Economic Impacts of Platform-Endorsed Quality Certification: Evidence from Airbnb

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Dewan, Sanjeev; Kim, Jooho; Nian, Tingting

Abstract: We contribute to the emerging literature on quality certification by digital pl ... Expand

Abstract: We contribute to the emerging literature on quality certification by digital platforms by studying the launch of the Airbnb Plus service, wherein the platform inspects properties and provides a badge that presumably signals the quality of the property and the reliability of the host. Our identification strategy relies on the fact that the Airbnb Plus service was launched in different cities at different times, and listings within the cities received the certification at different times. Using a staggered difference-in-differences estimation strategy in conjunction with suitable matching methods, we found that the Airbnb Plus certification increased the weekly booking rate of Plus listings by about 6.8% on average (direct effect). We also found some evidence that non-Plus listings saw a temporary decline in booking rate when one or more nearby properties received a Plus certification (externality effect). The net impact of the Airbnb Plus service on the platform itself was an annual increase in revenue of about $37,500 for the average 2-kilometer zone in a U.S. city that included one or more Plus listings, as compared to matched zones without any Plus listings (local platform effect). We performed additional analyses, including a randomized experiment, to demonstrate the robustness of our findings. Overall, our results suggest that platform-endorsed quality certification has significant economic impacts-not just on the listings that receive the certification but on other listings on the platform as well as on the platform itself. Collapse

Topics: Airbnb website sharing platform peer-to-peer model Google+

Methods: experimental group experiment propensity score method product review robustness check

A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

2023 | International Conference on Information Systems | Citations: 0

Authors: Dinh, Minh; Le, Mau Toan; Bui, Triet; Mai, Minh; Tran, Long; Nguyen, Nhan; Vo, Tan Hoang

Abstract: Machine learning techniques are successful for optical character recognition ta ... Expand

Abstract: Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively. Collapse

Topics: healthcare data digital transformation database system mobile system data security

Methods: experiment image processing deep learning computational algorithm machine learning

Exploring Personality-based Heterogeneity in Metaknowledge and Human-AI Collaboration

2023 | International Conference on Information Systems | Citations: 0

Authors: Gass, Daniel F

Abstract: While artificial intelligence (AI) has great potential to transform how we work ... Expand

Abstract: While artificial intelligence (AI) has great potential to transform how we work, it's clear that in many contexts, humans and AI will work side by side. According to recent research, humans' ability to capture complementarities while working collaboratively with AI is critically constrained by our limited ability to assess what we know, i.e., metaknowledge. However, extant research has largely overlooked a critical difference between human and AI: Human behavior is shaped by personality. I explore how differences arising from personality traits impact metaknowledge and delegation behavior towards AI. Using an image classification experiment, I find that higher levels of conscientiousness improve, while higher levels extraversion harm humans' ability to work collaboratively with AI. Differences in metaknowledge help to explain this relationship. These findings motivate further research on personality in human-AI collaboration and the design of collaborative systems which foster metaknowledge and improved performance. Collapse

Topics: artificial intelligence personality computer supported cooperative work database system human computer interaction

Methods: experimental group experiment survey linear regression analysis descriptive statistic

Theories: big five model

When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics

2023 | Information Systems Research | Citations: 0

Authors: Guan, Yue; Tan, Yong; Wei, Qiang; Chen, Guoqing

Abstract: Customer-generated images (CGIs) on e-commerce platforms have been widely adopte ... Expand

Abstract: Customer-generated images (CGIs) on e-commerce platforms have been widely adopted as a promotional tool to persuade customers into purchases. Despite their prevalent applications, the effect of CGIs on customer postpurchase satisfaction has not been extensively examined. This study postulates that CGIs could cause expectation disconfirmation and reduce product uncertainty for customers, therefore making their effect on subsequent product ratings complex. We leverage multiple methods and data sets to gain a better understanding of this problem and underlying mechanisms. We employ a difference-in-differences model to empirically test our hypotheses and find that CGIs lead to a decline in subsequent ratings compared with product ratings not exposed to CGIs. Further heterogeneity analyses demonstrate that high CGI review rating and high aesthetic quality exacerbate the negative effect, whereas reviewer face disclosure could alleviate the negative effect. Through cross-product analyses, we find that the negative effect is more prominent for experience goods (e.g., women’s dresses) than for search goods (e.g., lightning cables). Finally, the underlying mechanism is further validated through a laboratory experiment that shows participants experience significantly higher expectation and more negative disconfirmation in the CGI group with high review ratings, whereas uncertainty reduction effect is insignificant, which collectively explains the decline of subsequent product ratings. These findings suggest that platforms and retailers should be aware of the potential negative effect of CGIs on the rating dynamics and take appropriate measures to circumvent it. History: Ravi Bapna, Senior Editor; Gordon Burtch, Associate Editor. Funding: This work was supported in part by National Science Foundation of China [Grants 72202220, 72172070, and 71729001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1201. Collapse

Topics: online review experience good electronic commerce data breach digital good

Methods: experiment experimental group laboratory experiment computational algorithm product review

‘I could eat a horse’: The Impact of Hyperbole on Product Sales on Short Video Platforms

2023 | International Conference on Information Systems | Citations: 0

Authors: Han, Yutong; Yin, Bowen; Ren, Fei; Gu, Bin

Abstract: ... Expand

Abstract: Collapse

Topics: advertising management marketing management consumer behavior decision making Douyin

Methods: regression analysis method computational algorithm descriptive statistic natural language processing ImageNet

How Best to Hunt a Mammoth - Toward Automated Knowledge Extraction From Graphical Research Models

2023 | International Conference on Business Informatics | Citations: 0

Authors: Huettemann, Sebastian; Mueller, Roland M.; Larsen, Kai R.; Dinter, Barbara; Chiny, Joshua Campos

Abstract: ... Expand

Abstract: Collapse

Topics: knowledge repository database system knowledge base application programming interface Python

Methods: design science metamodel computational algorithm machine learning literature study

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts

2023 | International Conference on Business Informatics | Citations: 0

Authors: Jakubik, Johannes; Weber, Daniel; Hemmer, Patrick; Vössing, Michael; Satzger, Gerhard

Abstract: Information systems increasingly leverage artificial intelligence (AI) and mach ... Expand

Abstract: Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, humanin-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification. Collapse

Topics: human computer interaction team productivity human AI collaboration Google+ artificial intelligence

Methods: experiment machine learning sensitivity analysis ImageNet literature study

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

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

Authors: Joshi, Amogh Manoj; Nayak, Deepak Ranjan; Das, Dibyasundar; Zhang, Yudong

Abstract: Recent years have witnessed a rise in employing deep learning methods, especiall ... Expand

Abstract: Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. Collapse

Topics: covid 19

Methods: computed tomography ImageNet ablation analysis experiment machine learning

Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order

2023 | Information Systems Frontiers | Citations: 0

Authors: Klepetko, Randy; Krishnan, Ram

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition techno ... Expand

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition technology and found applications in various nonimage-related fields. For nonnatural data, such as cybersecurity packets, in which the data sample order is not defined by nature, some models trained on certain orderings of data perform better than when trained with other orderings. Some orderings create patterns from which the CNN extracts better features. Understanding how to best order the training data to improve CNN performance is not well-studied. In this study, we investigate this problem by examining malware infections using different CNN models and include visualization tools to enhance our analysis. We define a functional algorithm for ordering and show that order importance in CNNs is model dependent. In addition, we show that depending on the model, statistical relationships are crucial in establishing order with better performance. Collapse

Topics: malware mobile system cloud computing infrastructure as a service

Methods: experiment descriptive statistic machine learning computational algorithm Keras

Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks

2023 | Decision Support Systems | Citations: 0

Authors: Levich, Sergej; Lutz, Bernhard; Neumann, Dirk

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency o ... Expand

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model’s predictions in terms of Shapley additive explanations. Collapse

Topics: shapley additive explanation business process management artificial intelligence explainable artificial intelligence

Methods: BERT machine learning descriptive statistic artificial neural network business process modeling