment

Paper Typecorrelational Evidence Strengthlow — All results are based on cross-sectional, self-reported survey data from firms in a single city, so estimated relationships are associative and vulnerable to reverse causation, omitted variables, and common-method bias; mediation and moderation tests are informative but not causal. Methods Rigormedium — The study applies established multivariate techniques (PLS-SEM) with higher-order constructs and examines mediation and moderation, which is appropriate for theory testing with survey data; however, PLS-SEM has limitations relative to causal identification, and the paper appears to lack longitudinal data, experimental/quasi-experimental variation, and extensive robustness checks that would strengthen causal claims. SampleFirm-level survey of organizations in Makassar, Indonesia across creative industries, financial services, food & beverage, and technology sectors (manager/firm respondents); cross-sectional, self-reported measures of AI predictive-marketing capability, ethical perceptions (privacy/consent, transparency/explainability, fairness), governance practices, perceived manipulation and market concentration, and firm outcomes. Themesadoption governance innovation productivity IdentificationCross-sectional firm-level survey analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using higher-order constructs to estimate direct, mediated, and moderated associations between AI predictive-marketing capability, ethical perceptions, governance practices, and firm outcomes; no exogenous variation or quasi-experimental design—identification relies on model specification, measurement validity, and control variables. GeneralizabilitySingle-city (Makassar) and single-country (Indonesia) sample limits external validity to other national/regulatory contexts, Sector mix may not reflect national or global industry distributions; results may differ in heavy-tech or B2B settings, Cross-sectional, self-reported measures may not generalize to objective performance metrics or longitudinal outcomes, Cultural norms about privacy, consent, and trust in Indonesia may affect ethical perceptions differently than in Western markets, Likely over-representation of firms already experimenting with AI-PM (selection bias), limiting applicability to non-adopters

Claims (10)

Claim	Direction	Outcome	Confidence & Evidence	Details
Stronger AI-PM capability is associated with more favorable ethical perceptions (privacy and consent, transparency and explainability, fairness and non-discrimination). Ai Safety And Ethics	positive	ethical perceptions (privacy & consent; transparency & explainability; fairness & non-discrimination)	Reading fidelity high Study strength medium	not reported 0.3
Ethical perceptions relate positively to brand trust and credibility. Consumer Welfare	positive	brand trust and credibility	Reading fidelity high Study strength medium	not reported 0.3
Ethical perceptions relate positively to innovation readiness. Innovation Output	positive	innovation readiness	Reading fidelity high Study strength medium	not reported 0.3
Ethical perceptions relate positively to competitive advantage and firm performance. Firm Productivity	positive	competitive advantage and firm performance	Reading fidelity high Study strength medium	not reported 0.3
Governance practices (consent management, bias audits across pre-/in-/post-processing, explainability routines) act as the primary mechanism strengthening ethical perceptions and outcomes (mediation). Governance And Regulation	positive	ethical perceptions and related outcomes (brand trust, innovation readiness, competitive advantage, performance) via governance-mediated pathways	Reading fidelity high Study strength medium	not reported 0.3
Perceived manipulative design weakens capability–outcome links (negative moderation of AI-PM capability effects). Ai Safety And Ethics	negative	strength of links from AI-PM capability to ethical perceptions and downstream outcomes	Reading fidelity high Study strength medium	not reported 0.3
Perceptions of market concentration or data dominance reduce the ethical appraisal of personalization efforts. Ai Safety And Ethics	negative	ethical appraisal of personalization efforts	Reading fidelity high Study strength medium	not reported 0.3
Ethics-by-design functions as a market-based resource that renders data and algorithmic investments more legitimate and defensible over time. Governance And Regulation	positive	legitimacy and defensibility of data/algorithmic investments (conceptual claim)	Reading fidelity medium Study strength speculative	not reported 0.03
Managerially, firms should pair analytics stacks with governance stacks and invest in complementary IT and organizational readiness to realize ethical and strategic payoffs. Organizational Efficiency	positive	organizational readiness and the realization of ethical/strategic payoffs	Reading fidelity high Study strength speculative	not reported 0.05
Policymakers can enhance contestability and transparency to preserve choice and fairness in data-intensive markets. Governance And Regulation	positive	contestability, transparency, choice and fairness in data-intensive markets (policy recommendation)	Reading fidelity medium Study strength speculative	not reported 0.03