E-banking comes to town: Exploring how traditional UK high street banks are meeting the challenge of technology and virtual relationships

UK high street banks are continuing to extend the choice of channel through which customers can manage their moneycolon; an obvious example is e-banking. They have been keen to exploit technological advances and changing customer attitudes to alternative channels. Additionally, competition from leaner new market entrants has provided an added incentive to adopt new approaches. In the light of such developments, it is worth reflecting on the changing nature of the bank–customer relationship. This paper suggests that banks are finding it difficult to manage relationships effectively due, in large measure, to the sheer volume of customer data generated by new interactive, technology-based channels. Paradoxically, it seems that the more data banks have about customers the less likely they are to know them on a personal level. It is further suggested that channels such as e-banking potentially reduce the level of personal contact between bank and customer to the extent that a ‘virtual’ relationship develops. This paper concludes that, given the tendency towards ‘virtualisation’, it is inconceivable that bank–customer relationships will become any more intimate in the future. Indeed, a greater degree of personalisation in customer communication may be the very best that banks are able to offer.     

Strategizing through analyzing and influencing the network horizon

How does a firm keep on being valuable in a network? One requirement is that the firm has a sufficient overview of the network and its dynamics. In other words, a firm's strategy depends on the firm's overview of the network—its network horizon. How comprehensive or limited should its network horizon be? Is it necessary to know the network beyond the direct counterparts? Such issues have not received much attention. In this article, we discuss network horizons and argue that limited network horizons are both inevitable and useful. However, such myopia requires that a firm's counterparts effectively and efficiently mediate between the firm and the rest of the network. Based on a case study, we introduce and discuss three mediating functions of counterparts: a joining, a relating, and an insulating function, and we claim that in order to support a firm's strategizing, managers need to analyze and influence counterparts' mediating functions and thereby the firm's network horizon.     

Consistent estimation in the bilinear multivariate errors-in-variables model

A bilinear multivariate errors-in-variables model is considered. It corresponds to an overdetermined set of linear equations AXB=C, A∈ℝ^m×n, B∈ℝ^p×q, in which the data A, B, C are perturbed by errors. The total least squares estimator is inconsistent in this case.  An adjusted least squares estimator is constructed, which converges to the true value X, as m →∞, q →∞. A small sample modification of the estimator is presented, which is more stable for small m and q and is asymptotically equivalent to the adjusted least squares estimator. The theoretical results are confirmed by a simulation study. Acknowledgements. We thank two anonymous reviewers for their suggestions and corrections.? A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe.? S. Van Huffel is a full professor with the Katholieke Universiteit Leuven.? I. Markovsky is a research assistant with the Katholieke Universiteit Leuven.? This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP V-22), initiated by the Belgian State, Prime Minister's Office – Federal Office for Scientific, Technical and Cultural Affairs of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) “Predictive computer models for medical classification problems using patient data and expert knowledge”, of the FWO projects G.0078.01, G.0200.00, and G0.0270.02.? The scientific responsibility is assumed by its authors.