Automatic Teller Machines (ATMs) and Automated Payment Services ...... Media exploitation, email marketing, search engine marketing, mobile marketing).â.
University of Durham School of Economics, Finance and Business Submitted for the Qualification of MSc in Finance (Finance and Investment)
Thesis Title: Internet Banking and Diversification in Greek Banks
Anonymous Code: Z0947699 Dissertation Supervisor: Dr Tom McLean
Ustinov College
Word Count: 9,999
January 16, 2015 Page | 1
Declaration
“This dissertation is the result of my own work. Material from the published or unpublished work of others, which is referred to in the dissertation, is credited to the author in question in the text. The dissertation is approximately 9,999 words in length.”
Signature
Date 16 January 2015
Page | 2
Abstract
This thesis aims to evaluate the effectiveness of electronic banking diversification on both Greek retail and co-operative banks, using a combination of methodologies as suggested by Angelakopoulos and Mihiotis (2011) and by Palepu (1985). The former cover primarily the qualitative analysis of e-banking, while the latter the specific diversification measures. The researcher’ main findings suggest that co-operative banks’ e-banking differs from commercial banks in terms of market share, capitalisation, scope, regional constraints and regulations. Furthermore, co-operative banks’ e-banking offers only the two most popular echannels: internet banking and ATM, simultaneously requiring support from a third party. By contrast, retail e-banking tends to be more autonomous, providing more diversified eservices. Additionally, the researcher argues that a more related diversification e-banking strategy is more profitable than a less related one. However, the cost of such a strategy is usually higher, as it appears to be more sensitive to economic shocks. Overall, digital banking services strengthen the bank / consumer relationship and contribute to the general profitability and financial control of the institution. The optimal related / unrelated diversification strategy depends on the bank’s nature and economic environment.
Keywords: Electronic Banking; Diversification strategies; High/Low Total Diversification, High/Low Related/Unrelated Diversification, Profitability
Page | 3
Executive Summary
The purpose of this thesis is to evaluate the entire e-banking sector in Greece, using a combined methodology (Angelakopoulos and Mihiotis, 2011; Palepu, 1985), which allows the examination of a sample entailing ten banks, and their characteristics for a ten-year period (2004-2013). Much literature to date has focused only on commercial banks; this research goes a step further by including co-operative e-banking features. The sample’s data was obtained in accordance with Angelakopoulos and Mihiotis’ (2011) methodological advice – that is, interviews, questionnaires and desktop research. Then, using Jacquemin and Berry’s (1979) entropy measure of diversification and, in line with Palepu (1985), the researcher measured e-strategies performance. The sample was split into two groups (high and low Total/Related / Unrelated diversification strategies) before testing each of the thesis’ hypotheses (A, B, C and C’).
The thesis’ empirical results seem to agree with previous research. For instance, e-banking is offered for the same reasons in all Greek banks, as it provides the same benefits and challenges. However, co-operative e-banking is smaller in range and less autonomous. ATM and internet banking remain the most common e-banking services, while mobile banking and APS are the least popular ones. Further, consumer orientation / strength and consumer commitment is the number one reason behind the existence of such services, and both banks as well as customers benefit from their lower cost.
Additionally, the diversification-profitability relation shows that, even though there is no significant difference between high and low total diversification, a more related diversification strategy is more profitable. The researcher also concluded that the difference in cost might reflect an upcoming systemic crisis. Summing up, there are many parameters that suggest that there is no common recipe for the optimal diversification e-strategy.
Page | 4
Acknowledgements
I would like to thank first of all my supervisor, Mr. Tom McLean, Senior Lecturer in Accounting in the Durham University Business School, who encouraged and strongly supported this research all the way through. I would also like to thank:
The participant banks, especially the interviewees for their time and valuable feedback.
Special thanks go to Ioanna Archimandriti, Senior Manager Strategy Investment & Planning Group in Eurobank Ergasias, who not only helped with the contacts collection, but also provided the latest reports, supporting and solidifying this research every step of the way.
Additionally, I have to thank my professors and classmates, who made my time in Durham a unique experience, and helped me learn more not only about finance, but also about life.
Finally, I would like to thank my parents for their unlimited love and support.
Page | 5
Table of Contents
Page Declaration
2
Abstract
3
Executive
4
Summary Acknowledgements
5
Table of Contents
6
Index of Figures
8
Index of Tables
9
Chapter 1:
Introduction
10
Chapter 2:
Literature Review
14
2.1 Studies on the Diversification / Economic Performance relationship
Chapter 3:
14
2.2 Electronic Banking Literature Review
15
2.3 Electronic Banking in the Greek Market
19
Hypothesis, Methodology, Data and Measures
22
3.1 A Combined Methodology
22
3.2 Hypotheses to be tested
22
3.3 Diversification measures and how to overcome possible limitations
23
3.4 Methodological procedure and data
26
3.4.1. Qualitative methodology and data
27
3.4.2. Quantitative methodology and date
29 Page | 6
3.5 Descriptive statistics, sample and statistical tests
Chapter 4:
30
3.6 Other limitations faced by this method
33
Analysis and Results
34
4.1. A Qualitative results presentation
34
4.1.1. Survey Results
34
4.1.2 A Critical Evaluation of the Interviews
42
4.2. Quantitative data analysis and descriptive statistics
46
Chapter 5:
Conclusion and implications
53
Appendix 1:
Diversification Measures
56
Appendix 2:
Quantitative data evaluation- Hypothesis A
59
Appendix 3:
Quantitative data evaluation- Hypothesis B
65
Appendix 4:
Quantitative data evaluation- Hypothesis C
69
Appendix 5:
Quantitative data evaluation- Hypothesis C '
70
Appendix 6:
Quantitative data evaluation: further tests
73
Appendix 7:
Initial Report
78
Appendix 8:
Power Analysis
91
Appendix 9:
Qualitative descriptive statistics
92
References
94
Page | 7
Index of Figures Page Graph 3.1: Email Resources
29
Graph 4.1: Weight Classification of digital banking channels
35
Graph 4.2: Factors for offering e-banking services
36
Graph 4.3: Client benefits from e-banking
38
Graph 4.4: Classification of possible problems a client may face during ebanking
39
Graph 4.5: Common E-Banking challenges that Banks may face
40
Graph 4.6: Factors preventing clients from using e-banking services
41
Page | 8
Index of Tables Page Table 2.1: The latest map of Greek Banks which offer electronic services
20
Table 3.1: Research Hypotheses
23
Table 3.2: Participants Overview
28
Table 3.3: Summary Statistics and Correlations
32
Table 4.1: Hypotheses and strategies testing
47
Table 4.2: The performance of Total Diversification strategies for digital banking Table 4.3: A comparison between Related and Unrelated e-services Diversification performance Table 4.4: Related and Unrelated Diversification and Profitability growth Table 4.5: The performance of the banks which retain their Related and Unrelated diversification from 2004 up to 2009
48
49 50 52
Page | 9
Chapter 1: Introduction
According to Gort (1962, p. xix): “There never was a business with only one productive process and one final product; there never will be”. Of course Gort was referring to “diversification”, a term defined as the firm’s activities’ spread. Likewise, a bank has not only one service / product; a good example of this comes from the electronic banking (EB) division: it is widely accepted that EB services contribute to financial control, simultaneously increasing customer satisfaction and bank profitability. Obviously, an e-services diversification attracts more consumers and reduces (firm) risk. Grifell and Lovell (1999) identified the increasing importance of both diversification and technology for Spanish commercial banks, and Palepu (1985), using Jacquemin and Berry’s (1979) entropy measure, showed the significant relationship between diversification strategy and profitability for firms with the same production line.
Much literature to date (i.e. Pikkarainen et al., 2004); Xue et al., 2011)) has either studied the influence of electronic fund transfers from the perspective of their users (clients), or the same phenomenon from the perspective of the main providers of the e-services, i.e. banks (e.g. Gonzalez et al., 2004)). In particular, the EB services increasing development started with the Automatic Teller Machines (ATMs) and Automated Payment Services Terminals (APS), later spreading to phone banking (PB), internet banking (IB) and applications of mobile banking (MB). Research interest in EB is inseparable from modern advances in technology.
Needless to say that the current digital era has further increased the importance of EB; the efficient use of digital financial products, such as IB or MB seems to be the vehicle to strengthen the bank sector. Safe access to bank services anytime from anywhere enhances clients’ trust, minimizes transaction time / cost and generates profits. All the above have been confirmed in both academic literature and companies’ reports; for instance:
Page | 10
“The implication for banks is that as business models are transformed by the shift to digital channels, [electronic banking] opens up new opportunities for engaging and interacting with customers to build relationships and grow revenues. For banks that manage to engender a similar shift in their own distribution models, similar opportunities await.” (PWC 2011a, p.9).
Nevertheless, the last decade’s apparent development of the EB sector is not only an indication of the stiff competition within the field: it is also a requirement of the recent economic crisis. Economic recovery presupposes innovative ways to keep up with the challenging global environment; Vos and Koparanova (2
011)
have
reported
that
economies in the after-crisis phase are extremely sensitive to worldwide economic shocks. In addition, they proposed the diversification strategy as a vehicle to increase growth for institutes whose economies are affected the most by the recent crisis. Therefore, the examination of alternative ways of doing banking emerges as a strong requirement of the current global market.
The Greek economy is a typical example; after 2009, Greece asked for EU help and also received assistance from the IMF. This caused a dramatic change on the map of Greek Banks (MGB), emphasising, among other things, the need for further measures of financial control. Most of the studies up to now have focused on e-commerce and the adoptability of electronic banking in the Greek market: for instance, clients without prior experience are unaware of the security of their personal and account data, which is a strong inhibitory factor for the adoptability of electronic banking. Other studies, such as Angelakopoulos and Mihiotis’ (2011), have examined the main challenges and opportunities for Greek banks’ e-services. The latter conclude that, even though banks continue to develop e-services with the view of remaining competitive, there is much room for further improvement.
The present thesis will attempt to go beyond the above findings; it aims to go a step further by investigating the impact of the EB spread on the Greek banks joints, as they have been mapped from recent and old acquisitions in Greece. More specifically, the main purpose of Page | 11
this research is the investigation of the key features (both quantitative and qualitative) of EB, focusing on different categories of Greek banks that support e-services – from the big four (National Bank of Greece, Eurobank Ergasias S.A., Alpha Bank, Piraeus Bank) to the cooperative ones (see Table 2.1 later). The present study also aspires to opening up the field in the EB sector and its diversification. Using the quantitative methodology of Palepu (1985), in conjunction with the qualitative methodology of Angelakopoulos and Mihiotis (2011), this thesis examines, apart from the different features of diversification and related diversification, a possible relation between diversification and profitability.
Therefore, the main questions of this thesis can be summarized as follows:
1) Which are the key characteristics of EB in Greece and which are the diversification strategies in this area? 2) How does Greek EB perform and what is the impact on the new and previously established joints, as well as on the banking system as a whole? 3) Which Greek EB areas need further development and how can such development potentially result in a profitable diversification?
The data for this thesis were collected through interviews, questionnaires, bank reports and desktop research. Invaluable help was also received from the interviewed bank executives. The participants of the interviews and questionnaires were identified through careful study on their LinkedIn profiles – something necessary in order to meet the strict requirements set for this research and ensure data accuracy. Indeed, the present thesis relies on the most up-to-date data from the Greek banking sector, with almost all of the bank officers replying promptly and thus solidifying the research findings.
In short, up to now there has not been a quantitative EB evaluation in Greece, or a study that combines commercial and co-operative EB or the latest joints. However, the need to evaluate the performance of digital banking has confirmed the importance of this study. Next, chapter Page | 12
two presents the literature review, which includes the proposed theories and empirical work that explore EB services and their diversification. Chapter three illustrates the research hypothesis, methodology and data, before moving to the actual empirical results (chapter four) and the conclusion (chapter five).
Page | 13
Chapter 2: Literature Review
This chapter illustrates theories and studies underlying the diversification-performance relationship. It then provides deeper insight into e-banking, before presenting previous research on the Greek e-banking.
2.1 Studies on the Diversification – Economic Performance relationship
Corporate diversification and its relation to profitability is a subject of great debate: Utton (1979) reports that an increasing competition between large companies triggers diversification. In addition, both industrial organisation and strategic management studies have questioned whether a firm’s activity or heterogeneity can be seen as indicators of economic performance. What is more, while the industrial organisation researchers deny the significance of such a relationship, strategic management studies have found strong evidence of a relation between profitability and systematic diversification, especially for firms with extra-discriminated deeds.
The industrial organisation researchers’ point of view is exemplified by Gort (1962), who examined the impact of diversification strategies on corporate profits of US industries during 1947-1957. Gort’s measures are:
The sum of firms’ activities within industries,
The ratio of the industries return to the previously mentioned sum and
The product of the above combination.
However, Gort failed to prove a significant cross-sectional relationship between the aforementioned measures and corporate profitability. Then, Arnould (1969) added two more measures:
Page | 14
Relating the firm’s shares to the industry’s structure and
Evaluating the firm’s activities
Further, Markham (1974) investigated the main corporate diversification implications with respect to variables of profitability using regression models before his final estimation. Both the above researchers confirmed and extended Gort’s (1962) findings.
Nevertheless, strategic management researchers generally question the afore-mentioned findings, reprobating the industrial organisation null-hypothesis and introducing a different approach. For instance, Rumlet (1975) showed that, the more unrelated the industries, the higher the firms’ profitability, but the opposite also holds true. Rumlet’s (1975) primary methodological difference stems from the use of categorical measures related to a nine (and then seven) heterogeneous activities classification theme, which is not directly comparable to the afore-mentioned simple-product line indices, as it includes both quantitative and qualitative elements. Along the same lines, Christensen and Montgomery (1981) and, subsequently, Montgomery (1982) confirmed and extended Rumlet’s (1975) findings. Moreover, after conducting a methodological comparison, Montgomery (1982) concluded that strategic management measures are superior to the measures proposed by industrial organisation studies. The key indicator, as confirmed again by a different Rumlet (1982) study, was the clear line between related and unrelated diversification. Next, Jacquemin and Berry (1979) developed the entropy measure, which was later used in Palepu’s (1985) sameproduction-line food companies study to report a higher boost in profitability allotment from more related activities / heterogeneous firms.
2.2 Electronic Banking Literature Review
Bearing in mind previous studies (e.g. Palepu; 1985) about the link between diversification and profitability, the present study aims to explore this phenomenon in the e-banking industry as well. The diversification-performance relationship within the banking industry is further confirmed by the internal capital markets theory (ICT); (see Bernardo et al., 2006). Likewise,
Page | 15
ICT states that once the outer capital market defects, bank heterogeneity arises from competent assignment of internal management and administration of funds. Thus, service and assets modification allows unsystematic risk (Saunders, Strock and Travos, 1990). Much literature to date shows that, after a merger or acquisition (M&A), the least profitable divisions’ cross-subrogation minimises unsystematic risk (Klein and Saidenberg in Harker and Zenios, 2000). Similarly, researchers such as Amihud and Lev (1981) report that this kind of diversification might also be a management fortification form. In this case, various corporate finance researches (i.e. Clayman et al. 2012; Berk, DeMarzo; 2014) conclude that the managers acquire more power within the company (e.g. greater influence, bonuses, position retaining), regardless of their companies’ actual competitive advantage, or their shareholders’ wealth.
Moreover, literature also suggests that a diversified firm has defence mechanisms such as “cross-subsidization, predatory pricing, reciprocity in selling and buying, and barriers of entry” (Palepu 1985, p. 241-242), which come from the firm’s aptitude to increase its market share and control market power over its competitors. Another important function of a diversified firm is “information loss” (Palepu, 1985). With this term, Palepu (1985) refers to the firm’s ability to keep away from public information its comparative advantage – in other words, the advantage from the diversified profitable activities that allow the firm to retain its competitiveness over others. This results in wider diversification, which in turn increases the respective profits.
Other researchers have made direct and indirect allusions to the particular importance of information technology as a tool for diversification between commercial banks (Gadrey, Gallouj and Weinstein (1995); Drnevich and Croson, 2013). EB is also mentioned in the literature as an alternative channel for delivering banking services, while it simultaneously takes advantage of the technological benefits. A further integrated definition says that ebanking is:
Page | 16
“The use of the internet to organize, examine, and make changes to your bank accounts and investments, etc. electronically, or the use of the internet by banks to operate accounts and services.” (Cambridge online dictionary, http://dictionary.cambridge.org/dictionary/british/electronicbanking, 12.09.2014).
Horvitz and White (2000) report that banks first used EB during the 19th century to make transfers, with the aid of the relevant contemporary technology. This resulted in the creation of the Clearing House Interbank Payment System (CHIPS) in 1970 and the introduction of ATMs in 1969 by Chemical Bank in Rockville Centre, New York. By the 1980s ATMs were widely used, and credit cards have been popular for almost half a century. Along similar lines, Lin (2007), Ngai and Gunasekaran (2007), as well as Michael (2007), investigated how Information Technology (IT) can provide profitability. Moreover, one of the main questions that Michael (2007) tried to answer was whether the management of IT can be a tool of diversification. In addition, Ming-Chi Lee (2009) focused on the factors that highlight and intercept a successful adoption of IT services; Lee’s primary argument is that financial and security restrictions, privacy or ethical collusions, culture and further quantity and performance risks may pose an obstacle to the assimilation of new methods.
Gonzalez et al. (2004) concentrated on the e-banking sector in general, analysing management issues and further services under a quality function deployment (QFD) framework and looking at its approaches. Then, Pikkarainen et al. (2004) investigated consumer behaviour in the face of innovative digital services. Laukkanen and Pasanen (2008) went a step further by showing the main differences between m-Banking and other early IT innovations, using Logistic Regression Analysis (LRA). Their results indicate that mobile users have to be approached as a unique target group, even if their demographics are similar to other e-banking services.
There are also studies investigating the impact of innovation of one or more countries on their financial Institutions: Daniel (1999), for instance, studied UK and Irish banks and concluded that strategy success depends on endogenous traits. An IBM report (2011) proposed to Page | 17
simplify accessibility through the use of multiple channels and devices. With the use of less complex methods, companies (especially SMEs) and individuals will benefit in full from digital banking channels. Another survey on UK banks from PWC (2014b.) found that, by improving the online services, consumer trust will rise by 31%, thereby enhancing the communication between bank and consumer; more information and higher transparency of products and services, increasing providers’ numbers, and upturn of inside governance are some of the things that need to be changed in order to rebuild consumer trust.
Thus, most of the studies which concentrated on users’ (i.e. bank clients) points of view have confirmed the previously mentioned findings. For example, the user’s acceptance of digital retail banking in Malaysia was explored in Poon’s (2008) research, which outlined ten key characteristics: convenience, accessibility, feature availability, bank management and image, security, privacy, design, content, speed, and fees and charges. Another study in Hong Kong, conducted by Cheng, Lam and Yeung (2006), developed a theoretical model on the basis of Technology Acceptance Model (TAM) and Perceived Web Security. It empirically tested internet banking users, concluding that clients have to be in a position to understand the usefulness of information technology and be able to use it safely. Their data analysis, which relied on Structured Equation Modelling (SEM), highly supports the TAM model as a valuable method to measure consumer adaptability to information technology. Finally, Patsiotis et al. (2012) conducted an adaptability survey of 1200 consumers and found that demographics are statistically significant variables in relation to e-services trust; their main point states that:
“Service providers should target users and non-users across the segments differently. While the users identified require different retention policies, the resistance or non-resistance observed in non-users suggest the proper management of delay and rejection behaviours.” (Patsiotis et. al. 2012, p. 20).
Page | 18
2.3 Electronic Banking in the Greek Market
As shown previously, much literature to date has tried to identify the main factors that can influence the use of e-banking, either from a perspective that explores the effectiveness of the acceptance of e-banking for a financial institute’s clients (e.g. González et al., 2004), or from a perspective that emphasises an organisation’s approach to the new challenge (e.g. Daniel, 1999). Further studies focus on the e-banking sector of a particular country, such as Malaysia (e.g. Sohail and Shanmugham, 2003), or Greece (e.g. Angelakopoulos and Mihiotis, 2011). However, there has been little research on the diversification benefits or diversification strategies with respect to the Greek e-banking.
It has to be noted that banking co-operation was set as measure for economic recovery both by the G7 economies during their 2008 meeting and by the European Union for the euro zone countries four years later. Since then, we have seen many Greek bank joints. As stated in the relevant literature, the impact of various constraints on the target market, regional factors and legal limitations, implies that the Greek financial institutions (FI) need to apply different, innovative strategies.
A definition of a commercial (or retail) bank states that this is:
“a bank with branches in many different places that offers services to people and businesses, for example, keeping money in accounts and lending money” (Cambridge online dictionary (http://dictionary.cambridge.org/dictionary/businessenglish/commercial-bank)
The four big commercial Greek Banks at the moment are the National Bank of Greece S.A (ETE in the Athens Stock Exchange), Eurobank Ergasias S.A., Piraeus Bank and Alpha Bank. These banks offer various finance services, such as retail and corporate banking. Their wide range of activities, according to Bloomberg, Reuters and the official Bank sites (all Page | 19
shown in the next two Tables), cover a range of fields – from deposits acceptance to offering further financial products such as loans, lease financing, or mortgages, and from investing to insurance services for industrial, commercial, and consumer clients. Moreover, there have been many takeovers in the Greek banking market due to the recent financial crisis, especially in 2013; the most important ones are: 1) Piraeus Bank acquired Geniki Bank and ATEBank, (2) Eurobank S.A. took over TT Hellenic Postbank S.A. and New Proton Bank, and 3) Alpha Bank acquired Emporiki Bank. More details are shown in Table 2.1.
Table 2.1 The latest map of Greek Banks which offer Electronic Services
Similarly, even though co-operative banks have much in common with commercial banks, their business model differs. According to the banks’ own statements, the main differences of co-operative banks pertain not only to the after-effect of smaller capitalisation/market share, but also to the different values and activities, which are highly related to their district (even though some of them have branches in Athens too). Co-operative banks are more focused on a regional economic development than on global competition. The Hellenic Federation of Bank Unions (HBA) website reports that their main differences are: (a) financial credit goals, (b) capital formation and composition and (c) governance and operation transparency (they are subject to continuing social control). Hence, despite their being subject to the same regulations that apply to all financial institutions, they follow further regulations related to Page | 20
co-operation businesses. More information concerning the Map of Greek Banks will be given in chapter 4.
Overall, several studies have shown that diversification can be profitable for sameproduction-line corporations. Additionally, research on e-banking suggests that this area requires further development, as it can provide a competitive advantage. However, there has not been much research in the field of Greek e-banking diversification in relation to profitability. This study attempts to go a step further by investigating the sets of determinants of different strategies used for digital services as well as their impact on co-operative banks, commercial banks and their joints. Ultimately, by combining the qualitative evaluation of Angelakopoulos and Mihiotis (2011) with the diversification measures used by Palepu (1985), this thesis aims at giving greater insight into e-banking profitable tactics.
Page | 21
Chapter 3: Hypothesis, Methodology, Data and Measures
3.1 A Combined Methodology
The present research design combines Angelakopoulos and Mihiotis’ (2011) methodology (semi-structural interviews, questionnaire, and desktop research), with Palepu’s (1985) diversification measures. In addition to the formers’ qualitative methodological features, the latter’s methodology was used to construct present thesis’ hypotheses.
The main reason for this combination was dictated by the actual research questions, which require answers pertaining to both quantitative and qualitative parameters in order to analyse bank strategy profiles. In fact, the above profiles’ analysis is being conducted not only through an examination of their unique characteristics and of the clients / users target group, but also includes further regional and legal constraints. The key concept at this point will be to identify whether the management approach followed has resulted in Greek banks’ profitable diversification.
3.2 Hypotheses to be tested
Earlier research has tried to identify: 1) the main qualitative EB adaptability factors with respect to the Greek market or, 2) commercial banks’ EB characteristics. Nevertheless, there has not been a quantitative evaluation of any of the above parameters, and thus the main addition of this thesis is the analysis of both the qualitative and the quantitative EB features (including co-operative EB). Of particular importance here is the attempt to capture each EB strategy’s characteristics, as well as its relation to profitability, and examine diversified strategy performance.
Page | 22
The first thesis question discusses each EB system’s qualitative and quantitative elements, categorising different strategies according to their high and low diversified features. The next question deals with EB performance, also including the impact on new and past joints. Then, the third thesis question focuses on EB areas requiring further development, exploring to what extent such development can result in profitable diversification. Therefore, it is essential that the performance of both high and low, total (TD) and related (RD) /unrelated (UD) diversification be tested – something which indicates that Palepu’s (1985) EM must be employed so as to reveal the nature of each relationship.
More specifically, as the main reported argument is the proven significance of a relationship between diversification and profitability, the researcher tests the following hypotheses (which reflect Palepu’s (1985) hypotheses) as shown in table 3.1:
Table 3.1: Research Hypotheses Hypothesis A: Banks that offer e-services with higher total diversification are more profitable that those with low total diversification. Hypothesis B: The Performance of alternative banking channels with more related e-services is supperior to those with less related ones. Hypothesis C: Growth is higher in banks with more related digital services. The last one can be also expressed as: Hypothesis C': During a certain time period, the higher the related diversification of the e-banking activities, the higher the cost.
3.3 Diversification measures and how to overcome possible limitations
Following previous literature on the use of diversification measures and, in line with the requirements of the above hypotheses, the researcher chose the Entropy Measure (EM) (eq.3) form (Palepu, 1985; Jacquemin and Berry, 1979). This is because neither the general form
Page | 23
(eq.1.) nor the next most popular measure of the Berry–Herfindahl index (equation 3) (see also Palepu, 1985; Jacquemin and Berry, 1979; Kranenburg et al., 2004) has the EM ability to distinguish among TD (total diversification), RD (related diversification) and UD (unrelated diversification). Furthermore, the EM contributes to the investigation of the impact of the spread of e-banking on joint banks. Last but not least, previous research reports have confirmed that this option provides a much better measure of diversification, not only because it is simple and easy to apply, but also because it relates productivity to profitability:
"Preliminary empirical results relating the growth of large corporations to the pattern of diversification appear to confirm the advantage of the entropy measure and suggest a range of analytic applications." (Jacquemin and Berry; 1979, p. 368).
Diversification measures contain two parts, as shown in equations 1-3:
Classification or weighted classification measures with respect to each firm’s range of activities and
Profitability and / or growth rate indices.
For more information on the entropy measures, see Appendix 1 and the following:
Dg=Σ Ri*Wi
equation 3.1
D Berry-Herfindahl index = Σ Ri *Ri
equation 3.2
D entropy measure = Σ Ri*
equation3.3
Where: Ri is business’ classified revenues or sales cluster, While Wi is the weight of each industry / product / service.
Page | 24
In particular, Palepu used the Return on Sales (ROS) for a food industry study, and a Standard and Poors classification measure. The main reason behind Palepu’s use of ROS was to provide a more suitable measure of profitability for food business; however, this is not applicable to the present study. Much literature to date (Simpson and Kohers; 2002, Beltratti, Stulz; 2009) has recognised ROA (Rate of Assets) as a better estimator of bank profitability.
It is known that a main bank focus is buying and selling deposits, later reinvesting them in loans and other profitable activities under the assumption of a constant viable cost. ROA popularity derives from this procedure’s profitability capturing, which is unique only between financial institutions. ROA can be calculated as the ratio of net income to average total assets, illustrating how profitable the total asset allocation by the management is. Therefore, an efficient management allocation of assets will have a higher ROA. ROA is also sensitive in cases where there was a smaller capital allocation (fewer investments and stable assets). Thus, ROA, in combination with the self-survey obtained, yielded (as it is going to be shown later) more reliable results concerning the classification of e-banking activities, facilitating the sample’s allocation to higher and lower diversification class. Consequently, the present thesis’ reliance on ROA rather than ROE (Return on Equity), is because the latter may introduce a bias regarding the debt-equity mix of acquisition purchasing (Palepu, 1985).
The above parameters were then used to construct the diversification measures. “Total diversification” refers to the sum of investigated activities, while “related diversification” has been defined as the common features activity spread. Lastly, “unrelated diversification” signals loosely associated, or even no associated diversified activities.
A primary reason for using diversification measures is because all the relevant parameters are publicly available. However, at the initial stages of this thesis, the researcher tried to follow the widely used DEA model. However, several important limitations regarding quantitative data rendered this option infeasible. Examples would include the difficulty to collect further data due to inside information restrictions, which did not allow us to take full advantage of the quantitative data obtained through the semi-structural interviews1. As regards this thesis, 1
These data refer to question 8 (see interview questions in the appendix), covering the years 2011-2014.
Page | 25
especially, there is no access to some e-banking measures which cannot be published (inside information), but are particularly important for a DEA evaluation. Even though the aforementioned quantitative data could have been obtained from the semi-structural interviews or from some other source, a DEA model would still be inappropriate, due to the inconsistent bank environment of Greece. Similarly, the alternatives of a TAM and /or TPB, while widely used, are less relevant to this study, as they mostly measure consumer needs, thus falling beyond the criteria set by the present study’s questions.
Furthermore, as stated in the literature review, Montgomery’s (1982) comparison between the industrial organisation and strategic management measures concluded in the superiority of the latter. Diversification measures are better estimators of profit-to-activity range relationship, as they are not tested under the hypothesis of a significant cross-sectional correlation between products / services spread to profit; thus, they are not just simple indices. This conclusion can also be employed in the present project, as it further validates the accuracy of the researcher analysis.
Consequently, diversification measures are much easier to use, and the entropy measure version of Jacquemin and Berry (1979) clarified the picture. For example, Rumlet’s (1975) research had no evidence of the related or unrelated diversification impact on firms’ profitability. Another reason behind the choice of these methodological tools stems from the specific requirements of the particular sample examined, that is to say, the e-banking division of the banks which participated in the survey. Therefore, in addition to their reliability, their variability and their structure, diversification measures provide present thesis with noticeable advantages, while the required data for their design can be found easily [e.g. reports and Bloomberg (ROA), or survey questions (weighted average)].
3.4 Methodological procedure and data
As explained earlier on, Angelakopoulos and Mihiotis’ (2011) methodology was followed not only for the qualitative evaluation, but also for the whole sample (qualitative and quantitative Page | 26
data) collection. Panel data were collected from semi structural interviews, questionnaires and desktop research. Needless to say, the LinkedIn profiles of all interviewees and questionnaire participants were investigated carefully, so as to ensure compliance with the set research criteria. Additional data pertaining to the specific requirements of diversification measures were obtained from Bloomberg, as well as from publicly available yearly-reports.
3.4.1. Qualitative methodology and data
Firstly, the researcher has four bank executives’ semi-structural interviews (see overview in table 3.3). Participants represent three out of the four leading Greek banks. The interview parameters depend on the participant’s experience, his/her EB role in the bank and the bank’s background (for instance, its latest takeovers – in order to examine the M&A influence on EB). Although the researcher was able to interview half of the participants face-to-face, time and location constraints forced us to rely on LinkedIn and email interviews for collecting the remaining data.
Page | 27
Table 3.2: Participants Overview Participant referred in this thesis as:
Participant
Company
Position
A
Alpha Bank Project Managerrequest not to Alpha Bank Coordination 1 be named of Group Companies IT Division
B
Stathopoulos
Eurobank Ergasias
C
D
Years in Current Position
Form of interview
Date
Place
Interview Duration (minutes)
(9-12)-Jul2014
online
-
5
LinkedIn InMail
E-Banking Service Director
14
Email and 20, Amaliados & face-to-face 15-Jul-14 Ezlin,Ambelokip interview e, Athens, Greece
Sofocleous
Piraeus Bank
Business Development Group Digital Banking
7
Email
Parlavanzas
Piraeus Bank
E-Banking Excecutive
6
Email and face-to-face interview
08-Aug-14
online
163, Sugrou, N. 15-Jul-14 Smurni, Athens, Greece
90'
-
60'
notes: (1) details on the disc, named as "confident"
Of particular importance here is the attempt to examine different EB characteristics between the leading and secondary banks in Greece; this was done with questionnaires constructed in accordance with the issues that were repeatedly raised in the interviews, which were later sent to selected participants with knowledge of EB strategies. The main objective was to provide a broader idea of MGB and the latest EB developments. Thus, a ten-question-survey was designed, emailed to participants (see table 3.3) and completed with the aid of Qualtrics Online Survey Platform (QOSP) (https://www.qualtrics.com). The following Image 3.1 shows the email-resources: The following Image 3.1, shows the email-resources:
Page | 28
Graph 3.1: Email Resources
Dias Interbanking Systems S.A.
http://www.dias.com.gr/ +
Hellenic Federation of Bank Unions (HBA)
http://www.hba.gr
+ Association of Co-operative Banks in Greece (ΕΣΤΕ) http://www.este.gr/en/index.html
Then, ten out of the nineteen answers were analysed through an SPSS statistical tool, and question five was used in later quantitative analysis, providing greater insight into the investigated parameters. This was also achieved through further desktop research on the banks’ official sites, which gave more information about their IB and the identification of each financial institution’s EB strategy. Significant help was provided by Ioanna Arhimandriti, who gave us the latest bank reports (some of which will be published upon completion of this survey), and by the interviewees’ assistance and guidance. It is therefore obvious that the researcher has investigated carefully every Greek Bank site and every other site related to Greek EB, such as Banking Unions, Dias Interbanking Systems S.A, or the Association of Co-operative Banks.
3.4.2. Quantitative methodology
As mentioned before, the researcher has used Palepu’s (1985) method for the actual quantitative data. Palepu (1985) had used two parameters – ROS and Standard and Poors segment (which is a classified or weighted classified measure of industries’ spread) – to construct each of his diversification measures. By contrast, the present study used publicly available ROA instead of ROS, and the survey’s fifth-question responses instead of Standard and Poors segments. For the first part, ROA was either downloaded from Bloomberg (retail banks), or calculated from the banks’ own statements (co-operative banks) for the years 2004-2013. An average of ROA has also been estimated, not only for the purposes of
Page | 29
Hypothesis C, but also because Palepu (1985) suggested that such averages minimise the accounting figures’ noise.
The second part provides a purposive weighted average of the most common electronic banking services (ATM, APS, IB, PB, MB and DB), showing each bank’s EB weighted classified mix. Since the survey is anonymous, the identification of each bank number was done partly through personal communication with the participants via LinkedIn or email after they had completed the survey. Further check in QOSP IP collection verified the proper match of each bank’s ROA. Thus, restrictions on data availability were overcome.
Hypothesis testing requires EB systems evaluation in groups of High (H) and Low (L) HTD/HRD/HUD and LTD/LRD/LUD. With the help of Excel and Eviews, each TD/RD/UD was compared with the yearly respective diversification (above and below median). The first group was defined as High (H) “HTD/HRD/HUD”, and the second as Low (L) “LTD/LDR/LUD”. Then, the researcher matched the ROA of each group and examined the null of equal means/median group ROA (%) with the assumption of HTD/HRD/HUD group’s ROA (%) superiority to the other group.
3.5 Descriptive statistics, sample and statistical tests
The following statistical analysis relies on ten Greek EB departments. All the industry effects are under control as they come from the same industry. Moreover, the financial institutions used in this research had to provide electronic banking services, as the survey and, especially, the interview participants had to meet certain criteria regarding their knowledge and experience of e-banking strategies. Therefore, it is not a random sample with respect to probabilistic logic. The interviews took place between 1st -10th of July 2014, while the survey lasted from September to October 2014.
The key summary statistics and correlations from quantitative variables are shown in table 5. The latter shows many significant correlations for ROA, TD, RD and UD, though sometimes Page | 30
the relationship is negative. The mean and median of these variables are going to be used later in the researcher’ analysis. Further, qualitative descriptive statistics can be found in Appendix 9-Table 9.1 (surveys), supplementing table 3.3 (interviews). Additionally, Palepu (1985) proposes three kinds of tests for this part’s analysis, namely, the parametric t-test (which can be one-sided or two-sided with respect to each hypothesis tested), and two other non-parametric tests, a median test (the median ROA1 (%) and median ROA2 (%)), and the Mann-Whitney U-test. Even though the first test seems more appropriate for these hypotheses, the addition of the other two tests will reduce the noise from the small sample’s parameters, which may conclude in non-standing underlying hypotheses assumptions. Palepu (1985) based his interpretation more on those additional tests, due to their robustness for small samples. This interpretation comes from their assumptions, which are far more relaxed than a t-test’s distributional data properties assumption. In line with Palepu, therefore, the researcher has also used the above three tests in her analysis (see appendices 6 and 8).
Page | 31
Table 3.3: Correlations TableSummary 3.4. SummaryStatistics Statistics andand correlations name
Standard Deviation
Sample Variance
Count
1
2
3
4
5
TD2004 TD2005 TD2006 TD2007 TD2008 TD2009 TD2010 TD2011 TD2012 TD2013
0.94 1.03 0.12 1.15 1.00 0.80 -0.17 -6.86 0.44 0.40
0.81 0.87 0.13 1.19 1.02 0.79 0.34 -3.64 0.82 0.41
0.59 0.63 0.24 0.66 0.51 0.52 1.79 8.99 1.42 1.60
0.35 0.40 0.06 0.44 0.26 0.27 3.19 80.89 2.03 2.57
10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
1 93% 38% 79% 74% 31% 34% 15% 50% 63%
1 19% 94% 91% 61% 2% -20% 66% 67%
1 -14% 2% -21% 69% 46% 17% -35%
1 92% 71% -26% -41% 58% 74%
1 86% -30% -51% 70% 55%
1 -66% 1 -85% 88% 1 66% -20% -27% 1 29% 0% 0% 45%
1
RD2004 RD2005 RD2006 RD2007 RD2008 RD2009 RD2010 RD2011 RD2012 RD2013
0.10 -5.18 -3.83 -6.25 -3.78 0.80 9.21 66.56 14.40 0.54
-1.59 -1.11 -1.11 -2.11 -1.09 0.79 -0.13 45.31 14.82 0.60
18.35 7.74 11.87 10.02 7.10 0.52 30.89 74.24 12.14 14.94
336.66 59.98 140.86 100.34 50.34 0.27 953.99 5512.11 147.27 223.23
10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
1 66% 92% 79% 76% 97% 96% 58% 51% 0%
1 88% 95% 88% 65% 45% -20% -11% 16%
1 96% 90% 92% 81% 23% 29% 19%
1 96% 1 82% 83% 1 64% 64% 96% 1 -2% 4% 54% 75% 1 4% 1% 50% 68% 80% 1 22% 7% 8% 2% -28% 30%
1
UD2004 UD2005 UD2006 UD2007 UD2008 UD2009 UD2010 UD2011 UD2012 UD2013
0.09 0.13 0.12 0.22 0.12 0.03 -0.14 -1.63 -0.56 0.05
0.14 0.13 0.13 0.21 0.10 0.05 0.01 -1.83 -0.34 -0.01
0.35 0.23 0.24 0.26 0.15 0.16 0.48 1.15 0.57 0.89
0.12 0.05 0.06 0.07 0.02 0.03 0.23 1.31 0.32 0.79
10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
1 66% 92% 87% 87% 97% 88% 20% -14% 40%
1 88% 85% 82% 64% 35% -23% 20% 88%
1 93% 93% 92% 73% 1% 6% 66%
1 97% 85% 57% -26% -24% 68%
1 89% 64% -14% -17% 60%
1 91% 1 26% 58% 1 -4% 7% 50% 1 40% 8% -33% 25%
1
ROA2004 ROA2005 ROA2006 ROA2007 ROA2008 ROA2009 ROA2010 ROA2011 ROA2012 ROA2013
0.17 0.56 0.48 0.82 0.48 0.06 -0.85 -7.48 -2.11 -0.05
0.57 0.70 0.68 0.96 0.46 0.25 0.05 -6.38 -2.01 -0.11
1.76 0.80 1.12 0.92 0.64 0.84 2.88 6.19 1.66 2.53
3.10 0.64 1.26 0.85 0.42 0.71 8.27 38.33 2.75 6.40
10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 9.00
1 66% 92% 87% 87% 97% 88% 20% -14% 40%
1 88% 85% 82% 64% 35% -23% 20% 88%
1 93% 93% 92% 73% 1% 6% 66%
1 97% 85% 57% -26% -24% 68%
1 89% 64% -14% -17% 60%
1 91% 1 26% 58% 1 -4% 7% 50% 1 40% 8% -33% 25%
1
wi,ATM wi,APS
0.41 0.03
0.20 0.00
0.60 0.20
0.13 0.05
1.64 0.27
1 -77%
1
0.33
0.20
0.45
0.07
0.43
42% -57%
0.05
0.00
0.20
0.07
0.46
-69% 55% -21%
0.07
0.00
0.33
0.10
1.08
-63% 42% -40% 24%
0.11
0.00
0.30
0.10
1.04
wi, Internet
Mean Median
6
7
8
9
10
1
Banking
wi,Mobile
1
Banking
wi,Phone
1
Banking
wi,Direct
6%
-7% -36% -33% -42%
1
debits
Notes: (1) TD-entropy measure for total diversification. (2) RD-Entropy measure for related diversification. (3) UD-entropy measure for Unrelated diversification. (4) ROA- Return on Assets. (5) Wi weight of each service, as found later, in survey's question 5.
Page | 32
3.6 Other limitations faced by this method
Most of the limitations have to do with ethical reasons: with respect to the interview questions, for instance, the participants may have felt uncomfortable to elaborate on some points pertaining to inside data. This is probably why some sensitive quantitative data were hard to obtain. Moreover, time is a significant parameter for answering, both in email interactions and face-to-face interviews; consequently, the researcher tried to manage such limitations by recording time and by pre-testing the interviews / survey. However, the most important parameter throughout the process was to check every detail and meet all the ethical requirements, as this research project deals with sensitive data of financial institutions and especially, their people.
Page | 33
Chapter 4: Analysis and Results
This section presents the collected data and primary research findings.
4. 1. A Qualitative results presentation
In accordance with Angelakopoulos and Mihiotis’ (2011) methodological advice, the following section examines the qualitative components of the researcher’ hypotheses and provides a critical evaluation of the interviews.
4.1.1. Survey Results
From the initial survey questions, we can see that all sample EB services are six to ten years old. Furthermore, there is a special Department or Unit, running either independently (50%), or with a third party’s aid (50%). Key examples that stress an external partner involvement come from the co-operative banks’ EB, which was developed and further supported by Open Solutions Ltd. More information about EB Departments/Units is given in the interviews interpretation sub-section.
Page | 34
Graph 4.1: A weighted Classification of digital Banking Channels
source: Self-processed survey question 5
The results from question five are shown in graph 4.1. The figure shows each bank’s weighted e-banking classification, which was then used to assess the quantitative analysis of sub-section 4.3. Clearly, among the six depicted e-banking channels, ATM (41.15%) and IB (33.05%) were the most popular services (10/10 responses). Additionally, eight of the participants responded that their banks provided DB (11.10%) and six of them included PB (7.04%). MB (4.55%) and APS (3.11%) were delivered only by half the sample.
Page | 35
Graph 4.2: Factors for offering e-banking services
source: Self-processed survey question 6
Then, question six (Graph 4.2) presents the main factors for offering e-banking services to clients. It has to be noted that each bank has a different client focus group and a different business culture, thus, different priorities. However, looking at their average in the above illustration, the common features’ order of significance (22.27%-17.16%) is this:
(1) Consumer orientation / strength and consumer commitment, (2) e-banking can add value to banks and therefore contribute to their image and (3), the acquisition of new consumers is also quite important.
Page | 36
Other factors had a slightly lower average (11.18%-9.51%), and were ranked as less significant:
(4) Global trend (5) Revenue generation / potentially lower cost, (6) Fraud protection / security availability, and (7) local competition (e-banking is a global trend and local competitors offer it as well).
Local competition, in other words, seems to be playing a minor role today, something that, interestingly enough, contradicts the results of the research papers upon which present thesis’ methodology was based.
Page | 37
Graph 4.3: Clients Benefits from e-banking
source: Self-processed survey question 7
Graph 4.3 shows how much weight participant banks attribute to their clients’ e-banking benefits; the most significant ones are: (1.a) lower cost / money-saving ability of electronic transactions, (1.b) time-saving / quicker transaction procedure, as well as (1.c) transaction simplicity and (1.d) safety.
These are followed by: (2.a) making transactions anytime from anywhere and (2.b) keeping a transaction history record.
Page | 38
Somewhat curiously, however, transparency received a low score, thus contradicting the recent PWC (2014b.) report on UK banks-clients relationship.
Graph 4.4: Classification of possible problems a client may face during e-banking
source: Self-processed survey question 8
Question seven shows the most common e-transaction problems for users (graph 4.4). The popularity order is as follows: (1) Data analytics, that is, having difficulty to e-transact with some of the devices, (2) Payments / cross transfers, (3) Registration and Log in, (4) Poor transaction history record (for very old transactions), (5) the existence of too many security measures (certificates).
Page | 39
The last one can result in system access difficulties or prolonged transaction time. Clearly, after 2011, many financial regulations and bank priorities changed, affecting most of the participants’ e-strategies (see sub-section 4.2). Note that crucial parameters such as data analytics were less important in prior research (e.g. Angelakopoulos and Mihiotis, 2011).
Graph 4.5: Common E-Banking challenges that Banks may face
source: Self-processed survey question 9
The penultimate question, by contrast, shows that the most common bank problem is poor communication with a third party (graph 4.5). One of the current thesis’ interviewees, in fact, gave an example reporting that his bank’s conflict with a third party eventually led to a major difference that had to be resolved in court. Still, the bank’s main challenge was to deliver top Page | 40
quality services without disturbing client relation. Daniel and Storey (1997, p. 896), suggested that “The need for intermediaries is reduced if the bank offers on-line banking via a private dial-up service”. However, the cost of such a project is currently too high, and thus cannot be implemented by all banks.
Additionally, a multichannel approach (2) can also result in a major disruption: this refers to the situation where a service cannot be supported by some devices or programs. Other important problems include security issues like Phi-sing attacks (3), network instability and technical problems (4) and the difficulty of building up consumer trust (5).
Graph 4.6: Factors preventing clients from using e-banking services
source: Self-processed survey question 10
The last question (graph 4.6) refers to the main factors that have a negative impact on the usage of e-banking services by the customers. The number one factor was the adaptability for clients without prior (or with minimal) experience of such services, as they are hard to use, Page | 41
followed by the fear of losing private data / mistrust in e-banking services. These findings are identical to previous studies, but the researches has also examined aspects previously considered less significant – e.g. standardised transactions, technical problems, low eGovernment, or incorrect PIN / password.
Most of the current findings confirm previous studies (e.g. Angelakopoulos and Mihiotis, 2011); however, question five explores in greater detail the different e-channels contribution, and question six considers cost reduction as a less significant factor for offering EB. As for the discrepancies with the PWC (2014b.) recommendation, they might reflect the differences in the nature of the respective markets. However, the biggest discrepancy was the users’ issues during e-transactions; this is why sub-section 4.2 shows a significant change in ebanking tactics, a phenomenon that probably stems from the different bank perspectives after crises, as well as the different measures.
4.1.2. A critical evaluation of the Interviews
All participants reported that there is a full range of distinguished e-banking products and eservices provided by their banks for more than ten years. Moreover, their banks have a comprehensive, special department or Unit which does not only deal with the design and development of EB, but further supports existing services. This department is in charge of eservices’ efficient promotion, simultaneously supporting EB customers by solving any possible problems (C). Interviewees also reported innovative e-banking services for the Greek market, such “WinBank”, which is provided by Pireaus Bank and offers the prepaid card “WEBUY” together with car / motorbike e-insurance. A good example of how ebanking works in Greece was been from B’s (2014) fifty-employee-Unit, which is responsible for e-Services, EB and MB Channels, and for performing everyday transactions such as live payments. B (2014) also mentioned a specialised B2B e-commerce service for companies; among other things, he (2014) reported that:
Page | 42
“Some of our main services are: e-Banking (internet banking) for individuals and businesses, e-Statements, e-Products (i.e. Live deposit account), mobile banking / mobile apps, SMS and email alerts, e-Payments (including LivePay.gr), other b2b and specialized services (einvoicing, e-auctions). Furthermore, e-Business manages the Bank’s online presence and its interactive / digital marketing activities (i.e. websites development & management, Social Media exploitation, email marketing, search engine marketing, mobile marketing).” (Stathopoulos, 2014)
Although in some cases system support may be received from external partners (C; 2014), EB custody is primarily an internal portion of each bank (i.e. there is cooperation with other bank divisions). Moreover, interviewees stated that there are 3 million users / clients (1.2 million of which are active) in the Greek market. However, according to KTPAE (2014) report and B’s (2014) comment, e-transactions are much less used by Greek home-PC-users, who rely on the internet mainly for information, communication and entertainment reasons. In addition, even though mobiles gain more market share, their use for banking services is still very limited, as is their weight in relation to e-banking strategies (see fifth survey question).
Further answers are consistent with the results of previous surveys:
1) For instance, regarding cost reduction, C (2014) reported that the basic reason for offering e-services is due to the lower operational cost in comparison with in-store services. D (2014) added that the in-branch to alternative channel cost ratio is 1/20 for Greece and 1/3 for other EU countries.
2) Another reason was given by A (2014): A stated that both global and local competition explains why e-banking can be an advantage to a bank’s services portfolio, while safety (security) was also highly ranked among the interviewees. Regarding security, two interviewees commented on the Safety Alert importance for fraud prevention, which is more developed in other countries. According to Visa and MasterCard statements, the above Page | 43
developments are associated with the higher involvement of these countries’ laws, banks and other partners.
3) In addition to consumer experience and safety, D (2014) highlighted the importance of ebanking services transparency, as Greece loses 4-5 billion euros every year from tax evasion. However, he admitted that, despite the low share of e-payments, e-banking is growing steadily. A (2014) finally reported that while clients may have previously considered more traditional banking channels, they now find it easier to carry out a transaction anytime from anywhere and keep track of their money.
With respect to the next question, it has been suggested that some clients may face system limitations due to their hardware devices (e.g. mobile, laptop) or software settings (e.g. browser). Other technical failures may come from the bank, such as hard security measures (i.e. digital certificates) and old login wizards (difficulty in Registration / Log In). Similar problems such as security, network instability, and multichannel approaches were also reported by the banks, adversely influencing the use of e-banking services. However, nearly every bank plans to minimise such problems by introducing user navigation improvements, ebanking-related services promotions, and further investments (Piraeus, for instance, is redrawing their web banking). Another way to reduce security problems was introduced by D (2014), and that was:
“to educate customers, to provide them with security guarantees, to implement a new security platform that collects data from customers’ behaviour at login and during the usage of eBanking service, and, based on predefined rules, handle suspicious activity (real time fraud detection and prevention)”. (Stathopoulos, 2014)
Lastly, additional reasons for clients’ mistrust come from the actual lack of face-to-face contact and the broader economic environment. These were mentioned in both the interviews and the survey, and were further confirmed by the findings of the PWC report (2014b). Page | 44
All interviewees forecast a surge in the development of EB, with two of them stating that marketing will become more personalised through it (data exploitation – predictive analysis), something that will result in the acquisition of more consumers. They also predict that MB will gain a greater share in the years to come. Furthermore, banks will have the chance to use the new “internets of things” (Stathopoulos; 2014) – for example, cloud – together with devices such as tablets or smart phones. Likewise, social networks will allow the “always– on–banking to become a way of living” (Sofocleous; 2014). However, it has to be noted that these technologies are subject to security issues.
However, responses in the next question were contradictory: some interviewees think that ebanking in Greece is behind that of other E.U. countries, as it has focused more on beating local competition than meeting global trends; others believe that while e-banking is still developing, it nevertheless meets global standards, and there are also those who say that the answer depends on the way the question is being approached. For instance, D (2014) stated that, despite the high quality of services provided, there is still limited customer response, adding that banks should consider consumer experience and marketing / communication improvements. Further recommendations in order for e-banking to meet international criteria would include:
minimising regulations limitations,
increasing alternative channels investments
“E-Government initiatives [that] will further boost e-services adoption… [and]…help customers track and manage online their finances, as well as organize savings and investment goals through sophisticated online Personal Financial Manager tools” (Parlavanzas, 2014-translate from Greek),
2
strengthening the economy (A2; 2014),
Anonymity request.
Page | 45
And enhancing consumer experience and cross devices serving (B; 2014).
The final question illustrated the EB department / unit response to the recent, major M&A wave. As the banks merged, a common challenge was to migrate new services, consumers and systems. Suddenly, the bank becomes bigger (not only in numbers), but also has to integrate different cultures, perspectives and expertise. A common response was an EB department / Unit employee internal increase (as two EB departments / Units merged). The main reported challenge (D; 2014) is that back office work in such a way that clients notice no difference between the “before” and “after” M&A day (“No time for errors”; Parlavanzas, 2014, as translated from Greek). For instance, after Piraeus Bank’s acquisition of ATEbank, the former largely retained ATEbank’s unique culture and communication strategies, despite the economic issues. As D (2014) stated, the successful merger’s main requirement is client satisfaction, and therefore M&A requires time and slow changes. Another case that shows an important EB acquisition aftermath comes from A, who, considering Emporiki’s merger with Alpha bank, reported the challenge of co-ordinating twospeed EB departments (A; 2014).
In summary, the present interview evaluation seems to agree with previous research (i.e Angelakopoulos and Mihiotis, 2011; Daniel and Storey, 1997). However, it is not so clear whether Greek EB meets global criteria: while it meets the tools criteria (which are still more expensive compared to other EU countries), adaptability remains an issue. Additionally, participants seem to support previous forecasts of EB further development, making references to the new “internet of things”. Finally, Greece has to efficiently increase the user’s data protection and fraud prevention, in order to improve financial control.
4.2. Quantitative data analysis and descriptive statistics
Moving on to the quantitative analysis, the present section illustrates the findings of the aforementioned research hypotheses, following Palepu’s (1985) methodology and the entropy measure of diversification, and analysing yearly (panel) data. It thus elaborates on the strategy characteristics portraits, simultaneously showing the e-services’ diversificationPage | 46
profitability relation and comparing current thesis’ findings with previous research. The steps followed are described in detail in the methodology section.
Further, Table 4.1 presents the strategies and hypotheses examination procedure:
Table 4.1: Hypothesis and Strategies testing Hypothesis
Strategy 1
Strategy 2
A B C C'
HTD HRD&LUD HRD&LUD* HRD&LUD**
LTD LRD&HUD LRD&HUD* LRD&HUD**
Parametres ROA (%) ROA (%) ΔROA(%) ROA (%)
H0
H1
Sample
ROAHTD= ROALTD ROAHRD&LUD= ROALRD&HUD ΔROAHRD&LUD= ΔROALRD&HUD ROAHRD&LUD= ROALRD&HUD
ROAHTD >ROALTD ROAHRD&LUD> ROALRD&HUD ΔROAHRD&LUD> ΔROALRD&HUD ROAHRD&LUD>ROALRD&HUD
2004-2013 2004-2013 2004-2013 2004-2009
Notes:
(1) * is referring only for unchanged strategies, throughout 2004-2013. (2) ** is referring only for unchanged strategies, throughout 2004-2009. (3) There are also strategies of HRD&HUD and LRD&LUD. However, they are not applicable to either of our hypothesi (Hypothesis B,C and C'), and thus are not included.
Hypothesis A stated that there is higher profitability for banks with HTD in comparison with LTD banks. This was examined using the null hypothesis of the HTD-LTD strategy ROA equality, against the alternative that a HTD ROA exceeds the LTD ROA (see Table 4.1). The first t-tests’ results illustrated in Table 4.2 show that the null has failed to be rejected. However, the t-statistic of a small sample may result in a more apparent than significant value. Thus, the researcher used two more tests: Χ2 (1) and the Mann-Whitney U-test (the null tests equality of the median ROA of HTD and LTD, against ROA HTD median superiority). Following this, the thesis failed to reject the Χ2 (1) for all the years after 2009 (2010-2013), while prior that (2004-2009) the test rejected the null. Then, considering two or more tests’ agreement on H0 outcomes, this study and table 7 show that EB-profitability is the same, no matter the strategy used. These findings are in line with previous research (e.g. Palepu; 1985).
Page | 47
Table 4.2: The performance of total Diversification strategies for digital banking 2004 High RD & Low UD Number of banks mean ROA (%) t-statistic median ROA (%) Χ 2 (1) -statistic
5 0.90 0.91
Low RD & High UD 5 -0.55 -1.37** 0.17 3.6
Mann-Whitney Utest
2006
2007
2008
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
5 0.67
5 0.29
5 1.33
5 -0.37
5 1.03
5 0.62
5 0.63
5 0.33
0.04
1.22
0.04
1.33
0.60
0.87
-0.50** 1.22
-3.77**
-0.68**
-0.71** 0.30
3.6
10.00
3.6
3.6
10**
10**
10**
10**
10**
2009
2010
2011
2012
2013
High RD & Low UD
Number of banks mean ROA (%) t-statistic median ROA (%) 2 Χ (1) -statistic Mann-Whitney Utest
2005
Low RD & High UD
5 -0.05
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
5 0.18
5 0.07
5 -1.78
5 -3.78
5 -11.19
5 -2.09
5 -2.14
5 0
5 0
0.12
0.13
0.02
-5.58
-10.85
-2.02
-1.84
0
0.42** 0.37
-1.02**
-2.30**
3.6
0.40**
0.40**
10**
10**
10**
-0.05**
-0.22** 0
0.40**
0.09**
10**
9**
notes: (1) TD- High Total Diversification; ROA-Return on assets (2) t-test: Ho: equal means against H1: HTD mean exceeds LTD. With a t-statistic equal 2.0150 (5% level) and 3.3649 (1% level ), there is a rejection of null, if t-test exceed the respective t-statistic. On the other hand, for the other two tests, the study test the hypothesis Ho: equal median against H1: HTD median exceeds LTD. With a chi-square statistic equal 1.145 (5% level) and 0.5543 (1% level ), this study rejects the null, if chi-square exceeds chi-square statistics. For U-test, there is a rejection of the null when it is not exceed 2. (3) (**) very significant at level 1%, (*) significant at 5% level. (4) t-test and chi-square test statistics have been obtained from Chris Brooks (2002, p.669 and p.672) and U-test from University of Glasgow statistical tables.
Page | 48
Then, Table 4.3 illustrates Hypothesis B results. This hypothesis states that the performance of banks with more related e-services is superior to those with less related ones. In fact, the ttest rejects the H0 (see Table 4.1), while the non-parametric test failed to reject it. However, Χ2 (1), H0 has been rejected only for 2005 and 2011-2013. Thus, for those years, a strategy of relative digital-banking diversification is more profitable, something not applicable to 2004 and 2006-2010. Moreover, even with a t-test’s exclusion (small sample), the present thesis’ Hypothesis B agrees partly with Palepu (1985), whose outcomes were instead slightly significant. It seems that though sometimes a higher related diversification strategy may be more profitable, its results are often similar to less related strategies.
Table 4.3: A comparison between Related and Unrelated e-services' Diversification performance 2004 High RD & Low UD
2005 Low RD & High UD
4 4 Number of banks 1.16 mean ROA (%) -1.01 1.78** t-statistic 1.23 median ROA (%) 0.10 8.00 Χ 2 (1) -statistic
Mann-Whitney U-test
2006
2007
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
4 -0.14
5 1.30
4 -0.47
5 1.33
4 -0.05
5 1.06
12.65 -0.15
3.62* 1.31
0.04
5.76
1.22
0.14
Low RD & High UD
4 -0.07
5 0.96 3.58*
0.87
5.76
5.76
9**
9**
9**
2009
2010
2011
2012
4 Number of banks mean ROA (%) -0.49 t-statistic 1.89** median ROA (%) 0.07 Χ 2 (1) -statistic 3.60 9** Mann-Whitney U-test
High RD & Low UD
0.10
2.95*
8**
High RD & Low UD
2008
0.87 5.76 9**
2013
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
5 0.50
4 -3.05
5 0.16
2 0.06
2 -16.74
3 -2.53
3 -0.23
4 -1.53
5 1.69
0.37
-0.14
0.13
-16.74
0.06
-2.02
0.01
-1.59
1.47** 2.88 8**
3.85 4.00 4**
3.86 6.00 6**
2.88 0.75 5.76 9**
notes: (1) RD- Related diversification; UD- Unrelated diversification; ROA- return on assets (2) yearly: t-test: Ho: equal means against H1: HRD&LUD mean exceeds LRD&HUD With a t-statistic equal 2.1318 (5% level ) and 3.7469 (1% level), we reject null when t-test exceed t-statistic. On the other hand, for the other two test we test the hypothesis Ho: equal median against H1: HRD&LUD median exceeds LRD&HUD. With a chi-square statistic equal 0.7107 (5% level ) and 0.2971 (1% level), we reject the null when chi-square exceeds chi-square statistics. For U-test, we reject the null when it is less than 2. (3) t-test and chi-square test statistics have been obtained from Chris Brooks (2002 p.669 and p.672) and U-test from University of Glasgow statistical tables.
Page | 49
Next, Table 4.4 presents Hypotheses C. In order to investigate whether more related EB activities result in higher growth, the researcher focused upon diversification strategies that do not change over time. The previous table (4.3) shows that only one bank retained a HRD&LUD strategy and, likewise, one bank retained a LRD&HUD-strategy. This did not allow t-testing. Further, the growth was calculated as the percentage of ROA difference between 2004 and 2013.
The results were contradictory (Χ2
(1)
rejected the null).
Consequently, the aforementioned results do not fully agree with Palepu’s (1985) findings. As before, with respect to EB, the argument that “the more related the diversification, the higher the growth” is not always the case.
Table 4.4: Related and Unrelated Diverisfication and Profitability Growth High RD2004 & Low UD2004
Low RD2004 & High UD2004
1
1
0.03
0
2004 Number of banks mean ΔROA(%) t-statistic median ΔROA (%) Χ 2 (1) -statistic Mann-Whitney U-test
NA 0.03
0 2 2**
notes: (1) RD2004-related diversification; UD2004-Unrelated Diversification;ΔROA- growth rate of return on assets between 2004 and 2013, calculated as: (ROA2013-ROA2004)/ROA2004. (2) The Researcher tests the hypothesis Ho: equal median against H1: HRD&LUD median exceeds LRD&HUD. With a chi-square statistic equal 0.00016 (1% level ) and 0.00393 (5% level), present thesis rejects the null when chisquare exceeds chi-square statistics. For U-test, the null rejection comes from a value less than 0. (3) chi-square test statistics have been obtained from Chris Brooks (2002 p.672) and U-test from University of Glasgow statistical tables.
Page | 50
Finally, the researcher attempted to test whether a more related diversification strategy is more costly (Research Hypothesis C’). Previously, the interviewees had reported that EB cost is relatively lower, compared to other divisions, but still higher than that of other countries. Additionally, Table 4.3 had proved that banks’ strategies changed after the 2009 crisis and MGB allocation. Thus, 2004-2009 is the time-period used in order to obtain robust findings. That decision was made because research hypothesis C’ requires testing the same null and alternative as in Table 4.1 for banks that retain their HRD&LUD or LRD&HUD strategy over time.
Consequently, Table 4.5 presents five banks with a HRD&LUD strategy and four banks with a LRD&HUD strategy: First, the t-test failed to reject the null for all the years apart from 2005 and 2007. Then, X2(1) rejected it for all the years apart from 2007, in contrast to U-test, which failed to reject it even for 2007. Most of the above results are insignificant, but it appears that the cost is about the same for the majority of the examined years (excepting 2007). With respect to ICT, a RD-EB strategy was more costly in 2007, something that could stem from the fact that this is the year just before the crisis. Indeed, Wagner (2010) argues that there are some side-effects in the banking sector diversification: while bank activities become more alike, “systemic crises” are more likely to arise. Consequently, “the optimal degree of diversification may be arbitrarily low” (Wagner, 2010, p.337).
Page | 51
Table 4.5: The performance of the banks which retain their Related and unrelated diversification from 2004 up to 2009 2004
Number of banks mean ROA (%) t-statistic median ROA (%) Χ 2 (1) -statistic Mann-Whitney Utest
Number of banks mean ROA (%) t-statistic median ROA (%) Χ 2 (1) -statistic Mann-Whitney Utest
2005
2006
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
5 -0.73
4 1.16
5 -0.18
4 1.31
5 -0.37
4 1.30
1.23
-0.32
1.31
0.03
1.68** 0.17
12.24
3.2* 1.22
9.00
5.41*
9.00
9**
9**
9**
2007
2008
2009
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
High RD & Low UD
Low RD & High UD
5.00 0.08
4 1.48
5 0.00
4 0.80
5 -0.37
4 0.41
1.47
0.17
0.86
0.12
3.94 0.23
3.33*
1.46** 0.37
9.00
9.00
9.00
9**
9**
9**
notes: (1) RD- Related diversification; UD- Unrelated diversification; ROA- return on assets (2) yearly: t-test: Ho: equal means against H1: HRD&LUD mean exceeds LRD&HUD With a t-statistic equal 2.1318 (5% level ) and 3.7469 (1% level), I reject null when t-test exceed t-statistic. On the other hand, for the other two test we test the hypothesis Ho: equal median against H1: HRD&LUD median exceeds LRD&HUD. With a chi-square statistic equal 0.7107 (5% level ) and 0.2971 (1% level), we reject the null when chi-square exceeds chi-square statistics. For U-test, I reject the null when it is less than 2. (3) t-test and chi-square test statistics have been obtained from Chris Brooks (2002 p.669 and p.672) and U-test from University of Glasgow statistical tables
Thus, there is only partial agreement with Palepu’s (1985) conclusions, while Stomber’s (2006) findings are more applicable here. Relly and Brown (2012) report that such results could come from: a) different time (2004-2013), b) industry (FI behave differently) and c) economic conditions (the economic shock caused MGB allocation). Despite the limitations inherent in small samples, current thesis findings are in line with Stomber’s (2006) conclusions, which suggest that no single diversification strategy “should be optimal for all banks” (Stomber, 2006 p.35), since the preferred strategy depends on each bank’s nature.
Page | 52
Chapter 5: Conclusion and implications
This thesis attempts to give a deeper evaluation of the Greek electronic banking for both commercial and co-operative banks. The researcher has followed a combined methodology (Angelakopoulos and Mihiotis’ (2011); Palepu (1985)), which largely confirmed and extended the findings of previous studies. Some of the main results show that EB in Greece:
Enhances the bank-client relation, as it is simpler, less costly (for both parts) and saves time.
Clients are able to carry out e-transactions everywhere and anytime.
Provides higher profitability, adding value to the bank.
HTD and LTD strategies are equally profitable.
Has been firmly established for more than six years (research time range: 2004-2013).
All banks have a special department or unit for EB services. Co-operative e-banking is less autonomous, as it usually runs with the aid of a third party, and is thus more exposed to potential interest conflicts.
The e-services mix consists primarily of ATM and IT, while some banks also provide DB, PB and / or MB. The latter is provided by a shorter range.
It is subject to the same financial crises’ challenges, with the four big banks having to adapt to new cultures and sizes due to the recent mergers.
Page | 53
Many of the above findings are consistent with previous research; what differs can be summarized as follows:
The addition of the co-operative banks’ evaluation. The discrepancy with commercial banks might stem from the different perspectives after crises, as well as the different measures. However, it seems that other characteristics are equally important, such as:
o More regionally-focused development. o Additional regulation. o Smaller market share and capitalisation.
The outcomes in the problems their users may face during e-transactions, which can be attributed to the significant change in e-banking tactics, as suggested by the quantitative analysis.
The main difficulty a client may face is data analytics, followed by payments / cross transfers. The key source of misuse is poor adaptability, and this is why bank executives emphasize the need for better education.
Also, empirical results suggest that the optimal diversification strategy depends on the bank, while even EB can be affected by systemic crises’ risks, especially when it includes more related diversification activities.
Further empirical results provide the main reasons for offering e-services. With respect to the interviews evaluation, most of findings confirm and extend previous research (e.g. Angelakopoulos and Mihiotis, 2011; Daniel and Storey, 1997). The most important facts here pertain to the new information regarding M&A influence on banks, and especially on their ebanking Departments / Units. There was not a clear idea of whether Greek e-banking meets global criteria, but while it seems to have the tools (which are nevertheless more expensive in comparison with other EU countries), the respective client acceptance is still lacking.
Page | 54
Last but not least, the main limitations are:
Methodology: ethical reasons (inside data) and time restrictions.
Results: the small-sample restraints. This difficulty was overcome with the nonparametric test.
Much more remains to be researched in this field, especially parameters that relate to quantitative analysis:
Further models and measures.
Another interesting question could be the implications of EB diversification strategies.
A similar study combining EB and traditional banking products.
Page | 55
Appendix 1: Diversification Measures
Following Palepu’s methodology as described in Table 4.7 and the methodology section, and assuming that each bank has “M” e-services, then:
wi: is the weight given for each e-service, While wj : is the ratio of each e-service in relation to the total e-services of the bank.
Further, M, i=1…6 (where 1=ATM, 2=APS, 3= internet banking, 4= m-banking, 5=phone banking, 6= direct debits) and j=1…m, m≤6 (the amount of the e-services existing in the bank).
Then the researcher has calculated for each year, Pi (the share of the ith e-service in the total services of the bank) Pj (the share of the jth existing bank’s e-services within the total existing e-services of the bank) and Pij (the share of the e-service among the e-services that exist in the bank) as:
Pi =ROA*wi
Pj = ROA*wj
Pij = Pi/Pj= (ROA*Wi)/(ROA*Wj)= Wi/Wj
Especially for Hypothesis C, the researcher has calculated the ΔROA% = (ROA2013ROA2004)/(ROA2004).
Next, with the aid of the entropy measure of Jacquemin and Berry (1979), diversification measures have been computed as:
Total Diversification: TD = Σpi*ln(1/pi) Unrelated Diversification, UD= ΣPj*ln(1/pj) and Page | 56
Relative Diversification (RD):
Furthermore, the researcher found ΣDRj= ΣPij*ln(1/pij) and then computed 2nd DR=Σ DRj*Pj The present has conducted the aforementioned calculations with the aid of the initial data of the following table A1.1.
Page | 57
Table A1.1: Initial Data Wi
1
ROA FY 2004 BANK
Bank 1 Bank 2 Bank 3 Bank 4 Bank 5 Bank 6 Bank 7 Bank 8 Bank 9 Bank 10
FY 2005
FY 2006
FY 2007
FY 2008
FY 2009
FY 2010
FY 2011
FY 2012
2004-12-31 2005-12-31 2006-12-31 2007-12-31 2008-12-31 2009-12-31 2010-12-31 2011-12-31 2012-12-31 2013-12-31
0.17 0.03 1.23 -4.67 1.29 0.91 1.23 0.73 0.42 0.42
0.12 0.03 1.31 -0.40 1.31 1.30 1.31 1.29 -0.32 -0.32
0.18 0.04 1.22 -2.15 1.18 1.58 1.22 1.45 0.03 0.03
0.23 0.04 1.33 -1.07 1.63 1.61 1.33 1.95 0.60 0.60
0.17 0.04 0.87 -0.81 0.85 0.62 0.87 1.61 0.30 0.30
0.13 0.03 0.37 -2.23 0.52 0.37 0.37 0.86 0.12 0.12
0.17 0.02 0.08 -9.03 0.13 -0.04 0.08 0.35 -0.14 -0.14
0.11 0.01 -6.72 -21.10 -6.05 -12.37 -6.72 -10.85 -5.58 -5.58
0.12 0.01 -2.02 -3.56 -1.84 -0.83 -2.02 -2.01 -4.50 -4.50
0.13 n/A -1.59 -0.11 4.43 3.15 -1.59 0.75 -2.83 -2.83
X BANK
Bank 1 Bank 2 Bank 3 Bank 4 Bank 5 Bank 6 Bank 7 Bank 8 Bank 9 Bank 10
Q5. How much weight do you attribute to your electronic banking services? (total sum 100)
FY 2013
W_ATM
W_APS
1 1 1 1 1 1 1 1 1 1
0 0 1 1 0 1 1 1 0 0
W_APS
50% 33% 30% 20% 60% 20% 20% 45% 45% 40%
0% 0% 5% 10% 0% 10% 20% 4% 0% 0%
W_Internet W_Mobile_ W_phone_ W_Direct_ _Banking banking Banking Debits 30% 34% 45% 35% 40% 20% 20% 30% 35% 30%
0% 0% 10% 20% 0% 20% 5% 10% 0% 0%
0% 33% 5% 10% 0% 20% 20% 7% 0% 0%
20% 0% 5% 5% 0% 10% 15% 4% 20% 30%
Wj
W_Internet W_Mobile_ W_phone_ W_Direct_ _Banking banking Banking Debits 1 1 1 1 1 1 1 1 1 1
W_ATM
0 0 1 1 0 1 1 1 0 0
0 1 1 1 0 1 1 1 0 0
1 0 1 1 0 1 1 1 1 1
SUM 3 3 6 6 2 6 6 6 3 3
W_ATM
W_APS
17% 11% 5% 3% 30% 3% 3% 8% 15% 13%
0% 0% 1% 2% 0% 2% 3% 1% 0% 0%
W_Internet W_Mobile_ W_phone_ W_Direct_ _Banking banking Banking Debits 10% 11% 8% 6% 20% 3% 3% 5% 12% 10%
0% 0% 2% 3% 0% 3% 1% 2% 0% 0%
0% 11% 1% 2% 0% 3% 3% 1% 0% 0%
7% 0% 1% 1% 0% 2% 3% 1% 7% 10%
notes: (1a) ROA for banks 1 and 2 (Cooperative Bank of Chania and Cooperative Bank of Karditsa), have been computed with the aid of relevant bank annual reports for 2004-2013). More details are sited on refferencies Section more details are given in the References. (1b) ROA for bank 3 up to 10 have been obtained by bloomeberg (2) W i was taken from my survey's question 5. It shows the contribution of each particular service to the e-banking division of each bank. (3) X is a dummy which shows 1: if there is this service or 0: if there is not (4) wj is the weight (the share) of the service, in relation to the total services
Page | 58
Appendix 2: quantitative data evaluation: Hypothesis A
The researcher have tested thesis Hypothesis A with the null of Ho: HTD=LTD and the alternative Hypothesis of H1: HTD >LTD. Tables A2.1.1-A2.10.2 present the eviews output which corresponds to table 4.7 in chapter 4.
Table A2.1.1: 2004-T-test
Table A2.1.2: 2004-Chi-square and U-tests:
Page | 59
Table A2.2.1: 2005-T-test
Table A2.2.2: 2005-Chi-square and U-tests:
Table A2.3.1: 2006-T-test
Table A2.3.2: 2006-Chi-square and U-tests:
Page | 60
Table A2.4.1: 2007-T-test
Table A2.4.2: 2007-Chi-square and U-tests:
Table A2.5.1: 2008-T-test
Table A2.5.2: 2008-Chi-square and U-tests:
Page | 61
Table A2.6.1: 2009-T-test
Table A2.6.2: 2009-Chi-square and U-tests:
Table A2.7.1: 2010-T-test
Table A2.7.2: 2010-Chi-square and U-tests:
Page | 62
Table A2.8.1: 2011-T-test
Table A2.8.2: 2011-Chi-square and U-tests:
Table A2.9.1: 2012-T-test
Table A2.9.2: 2012-Chi-square and U-tests:
Page | 63
Table A2.10.1: 2013-T-test
Table A2.10.2: 2013-Chi-square and U-tests:
Page | 64
Appendix 3: quantitative data evaluation: Hypothesis B:
Present thesis tested thesis Hypothesis B with the null of Ho: ROAHRD&LUD = RoaLRD&HUD vs and the alternative Hypothesis of H1: ROAHRD&LUD > RoaLRD&HUD. Tables A3.1.1-A3.10.2 present the eviews output that corresponds to table 4.8 in chapter 4.
Ho: H1: Table A3.1.1: 2004-T-test
Table A3.1.2: 2004-Chi-square and U-tests:
t-test
chi-square and U-test
Test for Equality of Means of R_1
Test for Equality of Medians of R_1
Categorized by values of D2_1 Date: 11/15/14 Time: 16:47 Sample (adjusted): 1 8
Categorized by values of D2_1 Date: 11/15/14 Time: 17:05 Sample (adjusted): 1 8
Included observations: 8 after adjustments
Included observations: 8 after adjustments
Method
df
Value
Probability
Method
df
Value
Probability
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
6 3.030003 (1, 6) (1, 3.03)
1.779582 1.779582 3.166911 3.166911
0.1254 0.1723 0.1254 0.1723
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
1 1 1 1 1
2.165064 2.178067 8 4.5 5.333333 5.39759 5.065579
0.0304 0.0294 0.0047 0.0339 0.0209 0.0202 0.0244
Source of Variation
df
Sum of Sq. Mean Sq.
Between
1
9.485699
9.485699
Within
6
17.97152
2.995253
D2_1
Count Median
Median
Mean Rank Mean Score
0 1 All
4 4 8
4 0 4
6.5 2.5 4.5
2004
*Test allows for unequal cell variances Analysis of Variance
Total
7
27.45721
3.922459
Mean 1.16445 -1.013358 0.075546
Std. Dev. 0.172648 2.441454 1.98052
Std. Err. of Mean 0.086324 1.220727 0.70022
Category Statistics > Overall
1.228 0.096983 0.66475
0.634816 -0.638947 -0.002066
Category Statistics
D2_1 0 1 All
Count 4 4 8
Table A3.2.1: 2005-T-test
Table A3.2.2: 2005-Chi-square and U-tests:
Test for Equality of Means of R_2 Categorized by values of D2_2 Date: 11/15/14 Time: 16:51 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R_2 Categorized by values of D2_2 Date: 11/15/14 Time: 16:52 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 3.008012 (1, 7) (1, 3.00801)
12.64544 11.11156 159.9073 123.4667
0 0.0015 0 0.0015
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 7
4.645515 0.203359
4.645515 0.029051
Total
8
4.848874
0.606109
Mean 1.30294 -0.14291 0.66034
Std. Dev. 0.010624 0.260069 0.77853
Std. Err. of Mean 0.004751 0.130034 0.25951
2005
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.336772 5.76 2.975625 6 6.05042 5.762277
0.02 0.0195 0.0164 0.0845 0.0143 0.0139 0.0164
Median 1.3056 -0.145657 1.2859
> Overall Median 4 0 4
Mean Rank 7 2.5 5
Analysis of Variance Category Statistics
D2_2 0 1 All
Count 5 4 9
Mean Score 0.570123 -0.72523 -0.00559
Category Statistics
D2_2 0 1 All
Count 5 4 9
Page | 65
Table A3.3.1: 2006-T-test
Table A3.3.2: 2006-Chi-square and U-tests:
Test for Equality of Means of R_3 Categorized by values of D2_3 Date: 11/15/14 Time: 16:53 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R_3 Categorized by values of D2_3 Date: 11/15/14 Time: 16:53 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 3.119212 (1, 7) (1, 3.11921)
3.615226 3.195799 13.06986 10.21313
0.0086 0.0469 0.0086 0.0469
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 7
7.243095 3.879282
7.243095 0.554183
Total
8
11.12238
1.390297
Mean 1.33062 -0.474759 0.528229
Std. Dev. 0.176125 1.118809 1.179109
Std. Err. of Mean 0.078766 0.559405 0.393036
2006
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.336772 5.76E+00 2.975625 6 6.05042 5.663553
0.02 0.0195 0.0164 0.0845 0.0143 0.0139 0.0173
Median 1.223 0.035012 1.1769
> Overall Median 4 0 4
Mean Rank 7 2.5 5
Analysis of Variance Category Statistics
D2_3 0 1 All
Count 5 4 9
Mean Score 0.578763 -0.72523 -0.00079
Category Statistics
D2_3 0 1 All
Count 5 4 9
Table A3.4.1: 2007-T-test
Table A3.4.2: 2007-Chi-square and U-tests:
Test for Equality of Means of R_4 Categorized by values of D2_4 Date: 11/15/14 Time: 16:53 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R_4 Categorized by values of D2_4 Date: 11/15/14 Time: 16:54 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 4.488767 (1, 7) (1, 4.48877)
2.949831 2.759229 8.701504 7.613342
0.0214 0.0448 0.0214 0.0448
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 7
2.722976 2.190522
2.722976 0.312932
Total
8
4.913498
0.614187
Mean 1.05682 -0.05013 0.564842
Std. Dev. 0.402466 0.717079 0.783701
Std. Err. of Mean 0.179988 0.358539 0.261234
2007
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.327015 5.76 2.975625 6 6 5.63133
0.02 0.02 0.0164 0.0845 0.0143 0.0143 0.0176
Median 0.8659 0.135939 0.622
> Overall Median 4 0 4
Mean Rank 7 2.5 5
Analysis of Variance Category Statistics
D2_4 0 1 All
Count 5 4 9
Mean Score 0.580184 -0.72523 0.00E+00
Category Statistics
D2_4 0 1 All
Count 5 4 9
Table A3.5.1: 2008-T-test
Table A3.5.2: 2008-Chi-square and U-tests:
Test for Equality of Means of R_5 Categorized by values of D2_5 Date: 11/15/14 Time: 16:55 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R_5 Categorized by values of D2_5 Date: 11/15/14 Time: 16:56 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 5.489844 (1, 7) (1, 5.48984)
3.577034 3.452945 12.79517 11.92283
0.009 0.0157 0.009 0.0157
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 7
2.395112 1.310321
2.395112 0.187189
Total
8
3.705433
0.463179
Mean 0.96324 -0.074932 0.50183
Std. Dev. 0.375454 0.498818 0.680573
Std. Err. of Mean 0.167908 0.249409 0.226858
2008
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.336772 5.76E+00 2.975625 6 6.05042 5.679587
0.02 0.0195 0.0164 0.0845 0.0143 0.0139 0.0172
Median 0.8659 0.103187 0.622
> Overall Median 4 0 4
Mean Rank 7 2.5 5
Analysis of Variance Category Statistics
D2_5 0 1 All
Count 5 4 9
Mean Score 0.576776 -0.72523 -0.001894
Category Statistics
D2_5 0 1 All
Count 5 4 9
Page | 66
Table A3.6.1: 2009-T-test
Table A3.6.2: 2009-Chi-square and U-tests:
Test for Equality of Means of R_6 Categorized by values of D2_6 Date: 11/15/14 Time: 16:56 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R_6 Categorized by values of D2_6 Date: 11/15/14 Time: 16:57 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 3.160366 (1, 7) (1, 3.16037)
1.88534 1.670181 3.554505 2.789505
0.1014 0.1888 0.1014 0.1888
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 7
2.156457 4.246778
2.156457 0.606683
Total
8
6.403235
0.800404
Mean 0.4957 -0.489392 0.057882
Std. Dev. 0.212501 1.16421 0.894653
Std. Err. of Mean 0.095033 0.582105 0.298218
2009
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.336772 3.6 1.40625 6 6.05042 5.662153
0.02 0.0195 0.0578 0.2357 0.0143 0.0139 0.0173
Median 0.3696 0.072585 0.3664
> Overall Median 3 0 3
Mean Rank 7 2.5 5
Analysis of Variance Category Statistics
D2_6 0 1 All
Count 5 4 9
Mean Score 0.579779 -0.72523 -0.000225
Category Statistics
D2_6 0 1 All
Count 5 4 9
Table A3.7.1: 2010-T-test
Table A3.7.2: 2010-Chi-square and U-tests:
Test for Equality of Means of R_7 Categorized by values of D2_7 Date: 11/15/14 Time: 16:57 Sample (adjusted): 1 8 Included observations: 8 after adjustments
Test for Equality of Medians of R_7 Categorized by values of D2_7 Date: 11/15/14 Time: 16:57 Sample (adjusted): 1 8 Included observations: 8 after adjustments
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
6 2.001097 (1, 6) (1, 2.0011)
1.468447 1.072745 2.156337 1.150782
0.1924 0.3956 0.1924 0.3956
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 6
19.29583 53.69059
19.29583 8.948431
Total
7
72.98642
10.42663
Mean 0.159854 -3.048122 -1.043137
Std. Dev. 0.110712 5.178878 3.229029
Std. Err. of Mean 0.049512 2.990027 1.141634
2010
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.086997 2.099531 2.88E+00 0.888889 5 5.060241 4.8209
0.0369 0.0358 0.0897 0.3458 0.0253 0.0245 0.0281
Median 0.1256 -0.1419 0.0793
> Overall Median 3 0 3
Mean Rank 6 2 4.5
Analysis of Variance Category Statistics
D2_7 0 1 All
Count 5 3 8
Mean Score 0.483215 -0.805359 -5.55E-17
Category Statistics
D2_7 0 1 All
Count 5 3 8
Table A3.8.1: 2011-T-test
Table A3.8.2: 2011-Chi-square and U-tests:
Test for Equality of Means of R_8 Categorized by values of D2_8 Date: 11/15/14 Time: 16:58 Sample (adjusted): 1 4 Included observations: 4 after adjustments
Test for Equality of Medians of R_8 Categorized by values of D2_8 Date: 11/15/14 Time: 16:58 Sample (adjusted): 1 4 Included observations: 4 after adjustments
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
2 1.000266 (1, 2) (1, 1.00027)
3.849089 3.849089 14.81549 14.81549
0.0614 0.1618 0.0614 0.1618
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 2
282.2168 38.09754
282.2168 19.04877
Total
3
320.3144
106.7715
Mean 0.06371 -16.7356 -8.335945
Std. Dev. 0.071145 6.171911 10.33303
Std. Err. of Mean 0.050307 4.3642 5.166514
2011
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
1.161895 1.161895 4 1 2.4 2.4 2.328036
0.2453 0.2453 0.0455 0.3173 0.1213 0.1213 0.1271
Median 0.06371 -16.7356 -6.178998
> Overall Median 2 0 2
Mean Rank 3.5 1.5 2.5
Analysis of Variance Category Statistics
D2_8 [0, 0.2) [1, 1.2) All
Count 2 2 4
Mean Score 0.547484 -0.547484 5.55E-17
Category Statistics
D2_8 [0, 0.2) [1, 1.2) All
Count 2 2 4
Page | 67
Table A3.9.1: 2012-T-test
Table A3.9.2: 2012-Chi-square and U-tests:
Test for Equality of Means of R_9 Categorized by values of D2_9 Date: 11/15/14 Time: 16:59 Sample (adjusted): 1 6 Included observations: 6 after adjustments
Test for Equality of Medians of R_9 Categorized by values of D2_9 Date: 11/15/14 Time: 16:59 Sample (adjusted): 1 6 Included observations: 6 after adjustments
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
4 3.236128 (1, 4) (1, 3.23613)
3.861135 3.861135 14.90836 14.90836
0.0181 0.0268 0.0181 0.0268
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 4
7.929829 2.127619
7.929829 0.531905
Total
5
10.05745
2.01149
Mean -0.232316 -2.531567 -1.381941
Std. Dev. 0.522955 0.889004 1.41827
Std. Err. of Mean 0.301928 0.513267 0.579006
2012
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
1.745743 1.77123 6.00E+00 2.666667 3.857143 3.970588 3.751594
0.0809 0.0765 0.0143 0.1025 0.0495 0.0463 0.0528
Median 0.011875 -2.0183 -1.4255
> Overall Median 3 0 3
Mean Rank 5 2 3.5
Analysis of Variance Category Statistics
D2_9 0 1 All
Count 3 3 6
Mean Score 0.604511 -0.599928 0.002291
Category Statistics
D2_9 0 1 All
Count 3 3 6
Table A3.10.1: 2013-T-test
Table A3.10.2: 2013-Chi-square and U-tests:
Test for Equality of Means of R_10 Categorized by values of D2_10 Date: 11/15/14 Time: 16:59 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R_10 Categorized by values of D2_10 Date: 11/15/14 Time: 17:00 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 6.426357 (1, 7) (1, 6.42636)
2.87574 3.072261 8.269881 9.438787
0.0238 0.02 0.0238 0.02
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 7
23.06899 19.52663
23.06899 2.789519
Total
8
42.59563
5.324453
Mean 1.692863 -1.5291 0.26088
Std. Dev. 1.988827 1.111291 2.307478
Std. Err. of Mean 0.889431 0.555645 0.769159
2013
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.336772 5.76 2.975625 6 6.05042 5.672155
0.02 0.0195 0.0164 0.0845 0.0143 0.0139 0.0172
Median 0.75 -1.5891 0
> Overall Median 4 0 4
Mean Rank 7 2.5 5
Analysis of Variance Category Statistics
D2_10 0 1 All
Count 5 4 9
Mean Score 0.580184 -0.72097 0.001894
Category Statistics
D2_10 0 1 All
Count 5 4 9
Page | 68
Appendix 4: quantitative data evaluation: Hypothesis C:
The researcher have tested thesis Hypothesis C with the null of Ho: ΔROAHRD&LUD = ΔRoaLRD&HUD vs and the alternative Hypothesis of H1: ΔROAHRD&LUD > ΔRoaLRD&HUD. Tables A4.1.1-A4.1.2 present the eviews output that corresponds to table 4.9 in chapter 4.
Table A4.1.1: 2004-T-test:
Table A4.1.2: 2004-Chi-square and U-tests:
t-test
chi-square and U-test
Test for Equality of Means of R2_B Categorized by values of D2_B Date: 11/15/14 Time: 17:15 Sample: 1 24 Included observations: 24
Test for Equality of Medians of R2_B Categorized by values of D2_B Date: 11/15/14 Time: 17:15 Sample: 1 24 Included observations: 24
Method
df
Value
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
22 10.76022 (1, 22) (1, 10.7602)
2.353596 2.172444 5.539416 4.719511
Probability 0.0279 0.0531 0.0279 0.0531
*Test allows for unequal cell variances
Method Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
df
Value
1 1 1 1 1
Probability
2.694049 2.696395 8.223776 6.041958 7.414825 7.427743 6.338224
0.0071 0.007 0.0041 0.014 0.0065 0.0064 0.0118
Analysis of Variance Source of Variation
df
Sum of Sq. Mean Sq.
Between Within
1 22
59.09876 234.7129
59.09876 10.66877
Total
23
293.8117
12.77442
Category Statistics
D2_B
All
> Overall Count Median Median Mean Rank Mean Score 0 13 0.131664 10 16.11538 0.426766 1 11 -2.2344 2 8.227273 -0.504335 24 0.02135 12 12.5 1.14E-05
Category Statistics
D2_B
All
Std. Err. Count Mean Std. Dev. of Mean 0 13 -0.093264 0.999298 0.277155 1 11 -3.242657 4.719426 1.42296 24 -1.536736 3.574132 0.729567
Page | 69
Appendix 5: quantitative data evaluation: Hypothesis C’:
In order to test thesis Hypothesis C’, the researcher used those banks that retain their HRD&LUD or their LRD&HUD strategy over 2004-2009. Then, the researcher tested the null of Ho: ROAHRD&LUD = RoaLRD&HUD vs and the alternative Hypothesis of H1: ROAHRD&LUD > RoaLRD&HUD. Tables A5.1.1-A5.10.2 present the full eviews output that corresponds to the construction of table 4.10 (see chapter 4).
Table A5.1.1: 2004-T-test:
Table A5.1.2: 2004-Chi-square and U-tests: Test for Equality of Medians of R3B04
2004
Test for Equality of Means of R3B04 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:56 Sample: 1 9 Included observations: 9
Categorized by values of D3_04_09 Date: 11/17/14 Time: 16:07 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 4.060981 (1, 7) (1, 4.06098)
1.684109 1.906704 2.836222 3.635522
0.1360 0.1282 0.1360 0.1282
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq.
Mean Sq.
Category Statistics
Between Within
1 7
7.947551 19.61513
7.947551 2.802161
D3_04_09
*Test allows for unequal cell variances
df
Value
1.00 1.00 1.00 1.00 1.00
Probability
2.327015 0.0200 2.346653 0.0189 9.000000 0.0027 5.405625 0.0201 6.000000 0.0143 6.101695 0.0135 5.703652 0.0169
Analysis of Variance
Total
8
27.56268
0 1
3.445335 All
Count Median 4 1.228000 5 0.167125 9 0.420000
> Overall Median 4 0
Mean Rank 7.500000 3.000000 4 5.000000
Mean Score 0.720970 -0.579779 -0.001669
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
Mean 1.164450 -0.726687 0.113818
Std. Dev. 0.172648 2.209395 1.856161
Std. Err. of Mean 0.086324 0.988072 0.618720
Table A5.2.1: 2005-T-test:
Table A5.2.2: 2005-Chi-square and U-tests:
2005
Test for Equality of Means of R3B05 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:57 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R3B05 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:57 Sample: 1 9 Included observations: 9
Method
df
Value
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 12.23904 4.005146 13.87598 (1, 7) 149.7941 (1, 4.00515) 192.5429
Probability
Method
df
Value
Probability
0.0000 0.0002 0.0000 0.0002
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
1 1 1 1 1
2.327015 2.346653 9.000000 5.405625 6.000000 6.101695 5.791441
0.0200 0.0189 0.0027 0.0201 0.0143 0.0135 0.0161
*Test allows for unequal cell variances Analysis of Variance Source of Variation
df
Sum of Sq.Mean Sq.
Between Within
1 7
4.910325 4.910325 0.229463 0.032780
Total
8
5.139788 0.642474
Mean 1.307200 -0.179288 0.481373
Std. Dev. 0.005433 0.239465 0.801544
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
> Overall Median Median 1.308550 4 -0.324800 0 0.123174 4
Mean RankMean Score 7.500000 0.712654 3.000000 -0.576776 5.000000 -0.003696
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
Std. Err. of Mean 0.002716 0.107092 0.267181
Page | 70
Table A5.3.1: 2006-T-test:
Table A5.1.3: 2006-Chi-square and U-tests:
Test for Equality of Means of R3B06 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:58 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R3B06 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:58 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 4.356889 (1, 7) (1, 4.35689)
3.275727 3.682148 10.73039 13.55822
0.0136 0.0182 0.0136 0.0182
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq.
Mean Sq.
Category Statistics
Between Within
1 7
6.234738 4.067250
6.234738 0.581036
Total
8
10.30199
1.287749
Mean 1.301475 -0.373527 0.370918
Std. Dev. 0.188937 0.995007 1.134790
Std. Err. of Mean 0.094469 0.444981 0.378263
2006
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.346653 9.000000 5.405625 6.000000 6.101695 5.721592
0.0200 0.0189 0.0027 0.0201 0.0143 0.0135 0.0168
Median 1.223000 0.031400 0.180740
> Overall Median 4 0 4
Mean Rank 7.500000 3.000000 5.000000
Analysis of Variance
D3_04_09 0 1 All
Count 4 5 9
Mean Score 0.720970 -0.576776 0.000000
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
Table A5.4.1: 2007-T-test:
Table A5.1.4: 2007-Chi-square and U-tests:
Test for Equality of Means of R3B07 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:58 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R3B07 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:59 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 4.563582 (1, 7) (1, 4.56358)
3.937518 4.404508 15.50405 19.39969
0.0056 0.0086 0.0056 0.0086
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq.
Mean Sq.
Category Statistics
Between Within
1 7
4.335198 1.957320
4.335198 0.279617
Total
8
6.292518
0.786565
Mean 1.475800 0.079076 0.699842
Std. Dev. 0.164143 0.684925 0.886885
Std. Err. of Mean 0.082072 0.306308 0.295628
2007
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.346653 9.000000 5.405625 6.000000 6.101695 5.691712
0.0200 0.0189 0.0027 0.0201 0.0143 0.0135 0.0170
Median 1.471150 0.234271 0.595900
> Overall Median 4 0 4
Mean Rank 7.500000 3.000000 5.000000
Analysis of Variance
D3_04_09 0 1 All
Count 4 5 9
Mean Score 0.723453 -0.579779 -0.000565
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
Page | 71
Table A5.5.1: 2008-T-test:
Table A5.1.5: 2008-Chi-square and U-tests:
2008
Test for Equality of Means of R3B08 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:59 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R3B08 Categorized by values of D3_04_09 Date: 11/17/14 Time: 15:59 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 4.656097 (1, 7) (1, 4.6561)
3.330743 3.717743 11.09385 13.82161
0.0126 0.0156 0.0126 0.0156
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq.
Mean Sq.
Category Statistics
Between Within
1 7
1.429218 0.901808
1.429218 0.128830
Total
8
2.331027
0.291378
Mean 0.801900 -6.53E-05 0.356364
Std. Dev. 0.120069 0.463292 0.539795
Std. Err. of Mean 0.060034 0.207191 0.179932
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.346653 9.000000 5.405625 6.000000 6.101695 5.772622
0.0200 0.0189 0.0027 0.0201 0.0143 0.0135 0.0163
Median 0.859850 0.170836 0.299400
> Overall Median 4 0 4
Mean Rank 7.500000 3.000000 5.000000
Analysis of Variance
D3_04_09 0 1 All
Count 4 5 9
Mean Score 0.712654 -0.579779 -0.005365
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
Table A5.1.1: 2009-T-test:
Table A5.1.6: 2009-Chi-square and U-tests:
2009
Test for Equality of Means of R3B09 Categorized by values of D3_04_09 Date: 11/17/14 Time: 16:00 Sample: 1 9 Included observations: 9
Test for Equality of Medians of R3B09 Categorized by values of D3_04_09 Date: 11/17/14 Time: 16:00 Sample: 1 9 Included observations: 9
Method
df
Value
Probability
Method
t-test Satterthwaite-Welch t-test* Anova F-test Welch F-test*
7 4.052496 (1, 7) (1, 4.0525)
1.458537 1.651666 2.127331 2.727999
0.1881 0.1730 0.1881 0.1730
Wilcoxon/Mann-Whitney Wilcoxon/Mann-Whitney (tie-adj.) Med. Chi-square Adj. Med. Chi-square Kruskal-Wallis Kruskal-Wallis (tie-adj.) van der Waerden
Source of Variation
df
Sum of Sq.
Mean Sq.
Category Statistics
Between Within
1 7
1.330019 4.376437
1.330019 0.625205
Total
8
5.706456
0.713307
Mean 0.405300 -0.368333 -0.024496
Std. Dev. 0.075682 1.043941 0.844575
Std. Err. of Mean 0.037841 0.466865 0.281525
*Test allows for unequal cell variances
df
Value
Probability
1 1 1 1 1
2.327015 2.346653 9.000000 5.405625 6.000000 6.101695 5.693168
0.0200 0.0189 0.0027 0.0201 0.0143 0.0135 0.0170
Median 0.368000 0.115900 0.131664
> Overall Median 4 0 4
Mean Rank 7.500000 3.000000 5.000000
Analysis of Variance
D3_04_09 0 1 All
Count 4 5 9
Mean Score 0.723453 -0.578763 0.000000
Category Statistics
D3_04_09 0 1 All
Count 4 5 9
Page | 72
Appendix 6: quantitative data evaluation: further tests
Total Diversification = Related Diversification + Unrelated Diversification
Palepu (1985) has shown that total diversification is the sum of unrelated and related diversification. This thesis used eviews to further confirm Palepu’s results. First, Table A6.1.1 below illustrates the components of the equation:
, where a=0.59, C(1)=0.74 and C(2)=-0.08 , with standard errors3: (0.0.193389) (0.276346**)
Table A6.1.1: equation
3
(0.008406)
Table A6.1.2: Wald test:
(**) means it is significant at 5% level
Page | 73
Then, using a Wald test, the researcher tested the null that all coefficients are equal to zero (C (1) =C (2) =0) against the alternative that at least one of them is not zero. From Table A6.1.2, it is obvious that the aforementioned null has been rejected. Thus C (1), and/or C (2) is/are significantly different from zero. The coefficient covariance matrix (A6.1.3) is given below:
Table A6.1.3: Coefficient Covariance Matrix
coef.covariance matrix ud rd c 0.141637 0.002353 0.005467 0.002353 7.07E-05 -0.00014 0.005467 -0.00014 0.037399
ud rd c
Further, the researcher questions residuals’ normality, Heteroskedasticity and correlation. The residual graphs A6.1 and A.6.2 show whether their distribution is normal or not. Moreover, in A.6.2 graph, the test’s null states that there is a normal distribution, against the alternative that the residuals do not follow a normal distribution N(0,1) (mean (με) of residuals=0, σε2=1). Using the Jarque-Bera criterion, the null was rejected, while με =2.69e-17or 0 and σε2=3.44491789.
Graph A.6.1
Graph A.6.2 10 0 -10
8
-20
4
-30
0
-40
-4 -8 -12 10
20
30
40 Residual
50
60 Actual
70
80
90
100
Fitted
Page | 74
Next, the null of homoscedastic residuals was also tested, against the alternative that the residuals are heteroscedastic for the square of the residuals. Therefore tables A6.1.4a, A1.6.1.4b and A6.1.4.c. Show that both White tests (cross-sectional and non-cross-sectional), as well as the Breusch-Pagan-Godfrey test prove the rejection of the null. That result comes from the fact that the F-statistic exceeds 3.07 (at 5% level) and 4.79 (at 1% level)4. Thus the residuals are heteroscedastic.
Tables A6.1.4: Heteroskedasticity tests: a. White (non-cross-sectional) test
b. Breusch-Pagan-Godfrey test
c. White (cross-sectional) test
4
Values can be found in Brooks’ (2002) F-distribution tables, p. 670-671.
Page | 75
Tables A6.1.5: Autocorrelation and Partial Autocorrelation Correlograms:
a. AC and PAC of Residuals
b.AC and PAC of Squared residuals
The final thesis test focuses on residuals autocorrelation and partial autocorrelation. Thus, A6.1.5.a illustrates the failure of the null rejection (no autocorrelation / partial autocorrelation), something that happens also in table A6.1.5.b (for the residuals’ square).
Page | 76
In summary, it holds that:
or , (0.0.193389) (0.276346**)
(0.008406)
The residuals do not follow a normal distribution; they are heteroscedastic and not autocorrelated. Consequently, an F-test to test the relation between profitability and diversification is not applicable here (as residuals are not normally distributed). By contrast, the Chi-square test and the non-parametric test are more appropriate, as neither of them requires a normal distribution of residuals.
Page | 77
Appendix 7: Initial Report
This Appendix section presents the initial report, as obtained from QOSP. All tables (7.17a.10.2)
and
figures
(7.1-7.5)
of
this
section
are
a
product
of
QOSP
http://www.qualtrics.com/ (last access on 18.12.2014), and complement the self-processed (in SPSS) figures and tables of chapter 4.
Table 7.1 Question 1 A. Answers Question 1. Do you provide e-banking services? #
Answer
Response
%
1
Yes
10
100%
2
No
0
0%
Total
10
100%
B. Answer statistics Statistic
Value
Min Value
1
Max Value
1
Mean
1.00
Variance
0.00
Standard Deviation
0.00
Total Responses
10
Page | 78
Table 7.2 Question 2 a. Answers 2. How many years have you been providing e-banking services? #
Answer
Response
%
1
1-3 years
0
0%
2
3-5 years
0
0%
3
6-10 years
10
100%
0
0%
10
100%
we do not 4
provide ebanking services Total b. Answer statistics
Statistic
Value
Min Value
3
Max Value
3
Mean
3.00
Variance
0.00
Standard Deviation
0.00
Total Responses
10
Page | 79
Table 7.3 Question 3 a. Answers 3. Is there a Department or Unit specialized in e-banking services in your bank? If not, would you consider creating one in the future? #
Answer
1
Yes
Response
%
10
100%
0
0%
0
0%
10
100%
No, but we 2
are planning to create one in the future No, and we are not considering creating a
3
specialized ebanking Department / Unit in the future Total c. Answer statistics
Statistic
Value
Min Value
1
Max Value
1
Mean
1.00
Variance
0.00
Standard Deviation
0.00
Total Responses
10
Page | 80
Table 7.4 Question 4 a. Answers 4. Do you run e-banking services independently or do you receive support from a third party? #
Answer
Response
%
1
Independently
5
50%
5
50%
0
0%
Not applicable
0
0%
Total
10
100%
Partly, with the 2
aid of a third party A third party is
3
responsible for our e-banking services
4
b. Answer statistics Statistic
Value
Min Value
1
Max Value
2
Mean
1.50
Variance
0.28
Standard Deviation
0.53
Total Responses
10
Page | 81
Table 7.5 Question 5 a. Answers 5. How much weight do you attribute to your electronic banking services? (Total sum 100)
b. Statistics
#
Answer
1 2 3
4
5 6
Average
Standard
Value
Deviation
60.00
36.30
13.99
0.00
20.00
4.90
6.67
20.00
45.00
31.90
7.85
0.00
20.00
6.50
8.18
0.00
33.00
9.50
11.36
0.00
30.00
10.90
10.02
Min Value
Max Value
ATM
20.00
APS Internet Banking Mobile Banking Phone Banking Direct debits
Page | 82
Table 7.6 Question 6. Rank the following factors for offering e-banking services to your clients:
#
3
Answer Acquire new consumers
Total
1
2
3
4
5
6
7
0%
10%
10%
20%
30%
10%
10%
10
10%
0%
10%
10%
20%
20%
20%
10
10%
20%
0%
20%
10%
20%
20%
10
30%
20%
0%
0%
10%
10%
20%
10
10%
10%
40%
0%
10%
10%
20%
10
20%
20%
10%
10%
20%
0%
10%
10
0%
0%
10%
20%
20%
40%
0%
10
8
8
8
8
12
11
10
-
Responses
Consumer 1
orientation/strength consumer commitment Fraud protection/
7
Security availability
5 6
Global Trend Local competitors offers Revenue
4
generation/ lower cost
2
Value added/ Bank Image Total
Page | 83
Graphic 7.1 and table 7.7.1b. Statistics: Question 7:
Statistic
Min Value Max Value Mean Varianc e Standard Deviatio n Total Respons es
Consumer orientation/stren gth / consumer commitment
Valu e adde d/ Bank Imag e
Acquire new consume rs
Revenue generatio n/ lower cost
Glob al Tren d
Local competit ors offers
Fraud protectio n/ security availabili ty
1
3
2
1
1
1
1
30
20
20
10
10
7
7
7.40
6.50
6.10
4.00
4.20
4.00
4.40
66.49
23.61
25.88
8.22
10.40
4.44
4.71
8.15
4.86
5.09
2.87
3.22
2.11
2.17
10
10
10
10
10
10
10
Page | 84
Table 7.7 Question 7. How much weight do you attribute to the following benefits of ebanking for your clients? (Total sum 100)
# 1 2
3
4 5 6 7
Answer Time saving/ Quicker transactions Transaction Security No limits in time and place of transaction Reduce cost/ save money Keep track of their money Transparency Simplicity
Average Value
Standard Deviation
30.00
17.40
6.82
5.00
30.00
16.00
7.75
0.00
30.00
11.90
8.53
10.00
30.00
16.40
7.12
0.00
25.00
12.90
6.72
0.00 5.00
15.00 30.00
9.00 16.40
4.59 7.86
Min Value
Max Value
5.00
Graph 7.2: question 7
Page | 85
Graph 7.3: Question 8. Rate possible problems that may be faced by your clients during an electronic transaction:
Table 7.8. 1. Answers to question 8
# 1 2
3
4
5
Answer Registration and Log in Data Analytics Security measures (too many certificates) Payments /cross transfers Poor transaction history record Total
1
2
3
4
5
Total Responses
2
3
0
1
3
10
1
1
5
1
1
10
3
1
2
1
2
10
0
3
1
4
1
10
3
1
1
2
2
10
9
9
9
9
9
-
Page | 86
Table 7.8. 2. Statistics for answers to question 8 Security Statistic
Registration
Data
measures
and Log in
Analytics
(too many
Payments /cross transfers
certificates)
Poor transaction history record
Min Value
1
1
1
2
1
Max Value
20
30
10
20
20
Mean
4.70
5.70
3.50
5.00
4.60
Variance
31.57
74.01
7.61
28.89
31.82
5.62
8.60
2.76
5.37
5.64
10
10
10
10
10
Standard Deviation Total Responses
Table 7.9. 1. Answers to question 9: Answer
1
2
3
4
5
Network Instability/ technical problems Poor communication with a third party Multichannel Approach (for example, a service might not be supported by some devices or programs) Difficulty in building up consumer trust Security e.g. Phi-sing attacks, Trojan/ viruses Total
1
2
3
4
5
Total Responses
3
0
0
2
4
10
3
1
1
2
2
10
1
1
5
2
0
10
0
6
1
1
1
10
2
1
2
2
2
10
9
9
9
9
9
-
Page | 87
Graph 7.4: Question 9. Rate the problems your bank may face when providing ebanking services:
Table 7.9. 2. Statistics for answers to question 9
Statistic
Min Value Max Value Mean Variance Standard Deviation Total Responses
Network Instability/ technical Problems
Poor communication with a third party
1 10 4.10 7.43
1 30 5.60 76.04
Multichannel Approach (for example, a service might not be supported by some devices or programs) 1 30 5.60 74.27
2.73
8.72
10
10
Difficulty in Building up consumer Trust
Security e.g. Phi-sing attacks, Trojan/ viruses
2 10 3.40 6.49
1 10 3.80 6.84
8.62
2.55
2.62
10
10
10
Page | 88
Table 7.10. 1. Answers to question 10
10. Rank the main factors that have a negative impact on the usage of e-banking services for your customers: #
1
2
3
4 5 6
Answer Fear of losing private data/ mistrust in ebanking services Low eGovernment/ Have no previous experience/ Adaptability/ Hard to use Forgot Pin/ Password Standardized Transactions Technical Problems Total
1
2
3
4
5
6
Total Responses
0
0
4
3
1
1
10
0
3
2
1
2
0
9
1
1
1
3
0
3
10
2
0
3
2
2
0
10
1
3
1
0
3
0
9
4
2
0
1
0
2
10
8
9
11
10
8
6
-
Page | 89
Table 7.10. 2.Statistics for answers to question 10
Statistic
Min Value Max Value Mean Variance Standard Deviation Total Responses
Fear of losing Private data/ mistrust in Ebanking Services 3
Have no previous Low eexperience/ Government Adaptability/ Hard to use
Forgot Pin/ Password
Standardized Transactions
Technical Problems
2
1
1
1
1
20
10
30
10
20
10
5.50 26.94
4.00 6.50
6.60 70.49
3.90 6.54
5.00 34.00
3.40 9.38
5.19
2.55
8.40
2.56
5.83
3.06
10
9
10
10
9
10
Graph 7.5 Question 10
Page | 90
Appendix 8: Power Analysis The researcher tested the survey data with the aid of SPSS, in order to avoid type II errors. Thus, this thesis uses the null, that the level of power (probability) to avoid type II errors is not significant for an amount of answers equal to 40 (in total from the ten-question-survey), against the alternative that it is significant. Table A8.1 illustrates that p-value =100% > 5% and therefore the null failed to be rejected. If the null had been rejected, then the study would have been underpowered, likely yielding non-significant findings and showing that the present sample is inadequate (10 out of 19 answers). In that case, more banks would have been added to the sample. On the contrary, the present outcome (table A8.1) shows that a sample of 10 out of 19 banks is a good one.
Table A8.1 Multivariate Testsa Effect
Noncent. Parameter
Observed Power
Pillai's Trace
620486.908
1.000b
Wilks' Lambda
620486.908
1.000b
Hotelling's Trace
620486.908
1.000b
Roy's Largest Root
620486.908
1.000b
a
a. Design: Intercept Within Subjects Design: a b. Exact statistic
Page | 91
Appendix 9: Qualitative descriptive statistics In order to test the research hypotheses (A, B, C and C’), the researcher used Excel in combination with an eviews program (quantitative evaluation), and SPSS (qualitative part). All the interviews were critically evaluated. Table A.9, constructed in SPSS, gives the summary statistics for the quantitative data of the survey.
Page | 92
Page | 93
References
1.
Accenture (2013). Banking on Digital: Building Trust and Innovation in Financial Services. [Online]. Available at: http://www.accenture.com/gb-en/Pages/insightbuilding-trust-innovation-financial-services-summary.aspx (Accessed: 27 March 2014)
2.
Stomper A (2006). A Theory of Banks' Industry Expertise, Market Power, and Credit Risk. Management Science, 52(10) pp. 1618-1633. [Online]. Available at: http://www.jstor.org/stable/20110633 . (Accessed: 12 June 2014)
3.
Alexis P. Jacquemin and Charles H. Berry (1979). Entropy Measure of Diversification and Corporate Growth. The Journal of Industrial Economics. 27(4) pp. 359-369
4.
Alpha Bank (2008) Business Review 2008. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
5.
Alpha Bank (2009) Business Review 2009. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
6.
Alpha Bank (2010) Business Review 2010. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
7.
Alpha Bank (2011) Business Review 2011. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
8.
Alpha Bank (2012) Business Review 2012. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
9.
Alpha Bank (2013) Alpha Bank 2013 Financial Report. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
10.
Alpha Bank (2013) Business Review 2013. Greece: Alpha Bank Group. [Online]. Available at: http://www.alpha.gr/page/default.asp?la=2&id=57 (Accessed: 7 July 2014)
Page | 94
11.
Alpha Bank (2014) Annual Financial Report For the period from 1 January to 31 December 2013 (In accordance with Law 3556/2007) Greece: Alpha Bank Group. [Online]. Available at: https://www.alpha.gr/page/default.asp?la=2&id=11435 (Accessed: 8 July 2014)
12.
Alpha Bank [Online] http://www.alpha.gr (Accessed: 04 July 2014)
13.
Anita Elberse, Patrick Barwise, Kathy Hammond (2002), in Michael R. Baye (ed.). The impact of the internet on horizontal and vertical competition: Market efficiency and value chain reconfiguration, The Economics of the Internet and E-commerce (Advances in Applied Microeconomics, Volume 11) Emerald Group Publishing Limited, pp.1 – 27. Available in: http://www.emeraldinsight.com/doi/abs/10.1016/S0278-0984%2802%2911025-X (Accessed: 18.9. 2014)
14.
Anita K. Pennathur (2001). "Clicks and bricks": e-Risk Management for banks in the age of the Internet. Journal of Banking & Finance. 25(11), pp. 2103-2123
15.
Anthony Saunders, Elizabeth Strock and Nickolaos G. Travlos (1990), Ownership Structure, Deregulation, and Bank Risk Taking The Journal of Finance 45(2) pp. 643654
16.
Antonio E. Bernardo, Jiang Luo, James J.D. Wang (2006). A theory of socialistist internal capital markets. Journal of Financial Economics 80 (3), pp. 485–509. Available in: http://econpapers.repec.org/article/eeejfinec/default80.htm (Accessed: 3 November 2014)
17.
Arnould, R.J. (1969). Conglomerate growth and public policy, in Gordon, L. (ed.) Economics of Conglomerate Growth, Oregon State University Department of Agricultural Economics, Corvallis. pp. 72-80
18.
Association of Co-operative Banks in Greece http://www.este.gr/en/index.html (Accessed In: 12 May 2014)
19.
Athanasios G. Patsiotis, Tim Hughes, Don J. Webber (2012), Adopters and nonadopters of internet banking: a segmentation study. International Journal of Bank Marketing. Vol. 30 (1), pp. 20 - 42 , available in: www.emeraldinsight.com/02652323.htm (accessed 22.04.14)
20.
Attica Bank [Online] http://www.atticabank.gr/en/ (accessed: 1.09.2014)
21.
Bander Alsajjan and Charles Dennis (2010), Internet banking acceptance model: Cross-market examination. Journal of Business Research.63 (9-10), pp. 957–963
Page | 95
22.
Beltratti A., Stulz M.R. (2009). Why Did Some Banks Perform Better during the Credit Crisis? A Cross-Country Study of the Impact of Governance and Regulation. European Corporate Governance Institute. Finance Working Paper N°. 254/2009
23.
Berk J. DeMarzo P. (2014). Corporate Finance.3nd Edition - Global Edition. Essex: Pearson Education Limited.
24.
Bou-Wen Lin (2007), Information technology capability and value creation: Evidence from the US banking industry. Technology in Society. 29 (1), pp. 93–106
25.
IBM (2011). The future of Internet banking for business, Bray A., Watson J. United Kingdom: IBM Corporation. [Online]. Available at: http://www935.ibm.com/services/uk/gbs/pdf/IBM694_Corporate_Banking_Paper_Final_VISUA L_High_res.pdf (Accessed 22 December 2014)
26.
Brooks C. (2002). Introductory Econometrics for Finance. 1st Edition. Cambridge: Cambridge University Press.
27.
Bunmi Cynthia Adeleye, Fenio Annansingh, Miguel Baptista Nunes (2004), Risk management practices in IS outsourcing: an investigation into commercial banks in Nigeria. International Journal of Information Management. 24 (2), pp.167–180
28.
Cambridge online dictionary [Online] (http://dictionary.cambridge.org/dictionary/business-english/commercial-bank (Accessed: 18 September 2014)
29.
Cambridge online dictionary [Online] http://dictionary.cambridge.org/dictionary/british/electronic-banking (Accessed: 12 September 2014).
30.
Clayman R.M. Fridson M.S. George H.T. Mattheew S. (2012). Corporate Finance- A practical Approach. 2nd Edition. New Jersey: John Wiley & Sons, Inc.
31.
Co-operative Bank of Chania[Online]. Available at: http://www.chaniabank.gr/el/thegroup/homepage.aspx (accessed: 2.09.2014)
32.
Co-operative Bank of Chania (2004) Co-operative bank of Chania Annual Report 2004 Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
33.
Co-operative Bank of Karditsa (2004) Balance Sheet 2004. Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
Page | 96
34.
Co-operative Bank of Chania (2004) Balance Sheet 2004 Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
35.
Co-operative Bank of Chania (2005) Co-operative bank of Chania Annual Report 2005. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
36.
Co-operative Bank of Chania (2005) Balance Sheet 2005. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
37.
Co-operative Bank of Chania (2006) Co-operative bank of Chania Annual Report2006. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
38.
Co-operative Bank of Chania (2006) Balance Sheet 2006. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
39.
Co-operative Bank of Chania (2007) Co-operative bank of Chania Annual Report 2007.Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
40.
Co-operative Bank of Chania (2007) Balance Sheet Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
41.
Co-operative Bank of Chania (2008) Co-operative bank of Chania Annual Report2008. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
42.
Co-operative Bank of Chania (2008) Balance Sheet Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
43.
Co-operative Bank of Chania (2009) Co-operative bank of Chania Annual Report 2009. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at:
Page | 97
http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014) 44.
Co-operative Bank of Chania (2009) Balance Sheet Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
45.
Co-operative Bank of Chania (2010) Balance Sheet Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
46.
Co-operative Bank of Chania (2010) Co-operative bank of Chania Annual Report2010.Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
47.
Co-operative Bank of Chania (2011) Co-operative bank of Chania Annual Report2011. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
48.
Co-operative Bank of Chania (2011) Balance Sheet Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
49.
Co-operative Bank of Chania (2012) Balance Sheet. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
50.
Co-operative Bank of Chania (2012) Co-operative bank of Chania Annual Report2012. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
51.
Co-operative Bank of Chania (2013) Balance Sheet. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/thegroup/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
52.
Co-operative Bank of Chania (2013) Co-operative bank of Chania Annual Report 2013. Greece: Co-operative Bank of Chania (in Greek) [Online]. Available at: http://www.chaniabank.gr/el/the-group/investor-relations/annual-reports.aspx (accessed: 22 October 2014)
53.
Co-operative Bank of Epirus [Online] www.epirusbank.gr (accessed: 15.09.2014) Page | 98
54.
Co-operative Bank of Evros [online]www.evrosbank.gr (accessed: 20.09.2014)
55.
Co-operative Bank of Karditsa (2005) Balance Sheet 2005. Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
56.
Co-operative Bank of Karditsa (2006) Balance Sheet 2006. Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
57.
Co-operative Bank of Karditsa [online] www.bankofkarditsa.gr (accessed: 14.09.2014)
58.
Co-operative Bank of Karditsa (2007) Balance Sheet 2007. Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
59.
Co-operative Bank of Karditsa (2008) Balance Sheet 2008 Greece: Co-operative Bank of Karditsa (in Greek[Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
60.
Co-operative Bank of Karditsa (2009) Balance Sheet 2009 Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
61.
Co-operative Bank of Karditsa (2010) Balance Sheet 2010 Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
62.
Co-operative Bank of Karditsa (2011) Balance Sheet 2011 Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
63.
Co-operative Bank of Karditsa (2012) Balance Sheet 2012 Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/ (Accessed 10 September 2014)
64.
Co-operative Bank of Karditsa (2013) Balance Sheet 2013 Greece: Co-operative Bank of Karditsa (in Greek) [Online] Availabe at: http://www.bankofkarditsa.gr/attachments/article/59/%CE%99%CE%A3%CE%9F% CE%9B%CE%9F%CE%93%CE%99%CE%A3%CE%9C%CE%9F%CE%A3%2020 13.pdf (Accessed 10 September 2014)
65.
Co-operative Bank of Pieriea Co-op.LL “OLYMPIC CREDIT” [online]www.pieriabank.gr (accessed: 26.09.2014) Page | 99
66.
Co-operative bank of Thessaly Co-op. LL [online]www.bankofthessaly.gr (accessed: 23 Sep 2014)
67.
Co-operative bank of Serres Co-op. LL http://www.syntraser.gr/
68.
David Gefena, Dietmar W. Straubb (2004), Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services, Omega. International Journal of Management Science. 32 (6). pp.407-424
69.
Deloitte [ Online]. Europe’s Retail Banking Model Under Threat. New Technology: Five key threats revealed. Ed. Margaret Doyle. Available in: http://www.deloitte.com/view/en_GB/uk/industries/financial-services/bankingdisrupted/index.html (Accessed In 14.08.2014)
70.
Dewan S., Fei Ren F. (2011) Information Technology and Firm Boundaries: Impact on Firm Risk and Return Performance. Information Systems Research. 22(2), pp. 369–388. Available in: http://dx.doi.org/10.1287/isre.1090.0261 [last Accessed: 12.06.2014]
71.
Draskovic N.M. et al. (2011). Electronic Banking Models. International Journal of Economics and Law. 1( 3), pp. 24-37
72.
Drnevich P.L. Croson C.D (2013). Information Technology and Business-Level Strategy: Toward An Integrated Theoretical Perspective. MIS Quarterly. 37 (2), pp. 483-509
73.
E. Daniel and Storey Ch. (1997) On-line Banking: Strategic and Management Challenges. Long Range Planning, 30(6), pp. 890 to 898
74.
E. Grifell-Tatjé, C. A. K. Lovell (1999). Profits and Productivity. Journal of Management Science. 45(9), pp. 1177-1193 [Online]. Available in: http://www.jstor.org/stable/2634831 (Last Accessed: 12.06.2014).
75.
E.W.T. Ngai, A. Gunasekaran (2007). A review for mobile commerce research and applications. Decision Support Systems. Vo 43 (1), pp. 3 – 15
76.
Elizabeth Daniel, (1999). Provision of electronic banking in the UK and the Republic of Ireland. International Journal of Bank Marketing. 17(2), pp.72 - 83
77.
Erik Banks. (2001). e-Finance The Electronic Revolution. Imprint. West Sussex: John Wiley & Sons, Ltd.
78.
Association
of
Co-operative
Banks
in
Greece
(ΕΣΤΕ)
[online]
http://www.este.gr/index.asp (Accessed: 17 July 2014) 79.
Eurobank S.A (2004). Annual Financial Report 2004. Greece: Eurobank Ergasias EFG [Online] Available at: Page | 100
http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014) 80.
Eurobank S.A (2005). Annual Financial Report 2005. Greece: Eurobank Ergasias EFG[Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
81.
Eurobank S.A (2006). Annual Financial Report 2006. Greece: Eurobank Ergasias EFG [Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
82.
Eurobank S.A (2007). Annual Financial Report 2007. Greece: Eurobank Ergasias EFG [Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
83.
Eurobank S.A (2008). Annual Financial Report 2008. Greece: Eurobank Ergasias EFG [Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
84.
Eurobank S.A (2009). Annual Financial Report 2009. Greece: Eurobank Ergasias EFG[Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
85.
Eurobank S.A (2010). Annual Financial Report 2010. Greece: Eurobank Ergasias EFG[Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
86.
Eurobank S.A (2011). Annual Financial Report 2011. Greece: Eurobank Ergasias EFG[Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
87.
Eurobank S.A (2012). Annual Financial Report 2012. Greece: Eurobank Ergasias EFG [Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014) Page | 101
88. Eurobank S.A (2013). Annual Financial Report 2013. Greece: Eurobank Ergasias EFG [Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014) 89.
Eurobank S.A (2014). Full Year 2013 Results. Greece: Eurobank Ergasias EFG. [Online] Available at: http://www.eurobank.gr/online/HOME/generic.aspx?id=12&mid=342&lang=en (Accessed: 18 July 2014)
90.
Eurobank S.A [Online] www.eurobank.gr (Accessed: 03.07.2014)
91.
Ezzi, S. W. (2014). A theoretical Model for Internet banking: beyond perceived usefulness and ease of use. Archives of Business Research. 2(2), pp. 31-46
92.
Fotios Pasiouras, Audencia, Sailesh K. Tanna, Chrysovalantis Gaganis (2007). What Drives Acquisitions in the EU Banking Industry? The Role of Bank Regulation and Supervision Framework, Bank Specific and Market Specific Factors. Financial Markets, Institutions & Instruments, Forthcoming Coventry University, Economics, Finance and Accounting Applied Research Working Paper Series, No. 3http://papers.ssrn.com/sol3/papers.cfm?abstract_id=997751 (last accessed: 20/12/2014)
93.
Fotios Pasiouras, Kyriaki Kosmidou (2007), Factors influencing the profitability of domestic and foreign commercial banks in the European Union, Research in International Business and Finance, No 21 (2), pp. 222-237
94.
George E. Halkos, Dimitrios S. Salamouris (2004). Efficiency measurement of the Greek commercial banks with the use of financial ratios: a data envelopment analysis approach. Management Accounting Research. 5(2), pp .201–224
95.
George Georgiopoulos (March 26, 2014) UPDATE 1-Piraeus second Greek bank to complete share offering. Reuters [Online] Available in: http://www.reuters.com/article/2014/03/26/greece-piraeusbankidUSL5N0MN2ZN20140326 (Accessed In 10.11.2014)
96.
Georgios Angelakopoulos, Athanassios Mihiotis. (2011) E-banking: challenges and opportunities in the Greek banking sector. Electronic Commerce Research. 11(3), pp. 297-319
97.
Gort,M. (1962). Diversification and Integration in American Industry. 1st Edition. Princeton: Princeton University Press. General Series- National Bureau of economic research; 7. Page | 102
98.
H. B. Mann and D. R. Whitney (1947), On a Test of Whether One of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, 18(1) pp. 50-60. available in: http://www.jstor.org/stable/2236101 [Accessed: 22/12/2014]
99.
H. Kurt Christensen and Cynthia A. Montgomery (1981) Corporate Economic Performance: Diversification Strategy versus Market Structure. Strategic Management Journal. Vol. 2 (4), pp. 327-343.
100.
Montgomery C.A. (1982) the Measurement of Firm Diversification: Some New Empirical Evidence. Academy of Management. Vol. 25(2).pp. 299-307
101.
Hans van Kranenburg, John Hagedoorn, and Jacqueline Pennings (2004). Measurement of International and Product Diversification in the Publishing Industry. The Journal of Media Economics.17(2), pp.87–104
102.
Hare P.G. (2011) Chapter 2 - Institutions and diversification of the economies in transition. In: Rob Vos and Malinka Koparanova (ed.) Globalization and Economic Diversification: Policy Challenges for Economies in Transaction. London: Bloomsbury Academic, pp.11-35
103.
Hellenic Bank Association [Online] Available at: http://www.hba.gr/English/Index_en.asp (Accessed in: 11.07.2014)
104.
PWC (2011 a.) The new digital tipping point. Ed. Hobbs M. Whitehouse S. Bauer Scott, Mahdi S. Weggemans J.W. UK: PWC.[ Online] Available in: http://www.pwc.com/en_GX/gx/banking-capital-markets/publications/assets/pdf/pwcnew-digital-tipping-point.pdf (Accessed: 29.4.2014)
105.
Interbanking Systems S.A. [Online] Available at: http://www.dias.com.gr (Accessed In: 4 Sep 2014)
106.
Jean Gadrey, Faïz Gallouj, Olivier Weinstein, (1995). "New modes of innovation: How services benefit industry", International Journal of Service Industry Management, 6 (3), pp.4 – 16
107.
Joe Peek, Eric Rosengren (1995), Bank regulation and the credit crunch, Journal of Banking & Finance. 19(3-4), pp. 679-692
108.
Joris Claessens, Valentin Dem, Danny De Cock, Bart Preneel, Joos Vandewalle (2002). On the Security of Today’s Online Electronic Banking Systems. Computers & Security 21 (3) pp. 253–265
Page | 103
109.
Joseph E. Stiglitz and Andrew Weiss (1981). Credit Rationing in Markets with Imperfect Information. The American Economic Review. 71(3) pp. 393-410. [Online] Available in: http://www.jstor.org/stable/1802787 .(Accessed: 30/03/2014)
110.
K.A. Froot. d.C Stein(1998). Risk management, capital budgeting, and capital structure policy for financial institutions: an integrated approach. Journal of Financial Economics.47 (1). pp. 55-82
111.
Kathimerini (15 Oct 2014), Greek PM tries to restore calm after market upheaval. [Online].
Available
in:
http://www.ekathimerini.com/4dcgi/_w_articles_wsite1_1_15/10/2014_543770 (Accessed: 15 October 2014) 112.
Kathryn Cormican, David O’Sullivan (2004). Auditing best practice for effective product innovation management. Technovation. 24(10), pp. 819–829
113.
Kenneth A. Froot, Jeremy C. Stein (1998). Risk management, capital budgeting, and capital structure policy for financial institutions: An integrated approach. Journal of Financial Economics. 47 (1), pp. 55–82
114.
Koffa O. (2014) Semi-structural Interview - LinkedIn-InMail to A (confidential name, provided only in the CD under the folder with the name “Confident”). 9-12 Jul. 2014
115.
Koffa O. (2014) Semi-structural Interview-conversation to Parlavanzas V. 15.07.2014
116.
Koffa O. (2014) Semi-structural Interview-conversation to Stathopoulos A. 15.07.2014
117.
Koffa O. (2014) Semi-structural Interview-Email to Sofocleous N. 08.08.2014
118.
Co-operative Bank of Peloponnisos Co-op. LL [Online]. Available at: www.korinthiasbank.gr (accessed: 30.09.2014)
119.
Krishna Palepu (1985). Diversification strategy, profit performance and the entropy measure. Strategic Management Journal. 6(3), pp. 239–255
120.
KTPAE (2014) New Technologies in the Citizens daily life [Online] (translated from Greek) Available in: http://icteval.ktpae.gr/stats/delivery2/ (Accessed: 5.11.2014)
121.
Lecture notes of Lecturer Ludger Ever. Statistical Tables. Glasgow: Glasgow University, School of Mathematics and Statistics. [Online] Available at: http://www.stats.gla.ac.uk/~levers/software/tables/tables-uog.pdf (Accesed: 20 October 2014)
122.
Luis Garicano, Steven N. Kaplan (2002), in Michael R. Baye (ed.) Business-tobusiness e-commerce: Value creation, value capture and valuation. The Economics of the Internet and E-commerce (Advances in Applied Microeconomics, Volume 11) Page | 104
Emerald Group Publishing Limited, pp. 89–125 [Online] Available in: http://www.emeraldinsight.com/doi/abs/10.1016/S0278-0984%2802%2911025-X (last accessed: 18.9. 2014) 123.
M. J. Farrell (1967). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General). 120(3), pp. 253-290
124.
M. Sadiq Sohail, B. Shanmugham (2003). E-banking and customer preferences in Malaysia: An empirical investigation. Information Sciences. 150 (3-4). pp. 207–217
125.
Mahmood Hussain Shaha, Feroz A. Siddiquib, (2006) Organisational critical success factors in adoption of e-banking at the Woolwich bank. International Journal of Information Management. 26 (6) pp. 442–456
126.
Markham J.W (1974). Conglomerate Enterprise and Public Policy. Journal of Economic Literature. 12 (4), pp. 1358-1360. Available In: http://www.jstor.org/stable/2722407 [Accessed: 23.12.2014]
127.
Marvin E. González, Gioconda Quesada, Federico Picado, Carl A. Eckelman, (2004). Customer satisfaction using QFD: an e-banking case, Managing Service Quality.14(4), pp.317 – 330. Available in: www.emeraldinsight.com/0960-4529.htm [Accessed: 22.4.2014]
128.
Michel Rod, Nicholas J. Ashill, Jinyi Shao and Janet Carruthers (2009). An examination of the relationship between service quality dimensions, overall internet banking service quality and customer satisfaction. A New Zealand study. Marketing Intelligence & Planning. 27 (1) pp. 103-126
129.
Ming-Chi Lee (2009), Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications. Vo 8 (3), 130–141
130.
Miranda, Francisco Javier; Chamorro, Antonio; Rubio, Sergio; Morgado, Víctor (2013). Evaluation of Social Networks Sites in the Banking Sector: An Analysis of Top 200 International Banks. Journal of Internet Banking and Commerce. 18 (2) pp. 1-18
131.
Mohammed Amidua, Simon Wolfe (2013) Does bank competition and diversification lead to greater stability? Evidence from emerging markets. Review of Development Finance 3 (3) pp.152–166
132.
Mongtgomery C.A. (1982). The measurement of firm diversification and corporate growth. The Journal of Industrial Economics. 27 (4). pp.359-369. Available in: http://www.jstor.org/stable/255992 [Accessed 22.12.2014]
Page | 105
133.
Morna S.Y. Lee, Peter J. McGoldrick, Kathleen A. Keeling, Joanne Doherty (2003). Using ZMET to explore barriers to the adoption of 3G mobile banking services. International Journal of Retail & Distribution Management. International Journal of Retail & Distribution Management 31 (6). pp 240-348
134.
Nancy Jo Black, Andy Lockett, Heidi Winklhofer and Christine Ennew (2001). The adoption of Internet financial services: a qualitative study. International Journal of Retail & Distribution Management, 29 (8), pp.390 – 398
135.
National Bank of Greece (2009). Annual Report 2009. Greece: National Bank of Greece [Online]. Available at: https://www.nbg.gr/en/the-group/investorrelations/annual-report-offerring-circular (Accessed in 30 July 2014)
136.
National Bank of Greece (2010). Summary of the Annual Report 2010. Greece: National Bank of Greece [Online]. Available at: https://www.nbg.gr/en/thegroup/investor-relations/annual-report-offerring-circular (Accessed in 30 July 2014)
137.
National Bank of Greece (2011). Annual Report 2011. Greece: National Bank of Greece [Online]. Available at: https://www.nbg.gr/en/the-group/investorrelations/annual-report-offerring-circular (Accessed in 30 July 2014)
138.
National Bank of Greece (2012). Annual Report 2012. Greece: National Bank of Greece [Online]. Available at: https://www.nbg.gr/en/the-group/investorrelations/annual-report-offerring-circular (Accessed in 30 July 2014)
139.
National Bank Of Greece (2013). I-bank electronic services for retail customers and businesses. Greece: National Bank of Greece [Online]. Available at: https://www.nbg.gr/en/the-group/investor-relations/annual-report-offerring-circular (Accessed in 30 July 2014)
140.
National Bank of Greece (2013). National Bank of Greece FY 2013 Results. Greece: National Bank of Greece[Online]. Available at: https://www.nbg.gr/en/thegroup/investor-relations/annual-report-offerring-circular (Accessed in 30 July 2014)
141.
National Bank of Greece (2013). Summary of the Annual Report 2013. Greece: National Bank of Greece[Online]. Available at: https://www.nbg.gr/en/thegroup/investor-relations/annual-report-offerring-circular (Accessed in 30 July 2014)
142.
National Bank of Greece (2014). National Bank of Greece Group and Bank Annual Financial Report 31 December 2013[Online]. Available at: https://www.nbg.gr/en/the-group/investor-relations/annual-report-offerring-circular (Accessed in 30 July 2014)
143.
National Bank of Greece [Online] www.nbg.gr Accessed: 05.07.2014 Page | 106
144.
Pancreta Co-operative Bank [Online] www.pancretabank.gr (accessed: 23 Sep 2014)
145.
Panellinia
Bank
http://www.panelliniabank.gr/cms/createpage.aspx?pageid=25
[Online] (accessed:
23
Sep
2014) 146.
Pasiouras, F. Tanna S. and Gaganis, Ch, (2007). What Drives Acquisitions in the EU Banking Industry? The Role of Bank Regulation and Supervision Framework, Bank Specific and Market Specific Factors. Financial Markets, Institutions & Instruments, Forthcoming; Coventry University, Economics, Finance and Accounting Applied Research Working Paper Series, No.3. [Online] Available in: http://ssrn.com/abstract=997751 [Last accessed: 22/12/14]
147.
Visa [online]http://www.visa.co.uk/ (Accessed in: 3.12.2014)
148.
Mastercard [Online] http://www.mastercard.co.uk/ (Accessed in: 3.12.2014)
149.
Paul M. Horvitz, Lawrence J. White (2000). The Challenges of the New Electronic Technologies in Banking: Private Strategies and Public Policies. In: Patrick T. Harker and Stavros A. Zenios (ed). Performance of Financial Institutions Efficiency, Innovation, Regulation. Oxford: Cambridge University Press, pp. 367-387
150.
Peter G. Klein, Marc R. Saidenberg (2000). Diversification, Organisation, and Efficiency: Evidence from Bank Holding Companies. In: Patrick T. Harker and Stavros A. Zenios (ed). Performance of Financial Institutions Efficiency, Innovation, Regulation. Oxford: Cambridge University Press, pp. 153-174
151.
Philip Alexander (15th January 2014), Banking consolidation against crisis, European Business Review [online], Available from: http://www.europeanbusiness.gr/page.asp?pid=1169 , (Accessed: 29/4/2014)
152.
Piraeus Bank (2004). Annual Report 2004. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
153.
Piraeus Bank (2005). Annual Report 2005. Greece: Piraeus Bank[Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
154.
Piraeus Bank (2006). Annual Report 2006. Greece: Piraeus Bank[Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
Page | 107
155.
Piraeus Bank (2007). Annual Report 2007. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
156.
Piraeus Bank (2008). Annual Report 2008. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
157.
Piraeus Bank (2009). Annual Report 2009. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
158.
Piraeus Bank (2010). Annual Report 2010. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
159.
Piraeus Bank (2011). Annual Report 2011. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
160.
Piraeus Bank (2012). Annual Report 2012. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
161.
Piraeus Bank (2013). Annual Report 2013. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
162.
Piraeus Bank (2014). Annual Financial Report For the period from 1st January to 31st December 2013 Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
163.
Piraeus Bank (2014). Capital Strengthening Plan FY 2013 Results Presentation. Greece: Piraeus Bank [Online] Available at: http://www.piraeusbankgroup.com/en/investors/financials/annual-reports (Accessed: 01.07.2014)
164.
Piraeus Bank [Online] http://www.piraeusbank.gr (Accessed: 01.07.2014)
165.
Piraeus Bank [Online] www.winbank.gr (Accessed: 02.07.2014)
166.
Pooja Malhortra, Balwinder S. (2009). The Impact of Internet Banking on Bank Performance and Risk: The Indian Experience, Eurasian Journal of Business and Economics. 2(4) pp. 43-62 Page | 108
167.
Qualtrics Online Survey Platform (QOSP) (https://www.qualtrics.com), (Accessed In: 12 Nov 2014)
168.
Relly F.K., Brown K.C. (2012). Analysis of Investments of Portfolios. 10th Edition. India: Cengage Learning.
169.
Reuters (10.04.2008), Cooperation G7 - Banks against crisis, Kathimerini [online], Available at: http://www.kathimerini.gr/318877/article/oikonomia/die8nhsoikonomia/synergasia-g7---trapezwn-gia-antimetwpish-ths-krishs , (Accessed: 29/4/2014)
170.
Reuters (2013), Greece's Eurobank completes acquisitions of Postbank, Proton Bank. . [Online]. Available in: http://www.reuters.com/article/2013/07/15/greece-eurobankidUSL6N0FL3A920130715 (Accessed: 13 Jul. 2014)
171.
Rob Vos and Malinka Koparanova (2011). Chapter 2 Introduction: Globalization, transition and economic diversification - In: Rob Vos and Malinka Koparanova (ed.) Globalization and Economic Diversification: Policy Challenges for Economies in Transaction. London: Bloomsbury Academic, pp. 1-10
172.
Roth A.V, Jackson W.E. (1995). Strategic Determinants of Service Quality and Performance: Evidence from the Banking. Management Science 41(11), pp. 17201733 Available in: http://www.jstor.org/stable/2632869 [Last Accessed: 12/06/2014]
173.
Rumelt R. P. (1982). Diversification strategy and profitability. Strategic Management Journal. 3 (4) pp. 359-369
174.
Rumlet R.P (1975). Strategy, Structure, and Economic Performance. The Business History
Review.
49
(2),
pp.
282-284.
Available
in:http://www.jstor.org/stable/3113729 [Accessed: 23.12.2014] 175.
Ruud T Frambach, Harry G Barkema, Bart Nooteboom, Michel Wedel, (1998). Adoption of a service innovation in the business market: An empirical test of supplyside variables. Journal of Business Research. 41 (2), pp. 161–174
176.
Santouridis I., Kyritsi M. (2014). The Economies Of Balkan And Eastern Europe Countries In The Changed World (EBEEC 2013) Investigating The Determinants Of Internet Banking Adoption In Greece. Procedia Economics and Finance 9 (1) pp.501 – 510
177.
Seiford L.M and Zhu J. (1999). Profitability and Marketability of the Top 55 U.S. Commercial Banks. Management Science, 45 (9), pp. 1270-1288, available in: http://www.jstor.org/stable/2634837.[ Last Accessed: 12/06/2014]
Page | 109
178.
Serkan Akinci, Safak Aksoy and Eda Atilgan (2004). Adoption of Internet banking among sophisticated consumer segments in an advanced developing country. The International Journal of Bank Marketing. 22(3), pp. 212- 232
179.
Setia P., Venkatesh V., and Joglekar S. (2013). Leveraging Digital Technologies: How Information Quality Leads To Localized Capabilities And Customer Service Performance. MIS Quarterly. 37 (2), pp. 565-590
180.
Soteriou A., Zenios S.A. (1997). Efficiency, Profitability, and Quality in the Provision of Banking Service. [Online] The Wharton Financial Institutions Centre Working Paper. no.28, Pennsylvania Available in: http://fic.wharton.upenn.edu/fic/papers/97/zenios.pdf [Accessed: 20.11.2014]
181.
Soteriou A.C, Zenios S.A. (2003) Delivering e-banking services: An emerging internet business model and a case study. HERMES Center of Excellence on Computational Finance & Economics. Working Paper 02-13
182.
Steven C. Michael (2007). Can information technology enable profitable diversification? An empirical examination, Journal of Engineering and Technology Management. 24 (3), pp.167–185
183.
PWC (2014b.) [Online] How financial services lost its mojo - and how it can get it back Stylianides G. Pugh M., Lee Clarke. UK: PWC. [Online] Available in: http://www.pwc.co.uk/financial-services/financial-services-risk-and-regulation/howfinancial-services-lost-its-mojo.jhtml (Accessed in: 12.12.2014)
184.
T.C. Edwin Cheng, David Y.C. Lam, Andy C.L. Yeung (2006). Adoption of internet banking: An empirical study in Hong Kong, Journal Decision Support Systems. 42(3). pp.1558-1572
185.
Tanriverdi H. (2005). Information Technology Relatedness, Knowledge Management Capability, and Performance of Multibusiness Firms. MIS Quarterly, 29 (2), pp. 311334. Available in: http://www.jstor.org/stable/25148681 [ Last Accessed: 12/06/2014]
186.
Tero Pikkarainen, Kari Pikkarainen, Heikki Karjaluoto, Seppo Pahnila, (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Journal of Internet Research, 14 (3), pp.224 - 235
187.
Thatcher M.E., Pingry D.E. (2004). An Economic Model of Product Quality and IT Value. Information Systems Research. 15 (3). pp. 268–286. Available in: http://pubsonline.informs.org/doi/abs/10.1287/isre.1040.0029 [Last Accessed: 12/06/2014]
Page | 110
188.
Tommi Laukkanen and Mika Pasanen (2008), Analysis Paper, Mobile banking innovators and early adopters: How they differ from other online users? Journal of Financial Services Marketing. 13(2), pp. 86-94
189.
Ugo Albertazzi, Leonardo Gambacorta (2009) Bank profitability and the business cycle Journal of Financial Stability 5 (4) pp. 393–409
190.
Utton, M.A (1979). Diversification and Competition. 1st Edition. Cambridge: Cambridge University Press. Occasional papers-National Institute of Economic and Social Research; 31.
191.
Vincent S. Lai, Honglei Li (2005). Technology acceptance model for internet banking: an invariance analysis. Information & Management. 42 (2), pp. 373–386
192.
W. Gary Simpson and Theodor Kohers (2002). The Link between Corporate Social and Financial Performance: Evidence from the Banking Industry. Journal of Business Ethics, 35(2), pp. 97-109 available in: http://www.jstor.org/stable/25074660 (Accessed: 28/10/2014)
193.
Wadie Nasria, Lanouar Charfeddineb (2011). Factors Influencing the Adoption of Internet Banking in Tunisia. International Journal of Business and Management. 6(8) pp. 1-14
194.
Wai‐Ching Poon, (2008). Users' adoption of e‐banking services: the Malaysian perspective. Journal of Business & Industrial Marketing. 23 (1), pp.59 – 69
195.
Walfried M. Lassar, Chris Manolis, Sharon S. Lassar (2005). The relationship between consumer innovativeness, personal characteristics, and online banking adoption. International Journal of Bank Marketing. 23 (2), pp. 176-199
196.
Wolf Wagner (2010). Diversification at financial institutions and systemic crises. Journal of Financial Intermediation, 19 (3), pp. 373–386.
197.
Xue M. Hitt L.M. Chen Pei-yu (2011). The Determinants and Outcomes of Internet Banking Adoption. Management Science. 57(2). pp. 291 – 307
198.
Yakov Amihud, Baruch Lev and Nickolaos G. Travlos (1990). Corporate Control and the Choice of Investment Financing: The Case of Corporate Acquisitions. The Journal of Finance. 45(2 ), pp. 603-616
199.
Open Solutions [Online] http://www.open-solutions.gr/ (Accessed In:11.12.2014)
200.
Yi‐Shun Wang, Yu‐Min Wang, Hsin‐Hui Lin, Tzung‐I Tang, (2003). Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management., 14 (5), pp. 501 – 519
Page | 111
201.
Yousafzai S.Y., Pallister J.G. Foxall R.G. (2003). A proposed model of e-trust for electronic banking. Technovation 23 (11), pp. 847-860.
202.
Ziqi Liaoa, Michael Tow Cheung (2002) Internet-based e-banking and consumer attitudes: an empirical study. Information & Management 39(4), pp. 283–295
203.
Naftemporiki [Online], Focus Money: Investors’ trust is coming back to Greece, Naftemporiki [online], Available at: http://www.naftemporiki.gr/finance/story/801145/focus-money-epistrefei-iempistosuni-ton-ependuton-stin-ellada (Accessed in: 29.4.2014) (in Greek)
Page | 112
