Keeheon Lee

Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process

Research highlights? An agent-based model simulates the market-share dynamics of competing automobiles. ? Consumer-agents base their adoption on fuzzy, multi-attribute TOPSIS method. ? Social influence is raised by the interaction among the consumer-agents. ? An empirical study was conducted to verify the performance of the proposed model. This paper focuses on the product diffusion in a competitive automobile market. Since purchasing a car is costly, the consumers in the market tend to behave like rational decision makers. They naturally compare the attributes of cars (e.g., brand preference, fuel economy, safety, comfort) and make overall decisions. In this paper, we propose an agent-based (AB) diffusion model consisting of tens of thousands of interacting agents. In the model, an agent represents a consumer and bases its multi-attribute decision-making on fuzzy TOPSIS. The decision-making process integrates three purchasing forces: experts product information provided by mass media, subjective weights on product attributes assigned by individual consumers, and social influence (i.e., information delivered from a consumers neighbors who have already adopted products). The AB model executes the agents and observes the collective behavior. In this sense, the model can assist in the analysis of the complex market dynamics. We conducted an empirical study to verify the performance of the AB model.

Pricing and Timing Strategies for New Product Using Agent-Based Simulation of Behavioural Consumers

In this study, we are interested in the problem of determining the pricing and timing strategies of a new product by developing an agent-based product diffusion simulation. In the proposed simulation model, agents imitate behavioural consumers, who are reference dependent and risk averse in the evaluation of new products and whose interactions create word-of-mouth regarding new products. Pricing and timing strategies involve the timing of a new product release, the timing of providing a discount on a new product, and the relative rates of discounts. We conduct two experiments in this study. In both experiments, we consider the urban young person segment in the mobile phone market in Korea, in which three major new products - two smartphones and one convergence product - compete with one another. The first experiment is sensitivity analysis on the product life cycle and social influence. The objective is to observe how consumer agents behave as the product life cycle and the degree of sensitivity on social influence change. The second experiment is sensitivity analysis on time-to-market, time-to-discount, and amount-to-discount. The marketing strategy that maximises the sales volume (or revenue) of the new convergence product is sought from the sensitivity analysis. Based on the result, we provide pricing and timing implications for firms pursuing sales volumes (or revenue) increase.

Examining Characteristics of Traditional and Twitter Citation

Social media has attracted the attention of the academic community as an emerging communication channel. This channel opens a new opportunity to measure the impact of social use of scholarly publications in social media (altmetrics) which supplements our understanding on the scholarly impact of publications (bibliometrics). Two different channels, social media and journal, are known to establish various citation patterns statistically. However, thematic difference between altmetrics and bibliometrics structurally and contextually is unknown. Therefore, we perform document co-citation network analysis for structural comparison and topic modeling for contextual comparison. We also suggest Spearman’s correlation for statistical comparison. A case study is done for the publications from Journal of the Association for Information Science and Technology and the tweets mentioning the publications. We identified a weak correlation between scholarly impact and social use of these publications. We also found the structures of the traditional citations and Twitter citations share common but high interest in information retrieval system and impact analysis, while Twitter citations have diverse interest in data mining, network analysis, and information behavior as well. In addition, from content analysis, we found the two citation patterns to have both common and distinct characteristics. Specifically, the topics covered by both citation patterns show intersections and exclusive contexts. In conclusion, the traditional citation patterns and the Twitter citation patterns in Information Science are different statistically, structurally, and contextually. We suspect that intentional and unintentional citing behaviors are the main factor for the thematic difference and will be examined on the future.

Subject---method topic network analysis in communication studies

Communication studies depend on information and communication technology (ICT) and the behavior of people using the technology. ICT enables individuals to transfer information quickly via various media. Social changes are occurring rapidly and their studies are growing in number. Thus, a tool to extract knowledge to comprehend the quickly changing dynamics of communication studies is required. We propose a subject–method topic network analysis method that integrates topic modeling analysis and network analysis to understand the state of communication studies. Our analysis focuses on the relationships between topics classified as subjects and methods. From the relationships, we examine the societal and perspective changes relative to emerging media technologies. We apply our method to all papers listed in the Journal Citation Reports Social Science Citation Index as communication studies between 1990 and 2014. The study results allow us to identify popular subjects, methods, and subject–method pairs in proportion and relation.

Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model

Due to the recent development of Information Technology, the number of publications is increasing exponentially. In response to the increasing number of publications, there has been a sharp surge in the demand for replacing the existing manual text data processing by an automatic text data processing. Swanson proposed ABC model [1] on the top of text mining as a part of literature-based knowledge discovery for finding new possible biomedical hypotheses about three decades ago. The following clinical scholars proved the effectiveness of the possible hypotheses found by ABC model [2]. Such effectiveness let scholars try various literature-based knowledge discovery approaches [3, 4, 5]. However, their trials are not fully automated but hybrids of automatic and manual processes. The manual process requires the intervention of experts. In addition, their trials consider a single perspective. Even trials involving network theory have difficulties in mal-understanding the entire network structure of the relationships among concepts and the systematic interpretation on the structure [6, 7]. Thus, this study proposes a novel approach to discover various relationships by extending the intermediate concept B to a multi-leveled concept. By applying a graph-based path finding method based on co-occurrence and the relational entities among concepts, we attempt to systematically analyze and investigate the relationships between two concepts of a source node and a target node in the total paths. For the analysis of our study, we set our baseline as the result of Swanson [8]s work. This work suggested the intermediate concept or terms between Raynauds disease and fish oils as blood viscosity, platelet aggregability, and vasconstriction. We compared our results of intermediate concepts with these intermediate concepts of Swansons. This study provides distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.

Agent simulation-based ordinal optimisation for new product design

Abstract A decision tool that can accurately predict the market diffusion of a new product can be helpful in the establishment of product design. This study simulates product diffusion through multi-agent simulation (MAS) and determines product specifications that can maximise future sales by adapting ordinal optimisation. MAS constructs a virtual market of agents (consumers) and predicts the sales volume by simulating the product selection process of individual agents. By interacting with the MAS, the ordinal optimisation rapidly determines the product specifications of maximum sales with few computations. An empirical experiment conducted on the Korean smartphone market yielded interesting results, i.e., it is desirable for companies with high brand values not to offer low-performance products but to make additional efforts to increase satisfaction with hardware specifications, while a high-end policy of product quality and a low-price sales strategy may be more beneficial for increasing sales volumes for companies with low brand values.

Comparing Network Structures of Different Versions of a Book: A Case Study of the Bible

A book can be written in different versions even if their languages are the same. Or they can be published in different languages with the same message. Different versions can be represented as a co-word network or a cotopic network. In this study, I posit that different versions of a book may have similar but different topologies which imply slightly various interpretations. Two English versions of the Bible are used as a case study. Co-word networks and co-topic networks for topology comparison show that the King James Version and New International Version cover similar topics but have slightly different emphases in terms of network structure. This study shows that the comparisons among different versions of a book can be done through network analysis. Future work will be performed with domain experts. For example, with the Bible, experts in theology are required to connect network structural features to the present theology.