Part 1: How Research Is Used In The Banking Industry


Research is central to the daily operations of a company, without which no major development can take place. It is not surprising there that as part of its core business activities, firms identify research together with development in what has become known as research and development (R&D) (Lau et al., 2013). In this paper, how banks use quantitative research as part of their R&D activities is discussed. The discussion focuses on areas of research methods and tools selection, the effectiveness of these tools for specific areas of business operations, examples of how research has transformed banks, as well as the future of quantitative research within the banking industry and academic research as a whole.

There are a number of quantitative methods and tools that are used in the banking sector. In general, some of these quantitative methods and tools include surveys, pre/post designs, using pre-existing data, and pilot studies (Lau et al., 2013). In real cases, some banks resort to the use of experiments. Goyal, Rahman and Kazmi (2013) emphasized that the method and tools selected by the banks are largely determined by what they want to use their research to achieve. On the whole, it is known that quantitative research is used to measure observable variables through the use of mathematical and statistical indices. Meanwhile, there are very specific ways in which the banks do this, based on what they want to achieve. For example a bank that wants to measure the effectiveness of a new software on customer satisfaction would want to use surveys. Using surveys and questionnaire as the quantitative method and tools for such a study can be considered effective because it will give the bank the opportunity of sampling as many views from its customers as possible. These views can then be put together in an analytical way, using various statistical tool such as Microsoft Excel or SPSS. 

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Another bank that wants to determine employee reaction to proposed changes can use pilot studies. It would be noted that pilot studies are mostly mock researches which are done ahead of a major research. When the bank wants to know how employees will react to a proposed change at the workplace, it could find pilot studies as effective as it would present human resource managers the opportunity to introducing only few aspects of the proposed change to employees and then collecting data from the employees regarding how they perceived the outcome of the changes. The banking industry is a very sensitive one, which is constantly competing for growth (Goyal, Rahman & Kazmi, 2013). This is a reason that makes it necessary that when introducing new changes, it would do so in a systematic way that gives opportunity to collect data about the impact of the change on employee work output. Recently, Lloyds Bank reported on using such pilot study in determining employee reactions to change before implementing major policy changes at the workplace (Worthington, 2014).

The last quantitative method and tools that can be used is pre-existing data, which is a form of secondary research (Yilmaz, 2013). This can be considered effective when banks want to conduct research to know the impact of monetary policy on an intended area of financial investment such as mortgage. With this scenario, it would be noted that the bank will need a lot of past data on aspects of the economy such as inflation, interest rate, stock market performance, and GDP growth. Data in these areas could serve as independent variables, while data on mortgage prices serve as dependent variables. Because data of this nature are mostly published by agencies such as the central bank, it is always prudent to use pre-existing quantitative methods for studies of this kind instead of collecting data from respondents, who may not have enough information on the market trends with regards to monetary policy and its impact on mortgage investment. Most banks that have failed to take up quantitative research seriously when venturing into new areas of investments have been characterized with failure in those areas. Typical example of this is Lehmann Brothers Holdings Inc., whose wrong prediction of mortgage growth led to the collapse of the bank (Gambacorta & Mistrulli, 2014).

Going into the future, there are some changes that are expected with the use of quantitative research in the banking sector to be specific and academic research in general. Since technology is advancing by the day, this is expected to affect or impact the way banks conduct their quantitative researches. That is, they are expected to employ the use of a lot of technological tools in what will become known as e-research. As part of the e-research, the future of quantitative research is expected to be more efficient, whereby the banks and other researcher who engage in quantitative research will be able to do more with fewer researchers. Having made this observation however, the use of a lot of technology in research is not predicted to have major positive impact on effectiveness, which deals with quality of outcomes. This is because banks and other researchers may want to use robotics in research, which will mean that some human aspects will be eliminated. Once those human aspects are eliminated, it will become difficult to have results from quantitative research, which give a true representation of opinions, views and thoughts from the perspective of stakeholders such as customers, investors and employees (Yilmaz, 2013).

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  1. Gambacorta, L., & Mistrulli, P. E. (2014). Bank heterogeneity and interest rate setting: what lessons have we learned since Lehman Brothers?. Journal of Money, Credit and Banking, 46(4), 753-778.
  2. Goyal, P., Rahman, Z., & Kazmi, A. A. (2013). Corporate sustainability performance and firm performance research: literature review and future research agenda. Management Decision, 51(2), 361-379.
  3. Lau, M. M., Cheung, R., Lam, A. Y., & Chu, Y. T. (2013). Measuring service quality in the banking industry: a Hong Kong based study. Contemporary Management Research, 9(3), 263.
  4. Worthington, S. (2014). ‘Challenger banks’: Are they for real?. The Routledge Companion to Financial Services Marketing, 30.
  5. Yilmaz, K. (2013). Comparison of quantitative and qualitative research traditions: Epistemological, theoretical, and methodological differences. European Journal of Education, 48(2), 311-325.
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