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Publication records

Journal Article
Forthcoming

Granular search, market structure, and wages

Review of Economic Studies
Gregor Jarosch, Jan Sebastian Nimczik, Isaac Sorkin
Subject(s)
Economics, politics and business environment
Keyword(s)
Market Power, Search and Matching, Wages
JEL Code(s)
J31, J42
Journal Article
Forthcoming

It's not literally true, but you get the gist: How nuanced understandings of truth encourage people to condone and spread misinformation

Current Opinion in Psychology
Julia Langdon, Beth-Anne Helgason, Judy Qui, Daniel A. Effron
Subject(s)
Economics, politics and business environment; Ethics and social responsibility; Human resources management/organizational behavior; Management sciences, decision sciences and quantitative methods
Keyword(s)
misinformation, fake news, morality, fuzzy-trace theory, gist, verbatim, partisan politics
Journal Article
Forthcoming

The value of information design in supply chain management

Management Science
Ozan Candogan, Huseyin Gurkan
Subject(s)
Information technology and systems; Management sciences, decision sciences and quantitative methods; Technology, R&D management
Keyword(s)
information design, supply chain management, newsvendor model, forecast sharing
Journal Article
Forthcoming

Identification and demarcation—A general definition and method to address information technology in European IT security law

Computer Law & Security Review 52 (April): 105927
Nils Brinker
Subject(s)
Information technology and systems; Technology, R&D management
Keyword(s)
information technology, IT security law, cybersecurity, European regulation
Volume
52
Journal Pages
105927
ISSN (Online)
1873-6734
ISSN (Print)
0267-3649
Journal Article
Forthcoming

Peer evaluations: Evaluating and being evaluated

Organization Science
H. Klapper, H. Piezunka, Linus Dahlander
Subject(s)
Strategy and general management; Technology, R&D management
Peer evaluations place organizational members in a dual role: they evaluate their peers and are being evaluated by their peers. We theorize that when evaluating their peers, they anticipate how their evaluations will be perceived and adjust their evaluations strategically to be evaluated more positively themselves when their peers assess them. Building on this overarching claim of role duality resulting in strategic peer evaluations, we focus on a dilemma that evaluating members face: they want to leverage their evaluations of peers to portray themselves as engaged and having high standards, but at the same time, they must be careful not to offend anyone as doing so may cause retaliation. We suggest that organizational members about to be evaluated resolve this dilemma by participating in more peer evaluations but carefully targeting in which evaluations they participate. We test our theory by analyzing peer evaluations on Wikipedia, supplemented by in-depth semi-structured interviews. Our study informs research on peer evaluation and organizational design by revealing how being an evaluator and evaluated can make evaluations more strategic.
© 2023, INFORMS
Journal Article
Forthcoming

The new needs friends: Simmelian strangers and the selection of novelty

Strategic Management Journal
Athanasia Lampraki, Christos Kolympiris, Thorsten Grohsjean, Linus Dahlander
Subject(s)
Strategy and general management; Technology, R&D management
Keyword(s)
novelty, innovation, selection, simmelian strangers, secondments
Journal Article
Forthcoming

Coevolutionary lock-in in external search

Academy of Management Journal
Sanghyun Park, Henning Piezunka, Linus Dahlander
Subject(s)
Strategy and general management; Technology, R&D management
Keyword(s)
search, external search, ideas, crowdsourcing, co-evolutionary lock-in, attention
While external search allows organizations to source diverse ideas from people outside the organization, it often generates a narrow set of non-diverse ideas. We theorize that this result stems from an interplay between organizations’ selection of ideas and the external generation of ideas: an organization selects ideas shared by external contributors, and the external contributors, who strive to see their ideas selected, use the prior selection as a signal to infer what kind of ideas the organization is looking for. Contributors whose ideas are not aligned with the organization’s selection tend to stop submitting ideas (i.e., self-selection) or adjust the ideas they submit so that they correspond (i.e., self-adjustment), resulting in a less diverse pool of ideas. Our central hypothesis is that the more consistent organizations are in their selection, the stronger the co-evolutionary lock-in: organizations with greater selection consistency receive future ideas with lower content variety. We find support for these predictions by combining large-scale network analysis and natural language processing across a large number of organizations that use crowdsourcing. Our findings suggest a reconceptualization of external search: organizations are not simply passive receivers of ideas but send signals that shape the pool of ideas that externals share.
With permission of the Academy of Management
Journal Article
Forthcoming

Effectiveness and efficiency of state aid for new broadband networks: Evidence from OECD member states

Economics of Innovation and New Technology
Wolfgang Briglauer, Michał Grajek
Subject(s)
Economics, politics and business environment; Information technology and systems; Technology, R&D management
Keyword(s)
fiber optic technology, state aid, ex-post evaluation, efficiency, OECD countries
JEL Code(s)
C51, C54, H25, L52, O38
Journal Article
Forthcoming

Hybrid platform model: Monopolistic competition and a dominant firm

The RAND Journal of Economics
Simon P. Anderson, Özlem Bedre-Defolie
Subject(s)
Economics, politics and business environment
Keyword(s)
Trade platform, hybrid business model, antitrust policy, tax policy
JEL Code(s)
D42, L12, L13, L40, H25
We provide a canonical and tractable model of a trade platform enabling buyers and sellers to transact. The platform charges a percentage fee on third-party product sales and decides whether to be "hybrid", like Amazon, by selling its own product. It thereby controls the number of differentiated products (variety) it hosts and their prices. Using the mixed market demand system, we capture interactions between monopolistically competitive sellers and a sizeable platform product. Using long-run aggregative games with free entry, we endogenize seller participation through an aggregate variable manipulated by the platform's fee. We show that a higher quality (or lower cost) of the platform's product increases its market share and the seller fee, and lowers consumer surplus. Banning hybrid mode benefits consumers. The hybrid platform might favor its product and debase third-party products if the own product advantage is sufficiently high. We also provide some tax policy implications.
Journal Article
Forthcoming

Is your machine better than you? You may never know.

Management Science
Subject(s)
Information technology and systems; Management sciences, decision sciences and quantitative methods; Technology, R&D management
Keyword(s)
machine accuracy, decision making, human-in-the-loop, algorithm aversion, dynamic learning
Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stake decisions can properly assess whether the machine produces better recommendations. To that end, we study a set-up in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, she updates her belief about the machine. However, the DM is subject to a so-called verification bias such that the DM verifies the machine’s correctness and updates her belief accordingly only if she ultimately decides to act on the task. In this set-up, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better, i.e., she never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. These findings provide a novel explanation for human-machine complementarity and suggest guidelines on the decision to fully adopt or reject a machine.
© 2023, INFORMS