Open Science Research Excellence

Abdul Wahid

Publications

2

Publications

2
8035
Evaluating Telepresence Experience and Game Players' Intention to Purchase Product Advertised in Advergame
Abstract:

In line with changes of consumers modern lifestyle has call for the advertising strategy to change. This research is to find out how game with telepresence and product experience embedded in the computer game to affect users- intention to purchase. Game content developers are urging to consider of placing product message as part of game design strategy that can influence the game player-s intention to purchase. Experiment was carried out on two hundred and fifty undergraduate students who volunteered to participate in the Internet game playing activities. A factor analysis and correlation analysis was performed on items designed to measure telepresence, attitudes toward telepresence, and game player intention to purchase the product advertise in the game that respondents experienced. The results indicated that telepresence consist of interactive experience and product experience. The study also found that product experience is positively related to the game players- intention to purchase. The significance of product experience implies the usefulness of an interactive advertising in the game playing to attract players- intention to purchase the advertised product placed in the creative game design.

Keywords:
Purchase intention, telepresence, product experience, interactive experience.
1
10003871
Performance Evaluation of Data Mining Techniques for Predicting Software Reliability
Abstract:

Accurate software reliability prediction not only enables developers to improve the quality of software but also provides useful information to help them for planning valuable resources. This paper examines the performance of three well-known data mining techniques (CART, TreeNet and Random Forest) for predicting software reliability. We evaluate and compare the performance of proposed models with Cascade Correlation Neural Network (CCNN) using sixteen empirical databases from the Data and Analysis Center for Software. The goal of our study is to help project managers to concentrate their testing efforts to minimize the software failures in order to improve the reliability of the software systems. Two performance measures, Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Errors (MAE), illustrate that CART model is accurate than the models predicted using Random Forest, TreeNet and CCNN in all datasets used in our study. Finally, we conclude that such methods can help in reliability prediction using real-life failure datasets.

Keywords:
Classification, Cascade Correlation Neural Network, Random Forest, Software reliability, TreeNet.