Teaching Case: Learning Skills of the Data Analytics Lifecycle with Microsoft Power BI and National Parks Data

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Collier, Cassie

Abstract: Like many organizations, the U.S. National Parks Service has done an excellent ... Expand

Abstract: Like many organizations, the U.S. National Parks Service has done an excellent job of collecting and storing data for years about variables of interest. However, there are steps beyond the collection and storage of data that must be taken to transform that data into valuable information that can inform strategic business decisions. This teaching case introduces students to the data analytics lifecycle and the various processes for preparing data to make decisions. Students gain a familiarity with the leading data analytics software Microsoft Power BI as well as Microsoft Excel and SQL Server Reporting Services (SSRS) to inform data-driven decisions. This case is relevant to undergraduate and graduate students in information systems and other disciplines prioritizing data analytics. The case was not developed in conjunction with the National Parks Service; the case uses real-world data made publicly available by the National Parks Service but the narrative and questions posed are fictional. Collapse

Topics: data visualization analytical information system

Methods: exploratory data analysis

Integration of Computer Vision with Analogical Reasoning for Characterizing Unknowns

2023 | HICSS | Citations: 0

Authors: Combs, K.; Bihl, T.; Ganapathy, S.

Abstract: Current state-of-the-art artificial intelligence struggles with accurate interp ... Expand

Abstract: Current state-of-the-art artificial intelligence struggles with accurate interpretation of out-of-library (OOL) objects. One method proposed remedy is analogical reasoning (AR), which utilizes abductive reasoning to draw inferences on an unfamiliar scenario given knowledge about a similar familiar scenario. Currently, applications of visual AR gravitate toward analogy-formatted image problems rather than to computer vision data sets. The Image Recognition Through Analogical Reasoning Algorithm (IRTARA) approach described herein shows how AR can be leveraged to improve computer vision in OOL situations. IRTARA produces a word-based term frequency list that characterizes the OOL object of interest. To evaluate the quality of the results of IRTARA, both quantitative and qualitative assessments are used, including a baseline to compare the automated methods with human-generated results. Fifteen OOL objects were tested using IRTARA, which showed consistent results across all three evaluation methods on the objects that performed exceptionally well or poorly overall. Collapse

Topics: floppy disk Wikipedia Python ergonomics usability

Methods: computational algorithm experiment word embedding GloVe survey

Clustering and Topological Data Analysis: Comparison and Application

2023 | HICSS | Citations: 0

Authors: Combs, K.; Bihl, T.

Abstract: Clustering is common technique used to demonstrate relationships between data a ... Expand

Abstract: Clustering is common technique used to demonstrate relationships between data and information. Of recent interest is topological data analysis (TDA), which can represent and cluster data through persistent homology. The TDA algorithms used include the Topological Mode Analysis Tool (ToMATo) algorithm, Garin and Tauzin's TDA Pipeline, and the Mapper algorithm. First, TDA is compared to ten other clustering algorithms on artificial 2D data where it ranked third overall. TDA had the second-highest performance in terms of average accuracy (97.9%); however, its computation-time performance ranked in the middle of the algorithms. TDA ranked fourth on the qualitative "visual trustworthiness" metric. On realworld data, TDA showed promising classification results (accuracy between 80-95%). Overall, this paper shows TDA is a competitive algorithm performancewise, though computationally expensive. When TDA is used for visualization, the Mapper algorithm allows for unique alternative views especially effective for visualizing highly dimensional data. Collapse

Topics: Python cloud computing missing data

Methods: computational algorithm cluster analysis DBSCAN agglomerative hierarchical clustering BIRCH

Algorithm-Human-Algorithm: A New Classification Approach to Integrating Judgemental Adjustments

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Christopher; Jain, Nitish; Karamshetty, Varun

Abstract: Modern-day firms face the predicament of blending the comparative advantages of ... Expand

Abstract: Modern-day firms face the predicament of blending the comparative advantages of their two core resources: machines and humans. When forecasting demand (e.g. for a product), extant literature documents that always permitting (or prohibiting) human revision of a machine forecast is beneficial if the humans' private information role is larger (or smaller) than that of machine-accessible public information. We propose and test a complementary framework that shifts the focus to the regulation of each human revision; and in doing so, adjusts for human vulnerability to systematic biases. We collaborate with a European retailer to compile a large dataset (∼ 1.1 mn transactions) on machine-led demand forecasts. Humans revise nearly 38% of these forecasts, but revisions do not always yield an improved final forecast. Compared to the always permit or prohibit strategies, the ideal regulation of each of these revisions could reduce the absolute deviation of the final forecast from realized sales in close to 50% of instances. Any regulation to obtain the best of the machine and human-revised forecasts requires an ability to predict revision quality. In addition to private information indicators, we show that indicators of systematic bias in human judgement (e.g., indicators of cognitive load) can improve the prediction of revision quality. In an out-of-sample analysis, our revision-level regulation approach picks the best of available forecasts in 14% more instances, compared to an always-permit or prohibit strategy at the product-store level. Our paper provides practitioners with a novel approach to combining the machine-human output to improve demand forecasting performance. Collapse

Topics: privacy logistics management supply chain management analytical information system

Methods: computational algorithm machine learning descriptive statistic XGBoost gradient boosting

A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Question-and-Answer Platforms

2023 | Information Systems Research | Citations: 0

Authors: Chen, Chen; Walker, Dylan

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post h ... Expand

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors, and direct a bounty (monetary reward) to their most preferred answer, have become a prominent channel for patients to receive medical advice in China. To explore the quality of medical advice on these platforms, we analyzed data on patients’ evaluation of ∼497,000 answers to ∼114,000 questions on one of the most popular OHQPs, 120ask.com, over a three-month period. We assembled a panel of independent physicians and instructed them to evaluate the quality of ∼13,000 answers. We found that the quality of medical advice offered on the platform was high on average, and that low-quality answers were rare (6%). However, our results also indicate that patients lacked the ability to discriminate advice quality. They were as likely to choose the best answer as the worst. The medical accuracy of patient evaluation was worse in critical categories (cancer, internal medicine) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we trained deep neural networks to think like patients, allowing us to identify patients’ positive and negative responses to different heurist cues. Although our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues, which cannot effectively guide them toward the best advice. We also found that the platform reputation metric was not correlated with the quality of the advice giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Our analysis revealed bad actors on the platform, including drug promoters and spammers. Finally, we found that OHQPs exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings. History: Param Singh, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1158. Collapse

Topics: decision making local interpretable model agnostic explanation digital platform health information system pandemic

Methods: artificial neural network psychometrics survey deep learning natural language processing

Theories: lemon market theory

The Economic Implications of Privacy Dark Patterns (PDPs)

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Dawei; Hahn, Jungpil

Abstract: This study investigates the economic implications of privacy dark patterns (PDP ... Expand

Abstract: This study investigates the economic implications of privacy dark patterns (PDPs) through which firms "wisely" play privacy games. It is believed that PDPs advantage firms by deceiving consumers. However, it could also hinder firms' credibility. Thus, we aim to examine whether PDPs always benefit firms and hurt consumers. We also seek to answer whether market forces are sufficient to keep PDPs at relatively low levels. Our results show that PDPs make users weakly worse off and the seller weakly better off. Nevertheless, the seller has incentives to not utilize any PDPs when users' privacy cost is high, and the ratio of privacy concern and search cost is either too high or too low under which market shrinkage effect dominates market division effect. Finally, we show that a welfare maximizing social planner would allow the presence of PDPs when users' privacy cost is sufficiently low. Collapse

Topics: privacy information privacy concern price management data breach IS economics

Methods: cross sectional research game theoretical model literature study

Theories: game theory

Developing AI Literacy of Management Students using Problem and Project based Learning

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Chen, Elaine; Qin, Zhenxin

Abstract: Artificial Intelligence (AI) tools are becoming everyday tool for business of a ... Expand

Abstract: Artificial Intelligence (AI) tools are becoming everyday tool for business of all sizes. Business and management students who have good understanding of AI concepts, tools, technologies, and ethics will have an advantage in contributing to the integration of AI technologies in business. The objective of this study is to examine the effectiveness of using problem-based learning (PBL) and project-based learning (PjBL) to teach AI literacy to master's level management students in a university setting. A module on AI literacy was developed using PBL and PjBL, and a quantitative approach was employed to assess its effectiveness by measuring students' experiences and perceived AI literacy level before and after the module. The results indicate that both PBL and PjBL approaches are effective in developing AI literacy, with 69% of students reporting an increase in their AI literacy level after completing the module. The study also offers recommendations for future AI literacy education in business schools. Collapse

Topics: artificial intelligence analytical information system IT skill problem solving open source

Methods: survey simulation descriptive statistic cross sectional research survey design

Fit into work! From formalizing governance of gig platform ecosystems to helping gig workers craft their platform work

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Jianyu; Luo, Jinlian; Hu, Wenan; Ma, Jun

Abstract: The current governance model of gig platform ecosystems benefits gig platforms a ... Expand

Abstract: The current governance model of gig platform ecosystems benefits gig platforms and customers, but puts gig workers in the weakest position of the triangular relationship. Thus, gig workers experience difficulty in addressing hardships related to gig work. To this end, we must determine how gig platforms build a governance environment that matches gig workers to help them craft gig work. However, extant studies have remained in the qualitative research surface, lacked a certain empirical insight, and have not clarified the direction to creating an effective platform governance model. Thus, based on such literature, we first conceptualize platform formal­ ization governance. Drawing on person-environment fit theory, the PLS-SEM analysis of 290 data from gig workers finds that platform formalization governance directly promotes gig workers' job crafting and indirectly propels it by improving performance appraisal systems' transparency. However, platform work insecurity climate weakens such an direct/indirect positive relationship. These findings contribute to strategic values and effec­ tiveness of the governance model of gig platform ecosystems, thus balancing interests between gig platforms, gig workers, and customers. Gig platforms should consider appropriately formalizing the current governance model and improving work insecurity climate, which helps gig workers more easily address hardships associated with platform work through job crafting. Collapse

Topics: platform worker platform work digital ecosystem platform governance algorithmic management

Methods: survey partial least squares path modeling data transformation PLS tool double-barreled question

An Innovation Resistance Perspective on Seller Resistance to Digital Device Recycling Platforms

2023 | HICSS | Citations: 0

Authors: Chen, Leida; Tang, Zhenya

Abstract: Digital device recycling platforms (DDRP), popularized by the resale commerce (r ... Expand

Abstract: Digital device recycling platforms (DDRP), popularized by the resale commerce (reCommerce) movement, allows users to sell, trade-in, and recycle used digital devices online. Although DDRPs offer new opportunities for commerce and sustainability, challenges exist to usher in their wide adoption. This study aims to develop a deeper understanding of the reasons underlying seller resistance to the DDRP that is grounded in the innovation resistance theory (IRT). The research model of this study is validated using data collected from 307 potential sellers of used digital devices. Collapse

Topics: innovation management privacy word of mouth user resistance green IS

Methods: survey structural equation modeling factor analysis data transformation theoretical contribution

Introduction to the Special Issue on Smart Systems for Industry 4.0 and IoT

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

Authors: Chen, Mu-Yen; Thuraisingham, Bhavani; Egrioglu, Erol; Rubio, Jose De Jesus

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic gr ... Expand

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic growth of hybrid data, often in the form of complex sets of cross-media content including text, images, videos, audios, and time series. Tremendous volumes of these heterogeneous data are derived from multiple IoT sources and present new challenges for the design, development, and implementation of effective information systems and decision support frameworks to meet heterogeneous computing requirements. Emerging technologies allow for the near real-time extraction and analysis of heterogeneous data to find meaningful information.Machine-learning algorithms allow computers to learn automatically, analyzing existing data to establish rules to predict outcomes of unknown data. However, traditional machine learning approaches do not meet the needs for Internet of Things (IoT) applications, calling for new technologies. Deep learning is a good example of emerging technologies that tackle the limitations of traditional machine learning through feature engineering, providing superior performance in highly complex applications. However, these technologies also raise new security and privacy concerns. Technology adoption and trust issues are of timely importance as well.Industrial operations are in the midst of rapid transformations, sometimes referred to as Industry 4.0, Industrial Internet of Things (IIoT), or smart manufacturing. These transformations are bringing fundamental changes to factories and workplaces, making them safer and more efficient, flexible, and environmentally friendly. Machines are evolving to have increased autonomy, and new human-machine interfaces such as smart tools, augmented reality, and touchless interfaces are making interaction more natural. Machines are also becoming increasingly interconnected within individual factories as well as to the outside world through cloud computing, enabling many opportunities for operational efficiency and flexibility in manufacturing and maintenance.An increasing number of countries have put forth national advanced manufacturing development strategies, such as Germany's Industry 4.0, the United States' Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems), and China's Internet Plus Manufacturing and Made in China 2025 initiatives. Smart Manufacturing aims to maximize transparency and access of all manufacturing process information across entire manufacturing supply chains and product lifecycles, with the Internet of Things (IoT) as a centerpiece to increase productivity and output value. This manufacturing revolution depends on technology connectivity and the contextualization of data, thus putting intelligent systems support and data science at the center of these developments. ACM Reference format: Collapse

Topics: production management computer data storage smart manufacturing differential privacy privacy

Methods: computational algorithm convolutional neural network machine learning