30,825 research outputs found

    Consumer finance: challenges for operational research

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    Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

    Operations research in consumer finance: challenges for operational research

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    Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

    Perceived Justice and Email Service Recovery

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    This study adds to the limited research of email service recovery. It is perhaps the first non-US study of email service recovery as well as the first study to apply a theoretical perspective ¬– perceived justice – to email service recovery. The results of three annual studies using Australian data resemble US results and support extending perceived justice to service recovery via email. The distributive elements of replying and offering compensation, the procedural element of answering completely and the interactional element of thanking the customer showed significant positive relationships with customer satisfaction, positive word-of-mouth and repurchase intent. Perhaps most importantly for practitioners, the results of a stepwise regression showed that incorporating the simple phrase "thank-you" in the email reply was a strong predictor of successful email service recovery. Finally, this study found that response time might be less critical than previously thought

    e-Consumer Behaviour

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    Purpose – The primary purpose of this article is to bring together apparently disparate and yet interconnected strands of research and present an integrated model of e-consumer behaviour. It has a secondary objective of stimulating more research in areas identified as still being underexplored. Design/methodology/approach – The paper is discursive, based on analysis and synthesis of econsumer literature. Findings – Despite a broad spectrum of disciplines that investigate e-consumer behaviour and despite this special issue in the area of marketing, there are still areas open for research into econsumer behaviour in marketing, for example the role of image, trust and e-interactivity. The paper develops a model to explain e-consumer behaviour. Research limitations/implications – As a conceptual paper, this study is limited to literature and prior empirical research. It offers the benefit of new research directions for e-retailers in understanding and satisfying e-consumers. The paper provides researchers with a proposed integrated model of e-consumer behaviour. Originality/value – The value of the paper lies in linking a significant body of literature within a unifying theoretical framework and the identification of under-researched areas of e-consumer behaviour in a marketing context

    Cognitive finance: Behavioural strategies of spending, saving, and investing.

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    Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services

    Structural equation modeling of eBankQual scale: a study of E-Banking in India

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    This study assesses the relationship between perceived quality, brand perception and perceived value with satisfaction. For the data analysis structural equation modeling (SEM) method and path analysis method were used. A result indicates that, eBankQual model is fit to assess relationship between service quality, brand perception and perceived value with overall customers’ satisfaction in e-banking service. Result of regression SEM indicates that, all 14 variables found significant and good predictors of overall satisfaction in e-banking services. However, result of SEM analysis indicates that, data supports to eBankQual model and dimensions Compensation, Convenience, Contact Facilities, Easy to Use, Responsiveness, Cost Effectiveness and System Availability including brand perception and perceived value were found more significant factors in the eBankQual model.Structural Equation Modeling, Service quality, Brand perception, Perceived value, Satisfaction

    Using big data for customer centric marketing

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    This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

    Strategic Predictors of Successful Enterprise Systems Deployment

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    Purpose The delivered wisdom to date has enterprise system purchase and implementation as one of the most hazardous projects any organization can undertake. The aim was to reduce this risk by both theoretically and empirically finding those key predictors of a successful enterprise system deployment. Design/methodology/approach A representative sample of 60 firms drawn from the Fortune 1000 that had recently (1999-2000) adopted enterprise resource planning (ERP) systems was used to test a model of adoption performance with significant results. Findings Leadership (social learning theory), business process re-engineering (change the company not the technology) and acquisition strategy (buy, do not make) were found to be significant predictors of adoption performance (final model R 2=43 percent, F=5.5, pp Originality/value The “four factor” model we validate is a robust predictor of ERP adoption success and can be used by any organization to audit plans and progress for this undertaking

    Partnerships for skills : investing in training for the 21st century

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    Strategic Predictors of Successful Enterprise Systems Deployment

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    Purpose The delivered wisdom to date has enterprise system purchase and implementation as one of the most hazardous projects any organization can undertake. The aim was to reduce this risk by both theoretically and empirically finding those key predictors of a successful enterprise system deployment. Design/methodology/approach A representative sample of 60 firms drawn from the Fortune 1000 that had recently (1999-2000) adopted enterprise resource planning (ERP) systems was used to test a model of adoption performance with significant results. Findings Leadership (social learning theory), business process re-engineering (change the company not the technology) and acquisition strategy (buy, do not make) were found to be significant predictors of adoption performance (final model R 2=43 percent, F=5.5, pp Originality/value The “four factor” model we validate is a robust predictor of ERP adoption success and can be used by any organization to audit plans and progress for this undertaking
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