Using the juster scale to predict adoption of an innovative product

The Juster scale - a simple, self-reported measure of purchase probability, has been shown to be effective in predicting consumers' future purchasing behaviours. Purchase probability scales have often been shown to be better predictors of future behaviour than purchase intentions measures, the more widely used method. The vast majority of studies though have used the scale to predict the purchase of products or services the consumer is already familiar with. This research looks at how accurately the Juster scale can predict early adoption of an innovative new product prior to its’ launch. In a longitudinal study of market behaviour, these predictions are compared to actual adoption rates. The results show that there is only a moderate level of correlation between purchase probability scores and actual adoption behaviour, in both the short and long term, but they are better than intention measures. The main difficulty in predicting adoption behaviour seems to stem from consumers inability to foresee intervening situational factors, rather than inaccuracies in the probability scale itself.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

Predicting consumer innovative behavior using alternative theories and likelihood measures: a longitudinal study

This paper reports on a longitudinal study of consumers, where two dominant theories that purport to predict innovative behavior are applied and compared directly, using a methodology suggested as ideal by past researchers. Predictions made prior to launch were then evaluated against multiple measures of purchase likelihood, and against actual adoption behavior up to 12 months after launch. The results of this study suggest that perceptions of the innovations characteristics (PIC) predicted the selfreported likelihood of adoption better than the Domain Specific Innovativeness (DSI) scale, a personality-based measure. Prediction of actual adoption was largely inaccurate and both theories massively over predicted adoption levels, however the DSI scale was slightly more accurate. The conclusions here are that no one theory could make adequate predictions of behavior, that purchase likelihood measures are a poor substitute for measuring actual behavior but that purchase probability scales should be used more often in adoption research.

Innovative science education assessment : linking theory with practice

This paper presents an assessment approach which demonstrates the characteristics of excellent assessment. In a third year Science Education unit in an undergraduate degree in teacher education, assessment is seen as an integral part of the learning process. Learning outcomes are improved for the students and they appreciate the validity of the assessment. Assessment is an important aspect of any unit in higher education but should be seen, not as an end in itself, but rather as a means for educational improvement. Using the assessment task as an illustration, this paper will discuss the theoretical framework of assessment. It will describe how the unit, delivered in a school-based mode, provides opportunity for students to link their theoretical understandings with practical applications, drawing together the academic debates with the first-hand experiences. It will illustrate how the assessment task itself embeds the principles of constructivism and conceptual change models in science education. Finally, the paper will highlight the benefits of this assessment approach with particular reference to the student learning.

Who are "innovators" and do they matter?: A critical review of the evidence supporting the targeting of “innovative” consumers

Purpose – This paper aims to bring together and evaluate the reasons that have historically been advanced to justify the heavy emphasis on innovative consumers within the general context of the adoption of products and services, and to assess the strategic benefits to be gained from targeting such market segments.

Design/methodology/approach – The approach is critical review and analysis of the published
literature.

Findings – This paper finds that, although the benefits of identifying innovative early adopters are not as strong and clear-cut as is often claimed, they are still sufficient to warrant further research into methods that will accurately identify them and predict their purchasing behaviour.

Practical implications – Targeting strategies should distinguish carefully between truly innovative consumers and other early adopters. The costs of identifying them in a particular market need to be weighed against the potential benefits.

Originality/value – Although many studies have attempted to address the question of what drives individual adoption behaviour, the rationale for that focus has not been well established and is rarely critiqued. In clarifying the situation, this paper should provide guidance for academic researchers and marketing planners.

An innovative analyser for email classification based on grey list analysis

In this paper we propose a new technique of email classification based on grey list (GL) analysis of user emails. This technique is based on the analysis of output emails of an integrated model which uses multiple classifiers of statistical learning algorithms. The GL is a list of classifier/(s) output which is/are not considered as true positive (TP) and true negative (TN) but in the middle of them. Many works have been done to filter spam from legitimate emails using classification algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection the FP problem is unacceptable, sometimes. The proposed technique will provide a list of output emails, called "grey list (GL)", to the analyser for making decisions about the status of these emails. It has been shown that the performance of our proposed technique for email classification is much better compare to existing systems, in order to reducing FP problems and accuracy.

Human resource management : transforming theory into innovative practice

Facilitates the development of critical and innovative thinking, allowing readers to make Strategic Human Resources Management decisions in the light of the diverse features of any given business and its operating environment. Hartel, Fujimoto, Stybosch all from Monash University, Fitzpatrick, Victorian Chamber of Commerce.