The Sixth Washington Area IT Symposium (WAITS) will be hosted in Arlington Virginia by George Mason University the afternoon of April 29th, 2022 from Noon to 2:30pm. Pallab has promised sandwiches!
The goal of WAITS is to bring together like-minded IS scholars in the greater DC Metro area and facilitate inter-school dialogue and collaboration. While there are many business schools in the DMV, and many tremendous IS departments each with their own talented researchers, communication between the groups is limited. This stifles synergies, creative and innovative work, and the opportunity for each of us to learn from each other. It is the objective of WAITS to eliminate this problem by coming together every semester.
We are lucky to have two fantastic researchers giving talks.
Are Your Customers Paying Attention? Mobile Advertising in a Multiscreen Viewing Environment
It is increasingly common for consumers to use an additional device such as a smartphone while watching television, a phenomenon known as multiscreen viewing. While this additional device (or second screen) provides an extra advertising channel for marketers, little is known about how consumers respond to ads on the second screen during a multiscreen viewing experience. Thus, in this research, we use a series of behavioral experiments to understand how firms can optimize advertising on the second screen in light of how primary screen content influences consumer behavior on the second screen. Our results indicate an inverse relationship between primary and second screen viewing, suggesting consumers split their attention between the two screens. We also show that ad recognition is higher for ads appearing on the second screen when supplemental content is displayed on the primary screen, as opposed to when core content is displayed. Finally, recognition is higher for ads that are congruent with primary screen content. Results of this research contribute to the literature on second screen advertising in marketing and information systems, and also help marketers develop actionable strategies for advertising on the second screen in multiscreen viewing contexts.
Are Fairness-Aware Machine Learning Algorithms Really Fair? Examining the Predictive Bias of Using Machine Learning in Personnel Selection
The recent advances in machine learning (ML) brought considerable interests in the integration of ML in organizational decision-making processes, among which personnel selection is a prominent example. Initial attempts at directly using ML algorithms in personnel selection were blunted by excessive biases against protected groups (e.g., gender and racial minorities). To address this problem, numerous efforts in ML were devoted to develop what are now known as fairness-aware ML algorithms. While many of these algorithms were designed to limit the adverse impact of their predictions, what has received little attention is whether the predictions they generate could suffer from predictive bias towards the criterion of interest (e.g., job performance). Given the importance of predictive-bias assessment in personnel selection, we examine the existence, magnitude, and implications of predictive bias in the predictions made by fairness-aware ML algorithms for personnel selection. Our mathematical findings and Monte Carlo simulation studies reveal the prevalence of predictive bias in ML predictions and demonstrate how such bias could manifest as the exclusion of qualified minority candidates and a widened gap in criterion scores between the majority and minority candidates being selected. Our results show that ML predictive bias, if left unaddressed, could result in a range of downstream issues on performance appraisal and other criterion-based human resource functions, in addition to reinforcing rather than ameliorating the existing bias against protected groups.
Want to present at WAITS? Shoot me an email. You know how to find me.