Optimal Decision Rules for the Discursive Dilemma

2023 | Group Decision and Negotiation | Citations: 0

Authors: Alabert, Aureli; Farré, Mercè; Montes, Rubén

Abstract: We consider the classical problem in truth-tracking judgment aggregation of a co ... Expand

Abstract: We consider the classical problem in truth-tracking judgment aggregation of a conjunctive agenda with two premisses and one conclusion. We study this problem from the point of view of finding the best decision rule according to a quantitative criterion, under very mild restrictions on the set of admissible rules. The members of the deciding committee are assumed to have a certain probability to assess correctly the truth or falsity of the premisses, and the best rule is the one that minimises a combination of the probabilities of false positives and false negatives on the conclusion. Collapse

Topics: Python anonymity

Methods: machine learning statistical hypothesis test

The Interplay of Information Processing, Mindfulness, and Affective State in Phishing Detection

2023 | International Conference on Information Systems | Citations: 0

Authors: Bera, Debalina; Kim, Dan

Abstract: This study investigates the effect of individuals' information processing modes ... Expand

Abstract: This study investigates the effect of individuals' information processing modes on phishing email detection mechanisms and accuracy. Drawing on the heuristic-systematic model of processing, mindful decision-making, and affect-and-persuasion literature, this study examines the antecedents (i.e., heuristic, systematic processing, general and domain mindfulness, and affective state) and behavioral consequences (i.e., detection accuracy) in phishing identification. Using a scenario-based survey experiment this study shows mindfulness and affectivity navigate information processing mechanisms, thus influence phishing detection accuracy. It aims to contribute to information security literature by examining novel associations between general, domain mindfulness and its effect on online users' information processing strategy and phishing detection accuracy. Furthermore, this study contributes to phishing training and awareness by identifying the function of cognitive-affective (affective states, trait mindfulness) and cognitive-behavioral (domain mindfulness) factors on the choice of information processing modes and phishing detection accuracy. Finally, the affective states might help improve the efficacy of automatic filters. Collapse

Topics: phishing mindfulness electronic mail email phishing data security

Methods: experiment survey partial least squares regression machine learning laboratory experiment

Theories: protection motivation theory

Network Centralization and Collective Adaptability to a Shifting Environment

2023 | Organization Science | Citations: 1

Authors: Bernstein, Ethan S.; Shore, Jesse C.; Jang, Alice J.

Abstract: We study the connection between communication network structure and an organizat ... Expand

Abstract: We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed—interferes with collective problem-solving by obstructing the integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms responsible for that poor integration of ideas, information, and solutions would nevertheless prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an inferior consensus, requiring a collective shift of solution after more information emerged. We find that when the communication network within an organization is more centralized, it achieves the benefits of connectivity (spread of novel better solutions) without the costs (getting stuck on an existing inferior solution). We also find, however, that these benefits of centralization only materialize in networks with two-way flow of information and not when information only flows from the center of the network outward (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to reconceptualize theory on the impact of centralization—and how it affects conformity pressure (lock-in) and awareness of diverse ideas (learning)—on collective problem-solving that demands adaptation.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The authors acknowledge financial support from the Division of Research at the Harvard Business School for making this research possible. Collapse

Topics: social influence problem solving internet technology social network debugging

Methods: experiment machine learning simulation Poisson regression logistic regression

Theories: organizational theory

DrugExBERT for Pharmacovigilance – A Novel Approach for Detecting Drug Experiences from User-Generated Content

2023 | International Conference on Information Systems | Citations: 0

Authors: Bohnen, Eva; Erlebach, Stefanie; Zimmermann, Steffen

Abstract: Pharmaceutical companies have to maintain drug safety through pharmacovigilance ... Expand

Abstract: Pharmaceutical companies have to maintain drug safety through pharmacovigilance systems by monitoring various sources of information about adverse drug experiences. Recently, user-generated content (UGC) has emerged as a valuable source of real-world drug experiences, posing new challenges due to its high volume and variety. We present DrugExBERT, a novel approach to extract adverse drug experiences (adverse reaction, lack of effect) and supportive drug experiences (effectiveness, intervention, indication, and off-label use) from UGC. To be able to verify the extracted drug experiences, DrugExBERT additionally provides explications in the form of UGC phrases that were critical for the extraction. In our evaluation, we demonstrate that DrugExBERT outperforms state-of-the-art pharmacovigilance approaches as well as ChatGPT on several performance measures and that DrugExBERT is data-and drug-agnostic. Thus, our novel approach can help pharmaceutical companies meet their legal obligations and ethical responsibility while ensuring patient safety and monitoring drug effectiveness. Collapse

Topics: health information system application programming interface generative AI database system

Methods: chatGPT natural language processing RoBERTa BERT machine learning

Low-Coders, No-Coders, and Citizen Developers in Demand: Examining Knowledge, Skills, and Abilities Through a Job Market Analysis

2023 | International Conference on Business Informatics | Citations: 0

Authors: Binzer, Björn; Winkler, Till J.

Abstract: The emergence of low-code/no-code (LCNC) platform technologies and the resultin ... Expand

Abstract: The emergence of low-code/no-code (LCNC) platform technologies and the resulting increase in citizen development programs are facilitating the democratization of the design, development, and deployment of digital solutions. Citizen developers, non-technical employees who leverage LCNC platforms, are at the heart of this trend. While many firms perceive LCNC and citizen development as a crucial component of their digital transformation strategy, little is known about the evolving roles in this field or the necessary knowledge, skills, and abilities (KSA). To address this knowledge gap, we processed 113,106 job postings published on Indeed.com. Our topic modeling methodology identified 34 KSA topics and classified them into the three domains platform, business, and technology. We contribute to research by empirically demonstrating which competencies are required to successfully work in the LCNC field. Our findings can guide individual professionals and organizations alike. Collapse

Topics: online labor market Indeed.com low code development platform Python project management

Methods: topic model natural language processing computational algorithm latent dirichlet allocation NLTK

Too Good to Be True? An Empirical Study of ChatGPT Capabilities for Academic Writing and Implications for Academic Misconduct

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Busch, Peter André; Hausvik, Geir Inge

Abstract: This study evaluated the capabilities of ChatGPT, a conversational artificial i ... Expand

Abstract: This study evaluated the capabilities of ChatGPT, a conversational artificial intelligence (AI) agent, to generate quality answers to exam questions that are indistinguishable from human-generated text, normally requiring high-level cognitive abilities. This potential capacity is a major threat to academic integrity since ChatGPT may be used for academic misconduct. To determine its capabilities, we asked 15 faculty members to assess a total of ten answers (two each) to an actual IS exam question, of which students wrote five, and ChatGPT generated five. The study found that ChatGPT can generate answers of generally good quality that may pass as human-written text by examiners. The contributions of the study lay in providing empirical results in a novel research area, providing recommendations for teachers, and suggesting ways forward for research. Collapse

Topics: artificial intelligence smartphone evaluation criteria smartphone addiction mobile system

Methods: chatGPT machine learning descriptive statistic correlation analysis personal interview

Business Inferences and Risk Modeling with Machine Learning; The case of Aviation Incidents

2023 | HICSS | Citations: 0

Authors: Cankaya, B.; Topuz, K.; Glassman, A.

Abstract: Machine learning becomes truly valuable only when decision-makers begin to depe ... Expand

Abstract: Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "highimpact, low-probability" (HILP) events. Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study provides an interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers. In short, we combine predictive results with simulations to interpret findings and prescribe actions.A machine-learning (ML) algorithm was chosen for its fast performance, short run-time, and interpretability. The ML algorithms were compared and evaluated on a confusion matrix for their mean receiver operating characteristic curve (ROC), Collapse

Topics: decision making decision support system graphical user interface usability missing data

Methods: simulation machine learning multinomial logistic regression computational algorithm support vector machine

Context and Aggregation: An Experimental Study of Bias and Discrimination in Organizational Decisions

2023 | Organization Science | Citations: 1

Authors: Christensen, Michael; Dahl, Christian M.; Knudsen, Thorbjørn; Warglien, Massimo

Abstract: This paper addresses a notable gap at the intersection of organizational economi ... Expand

Abstract: This paper addresses a notable gap at the intersection of organizational economics and organization science: how does organizational context influence aggregation of individual behavior in organizational decisions? Using basic centralized versus decentralized organizational structures as building blocks for our experimental design, we examine whether assignment of organizational positions, incentive schemes, and structural configuration induce endogenous adaptation in the form of change in reservation levels (bias) or modified discrimination capability in subjects’ behavior. We found that evaluators adapted their reservation and discrimination levels in centralized structures, whereas they did not generally adapt their reservation and discrimination levels when placed in decentralized structures. We identify mechanisms that explain these findings; explain how they influence aggregate, organizational behavior; and discuss implications for research and practice.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: M. Warglien acknowledges financial support from the European Union's Horizon 2020 research and innovation programme under [Grant Agreement 732942].Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1502. Collapse

Topics: organization structure organizational context decision making organizational productivity graphical user interface

Methods: experiment statistical hypothesis test experimental design theoretical contribution cluster analysis

Theories: organizational behavior theory

Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Shuixia; Ngai, Eric W.T.; Ku, Yaoyao; Xu, Zeshui; Gou, Xunjie; Zhang, Chenxi

Abstract: Reliable hotel cancellation prediction can help establish appropriate operationa ... Expand

Abstract: Reliable hotel cancellation prediction can help establish appropriate operational strategies for hotel manage­ ment. In this sector, personal name records (PNR) data may be the most representative information source for prediction tasks. Despite the popularity of PNR, its inherent lack of availability has been commonly disregarded in the literature. Existing studies have directly input PNR into high-dimensional machine learning (ML) models to achieve cancellation predictions. Another type of model generates cancellation prediction based on the proba­ bility modeling of samples. In this study, we propose an interpretable feature interaction method to enrich the existing PNR information. Thereafter, we empirically assess the prediction performance of the two model classes. This study specifically determines whether or not the two methods can cross-fertilize each other to improve cancellation prediction. To do so, we propose a model integrating Bayesian networks (BNs) and Lasso regression for this prediction task. This study utilizes BNs for the probability model consistent with our correlated variables and dichotomous prediction setting. Moreover, we use a linear ML model (i.e., Lasso regression), given its ad­ vantages in reducing ineffective predictors and transparency for ranking feature importance. Empirical results show that the proposed integration model has better prediction performance, and the obtained BN estimators and interactive features are the most important predictors. This study contributes to the booking cancellation liter­ ature by proposing an interpretable feature interaction and a prediction method integrating two types of effective models. The obtained accurate and interpretable cancellation prediction further contributes to offering practical implications to hoteliers in managerial decision-making. Collapse

Topics: missing data decision making competitive advantage price management marketing management

Methods: lasso regression experiment computational algorithm robustness check parametric test

How can Blockchain technology support patent management? A systematic literature review

2023 | International Journal of Information Management | Citations: 0

Authors: Denter, Nils M.; Seeger, Fabian; Moehrle, Martin G.

Abstract: Patent management tasks involve many stakeholders from diverse disciplines and h ... Expand

Abstract: Patent management tasks involve many stakeholders from diverse disciplines and hierarchies as well as from inside and outside the organization. By drawing on the transaction cost and stakeholder theories, Blockchain technology offers new opportunities to rethink the way in which stakeholders are organized and collaborate. Although Blockchain technology receives considerable attention from both the public and academia, the question of how Blockchain technology leverages patent management in particular has so far been neglected, although it holds great promise. Consequently, this is what we choose to be our basic research question. In response to it, we conduct a systematic literature review, following a procedure in three steps. Out of an initial sample of 714 articles, we select 52 as being particularly relevant, and classify them in accordance with the dimensions of the 7D Patent Management Maturity Model. Three main foci regarding the use of Blockchain technology emerge – i. e. for the generation, the enforcement, and the exploitation of patents. This study contributes to scholarship and management by showing that Blockchain technology mitigates environmental and behavioral uncertainty, while stimulating new governance forms and business models by reorganizing or discarding stakeholders. Collapse

Topics: blockchain database system Bitcoin anonymity design thinking

Methods: literature sample literature study structured literature research topic model qualitative content analysis

Theories: transaction cost economics stakeholder theory