An Empirical Investigation of the Auction Buyer's Choice to Buy Out or Bid: Cry of Regret or Laugh of Satisfaction?
Online auction buyers often face the predicament of choosing between the buyout strategy and the bidding strategy to acquire an auctioned product. A buyer who chooses to buy out will obtain the item immediately at the posted price while the one who chooses to bid will have to monitor the bidding process in the hope of acquiring the product at a price lower than the posted one. Despite the wealth of auction literature, relatively few studies empirically assess the buyout option. This study seeks to bridge the gap by investigating how the buyout option (permanent or temporary) and price parameters (starting price and buyout price) affect the formation of two emotions, namely anticipated satisfaction of choice and anticipated regret of outcome, which in turn affect the buying strategy (i.e., buy out or bid). We employ laboratory experiments in our study and our results indicate two interesting findings. First, when the permanent buyout option is available, a buyer is more likely to exhibit loss aversion characteristics and prefer to adopt the buyout strategy. Second, when the temporary buyout option is present, the thought that one could avoid premature ending of auction by exercising the bidding option at the start decreases one’s preference for adopting the buyout strategy. Implications of these results for further research and practice are discussed.

title: Comparison-Shopping Websites: An Empirical Investigation on the Influence of Decision Aids and Information Load on Consumer Decision-Making Behavior
abstract: Rapid advances in Internet commerce technology have brought about the emergence of comparison-shopping Websites, which act as agents to consolidate vast amounts of product information. Researchers have traditionally focused on incorporating the latest database interrogation technology and investigating the economic implications of the existence of comparison-shopping Websites. Little attention has been paid to understanding whether the decision aids provided are capable of assisting a consumer in managing a large quantity of information or, more importantly, are compatible with the decision-making behavior of the consumer. This study surveys several successful comparison-shopping Websites, enumerates two comercially successful forms of decision aids (i.e., screening and sorting), and examines their effects on decision effectiveness under different information loads. Based on a 3×2×2 factorial controlled experiment, we had three major findings. First, more sophisticated screening aids that assist a consumer in filtering a large quantity of information do not necessarily improve decision effectiveness. Surprisingly, our results even suggest that in some circumstances the opposite could be true. Second, the results indicate that the effectiveness of decision aids, to a large extent, depends on the information load. Third, decision makers are adaptive. One may supplement the absence of more sophisticated screening aids with the sorting aid.

title: An Investigation of Online Group-Buying Institution and Buyer Behavior
abstract: This study draws on interdependency theory to propose and assess the effects of two artifacts - conditional purchase and information cue - on decisional choice in the online group-buying context. Our results suggest that buyers presented with conditional purchase options (i.e., of not honoring the purchase when the expected discount price is not met) are more willing to make a purchase choice (i.e., deviate from an inactive inertia). Counter-intuitively, provision of information cues (i.e., information about procurement actions of the other buyers) appears to induce inaction. However, when a choice has to be made between a riskier and a less risky choice, the presence of information cues leads to a higher propensity for the riskier choice. Implications for theory and practice are further discussed.

title: Hough technique for bar charts detection and recognition in document images
abstract: Charts are common graphic representation for scientific data in technical and business papers. We present a robust system for detecting and recognizing bar charts. The system includes three stages, preprocessing, detection and recognition. The kernel algorithm in detection is newly developed modified probabilistic Hough transform algorithm for parallel lines clusters detection. The main algorithms in recognition are bar pattern reconstruction and text primitives grouping in the Hough space which are also original. The experiments show the system can also recognize slant bar charts, or even hand-drawn charts.

title: 3D geometric and optical modeling of warped document images from scanners
abstract: When one scans a document page from a thick bound volume, the curvature of the page to be scanned results in two kinds of distortion in the scanned document images: i) shade along the 'spine' of the book; and ii) warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. We first build practical models namely a 3D geometric model and a 3D optical model for the practical scanning conditions to reconstruct the 3D shape of book surface. We next restore the scanned document image using this shape based on de-shading and de-warping models. Finally, we evaluate the restoration results by comparing the OCR (optical character recognition) performance on the original and restored document images. The experiments show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.

title: Improving Noun Phrase Coreference Resolution by Matching Strings
abstract: In this paper we present a noun phrase coreference resolution system which aims to enhance the identification of the coreference realized by string matching. For this purpose, we make two extensions to the standard learn-ing-based resolution framework. First, to improve the recall rate, we introduce an additional set of features to capture the different matching patterns between noun phrases. Second, to improve the precision, we modify the instance selection strategy to allow non-anaphors to be included during training instance generation. The evaluation done on MEDLINE data set shows that the combination of the two extensions provides significant gains in the F-measure.

title: Separation of overlapping text from graphics
abstract: The separation of overlapping text from graphics is a challenging problem in document image analysis. This paper proposes a specific method for detecting and extracting characters that are touching graphics. It is based on the observation that the constituent strokes of characters are usually short segments in comparison with those of graphics. It combines line continuation with the feature line width to decompose and reconstruct segments underlying the region of intersection. Experimental results showed that the proposed method improved the percentage of correctly detected text as well as the accuracy of character recognition significantly.

title: Chart analysis and recognition in document images
abstract: Hidden Markov models are a probabilistic modeling tool for time series data. It has been successfully applied to many areas, such as speech recognition, hand-written character recognition, etc. In this paper, we present a novel statistical approach using ergodic hidden Markov models to recognize scientific charts. We also present a newly developed feature extraction method for chart images. Unlike traditional primitive-based diagram recognition method, our approach need not recognize the graphic primitives in charts thus bypassing the recognition error problem caused by the inaccurate primitive extraction that is also a major obstacle to the construction of a general chart recognition system.