Quantitative Modelling in Marketing and Management
The field of marketing and management has undergone immense changes over the past decade. These dynamic changes are driving an increasing need for data analysis using quantitative modelling.
Problem solving using the quantitative approach and other models has always been a hot topic in the fields of marketing and management. Quantitative modelling seems admirably suited to help managers in their strategic decision making on operations management issues. In social sciences, quantitative research refers to the systematic empirical investigation of social phenomena via statistical, mathematical or computational techniques.
This book focuses on the description and applications of many quantitative modelling approaches applied to marketing and management. The structure encompasses statistical, computer and mathematical as well as other models. The topics range from fuzzy logic and logical discriminant models to growth models and k-clique models. It also covers current research being conducted in the field.
Readership: Undergraduates and postgraduates of management and business administration, academic researchers marketing professionals, financial professionals and business consultants.
main antecedent is the perceived usefulness of the service (H3: β = 0.567; p < 0.01), as it makes easier and quicker banking activities, increasing perceived productivity. However, the role of the ‘ease of use’ is also very relevant. Results show that it becomes difﬁcult to perceive a technology as useful which is not easy to use, as easiness is usefulness’s main antecedent (H1: β = 0.802; p < 0.01). The effect of the perceived ease of use of internet banking on the attitude to this technology is
15.4 25.1 48 36 6 6 6 < 2.7e−09 < 2.2e−06 0.00061 0.02360 0.00012 Coefﬁcients TourType Country Age Gender Earnings (see Hutcheson and Moutinho (2008) for a detailed explanation of the use of analysis of deviance tables with logit models). 2.2. Union membership Data taken from the current population survey (see Berndt, 1991) was used to model the probability of someone being a member of a Union (see Hutcheson and Moutinho (2008, Chapter 3) for a detailed discussion of these data).
relationship Negative impact of hidden Structural model: layer loyalty: The hidden neuron In the hypothesised and transactional factor has equivalent models the inhibitory weight on loyalty constructs of relational nature are stressed very rational industry and their presence is due to the involvement of people, in this case, the account manager. These are some of the ﬁndings that, more than conﬁrming a theory, present the opportunity for new developments in the loyalty ﬁeld. page 132 October
and the potential of both modelling approaches. Furthermore, it reinforces the idea that these speciﬁc modelling techniques can be used in a complementary way: SEM with a more conﬁrmatory approach and ANNs as a more exploratory tool. Studies conducted in the loyalty ﬁeld rely, excessively, on a set of assumptions perfectly determined. Consequently, advances on the study of the determinants and causes of loyalty are scarce. Using a tool like ANNs, will help to discover new links between variables,
research is to develop and employ mathematical models, theories and/or hypotheses pertaining to Quantitative Modelling in Marketing and Management phenomena. In the social sciences, quantitative research refers to the systematic empirical investigation of social phenomena via statistical, mathematical or computational techniques. Quantitative research is used widely in social sciences such as in marketing and management. In the social sciences, the ‘quantitative’ term relates to empirical