Quantitative Analysis Of Ai-Driven Systemic Bias In Customer Experience

Trust, system-drivenpersonalization, user autonomy, andcustomerengagement emerge as the four central influencing factors due to technological advances inAIand AgenticAI. Methodologically, CX research has evolved from survey-based models to structural equation modeling, sentimentanalysis, andAI-integrated frameworks.

These remedies are vital for mitigatingbias, and more work remains. Yet, as illus-trated in Fig. 1, human andsystemicin-stitutional and societal factors are sig-nificant sources ofAIbiasas well, and are currently overlooked. Successfully meeting this challenge will require tak-ing all forms ofbiasinto account.

This particular example perfectly highlights why Quantitative Analysis Of Ai-Driven Systemic Bias In Customer Experience is so captivating.

This article provides a synthesis based on anin-depthanalysisofeleven empirical studies published as part of a special issue of Computers in Human Behavior, entitled "Using artificial intelligence to enhancecustomerexperienceand to develop strategic marketing."

Although this relationship has long been examined in the research domains of consumer behavior andcustomerrelationship management, the newAI- enabledcustomerjourney has been reshaped by data-drivenco- creation and predictive analytics, which enhance personalization and real- time engagement to unprecedented levels.

By employing these two dataanalysissteps—aquantitativesystematic review approach and thematic analysis—we aim to uncover theoretical underpinnings, identify key concepts and themes, and organizeAIcategories into distinct levels.

Artificial intelligence (AI) is transformingcustomerexperiencemanagement (CXM) by enabling real-time, data-driven, and personalized interactions across digital touchpoints, including chatbots, voice assistants, generativeAI, and immersive platforms. This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining howAI...

This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining howAI-enabled personalization, privacy concerns, and ...