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Profiling internet banking users: A knowledge discovery in data mining process model based approach

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Abstract

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

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Author information

Authors and Affiliations

  1. Department of Computing, The University of the West Indies, Kingston, Jamaica

    Gunjan Mansingh

  2. Mona School of Business and Manangement, The University of the West Indies, Kingston, Jamaica

    Lila Rao

  3. School of Business, Virginia Commonwealth University, Richmond, VA, USA

    Kweku-Muata Osei-Bryson

  4. Department of Accounting and Information Systems, University of Canterbury, Christchurch, New Zealand

    Annette Mills

Authors
  1. Gunjan Mansingh
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  2. Lila Rao
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  3. Kweku-Muata Osei-Bryson
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  4. Annette Mills
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Corresponding author

Correspondence to Gunjan Mansingh.

Appendix A

Appendix A

 

Construct/Variable Description

Field Names

Banking Use Demographics

 

1.

Internet Banking (IB) User (Yes/No)

IBUSER

2.

Frequency of Banking Use (General use)

BNKFREQ

3.

Bank User (Yes/No)

BANKUSER

4.

Credit Union/Building Society User (Yes/No)

CREDITUNION_BUISOCIETY

5.

Internet Banking Use (# of Years)

IBYRS

Internet Use Demographics

 

6.

Internet Access through:

 

7.

- Home (Yes/No)

INTHOME

8.

- School (Yes/No)

INTSCH

9.

- Work (Yes/No)

INTWRK

10.

Ready Access to Internet (Yes/No)

RDYACCESS

11.

Computer Use (# of Years)

COMPYRS

12.

Internet Use (# of Years)

INTYRS

Personal Demographics

 

13.

Gender

GENDER

14.

Age

AGE

15.

Educational Level

HGHEDUC

16.

Occupation Classification Code

OCCUPATIONRECODED

17.

Head of Household

HEADHSE

18.

Primary Income Earner

PEARNER

19.

Household Structure

HSESTRUC

20.

No. of Dependents

DPNDENTS

21.

Income

INCOME

Attitudinal and Behavioral Variables

 

22.

Attitude

ATITUDE_FS

23.

Perceived Usefulness

PUSE_FS

24.

Perceived Ease of Use

PEOU_FS

25.

Compatibility

COMPATIBILITY_FS

26.

Confidence

CONFIDENCE_FS

27.

Personal Innovativeness

INNOVATIVENESS_FS

28.

Perceived Behavioral Control

PBS_FS

29.

Subjective Norm

SUBNRM_FS

30.

Need for Human Interaction

HUMINTRACT_FS

31.

Perceived Cost (Effort)

PCOST_FS

32.

Switching Cost (Inertia)

SWITCH_FS

33.

Perceived Security

SECURITY_FS

34-39

IB activities (Users only) (e.g. view transactions and balances; transfer funds between bank accounts; pay personal bills; subscribe to Alert services, etc.)

USEFRQ1…USEFREQ6

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Mansingh, G., Rao, L., Osei-Bryson, KM. et al. Profiling internet banking users: A knowledge discovery in data mining process model based approach. Inf Syst Front 17, 193–215 (2015). https://doi.org/10.1007/s10796-012-9397-2

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