The Impact of Information Technology on the Banking Industry: Theory and Empirics (Theoretical Paper)

Authors: Ho, Shirley; Mallick, Sushanta
Title of Journal: Information System Research
Volume Number: 9
Date: November 2006

Author’s Purpose: This study was conducted to develop and test a model to see the effects that information technology has on the US banking industry.

 Approach or Methods: The method they use was a case study on a panel that lasted over 20 years on 68 US banks. They tested the profits of these banks to see if they were making more money or less money due to the integration of information technology and information systems.

 Hypothesis: The authors’ hypothesis is that IT helps to improve the performance of a bank in two distinct functions: information technology and information systems can help lessen the operational cost (or cost effect) of a bank, and they can also help ease the transactions between customers within a single network (known as the network effect).

 Review and Remarks: The inconsistency between the authors’ initial hypothesis and their final conclusion is perhaps the most interesting part of this theoretical paper. As this paper is eleven years old, the results of their conclusion may no longer be consistent with what they are today. Perhaps the initial adoption of information systems by the banking industry was incorrectly done, as they were still unfamiliar with the best way to shift their services to one that was run or at least augmented by information systems. What we can garner from this information is that there will always be difficulties when it comes to translating something extremely complex (such as a financial institution’s services) to run under the performance of an information system. This is something the banking industry should always have in mind—never begin implementing until you understand the effects of a certain aspect of information systems completely.

Conclusion: The conclusion was not similar to their hypothesis. They found that there was an inconsistency when it came to studying this hypothesis, with some studies agreeing and some not. They tested this inconsistency on 68 US banks over two decades, and they finally concluded that the decline of bank profits is because of the diffusion and adoption of IT investment. This shows that ultimately there was a negative effect of the information system industry on the banking systems. They also found that the relationship between IT and IS costs and the financial performance of the bank was conditional on the type of network effect they were involved with, suggesting that the network effect is high when it comes to the US banking industry.


Shirley, H; Sushanta, M. (2006). The Impact of Information Technology on the Banking Industry: Theory and Empirics. Vol. 9. Information System Research. 58-67.

The Evolving Understanding of Information Systems: A Response to P.B. Checkland’s Information Systems and Systems Thinking: Time to Unite?

AUTHOR:                       P. B CHECKLAND

YEAR:                             1988 (VOL 8 PG 239- 248)

In P.B. Checkland’s 1988 theoretical paper entitled Information Systems and Systems Thinking: Time to Unite?, his primary discussion point was the social understanding of computers and information systems, and how this understanding was driven by certain points and notions that are stored in the social communal memory. For example, calling a computer’s storage “memory” instead of “storage”, and referring to a computer’s transmissions as a transmission of “information” instead of a transmission of “signals”, which would have been much more accurate. The first ways in which we understood what computers were and what information systems represented were done so through anthropomorphic means, perhaps as a measure of acclimatizing a non-computer literate general population towards a world of computers. Checkland discusses the differences in understanding when computers first arose in the 1950s and 1960s, to the new developments of thinking in the 1970s and 1980s, and how this transformation of understanding affected the application of information systems.

With nearly three decades now between the original publish date of this paper and today, it can be interesting to analyze whether Checkland’s theories of how information systems and its application would continue to evolve were accurate or not, and why. At first interpretation, we can see that while Checkland was perhaps off the mark, he did shoot in the right direction—the way in which he predicted the established relationship between the end user and information systems was slightly accurate. The biggest thing holding back his estimations was his underestimation of how much the integration of information systems in the lives of end users would ultimately take over; he imagined a world in which the human element was much more relevant than it is today.

For example, Checkland was correct in predicting that systems would be built up in which designers and users could learn their way to an adequate arrangement.  He was also correct in his prediction that the user could create their own information system with the help of system-developed support tools. However, he overestimated the relevance of human issues like politics and perceptions; these, largely have become irrelevant in defining the relationship of user and software. He also underestimated the potential of information systems in his prediction that it would remain static only for the most basic mechanical processes, such as logging the receipt of mail in a mail-order company. For the most part, technology and information systems now runs most of the processes in the modern world, and any claim otherwise would be a widely inaccurate portrayal.

Ultimately, Chuckland’s models could be deemed to be a slightly successful predictor of what was to come, but its trajectory was lower than it should have been. The systemic systems thinking that he discusses being relevant in the 1980s perhaps played a role in how much he held back in his theoretical models; he could not truly foresee the potential of information systems because of his reliance on understanding what was before him at that time, rather than predicting continued leaps in development. The pragmatic thinking that had to be adopted perpetuated still in his own models.


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