MIS Distinguished Speaker Series

Temple University

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Sezgin Ayabakan

Feb 4 – Youngjin Kwon to present “The Unvaccinated against Covid-19: The Perils of (In)Voluntary Disclosure of Unvaccinated Status in Job Applications”

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The Unvaccinated against Covid-19: The Perils of (In)Voluntary Disclosure of Unvaccinated Status in Job Applications

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Youngjin Kwon

Ph.D. Student
Management Information Systems
Fox School of Business
Temple University

Friday, Feb 4

In-person

11:00 am – 12:30 pm | Speakman 200 

Abstract:

While 35% of people aged 18-39 years are not still fully vaccinated, nearly 8 in 10 vaccinated Americans blame the high number of Covid-19 cases on them. Further, many politicians have taken strong stances against the unvaccinated. Such examples have evoked concerns that the unvaccinated are stigmatized. In the meanwhile, the unvaccinated are on the verge of losing jobs and being rejected by their families, friends, and coworkers. Particularly in a management context, almost one-third of employers responded that they would automatically reject applicants without information about their vaccination, but, at the same time, firms are struggling with labor shortages during the Covid-19 pandemic. In this study, we examine the effects of (in)voluntary disclosure of unvaccinated status on hiring outcomes. Drawing upon stigma theory, intergroup theory, and relative deprivation theory, we study how (a) discovery on social media and (b) disclosure on resumes of unvaccinated status influence hiring outcomes. Our research model hypothesizes that hiring managers would negatively evaluate the unvaccinated because of (a) negative stereotyping about them (rule-following behavior), (b) concerns about potential coworkers (coworker stigmatization), and (c) hiring managers’ affect toward them (liking). Through an online experiment, we found that both discovery and disclosure negatively affect the three factors. Interestingly, however, disclosure can be an effective strategy to reduce negative stereotyping compared to discovery. By studying discovery and disclosure of stigma in a unique context, this study would provide not only theoretical contributions in the relevant literature but also practical implications regarding how firms and unvaccinated applicants address job applications.

Jan 21 – Hyeonsik Shin to present “Entry of Online Grocery Delivery Services in the U.S. and Widening of the Nutritional Inequalities”

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Entry of Online Grocery Delivery Services in the U.S. and Widening of the Nutritional Inequalities

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Hyeonsik Shin

Ph.D. Student
Management Information Systems
Fox School of Business
Temple University

Friday, Jan 21

11:00 am – 12:30 pm | Zoom

Abstact:

Despite a series of policies, campaigns, and investments to curb the obesity epidemic, the obesity rate in the US has continuously risen for the past few decades. This paper examines the effect of third-party online grocery delivery services (OGDS) on the obesity rate. We posit that OGDS can reduce the obesity rate by increasing consumers’ access to healthy foods in popular grocery stores and promoting healthy dietary habits. By exploiting the nationwide quasi-experimental setting of Instacart’s staggered entries in the US county-level from 2004 to 2017, we find that the entry of Instacart lowers the obesity rate by 0.64%. Further analyses show, however, that the decrease in obesity rates following Instacart’s entry is greater in counties with higher personal income, more grocery stores, and lower child food insecurity rates. As the low-income population with poor access to healthy foods has been more vulnerable to obesity, our findings indicate that OGDS widen nutritional inequality gaps between the wealthy and the poor. This study contributes to the information systems (IS) literature by investigating an understudied type of digital platform offering necessity-based goods/services (i.e., provision of nutrition). Moreover, our study contributes to the IS literature on digital platforms by providing robust empirical evidence that points to the role of one of these platforms in exacerbating societal inequalities.

Jan 14 – Yiwen Gao to present “Biding Their Time: The Influences of Executive Compensation and Board Cybersecurity Intensity on Firms’ Strategic SEC Data Breach Notification Delays”

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Biding Their Time: The Influences of Executive Compensation and Board Cybersecurity Intensity on Firms’ Strategic SEC Data Breach Notification Delays

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Yiwen Gao

Ph.D. Student
Management Information Systems
Fox School of Business
Temple University

Friday, Jan 14

11:00 am – 12:30 pm | Zoom

Abstact:

The U.S. Securities and Exchange Commission (SEC) requires firms to notify investors in an SEC filing of a data breach if it constitutes a material event. Importantly, the determination of materiality lies with executives, which has resulted in firms failing to disclose breaches to the SEC or purposely delaying notifications. We draw from the behavioral theory of the firm and executive compensation literature to develop predictions about the influence of IT and non-IT executives’ compensation on firms’ SEC data breach notification delays. Given the possibility of competing priorities and goals of the two executive groups, we argue that increased IT executive compensation leads to fewer delays, whereas increased non- IT executive compensation has the opposite effect. Because corporate boards of directors have oversight and advise on firms’ cybersecurity matters, we argue that the cybersecurity intensity of the firm’s board (i.e., social ties to breached firms) moderates the relationships between IT and non-IT executive compensation and notification delays. To test our hypotheses, we constructed a panel dataset from public sources and performed a series of econometric analyses. Our results suggest that the influence of executive compensation on notification delays differs for IT and non-IT executives in the manner hypothesized. However, for both types of executives, the moderating influence of the board’s cybersecurity intensity works to increase notification delays. Counter to the conventional view that increased cybersecurity experience on the board benefits timely data breach notification, our findings suggest that greater board experience results in delays of timely communications about data breaches via 8-K filings.

Dec 3 – Jingjing Zhang to present “Longitudinal Impact of Preference Biases on Recommender Systems’ Performance”

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Longitudinal Impact of Preference Biases on Recommender Systems’ Performance

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Jingjing Zhang

Associate Professor
Fettig/Whirlpool Faculty Fellow
Department of Operations & Decision Technologies
Kelley School of Business
Indiana University

Friday, Dec 3

10:30am – 12 pm | Alter 603

Abstact:

Research studies have shown that recommender systems’ predictions that are observed by users can cause biases in users’ post-consumption preference ratings. Because users’ preference ratings are typically fed back to the system as training data for future predictions, this process is likely to influence the performance of the system in the long run. We use a simulation approach to study the longitudinal impact of preference biases (and their magnitude) on the dynamics of recommender systems’ performance. We look at the influence of preference biases in two conditions: (i) during the normal system use, where biases are typically caused by the system’s inherent prediction errors, and (ii) in the presence of external (deliberate) recommendation perturbations. Our simulation results show that preference biases significantly impair the system’s prediction performance (i.e., prediction accuracy) as well as users’ consumption outcomes (i.e., consumption relevance and diversity) over time. The impact is non-linear to the size of the bias, i.e., large bias causes disproportionately large negative effects. Also, items that are less popular and less distinctive (in terms of their content) are affected more by preference biases. Additionally, intentional recommendation perturbations, even on a small number of items for a short time, substantially amplify the negative impact of preference bias on a system’s longitudinal dynamics and causes long-lasting effects on users’ consumption. Our findings provide important implications for the design of recommender systems.

Nov 12 – Sumit Sarkar to present “Modeling User Choice to Compose Offer Sets”

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Modeling User Choice to Compose Offer Sets

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Sumit Sarkar

Charles and Nancy Davidson Chair
Professor of Information Systems
Director, PhD Programs
Naveen Jindal School of Management
The University of Texas at Dallas

Friday, Nov 12

10 – 11 am | Zoom

Abstact:

Firms are increasingly using clickstream and transactional data to tailor product offerings to visitors (users) at their site. The sites have the opportunity, at each interaction (i.e., whenever a user clicks on a link), to offer multiple items (referred to as an offer set) that might be of interest to the user. By displaying links to multiple items, the site hopes to increase the chance that the user will find at least one of those items to be interesting, thereby increasing the probability the user will make a purchase. We examine the problem of composing an offer set that maximizes the firm’s expected payoff over a user’s session, which would typically include multiple interactions. An offer set provided to a user has important implications: the composition of the offer set impacts the immediate choice of the user (item externality), and the user’s choice in one interaction may influence the subsequent actions the user will take during that session. Hence, in order to determine the full impact of an offer set, it is necessary to consider not only the likelihood of the user purchasing one of the offered items, but to also evaluate how it may impact the choices of a user at subsequent interactions. By providing the right offer set a site can guide a user to paths that lead to items with higher likelihood of conversion while simultaneously learning what other items the user may examine (view) before making a purchase. We show that identifying the optimal offer set is a difficult problem in general, and develop an efficient heuristic that can be used in real time. Simulated experiments based on real data show that the joint consideration of item externalities and of looking ahead lead to both higher conversion rates and longer user sessions on average, and consequently to increased total sales.

Oct 29 – Maximilian Schreieck to present “From Product Platform Ecosystem to Innovation Platform Ecosystem: An Institutional Perspective on the Governance of Ecosystem Transformations”

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From Product Platform Ecosystem to Innovation Platform Ecosystem: An Institutional Perspective on the Governance of Ecosystem Transformations

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Maximilian Schreieck

Visiting Postdoc
The Wharton School, The University of Pennsylvania
Postdoc
Krcmar Lab, Technical University of Munich

Friday, Oct 29

10:30am – 12 pm | Alter 603

Abstact:

With the advance of cloud computing, incumbent companies across different industries such as banking, insurance, and enterprise software have the opportunity to transform existing product platform ecosystems into innovation platforms ecosystems, increasing generativity in their ecosystems. This transformation is challenging because it not only entails a technological shift but also changes to the complex interactions between the ecosystem orchestrator and ecosystem actors such as partners and customers. To study how incumbent companies can govern ecosystem transformations, we interpret ecosystems as institutional fields. We analyze how incumbent companies can leverage the three institutional pillars (regulatory, normative, and cultural-cognitive) to address governance tradeoffs that arise once the transformation is triggered. In a multiyear, grounded theory study, we analyze SAP’s introduction of a cloud platform for enterprise software applications and show that the company aligned its governance approach with the three institutional pillars, iteratively increasing the legitimacy of the transformed ecosystem among partners and customers. We contribute to the literature on ecosystem transformation and platform governance by showing that a transformed ecosystem needs to gain legitimacy among ecosystem actors, a process that can be supported by the ecosystem orchestrator through governance mechanisms based on the three institutional pillars. We also highlight the potential of institutional theory as a lens for understanding dynamic changes in ecosystems and provide practical implications for incumbent companies that undergo ecosystem transformations.

Oct 1 – Manju Ahuja to present “Maladaptive Mobile Use and Family-work Conflict: A Resource Drain Theory Approach to Examine Their Effects on Productivity and Well-being”

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Maladaptive Mobile Use and Family-work Conflict: A Resource Drain Theory Approach to Examine Their Effects on Productivity and Well-being

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Manju Ahuja

Frazier Family Professor of Computer Information Systems
College of Business
University of Louisville

Friday, Oct 1

10:30am – 12 pm | Alter 603

Abstact:

While acknowledging the many benefits of anytime-anywhere connectivity, recent research has called for further investigation into the maladaptive side of mobile technology use in the work-family interface realm. We rely on Resource Drain Theory to investigate how family-work conflict (FWC) leads to excessive use of mobile devices for work purposes during non-work hours, which, in turn, affects individual productivity and well-being. Further, we examine the role of competitive climate as a boundary condition. We conducted a field study across two measurement periods involving 324 individuals and their partners. Our results suggest that FWC affects productivity and well-being through excessive mobile use, and that competitive climate amplifies these effects. The study contributes by providing a better understanding of excessive mobile use phenomenon in terms of its determinants and consequences. We discuss the theoretical and practical implications of our findings, and outline directions for future research.

Sep 24 – Lauren Rhue to present “Man vs. Machine: The Substitutability of AI and Expert Evaluations of Initial Coin Offerings (ICOs)”

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Man vs. Machine: The Substitutability of AI and Expert Evaluations of Initial Coin Offerings (ICOs)

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Lauren Rhue

Assistant Professor of Information Systems
Department of Decision, Operations and Information Technologies
Robert H. Smith School of Business
University of Maryland

Friday, Sep 24

9 – 10 am | Zoom

Abstact:

Initial coin offerings (ICOs) were heralded as a popular method for emerging blockchain and technology ventures to raise capital for their businesses; however, several high-profile ICO scams generated concerns about ICO legitimacy. We examine an ICO-rating platform that provides two evaluation sources, artificial intelligence (AI) and experts, as well as qualitative and quantitative expert evaluations to evaluate ICOs. This study compares the informativeness of the information sources and information types to understand how AI ratings for uncertain quality items, like ICOs, compares to the expert ratings. Using dual-processing theory and cognitive biases, we posit substitutability for quantitative evaluations but complementarity between quantitative and qualitative evaluations. Using nearly 5,000 ICOs and more than 14,000 expert evaluations, we find that 1) experts’ decisions on which ICOs to evaluate contain relevant information, 2) experts and AI quantitative evaluation are substitutes, and 3) quantitative evaluations complement qualitative evaluations. Our paper makes several contributions to the information systems literature related to the substitutability of automation systems for online human reviews, the different processing pathways for qualitative and quantitative evaluations, and the unexpected benefit of cognitive biases.

Sep 10 – Amit Basu to present “Preference Uncertainty and Information Asymmetry in Online Matching Platforms”

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Preference Uncertainty and Information Asymmetry in Online Matching Platforms

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Amit Basu

Professor
Carr P. Collins Chair in Management Information Science
Cox School of Business
Southern Methodist University

Friday, Sep 10

10 – 11 am | Zoom

Abstact:

A firm seeking a business partner, or an individual searching for a life partner, can use an online matching platform not only to efficiently search for available candidates, but also to address two related challenges. First, due to uncertainty in their subjective preferences, match-seekers may not know what candidates would be compatible with them. And second, due to information asymmetry in online settings, candidates may misrepresent their credentials. In this paper, we model and analyze whether an online matching platform should enhance search with a positioning capability that helps match-seekers determine the subjective compatibility of potential matches, and also whether it should offer an authentication service that reliably verifies the objective quality of match-seekers. We analyze the equilibrium behavior of match-seekers, and show how this behavior impacts the optimal strategy of the platform with respect to positioning and authentication. Our analysis provides insights on the relative value of authentication and positioning, and identifies conditions under which the platform should focus on each of these services. We also show that positioning and authentication reinforce each other (act as complements) for some levels of market quality and the platform’s positioning capability, while they detract from each other (act as substitutes) in others. Our results help us develop guidelines for the platform’s pricing decisions, and provide valuable practical insights for owners and operators of match-making platforms, by helping them understand the interplay between these two important and orthogonal features in online matching.

Sep 3 – Huaxia Rui to present “Probabilistic Selling in Vertically Differentiated Markets”

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Probabilistic Selling in Vertically Differentiated Markets

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Huaxia Rui

Professor
Xerox Chair of Computer and Information Systems
Simon Business School
University of Rochester

Friday, Sep 3

9 – 10 am | Zoom

Abstact:

This paper studies two fundamental questions regarding probabilistic selling in a vertical market: when is probabilistic selling profitable and how to design it optimally? For the first question, we identify an important but overlooked economic mechanism driving probabilistic selling in vertical markets: convexity of consumer preferences. In stark contrast with the literature finding that probabilistic selling is never profitable unless there is excess capacity or bounded rationality, we find that with an alternative utility function capable of representing convex preference, probabilistic selling is always profitable without excess capacity and with rational consumers. For the second question, we study the optimal design of probabilistic goods where the two component goods are of different qualities and their prices are endogenous. We develop an efficient algorithm to compute the optimal design when consumers have Cobb-Douglas utility functions and obtain an important structural property of the optimal prices of the two component goods: the optimal price of the high-quality good increases while the optimal price of the low-quality good decreases upon the introduction of probabilistic selling, thereby increasing the market coverage of the goods without launching an actual new product line. We also obtain closed-form solutions for a special case of Cobb-Douglas utility function that is widely used in the economics literature on vertical product differentiation.