TY - GEN
T1 - AI-Enhanced SEM Analysis
T2 - 3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023
AU - Singh, Gurinder
AU - Sao, Ameet
AU - Singh, Sakshee
AU - Hinchey, Mike
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The current study focuses on the integration of Artificial Intelligence (AI) within Electronic Customer Relationship Management (ECRM) systems in the ever-evolving landscape of financial services, particularly in the private banking sector of Delhi, India. It explores how AI-driven ECRM can affect the customer experience and satisfaction. The research, grounded in empirical data from 323 structured surveys across 23 bank branches, employs advanced AI analytical tools and ML algorithms taking Explanatory Factor Analysis (EF A), Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) as base to interpret customer feedback. It is concluded that AI-driven ECRM plays a pivotal role in shaping the customer experience, leading to higher satisfaction levels. The ML output confirms with strong significance that ECRM positively impacts customer experience (ß= 0.37, p=0.001) and satisfaction (ß = 0.43, p=0.001), while also establishing customer experience as a crucial intermediary between ECRM and overall customer satisfaction. This underscores the potential of AI in transforming customer relationship strategies and achieving a competitive edge in the financial sector through improved ECRM.
AB - The current study focuses on the integration of Artificial Intelligence (AI) within Electronic Customer Relationship Management (ECRM) systems in the ever-evolving landscape of financial services, particularly in the private banking sector of Delhi, India. It explores how AI-driven ECRM can affect the customer experience and satisfaction. The research, grounded in empirical data from 323 structured surveys across 23 bank branches, employs advanced AI analytical tools and ML algorithms taking Explanatory Factor Analysis (EF A), Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) as base to interpret customer feedback. It is concluded that AI-driven ECRM plays a pivotal role in shaping the customer experience, leading to higher satisfaction levels. The ML output confirms with strong significance that ECRM positively impacts customer experience (ß= 0.37, p=0.001) and satisfaction (ß = 0.43, p=0.001), while also establishing customer experience as a crucial intermediary between ECRM and overall customer satisfaction. This underscores the potential of AI in transforming customer relationship strategies and achieving a competitive edge in the financial sector through improved ECRM.
KW - (SEM)
KW - Artificial Intelligence
KW - business analytics
KW - Electronic Customer Relationship Management
KW - Structural Equation Modeling
UR - http://www.scopus.com/inward/record.url?scp=85185385782&partnerID=8YFLogxK
U2 - 10.1109/ICTACS59847.2023.10390044
DO - 10.1109/ICTACS59847.2023.10390044
M3 - Conference contribution
AN - SCOPUS:85185385782
T3 - Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023
SP - 957
EP - 962
BT - Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023
A2 - Chaudhary, Naina
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 November 2023 through 3 November 2023
ER -