Perceived Algorithmic Control: Conceptualization and Scale Development

2023 | European Conference On Information Systems | Citations: 1

Authors: Alizadeh, Armin; Hirsch, Felix; Benlian, Alexander; Wiener, Martin; Cram, W.

Abstract: Organizations today leverage big data analytics combined with modern informatio ... Expand

Abstract: Organizations today leverage big data analytics combined with modern information and communication technology (ICT) to control workers in a largely automated manner. This phenomenon is commonly referred to as algorithmic control (AC). Research has shown that worker perceptions of AC applications can impact critical outcomes such as worker resistance, continuance intention, and well-being. Despite recent efforts to better understand these worker-level effects of AC, no systematic measures of workers' perceptions of AC exist to date. In this study, we develop and present a scale to empirically measure AC at the worker level. In particular, following established scale development guidelines, we conceptualize AC in terms of seven distinct forms, and derive, evaluate, and validate a final set of 33 measurement items. In doing so, our study paves the way for a much-needed, more systematic investigation of AC and its worker-level implications. Collapse

Topics: database system video conference social media

Methods: qualitative interview survey design psychometrics card sorting descriptive statistic

Benchmarking the Robustness of Phishing Email Detection Systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ampel, Benjamin; Gao, Yang; Hu, James; Samtani, Sagar; Chen, Hsinchun

Abstract: Social engineering attacks are currently the most cited cybersecurity threat to ... Expand

Abstract: Social engineering attacks are currently the most cited cybersecurity threat to organizations. Phishing emails are the most salient form of social engineering attacks. Organizations are increasingly implementing AI-enabled systems to detect phishing emails. However, AI-enabled systems are often susceptible to textual perturbations, where an adversary makes a small change to cause a misclassification. In this study, we sought to identify the performance of prevailing phishing email detection systems (PEDS) against character, word, sentence, and multi-level adversarial text perturbations. Through a principled benchmarking framework, we quantitatively demonstrated the lack of robustness prevailing PEDS have to specific types of text-based adversarial perturbations (e.g., character, word, sentence, multi-level). The results of this study provide new insights into the robustness of AI-based PEDS and highlight the need for organizations to adopt a multi-layered approach to phishing protection. Additionally, organizations can implement our benchmark framework to test their PEDS against adversarial perturbations. Collapse

Topics: artificial intelligence electronic mail email phishing phishing IT security

Methods: word2vec random forest classification naïve bayes literature study pre-trained language model

On Predicting ESG Ratings Using Dynamic Company Networks

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ang, Gary; Guo, Zhiling; Lim, Ee-Peng

Abstract: Environmental, social and governance (ESG) considerations play an increasingly i ... Expand

Abstract: Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or financial fundamentals of companies, but few works study how different types of company information can be utilized to predict ESG ratings. Previous works also largely focus on using only the financial information of individual companies to predict ESG ratings, leaving out the different types of inter-company relationship networks. Such inter-company relationship networks are typically dynamic, i.e., they evolve across time. In this paper, we focus on utilizing dynamic inter-company relationships for ESG ratings prediction, and examine the relative importance of different financial and dynamic network information in this prediction task. Our analysis shows that utilizing dynamic inter-company network information, based on common director, common investor and news event-based knowledge graph relationships, can significantly improve ESG rating prediction performance. Robustness checks over different time-periods and different number of time-steps in the future further validate these insights. Collapse

Topics: shapley additive explanation knowledge graph database system missing data network effect

Methods: machine learning econometric modeling longitudinal research Granger causality robustness check

TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

2023 | Decision Support Systems | Citations: 0

Authors: Antunes, Jorge; Hadi-Vencheh, Abdollah; Jamshidi, Ali; Tan, Yong; Wanke, Peter

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinc ... Expand

Abstract: This paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous. Collapse

Topics: health information system IT productivity human resource management service quality productivity

Methods: TOPSIS trade-off analysis method machine learning artificial neural network literature study

Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

2023 | Information Systems Frontiers | Citations: 0

Authors: Azri, Abderrazek; Favre, Cécile; Harbi, Nouria; Darmont, Jérôme; Noûs, Camille

Abstract: The proliferation of rumors on social media has become a major concern due to it ... Expand

Abstract: The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance. Collapse

Topics: Twitter social network Python microblog logistics management

Methods: computational algorithm machine learning experiment meta learning ensemble learning

Single-Sourcing vs. Multisourcing: An Empirical Analysis of Large Information Technology Outsourcing Arrangements

2023 | Information Systems Research | Citations: 0

Authors: Bapna, Ravi; Gupta, Alok; Ray, Gautam; Singh, Shweta

Abstract: As the information technology (IT) services landscape matures, clients are incre ... Expand

Abstract: As the information technology (IT) services landscape matures, clients are increasingly adopting multisourcing arrangements that involve multiple vendors. Although a large body of information systems (IS) literature addresses issues of whether to outsource (to a single vendor), what types of contracts to use, and how to achieve optimal relational governance, little is known about the antecedents and consequents of the single versus multisourcing decision. Moreover, while conceptual and analytical models of single-sourcing versus multisourcing have been developed, there is no empirical IS research using a large-scale data set with rigorous econometric analysis that examines the antecedents and consequents of multisourcing in the IT context. This paper fills this void, using the transaction cost economic lens and a data set of 49,057 large IT outsourcing arrangements that spans multiple industries and dates back 25 years. We find that there is a curvilinear relationship between number of IT services in an IT outsourcing arrangement and the likelihood of multisourcing. This relationship increases as the number of IT services increases to up to five services and then decreases. For managers who plan to multisource IT outsourcing arrangements, this research provides guidance to minimize exchange hazards through a better understanding of the relationship between sourcing choice, client IT outsourcing capabilities, the competitiveness of the vendor landscape, and the number of IT services in an IT outsourcing arrangement. We provide empirical evidence that the choice between single-sourcing and multisourcing is material to the performance of outsourcing contracts as an incorrect sourcing choice is likely to result in negative contract outcomes. History: Paul A. Pavlou, Senior Editor; Ning Su, Associate Editor. Collapse

Topics: outsourcing system integration IS management service desk data center

Methods: XGBoost robustness check machine learning k-nearest neighbor statistical hypothesis test

Theories: transaction cost economics game theory

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of a ... Expand

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ~0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making. Collapse

Topics: healthcare data decision making big data missing data data warehouse

Methods: machine learning computational algorithm XGBoost descriptive statistic random forest classification

Save Our Souls: Study of Twitter Use during India’s COVID-19 Pandemic

2023 | International Conference on Information Systems | Citations: 0

Authors: Bhattacharyya, Samadrita; Mojumder, Probal

Abstract: Twitter is a commonly used social platform for communication during disasters. ... Expand

Abstract: Twitter is a commonly used social platform for communication during disasters. Tweets by citizens during disasters to share information, seek, and offer help create a body of spontaneous, decentralized, emergent social media communication. Users' exploit Twitter's reach-enabling technological functionalities (hashtags (#), mentions (@), and 'reply-to') to draw attention to the messages. Set in context of the second wave of COVID-19 in India, that saw a surge in citizen-driven tweets seeking healthcare resources from fellow citizens and officials (i.e., SOS tweets), our paper empirically analyses the effects of Twitter's reach-enabling functionalities on online responses (i.e., retweets and replies) to these SOS tweets. We investigate the effects of inclusion of hashtags, mentions, and 'reply to' SOS tweets. We also examine the moderating effect of how the effects of the reach-enabling functionalities change as the social platform gets crowded with SOS tweets. The study offers various academic and practical implications. Collapse

Topics: Twitter pandemic social media Python societal evaluation criteria

Methods: propensity score method machine learning propensity score matching econometric modeling robustness check

Analyzing Patients’ EHR: Predicting and Explaining Admission Consequences for COPD and Liver Disease Patients

2023 | International Conference on Information Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Geva, Gaya

Abstract: This study analyzed the admission outcomes in chronic patients (with COPD, and ... Expand

Abstract: This study analyzed the admission outcomes in chronic patients (with COPD, and Liver disease) to demonstrate the feasibility of applying prediction methods on EHR records while incorporating an explainable AI technique. We predicted three target variables: 30-day readmission, Medium&Long Length of Stay and Single-day admission and analyzed the features using an explainable AI technique, the SHapley Additive exPlanations (SHAP). The results show that Readmission had higher prediction scores than all other dependent variables. Some features affected all target variables with either positive or negative influence including: Age, Charlson comorbidity index, Day-Shift, Gender, using EHR screens and Insurance cover level. These findings thus point to the value of using Machine-Learning combined with an explainable AI method to understand and assess the risks factors. The assessment of the potential factors leading to multiple complications can bolster prevention-oriented medical decisions to groups of patients but can also be tailored to the patient level. Collapse

Topics: shapley additive explanation explainable artificial intelligence Python IT work artificial intelligence

Methods: machine learning XGBoost logistic regression artificial neural network classification method

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