Understanding what drives the purchase decision in pension and investment products

The aims of this paper are to first seek an understanding of consumer decision-making when purchasing pension and investment products, and second to ascertain how this decision-making affects the consumer's choice of distribution route. The study employed both focus groups and postal questionnaire survey methods based on the framework of a classical decision-making model that investigated problem recognition, information search, evaluation tools used and post-purchase. The findings show that the decision-making process experience differed to a lesser or greater degree depending on the distribution route. The majority of respondents had recognised the need to make a purchase decision long before seeking information. Younger respondents on all incomes believed that they must make some pension provision for themselves as opposed to relying on the government's retirement provision. Many changed channels for information searches, but tended to settle with the Independent Financial Adviser (IFA). The two main evaluation tools for pension and investment were found to be the ‘charges’ and ‘historic fund performance’. The vast majority of respondents reiterated their worry that the outcomes would not be known until retirement. In terms of analysis by the level of ‘financial literacy’, respondents who scored in the upper quartile were more inclined to be on a higher income, less inclined to evaluate on charges and more proactive in discussing the investment strategy of their pension fund. Respondents who scored in the lower quartile had opposite results. One of the implications of these findings is that the younger respondents’ recognition of pension savings favours the government's intention to reverse the existing balance of pension distribution. The other main implication is that the findings will be of help to managers in appreciating the dominance of the IFA channel by providing an explanation of why consumers choose this route, and, additionally, can assist direct marketing managers in identifying customers who will be more likely to use multichannel or single-channel shoppers. It can also help the marketing manager increase the usage of different channels by addressing the factors driving the purchase decision and distribution choice.

The personality attributes and leisure activities of the internet users: a Taiwanese case study

This study aims to examine the relationship between the personality attributes of Internet users and their leisure activities. A questionnaire survey was undertaken which revealed that most Internet users are single males aged between 21-30 belonging to the lower income groups, employed in information technology or related fields. The personality attributes of the sample showed a tendency towards a mixed locus control category. The survey indicated that the preferred leisure activities of this population group are reading, collecting and computer-based activities. However, ‘movement’ and collecting were the only leisure activities to show a significant correlation with the users’ personality attributes.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.