arbiter
JournalsAccount

AI-Driven Phygitalization: Enhancing Customer and Employee Experience for Improved Well-Being in Financial Services

Claire Roederer et al.

Journal of Macromarketing2026https://doi.org/10.1177/02761467251415501article
AJG 2ABDC A
Weight
0.50

Abstract

Drawing on the phygital experience framework and integrated well-being dimensions, this paper conducts a systematic literature review of 75 conceptual and empirical studies across banking, insurance, and fintech. It addresses two research questions: What artificial intelligence (AI)-based enablers are used in financial services interactions from employee and customer perspectives? How are phygital experience constructs operationalized in the literature and linked to employee and customer well-being outcomes? The review aimed to identify how AI influences the phygital experience and how this subsequently affects employee and customer well-being. This paper extends existing frameworks, offers a typology of AI-based enablers, and proposes a research agenda. The findings show that AI increasingly acts as a third agent that shapes affective, cognitive, and relational outcomes in service ecosystems, thereby transforming customer and employee dynamics. Strategic recommendations are provided for marketers, managers, and researchers navigating the evolving financial services landscape.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1177/02761467251415501

Or copy a formatted citation

@article{claire2026,
  title        = {{AI-Driven Phygitalization: Enhancing Customer and Employee Experience for Improved Well-Being in Financial Services}},
  author       = {Claire Roederer et al.},
  journal      = {Journal of Macromarketing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/02761467251415501},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

AI-Driven Phygitalization: Enhancing Customer and Employee Experience for Improved Well-Being in Financial Services

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.