The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Adopters and non-adopters of Internet banking: A segmentation study
Open accessAbstract
Purpose: This study examines internet banking adoption and resistance behaviour in Greece in order to develop profiles of adopters and non‐adopters of the service. The aim is to illustrate customers' resistance behaviour towards internet banking. The existing research does not explain resistance behaviour, since it does not clearly distinguish non‐adoption from resistance. Consequently, it has not recognised the different types of non‐adoption. Design/methodology/approach: A measuring instrument was developed and utilised in a survey of a convenience sample of 1,200 customers. The derived dimensionality of the relevant perceptual variables was used to explore the existence of different customer segments through cluster analysis. Findings: Three segments were identified, where the description of their profiles is based on customer perceptions of the service and general usage data. Across these segments adopters and non‐adopters were found to have different characteristics. With regard to demographics, only income was found to be associated with segment membership. Research limitations/implications: Perceptual and usage variables are useful in market segmentation. The results also suggest the possible existence of sub‐groups within each segment characterised by different aspects of resistance behaviour. Further research could identify and explore their potential and study non‐adopter behaviour. Practical implications: 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. Originality/value: The customer segments identified in this study are based on new links found between the factors that drive diffusion and resistance to diffusion and general usage data. Non‐adopters across the segments resist for different reasons, or not resist.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Using AI goods and services may face social resistance at least for some considerable time after these services will be possible to offer on the market.
“AI might be more efficient and reliable, but using these types of goods and services may face social resistance at least for some considerable time after these services will be possible to offer on the market (Patsiotis et al. 2012).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Patsiotis et al. 2012
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549
Extrinsic and Intrinsic Motivation to Use Computers in the Workplace 1
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1992.tb00945.x · Cited by 6,231
Flagged first, then by citations
Research works printed but not linked: 44
- Alfansi, L. and Sargeant, A. (2000), “Market segmentation in the Indonesian banking sector: the relationship between demographics and desired customer benefits”, International Journal of Bank Marketing, Vol. 18 No. 2, pp. 64‐74. · DOI
- Beane, T.P. and Ennis, D.M. (1987), “Market segmentation: a review”, European Journal of Marketing, Vol. 21 No. 5, pp. 20‐42. · DOI
- Black, N.J., Lockett, A., Winklhofer, H. and Ennew, C. (2001), “The adoption of internet financial services: a qualitative study”, International Journal of Retail & Distribution Management, Vol. 29 No. 8, pp. 390‐8. · DOI
- Burke, R.R. (2002), “Technology and the customer interface: what consumers want in the physical and virtual store”, Journal of the Academy of Marketing Science, Vol. 30 No. 4, pp. 411‐32. · DOI
- Chen, R. and He, F. (2003), “Examination of brand knowledge, perceived risk and consumers' intention to adopt an online retailer”, TQM & Business Excellence, Vol. 14 No. 6, pp. 677‐93. · DOI
- Cheung, C.M.K. and Lee, M.K.O. (2001), “Trust in internet shopping: instrument development and validation through classical and modern approaches”, Journal of Global Information Management, Vol. 9 No. 3, pp. 23‐35. · DOI
- Curran, J.M. and Meuter, M.L. (2005), “Self‐service technology adoption: comparing three technologies”, Journal of Services Marketing, Vol. 19 No. 2, pp. 103‐13. · DOI
- Durkin, M. (2007), “Understanding registration influences for electronic banking”, International Review of Retail, Distribution and Consumer Research, Vol. 17 No. 3, pp. 219‐31. · DOI
- Ellen, P.S., Bearden, W.O. and Sharma, S. (1991), “Resistance to technological innovations: an examination of the role of self‐efficacy and performance satisfaction”, Journal of the Academy of Marketing Science, Vol. 19 No. 4, pp. 297‐307. · DOI
- Gatignon, H. and Robertson, T.S. (1989), “Technology diffusion: an empirical test of competitive effects”, Journal of Marketing, Vol. 53 No. 1, pp. 35‐49. · DOI
- Gerrard, P. and Cunningham, J.B. (2003), “The diffusion of internet banking among Singapore consumers”, International Journal of Bank Marketing, Vol. 21 No. 1, pp. 16‐28. · DOI · Paper page
- Gerrard, P., Cunningham, J.B. and Devlin, J.F. (2006), “Why consumers are not using internet banking: a qualitative study”, Journal of Services Marketing, Vol. 20 No. 3, pp. 160‐8. · DOI
- Hair, J.F., Bush, R.P. and Ortinau, D.J. (2003), Marketing Research – Within a Changing Information Environment, 2nd ed., McGraw‐Hill/Irwin, New York, NY.
- Hair, J.F. Jr, Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, Prentice Hall, Englewood Cliffs, NJ.
- Harrison, T. (1994), “Mapping customer segments for personal financial services”, International Journal of Bank Marketing, Vol. 12 No. 8, pp. 17‐25. · DOI
- Hughes, T. (2006), “New channels/old channels – customer management and multi‐channels”, European Journal of Marketing, Vol. 40 Nos 1/2, pp. 113‐29. · DOI
- Janssens, W., Wijnen, K., De Pelsmacker, P. and Van Kenhove, P. (2008), Marketing Research with SPSS, Prentice Hall, Harlow.
- Kimiloglou, H. (2004), “The e‐literature: a framework for understanding the accumulated knowledge about internet marketing”, Academy of Marketing Science Review, Vol. 8 No. 6, pp. 1‐36, available at: www.amsreview.org/articles/kimiloglu06‐2004.pdf (accessed 13 January 2011).
- Kotler, P. (2000), Marketing Management, Prentice Hall, Englewood Cliffs, NJ.
- Lassar, W.M., Manolis, C. and Lassar, S. (2005), “The relationship between consumer innovativeness, personal characteristics, and online banking adoption”, International Journal of Bank Marketing, Vol. 23 No. 2, pp. 176‐99. · DOI
- Laukkanen, P., Sinkkonen, S. and Laukkanen, T. (2008), “Consumer resistance to internet banking: postponers, opponents and rejectors”, International Journal of Bank Marketing, Vol. 26 No. 6, pp. 440‐55. · DOI
- Lee, E.‐J., Kwon, K.‐N. and Schumann, D.W. (2005), “Segmenting the non‐adopter category in the diffusion of internet banking”, International Journal of Bank Marketing, Vol. 23 No. 5, pp. 414‐37. · DOI
- Lockett, A. and Littler, D. (1997), “The adoption of direct banking services”, Journal of Marketing Management, Vol. 13 No. 8, pp. 791‐811. · DOI
- McKechnie, S., Winklhofer, H. and Ennew, C. (2006), “Applying the technology acceptance model to online retailing of financial services”, International Journal of Retail & Distribution Management, Vol. 34 Nos 4/5, pp. 388‐410. · DOI
- McMillan, S.J. and Hwang, J.‐S. (2002), “Measures of perceived interactivity: an exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity”, Journal of Advertising, Vol. 31 No. 3, pp. 29‐42. · DOI
- Machauer, A. and Morgner, S. (2001), “Segmentation of bank customers by expected benefits and attitudes”, International Journal of Bank Marketing, Vol. 19 No. 1, pp. 6‐17. · DOI
- Minhas, R.S. and Jacobs, E.M. (1996), “Benefit segmentation by factor analysis: an improved method of targeting customers for financial services”, International Journal of Bank Marketing, Vol. 14 No. 3, pp. 3‐13. · DOI
- Molesworth, M. and Suortti, J.‐P. (2001), “Buying cars online: the adoption of the web for high‐involvement, high‐cost purchases”, Journal of Consumer Behaviour, Vol. 2 No. 2, pp. 155‐68. · DOI
- Moore, G.C. and Benbasat, I. (1991), “Development of an instrument to measure the perceptions of adopting an information technology innovation”, Information Systems Research, Vol. 2 No. 3, pp. 192‐222. · DOI · Paper page
- Nysveen, H., Pedersen, P.E. and Thorbjornsen, H. (2005), “Intentions to use mobile services: antecedents and cross‐service comparisons”, Journal of the Academy of Marketing Science, Vol. 33 No. 3, pp. 330‐46. · DOI
- Ozdemir, S. and Trott, P. (2009), “Exploring the adoption of a service innovation: a study of internet banking adopters and non‐adopters”, Journal of Financial Services Marketing, Vol. 13 No. 4, pp. 284‐99. · DOI
- Ram, S. and Sheth, R. (1989), “Consumer resistance to innovations: the marketing problem and its solutions”, Journal of Consumer Marketing, Vol. 6 No. 2, pp. 5‐14. · DOI
- Rogers, E.M. (1995), Diffusion of Innovations, The Free Press, New York, NY.
- Sathye, M. (1999), “Adoption of internet banking by Australian consumers: an empirical investigation”, International Journal of Bank Marketing, Vol. 17 No. 7, pp. 324‐34. · DOI
- Schmidt, M.J. and Hollensen, S. (2006), Marketing Research – An International Approach, Prentice Hall, Harlow.
- Sheth, J.N. (1981), “Psychology of innovation resistance: the less developed concept (LDC) in diffusion research”, in Sheth, J.N. (Ed.), Research in Marketing, Vol. 4, JAI Press, Greenwich, CT, pp. 273‐82.
- Stafford, M.R. (1996), “Demographic discriminators of service quality in the banking industry”, Journal of Services Marketing, Vol. 10 No. 4, pp. 6‐22. · DOI
- Suzuki, Y. and Williams, L.R. (1998), “Analysis of EDI resistance behaviour”, Transportation Journal, Vol. 37 No. 4, pp. 36‐44.
- Walker, R.H. and Johnson, L.W. (2006), “Why consumers use and do not use technology‐enabled services”, Journal of Services Marketing, Vol. 20 No. 2, pp. 125‐35. · DOI
- Wang, Y.‐S., Wang, Y.‐M., Lin, H‐H. and Tang, T.‐I. (2003), “Determinants of user acceptance of internet banking: an empirical study”, International Journal of Service Industry Management, Vol. 14 No. 5, pp. 501‐19. · DOI
- Watson, L. and Spence, M.T. (2007), “Causes and consequences of emotions on consumer behaviour – a review and integrative cognitive appraisal theory”, European Journal of Marketing, Vol. 41 Nos 5/6, pp. 487‐511. · DOI
- Wood, S.L. and Moreau, C.P. (2006), “From fear to loathing? How emotion influences the evaluation and early use of innovations”, Journal of Marketing, Vol. 70 No. 3, pp. 44‐57. · DOI
- Yadav, M.S. and Varadarajan, R.R. (2005a), “Understanding product migration to the electronic marketplace: a conceptual framework”, Journal of Retailing, Vol. 81 No. 2, pp. 125‐40. · DOI
- Yadav, M.S. and Varadarajan, R.R. (2005b), “Interactivity in the electronic marketplace: an exposition of the concept and implications for research”, Journal of the Academy of Marketing Science, Vol. 33 No. 4, pp. 585‐603. · DOI
Cited by
Sorted by citations
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
“AI might be more e ffi cient and reliable, but using these types of goods and services may face social resistance at least for some considerable time after these services will be possible to o ff er on the market (Patsiotis, Hughes, & Webber, 2012).”
Background
Metadata
- Authors
- Patsiotis, A. G., Hughes, T., & Webber, D. J.
- Affiliations
- The American College of Greece (GR)
- University of the West of England (GB)
- Auckland University of Technology (NZ)
- Venue
- International Journal of Bank Marketing
- Publisher
- Emerald
- Published
- 2012-01-27
- Volume
- 30
- Issue
- 1
- Pages
- 20-42
- ISSN
- 0265-2323 (ISSN-L)
- OpenAlex
- W2052392449
- Cited by
- 85
- References
- 3
- Citations by year
- 2025: 2 · 2024: 10 · 2023: 3 · 2022: 5 · 2021: 9 · 2020: 6 · 2019: 6 · 2018: 8 · 2017: 4 · 2016: 5 · 2015: 10 · 2014: 3 · 2013: 12 · 2012: 2
- FWCI
- 17.64
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.uwe.ac.uk
- Licence
- Locations
- International Journal of Bank Marketing (publishedVersion)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- internet banking, technology adoption, non-adopters, customer segmentation, consumer profile, diffusion of innovations, consumer perception, Greece
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review