Pub Date : 2024-07-01DOI: 10.1287/isre.2024.intro.v35.n2
Ahmed Abbasi, Robin Dillon, H. Raghav Rao, Olivia R. Liu Sheng
“The Century of Disasters” refers to the increased frequency, complexity, and magnitude of natural and man-made disasters witnessed in the 21st century: the impact of such disasters is exacerbated by infrastructure vulnerabilities, population growth/urbanization, and a challenging policy landscape. Technology-enabled disaster management (TDM) has an important role to play in the Century of Disasters. We highlight four important trends related to TDM, smart technologies and resilience, digital humanitarianism, integrated decision-support and agility, and artificial intelligence–enabled early warning systems, and how the confluence of these trends lead to four research frontiers for information systems researchers. We describe these frontiers, namely the technology-preparedness paradox, socio-technical crisis communication, predicting and prescribing under uncertainty, and fair pipelines, and discuss how the eight articles in the special section are helping us learn about these frontiers.History: Senior editor, Suprateek Sarker.Funding: This study was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915]. H. R. Rao is also supported in part by the NSF [Grant 2020252]. The usual disclaimer applies.
{"title":"Preparedness and Response in the Century of Disasters: Overview of Information Systems Research Frontiers","authors":"Ahmed Abbasi, Robin Dillon, H. Raghav Rao, Olivia R. Liu Sheng","doi":"10.1287/isre.2024.intro.v35.n2","DOIUrl":"https://doi.org/10.1287/isre.2024.intro.v35.n2","url":null,"abstract":"“The Century of Disasters” refers to the increased frequency, complexity, and magnitude of natural and man-made disasters witnessed in the 21st century: the impact of such disasters is exacerbated by infrastructure vulnerabilities, population growth/urbanization, and a challenging policy landscape. Technology-enabled disaster management (TDM) has an important role to play in the Century of Disasters. We highlight four important trends related to TDM, smart technologies and resilience, digital humanitarianism, integrated decision-support and agility, and artificial intelligence–enabled early warning systems, and how the confluence of these trends lead to four research frontiers for information systems researchers. We describe these frontiers, namely the technology-preparedness paradox, socio-technical crisis communication, predicting and prescribing under uncertainty, and fair pipelines, and discuss how the eight articles in the special section are helping us learn about these frontiers.History: Senior editor, Suprateek Sarker.Funding: This study was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915]. H. R. Rao is also supported in part by the NSF [Grant 2020252]. The usual disclaimer applies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"17 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.
{"title":"KETCH: A Knowledge-Enhanced Transformer-Based Approach to Suicidal Ideation Detection from Social Media Content","authors":"Dongsong Zhang, Lina Zhou, Jie Tao, Tingshao Zhue, Guodong (Gordon) Gao","doi":"10.1287/isre.2021.0619","DOIUrl":"https://doi.org/10.1287/isre.2021.0619","url":null,"abstract":"Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"19 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online searches are often accompanied by sponsored content (e.g., targeted ads), which sometimes seem irrelevant but could be good alternatives to expand users’ consideration space. The sponsored search results (SSRs) often trigger suspicions among users. This study examines the potential of customer ratings and reviews of the SSRs to mitigate such suspicion and enhance user engagement with the SSRs. The research reveals that when SSRs for well-known brands are paired with positive customer ratings, users’ suspicion toward the SSRs can be reduced. However, for lesser-known brands, only ads with high ratings can effectively reduce users’ suspicion. This study further reveals that addressing users’ uncertainty in evaluating SSRs and concerns about the platform’s intentions in providing them is paramount to minimizing users’ suspicion. Our study holds significant practical implications for online platforms seeking to optimize the presentation of SSRs either with famous or unknown brands alongside organic search results. The findings underscore the importance of strategically integrating user-generated content and ratings to reduce the suspicion of users navigating SSRs. It offers actionable insights for e-commerce platforms aiming to enhance users’ decision-making processes by better utilizing SSRs with positive customer ratings.
在线搜索往往伴随着赞助商内容（如定向广告），这些内容有时看似无关紧要，但可能是扩大用户考虑空间的好选择。赞助商搜索结果（SSR）往往会引发用户的怀疑。本研究探讨了客户对赞助商搜索结果的评分和评论在减少这种怀疑和提高用户对赞助商搜索结果的参与度方面的潜力。研究发现，当知名品牌的广告搜索结果与正面的客户评价相匹配时，用户对广告搜索结果的怀疑就会减少。然而，对于知名度较低的品牌，只有高评分的广告才能有效减少用户的怀疑。本研究进一步揭示，解决用户在评价 SSR 时的不确定性以及对平台提供 SSR 的意图的担忧，对于最大限度地减少用户的怀疑至关重要。我们的研究对网络平台在有机搜索结果中优化知名或不知名品牌的 SSR 呈现具有重要的现实意义。研究结果强调了战略性地整合用户生成的内容和评价以减少用户在浏览 SSR 时的怀疑的重要性。它为电子商务平台提供了可操作的见解，这些平台旨在通过更好地利用具有正面客户评价的 SSR 来增强用户的决策过程。
{"title":"Addressing Online Users’ Suspicion of Sponsored Search Results: Effects of Informational Cues","authors":"Honglin Deng, Weiquan Wang, Kai H. Lim","doi":"10.1287/isre.2021.0364","DOIUrl":"https://doi.org/10.1287/isre.2021.0364","url":null,"abstract":"Online searches are often accompanied by sponsored content (e.g., targeted ads), which sometimes seem irrelevant but could be good alternatives to expand users’ consideration space. The sponsored search results (SSRs) often trigger suspicions among users. This study examines the potential of customer ratings and reviews of the SSRs to mitigate such suspicion and enhance user engagement with the SSRs. The research reveals that when SSRs for well-known brands are paired with positive customer ratings, users’ suspicion toward the SSRs can be reduced. However, for lesser-known brands, only ads with high ratings can effectively reduce users’ suspicion. This study further reveals that addressing users’ uncertainty in evaluating SSRs and concerns about the platform’s intentions in providing them is paramount to minimizing users’ suspicion. Our study holds significant practical implications for online platforms seeking to optimize the presentation of SSRs either with famous or unknown brands alongside organic search results. The findings underscore the importance of strategically integrating user-generated content and ratings to reduce the suspicion of users navigating SSRs. It offers actionable insights for e-commerce platforms aiming to enhance users’ decision-making processes by better utilizing SSRs with positive customer ratings.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"5 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Practice- and Policy-Oriented AbstractMany organizations recognize the capacity of online communities to generate knowledge and create value. However, firm-sponsored online communities are composed of both community and firm stakeholders, where the goals and desires of each side can differ. This dichotomy of goals can create challenges when determining how best to govern a firm-sponsored online community, such as how much control the firm should exert on community behavior. Our work shows that community governance need not stem solely from the firm or the community. Rather, a successful and vibrant community that achieves the goals of all its stakeholders is achieved through participatory governance, which adopts both firm- and community-based governance modes. Drawing from a case study from Mayo Clinic Connect, a successful firm-sponsored online community that employs a participatory governance model, we discovered governance alignment as a capability that improves participatory governance. Governance alignment is an adaptive process that effectively balances the sponsoring firm’s goals with the community members’ needs and participation. In this paper, we present specific practices and actionable examples for governance alignment, such as standardizing organic community content, training community super users, and more. These actionable insights can enhance the value that firms hope to achieve when leveraging online communities.
{"title":"Firm-Sponsored Online Communities: Building Alignment Capabilities for Participatory Governance","authors":"Hani Safadi, Tanner Skousen, Elena Karahanna","doi":"10.1287/isre.2021.0578","DOIUrl":"https://doi.org/10.1287/isre.2021.0578","url":null,"abstract":"Practice- and Policy-Oriented AbstractMany organizations recognize the capacity of online communities to generate knowledge and create value. However, firm-sponsored online communities are composed of both community and firm stakeholders, where the goals and desires of each side can differ. This dichotomy of goals can create challenges when determining how best to govern a firm-sponsored online community, such as how much control the firm should exert on community behavior. Our work shows that community governance need not stem solely from the firm or the community. Rather, a successful and vibrant community that achieves the goals of all its stakeholders is achieved through participatory governance, which adopts both firm- and community-based governance modes. Drawing from a case study from Mayo Clinic Connect, a successful firm-sponsored online community that employs a participatory governance model, we discovered governance alignment as a capability that improves participatory governance. Governance alignment is an adaptive process that effectively balances the sponsoring firm’s goals with the community members’ needs and participation. In this paper, we present specific practices and actionable examples for governance alignment, such as standardizing organic community content, training community super users, and more. These actionable insights can enhance the value that firms hope to achieve when leveraging online communities.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"44 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141196086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Practice- and policy-oriented abstract:The success of on-demand service platforms crucially hinges upon their ability to make fast and accurate demand forecasts so that its workers are always at the right time and location to serve customers promptly. Yet demand forecasting is challenging for several reasons. First, demand data are typically released as high-frequency streaming time series, which requires an algorithm that has a fast processing time. Second, a digital platform often operates in many different geographic regions, thereby giving rise to a large heterogeneous geographical collection of high-frequency demand streams that need to be forecast and requiring a scalable algorithm. Third, a platform business usually operates in an unstable, rapidly changing environment and faces irregular growth patterns, which requires agility when forecasting demand because slow reactions to such instabilities causes forecast performance to break down. We offer a novel forecast framework called fast forecasting of unstable data streams that is fast and scalable and automatically assesses changing environments without human intervention. We test our framework on a unique data set from a leading European on-demand delivery platform and a U.S. bicycle sharing system and find strong (i) forecast performance gains, (ii) financial gains, and (ii) computing time reduction from using our framework against several industry benchmarks.
{"title":"Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms","authors":"Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms","doi":"10.1287/isre.2023.0130","DOIUrl":"https://doi.org/10.1287/isre.2023.0130","url":null,"abstract":"Practice- and policy-oriented abstract:The success of on-demand service platforms crucially hinges upon their ability to make fast and accurate demand forecasts so that its workers are always at the right time and location to serve customers promptly. Yet demand forecasting is challenging for several reasons. First, demand data are typically released as high-frequency streaming time series, which requires an algorithm that has a fast processing time. Second, a digital platform often operates in many different geographic regions, thereby giving rise to a large heterogeneous geographical collection of high-frequency demand streams that need to be forecast and requiring a scalable algorithm. Third, a platform business usually operates in an unstable, rapidly changing environment and faces irregular growth patterns, which requires agility when forecasting demand because slow reactions to such instabilities causes forecast performance to break down. We offer a novel forecast framework called fast forecasting of unstable data streams that is fast and scalable and automatically assesses changing environments without human intervention. We test our framework on a unique data set from a leading European on-demand delivery platform and a U.S. bicycle sharing system and find strong (i) forecast performance gains, (ii) financial gains, and (ii) computing time reduction from using our framework against several industry benchmarks.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"31 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective customer acquisition is crucial for digital platforms, with sequential targeting ensuring that marketing messages are both timely and relevant. The proposed deep recurrent Q-network with attention (DRQN-attention) model enhances this process by optimizing long-term rewards and increasing decision-making transparency. Tested with a data set from a digital bank, the DRQN-attention model has proven to enhance clarity in decision making and outperform traditional methods in boosting long-term rewards. Its attention mechanism acts as a strategic tool for forward planning, pinpointing crucial ad marketing channels that are likely to engage and convert prospects. This capability enables marketers to understand the dynamic targeting strategies of the proposed model that align with customer profiles, dynamic behaviors, and the seasonality of the markets, thereby boosting confidence and effectiveness in their customer acquisition strategies.
{"title":"Customer Acquisition via Explainable Deep Reinforcement Learning","authors":"Yicheng Song, Wenbo Wang, Song Yao","doi":"10.1287/isre.2022.0529","DOIUrl":"https://doi.org/10.1287/isre.2022.0529","url":null,"abstract":"Effective customer acquisition is crucial for digital platforms, with sequential targeting ensuring that marketing messages are both timely and relevant. The proposed deep recurrent Q-network with attention (DRQN-attention) model enhances this process by optimizing long-term rewards and increasing decision-making transparency. Tested with a data set from a digital bank, the DRQN-attention model has proven to enhance clarity in decision making and outperform traditional methods in boosting long-term rewards. Its attention mechanism acts as a strategic tool for forward planning, pinpointing crucial ad marketing channels that are likely to engage and convert prospects. This capability enables marketers to understand the dynamic targeting strategies of the proposed model that align with customer profiles, dynamic behaviors, and the seasonality of the markets, thereby boosting confidence and effectiveness in their customer acquisition strategies.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"22 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141148144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Subrahmanyam Aditya Karanam, Deepa Mani, Rajib L. Saha
In today’s rapidly evolving technological landscape, industries across various sectors are increasingly leveraging information and communication technologies (ICT) to drive innovation and gain a competitive edge. Our study reveals that, as industries become more closely connected to the ICT sector, they experience a significant shift in their innovation processes and outcomes. By analyzing 1.3 million U.S. patents granted between 1981 and 2010, we demonstrate that industries with stronger ties to the ICT sector (i.e., higher “ICT-closeness”) exhibit a greater proportion of ICT technologies in their patent portfolios and enhanced complementarity between ICT and non-ICT patents. Furthermore, ICT-Closeness results in greater innovation efficiency (the number of patents per R&D capital), recombinant creation (the creation of new technological combinations), recombinant reuse (the refinement and reuse of known technological combinations), and the creation of new business models. These findings have important implications for practitioners. Specifically, our research highlights the importance of strategically integrating ICT into their technological innovations. Managers should actively seek opportunities to collaborate with and learn from the ICT sector to enhance their innovative capabilities, create new products, services, and business methods, and ultimately gain a competitive advantage. However, they must also be prepared for the heightened competition that comes with increased ICT-closeness, as it can lead to winner-take-all dynamics and market turbulence.
{"title":"Growing Technological Relatedness to the ICT Industry and Its Impacts","authors":"Subrahmanyam Aditya Karanam, Deepa Mani, Rajib L. Saha","doi":"10.1287/isre.2020.0627","DOIUrl":"https://doi.org/10.1287/isre.2020.0627","url":null,"abstract":"In today’s rapidly evolving technological landscape, industries across various sectors are increasingly leveraging information and communication technologies (ICT) to drive innovation and gain a competitive edge. Our study reveals that, as industries become more closely connected to the ICT sector, they experience a significant shift in their innovation processes and outcomes. By analyzing 1.3 million U.S. patents granted between 1981 and 2010, we demonstrate that industries with stronger ties to the ICT sector (i.e., higher “ICT-closeness”) exhibit a greater proportion of ICT technologies in their patent portfolios and enhanced complementarity between ICT and non-ICT patents. Furthermore, ICT-Closeness results in greater innovation efficiency (the number of patents per R&D capital), recombinant creation (the creation of new technological combinations), recombinant reuse (the refinement and reuse of known technological combinations), and the creation of new business models. These findings have important implications for practitioners. Specifically, our research highlights the importance of strategically integrating ICT into their technological innovations. Managers should actively seek opportunities to collaborate with and learn from the ICT sector to enhance their innovative capabilities, create new products, services, and business methods, and ultimately gain a competitive advantage. However, they must also be prepared for the heightened competition that comes with increased ICT-closeness, as it can lead to winner-take-all dynamics and market turbulence.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"26 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Guided Diverse Concept Miner (GDCM) is an innovative deep learning algorithm tailored for the extraction of managerially relevant concepts from textual data, emphasizing the autonomy in discovering insights without predefined labels or guidance. This tool stands out by embedding words, documents, and concepts within the same vector space, which simplifies the interpretation of unearthed concepts and ensures their alignment with managerial outcomes. Central to GDCM’s methodology is its capacity to focus on concepts that are highly correlated with user-specified managerial outcomes, termed guiding variables, thereby enhancing the relevance and application of extracted insights in decision-making processes. The algorithm’s design inherently promotes the diversity of the recovered concepts, ensuring a broad spectrum of insights. Through practical application in analyzing customer reviews related to online purchases, GDCM not only identified key concepts influencing conversion rates but also validated its findings against established theories and prior causal research. This validation underscores GDCM’s utility in generating actionable, diverse insights tailored to specific managerial contexts, marking a significant advancement in how businesses leverage textual data for strategic decisions.
{"title":"Guided Diverse Concept Miner (GDCM): Uncovering Relevant Constructs for Managerial Insights from Text","authors":"Dokyun “DK” Lee, Zhaoqi “ZQ” Cheng, Chengfeng Mao, Emaad Manzoor","doi":"10.1287/isre.2020.0494","DOIUrl":"https://doi.org/10.1287/isre.2020.0494","url":null,"abstract":"The Guided Diverse Concept Miner (GDCM) is an innovative deep learning algorithm tailored for the extraction of managerially relevant concepts from textual data, emphasizing the autonomy in discovering insights without predefined labels or guidance. This tool stands out by embedding words, documents, and concepts within the same vector space, which simplifies the interpretation of unearthed concepts and ensures their alignment with managerial outcomes. Central to GDCM’s methodology is its capacity to focus on concepts that are highly correlated with user-specified managerial outcomes, termed guiding variables, thereby enhancing the relevance and application of extracted insights in decision-making processes. The algorithm’s design inherently promotes the diversity of the recovered concepts, ensuring a broad spectrum of insights. Through practical application in analyzing customer reviews related to online purchases, GDCM not only identified key concepts influencing conversion rates but also validated its findings against established theories and prior causal research. This validation underscores GDCM’s utility in generating actionable, diverse insights tailored to specific managerial contexts, marking a significant advancement in how businesses leverage textual data for strategic decisions.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"26 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces machine learning–based methods designed to measure the evasiveness and incoherence of responses from more-informed individuals during real-time strategic conversations. It tests the efficacy of these methods using the question-and-answer segments of earnings conference calls, where managers are subjected to scrutiny by analysts. The article underscores the largely untapped potential for extracting valuable financial insights from the dialogues between managers and analysts during these calls—a data source that current fintech solutions have largely ignored.Furthermore, the research breaks new ground by integrating machine learning with asset pricing, a promising avenue in light of rapid technological advances in artificial intelligence. From a practical standpoint, the study provides less-informed participants in strategic conversations with tools to identify when their more-informed counterparts are being evasive or incoherent. This ability allows them to pose more incisive questions, leading to better-informed decisions in various fields, including investing and hiring. Moreover, the paper contends that as AI technology continues to evolve, it will compel more-informed parties to adopt greater transparency. This shift will enhance both the efficiency and the transparency of markets and institutions, ultimately benefiting society as a whole.
{"title":"Conversation Analytics: Can Machines Read Between the Lines in Real-Time Strategic Conversations?","authors":"Yanzhen Chen, Huaxia Rui, Andrew B. Whinston","doi":"10.1287/isre.2022.0415","DOIUrl":"https://doi.org/10.1287/isre.2022.0415","url":null,"abstract":"This paper introduces machine learning–based methods designed to measure the evasiveness and incoherence of responses from more-informed individuals during real-time strategic conversations. It tests the efficacy of these methods using the question-and-answer segments of earnings conference calls, where managers are subjected to scrutiny by analysts. The article underscores the largely untapped potential for extracting valuable financial insights from the dialogues between managers and analysts during these calls—a data source that current fintech solutions have largely ignored.Furthermore, the research breaks new ground by integrating machine learning with asset pricing, a promising avenue in light of rapid technological advances in artificial intelligence. From a practical standpoint, the study provides less-informed participants in strategic conversations with tools to identify when their more-informed counterparts are being evasive or incoherent. This ability allows them to pose more incisive questions, leading to better-informed decisions in various fields, including investing and hiring. Moreover, the paper contends that as AI technology continues to evolve, it will compel more-informed parties to adopt greater transparency. This shift will enhance both the efficiency and the transparency of markets and institutions, ultimately benefiting society as a whole.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"2016 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our study, conducted through a field experiment with a major Asian microloan company, examines the interaction between information complexity and machine explanations in human–machine collaboration. We find that human evaluators’ loan approval decision-making outcomes are significantly enhanced when they are equipped with both large information volumes and machine-generated explanations, underscoring the limitations of relying solely on human intuition or machine analysis. This blend fosters deep human engagement and rethinking, effectively reducing gender biases and increasing prediction accuracy by identifying overlooked data correlations. Our findings stress the crucial role of combining human discernment with artificial intelligence to improve decision-making efficiency and fairness. We offer specific training and system design strategies to bolster human–machine collaboration, advocating for a balanced integration of technological and human insights to navigate intricate decision-making scenarios efficiently. Specifically, the study suggests that, whereas machines manage borderline cases, humans can significantly contribute by reevaluating and correcting machine errors in random cases (i.e., those without explicitly congruent feature patterns) through stimulated active rethinking triggered by strategic information prompts. This approach not only amplifies the strengths of both humans and machines, but also ensures more accurate and fair decision-making processes.
{"title":"1 + 1 > 2? Information, Humans, and Machines","authors":"Tian Lu, Yingjie Zhang","doi":"10.1287/isre.2023.0305","DOIUrl":"https://doi.org/10.1287/isre.2023.0305","url":null,"abstract":"Our study, conducted through a field experiment with a major Asian microloan company, examines the interaction between information complexity and machine explanations in human–machine collaboration. We find that human evaluators’ loan approval decision-making outcomes are significantly enhanced when they are equipped with both large information volumes and machine-generated explanations, underscoring the limitations of relying solely on human intuition or machine analysis. This blend fosters deep human engagement and rethinking, effectively reducing gender biases and increasing prediction accuracy by identifying overlooked data correlations. Our findings stress the crucial role of combining human discernment with artificial intelligence to improve decision-making efficiency and fairness. We offer specific training and system design strategies to bolster human–machine collaboration, advocating for a balanced integration of technological and human insights to navigate intricate decision-making scenarios efficiently. Specifically, the study suggests that, whereas machines manage borderline cases, humans can significantly contribute by reevaluating and correcting machine errors in random cases (i.e., those without explicitly congruent feature patterns) through stimulated active rethinking triggered by strategic information prompts. This approach not only amplifies the strengths of both humans and machines, but also ensures more accurate and fair decision-making processes.","PeriodicalId":48411,"journal":{"name":"Information Systems Research","volume":"30 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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