Does the Electronic Medical Record (EMR) Adoption Matter? Exploring Patterns of EMR Implementation and its Impact on Hospital Performance

2023 | HICSS | Citations: 0

Authors: Lee, J.; Kim, J.; Shin, S.

Abstract: We aimed to explore the patterns of electronic medical records (EMR) adoption a ... Expand

Abstract: We aimed to explore the patterns of electronic medical records (EMR) adoption and its effects on hospital performance. We analyzed hospital-level panel data from 2008 to 2013 using Bayesian regression and the Naïve Bayes model. Our research analysis revealed 38 different adoption patterns for 1,919 hospitals that completed EMR implementation (having all of the four components) and 42 different adoption patterns for 1,341 hospitals that could not complete the EMR implementation. We examined the hospitals' EMR adoption patterns that were not completed; but predicted as completed using the Naïve Bayes model. Our results revealed that the hospitals that completed EMR adoption showed higher performance in terms of patient recommendation and net patient revenue than those that did not complete EMR adoption. More importantly, most of hospitals that observed as "not completed" but predicted as "completed" showed lower performance in terms of patient recommendation as well as net patient revenue. Collapse

Topics: technology adoption artificial intelligence database system missing data electronic health record

Methods: naïve bayes machine learning survey regression analysis method Markov chain Monte Carlo

Personalized Ranking at a Mobile App Distribution Platform

2023 | Information Systems Research | Citations: 0

Authors: Mao, Shengjun; Dewan, Sanjeev; Ho, Yi-Jen (Ian)

Abstract: The ease of customer data collection has enabled the widespread personalization ... Expand

Abstract: The ease of customer data collection has enabled the widespread personalization of content and services in digital platforms. We examine personalization in a hitherto unaddressed context: that of mobile app distribution. Specifically, we develop a comprehensive framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action (CPA) margin, which is the revenue earned by the platform per app installation. To this end, we specify a structural model of click and installation choices, jointly estimated as a function of a comprehensive set of numerical (screen rank, quality, and popularity) and textual (titles, descriptions, and reviews) covariates. Our novel data set is at the granular user-impression level and uniquely includes app CPA margins paid to the platform. We conduct a series of policy experiments to quantify the value of personalization. Specifically, we show that a personalized hybrid margin and utility margin ranking scheme outperforms other personalized methods, including those based on utilities alone or a combination of utilities and margins. Overall, our analysis demonstrates how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution.History: Param Singh, Senior Editor; Juan Feng, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1156. Collapse

Topics: mobile application personalization usability search engine accounting

Methods: experiment mathematical model robustness check latent dirichlet allocation Markov chain Monte Carlo

Theories: utility theory

Handling Missing Values in Information Systems Research: A Review of Methods and Assumptions

2023 | Information Systems Research | Citations: 0

Authors: Peng, Jiaxu; Hahn, Jungpil; Huang, Ke-Wei

Abstract: In today’s big data environment, missing values continue to be a problem that ha ... Expand

Abstract: In today’s big data environment, missing values continue to be a problem that harms data quality. The bias caused by missing values raises the highest concern, as it cannot be eliminated simply by increasing the sample size. Although the statistics literature has developed approaches to handling missing values and formulated assumptions regarding when these approaches generate valid statistical inferences, these prescriptions have yet to be broadly accepted by many social science disciplines, including the information systems (IS) discipline. By reviewing recently published empirical research in information systems, we find that missing values are indeed an important and pervasive problem. We believe that a review of missing value theory is necessary and timely for the IS community to understand the nature of missing values and to promote more rigorous research practice when missing values are often unavoidable. In addition, the missing not at random (MNAR) mechanism brings about challenges in parameter estimation. We contribute to research practice by proposing and demonstrating the superior performance of a Monte Carlo likelihood approach in correcting bias in parameter estimation. We conclude by suggesting that research validity can be enhanced through a reasoned adoption of the missing value handling method and structured missing value reporting practices.History: Giri Kumar Tayi, Senior Editor; Pei-Yu Chen, Associate Editor.Funding: This work was supported by the Singapore Ministry of Education [Grants R-253-000-136-114, R-253-000-149-490, and R-253-000-158-114] the National Natural Science Foundation of China [Approval No. 72201288], and the Program for Innovation Research in Central University of Finance and Economics. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Singapore Ministry of Education, the Singapore Government or the Central University of Finance and Economics.Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1104. Collapse

Topics: missing data research and development electronic commerce logistics management mindfulness

Methods: simulation regression analysis method robustness check literature sample data imputation

Distance and Local Competition in Mobile Geofencing

2020 | Information Systems Research | Citations: 5

Authors: Ho, Yi-Jen (Ian); Dewan, Sanjeev; Ho, Yi-Chun (Chad)

Abstract: This research studies the performance of geofencing, a practice where mobile use ... Expand

Abstract: This research studies the performance of geofencing, a practice where mobile users are targeted within a predefined virtual geographic boundary around an advertiser’s establishment. We argue the significance of distance (i.e., the mileage from a consumer to a focal establishment) and local competition (i.e., the number of alternatives in consumer vicinity) in ad responses. Drawing on the notion of the purchase funnel, we develop a two-stage hierarchical Bayesian model to examine consumer click and conversion choices. A unique data set of geofencing ad impressions is collected from one of the largest location-based marketing agencies in the United States. The results suggest that local competition matters in the click stage, whereas distance influences the propensity of conversion. Quantitatively, one additional competitor in the consumer vicinity zone lowers the click-through rate by 1.03%, whereas a 1-mile increase in distance results in a 17.64% decrease in the conversion rate. We also find a significant interactive effect, whereby a higher degree of local competition amplifies the negative impact of distance on the likelihood of conversions. Additionally, product differentiation ameliorates the effects of distance and local competition, whereas these effects are found to be more prominent during office working hours. This study discovers the stage-varying roles of distance and local competition along the customer journey and offers new directions for more effective location-based targeting. Collapse

Topics: advertising management product differentiation mobile application marketing management mobile system

Methods: descriptive statistic robustness check longitudinal research Bayesian analysis simulation

Theories: utility theory

When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases

2019 | Information Systems Research | Citations: 0

Authors: Choi, Angela Aerry; Cho, Daegon; Yim, Dobin; Moon, Jae Yun; Oh, Wonseok

Abstract: Online reviews offer consumers the indirect experience of products through other ... Expand

Abstract: Online reviews offer consumers the indirect experience of products through others’ consumption evaluations, whereas previews afford them direct experience through product trials. Although conceptual and empirical studies on the business ramifications of online reviews abound, little is known about how online previews moderate the effects of online reviews on sales. To cast light on this issue, the current research investigated the interactive effects of exposure to online previews and reviews on individual purchase decisions. We analyzed a unique two-month panel data set on 270,260 sessions that comprise clickstream data on consumers’ exposure to previews and reviews and data on their subsequent purchase behaviors. On the basis of analyses underlain by a two-stage hierarchical Bayesian framework, we found that online previews positively influence individual purchase decisions. More importantly, significant interactions exist between previews and reviews, as evidenced by the decreasing positive effect of previews with increasing review volume and average review rating. In addition, previews can complement reviews when a high variance in the latter renders purchase decisions difficult. We further examined the sequence effect of exposure to previews and reviews and discovered that exposure to previews following the experience of reviews may exert a considerable positive influence on individual purchase decisions. The results from an additional field experiment and a text-based sentiment analysis reinforced the validity of our main findings by mitigating concerns regarding the endogeneity and the accuracy of the review quality, respectively. The findings provide practical implications with regard to the design of optimal strategies for releasing preview information to digital platforms. Collapse

Topics: ebook digital good online review decision making electronic market

Methods: experimental group sentiment analysis instrumental variables estimation longitudinal research field experiment

Theories: construal level theory

Optimal pricing in e-commerce based on sparse and noisy data

2018 | Decision Support Systems | Citations: 0

Authors: Bauer, Josef; Jannach, Dietmar

Abstract: In today's transparent markets, e-commerce providers often have to adjust their ... Expand

Abstract: In today's transparent markets, e-commerce providers often have to adjust their prices within short time intervals, e.g., to take frequently changing prices of competitors into account. Automating this task of determining an “optimal” price (e.g., in terms of profit or revenue) with a learning-based approach can however be challenging. Often, only few data points are available, making it difficult to reliably detect the relationships between a given price and the resulting revenue or profit. In this paper, we propose a novel machine-learning based framework for estimating optimal prices under such constraints. The framework is generic in terms of the optimality criterion and can be customized in different ways. At its core, it implements a novel algorithm based on Bayesian inference combined with bootstrap-based confidence estimation and kernel regression. Simulation experiments show that our method is favorable over existing dynamic pricing strategies. Furthermore, the method led to a significant increase in profit and revenue in a real-world evaluation. Collapse

Topics: price management dynamic pricing electronic commerce online learning system

Methods: computational algorithm simulation experiment decision tree classification multi-armed bandit

Disconfirmation Effect on Online Rating Behavior: A Structural Model

2017 | Information Systems Research | Citations: 83

Authors: Ho, Yi-Chun (Chad); Wu, Junjie; Tan, Yong

Abstract: This research studies the effect of disconfirmation—the discrepancy between the ... Expand

Abstract: This research studies the effect of disconfirmation—the discrepancy between the expected and experienced assessment of the same product—on the behavior of consumers leaving online product reviews. We propose a modeling framework in which an individual’s prepurchase expectation is shaped by (1) the product ratings she observes and (2) the perception of the review system she has at the time of the purchase. Upon product consumption, the individual obtains the postpurchase evaluation and encounters a certain level of disconfirmation. Drawing on the Bayesian learning framework, we model individual perception of the review system as a subjective attitude underlying how well the aggregate ratings match one’s own usage experience. A hierarchical Bayesian model is developed and estimated using a rich data set comprising complete purchasing and rating activities on an e-commerce website. Our results suggest that an individual’s decisions of whether to post a rating and what rating to post are affected by disconfirmation in two distinct manners. Specifically, an individual is more likely to leave a review when the magnitude of disconfirmation she encounters is larger. In addition, when the individual decides to review a product, the rating she chooses may not neutrally reflect her postpurchase evaluation; the direction of such a bias is in accordance with the sign of disconfirmation. We also observe several moderating effects: the disconfirmation effect on posting is attenuated by the time gap between purchase and receipt of the same product but accentuated by the dissension in product evaluations among peer consumers. A more granular examination reveals that infrequent raters are systematically more susceptible to disconfirmation than frequent posters. The insights from this research lead to actionable strategies for marketers and designers of recommender systems. Collapse

Topics: electronic commerce online review website experience good online word of mouth

Methods: simulation descriptive statistic robustness check econometric model longitudinal research

Efficacy of a Health App for Obesity and Overweight Management: A Hidden Markov Model

2017 | International Conference on Information Systems | Citations: 1

Authors: Kwon, Hyeokkoo Eric; Dewan, Sanjeev; Oh, Wonseok; Kim, Tae Kyung

Abstract: We examined the efficacy of a mobile app for personal weight management by using ... Expand

Abstract: We examined the efficacy of a mobile app for personal weight management by using a hidden Markov modeling approach, which allows us to capture the dynamics in the intrinsic motivation, or state of self-regulation, of individuals to achieve their desired weight reduction objectives. The model illuminates the direct and indirect treatments that affect self-regulation state and success of overweight and obesity management. Results show that two selfregulation states (weak and strong) are optimal for describing the dynamics in the data. Relatively easy direct treatments, such as reducing calorie intake and relying on artificial electronic assistance, are more effective in the weak self-regulation state, whereas strenuous interventions, such as exercising, are more helpful in the strong state. The use of the mobile app is considerably more effective than the use of its PC channel in influencing users’ selfregulation states. Individuals who do not use either channel fail to transition from the weak to the strong self-regulation state. Overall, the intensive use of the mobile app increases the probability of successful overweight and obesity management by 5.2%. Collapse

Topics: mobile channel mobile application mobile health intrinsic motivation individual characteristic

Methods: survey longitudinal research hidden Markov model design artifact descriptive statistic

Can I See Beyond What You See? Blending Machine Learning and Econometrics to Discover Household TV Viewing Preferences

2017 | HICSS | Citations: 0

Authors: Li, Zhuolun; Kauffman, Robert; Dai, Bingtian

Abstract: This research blends machine learning-based discovery of preference patterns tha ... Expand

Abstract: This research blends machine learning-based discovery of preference patterns that uses natural language processing for TV viewing data with explanatory modeling that uses econometrics, as a basis for understanding TV viewing preferences at the household-level. We employ a dataset of about 1.1 million observations that was collected via set-top box technology that tracked household-level consumption of the content of its channel subscription package. The data describe the details of what households watched on TV, including the channels and shows, start times and durations, and overall viewing times for content from different digital entertainment genres. This research demonstrates the efficacy of our machine learning and explanatory econometrics approach, and presents insights on consumer behavior and content bundling that are useful for firm strategy in digital entertainment services. Collapse

Topics: digital entertainment marketing management consumer behavior

Methods: econometric modeling machine learning data transformation principal component analysis natural language processing

The Pursuit of Conversion: Effects of Mediating Channels on Product Choices and Purchase Propensities in Social Commerce Platforms

2015 | International Conference on Information Systems | Citations: 0

Authors: So, Hyunji; Oh, Wonseok; Kim, Minki; Park, Sunghyuk

Abstract: This study elucidates the effectiveness of intermediary channels in driving sale ... Expand

Abstract: This study elucidates the effectiveness of intermediary channels in driving sales at social commerce sites (SCSs). Using a panel data, we investigate how the external intermediary channels through which consumers arrive at SCSs influence product choice and purchase likelihood. In addition, we scrutinize the extent to which product categories with varying quality moderate the relationship between consumers’ channel-related behaviors and purchase propensities. Furthermore, we examine how external channels “collaborate” with internal channels to increase purchase likelihood. The findings suggest that consumers who enter the SCS through direct apps and portals engage in more proactive purchasing than do consumers landing at the SCS via metasites or e-mail promotions. Consumers who are directed to the SCS through metasites or e-mail promotions are more likely to purchase experience goods than search goods. Contrary to previous findings, consumers’ purchasing propensities decline, rather than increase, across all channels after the implementation of a recommendation system. Collapse

Topics: recommender system social commerce experience good mobile application electronic mail

Methods: longitudinal research Gibbs sampling field experiment Bayesian analysis Metropolis–Hastings method