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When product or service quality cannot be well-specified ex-ante because of uncertainty and information asymmetry, consumer surplus (CS) is not well-defined or known ex-ante. Traditional measures of CS have implicitly assumed that the quality expected is the same as the quality that is paid for. However, when the service and price exchange doesn’t take place at the same time and in the presence of information asymmetry, the expected service quality may not necessarily realize, which may either overestimate or under-estimate its real level, thus misrepresenting the surplus e-markets offer to customers (service buyers).In this paper, using global e-markets for services outsourcing as a context, we propose a quality-adjusted measure for CS. We first relax the assumption that buyers always get what they expected. Second, we include the psychological factors of disconfirmation on satisfaction to construct the functional form for the measure. We leverage expectation-confirmation theory to theorize the way expected service quality and ex-post consumer satisfaction affect CS and derive a quality-adjusted measure for CS.Empirically, using transaction data from a leading e-market for services outsourcing, we find the following: First, the quality-adjusted measure of CS for the initial project of a buyer predicts whether he decides to stay on the marketplace, and even his subsequent payments and projects; while the traditional measure of CS does not have prediction power. Second, we propose the difference between the quality adjusted measure and traditional measure of CS ( ΔCS ) to be an indicator of ex-post differential satisfaction, and find buyer experience, repeat purchase, lack of language barrier and labor arbitrage to be positively associated with ΔCS , while number of bids is negatively associated with ΔCS . Third, the results also show that buyers reap an average surplus of $94.4-$175.5, even the lower-bound estimate is surprisingly high (about 69% of the winning bid price ($137)). Also, providers’ CS will be inflated if the quality-adjusted measure is not employed. We discuss validity of the quality-adjusted measure of CS and call for revisiting the estimation of CS toward economic reality.

Technology in general and the Internet in particular have often been seen as the "great equalizer" in that it provides a level playing field for all individuals in the society in terms of competing for social and economic opportunities. However, technology philosophers such as Andrew Feenberg have argued that technology diffusion mirrors the existing social order. Which of these worldviews actually holds is an open question, and in this research, we try to answer it using data on adoption of multiple technologies by individuals in the US over different time periods. Our results suggest that technology diffusion largely takes place along existing social class lines, and that the arrival of newer technologies ensures that the digital divide perpetuates.

The resourced-based view (RBV) is one of the most frequently cited theories in organization research. In the field of Information Systems (IS), the RBV has been used to explain organizations’ IT capabilities and firm performance. Recently, small online vendors have emerged as a viable organizational form in the Internet business arena, and their population is growing. This paper employs a grounded theory approach to develop an understanding of how small online vendors use IT artifacts to enhance their organization capabilities and, ultimately, to succeed. Based on content analysis of 176 interviews with small, successful vendors in China’s largest online marketplace (Taobao), we find that use of instant messaging software and web 2.0 technologies (e.g. blogs, social networking sites) contribute significantly to small online vendor’s learning and social networking capability, which in turn allows them to succeed. Theoretical and managerial implications are discussed.

Due to the dynamics of the environment and the variability in product usage, product units in the field are usually exposed to varying failure-causing stresses. Some products are equipped with sensors and smart chips that measure and record usage/environmental information over the life of the product. For some products, it is possible to track environmental variables dynamically, even in real time, providing useful information for field-failure prediction. In many applications, predictions are needed for individual units, giving the remaining life of individuals, and for the population, giving the cumulative number of failures at a future time. It is always desirable to obtain more accurate predictions for both the population and the individuals. This paper outlines a model and methods that can be used for field-failure prediction using dynamic environmental data. Multivariate time series models are also used to describe the dynamic covariate information. The cumulative exposure model is used to link the explanatory variables which are recorded as a multivariate time series, and the failure-time model.

This paper examines the antecedents and consequences of product uncertainty in online marketplaces by conceptualizing the dimensions of product uncertainty - description uncertainty (identifying product characteristics), performance uncertainty (inferring product’s future performance) and fit uncertainty (matching product’s characteristics with buyer’s needs). It also theorizes the distinction, relationship, and effects of the dimensions of product uncertainty. Finally, it proposes a set of IT-enabled mechanisms to reduce product uncertainty. The hypotheses are tested with data from 274 buyers in the largest online marketplace in China (Taobao). The results first demonstrate the distinction between the dimensions of product uncertainty, show that relative to description and performance uncertainty, fit uncertainty has the only significant effect on price premiums, satisfaction, product returns, and repurchase intentions, and support the effects of computer-mediated communication (between buyers and sellers and among buyers), product forums, and decision support systems on fit uncertainty. Implications for theory and practice are discussed.

Prediction of the remaining life of high-voltage power transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Lifetime data for such transformers are complicated because transformer lifetimes can extend over many decades and transformer designs and manufacturing practices have evolved. We were asked to develop statistically-based predictions for the lifetimes of an energy company's fleet of high-voltage transmission and distribution transformers. The company's data records begin in 1980, providing information on installation and failure dates of transformers. Although the dataset contains many units that were installed before 1980, there is no information about units that were installed and failed before 1980. Thus, the data are left truncated and right censored. We use a parametric lifetime model to describe the lifetime distribution of individual transformers. We develop a statistical procedure, based on age-adjusted life distributions, for computing a prediction interval for remaining life for individual transformers now in service. We then extend these ideas to provide predictions and prediction intervals for the cumulative number of failures, over a range of time, for the overall fleet of transformers., Comment: Published in at http://dx.doi.org/10.1214/00-AOAS231 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The emission of toxic gas, sulfur oxides and nitrogen oxides has become serious issue in the world. A review of EB method to remove the SO/sub x/ and NO/sub x/ in flue gas from coal-fired power plants was described in this paper. A design of an electron curtain accelerator of 600 keV was given, and a facility for purification of flue gas from coal combustion power plant of 10 MW was described here.

Strategic pricing in online markets has been of much interest to academics and practitioners, particularly for unique IT services. We build upon information signaling and uncertainty theory to exam...

Online markets pose a difficulty for evaluating products, particularly experience goods, such as used cars, that cannot be easily described online. This exacerbates product uncertainty, the buyer's difficulty in evaluating product characteristics, and predicting how a product will perform in the future. However, the IS literature has focused on seller uncertainty and ignored product uncertainty. To address this void, this study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars (eBay Motors). Extending the information asymmetry literature from the seller to the product, we first theorize the nature and dimensions (description and performance) of product uncertainty. Second, we propose product uncertainty to be distinct from, yet shaped by, seller uncertainty. Third, we conjecture product uncertainty to negatively affect price premiums in online markets beyond seller uncertainty. Fourth, based on the information signaling literature, we describe how information signals (diagnostic product descriptions and third-party product assurances) reduce product uncertainty. The structural model is validated by a unique dataset comprised of secondary transaction data from used cars on eBay Motors matched with primary data from 331 buyers who bid on these used cars. The results distinguish between product and seller uncertainty, show that product uncertainty has a stronger effect on price premiums than seller uncertainty, and identify the most influential information signals that reduce product uncertainty. The study's implications for the emerging role of product uncertainty in online markets are discussed.