Seung-Pyo Jun

An empirical study of users' hype cycle based on search traffic: the case study on hybrid cars

Many forms of technology cycle models have been developed and utilized to identify new/convergent technologies and forecast social changes, and among these, the technology hype cycle introduced by Gartner has become established as an effective method that is widely utilized in the field. Despite the popularity of this commonly deployed model, however, the currently existing research literature fails to provide sufficient consideration of its theoretical frame or its empirical verification. This paper presents a new method for the empirical measurement of this hype cycle model. In particular, it presents a method for measuring the hype of the users rather than the hype cycle generated by research activities or by the media by means of analyzing the hype cycle using search traffic analysis. The analytical results derived from the case study of hybrid automobiles empirically demonstrated that following the introductory stage and the early growth stage of the life cycle, the positive hype curve and the negative hype curve, the representative figures of the hype cycle, were present in the bell curve for the users’ search behavior. Based on this finding, this paper proposes a new method for measuring the users’ expectation and suggests a new direction for future research that enables the forecasting of promising technologies and technological opportunities in linkage with the conventional technology life cycle model. In particular, by interpreting the empirical results using the consumer behavior model and the adoption model, this study empirically demonstrates that the characteristics of each user category can be identified through differences in the hype cycle in the process of the diffusion of new technological products discussed in the past.

Visualization of brand positioning based on consumer web search information: Using social network analysis

Purpose Online web searches have played crucial roles in influencing consumers’ purchasing decisions. Web search traffic information enables researchers and practitioners to better understand consumers in terms of their preferences and interests, among other things. The purpose of this paper is to use web search traffic information provided by Google Trends to derive relationships among product brands as well as those between product brands and product attributes to propose a method to enhance the visibility of consumer brand positioning. Design/methodology/approach This study builds upon the interesting observation that consumers’ behavior in performing simultaneous searches, or searches including two or more keywords, can be converted into data indicating relationships among brands as well as those between brands and their attributes. The study focuses on the cases of hybrid cars and tablet PCs, and applies a social network analysis method to identify these relationships. Time series information on web search traffic is used because it can track these two product groups from the early stages to the present. This step is completed to verify the changes in the status of each brand and in their relationships that occurred in consumers’ minds over time. Findings Results show that consumers’ web search behaviors reveal the brand positioning and brand-attribute associations in their minds. Specifically, using consumers’ simultaneous search data, the authors derived relationships among brands (brand-brand network) from consumers’ behaviors of searching simultaneously for two brands and the relationships between brands and attributes (brand-product attributes network) from consumers’ behavior of searching simultaneously for a specific brand and certain product attributes. Originality/value Theoretically, this study verifies that consumers’ web search traffic information can be used to microscopically identify the positions of individual brands and their relationships in the minds of consumers. Regarding practical applications, this study proposes a method that can be used by companies to track how consumers perceive their brands by performing a simple and cost-effective analysis using the free search traffic information provided by Google.

Database Valuation: How to Determine the Economic Lifetime and Contribution of DB Assets?

Regardless of the increasing interest in practical uses of technology valuation, there exists no valuation model for measuring appropriate values of database assets. Cost approach which involves the calculation of input cost such as software database can be utilized for calculating the establishing expenses of databases, but we need to propose an optimal valuation model to properly reflect future business profitability and standardize how to estimate and apply to primary variables associated. In this study, we look into how to determine the economic life expectancy of database assets based on durable period of 5 years and the contribution of database assets to the entire business value created through the business model therein. Since there has been no valuation model to date to lead to an objective market value of database assets, it is expected that database valuation framework proposed herein would be significantly useful when various data-driven business models are involved.