چکیده (انگلیسی):

Automatic teller machines (ATMs) being developed today may astonish customers. Some of these sophisticated new ATMs deliver full banking services, dispense theater tickets, and cruise the World Wide Web. What may be more striking is the fact that, since the inception of ATMs 30 years ago, bankers, developers, and deployers have not had precise information on how clients perform in real time when using even the simplest ones. Consequently, from the point of view of cognitive research, it is not surprising that, although the banking industry has considerable capital investment tied up in ATMs, banks do not obtain the desired returns. ATMs would need to be utilized more often and for more profitable transactions, which generally means more cognitively demanding ones, in order to bring in profits. To accomplish that mission, designers and bankers need precise information about what customers really do when using the machines. This paper discusses a study conducted with a new methodology that allows the capture of relevant, detailed, and real-time data from electronic self-service systems. That same methodology can be applied to businesses as diverse as airlines, hotels, internet transactions, and many others. In our study, the data were collected during the challenging times of a merger of two large international banks that jointly have over 550,000 customers and 1000 ATMs. A sample of 15,099 users, ages 16–79 years old, participated. These customers performed 60,259 financial transactions during their 44,435 visits to 103 ATMs in 22 days. Accuracy, as measured by success and errors in obtaining the desired outcome, and time spent with input, measured on-line in milliseconds, were the dependent variables. The data were cross-referenced to identify education, gender, age, experience, and other demographic variables. The implications of the findings for the study of human–computer interaction, practical intelligence, and procedural knowledge are discussed.