TAXONOMY RESEARCH IN INFORMATION SYSTEMS: A SYSTEMATIC ASSESSMENT

Today’s world is changing at unprecedent speed and scale becoming more complex to understand. Taxonomies represent an important tool for understanding and analyzing complex domains based on the classification of objects. In the Information Systems (IS) domain, Nickerson et al. (2013) were the first to propose a taxonomy development method, addressing the observation that many taxonomies have been developed in an ‘ad-hoc’ approach. More than five years after Nickerson et al.’s (2013) publication, we examined to what extent recently published taxonomy articles account for existing methodological guidance. Therefore, we identified and reviewed 33 taxonomy articles published between 2013 and 2018 in leading Information Systems journals. Our results were sobering: We found few taxonomy articles that followed any specific development method. Although most articles correctly understood taxonomies as conceptually or empirically derived groupings of dimensions and characteristics, our study revealed that the development process often remained opaque and that taxonomies were hardly evaluated. We discuss these findings and potential root causes related to method design, method adoption, and the general positioning of taxonomy research in the IS domain. Our study proposes stimulating questions for future research and contributes to the IS community’s progress towards methodologically well-founded taxonomies

Optimal Pricing Strategy for Multichannel Healthcare Services

As a combination of online and offline channel services, multichannel healthcare services currently play important roles in helping consumers solve their health problems. In this study, we establish a stylized model to investigate how healthcare service providers should price in multi-channels and when consumers should choose online service, taking misdiagnosis rate and the severity of disease problems into account. Our results show that the prices of the online channel and offline channel can increase when the misdiagnosis rate is low and minor problem inspection rate online is high. Moreover, when the diagnosis rate is high, the profit of online channel would increase, and then improve the profit of multichannel services. These findings provide insights for the theoretical research of online healthcare services and practice management on pricing strategies in multichannel healthcare services

Effects of online one-yuan Dutch auction on the seller’s revenue: Evidence from an online community for auctioning agricultural and subsidiary products in China

The traditional Dutch auction usually sets the starting price but does not set the final price. The effects and impact factors on the seller’s revenue have not been discussed if the final price of online Dutch auction is set to a teeny number (i.e., one RMB) which is termed as an one-RMB Dutch auction. Based on the regret theory and related literature, the effects of starting price, time pressure (one day and 15 minutes of time interval respectively) and product perishability on buyers\u27 choice behavior and the revenue of sellers were examined. The results showed that there is a negative effect coming from the starting price, time pressure and perishability of products and the overall discount rate of product auction; and buyers are more inclined to bid in the penultimate round of the price reduction cycle

Information Transparency in B2B Auction Markets: The Role of Winner Identity Disclosure

We study the impact of information transparency in B2B auctions. Specifically, we measure the effect of concealing winners’ identities on auction outcomes using a large-scale, quasi-natural field experiment. Contrary to the conventional wisdom that “the more information, the better,” we find that concealing winners’ identities leads to a significant increase in price by approximately 6%, and such effect holds true across both online and offline channels as well as different types of bidders. We further explore the mechanism that drives the observed effect. The empirical analysis suggests that the price increase may primarily stem from the disruption of imitative bidding which relies on the identification of fellow competitors. Our findings have important implications for the design of auction markets, especially multi-channel B2B markets

Beyond posted prices: the past, present and future of participative pricing mechanisms

Driven by the low transaction costs and interactive nature of the internet, customer participation in the price-setting process has increased. These changes were first brought about by the rise of online auctions in the early 2000s, followed by the emergence of newer participative mechanisms. Today, platforms such as eBay have popularized online auctions on a global scale, Priceline has made headlines with its name-your-own-price (NYOP) business model, and Humble Bundle has enabled independent musicians and game developers to market their works through pay-what-you-want (PWYW) pricing. Advertising exchanges conduct several hundred million individual auctions per day to sell online advertising slots. These are just a few examples of participative pricing in transactions among consumers or businesses. In parallel, academic research on participative pricing has blossomed in recent years, with an overarching concern over the profitability and other marketing implications these mechanisms have on sellers and buyers. The present paper contributes to this literature in three ways. First, we propose a definition of participative pricing mechanisms, as well as a useful taxonomy. Second, we discuss the current understanding by synthesizing conceptual and empirical academic literature. Third, we outline promising research questions with a key focus on the related behavioral aspects of buyers and sellers

Exploring Bidder Heterogeneity in Multichannel Sequential B2B Auctions

The proliferation of online auctions has attracted significant research interest in understanding real-life bidding behavior. However, most of the empirical work has focused on business-to-consumer (B2C) auctions. A natural question is whether the findings obtained from B2C auctions are applicable to business-to-business (B2B) auctions, which often involve much higher stakes. In this paper, we examine how professional bidders choose their bidding strategies in multichannel, sequential B2B auctions. Using an extensive data set from the world’s largest B2B market for cut flowers, we find a stable taxonomy of bidding behavior and identify five distinctive bidding strategies. In addition, we demonstrate that bidders’ choice of strategies is associated with their demand, budget constraint, and transaction cost. These findings challenge the conventional view that bidders’ bidding strategies will converge as they gain experience. We also analyze the economic impacts of different strategies. Our results provide useful implications for practical design of B2B auctions

Surveillance of Complex Auction Markets: a Market Policy Analytics Approach

The dissertation consists of four essays that investigates the merits of big data-driven decision-making in the surveillance of complex auction markets. In the first essay, Avci and her co-researchers examine the aggregate-level bidding strategies and market efficiency in a multi-time tariff setting by using parametric and semi parametric methods. In the second essay, they address three key forecasting challenges; risk of selection of an inadequate forecasting method and transparency level of the market and market-specific multi-seasonality factors in a semi-transparent auction market. In the third essay, they demonstrate the effect of information feedback mechanisms on bidders’ price expectations in complex auction markets with the existence of forward contracts. They develop a research model that empirically tests the impact of bidders’ attitudes on their price expectation through their trading behavior and tested their hypotheses on real ex-ante forecasts, evaluated ex-post. In the fourth essay, they investigate characterization of bidding strategies in an oligopolistic multi-unit auction and then examine the interactions between different strategies and auction design parameters. This dissertation offers important implications to theory and practice of surveillance of complex auction markets. From the theoretical perspective, this is, to our best knowledge, the first research that systematically examines the interplay of different informational and strategic factors in oligopolistic multi-unit auction markets. From the policy perspective, Avci’s research shows that integration of big data analytics and domain-specific knowledge improves decision-making in surveillance of complex auction markets