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Working Paper
SSRN Working Paper
Daniel Grodzicki, Alexei Alexandrov, Özlem Bedre-Defolie, Sergei Koulayev (2021)
Subject(s)
Economics, politics and business environment; Finance, accounting and corporate governance
Keyword(s)
Credit card demand reactions to fees, late fee regulation, limited attention
JEL Code(s)
D12, D90, G50
We introduce a model of a rational credit card user's rather complex usage choices and develop an empirical framework to test its predictions. Employing a large national database of U.S. card accounts, we estimate how prices impact card usage and find that price effects are mostly well explained within our model. An exception is less borrowing in response to declining late-fees among low credit-score (subprime) users. Extension of our model based on "focusing theory" predicts this behavior. It also implies substantial indirect benefits of the CARD Act's late-fee cap due to subprime users re-focusing toward reducing their debt.
Pages
46
ESMT Working Paper
ESMT Working Paper No. 19-02 (R2)
Işık Biçer, Florian Lücker, Tamer Boyaci (2021)
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management
Keyword(s)
Product proliferation, lead-time reduction, process redesign, delayed differentiation
Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, manufacturers can establish market responsiveness and/or cost efficiency in alternative ways. Delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (1) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (2) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high-cost operations. We consider a multi-echelon and multi-product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast-evolution model to characterize the demand-updating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. Using anonymized data of Kordsa Inc., a global manufacturer of advanced composites and reinforcement materials, we show that our model outperforms a theoretical benchmark of the repetitive newsvendor model. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.
Pages
47
ISSN (Print)
1866–3494
ESMT Working Paper
ESMT Working Paper No. 20-03 (R2)
Forthcoming in Management Science.
Francis de Véricourt, Huseyin Gurkan, Shouqiang Wang (2021)
Subject(s)
Health and environment; Information technology and systems
Keyword(s)
Public health, epidemic control, information design, strategic behavior
This paper explores how governments may efficiently inform the public about an epidemic to induce compliance with their confinement measures. Using an information design framework, we find the government has an incentive to either downplay or exaggerate the severity of the epidemic if it heavily prioritizes the economy over population health or vice versa. Importantly, we find that the level of economic inequality in the population has an effect on these distortions. The more unequal the disease's economic impact on the population is, the less the government exaggerates and the more it downplays the severity of the epidemic. When the government weighs the economy and population health sufficiently equally, however, the government should always be fully transparent about the severity of the epidemic.


Pages
41
ISSN (Print)
1866–3494
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
Machine-learning, rational inattention, human-machine collaboration, cognitive effort
The rapid adoption of AI technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision-makers (DM) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors as well as the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions, but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, even though its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial.
Pages
40
ISSN (Print)
1866–3494
Working Paper
SSRN Working Paper
Gishan Dissanaike, Wolfgang Drobetz, Paul P. Momtaz, Jörg Rocholl (2020)
Subject(s)
Economics, politics and business environment; Finance, accounting and corporate governance
Keyword(s)
mergers and acquisitions, acquirer returns, law and finance, takeover law, law enforcement
JEL Code(s)
G30, G34, G38, K20, K22
This paper examines the impact of takeover law enforcement on corporate acquisitions. We use the European Takeover Directive as a natural experiment, which harmonizes takeover law across countries, while leaving its enforcement to the discretion of individual countries. We exploit this heterogeneity in enforcement quality across countries in a difference-in-differences-in-differences model, while employing an overall inductive research approach, following Karpoff and Whittry’s (2018) recommendation. We find that acquirer returns increase in countries with improvements in takeover law, driven by better target selection and lower cost of financing. The increase in acquirer returns is lower in weak enforcement jurisdictions, which we identify by developing a novel Takeover Law Enforcement Index (TLEI). The findings show that takeover law can mitigate agency conflicts, but its true value depends on its enforcement. Our results are robust to a number of robustness tests.
Pages
56
Subject(s)
Management sciences, decision sciences and quantitative methods; Product and operations management; Technology, R&D management
Keyword(s)
Data, machine learning, data product, pricing, incentives, contracting
This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which, as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider's incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics between machine learning, data acquisition, pricing, and contracting. We find that the firm's decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider's effort. If this effort has a more (resp. less) significant impact on accuracy for larger volumes of data, the firm underprices (resp. overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect have important implications for the firm's data collection strategy. In particular, the firm can boost its profit by increasing the product's capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm's profit may actually decrease. As a result, the firm should consider reducing its product's data acquisition capacity when its initial dataset to train the algorithm is large enough.
Pages
48
ISSN (Print)
1866–3494
Working Paper
SSRN Working Paper
Katja Kisseleva, Aksel Mjos, David T. Robinson (2020)
Subject(s)
Finance, accounting and corporate governance
Keyword(s)
returns, investment, entrepreneurship, venture capital, early-stage financing
JEL Code(s)
G11, G23, G24, G32
This paper uses highly detailed administrative records from the Norwegian Tax Authority to provide direct measures of the returns from investing in newly established, innovative companies. We trace out the entire funding and pricing histories of each firm and study performance measures at the transaction and investment levels. Many investments result in total loss, but returns exhibit extreme right-skewness. Cross-sectional analysis shows that different investor types earn widely differing returns even in the same investment. This arises not just because they invest on different terms, but because they make different decisions about holding or selling shares. The opaqueness and uncertainty implied by this heterogeneity is indicative of the market frictions associated with early-stage investment in innovation.
Pages
52
ESMT Working Paper
ESMT Working Paper No. 18-04 (R1)
Subject(s)
Management sciences, decision sciences and quantitative methods
Keyword(s)
Service operations, rational inattention, strategic customers, rational queueing, information costs, system throughput, social welfare
Problem description: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly as it requires time, attention, and cognitive capacity which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium and delineate the impact of service characteristics and information costs. We numerically show that when customers allocate their attention to learn uncertain queue length, limited attention of customers improves throughput in a congested system that customers value reasonably highly, while it can be detrimental for less popular services that customers deem rather unrewarding. This is also reflected in social welfare if the firm's profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: Our results shed light on optimal information provision and physical design strategies of service firms and social planners by identifying service settings where they should be most cautious for customers' limited attention. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design.
Pages
42
ISSN (Print)
1866–3494
Working Paper
Bocconi University Management Research Paper
Forthcoming in Organization Science.
Paola Criscuolo, Linus Dahlander, Thorsten Grohsjean, Ammon Salter (2020)
Subject(s)
Technology, R&D management
Keyword(s)
Sequence effect, law of small numbers, gambler’s fallacy, contrast effect, quota model, R&D project selection, innovation, decision-making, panel, professional service firm
We examine how groups fall prey to the sequence effect when they make choices based on informed assessments of complex situations; for example, when evaluating research and development (R&D) projects. The core argument is that the temporal sequence of selection matters because projects that appear in a sequence following a funded project are themselves less likely to receive funding. Building on the idea that selecting R&D projects is a demanding process that drains participants’ mental and emotional resources, we further theorize the moderating effect of the influence of the timing of the panel meeting on the sequence effect. We test these conjectures using a randomization in sequence order from several rounds of R&D project selection at a leading professional service firm. We find robust support for the existence of a sequence effect in R&D as well as for the moderating effect. We further explore different explanations for the sequence effect and how it passes from the individual to the panel. These findings have broader implications for the literatures on innovation and search in general and on group decision-making for R&D, specifically, as they suggest that a previously overlooked dimension affects selection outcomes.
Pages
44
Subject(s)
Economics, politics and business environment
Keyword(s)
Cartels, private damages, competition law
Private cartel damages litigation is on the rise in Europe since early 2000. This development has been initiated by the European courts and was supported by various policy initiatives of the European Commission, which found its culmination in the implementation of the EU Directive on Antitrust Damages end of 2016. This paper explores the impact of this reform process on effective compensation of damaged parties of cartel infringements. For that purpose we analyse all European cartel cases with a decision date between 2001 and 2015, for which we analyse litigation activity and speed. Overall, we find a substantial reduction of the time until first settlement (increase in litigation speed) together with a persisting high share of cases being litigated (high litigation activity). This supports the view that the reform not only increased the claimant’s expectation about the amount of damages being awarded, but also resulted in an alignment in the expectations of claimants and defendants in the final damages amount, i.e. the European Commission succeeded in reaching its objective to clarify and harmonize legal concepts across Europe.
Pages
39
ISSN (Print)
1866–3494