Seongmin Jeon

Mining customer requirements from online reviews

We propose a filtering model to predict helpfulness of reviews for product design.We provide a way to use the KANO model based on online reviews.We explore how to obtain insights from Big Data through knowledge-based view. Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.

Using the experience curve model to project carbon dioxide emissions through 2040

Abstract Projections of long-term carbon dioxide (CO2) emissions in the literature vary over a wide range depending on both socio-economic development and mitigation policy. Therefore, any attempt to improve the accuracy of such projections is important. We present a simple aggregate model to project the future carbon intensity of economic output, which is then used to forecast energy-related CO2 emissions through 2040 for seven countries and three regions of the world. Our projection results are compared to those from the International Energy Outlook 2013 report. We find that our projections are significantly higher than those reported in the International Energy Outlook 2013 report.

An analysis of popularity information effects

Analyzing empirically the effects of the popularity information on sales.Presenting the results from controlled field experiments in an online store on Tmall.Identifying causality using difference-in-differences and propensity score matching.Presenting the hit list effects varying over subgroups of price and category. Popularity information is identified as one of the important factors for businesses in the online marketplace and is normally expected to strengthen sales performance. At the same time, the effects of popularity information are found to vary across product types. In this study, we attempt to identify the effects of popularity information on product sales through an analysis of the subgroups of category and price. Two sequential field experiments in an online apparel store on Alibabas Tmall, the most dominant online brand marketplace in China, are conducted to capture the causal effects of the popularity information on sales. After observing the possible existence of popularity information effects through a pilot test of 17 products, we conduct the main experiment with 290 products, recording the daily sales for each by posting selected products on the hit list. The difference-in-differences method and propensity score matching are used to analyze the effects. The results show that once the products are displayed on the hit list, product sales increase by an average of 1.3units per day. One subgroup of the niche product category is found to be influenced more significantly by hit list information than are other subgroups in the broad appeal category. Furthermore, after the hit list information is presented, more units of mid-price products are likely to be sold than units of products with high and low prices.

Will Insta-Business be the Electronic Contemporary Bazaar?: An Exploratory Analysis on Electronic Commerce in Kuwait

Electronic Commerce is shaping many of our daily tasks and, with the introduction to new aspects of social interaction on the net, a new phenomenon is emerging in the arena of e-commerce known as social commerce. Within this new trend in social commerce, Instagram, a simple yet powerful app, is now playing a new role in combining both the advantage of social networks with its design features and capabilities. In this paper we examine how a simple yet powerful picture-based social network app like Instagram is shaping the electronic commerce arena globally, using Kuwait as an example. Instagram has become a major part of most Kuwaiti individual lives, for in Kuwait Instagram is currently an hourly stop instead of a daily one. Instagram transformed from being a voyeuristic tool to becoming a handy shopping outlet. Everything from exotic pets to home car washing and detailing service can be found on Instagram, whose usage in Kuwait is now ubiquitous: from the old widower selling home-cooked traditional dishes to multimillion real estate investment companies. In this paper we explore the factors and barriers that helped Instagram become a social commerce outlet for many users in Kuwait and around the world.

Do local consumers behave differently from visitors? An exploratory study in online group buying

Abstract With the rapid increase in Groupon-like online daily deals, the behavior of consumers within online group buying has become a popular research topic. In addition, the increase of smart phone group buying has led merchants to consider increasing numbers of visitors beyond local consumers. The effects of the period, price, discount rate, and product category on sales are examined in the context of local consumers and visitors purchasing restaurant coupons in the group-buying electronic marketplace. Using linear and non-linear regressions and conjoint analysis as method, the actual transactional data sets of Dianping.com—one of Asia’s largest online group-buying businesses—is studied. The results show that there is a clear discrepancy between the preferences of the two consumer groups. Local consumers tend to think highly of discounts, while visitors are likely to care more about the product category. In addition, the behavior of the two groups differs in terms of product attribute selections. The findings could be useful to businesses in terms of understanding consumers’ purchasing and product designs.

How Much Wind-Powered Electricity May Be Generated in 2040 by China, the U.S. and Four Other Countries?

Past projections of wind-powered electricity by several organizations including Energy Information Administration have been too conservative in the past. We generate alternative projections for China, the U.S., India, Canada, Japan and South Korea and compare them against the projections made by EIA for the years of 2020, 2030, and 2040. Although our projections are much higher than the EIA’s for all five countries with the exception of Japan, our projections are supported by several projections made by other organizations including the respective government sources. Implications from our findings will be discussed as well.

Does Too Much Regulation Kill the Online Gambling Industry?: An Empirical Analysis of Regulation Effects Using VAR Model

Motivated by the growing interest of online gambling regulation, we empirically investigate whether the regulation successfully decreases online gambling addiction. For this purpose, we use a vector autoregression (VAR) model to identify interrelationship changes among online gambling games and service platforms due to the regulation. In particular, we propose three theoretical perspectives: the role of prior experience, switching costs and network externalities. We find that the impact size of the regulation is different depending on levels of prior experience, and the regulation significantly affects the switching behavior of users on online gambling platform. Therefore, we offer one of the first empirical evidences that examine the regulation effects on online gambling using VAR model. We also suggest the policy makers should make suitable regulations for each user group to effectively avoid generating gambling addicts without interrupting the economic growth of the online gambling industry.

Measuring the Effects of Regulation Policy on Online Game: A Vector Autoregressive (VAR) approach

The purpose of this study is to study the effects of regulatory policy on online gambling, one of increasingly popular types in the online game industry. Previous information systems (IS) studies on online game primarily aim user behavior. Nowadays, instead of heuristic approaches on individual behavior, there is a growing need to examine the effects of regulatory policy on dynamic changes of games or game providers. Standing above the approaches of prior studies, we empirically test the regulatory policy effect with two theoretical viewpoints: social influence and previous experience. We use a vector autoregression (VAR) analysis to predict game usage and to model various forms of the co-movement of online games. We provide also evidence of strong Granger-causal interdependencies within games and game providers. This study offers one of the first empirical evidences studying the effects of regulatory policies on online game industry. In research methodology point, this study also introduces an explanation of VAR methodology in IS research. Thus, it delivers advanced knowledge on gaming behaviors as well as helps develop suitable regulatory policies to satisfy policymakers and to protect users of online game.

Business Analytics in Action: The Case of Asset Pricing for a Rent-a-Car Company

The Internet enables businesses to acquire a great deal of reference information, including the price in the open markets, for estimating appropriate prices of the assets to sell off. In particular, what is the value of reference price information to a company in the market and how can the company make use of this information? Using a multiple regression model for price estimation applied to used cars for a rent-a-car company, we propose a framework to collect the reference price information, in order to estimate appropriate prices in the future. The result is a new information system that collects online reference price information and provides very accurate information in estimating future prices of used cars and rental prices.

An Empirical Analysis of Regulation Policy Effects on Social Casino Using Vector Autoregression

The objective of this study is to examine the effects of regulation policy on online gambling, an increasingly popular type of entertainment in the online game industry. Prior information systems (IS) studies on online game focus primarily on user behavior. However, there is a growing need to investigate the effects of regulation policy on dynamic changes of games or service providers instead of ad hoc heuristic approaches on individual behavior. Going beyond the approaches of previous studies, this study empirically test the regulation policy effect with three theoretical perspectives: social influence, prior experience and perceived switching cost. A vector autoregression (VAR) methodology is used to forecast game usage and to model several patterns of the co-movement of online games. Evidence is also provided of strong Granger-causal interdependencies within games and service providers. This study provides one of the first empirical evidences that examine the effects of regulation policies on online game. In research methodology aspect, this study also introduce an exposition of VAR methodology in IS research. Therefore, it provides advanced knowledge on gambling behavior and helps develop suitable regulation policy to protect users of online gambling as well as to satisfy policy makers.