The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

Although prior research has addressed the influence of corporate social responsibility (CSR) on perceived customer responses, it is not clear whether CSR affects market value of the firm. This study develops and tests a conceptual framework, which predicts that (1) customer satisfaction partially mediates the relationship between CSR and firm market value (i.e., Tobin’s q and stock return), (2) corporate abilities (innovativeness capability and product quality) moderate the financial returns to CSR, and (3) these moderated relationships are mediated by customer satisfaction. Based on a large-scale secondary data set, the results show support for this framework. Notably, the authors find that in firms with low innovativeness capability, CSR actually reduces customer satisfaction levels and, through the lowered satisfaction, harms market value. The uncovered mediated and asymmetrically moderated results offer important implications for marketing theory and practice.

Corporate Social Responsibility, Customer Satisfaction, and Market Value

Although prior research has addressed the influence of corporate social responsibility (CSR) on perceived customer responses, it is not clear whether CSR affects market value of the firm. This study develops and tests a conceptual framework, which predicts that (1) customer satisfaction partially mediates the relationship between CSR and firm market value (i.e., Tobin's q and stock return), (2) corporate abilities (innovativeness capability and product quality) moderate the financial returns to CSR, and (3) these moderated relationships are mediated by customer satisfaction. Based on a large-scale secondary data set, the results show support for this framework. Notably, the authors find that in firms with low innovativeness capability, CSR actually reduces customer satisfaction levels and, through the lowered satisfaction, harms market value. The uncovered mediated and asymmetrically moderated results offer important implications for marketing theory and practice.

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The authors develop a conceptual model that links Web site and consumer characteristics, online trust, and behavioral intent. They estimate this model on data from 6831 consumers across 25 sites from eight Web site categories, using structural equation analysis with a priori and post hoc segmentation. The results show that the influences of the determinants of online trust are different across site categories and consumers. Privacy and order fulfillment are the most influential determinants of trust for sites in which both information risk and involvement are high, such as travel sites. Navigation is strongest for information-intensive sites, such as sports, portal, and community sites. Brand strength is critical for high-involvement categories, such as automobile and financial services sites. Online trust partially mediates the relationships between Web site and consumer characteristics and behavioral intent, and this mediation is strongest (weakest) for sites oriented toward infrequently (frequ...

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Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study

Multichannel customer management is the design, deployment, coordination, and evaluation of channels through which firms and customers interact, with the goal of enhancing customer value through ef...

/pdf/challenges-and-opportunities-in-multichannel-customer-114cpfdczl.pdf

Challenges and opportunities in multichannel customer management

It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that price dispersion - the distribution of prices of an item indicated by measures such as range and standard deviation - of an item with the same measured characteristics across sellers of the item at a given point in time for identical products sold by e-tailers online (on the Internet) should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) speculates that this is due to heterogeneity among e-tailers in such factors as shopping convenience and consumer awareness. Based on an empirical analysis of 105 e-tailers comprising 6739 price observations for 581 items in eight product categories, we show that online price dispersion is persistent, even after controlling for e-tailer heterogeneity. Our general conclusion is that the proportion of the price dispersion explained by e-tailer characteristics is small. This evidence is contrary to the hypothesis that search costs in online markets are low, or that online markets are highly competitive. The results also show that after controlling for differences in e-tailer service quality, prices at pure play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID328821_code021211500.pdf?abstractid=328821&mirid=1

Can Price Dispersion in Online Markets be Explained byDifferences in e-Tailer Service Quality?

It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that price dispersion of an item with the same measured characteristics across sellers at a given point in time for identical products sold by e-tailers online should be smaller than it is offline, but some recent empirical evidence reveals the opposite. Based on an empirical analysis of 105 e-tailers comprising 6.739 price observations for 581 items in eight product categories, the authors show that online price dispersion is persistent, even after controlling for e-tailer heterogeneity. The general conclusion is that the proportion of the price dispersion explained by e-tailer characteristics is small. Also, after controlling for differences in e-tailer service quality, prices at pure-play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.

Can price dispersion in online markets be explained by differences in e-tailer service quality?

Frictionless e-commerce implies that price dispersion for identical products sold by different e-tailers should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) suggests that such a phenomenon may be due to heterogeneity among e-tailers in such factors as shopping convenience, consumer awareness, and trust. These hypotheses, however, remain untested. In this paper, we extend previous research by developing a comprehensive framework of the drivers of online price dispersion that includes market characteristics such as number of competitors, consumer involvement, and product popularity, in addition to e-tailer characteristics and product category differences. We also empirically test our propositions in a more comprehensive manner than prior research by using a range of price dispersion measures covering 6,739 price quotes for 581 products from 105 e-tailers in a variety of product categories including books, CDs, DVDs, desktop computers, laptop computers, PDAs, computer software, and consumer electronics. Specifically, we (1) identify some key dimensions of e-tailer heterogeneity using factor analysis; (2) identify clusters of e-tailers on these dimensions using cluster analysis; (3) analyze how market factors affect price dispersion using regression analyses; and (4) examine how heterogeneity among e-tailers is related to their prices using hedonic regressions by category and by cluster. Our results show that e-tailer services can be characterized by five underlying factors, namely, shopping convenience, reliability in fulfillment, product information, shipping and handling, and pricing policy. There are three clusters of e-tailers who target different consumer groups and position themselves differently along these five factors. Even after controlling for e-tailer characteristics, online price dispersion is large. Market characteristics drive a large portion of this e-tailer price dispersion. Specifically, price dispersion increases with involvement or average price level of items, albeit at a decreasing rate, and decreases with the number of competitors, but at a diminishing rate. The models explain over 92% of the variance in price dispersion. E-Tailers charge prices in line with their characteristics, but do not necessarily command higher prices for superior services. The drivers of e-tailer prices also vary significantly by the cluster to which the e-tailers belong.

Why Aren't the Prices of the Same Item the Same at Me.com and You.com?: Drivers of Price Dispersion Among E-Tailers

In this paper, we first develop a game theoretic model of price competition between a pure play e-tailer and a bricks-and-clicks e-tailer. We show that in general, the pure play e-tailer has a lower equilibrium price. We then develop a simultaneous equation model of e-tailer price and traffic and estimate this model using data collected from 905 e-tailers across eight product categories. The empirical results show that after controlling for the effects of other variables, prices at pure play e-tailers are generally lower. E-tailers with high traffic do not always command higher prices. E-tailers with high level of reliability, shopping convenience, and deep information, generally do not generate high web traffic and do not enjoy high prices. However, trust enhances e-tailer traffic and early online entry is associated with both high e-tailer traffic and high prices.

Price competition between pure play versus bricks-and-clicks e-tailers: Analytical model and empirical analysis

The authors examine the consumer welfare implications of changes in the structure of e-commerce markets employing comprehensive data collected from BizRate.com in November 2000 and November 2001. They find that price dispersion decreased substantially between these two periods and that measured differences in e-tailer services bear little relation to e-tailer prices.

Xing Pan

Papers

Can Price Dispersion in Online Markets be Explained byDifferences in e-Tailer Service Quality?

Can price dispersion in online markets be explained by differences in e-tailer service quality?

Why Aren't the Prices of the Same Item the Same at Me.com and You.com?: Drivers of Price Dispersion Among E-Tailers

Price competition between pure play versus bricks-and-clicks e-tailers: Analytical model and empirical analysis

On the Efficiency of Internet Markets for Consumer Goods