Branch Employees' Perceptions Towards the Implementation of Big Data Analysis in Retail Banking-Charlee

1Student Number: 1111415
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Module Code MG3119
Module Title Issues and Controversies in
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Tutor: Dr. Lynne Baldwin
Student ID number 1111415
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Title Page
MG3119 – Issues and Controversies in Management
Project
Academic Year 2014 -2015
“BRANCH EMPLOYEES’ PERCEPTIONS
TOWARDS THE IMPLEMENTATION OF BIG DATA
ANALYSIS IN RETAIL BANKING”
BSc (Hons) International Business
Brunel Business School
Student Name: Charlotte Lockhart
Student ID: 1111415
Project Supervisor: Dr. Lynne Baldwin
Date Submitted: 5th March 2015
Word Count: 7949
Brunel University
Uxbridge, Middlesex UB8 3PH
United Kingdom
Tel: +44 (0) 1895 267007

Abstract
PURPOSE
This paper aims to define and analyse the implications of Big Data analysis in retail
banking and investigate branch employees’ perceptions towards its implementation
in the sector. An additional purpose of the study was to determine whether there is
any relationship between the found perceptions and the characteristics of
employees.
METHODOLOGY/ APPROACH
A review of the current climate of the UK retail banking industry identified the need
for radical change in order to preserve relationships with increasingly demanding
and disloyal consumers. An investigation into the emergence of Big Data signified
that it could be the solution. This notion was explored further and 3 benefits and one
risk of Big Data analytics within the retail banking industry were identified. Based
on these findings, questionnaires were sent to a random sample of a bank’s branches
within Greater London with the aim of revealing the perceptions of customer-facing
bank employees on the Big Data Phenomenon.
FINDINGS
Statistical analysis revealed that branch employees’ tend to be positive towards the
adoption of Big Data analytics and provided evidence of relationships between
perceptions and some personal characteristics. Based on these findings,
recommendations on improving the flow of information to branch employees and
exploiting the benefits of Big Data were suggested. Recommendations were also
made for future research based on the limitations and findings of this study.

Acknowledgements
I would like to give my thanks to my supervisor Dr. Lynne Baldwin for all of her support
and guidance throughout this process and to the branch employees for taking the time to
respond to the questionnaires.
Thank you to my Dad for inspiring me.

Contents
COVER SHEET........................................................................................................................................1
TITLE PAGE............................................................................................................................................2
ABSTRACT.............................................................................................................................................3
ACKNOWLEDGEMENTS.........................................................................................................................4
LIST OF FIGURES ...................................................................................................................................7
LIST OF TABLES .....................................................................................................................................7
1. INTRODUCTION ............................................................................................................................8
1.1 WHAT IS BIG DATA? ...................................................................................................................................................8
1.1.1 The ‘3Vs’ Model...............................................................................................................................................9
1.2 UK RETAIL BANKING INDUSTRY REVIEW ................................................................................................................... 11
1.2.1 UK Retail Banking Industry PEST Analysis................................................................................................ 12
1.3 CHAPTER SYNOPSIS & RESEARCH JUSTIFICATION ..................................................................................................... 13
1.4 RESEARCH QUESTIONS.............................................................................................................................................. 13
2. LITERATURE REVIEW ...................................................................................................................14
2.1 INTRODUCTION ......................................................................................................................................................... 14
2.2 GARTNER’S HYPE CYCLE & THE S-CURVE MODEL..................................................................................................... 14
2.3 PUNCTUATED EQUILIBRIUM THEORY .......................................................................................................................... 17
2.4 BIG DATA ADVANTAGES AND ISSUES....................................................................................................................... 18
2.4.1 Increase of Sales........................................................................................................................................... 18
2.4.2 Build Customer Relationships and Loyalty ................................................................................................ 19
2.4.3 Innovator’s Advantage................................................................................................................................ 21
2.4.4 Privacy Issues................................................................................................................................................. 22
2.5 CHAPTER SYNOPSIS .................................................................................................................................................. 24
3. RESEARCH METHODOLOGY........................................................................................................25
3.1 INTRODUCTION ......................................................................................................................................................... 25
3.2 PHILOSOPHIES AND APPROACH................................................................................................................................ 25
3.3 RESEARCH DESIGN .................................................................................................................................................... 25
3.3.1 Sampling ........................................................................................................................................................ 26
3.3.2 Questionnaire ................................................................................................................................................ 28
3.4 ETHICS....................................................................................................................................................................... 30
3.4.1 Integrity & Transparency............................................................................................................................. 31
3.4.2 Informed & Consenting................................................................................................................................ 31
3.4.3 Confidentiality & Anonymity....................................................................................................................... 31
3.4.4 Voluntary Participation................................................................................................................................ 32
3.4.5 Independent and Impartial.......................................................................................................................... 32
3.4.6 Not Detrimental............................................................................................................................................. 32
3.5 DATA ANALYSIS METHOD......................................................................................................................................... 32
3.6 LIMITATIONS.............................................................................................................................................................. 32
3.7 CHAPTER SYNOPSIS .................................................................................................................................................. 33
4. FINDINGS & ANALYSIS................................................................................................................33
4.1 INTRODUCTION ......................................................................................................................................................... 33
4.2 SAMPLE ANALYSIS..................................................................................................................................................... 33

4.3 RQ2: WHAT ARE BANK BRANCH EMPLOYEES’ PERCEPTIONS OF BIG DATA IMPLICATIONS?.................................... 36
4.4 RQ3: ARE PERCEPTIONS LINKED WITH PARTICIPANT’S VARYING CHARACTERISTICS?................................................. 37
4.5 SUMMARY ................................................................................................................................................................. 40
5. CONCLUSION..............................................................................................................................41
6. RECOMMENDATIONS .................................................................................................................43
REFERENCES........................................................................................................................................44
APPENDICES........................................................................................................................................50
APPENDIX 1 – QUESTIONNAIRE....................................................................................................................................... 50
APPENDIX 2 – SPEARMAN’S RANK CORRELATIONS ......................................................................................................... 54
APPENDIX 3 – COMMUNICATIONS WITH BRANCH AREA DIRECTOR ................................................................................ 60
APPENDIX 4 – ETHICAL APPROVAL .................................................................................................................................. 62

List of Figures
Figure 1: The ‘3Vs’ Mode……………………………………………………………………… 9
Figure 2: The Increasing Capacity of Data Storage Over Time………………………………. 10
Figure 3: The Two Curves of the Hype Cycle………………………………………………….. 15
Figure 4: The Stages of the Hype Cycle………………………………………………………. 15
Figure 5: 2014 Gartner Hype Cycle for Emerging Technologies……………………………... 16
Figure 6: O’Brien & Jones’s (1995) Loyalty Scheme Value Elements………………………….. 20
Figure 7: Tesco Clubcard Customer Value Analysis…………………………………………… 21
Figure 8: Financial Services Data Loss………………………………………………………… 23
Figure 9: Scoping & Sampling Methodology………………………………………………….. 27
Figure 10: Framework for Research Ethics…………………………………………………….. 31
Figure 11: Participant Ages…………………………………………………………………… 33
Figure 12: Respondent Education Levels………………………………………………………. 34
Figure 13: Length of Industry Experience…………………………………………………….... 34
Figure 14: Respondent Role Variety…………………………………………………………... 35
Figure 15: Respondent Gender………………………………………………………………... 35
Figure 16: Branch Employee Perceptions of the Four Outlined Implications of Big Data……… 36
Figure 17: Branch Employees' Opinions on Whether Their Bank Should Adopt a Big Data
Strategy…………………………………………………………………………………….......
40
List of Tables
Table 1: Differences Between Traditional Data and Big Data…………………………............... 8
Table 2: Potential Big Data Privacy Issues……………………………………………………… 23
Table 3: Survey Question Analysis………………………………………………………………. 29
Table 4: Branch Employees' Perceptions of the Four Outlined Implications of Big Data
Adoption…………………………………………………………………………………..…….
36
Table 5: Statistical Analysis of Findings……………………………………………………….... 38
Table 6: Summary of Spearman's Rank Test Findings……………………………….………….. 38
Table 7: Correlation Between Gender and Privacy Risk Rating………………………………... 39
Table 8: Correlation Between Roles and Innovator’s Advantage Rating……………………….. 39

1.Introduction
Big Data is a modern-day phenomenon that is rapidly changing the way we do business.
The novelty of this innovation, coupled with the shortage of wide-spread understanding of
it outside of the data science and IT professions, necessitates further research. Big Data
has become somewhat controversial owing to privacy risks and a perceived ‘big brother’
omniscience. Large quantities of real time data means target marketing can now be more
personalised than ever, firms can get to know their customers without needing to interact
with them directly and companies can rapidly respond to change. Despite potential
drawbacks in reliability and privacy concerns Big Data has already proven successful in
numerous industries.
The UK banking sector has recently been under siege by regulatory authorities over the
miss-selling of PPI. Additionally, the recent recession has left UK banks with shattered
reputations and diminished customer loyalty. With Big Data on the rise, now is the time to
explore its potential to change the UK retail banking industry and how this innovation is
perceived by ‘front line’ employees.
This chapter discusses the current climate of the UK retail banking industry and reviews the
Big Data phenomenon, providing background to and justification of this study.
1.1 What is Big Data?
Every day Google receives over 3 billion search queries, more than 10 million photos are
uploaded to Facebook every hour and by 2012 Twitter had exceeded 400 million tweets
per day (Mayer-Schönberger & Cukier, 2013). All of this information is saved; millions of
consumers around the globe volunteer, often unknowingly, trillions of bytes of data
(Manyika et al., 2011), left as a ‘digital exhaust’ (Mayer-Schönberger & Cukier, 2013).
Table 1: Differences Between Traditional Data and Big Data
Reproduced from Davenport (2014, p. 4)

Big Data comes in various forms; purchasing decisions tracked by loyalty cards and
internet shopping (Manyika et al., 2011), energy usage from smart meters, communication
patterns and social interactions from social media (Machanavajjhala & Reiter, 2012) to
name a few. Big Data analytics differs from that of traditional data in that the data set
is larger, more varied and can provide real-time insights, as summarised by Davenport
(2014) in Table 1.
1.1.1 The ‘3Vs’ Model
The ‘3Vs’ model established by Gartner analyst Douglas Laney describes three widely-
agreed upon properties of Big Data, summarised in Figure 1.
Interpreted from Laney (2001)
Figure 1: The '3Vs' Model

Volume
Mayer-Schönberger
& Cukier (2003)
describe how the cost
of digital storage has
been continually
decreasing over the
past 50 years, while
storage capacity
increases
respectively. Figure 2 demonstrates how global data storage capacity has rapidly
increased and shifted distinctly from analog to digital since 2000. The availability of large
amounts of cheap data storage is arguably one factor that has facilitated the emergence
of Big Data analytics.
Variety
There are numerous uses for Big Data made possible by the extensive variety of different
data types. Vast amounts of unstructured information has always existed, however the
ability to collect, store and analyse it has only recently been realised. Big Data analytics
and the new-found capability to unearth valuable data from unlikely sources means
information that was previously considered unquantifiable or of minimal value can be
utilised. Mayer-Schönberger & Cukier (2013) coined the term ‘datafication’ to describe
this phenomenon.
Business examples of ‘datafication’ include Amazon, which tracks consumer’s purchasing
behaviour; how long they look at certain items to what items they purchase at the same
time (Mayer-Schönberger & Cukier, 2013). AirSage collects and analyses location data
from over 15 billion wireless device locations across the US every day to support
applications for target marketing and enable large-scale transport planning (Airsage,
2014).
Figure 2: The Increasing Capacity of Data Storage Over Time
Source: Hilbert & Lopez (2011) cited in Manyika et al. (2011,
p. 17)

Velocity
The ability to quickly and efficiently process large quantities of data, using tools such as
Machine Learning and Hadoop (Davenport, 2014), enables analysts to fully evaluate
large data sets, eliminating the need for sampling (Mayer-Schönberger & Cukier, 2013).
A primary benefit of analysing whole data sets is the availability of insights on a granular
level; into subcategories and submarkets which sampling cannot asses (Mayer-
Schönberger & Cukier, 2013). However Mayer-Schönberger & Cukier (2013) point out
that as the volume of data increases, the number of inaccuracies increase concurrently and
thus the mindset of data analysts and business decision-makers must shift away from the
need for exactitude and to begin simply asking what instead of why.
1.2 UK Retail Banking Industry Review
To understand how Big Data can potentially benefit the retail banking industry it is
important to consider the current industry climate. The miss-selling of Payment Protection
Insurance (PPI) by UK banks had a profoundly negative effect on the industry. Questions
surrounding the value of PPI and its compliance with the Financial Services Authority (FSA)
regulations originated in the 1990s (Evans, 2011) (Financial Services Authority, 2005, p.2).
A 2005 FSA report on the selling of PPI by banks and retail lenders exposed generally
poor quality of advice, lack of disclosure of costs and high risk of inappropriate selling
(Financial Services Authority, 2005, p.3-4). The FSA resolved to impose fines and strict
regulations on the selling of PPI (Evans, 2011). By 2014 the scandal had cost the banking
industry almost £20bn in customer compensation payments (Goff & Cadman, 2014).
Furthermore, the global financial crisis which led to the UK’s double-dip recession,
arguably caused by loose monetary policy and regulations (Martin & Milas, 2010), had
a substantial impact on the UK banking sector. Loyalty is therefore scarce as consumers
search for the best deals (Jones, 2010) and banks struggle with low interest rates and
small profit margins (Yell et al., 2012). Thus, it is fair to say that the industry is struggling
with customer retention and financial loss from regulatory penalties.

1.2.1 UK Retail Banking Industry PEST Analysis
The following PESTLE analysis has been produced in order to understand the industry
environment and how it may be impacted by Big Data.
Political & Legal
The Financial Services Act 2013 (Great Britain) encourages structural and cultural changes
to the UK banking system to better prepare the industry for future crisis and prevent the
exploitation of consumer interests. The privacy risks associated with Big Data should
therefore be carefully considered in order to uphold ethical standards.
Economic
Grant Thornton (2013) describe how regulatory costs have put intense pressure on bank
profit margins, creating a paradox in that banks need to proactively reduce costs while
also embrace innovative solutions to attract new customers.
Low entry barriers have resulted in increased competition from smaller firms including
Tesco Bank, TSB and Metro Bank. These new entrants have the advantage of untarnished
reputations (Peachey, 2014). The concept of the innovator’s advantage suggests that Big
Data could be essential for industry competition and growth, especially for incumbent firms
that need to defend their market share.
Social
Due to the interdependance of the UK retail banking industry and the UK economy, banks
are subject to various matters of corporate social responsibility. Battling financial crime,
ensuring the ethical treatment of employees and the reasonable handling of customer
complaints are just some examples (Santander, 2013).
Large-scale use of customer data means banks are socially and legally obliged under the
Data Protection Act to manage information in accordance to the Data Protection Rights
(Information Commissioner’s Office, 2014a). Accordingly, stringent codes of conduct would
be necessary if a bank were to adopt a Big Data strategy.

Technological
Lacmanovic et al. (2012) discuss how the internet is one of the fastest growing channels to
market in the world and has thus created new opportunities for financial institutions. In their
study, Lymperopoulos & Chaniotakis (2004) deliberate the benefits of e-banking for both
consumers and banks including cost reduction, queue minimization and increased sales.
Similarly, Big Data has the potential to revolutionize the banking industry with advanced
customer profiling.
1.3 Chapter Synopsis & Research Justification
This chapter has discussed the UK retail banking sector’s need for a strategy to improve
customer loyalty and increase profit margins. The emerging popularity of Big Data and
it’s huge potential justfies research into the implementation of Big Data in retail banking.
Because the majority of literature around Big Data focuses on strategic and decision-
making issues and neglects to explore the phenomenon from a customer-service
perspective, the investigation of the perceptions of employees on the ‘front line’ of
customer service is also justified.
1.4 Research Questions
The aim of this research is to discover the perceptions of UK retail bank branch employees’
on the topic of Big Data in retail banking and conclude whether attributes influence
perceptions. The key objectives to achieve this aim are:
 Identify and analyse the benefits and drawbacks of Big Data adoption in retail
banking, based on the implementation of Big Data in other industries.
 Collect primary data on branch employees’ opinions of the stated benefits and
issues and explore whether there is any correlation between their opinions and
personal characteristics.

Thus, the following research questions have been designed:
2.Literature Review
2.1 Introduction
This chapter examines various academic sources concerning models and theories of
innovation and marketing. Four implications of Big Data in the context of retail banking
are outlined based on these models and theories, reinforced with business examples.
2.2 Gartner’s Hype Cycle & the S-curve Model
In order to understand the emergence of Big Data and its characteristics in the different
stages of its lifecycle, Gartner’s Hype Cycle model can be applied. Steinert & Leifer
(2010) describe the model as a tool which demonstrates the various levels of value
expectation of a technology over time, recognising it as a prominent consulting model for
large businesses. The model is formed of a bell-shaped curve which represents the
preliminary positive reaction typically received by emerging technologies (Steinert &
Leifer, 2010). The second part of the curve incorporates the technology S-Curve diffusion
model; demonstrating the notion that the maturity of a technology at first develops slowly
and then reaches a turning point, where-after development quickens until the technology
meets its natural limit (Steinert & Leifer, 2010). Figure 3 demonstrates how Gartner has
integrated the two models into one tool. Figure 4 illustrates the stages and indicators of
the Hype Cycle.

Fox and Do (2013) discuss how hype is often influential in organisational decision making,
arguing that companies risk losing competitive advantage and relevance in an industry if
they fail to respond to hype. For example, software giant Microsoft failed to respond in
the early stages of the smartphone hype. In 2014 Microsoft acquired Nokia’s smartphone
business in an attempt to enter the market adopting a follower strategy, however the lack
of applications compatible with the Windows Phone platform, compared to IOS and
Android, is arguably deterring potential customers (Bosker, 2013). Debatably, because
Microsoft did not react quickly enough to this hype it has lost out on the ‘innovators
advantage’ of early adoption and is thus lagging behind in market share. It could

therefore be argued that retail banks may be disadvantaged if they fail to embrace Big
Data during its current hype.
Concurrently, investing in emerging technologies can be risky. Fox and Do describe hype
as being both positive and vague, referring specifically to the lack of stipulation around
Big Data’s definition. This elicits questions around how strategic decisions concerning a
technology can be made if the technology is not entirely defined and understood. Thus the
uncertainty surrounding new technologies arguably increases the risk involved in their
adoption.
Criticism of the model highlights risks associated with basing decisions on its predictions.
Steinert & Leifer (2010) argue that though highly utilized, the model is still fairly new and
has more prominence online than in literature. Steinert and Leifer’s findings did not
correspond to the model’s predictions, consequently leading them to question its reliability.
Gartner’s Hype Cycle is therefore arguably a useful tool when considered alongside other
factors in the decision-making process.
As highlighted in Figure 5, Gartner placed Big Data on the border of the second and third
phases of the 2014 cycle, implying that the technology is still experiencing high degrees
of hype and the diffusion process is still in the early stages. Gartner (2014b) describes
the ‘Trough of Disillusionment’ stage, as being the deciding point of a technology’s future.
If the ROI are satisfactory to early adopters of the technology, others may pursue a

follower strategy. Now that the initial hype has receded, Big Data business cases will
reveal whether it has lived up to former expectations.
Based solely on Gartner’s 2014 Hype Cycle, UK retail banks are essentially left with three
options. The first is to risk the uncertainty surrounding Big Data and hope to achieve the
‘innovator’s advantage’ or adopt a follower strategy when Big Data has been proven by
early-adopters to be a justifiable investment. The alternative would be to disregard Big
Data, yet the consequences for doing so may not be realised until the technology has
matured. Either way, this decision arguably should not be based exclusively on the Gartner
model but also on the exigencies and ambitions of the individual organisation.
2.3 Punctuated Equilibrium Theory
Punctuated Equilibrium theory can be applied to Big Data to predict the impact of its
disruption of the retail banking industry. Romanelli & Tushman (1994) express equilibrium
as a period of stability, discerning that it is punctured by brief surges of fundamental
change. This change eventually yields to a new equilibrium and the cycle repeats. Loch &
Huberman (1999) describe punctuated equilibrium in the context of innovation; radical
innovation brings about instability and experimentation. Once the innovation is better
understood, a renewed equilibrium descends and a period of incremental innovation is
experienced until it is again destabilised by radical innovation.
Loch and Huberman (1999) argue that long periods of incremental change are not
necessarily due to declines in radical innovation but can be brought about by the resistance
to change of firms and entire industries. It is possible for a radical innovation to disjoint an
industry to the extent that it destroys a firm’s competencies. Absorptive capacity theory
suggests firms with large investments sunk into existing infrastructure and processes are
often unable to react quickly to innovation or that doing so may not be financially viable.
Consequently, industries are left open to new competitors that, unlike incumbent firms, are
not restricted by sunk costs and can embrace innovation. Former DVD rental market leader
Blockbuster did not appropriately react to the emergence of the internet due to process
rigidity and thus lost the market to online services including Netflix and Blinkbox (Satell,
2014).
Web 2.0 is a phenomenon that emerged in the early 2000s, facilitating online networking
and content sharing. While Mitic and Kapoulas point out that US banking organisations,
e.g. Citibank, have been engaging in Web 2.0 activities, Klimis (2010, cited by Mitic and

Kapoulas) found that European banks tend to be more reserved. This observation is
reinforced by Yell et al. (2012) who attribute this to the bureaucratic and process-driven
nature of the industry. They forecast that UK banks will lose competitive advantage if they
fail to embrace Web 2.0 in the near future.
Web 2.0 is the most recent innovation to punctuate the UK banking industry’s equilibrium
and according to its positioning on the 2014 Hype Cycle, Big Data is likely to be the next.
PriceWaterhouseCoopers (2014) emphasises Big Data as a crucial instrument for the
success of UK retail banks in the near future.
Yet it is clear that it must be considered not only whether UK retail banks will take the risk
with Big Data in hopes to gain first-mover advantage, but also whether they have the
financial, structural and cultural capacity to respond to this game-changing phenomenon.
2.4 Big Data Advantages and Issues
The following advantages and issues have been collated from literature on Big Data, retail
banking and Relationship Marketing and serve to answer RQ1.
2.4.1 Increase of Sales
Direct marketing was not significant in mainstream business until the 1990s, when
computers had advanced enough to enable the storage and analysis of copious quantities
of data (Breur, 2011). Direct marketing has experienced rapid popularity growth,
predominantly because companies can communicate tailored marketing more efficiently
and ROI can be measured (Fletcher et al., 1996). This can increase sales by targeting
consumers with marketing communications tailored based on their unique attributes, which
Fletcher et al. (1996) recognise as essential due to the increasing fragmentation of
markets. This therefore increases the likelihood of communications receiving a direct
response, the fundamental intention of this approach.
Through clickstreams, social media, loyalty cards and more, companies can derive not only
what customers bought but also when and how. Furthermore, artificial intelligence enables
the text mining of social media communications, interpreting qualitative data posted online
by millions of consumers. The manipulation of this ‘messy’ data was previously
unachievable, however Big Data and the process of data fusion facilitate the combination
of behavioural and attitudinal data (Breur, 2011).

US President Barak Obama’s re-election campaign is a noteworthy example of Big Data’s
utilisation for direct marketing. McGregor (2012) describes how information on
individual’s marital status, home ownership and income as well as their attitudes towards
various social and political causes were analysed so that marketing could be tailored
based on their personal values. For example, someone who habitually read and shared
messages relating to healthcare would be targeted with communications concerning
Obama’s propositions around healthcare.
Translated into the context of retail banking, this could increase sales by ensuring only
relevant products and offers are communicated to customers so as to elicit positive
responses, as argued by Akaah et al. (1995). Furthermore, strategic analysis of consumer’s
online habits could be used to target individuals with relevant marketing during key life
events. For example, online activity such as browsing car sales adverts, reading car
reviews and talking about buying a car on social media could be aggregated with an
individual’s credit score and employment status for banks to offer tailored direct
marketing around car loans. This would provide convenience for the consumer and a higher
likelihood of ROI for the bank.
2.4.2 Build Customer Relationships and Loyalty
The benefits of personalised direct marketing can be related to relationship marketing
theory. Morgan and Hunt (1994) define relationship marketing in terms of establishing,
developing and maintaining transactional relationships through marketing. They conclude
that there are ten types of relationship marketing encompassing relations between a firm
and employees, suppliers, business units and more. The relationship between that of UK
retail banks and ‘Ultimate Customers’, which they describe as long-term, is the focus of this
study. It can be argued that banking is one of a few industries in which mostly all customers
engage in long-term relationships with firms, as services provided are intended to be
recurrent and are measured by time (e.g. Five year loan agreements). Banks are thus
reliant on relationships with and the loyalty of customers, which according to Hallowell
(1996) can be identified by the continuity and increase of scale and scope of these
relationships.
Morgan and Hunt highlight commitment and trust as being fundamental to the success of
relationship marketing. Taking into account the recent PPI and exchange rate scandals UK
banks are finding it challenging to win the trust and commitment of consumers (Yell et al,
2012). Furthermore, Yell et al. (2012) recognised that the homogeneity of banks’ offerings

make differentiation challenging, denoting the importance of building customer
relationships and creating positive customer experiences.
Loyalty reward schemes are one aspect of relationship marketing in which retailers are
reaping the benefits of Big Data. Ferguson (2013) describes how Tesco’s Clubcard collects
data on consumer shopping habits to help build consumer profiles. This information is used
to tailor customer experience, for example personalising product suggestions on its
website, and also for rewarding customer loyalty. O’Brien & Jones (cited by Rowley,
2000; 2004) argue customer loyalty can be gained through rewards that correspond to
customer values, which are depicted in Figure 6.
Figure 7 depicts how Tesco’s Clubcard provides value to customers in line with O’Brien and
Jones’s customer values. UK retail banks are currently using rewards as incentives for
customer loyalty, however they are arguably achieving only a few of the outlined values.
NatWest’s ‘Cashback Plus’ (NatWest, 2014) allows customers to collect points when
shopping with specific retailers which can be spent in selected stores. However there are
currently very few participating retailers and thus there is limited scope for reward.
Figure 6: O’Brien & Jones’s (1995) Loyalty Scheme Value Elements
Source: Adapted from Rowley (2000; 2004)

One suggestion for banks to improve reward schemes is to use Big Data in a similar way
to Tesco. Banks have the potential to analyse Big Data to find out customer’s hobbies,
interests and favourite retailers. This information could be used to tailor rewards
specifically for individual customers, similar to how Tesco issues discount vouchers to
Clubcard members for items they regularly purchase. For example, a customer that
regularly spends money on cinema visits and reads numerous online movie reviews could
be rewarded with half-price cinema tickets for their loyalty. This could increase the
likelihood of the reward being of value to the customer and thus strengthen the customer
relationship.
Research reveals that banks are reliant on relationships with long-term customers and thus
relationship marketing is evidently key to customer retention. As seen from Tesco’s
Clubcard success, Big Data has the potential to revolutionise the way UK retail banks build
customer relationships and loyalty.
2.4.3 Innovator’s Advantage
Investing in new innovations entails high levels of risk and therefore firms must consider at
what point in the hype cycle would be the most strategic to implement Big Data
technologies. PriceWaterhouseCoopers (2014) suggest that UK retail banks which are

quick to embrace Big Data will achieve competitive advantage in customer experience,
risk management and cost reduction.
Bower and Christensen (1995) discuss how empirical evidence has revealed that a distinct
cause of companies losing their lead in the market is their failure to quickly respond to
disruptive technologies. As demonstrated by Gartner’s Hype Cycle, Big Data is a current
disruptive technology impacting numerous industries. Though retail banks arguably cannot
be classed as high-tech, their infrastructures are heavily reliant on technology and thus
sensitive to technological change.
Furthermore, Wigan and Clarke (2013) discuss how Big Data can be identified as
intellectual property and can therefore be subjected to copyright. This supports the
concept of the innovator’s advantage through the potential monopolistic power attainable
by banks which act fast in the Big Data hype.
It is suggested that Big Data will eventually be a necessity for banks to remain competitive
(Mitic and Kapoulas, 2012; Marous, 2012; PriceWaterhouseCoopers, 2014), thus
arguably the sooner banks adopt a Big Data strategy, the sooner they can harness the
associated benefits.
2.4.4 Privacy Issues
Despite the benefits associated with Big Data, potential risks must also be assessed when
considering a Big Data strategy. Wigan and Clarke (2013) argue that even when there
are no explicit identifiers within a dataset, the depth of the data can still derive inferences,
potentially making individuals re-identifiable. Machanavajjhal and Reiter (2012)
elaborate on how quasi-identifiers, such as geographic and demographic data, can be
matched to other datasets, resulting in loss of anonymity. A recent report by a US
government organisation (US. President’s Council of Advisors on Science and Technology,
2014) highlights how the capability to rapidly collect and efficiently analyse data in mass
quantities means that companies can derive more information than consumers may
anticipate. Table 2 summarises the potential privacy concerns identified in the report.
These concerns suggest that companies have the potential to, whether intentionally or not,
misuse data in a way that could have adverse effects on consumers.

KPMG (2012) identified hacking as the primary cause of data loss, accounting for 67.2%
of all incidents in 2012, and the financial services sector as being within the top five
industries most affected by data loss between 2008 and 2009. Figure 8 illustrates how
fraud and hacking are the two largest causes of data loss in the financial services industry.
Consumers are at risk not only from the potential misuse of data by companies, but also
from the possibility of data being lost and used with malicious intent by unauthorised
entities. The US retailer Target was victim of a data hack in which up to 70 million of its
customers had personal data stolen including their names, email address and credit card
information (Kuchler, 2014), resulting in numerous cases of identity theft.
Source: US. President’s Council of Advisors on Science and Technology (2014)
Table 2: Potential Big Data Privacy Issues
Figure 8: Financial Services Data Loss
Source: KPMG (2012)

The consequences of data-loss and misuse are not only detrimental to consumers;
companies face heavy fines for data loss as well as loss of customer trust. Zurich insurance
was fined £2.2 million by the FSA for losing 46,000 customer’s personal data (Masters,
2010) and the UK Ministry of Justice was fined £180,000 for losing confidential data on
over 3000 prisoners (Nuttall, 2014).
It is evident that privacy is an existing issue for companies and their customers. The fact
that Big Data provides much more thorough and detailed consumer profiles than
traditional data implies that consumer privacy is at even greater risk (Information
Commissioner’s Office, 2014b). Retail banks must therefore proactively analyse and
prepare for potential risks to both customers and the firm when considering a Big Data
strategy.
2.5 Chapter Synopsis
This chapter has applied Gartner’s Hype Cycle Model and the theory of Punctuated
Equilibrium to Big Data. Analysis of existing literature and business cases has revealed
increased sales, customer relationship building and the innovator’s advantage as three
key potential benefits of Big Data in the retail banking industry. Furthermore, business
cases and statistics have highlighted the risk precautions necessary for companies handling
confidential data which would be applicable to banks adopting a Big Data strategy.

3.Research Methodology
3.1 Introduction
This chapter reviews the approach taken in the collection and analysis of data for this
research. Analysis of the philosophical approach adopted justifies the design of this
research. Furthermore, ethical issues and research limitations are discussed.
3.2 Philosophies and Approach
A positivist approach has been adopted for this research with the research questions
having been formulated based on the observable reality of the Big Data phenomenon in
various industries. The nature of this philosophy is reflected in quantifiable observations of
this study being statistically analysed.
The below analysis of the positivist approach of this research is based on Creswell’s (1994)
coalescing of three interrelated-assumptions.
Ontological assumption – In this research, reality is considered objective and singular,
separate from the researcher (Saunders et al., 2012).
Epistemological assumption – The view that only observable and measurable outcomes
can be considered as valid findings is embraced, with the researcher taking an objective
stance (Hussey & Hussey, 1997).
Axiological assumption – The focus of the research is orientated around the relationship
between variables and is less concerned with the behaviours of people.
Interpretivism advocates the understanding of differences between researching humans
and objects (Saunders et al., 2012) and thus did not correspond to the deductive approach
of this research.
3.3 Research Design
This research is descripto-explanatory as it seeks to identify the perceptions of branch
employees’ and subsequently endeavours to explain the reasons behind these perceptions.
A concurrent mixed research method was used as qualitative data was ‘quantitised’ and
data collection was single-phased (Saunders et al., 2012). The quantitative analysis of

data and the search to explain the relationships between variables in indicative of the
deductive approach of this research (Saunders et al., 2003). This study, including its
methodology and overall layout, has been inspired by Lymperopoulos & Chaniotakis’s
2004 study entitled ‘Branch Employees’ Perceptions towards the implications of e-banking
in Greece’.
3.3.1 Sampling
As the population for this study was much larger than 50, in line with Henry’s (1990)
recommendations, probability sampling was utilised for data collection.
Once the sampling frame was scoped down to one firm with branches located within
Central and Greater London, simple random sampling was implemented by numbering all
of the branches within the region and using the Excel formula ‘=RAND()’ to select twenty
branches.
The advantages of this sampling method is that it should ensure a completely unbiased
sample (Hussey & Hussey, 1997) and is well suited to postal questionnaires (Saunders et
al., 2003).

Figure 9: Scoping & Sampling Methodology

3.3.2 Questionnaire
The use of questionnaires (see appendix 1) was chosen over alternative research
instruments as quantifiable data were desired for ease of analysis. Furthermore, the
questionnaire allowed for the gathering of standardised data to be collected
anonymously. The style of the questions was based around a similar study by
Lymperopoulos & Chaniotakis (2004).
The questions were fabricated to be self-completed and hard copies were distributed via
the internal postal system of the participating bank. This afforded high confidence of the
correct participants responding and the anonymity of responses discouraged the need to
answer dishonestly on the basis of social desirability or self-presentation (Auger and
Devinney, 2007). A key benefit of this distribution channel was that it eliminated the
impediment of branches being geographically dispersed as face to face contact with
participants was not required. Another method would have been to use an online
distribution channel, however the internet security restrictions of the bank made this
unfeasible.
Prior to distribution, a pilot study was carried out within one bank branch to highlight any
unethical or unclear questions. Subsequently, the questionnaire was sent to the banks’
London and South-East area manager for ethical approval (See appendix 3).
Considering time-efficiency and simplicity of completion, the research aims were fashioned
into twelve closed, multiple-choice questions including Likert-scales, as summarised in Table
3. Both attribute and opinion data were collected in the questionnaire as the purpose of
the research was to define perceptions and identify trends between attributes and
opinions.

Table 2 Survey Question Analysis
Question Open/Closed Purpose
Type of Variable
(Dillman, 2009)
Data Type
(Stevens,
1946)
Question
Style
What age category do
you belong to?
Closed
Facilitating in the
deducing of whether
age impacts
perception
Attribute Ordinal Category
What is the highest
level of education you
have successfully
completed?
Closed
Facilitating in the
deducing of whether
education level
impacts perception
Attribute Ordinal List
What gender group do
you belong to?
Closed
Facilitating in the
deducing of whether
gender impacts
perception
Attribute
Nominal
(Dichotomous)
Category
How many years of
experience do you
have in retail banking
(All companies and
roles)?
Closed
Facilitating in the
deducing of whether
experience impacts
perception
Attribute Ordinal Category
What position do you
currently hold in
branch?
Closed
Facilitating in the
deducing of whether
role/seniority impacts
perception
Attribute Ordinal List
Increase ‘customer
outcomes’ through
target marketing
Closed
Identifying the
participant’s
perception of Big
Data’s ability to
increase sales through
target marketing
Opinion Ordinal
Rating –
Likert-style
within a
matrix
Put the bank at an
advantage to
competitors by being
one of the first UK
banks to fully embrace
the technique
Closed
Identifying the
participant’s
perception of Big
Data’s ability to
create competitive
advantage
Opinion Ordinal
Rating –
Likert-style
within a
matrix
Enabling the tailoring
of marketing and
Closed
Identifying the
participant’s
Opinion Ordinal
Rating –
Likert-style

incentives to customer
needs and values
perception of Big
Data’s ability to tailor
marketing and
incentives
within a
matrix
Pose a threat to
customer's privacy
Closed
Identifying the
participant’s
perception of Big
Data’s potential
privacy issues
Opinion Ordinal
Rating –
Likert-style
within a
matrix
Were you aware of the
concept of Big Data
before taking part in
this study?
Closed
Discover how many
employees were
previously aware of
Big Data
Attribute
Nominal
(Dichotomous)
List
Do you feel that you
have a good general
understanding of Big
Data and its
advantages and
disadvantages?
Closed
To determine whether
perceptions are
affected by lack of
subject understanding
Attribute
Nominal
(Dichotomous)
List
Would you like to see
the bank embrace a Big
Data strategy?
Closed
To summarise whether
or not employees
want to see a Big
Data strategy
implemented by the
bank
Opinion Nominal List
3.4 Ethics
ESRC (The Economic and Social research Council) (2012) devised the Framework for
Research Ethics (FRE) (See Figure 10) with the aim of protecting all that are involved in
research. The ethical issues associated with this research have been analysed in
accordance with this framework.

3.4.1 Integrity & Transparency
The research proposal and accompanying ethical documents (see appendix 4) were
approved by Brunel’s Business School prior to data collection. The aims and purpose of
the research were made clear within both the proposal and the questionnaire introduction.
3.4.2 Informed & Consenting
The first section of the questionnaire introduced participants to the researcher and related
the topic and research aims. Questionnaires were sent addressed to branches rather than
individuals and thus participation was voluntary, signifying that all contributors gave
consent for the data to be used for the stated purpose.
3.4.3 Confidentiality & Anonymity
Directly identifiable data such as name and branch location were omitted. Questionnaires
were returned directly to one branch via internal mail so the researcher was unaware of
which branch each of the questionnaires returned from.
Figure 10: Framework for Research Ethics
Adapted from Economic and Social Research Council (2012)

3.4.4 Voluntary Participation
The introduction to the questionnaire indicated that it was not compulsory for individuals
to partake in the research. No individual was specifically sent a questionnaire and the
identities of those who did and did not partake remain anonymous, thus there was no
pressure or coercion towards contribution.
3.4.5 Independent and Impartial
This research is entirely independent and is solely for the purpose of the completion of an
undergraduate degree. Thus the researcher is entirely impartial to the results of the data.
3.4.6 Not Detrimental
Because this research has been conducted within a workplace it was a priority to ensure
that all involved were not ill-effected by the process or outcome of this research. The
findings of the research was not shared with the participating bank in order to protect the
interest of its employees.
3.5 Data Analysis Method
A combination of Microsoft Excel 2013 and SPSS were used to analyse the data, taking
advantage of the unique benefits of both programmes. Excel was utilised to calculate
basic statistics such as the mean, mode and standard deviations of the data and also for
data presentation. SPSS was used to carry out more complex calculations in the form of
Spearman’s Rank Correlation Coefficient.
3.6 Limitations
Quantitising qualitative data potentially results in a loss of exploratory or explanatory
richness (Saunders et al., 2012). The lack of qualitative analysis means the underlying
reasons behind participant perceptions cannot be explained beyond the given variables.
Time constraints and reliance on voluntary participation have resulted in a small sample
size and a likelihood of bias due to non-respondents, indicating the sample is not wholly
representative of the population (Saunders et al., 2012). Therefore qualitative data

analysis and a larger sample would have provided a greater insight into the topic.
Nevertheless Saunders et al. (2012) argue that a sample size of 30 or more has been
proven sufficient to provide a sampling distribution similar to that of the entire population
and thus the sample collected is considered acceptable for the purposes of this research.
3.7 Chapter Synopsis
A positivist approach had been adopted and simple random sampling was used with a
questionnaire chosen as the instrument for data collection. The Economic and Social
Research Council’s ‘Framework for Research’ was used to analyse relevant ethical issues.
Time constraints and a small sample size are recognised as limitations of the research.
4.Findings & Analysis
4.1 Introduction
This chapter presents the key findings of the research. These findings identify the
perceptions of branch employees’ in regards to the four outlined implications of Big Data
and deduce whether employees’ would like to see their employer adopt a Big Data
Strategy. Furthermore the data is used to derive whether demographic variables have
any influence over the found perceptions.
4.2 Sample Analysis

Figure 11 illustrates the sample’s age distribution. 37.84% of the 37 total respondents
fall within the youngest age category, thus there is bias towards younger employees.
Figure 12 indicates that the majority of participants were educated at Undergraduate
level or below, the categories of which are fairly evenly represented. The mode category
for industry experience was 1 – 5 years, with 70.28% of the sample having 10 years or
less experience.

Just under half of the sample is made up of Customer Assistants, implying bias towards
this role. Furthermore, the sample consists of just under 60% female employees, again
insinuating a slight bias but providing adequate representation of both genders.

4.3 RQ2: What are Bank Branch Employees’ Perceptions of Big
Data Implications?
Question Mean Mode
Standard
Deviation
Variance
Table 4: Branch Employees' Perceptions of the Four Outlined Implications of Big Data Adoption
0.99
0.78
0.51
1.45
4
4
4
2
Increase sales through target marketing so that
customers are only subjected to marketing
communications which are relevant to them
Put the bank at an advantage to competitors by being
one of the first UK banks to embrace the technique
Benefit customers by better enabling the tailoring of
products and services to individual needs
Pose a threat to customer privacy
3.89
4.35
Note: 1 = Strongly Disagree to 5 = Strongly Agree
0.72
2.86 1.21
0.99
4 0.88

Figure 16 and Table 4 depict a collectively positive attitude towards Big Data. Of the
three potential benefits of Big Data, employees seemed the most sceptical about the
technology’s potential to increase sales, as reflected by the marginally lower mean and
higher standard deviation. Interestingly, the results indicate that the tailoring of products
and marketing was seen as having the biggest benefit with 90% of respondents agreeing
with the statement. There was a much higher ‘neutral’ response to the ‘innovators
advantage’ benefit, suggesting a higher level of uncertainty. This could be explained by
the notion that employees lower within the organisational hierarchy are typically less
exposed to the innovation process than those at management level (Kesting & Ulhøi, 2010).
The mean and mode in Table 4 also suggest that overall, employees are divided on
whether Big Data is a risk to customer privacy with over 50% disagreeing with the
statement. However, the results for this implication reveal a much higher standard
deviation and variance; the widely varied opinions suggesting a high level of scepticism
and uncertainty. This could arguably be explained by the general risk-averse nature of
the industry coupled with Big Data’s high degree of novelty.
To summarise, findings suggest branch employees generally have a positive perception of
Big Data however tend to be more uncertain when considering potential privacy risk.
4.4 RQ3: Are perceptions linked with participant’s varying
characteristics?
Table 5 provides a breakdown of the survey findings. From ‘eyeballing’ the data, it is
possible to see that in regards to the three implied benefits of Big Data, men tend to be
more positive than women. Furthermore there is evidence that those in senior branch roles
tend to be more in agreement with the inferred benefits of Big Data than those in junior
roles. These observations have been highlighted in Table 5 in Yellow.

However, calculating and interpreting the averages and standard deviations of the data
does not provide enough evidence to conclude findings. In order to gain an in-depth insight
into the data, Spearman’s Rank Correlation Coefficient has been calculated to determine
whether there any linear associations between the variables. Analysis was carried out
between all five participant variables and each of the four statements; the results of which
are presented in Appendix 2. These results have been summarised in Table 6 below.
Respondents' Characteristics Factors
Number of
Respondents
%
Age Mean SD Mean SD Mean SD Mean SD Mean SD
Age 16- 25 14 37.84% 4.14 0.87 4.00 0.78 4.14 0.66 2.64 1.08 4.10 0.75
26- 35 8 21.62% 4.00 0.93 3.88 0.83 4.75 0.46 3.00 1.41 4.21 0.83
36- 45 6 16.22% 3.50 1.22 4.17 0.75 4.33 0.52 3.17 1.17 4.00 0.90
46- 55 5 13.51% 3.60 1.14 4.00 1.22 4.60 0.55 2.80 1.45 4.07 1.03
56- 65 3 8.11% 3.33 1.15 4.67 0.58 4.00 1.73 3.33 1.15 4.00 1.20
66+ 1 2.70% - - - - - - - - - -
Trend
Education OLevels/GCSEs 10 27.03% 3.60 1.35 3.80 1.23 4.50 0.71 2.90 1.29 3.97 1.15
BTEC/AS/ALevels 13 35.14% 3.77 0.93 4.08 0.86 4.23 0.83 2.69 1.18 4.03 0.87
UndergraduateDegree 11 29.73% 4.18 0.75 4.09 0.54 4.55 0.52 3.09 1.30 4.27 0.62
Masters 3 8.11% 4.33 0.58 4.00 1.00 3.67 0.58 2.67 1.15 4.00 0.70
Trend
Gender Male 15 40.54% 4.07 1.15 3.93 0.84 4.60 0.80 3.40 1.14 4.20 0.78
Female 22 59.46% 3.77 0.70 4.05 0.96 4.18 0.51 2.50 1.12 4.00 0.94
Work Experience Under1Year 6 16.22% 3.33 1.03 3.50 0.55 3.83 0.41 1.83 0.41 3.56 0.70
1- 5Years 14 37.84% 4.29 0.61 4.14 0.77 4.57 0.65 2.79 1.05 4.33 0.68
6- 10years 6 16.22% 4.00 1.10 3.67 1.21 4.67 0.52 4.17 1.17 4.11 1.02
11- 15Years 4 10.81% 4.00 0.00 4.75 0.50 4.50 0.58 3.50 0.58 4.42 0.51
16- 20Years 3 8.11% 3.33 2.08 3.67 1.53 4.33 0.58 3.00 1.73 3.78 1.39
21+Years 4 10.81% 3.50 1.29 4.25 0.50 3.75 1.26 2.00 0.82 3.83 1.02
Trend
Role CustomerAssistant 16 43.24% 3.69 1.30 3.50 0.89 4.25 0.58 2.63 1.31 3.81 1.00
PersonalBanker 5 13.51% 3.60 0.89 4.00 0.71 3.60 0.89 2.20 0.45 3.73 0.79
PersonalBankingManager 6 16.22% 4.17 0.41 4.50 0.55 5.00 0.00 3.50 1.05 4.56 0.51
BranchManager 5 13.51% 4.40 0.55 4.40 0.89 4.40 0.89 3.60 1.14 4.40 0.73
MortgageAdvisor 3 8.11% 4.00 0.00 4.67 0.58 4.67 0.58 3.67 0.58 4.44 0.52
Other 2 5.41% 4.00 1.41 4.50 0.71 4.50 0.71 1.50 0.71 4.33 0.81
Trend
Table5: StatisticalAnalaysisof Findings
1 - Increase Sales
2 - Innovators
Advantage
3 - Tailored Offerings 4 - PrivacyThreat
CategoryAverage
for 1, 2 & 3
Age
Education
Gender
Experience
Role
Table 6: Summary of Spearman's Rank Test Findings
No correlation No correlation
No correlation
No correlation
No correlation
No correlation
Negative correlation
significant at the 5%
level
No correlation
No correlation
No correlation
No correlation
No correlation
No correlation
No correlation
No correlation
No correlation
Positive correlation
significant at the 5%
level
1 - Increase Sales
2 - Innovators
Advantage
3 - Tailored
Offerings
4 - Privacy Threat
No correlation No correlation No correlation

Two significant correlations were found in the data, which are highlighted in green in Table
6. For all fields marked ‘no correlation’, the test returned high ‘P’ Values, indicating that
the results were not significant at the 5% level and the correlations found were likely to
be as a result of random sampling and were thus rejected.
Table 7: Correlation Between Gender and Privacy Risk Rating
Gender Pose a Threat to
Customer Privacy
Spearman's rho
Gender
Correlation Coefficient 1.000 -.324
Sig. (2-tailed) . .051
N 37 37
Pose a Threat to Customer Privacy
Correlation Coefficient -.324 1.000
Sig. (2-tailed) .051 .
N 37 37
Table 7 presents the low negative correlation (ƿ = -0.324) between gender and the
potential privacy threat of Big Data. This correlation indicates that men perceive Big Data
to carry greater risks than women do. The P Value (0.051) indicates that the results are
significant within the sample size and the correlation can be accepted as a relevant
finding.
Table 8: Correlation Between Roles and Innovator’s Advantage Rating
Participant's
Current Role
Put the Bank at an
Advantage to
Competitors by
Being One of the
First to embrace
Big Data
Spearman's rho
Participant's Current Role
Correlation Coefficient 1.000 .519**
N 37 37
Put the Bank at an Advantage to
Competitors by Being One of the
First to embrace Big Data
Correlation Coefficient .519**
1.000
N 37 37
**. Correlation is significant at the 0.01 level (2-tailed).
Table 8 represents the moderately strong correlation found between participant roles
within the branch and their agreement with the innovator’s advantage statement. The P
Value returned at 0.001 indicates that the correlation is highly significant and has
therefore been accepted as a relevant finding. The correlation depicts that positivity

towards, and thus arguably the understanding of, the benefits of innovation increases as
role seniority increases. Again, this can arguably be explained in part by Kesting & Ulhøi’s
(2010) argument that exposure to innovation is dependent on the seniority of the role
within a firm’s hierarchy.
One of the limitations of this research is that it seeks what not why and thus the causation
of these findings cannot be deduced. However it can be speculated that the partition
between branch and head office staff may be one cause for the asymmetric flow of
knowledge in regards to innovation. Branch Managers are perceived to be the most
influential change agents (Lymperopoulos & Chaniotakis, 2004) and key decision makers
within bank branches and, along with Mortgage Advisors, tend to be frequently involved
in head office activity. Thus, they typically have more exposure to the innovation process
than other staff such as Customer Assistants.
In regards to the Hype Cycle, Branch Managers are arguably the most likely of all the
branch employees to be exposed to the hype of new innovations, as the role requires the
ability to implement and understand strategic activities at the branch level. This therefore
provides one potential explanation for why senior branch staff are more positive about
the concept of innovator’s advantage. Lymperopoulos & Chaniotakis’s also found that
employees’ lower in the branch hierarchy tended to be more sceptical around the
implementation of e-banking. Thus, their recommendation of utilising Branch Managers to
inform and persuade junior staff about the positive effects of new innovations is also
applicable in the case of Big Data.
4.5 Summary

The key findings of the data included that overall, branch employees have a positive
perception of Big Data, though there are mixed feelings in regards to the privacy risks
involved. Furthermore, statistical testing revealed that men are the most wary of the
privacy risks associated with Big Data while also the most positive towards the benefits.
There is a positive relationship between role seniority and positive perceptions towards
the concept of the innovator’s advantage, suggesting asymmetric communication flows are
hindering lower-level employee knowledge.
It seems that though employees’ are generally positive about Big Data, just under 25% of
participants were still unsure of whether they would like to see their employer adopt a
Big Data strategy. Furthermore, 47% of the sample felt that they did not have a good
understanding of the concept of Big Data. This could arguably be due to the fact that Big
Data is still in the early stages of the Hype Cycle and also because of the lack of branch
employee exposure to the technology, as the participating bank is yet to adopt a Big
Data strategy. This figure suggests that education is needed around Big Data in order for
employees’ to be able to make informed decisions and opinions about this innovation. In
which case, it can be assumed that if this study were to be carried out again, findings may
be quite different. Arguably, time is a critical factor in the diffusion of Big Data as a
widely-recognised technology and unless banks invest in training and improved
communications around innovation and strategy, branch employees’ will remain
segregated from and poorly informed of industry innovation.
5.Conclusion
Big Data is a recent radical innovation which has punctuated the equilibrium of many
industries and is arguably soon to have the same effect on the UK retail banking sector.
There is therefore a need to understand how retail banks can benefit from this innovation
and whether branch employees have a good understanding and are supportive of the
coming change. The aim of this study was to answer the three research questions below.
RQ1 was approached in the Literature Review, where the potential to increase sales, build
customer relationships and loyalty and gain first-mover advantage were outlined as the
key benefits of Big Data adoption. The threat to consumer privacy due to the potential of
data loss or misuse was highlighted as an important risk consideration. Questions 2 & 3
were successfully answered by the data analysis in Chapter 4.

Big Data is still an emerging innovation in the infancy stages of the hype cycle. Though it
has proven to be both successful and risky in various industries, its impact on the UK retail
banking sector can still only be speculated upon. This research has contributed to the
limited literature on Big Data by providing arguments and insights relating to the
implementation of the innovation in the UK retail banking sector. As highlighted in Chapter
1, there is a need for radical change in order to overcome the challenges instigated by
recent industry scandals and the economic downturn. The high data dispersion found in the
results of this research as well as the lack of industry examples of Big Data use within
banking, suggests that retail banks and their employees’ are still a long way from
adopting and fully understanding Big Data. It can therefore be argued that companies
within the sector have low absorptive capacity when it comes to technological change at
the branch level.
Based on the findings in the Literature Review, Big Data has the potential to revolutionise
the UK Retail Banking Industry by providing customer profiling that is more in-depth and
dynamic than anything available from previous technologies. Harnessing the power of this
technology will enable the industry to understand customer needs and desires better than
ever before, creating huge potential for customer relationship building and tailored
products and marketing. The findings of this study suggests that branch employees are on
the whole open and positive towards innovation, recognising the significance of the
innovator’s advantage, however are still somewhat sceptical towards Big Data in
particular.
Based on the findings of this study it is recommended that banks exploit the full potential
of Big Data to overcome current and future industry challenges by creating and building
upon complex and personalised long-term customer relationships. Banks should improve
the flow of communication to branch employees around topics of innovation to break down
the divide between roles and ease the diffusion process of new technologies within the
firm in order to increase absorptive capacity.

6.Recommendations
A cross-sectional view was adopted for this study at the time of Big Data being in the
trough of disillusionment phase of the Gartner Hype Cycle. Thus, it is recommended that
further research be carried out when the innovation has moved to a new section of the
cycle, which can be compared to the findings of this study and also provide further insight
into the impact of the innovation of Big Data in the UK retail banking industry.
This research concentrated specifically on the perceptions of branch employees’ of one
bank within Greater London. There is great scope for expanding upon this study, including
investigations on a larger scale across and outside of the UK and also including various
industry competitors. A similar study investigating the perceptions of retail banking head
office employees’ would complement this research by providing a basis for comparison.

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Appendices
Appendix 1 – Questionnaire

Appendix 2 – Spearman’s Rank Correlations
Privacy Threat
Correlations
Participant Age Pose a Threat to
Customer Privacy
Spearman's rho
Participant Age
Correlation Coefficient 1.000 .115
N 37 37
Correlation Coefficient .115 1.000
N 37 37
Correlations
Participant
Education Level
Pose a Threat to
Customer Privacy
Spearman's rho
Participant Education Level
N 37 37
N 37 37
Correlations
Participant
Experience Length
Pose a Threat to
Customer Privacy
Spearman's rho
Participant Experience Length
N 37 37
N 37 37

Correlations
Participant's Current
Role
Pose a Threat to
Customer Privacy
Spearman's rho
N 37 37
N 37 37
Increase Sales
Correlations
Gender Increase 'Customer
Outcomes' Through
Target Marketing
Spearman's rho
Gender
N 37 37
Increase 'Customer Outcomes'
Through Target Marketing
N 37 37
Correlations
Participant Age Increase 'Customer
Outcomes' Through
Target Marketing
Spearman's rho
Participant Age
N 37 37
N 37 37
Correlations
Participant
Education Level
Increase 'Customer
Outcomes' Through
Target Marketing
Spearman's rho
N 37 37
N 37 37

Correlations
Participant
Experience Length
Increase 'Customer
Outcomes' Through
Target Marketing
Spearman's rho
N 37 37
N 37 37
Correlations
Role
Increase 'Customer
Outcomes' Through
Target Marketing
Spearman's rho
N 37 37
N 37 37
Innovator’s Advantage
Correlations
Gender Put the Bank at an
Advantage to
Competitors by
Being One of the
First to embrace Big
Data
Spearman's rho
Gender
N 37 37
N 37 37

Correlations
Participant Age Put the Bank at an
Advantage to
Competitors by
Being One of the
Data
Spearman's rho
Participant Age
N 37 37
N 37 37
Correlations
Participant
Education Level
Put the Bank at an
Advantage to
Competitors by
Being One of the
Data
Spearman's rho
N 37 37
N 37 37
Correlations
Participant
Experience Length
Put the Bank at an
Advantage to
Competitors by
Being One of the
Data
Spearman's rho
N 37 37
N 37 37

Correlations
Role
Put the Bank at an
Advantage to
Competitors by
Being One of the
Data
Spearman's rho
Correlation Coefficient 1.000 .519**
N 37 37
Correlation Coefficient .519**
1.000
N 37 37
**. Correlation is significant at the 0.01 level (2-tailed).
Tailoring of Marketing and Incentives
Correlations
Participant Age Enable the Tailoring
of Marketing and
Incentives
Spearman's rho
Participant Age
N 37 37
Enable the Tailoring of Marketing
and Incentives
N 37 37
Correlations
Gender Enable the Tailoring
of Marketing and
Incentives
Spearman's rho
Gender
N 37 37
and Incentives
N 37 37

Correlations
Participant
Education Level
Enable the Tailoring
of Marketing and
Incentives
Spearman's rho
N 37 37
and Incentives
N 37 37
Correlations
Participant
Experience Length
of Marketing and
Incentives
Spearman's rho
N 37 37
and Incentives
N 37 37
Correlations
Role
of Marketing and
Incentives
Spearman's rho
N 37 37
and Incentives
N 37 37

Appendix 3 – Communications with Branch Area Director

Appendix 4 – Ethical Approval

Research Ethics
The Participant Information Sheet is designed for
participants’ use only and should contain required
information related to the research process
(collection methods, video or tape recording; length
of interviews, and the subsequent use of data).
Respondents also need to be informed that they
have the freedom and opportunity to withhold
consent at any point during the research process.

Research Ethics
Participant Information Sheet
1. Title of Research: Branch Employees’ Perceptions Toward the
Implementation of Big Data Analysis in Retail Banking
2. Researcher: Student Charlotte Lockhart on International Business BSc.
Brunel Business School, Brunel University
3. Contact Email: 1111415@my.brunel.ac.uk
4. Purpose of the research: The aim of this research is to discover the perceptions
of UK bank employees’ on the topic of Big Data in retail banking and explore whether
there is any correlation between their opinions and personal characteristics.
5. What is involved: Reading a fact sheet about Big Data and filling in a
questionnaire.
6. Voluntary nature of participation and confidentiality. Participation in this
research is voluntary; there is no obligation however your participation is much
appreciated. Participant’s identities will remain anonymous.

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