ABSTRACT. In recent years, the advent of electronic commerce has led to the creation of many new and interesting business models for Internet-based selling. In this paper, we will explore a variant of the typical dynamic pricing mechanism, in which buyers and sellers actively engage in the price discovery process, that emphasizes the power of group buying. Dynamic pricing approaches are used by many well known Internet-based firms, including firms that offer online auctions such as eBay and Amazon.com. A group-buying discount is a dynamic pricing mechanism that mimics the general approach of traditional “discount shopping clubs. ” Group buying pricing mechanisms permit buyers to aggregate their purchasing power and obtain lower prices than they otherwise would be able to get individually. However, with the recent closing of Mercata.com, a leading group-buying Web site, and the change in strategic direction of another market leader, Mobshop.com, the future of group-buying discount business models in Internet-based selling is no longer clear. In this essay, we will: (1) introduce the innovations associated with group-buying business models in Internet-based selling; (2) characterize the operational aspects of dynamic pricing mechanisms for group-buying through a discussion of a series of mini-cases with different firms that are widely recognized as the innovators in this area; (3) assess the quality of their business models relative to other new business models for Internet-based selling; and (4) draw

Over the past year, the shakeout in the electronic commerce marketplace has redirected the attention of entrepreneurs and investors from the funding hype surrounding Internet startups to the traditional bottom line of business: firm profitability. To improve profits and achieve strategic sustainability in a rapidly changing competitive environment, many DotComs have been repackaging themselves by targeting new markets, expanding into the offline world, forming alliances, licensing software, and adjusting their core offerings to focus on the most profitable products and customers (Chircu and Kauffman, 2000). This research develops an evolutionary game theory-motivated framework (Ba et al., 2000; Smith, 1992; Weibull, 1997) that helps academic researchers and industry practitioners to understand the “strategic morphing ” of DotComs (Miller, 2001). Our framework applies an analogy from the theory of bio-diversity and genetic survivability in population ecology to different species in a highly competitive organic biome. In the e-commerce context, however, we emphasize survivability in terms of a set of characteristic categories and constructs that provide explanatory power for firm “strategic fitness ” in competition with other firms and in the marketplace in general. We illustrate our framework using mini-case studies on DotCom success and failure.

Daily deal sites have become the latest Internet sensation, providing discounted offers to customers for restaurants, ticketed events, services, and other items. We begin by undertaking a study of the economics of daily deals on the web, based on a dataset we compiled by monitoring Groupon and LivingSocial sales in 20 large cities over several months. We use this dataset to characterize deal purchases; glean insights about operational strategies of these firms; and evaluate customers’ sensitivity to factors such as price, deal scheduling, and limited inventory. We then marry our daily deals dataset with additional datasets we compiled from Facebook and Yelp users to study the interplay between social networks and daily deal sites. First, by studying user activity on Facebook while a deal is running, we provide evidence that daily deal sites benefit from significant word-of-mouth effects during sales events, consistent with results predicted by cascade models. Second, we consider the effects of daily deals on the longer-term reputation of merchants, based on their Yelp reviews before and after they run a daily deal. Our analysis shows that while the number of reviews increases significantly due to daily deals, average rating scores from reviewers who mention daily deals are 10 % lower than scores of their peers on average.

Online retailing (“e-tailing”) provides an opportunity for new pricing options that are not feasible in traditional retail settings. This paper proposes a dynamic pricing strategy from the perspective of bundling to derive added savings for customers while maximizing profits for e-tailers. Given product costs, posted prices and customers ’ reservation prices, we propose a nonlinear mixed-integer programming model to increase e-tailers ’ profits and enhance customers’ savings. We next extend our dynamic bundle pricing model to accommodate (a) customer budget constraints and (b) e-tailers ’ coupons. The computational results suggest that the proposed model not only attracts more customers to buy products at the bundled prices, but also increases profits for the e-tailers.

Most observers would agree that the explanatory capabilities of current theories of electronic intermediation on the Internet fall short of having the desired power. Instead, the patchwork quilt of theoretical assertions, conflicting empirical results, and revealing case studies that characterize the existing literature point to the need for a new synthesis. This article makes an attempt to do just that by proposing an explanatory and predictive contingency model for intermediated and non-intermediated market structures. We distinguish between direct and intermediated markets, as well as traditional and electronic markets in a two-by-two classification grid allowing a number of industry examples to be typed. Explaining why the industry examples end up in those “buckets ” required deeper reasoning, on the basis of a blend of existing and emerging theory, not all of which delivers the same predictions. For this reason, our proposed contingency model leverages a variety of elements of the existing theories and empirical findings (including transaction price, search cost, liquidity, trust, expertise, risk exposure and technological adaptation cost), which enable us to usefully interpret three illustrative mini-cases involving the airline ticket market, the used car market, and the antiques and collectibles market. Our findings provide preliminary support for contingency model as interpretative tool for management.

Viral Marketing, sometimes described as word of mouse publicity, is a tactic that leverages the considerable power of individuals to influence others in their online social networks using computer aided communication media such as email, instant messaging and online chat. Viral marketing is increasingly being recognized as an important way for firms to spur the trial, use and adoption of products and services. While viral marketing has been wildly successful for some products, it has not worked as well for others. What is clearly needed is an informed approach to the issue through a greater understanding of the complex nature of persuasion and compliance in online social networks. This paper proposes a framework highlighting the behavioral mechanisms underlying knowledge sharing, influence and compliance in online social networks. The framework provides a basis to determine the characteristic of appropriate viral marketing strategies in different contexts and can be a useful guide for managers and for future research into this important phenomenon. 2

Daily-Deal Sites (DDS) like Groupon, LivingSocial, Amazon’s Goldbox, and many more, have become particularly popular over the last three years, providing discounted offers to customers for restaurants, ticketed events, services etc. In this paper, we study the following problem: among a set of candidate deals, which are the ones that a DDS should featureas daily-dealsinorderto maximizeits revenue? Ourfirst contribution lies in providing two combinatorial formulations of this problem. Both formulations take into account factors like the diversification of daily deals and the limited consuming capacity of the userbase. We prove that our problems are NP-hard and devise pseudopolynomial – time approximation algorithms for their solution. We also propose a set of heuristics, and demonstrate their efficiency in our experiments. In the context of deal selection and scheduling, we acknowledge the importance of the ability to estimate the expected revenue of a candidate deal. We explore the nature of this task in thecontextof real data, andpropose a framework for revenue-estimation. We demonstrate the effectiveness of our entire methodology in an experimental evaluation on a large dataset of daily-deals from Groupon.