Data on Banking Chatbot Service Quality

A structured questionnaire was initially distributed to 800 Indian Chatbot users in Bangalore, India, via the Redcap survey tool and participants were presented with explicit ethical instructions and guaranteed the confidentiality of their input. Out of the 800 recipients, 738 responded to the survey. However, 124 of these responses were incomplete. As a result, a total of 614 completed responses were obtained initially. Subsequently, reminder emails were sent to a subset of 465 respondents, which included those with incomplete surveys. Within this group, 211 respondents completed the survey. Following this, another round of reminders was sent. All of the remaining respondents in this group including 124 respondents filled out the questionnaire. All items were assessed using a five-point Likert-type scale, where "Strongly agree" was assigned a value of 1, "Agree" as 2, "Neutral" as 3, "Disagree" as 4, and "Strongly disagree" as 5. Chabot service quality was measured using AISQC (Artificial Intelligence Service Quality ) scale which is adapted .The study selected participants from Indian banking customers from Bangalore, India. The target group for data collection consisted of individuals aged 18 and above with prior experience using Chatbot in the banking sector. The study concentrated on individuals utilizing chatbot provided by leading Indian banks such as HDFC Bank, ICICI Bank, SBI Bank, YES Bank, City Union Bank, Bank of Baroda, Indus Bank, Andhra Bank, Kotak Mahindra Bank, and AXIS Bank. The uncontrolled quota sampling method, a non-probability sampling technique, was used. The variables in the study are Customer value (CV),customer satisfaction (CSAT) ,Customer trust (CT) and customer loyalty (CL) which i are dependent variable , semantic understanding (SEM), human collaboration (HC) , Human-Like (HL), Continuous Improvement(CI), Personalization (PERS), Cultural adaptation (CA) and efficiency (EY)is independent variable.
Note : The incomplete data or missing data is represented by o in the raw data set