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

1 Student Number: 1111415

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MG3119

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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 ModeStandard

DeviationVariance

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 FactorsNumber 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 O Levels/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/A Levels 13 35.14% 3.77 0.93 4.08 0.86 4.23 0.83 2.69 1.18 4.03 0.87

Undergraduate Degree 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 Under 1 Year 6 16.22% 3.33 1.03 3.50 0.55 3.83 0.41 1.83 0.41 3.56 0.70

1 - 5 Years 14 37.84% 4.29 0.61 4.14 0.77 4.57 0.65 2.79 1.05 4.33 0.68

6 - 10 years 6 16.22% 4.00 1.10 3.67 1.21 4.67 0.52 4.17 1.17 4.11 1.02

11 - 15 Years 4 10.81% 4.00 0.00 4.75 0.50 4.50 0.58 3.50 0.58 4.42 0.51

16 - 20 Years 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 Customer Assistant 16 43.24% 3.69 1.30 3.50 0.89 4.25 0.58 2.63 1.31 3.81 1.00

Personal Banker 5 13.51% 3.60 0.89 4.00 0.71 3.60 0.89 2.20 0.45 3.73 0.79

Personal Banking Manager 6 16.22% 4.17 0.41 4.50 0.55 5.00 0.00 3.50 1.05 4.56 0.51

Branch Manager 5 13.51% 4.40 0.55 4.40 0.89 4.40 0.89 3.60 1.14 4.40 0.73

Mortgage Advisor 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

Table 5: Statistical Analaysis of Findings

1 - Increase Sales2 - Innovators

Advantage3 - Tailored Offerings 4 - Privacy Threat

Category Average

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 correlationNegative correlation

significant at the 5%

levelNo 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 Sales2 - Innovators

Advantage

3 - Tailored

Offerings4 - 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.


References

AirSage (2014) How it Works. Available at: http://www.airsage.com/Technology/How-

it-works/ [Accessed: 2nd November 2014].

Akaah, I. P., Korgaonkar, P. K. & Lund, D. (1995) ‘Direct Marketing Attitudes’, Journal of

Business Research, 34(3), pp. 211 – 219.

Auger, P. & Devinney T. M. (2007) ‘Do What Consumers Say Matter? The Misalignment

of Preferences with Unconstrained Ethical Intentions’, Journal of Business Ethics, 76(4), pp.

361–383.

Bosker, B. (2013) ‘Why People Don't Buy Microsoft's Phones (and Might Not Start)’, The

Huffington Post, 3 September [Online]. Available at:

http://www.huffingtonpost.com/2013/09/03/microsoft-nokia-windows-

phone_n_3861707.html [Accessed: 13th November 2014].

Bower, J. & Christensen, C. M. (1995) ‘Disruptive Technologies: Catching the Wave’,

Harvard Business Review, 73(1), pp. 43 – 53.

Breur, T. (2011) ‘Data Analysis Across Various Media: Data Fusion, Direct Marketing,

Clickstream Data and Social Media’, Journal of Direct, Data and Digital Marketing

Practice, 13 (2), pp. 95 – 105.

Creswell, J. W. (1994) Research Design: Qualitative and Quantitative Approaches.

Thousand Oaks: Sage.

Davenport, T. H. (2014) Big Data at Work. Boston, Massachusetts: Harvard Business

Review Press.

Dillman, D. A. (2009) Internet, Mail and Mixed Mode Surveys: The Tailored Design

Method. 3rd edn. New York: Wiley.

Economic and Social Research Council (2012) ESRC Framework for Research Ethics (FRE)

2010 Updated September 2012 [Online]. Available at: http://www.esrc.ac.uk/about-

esrc/information/research-ethics.aspx [Accessed: 5th January 2015].

Evans, R. (2011) ‘Timeline: how the PPI scandal unfolded’, The Telegraph, 4 August

[Online]. Available at:

http://www.airsage.com/Technology/How-it-works/

http://www.airsage.com/Technology/How-it-works/

http://www.huffingtonpost.com/2013/09/03/microsoft-nokia-windows-phone_n_3861707.html

http://www.huffingtonpost.com/2013/09/03/microsoft-nokia-windows-phone_n_3861707.html

http://www.esrc.ac.uk/about-esrc/information/research-ethics.aspx

http://www.esrc.ac.uk/about-esrc/information/research-ethics.aspx


http://www.telegraph.co.uk/finance/personalfinance/insurance/incomeprotection/8681

344/Timeline-how-the-PPI-scandal-unfolded.html [Accessed: 3rd November 2014].

Ferguson, D. (2013) ‘How Supermarkets Get Your Data – and What They Do with It’, The

Guardian, 8 June [Online]. Available at:

http://www.theguardian.com/money/2013/jun/08/supermarkets-get-your-data

[Accessed: 3rd June 2014].

Financial Services Authority (2005) The Sale of Payment Protection Insurance – Results of

Thematic Work [Online]. Available at:

www.fsa.gov.uk/pubs/other/ppi_thematic_report.pdf [Accessed: 3rd November 2014].

Financial Services Authority (2014) PPI Payouts [Online]. Available at:

http://www.fsa.gov.uk/static/pubs/consumer-research/ppi-graph-jan13.pdf [Accessed:

3rd November 2014].

Fletcher, K., Wright, G. & Desai, C. (1996) ‘The Role of Organisational Factors in the

Adoption and Sophistication of Database Marketing in the UK Financial Services

Industry’, Journal of Direct Marketing, 10(1), pp. 10 – 21.

Fox, S. & Do, T. (2013) ‘Getting Real About Big Data: Applying Critical Realism to

Analyse Big Data Hype’, International Journal of Managing Projects in Business, 6(4), pp.

739 – 760.

Gartner (2014a) Gartner's 2014 Hype Cycle for Emerging Technologies Maps the Journey

to Digital Business. Available at: http://www.gartner.com/newsroom/id/2819918

[Accessed: 24th November 2014].

Gartner (2014b) Gartner Hype Cycle. Available at:

http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp [Accessed:

26th October 2014].

Goff, S. & Cadman, E. (2014) ‘UK Banks Count Climbing Cost of PPI Mis-selling’, The

Financial Times, 3 February [Online]. Available at:

http://www.ft.com/cms/s/0/8310b6ec-8ced-11e3-ad57-

00144feab7de.html#axzz3I0ojR7lK [Accessed: 3rd November 2014].

Grant Thornton (2013) Retail Banking: Innovation distinguishes between the leaders and

the followers. Available at: http://www.grant-

thornton.co.uk/Global/Publications%20thumbnails/V23384%20Retail%20Banking%20

8pp_email.pdf [Accessed: 21st December 2014].

http://www.telegraph.co.uk/finance/personalfinance/insurance/incomeprotection/8681344/Timeline-how-the-PPI-scandal-unfolded.html

http://www.telegraph.co.uk/finance/personalfinance/insurance/incomeprotection/8681344/Timeline-how-the-PPI-scandal-unfolded.html

http://www.theguardian.com/money/2013/jun/08/supermarkets-get-your-data

http://www.fsa.gov.uk/pubs/other/ppi_thematic_report.pdf

http://www.fsa.gov.uk/static/pubs/consumer-research/ppi-graph-jan13.pdf

http://www.gartner.com/newsroom/id/2819918

http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp

http://www.ft.com/cms/s/0/8310b6ec-8ced-11e3-ad57-00144feab7de.html#axzz3I0ojR7lK

http://www.ft.com/cms/s/0/8310b6ec-8ced-11e3-ad57-00144feab7de.html#axzz3I0ojR7lK

http://www.grant-thornton.co.uk/Global/Publications%20thumbnails/V23384%20Retail%20Banking%208pp_email.pdf




Great Britain. Financial Services (Banking Reform) Act 2013 (2013) London: The

Stationary Office.

Hallowell, R. (1996) ‘The Relationships of Customer Satisfaction, Customer Loyalty, and

Profitability: an Empirical Study’, International Journal of Service Industry Management,

7(4), pp. 27 – 42.

Henry, G. T. (1990) Practical Sampling. Newbury Park: Sage.

Hilbert, M. & Lopez, P. (2011) ‘The World’s Technological Capacity to Store,

Communicate, and Compute Information’, Science, 332(60), pp. 60 - 65

Hussey, J. & Hussey, R. (1997) Business Research: A Practical Guide for Undergraduate

and Postgraduate Students. London: Macmillan.

Information Commissioner’s Office (2014a) Data Protection Principles. Available at:

http://ico.org.uk/for_organisations/data_protection/the_guide/the_principles


Information Commissioner’s Office (2014b) Big Data and Data Protection. Available at:

https://ico.org.uk/media/for-organisations/documents/1541/big-data-and-data-

protection.pdf [Accessed: 14th December 2014].

Kesting, P. & Ulhøi, J. P. (2010) ‘Employee‐driven Innovation: Extending the License to

Foster Innovation’, Management Decision, 48(1), pp. 65 – 84.

KPMG (2012) Data Loss Barometer. Available at:

http://www.kpmg.co.uk/email/12Dec12/276985/PageTurner/index.html [Accessed:

14th December 2014].

Kuchler, H. (2014) ‘Target Data Theft Sounds Wake-up Call for Retailers’, The Financial

Times, 15 January [Online]. Available at: http://www.ft.com/cms/s/0/7d5f28bc-7d81-

11e3-81dd-00144feabdc0.html#axzz3Lsu0mBhf [Accessed: 14th December 2014].

Lacmanovic, D., Lacmanovic, I. & Markoski, B. (2012) ‘Mobile Banking – Financial

Services Technology’, 35th International Convention on Information and Communication

Technology, Electronics and Microelectronics, MIPRO, Croatia, 21 to 25 May 2012.

IEEEXplore [Online]. Available at:

http://ieeexplore.ieee.org.ezproxy.brunel.ac.uk/xpls/abs_all.jsp?arnumber=6240881


http://ico.org.uk/for_organisations/data_protection/the_guide/the_principles

https://ico.org.uk/media/for-organisations/documents/1541/big-data-and-data-protection.pdf

https://ico.org.uk/media/for-organisations/documents/1541/big-data-and-data-protection.pdf

http://www.kpmg.co.uk/email/12Dec12/276985/PageTurner/index.html

http://www.ft.com/cms/s/0/7d5f28bc-7d81-11e3-81dd-00144feabdc0.html#axzz3Lsu0mBhf

http://www.ft.com/cms/s/0/7d5f28bc-7d81-11e3-81dd-00144feabdc0.html#axzz3Lsu0mBhf

http://ieeexplore.ieee.org.ezproxy.brunel.ac.uk/xpls/abs_all.jsp?arnumber=6240881


Laney, D. (2001) ‘3D Data Management: Controlling Data Volume, Velocity, and

Variety’, Gartner Blog, 14 January. Available at: http://blogs.gartner.com/doug-

laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-

data/ [Accessed: 20th October 2014].

Loch, C. H. & Huberman, B. A. (1999) ‘A Punctuated-Equilibrium Model of Technology

Diffusion’, Management Science, 45(2), pp. 160 – 177.

Lymperopoulos, C. & Chaniotakis, I. E., (2004) ‘Branch employees’ perceptions towards

implications of e‐banking in Greece’, International Journal of Retail & Distribution

Management, 32(6), pp. 302 – 312.

Machanavajjhala, A. & Reiter, J. P. (2012) ‘Big Privacy: Protecting Confidentiality in Big

Data’, XRDS Crossroads, 19(1), pp. 20 – 23.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. & Byers, A. H. (2011)

Big Data: The Next Frontier for Innovation, Competition and Productivity [Online].

Available at:

http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for

_innovation [Accessed: 9th June 2014].

Marous, J. (2012) ‘Big Data: Big Opportunity In Banking… Or Big B.S.?’, The Financial

Brand, 26 November [Online]. Available at: http://thefinancialbrand.com/26363/big-

data-analytics-retail-banking-jm/ [Accessed 3rd October 2014].

Martin, C. & Milas, C. (2010) ‘Financial Market Liquidity and the Financial Crisis: An

Assessment Using UK Data’, International Finance, 13(3), pp. 443 - 459.

Masters, B. (2010) ‘FSA Fines Zurich UK £2.27m for Data Loss’, The Financial Times, 24

August [Online]. Available at: http://www.ft.com/cms/s/0/09d10330-af75-11df-

a172-00144feabdc0.html#axzz3Lsu0mBhf [Accessed: 14th December 2014].

Mayer-Schönberger, V. & Cukier, K. (2013) Big Data: A Revolution That Will Transform

How We Live, Work and Think. London: John Murray.

McGregor, R. (2012) ‘Inside Obama’s HQ’, The Financial Times, 14 September [Online].

Available at: http://www.ft.com/cms/s/2/0df7cc4a-fd35-11e1-a4f2-

00144feabdc0.html#slide0 [Accessed: 6th December 2014].

Mitic, M. & Kapoulas, A. (2012) ‘Understanding the Role of Social Media in Bank

Marketing’, Market Intelligence & Planning, 30(7), pp. 668 – 686.

http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/



http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

http://thefinancialbrand.com/26363/big-data-analytics-retail-banking-jm/

http://thefinancialbrand.com/26363/big-data-analytics-retail-banking-jm/

http://www.ft.com/cms/s/0/09d10330-af75-11df-a172-00144feabdc0.html#axzz3Lsu0mBhf

http://www.ft.com/cms/s/0/09d10330-af75-11df-a172-00144feabdc0.html#axzz3Lsu0mBhf

http://www.ft.com/cms/s/2/0df7cc4a-fd35-11e1-a4f2-00144feabdc0.html#slide0

http://www.ft.com/cms/s/2/0df7cc4a-fd35-11e1-a4f2-00144feabdc0.html#slide0


Morgan, R. M. & Hunt, S. D. (1994) ‘The Commitment-Trust Theory of Relationship

Marketing’, Journal of Marketing, 58(3), pp. 20 – 38.

NatWest (2014) Cashback Plus. Available at: https://www.cashbackplus.natwest.com/

[Accessed: 9th December 2014].

Nuttall, C. (2014) ‘UK Ministry of Justice Fined Over Prison Data Loss’, The Financial

Times, 26 August [Online]. Available at: http://www.ft.com/cms/s/0/240e2eb2-2d0c-

11e4-8105-00144feabdc0.html#axzz3Lsu0mBhf [Accessed: 14th December 2014].

Peachey, K. (2014) ‘Can Supermarkets Steal More of Big Banking's Business?’, BBC, 21

February [Online]. Available at: http://www.bbc.co.uk/news/business-26289191


Press Association (2014) ‘Banks Fined £2bn Over Forex Scandal’, Mail Online, 12

November [Online]. Available at: http://www.dailymail.co.uk/wires/pa/article-

2831019/Banks-face-1bn-forex-penalty.html [Accessed: 12th November 2014].

PriceWaterhouseCoopers (2014) Retail Banking 2020 [Online]. Available at:

http://www.pwc.com/gx/en/banking-capital-markets/banking-2020/download-and-

read-the-report.jhtml [Accessed 26th October 2014].

Romanelli, E. & Tushman, M, L. (1994) ‘Organisational Transformation as Punctuated

Equilibrium: An Empirical Test’, Academy of Management, 37(5), pp. 1141 – 1166.

Rowley, J. (2000) ‘Loyalty Kiosks: Making Loyalty Cards Work’, British Food Journal,

102(5/6), pp. 390 – 398.

Rowley, J. (2004) ‘Loyalty and Reward Schemes: How Much is Your Loyalty Worth?’, The

Marketing Review, 4(2), pp. 121 – 138.

Santander (2013) Corporate Social Responsibility Report 2013 [Online]. Available at:

http://www.santander.co.uk/uk/about-santander-uk/csr/ [Accessed: 16th November].

Satell, G. (2014) ‘A Look Back At Why Blockbuster Really Failed and Why It Didn't

Have To’, Forbes, 5 September [Online]. Available at:

http://www.forbes.com/sites/gregsatell/2014/09/05/a-look-back-at-why-

blockbuster-really-failed-and-why-it-didnt-have-to/ [Accessed: 25th November 2014].

Saunders, M., Lewis, P. & Thornhill, A. (2003) Research methods for Business Students. 3rd

edn. England: Pearson.

https://www.cashbackplus.natwest.com/

http://www.ft.com/cms/s/0/240e2eb2-2d0c-11e4-8105-00144feabdc0.html#axzz3Lsu0mBhf

http://www.ft.com/cms/s/0/240e2eb2-2d0c-11e4-8105-00144feabdc0.html#axzz3Lsu0mBhf

http://www.bbc.co.uk/news/business-26289191

http://www.dailymail.co.uk/wires/pa/article-2831019/Banks-face-1bn-forex-penalty.html

http://www.dailymail.co.uk/wires/pa/article-2831019/Banks-face-1bn-forex-penalty.html

http://www.pwc.com/gx/en/banking-capital-markets/banking-2020/download-and-read-the-report.jhtml

http://www.pwc.com/gx/en/banking-capital-markets/banking-2020/download-and-read-the-report.jhtml

http://www.santander.co.uk/uk/about-santander-uk/csr/

http://www.forbes.com/sites/gregsatell/2014/09/05/a-look-back-at-why-blockbuster-really-failed-and-why-it-didnt-have-to/

http://www.forbes.com/sites/gregsatell/2014/09/05/a-look-back-at-why-blockbuster-really-failed-and-why-it-didnt-have-to/


Schäfer, D. & Fleming, S. (2014) ‘Forex Scandal puts London’s Reputation on the Line’,

The Financial Times, 5 March [Online]. Available at:

http://www.ft.com/cms/s/0/36196e7a-a48e-11e3-b915-

00144feab7de.html#axzz3IyRin13G [Accessed: 14th November 2014].

Steinert, M. & Leifer, L. (2010) ‘Scrutinizing Gartner's Hype Cycle Approach’, Technology

Management for Global Economic Growth (PICMET), 2010 Proceedings of PICMET '10:.

Phuket, 18 – 22 July. IEEE. Available at:

http://ieeexplore.ieee.org.ezproxy.brunel.ac.uk/xpl/articleDetails.jsp?tp=&arnumber=

5603442&queryText%3DScrutinizing+Gartner%27s+hype+cycle+approach [Accessed

28th October 2014].

Stevens, S. S. (1946) ‘On the Theory of Scales of Measurement’, Science, 103(2684), pp.

677-680.

Tesco (2014) Tesco Clubcard. Available at: http://www.tesco.com/clubcard/about/

[Accessed: 9th December 2014].

US. President’s Council of Advisors on Science and Technology (2014) Big Data and

Privacy: A Technological Perspective [Online]. Available at: www.

whitehouse.gov/sites/default/files/ microsites/ostp/PCAST/pcast_big_

data_and_privacy_-_may_2014 [Accessed: 13th December 2014].

Wigan, M. R. & Clarke, R. (2013) ‘Big Data's Big Unintended Consequences’, Computer,

46(6), pp. 46 – 53.

Wray, R. (2009) ‘T-Mobile Confirms Biggest Phone Customer Data Breach’, The

Guardian, 17 November [Online]. Available at:

http://www.theguardian.com/uk/2009/nov/17/t-mobile-phone-data-privacy


Yell, G., Lopianowski, A., Wildman, J. and Maxwell, P. (2012) Protection from the Perfect

Storm: the Future of UK Retail Banking [Online]. Available at:

http://www.haygroup.com/uk/downloads/Details.aspx?ID=35071 [Accessed: 26th

October 2014].

http://www.ft.com/cms/s/0/36196e7a-a48e-11e3-b915-00144feab7de.html#axzz3IyRin13G

http://www.ft.com/cms/s/0/36196e7a-a48e-11e3-b915-00144feab7de.html#axzz3IyRin13G

http://ieeexplore.ieee.org.ezproxy.brunel.ac.uk/xpl/articleDetails.jsp?tp=&arnumber=5603442&queryText%3DScrutinizing+Gartner%27s+hype+cycle+approach

http://ieeexplore.ieee.org.ezproxy.brunel.ac.uk/xpl/articleDetails.jsp?tp=&arnumber=5603442&queryText%3DScrutinizing+Gartner%27s+hype+cycle+approach

http://www.tesco.com/clubcard/about/

http://www.theguardian.com/uk/2009/nov/17/t-mobile-phone-data-privacy

http://www.haygroup.com/uk/downloads/Details.aspx?ID=35071


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: [email protected]

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.

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

Documents

student number

branch employees perceptions

benefits of big data

big data phenomenon

emergence of big data

characteristics of employees

adoption of big data

risk of big data analytics