By Wael Jabr📧, and Suvrat Dhanorkar📧
In International Conference on Information Systems (ICIS) 2023 Proceeddings, 2023, 13. https://aisel.aisnet.org/icis2023/soc_impactIS/soc_impactIS/13
When stakeholders are unaware of information that is of relevance to them and when this information is costly to obtain (due to costs associated with information awareness, acquisition, and integration), those stakeholders suffer from information asymmetry vis-à-vis the producer and owner of this information. To overcome those conditions, mandated disclosure has been used in a number of settings, requiring the owner of information such as a chemical manufacturer to disclose the types and level of toxic releases it produces. However, without the ease of information acquisition and subsequent dissemination, such disclosure’s value remains limited. In this paper, we study broadband penetration in the United States as an enabler of the public to be better informed about manufacturers’ pollution and its implications on curbing toxic releases. Because some marginalized communities have been documented to suffer more from such toxic releases, we also study the disproportionate impact of broadband on Black communities.
State judicial systems face the challenge of managing overwhelming prisoner populations and record-high incarceration costs. To be efficient, judicial systems are adopting artificial intelligence (AI) to assess offenders’ recidivism risks and recommend alternative punishments (instead of incarceration) for low-risk offenders. However, the impacts of such AI initiates on judges’ decision-making, offenders’ fairness, and public safety remain unknown. We investigate the effects of AI recommendations on judges’ sentencing decisions and the subsequent societal impact on public safety. Using a regression discontinuity design and unique data from 56,941 sentencing cases in Virginia, we first note that AI recommendations significantly increase the probability of offering alternative punishments, lower the probability of incarceration, and shorten the length of imprisonment. More importantly, we show that AI can promote or demote judicial fairness. While judges are more lenient toward females than males, AI helps alleviate such a gender-based difference. In addition, judges stay fair when sentencing risky offenders but give more favor to whites than blacks, both of whom receive AI alternative punishment recommendations. We last analyze the quality of judges’ decisions regarding offenders’ recidivism. The results indicate that judges’ leniency towards females and whites and strictness towards males and blacks hurt public safety. We compile the results to provide actionable implications for the public, judges, and policymakers to promote judicial fairness with AI support.
Ride-hailing platforms such as Uber and Lyft promise to reduce the negative externalities of driving and improve access to transportation. However, recent empirical evidence has been mixed about the impact of ride-hailing on US cities, often resulting in a net increase in traffic congestion and greenhouse gas (GHG) emissions, largely due to increased travel demand and competition with public transit. Pooled rides, in which multiple passengers share a single vehicle, are an effective solution to improve the sustainability of ride-hailing, reducing GHG emissions and traffic congestion and appealing to price-sensitive population segments by offering relatively cheaper rides. Yet, most ride-hailing trips are unprofitable currently, resulting from ride-hailing rides being subsidized (especially pooled) to compete with cheaper transportation alternatives such as public transit. In this paper, we consider whether price optimization can be used to improve ride-hailing revenues while also reducing the environmental impacts of ride-hailing, particularly as the cost of ride-hailing is expected to fall into the future with the introduction of automated vehicles. Using a discrete choice experiment and multinomial logit choice model with a representative sample of the US population, we estimate consumer preferences for the attributes of ride-hailing services and use them to explore how ride prices affect the revenue of ride-hailing platforms and the total vehicle miles traveled (VMT) by the ride-hailing fleet. We show that as the costs of driving fall, continuously increasing the difference between the prices of individual and pooled rides is financially optimal for ride-hailing platforms. Importantly, this pricing strategy also significantly reduces total VMT, resulting in a win–win for ride-hailing platforms and cities. We perform extensive sensitivity analyses and show that our results are qualitatively robust under a wide range of consumer preferences and market conditions but that the optimal trajectory of prices and realized gains vary, highlighting opportunities for ride-hailing services to influence the future of urban transportation.
Enterprises are increasingly realizing the ability of digital technologies to create a competitive advantage in the procurement function. To date, digital technologies like analytics, artificial intelligence, and robotics process automation have been widely deployed in various procurement domains. Now, a much-hyped newcomer, blockchain, is swiftly gathering momentum. Blockchain has advanced significantly since its early application as the underlying technology of Bitcoin, expanding its field of possible applications. In particular, its ability to enable transparency, traceability, operational efficiency, and trust among users could potentially disrupt procurement operations. Supply chain executives cannot afford to ignore this promising, but yet-to-mature technology. However, blockchain’s novelty and dynamic innovations can make it hard to grasp how this evolving technology could be applied in the real world. To help procurement organizations catch the wave of blockchain technology, this article examines blockchain applications in digital procurement. It highlights areas of applicability and discusses how different blockchain utilities can be advantageously harnessed across procurement processes.
By empowering customers to make fitting purchases, user reviews play an important role in reducing inefficiencies in the provisioning of product information. Because of the abundance of reviews and the signals they provide, this information may become confusing and risks overloading customers. Consequently, review hosting platforms have adjusted their designs to feature a signal “distilled” from a selective set of “top reviews” and their valences. The expected ease with which customers process this signal is intended to increase their satisfaction, thus reducing dispersion in their subsequent review ratings. In this study, we analyze the influential role that top reviews and their valence play under various scenarios: when customers are overloaded by a large number of reviews, when top reviews themselves are not parsimonious in number, and when the signals from top reviews are not in concordance with that from all the other reviews. We find that the valence of top reviews plays a central role in mitigating information overload. However, the influence of those top reviews diminishes when they too pose an overload risk but is strengthened when their signal is reaffirmed by signals from all other reviews. Finally, the impact of top reviews is weaker for less popular products.
Gamification utilizes game-like features to engage participants, widely implemented in a variety of contexts. Such an IT-enabled engagement strategy serves as a marketing device to boost sales and customer loyalty. This study focuses on two significant game elements (i.e., badges and leaderboards) that promote consumer motivations and social comparisons. To qualify the impacts, we conduct a randomized field experiment at one of the largest shopping malls in Asia. In the experiment, we contrast the two elements against coupons regarding various shopping outcomes. A two-period design (consisting of the treatment and posttreatment periods) identifies the long-term behavior changes after the treatment removals. The main results suggest that badging and leaderboarding promote sales by 21.5% and 22.5% in the treatment period, respectively, whereas couponing delivers a more potent effect of 31.7%. In the posttreatment period, the gamification impacts remain significant compared with the baseline, but the influence of couponing fades out. Besides, the additional analyses document the salient heterogeneous treatment effects across demographics. We further discover the substantial differences in the within-group heterogeneity across the treatments. Specifically, badging is a balanced tool for attracting the general public, whereas leaderboarding is a double-edged sword that could encourage self-reinforcing or self-banishing. Finally, gamification brings more explorations that lead to additional sales and engagements. Overall, the robust results can be translated into actionable strategies to utilize gamification proactively.
Technological applications disrupt the way to assess risks in the auto-insurance business. Contrasted with the common practice based on static demographics, usage-based insurance predicts risks using driving data collected from Internet-of-things–enabled telematics. This study proposes a novel solution leveraging the synergy between big data and hierarchical modeling. We specifically consider two aspects of mobility, namely, trait and trajectory, monitored by global positioning system (GPS), on-board diagnostics, and in-vehicle cameras in real time. Traits here refer to drivers’ distinctive driving behaviors (styles), whereas trajectories consist of the vehicle motion sequences and the contextual factors on trips. We operationalize semantic features of the two to assess risks at both trip and driver levels. Using fine-granular driving data and crash reports, we find that behavioral traits play a significant role in predicting crashes, given individual heterogeneity and temporal dynamics. In a series of empirical validations, the proposed solution outperforms the current practice and alternative predictive models considered by prior literature. We show that the mobility-based models are superior to the demographic-based ones. Moreover, our model achieves the comparable performance of neural networks, improving the recall of class-weighted logistic regression, nested support vector machine, and cost-sensitive random forests by 44.23%, 29.18%, and 24.59%, respectively. Last, our approach is robust, data independent, and computationally efficient for skewed and small samples. This study provides several managerial implications and a blueprint for the auto-insurance industry to operationalize IoT-enabled risk assessments in the era of 5G communication.