Influence of personality factors on the consumption of personal care products

This research was undertaken with the primary objective of explaining differences in consumption of personal care products using personality variables. Several streams of research reported were reviewed and a conceptual model was developed. Theories on the relationship between self concept and behaviour was reviewed and the need to use individual difference variables to conceptualize and measure the salient dimensions of the self were emphasized. Theories relating to social comparison, eating disorders, role of idealized media images in shaping the self-concept, evidence on cosmetic surgery and persuasibility were reviewed in the study. These came from diverse fields like social psychology, use of cosmetics, women studies, media studies, self-concept literature in psychology and consumer research, and marketing. From the review three basic dimensions, namely self-evaluation, self-awareness and persuasibility were identified and they were posited to be related to consumption. Several personality variables from these conceptual domains were identified and factor analysis confirmed the expected structure fitting the basic theoretical dimensions. Demographic variables like gender and income were also considered.It was found that self-awareness measured by the variable public self-consciousness explain differences in consumption of personal care products. The relationship between public self-consciousness and consumption was found to be most conspicuous in cases of poor self-, evaluation measured by self-esteem. Susceptibility to advertising also was found to explain differences in consumption.From the research, it may be concluded that personality variables are useful for explaining consumption and they must be used together to explain and understand the process. There may not be obvious and conspicuous links between individual measures and behaviour in marketing. However, when used in proper combination and with the help oftheoretical models personality offers considerable explanatory power as illustrated in the seventy five percent accuracy rate of prediction obtained in binary logistic regression.

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis