本文精选了信息管理类国际顶刊《Information Systems Research》6月发表的论文，提供信息管理研究领域最新的学术动态。

Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation

原刊和作者：

Information Systems Research 2022年 6月

Ranjit M. Christopher （University of Missouri–Kansas City）

Sungho Park （Seoul National University）

Sang Pil Han （Arizona State University）

Min-Kyu Kim （Arizona State University）

Abstract

Demand-side platforms (DSPs) that purchase digital ad space using real-time bidding (RTB) systems employ “black-box” performance optimizers to adjust bids at run time. Advertisers using field experiments to estimate the marginal value of display ads need to contend with the selective targeting of users by optimizers that adjust bids to target users with a greater propensity to respond favorably (i.e., click or conversion). In this paper, we propose an alternative approach for advertisers who choose to bypass their DSP’s performance optimizers for the purpose of assessing the value of their ads. We show that external frequency caps that set upper limits on the number of ad impressions outside the purview of bidding algorithms can serve as a suitable instrumental variable. Eliminating performance optimizers allows the advertiser to value ads without relying on the support services of the DSP with the added benefit of a broader customer reach and a markedly lower cost. As the focal advertiser disables performance optimizers, any overbidding or underbidding vis-à-vis competition that employs them results in a negative correlation between the numbers of ad impressions won and their underlying quality in real time. Using two large-scale randomized field experiments in different geographies (United States and Asia) and different devices (PC and mobile), we validate the proposed approach and report a positive effect of ad impression count after adjusting for net negative bias.

Link: https://doi.org/10.1287/isre.2021.1050

Developing a Composite Measure to Represent Information Flows in Networks: Evidence from a Stock Market

原刊和作者：

Information Systems Research 2022年 6月

Wuyue (Phoebe) Shangguan （Xiamen University）

Alvin Chung Man Leung （City University of Hong Kong）

Ashish Agarwal （The University of Texas at Austin）

Prabhudev Konana （University of Maryland）

Xi Chen （Zhejiang University）

Abstract

There is increasing interest in information systems research to model information flows from different sources (e.g., social media, news) associated with a network of assets (e.g., stocks, products) and to study the economic impact of such information flows. This paper employs a design science approach and proposes a new composite metric, eigen attention centrality (EAC), as a proxy for information flows associated with a node that considers both attention to a node and coattention with other nodes in a network. We apply the EAC metric in the context of financial market where nodes are individual stocks and edges are based on coattention relationships among stocks. Composite information from different channels is used to measure attention and coattention. To evaluate the effectiveness of the EAC metric on predicting outcomes, we conduct an in-depth performance evaluation of the EAC metric by (1) using multiple linear and nonlinear prediction methods and (2) comparing EAC with a benchmark model without EAC and models with a set of alternative network metrics. Our analysis shows that EAC significantly outperforms other measures in predicting the direction and magnitude of abnormal returns of stocks. Besides, our EAC specification has better predictive performance than alternative specifications, and EAC outperforms direct attention in predicting abnormal returns. Using the EAC metric, we derive a stock portfolio and develop a trading strategy that provides significant and positive excess returns. Lastly, we find that composite information has significantly better predictive performance than separate information sources, and such superior performance owes to information from social media instead of traditional media.

Link: https://doi.org/10.1287/isre.2021.1066

Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model

原刊和作者：

Information Systems Research 2022年 6月

Chenshuo Sun （New York University）

Panagiotis Adamopoulos （Emory University）

Anindya Ghose （New York University）

Xueming Luo （Temple University）

Abstract

The proliferation of omnichannel practices and emerging technologies opens up new opportunities for companies to collect voluminous data across multiple channels. This study examines whether leveraging omnichannel data can lead to, statistically and economically, significantly better predictions on consumers’ online path-to-purchase journeys, given the intrinsic fluidity in and heterogeneity brought forth by the digital transformation of traditional marketing. Using an omnichannel data set that captures consumers’ online behavior in terms of their website browsing trajectories and their offline behavior in terms of physical location trajectories, we predict consumers’ future path-to-purchase journeys based on their historical omnichannel behaviors. Using a state-of-the-art deep-learning algorithm, we find that using omnichannel data can significantly improve our model’s predictive power. The lift curve analysis reveals that the omnichannel model outperforms the corresponding single-channel model by 7.38%. This enhanced predictive power benefits various heterogeneous online firms, regardless of their size, offline presence, mobile app availability, or whether they are selling single- or multicategory products. Using an illustrative example of targeted marketing, we further quantify the economic value of the improved predictive power using a cost-revenue analysis. Our paper contributes to the emerging literature on omnichannel marketing and sheds light on the inherent dynamics and fluidity in consumers’ online path-to-purchase journeys.

Link: https://doi.org/10.1287/isre.2021.1071

Configuring the Enterprise Systems Portfolio: The Role of Information Risk

原刊和作者：

Information Systems Research 2022年 6月

Chaitanya Sambhara （University of Texas）

Arun Rai （Georgia State University）

Sean Xin Xu （Tsinghua University）

Abstract

We investigate how public firms configure their enterprise systems (ES) portfolio when faced with information risk, which refers to the likelihood that corporate financial information is of poor quality. We focus on firms’ configuration of their ES portfolio by introducing a novel construct: ES portfolio balance, or the relative proportion of two categories of ES modules, operational and functional. We draw on the theory of information processing to hypothesize the impact of information risk on ES portfolio balance and how this impact is affected by internal controls. We construct a multisource panel data set of 697 firms and 1,993 firm-year observations from 2005 to 2008 and use econometric and multivariate procedures to test our hypotheses. We find that when faced with an increase in information risk, firms change their ES portfolio balance more toward operational modules. However, when such firms are also faced with materially weak internal controls, they change their ES portfolio balance more toward functional modules instead. These findings expand our understanding of how firms’ information processing needs drive the configuration of their ES portfolio and, more broadly, IT resources portfolio.

Link: https://doi.org/10.1287/isre.2021.1075

Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms

Information Systems Research 2022年 6月

Karthik Kannan （Purdue University）

Rajib L. Saha （Indian School of Busines）

Warut Khern-am-nuai （McGill University）

Abstract

Consumer profiling has become one of the most common practices on online trading platforms. Many platforms strive to obtain and implement technological innovations that allow them to understand and identify consumers’ needs, and, thereafter, monetize this capability by charging sellers to present and/or sell their products or services based on consumers’ interests. However, an interesting and relevant question arises in this context: Does the platform have an incentive to profile its buyers as accurately as possible? This paper develops and analyzes a parsimonious game-theoretic model to answer this research question. We find that, surprisingly, platforms that charge sellers for discoveries have a perverse incentive to deviate from accurate buyer profiling. However, such a perverse incentive does not exist for platforms that charge sellers for transactions. As a result, with such a perverse incentive, social welfare under discovery-based pricing is lower than that under transaction-based pricing.

Link: https://doi.org/10.1287/isre.2021.1077