Pub Date : 2023-12-01DOI: 10.25300/misq/2023/17330
Tian Lu, Yingjie Zhang, Beibei Li
The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., “alternative data”) stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel “meta” experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes.
{"title":"Profit vs. Equality? The Case of Financial Risk Assessment and a New Perspective on Alternative Data","authors":"Tian Lu, Yingjie Zhang, Beibei Li","doi":"10.25300/misq/2023/17330","DOIUrl":"https://doi.org/10.25300/misq/2023/17330","url":null,"abstract":"<style>#html-body [data-pb-style=OLYHIW7]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style><br/>The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., “alternative data”) stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel “meta” experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":" 730","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138475763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.25300/misq/2023/17413
Runyu Shi, Aleksi Aaltonen, Ola Henfridsson, Ram D. Gopal
Research on platform owners’ entry into complementary markets points in divergent directions. One strand of the literature reports a squeeze on post-entry complementor profits due to increased competition, while another observes positive effects as increased customer attention and innovation benefit the complementary market as a whole. In this research note, we seek to transcend these conflicting views by comparing the effects of the early and late timing of platform owners’ entry. We apply a difference-in-differences design to explore the drivers and effects of the timing of platform owners’ entry using data from three entries that Amazon made into its Alexa voice assistant’s complementary markets. Our findings suggest that early entry is driven by the motivation to boost the overall value creation of the complementary market, whereas late entry is driven by the motivation to capture value already created in a key complementary market. Importantly, our findings suggest that early entry, in contrast to late entry, creates substantial consumer attention that benefits complementors offering specialized functionality. In addition, the findings also suggest that complementors with more experience are more likely to benefit from the increased consumer attention. We contribute to platform research by showing that the timing of the platform owner’s entry matters in a way that can potentially reconcile conflicting findings regarding the consequences of platform owners’ entry into complementary markets.
{"title":"Comparing Platform Owners’ Early and Late Entry into Complementary Markets","authors":"Runyu Shi, Aleksi Aaltonen, Ola Henfridsson, Ram D. Gopal","doi":"10.25300/misq/2023/17413","DOIUrl":"https://doi.org/10.25300/misq/2023/17413","url":null,"abstract":"<style>#html-body [data-pb-style=D17AMR8]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Research on platform owners’ entry into complementary markets points in divergent directions. One strand of the literature reports a squeeze on post-entry complementor profits due to increased competition, while another observes positive effects as increased customer attention and innovation benefit the complementary market as a whole. In this research note, we seek to transcend these conflicting views by comparing the effects of the early and late timing of platform owners’ entry. We apply a difference-in-differences design to explore the drivers and effects of the timing of platform owners’ entry using data from three entries that Amazon made into its Alexa voice assistant’s complementary markets. Our findings suggest that early entry is driven by the motivation to boost the overall value creation of the complementary market, whereas late entry is driven by the motivation to capture value already created in a key complementary market. Importantly, our findings suggest that early entry, in contrast to late entry, creates substantial consumer attention that benefits complementors offering specialized functionality. In addition, the findings also suggest that complementors with more experience are more likely to benefit from the increased consumer attention. We contribute to platform research by showing that the timing of the platform owner’s entry matters in a way that can potentially reconcile conflicting findings regarding the consequences of platform owners’ entry into complementary markets.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":" 734","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138475759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer to investigate how intelligent image processing (IIP)-based shelf monitoring aids manufacturers’ shelf management by using data from a quasi-experiment and a field experiment. We discovered that such artificial intelligence (AI) assistance significantly and consistently improves product sales. Several underlying mechanisms were revealed by our quantitative and qualitative analysis. First, retailers are more likely to comply due to the greater monitoring effectiveness enabled by AI assistance. Second, the positive effect of IIP-based shelf monitoring partially persists after it is terminated, implying that human learning takes place. Third, the value of IIP-based shelf monitoring can be attributed to independent retailers rather than chain retailers. Since the degree of contract heterogeneity is the major difference between these retailers in terms of monitoring, this finding further suggests that AI is relatively more scalable when coping with more heterogeneous instances. Apart from these great benefits, we demonstrate the low marginal costs of implementing IIP-powered shelf monitoring, which indicates its long-term applicability and potential to generate incremental value. Our research contributes to several literature streams and provides managerial insights for practitioners who consider AI-assisted operational models.
{"title":"Let Artificial Intelligence Be Your Shelf Watchdog: The Impact of Intelligent Image Processing-Powered Shelf Monitoring on Product Sales","authors":"Yipu Deng, Jinyang Zheng, Liqiang Huang, Karthik Kannan","doi":"10.25300/misq/2022/16813","DOIUrl":"https://doi.org/10.25300/misq/2022/16813","url":null,"abstract":"<style>#html-body [data-pb-style=VPWOU9T]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer to investigate how intelligent image processing (IIP)-based shelf monitoring aids manufacturers’ shelf management by using data from a quasi-experiment and a field experiment. We discovered that such artificial intelligence (AI) assistance significantly and consistently improves product sales. Several underlying mechanisms were revealed by our quantitative and qualitative analysis. First, retailers are more likely to comply due to the greater monitoring effectiveness enabled by AI assistance. Second, the positive effect of IIP-based shelf monitoring partially persists after it is terminated, implying that human learning takes place. Third, the value of IIP-based shelf monitoring can be attributed to independent retailers rather than chain retailers. Since the degree of contract heterogeneity is the major difference between these retailers in terms of monitoring, this finding further suggests that AI is relatively more scalable when coping with more heterogeneous instances. Apart from these great benefits, we demonstrate the low marginal costs of implementing IIP-powered shelf monitoring, which indicates its long-term applicability and potential to generate incremental value. Our research contributes to several literature streams and provides managerial insights for practitioners who consider AI-assisted operational models.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 9","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leveraging a combination of analytical frameworks and empirical assessments, this study investigates the effects of wait-for-free (WFF) pricing schemes on the monetization of serialized, digital entertainment content, which has become increasingly pervasive on online platforms. WFF pricing is a strategy in which consumers are given the option to either wait a certain amount of time to acquire digital content at no cost or pay to consume it immediately. We evaluate the extent to which habit formation and present-biased preferences driven by the consumption of addictive stock affect individual consumers’ willingness to wait (or pay) for content, which, in turn, determines the efficacy of WFF pricing. We also examine the conditions under which consumers switch from waiting for free content to instantaneously purchasing content. Our findings indicate that WFF pricing increases the sales of serialized digital content, generating new demand from customers who would otherwise forgo participation in the market. In addition, the pricing design effectively generates sustained profits in the long run. We found that most consumers who initiate a purchase either upon initial market entry or upon switching continue to purchase as new episodes become available. Moreover, the results indicate that as a user accumulates free episodes of a specific series, given extended waiting periods, the likelihood of their conversion from a wait-for-free customer to an instant-purchase customer increases. In particular, WFF pricing effectively augments the willingness to pay of low-valuation consumers as habit formation builds up through time with the free consumption of serialized content. One free episode can elevate the likelihood of consumer purchase by up to 13%. However, as the number of free episodes consumed goes beyond a threshold, the likelihood of conversion decreases. We conclude with a discussion of managerial implications that can help content providers monetize their serialized digital content products.
{"title":"The Cost of Free: The Effects of “Wait-for-Free” Pricing Schemes on the Monetization of Serialized Digital Content","authors":"Angela Aerry Choi, Ki-Eun Rhee, Chamna Yoon, Wonseok Oh","doi":"10.25300/misq/2022/17196","DOIUrl":"https://doi.org/10.25300/misq/2022/17196","url":null,"abstract":"<style>#html-body [data-pb-style=GGMH70J]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Leveraging a combination of analytical frameworks and empirical assessments, this study investigates the effects of wait-for-free (WFF) pricing schemes on the monetization of serialized, digital entertainment content, which has become increasingly pervasive on online platforms. WFF pricing is a strategy in which consumers are given the option to either wait a certain amount of time to acquire digital content at no cost or pay to consume it immediately. We evaluate the extent to which habit formation and present-biased preferences driven by the consumption of addictive stock affect individual consumers’ willingness to wait (or pay) for content, which, in turn, determines the efficacy of WFF pricing. We also examine the conditions under which consumers switch from waiting for free content to instantaneously purchasing content. Our findings indicate that WFF pricing increases the sales of serialized digital content, generating new demand from customers who would otherwise forgo participation in the market. In addition, the pricing design effectively generates sustained profits in the long run. We found that most consumers who initiate a purchase either upon initial market entry or upon switching continue to purchase as new episodes become available. Moreover, the results indicate that as a user accumulates free episodes of a specific series, given extended waiting periods, the likelihood of their conversion from a wait-for-free customer to an instant-purchase customer increases. In particular, WFF pricing effectively augments the willingness to pay of low-valuation consumers as habit formation builds up through time with the free consumption of serialized content. One free episode can elevate the likelihood of consumer purchase by up to 13%. However, as the number of free episodes consumed goes beyond a threshold, the likelihood of conversion decreases. We conclude with a discussion of managerial implications that can help content providers monetize their serialized digital content products.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 1","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.25300/misq/2022/17171
Xiaohang Zhao, Xiao Fang, Jing He, Lihua Huang
Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.
{"title":"Exploiting Expert Knowledge for Assigning Firms to Industries: A Novel Deep Learning Method","authors":"Xiaohang Zhao, Xiao Fang, Jing He, Lihua Huang","doi":"10.25300/misq/2022/17171","DOIUrl":"https://doi.org/10.25300/misq/2022/17171","url":null,"abstract":"<style>#html-body [data-pb-style=NRKL16R]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 7","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Technology-augmented choice-making impacts many facets of business. The use of economic incentives under the ubiquitous mobile ecosystem for prosocial behavior has been shown to be particularly effective. We build on the previous work on this topic and study how mobile-based economic incentives and environments influence charitable giving behavior. In contrast to traditional fund-raising, we consider the use of mobile devices to generate giving in small denominations, which we term microgiving. In collaboration with a US-based mobile app provider, we incorporated a functionality that allowed users to contribute their in-app reward points to charity. To encourage donations, we used economic incentives in the form of monetary subsidies, i.e., rebates or matching grants, as well as digital nudges in the form of push notifications. We studied the effects of these factors on giving behavior across two large-scale field experiments. Focusing on the different aspects of smartphones that could differentially impact charitable giving behavior—namely the intensely private and personal nature of smartphones—we examined how the visibility of donation decisions affects giving behavior by toggling audience effects. Our results show that the effectiveness of incentives is contingent upon the magnitude of the incentive as well as the extent to which individual decisions are visible to others. To situate our results in relation to the traditional medium of charitable giving, we propose an analytical model that internalizes the subsidy rates and the audience effect. This study provides initial empirical evidence and an analytical model to advance technology-augmented charitable giving that can provide insights to organizations and service providers.
{"title":"Nudging Private Ryan: Mobile Microgiving under Economic Incentives and Audience Effects","authors":"Dongwon Lee, Anandasivam Gopal, Dokyun Lee, Dongwook Shin","doi":"10.25300/misq/2022/16643","DOIUrl":"https://doi.org/10.25300/misq/2022/16643","url":null,"abstract":"<style>#html-body [data-pb-style=OQ4RCF8]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Technology-augmented choice-making impacts many facets of business. The use of economic incentives under the ubiquitous mobile ecosystem for prosocial behavior has been shown to be particularly effective. We build on the previous work on this topic and study how mobile-based economic incentives and environments influence charitable giving behavior. In contrast to traditional fund-raising, we consider the use of mobile devices to generate giving in small denominations, which we term microgiving. In collaboration with a US-based mobile app provider, we incorporated a functionality that allowed users to contribute their in-app reward points to charity. To encourage donations, we used economic incentives in the form of monetary subsidies, i.e., rebates or matching grants, as well as digital nudges in the form of push notifications. We studied the effects of these factors on giving behavior across two large-scale field experiments. Focusing on the different aspects of smartphones that could differentially impact charitable giving behavior—namely the intensely private and personal nature of smartphones—we examined how the visibility of donation decisions affects giving behavior by toggling audience effects. Our results show that the effectiveness of incentives is contingent upon the magnitude of the incentive as well as the extent to which individual decisions are visible to others. To situate our results in relation to the traditional medium of charitable giving, we propose an analytical model that internalizes the subsidy rates and the audience effect. This study provides initial empirical evidence and an analytical model to advance technology-augmented charitable giving that can provide insights to organizations and service providers.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"18 25","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.25300/misq/2022/16620
Sunghan Ryu, Keongtae Kim, Jungpil Hahn
This study adopts a signaling theory perspective to examine whether and how crowdfunding (relative to angel financing) influences subsequent venture capital (VC) investments in startups. We used a bivariate probit model with propensity score matching to address the potential endogeneity of the initial funding choice. Subsequently, we found that crowdfunded startups have a lower chance of receiving VC funding than angel-financed startups and that the effect is more negative for startups located outside of startup cluster cities. We show that corporate VCs, unlike independent VCs comprising the majority of VCs, favor crowdfunded startups. Our study contributes to the literature on crowdfunding, startup finance, and the transformative effects of IT-enabled platforms. This study further discusses the practical implications of crowdfunding in startup finance ecosystems.
#html-body [data- pp -style=QU98EO3]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat: not -repeat;background-attachment:scroll}本研究采用信号理论的视角来考察众筹(相对于天使融资)是否以及如何影响创业公司后续的风险投资。我们使用具有倾向得分匹配的双变量概率模型来解决初始资金选择的潜在内生性。随后，我们发现众筹创业公司比天使融资创业公司获得风险投资的机会更低，并且对于创业集群城市以外的创业公司来说，这种影响更为消极。我们发现，与独立风投不同，企业风投更青睐众筹创业公司。我们的研究为众筹、创业融资和it平台的变革效应的文献做出了贡献。本研究进一步探讨了众筹在创业金融生态系统中的实际意义。
{"title":"Crowdfunding Success Effects on Financing Outcomes for Startups: A Signaling Theory Perspective","authors":"Sunghan Ryu, Keongtae Kim, Jungpil Hahn","doi":"10.25300/misq/2022/16620","DOIUrl":"https://doi.org/10.25300/misq/2022/16620","url":null,"abstract":"<style>#html-body [data-pb-style=QU98EO3]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>This study adopts a signaling theory perspective to examine whether and how crowdfunding (relative to angel financing) influences subsequent venture capital (VC) investments in startups. We used a bivariate probit model with propensity score matching to address the potential endogeneity of the initial funding choice. Subsequently, we found that crowdfunded startups have a lower chance of receiving VC funding than angel-financed startups and that the effect is more negative for startups located outside of startup cluster cities. We show that corporate VCs, unlike independent VCs comprising the majority of VCs, favor crowdfunded startups. Our study contributes to the literature on crowdfunding, startup finance, and the transformative effects of IT-enabled platforms. This study further discusses the practical implications of crowdfunding in startup finance ecosystems.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 6","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.25300/misq/2022/16958
Sanjeev Dewan, Jooho Kim, Tingting Nian
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.
{"title":"Economic Impacts of Platform-Endorsed Quality Certification: Evidence from Airbnb","authors":"Sanjeev Dewan, Jooho Kim, Tingting Nian","doi":"10.25300/misq/2022/16958","DOIUrl":"https://doi.org/10.25300/misq/2022/16958","url":null,"abstract":"<style>#html-body [data-pb-style=XUI02HN]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>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.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 4","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.25300/misq/2022/16957
Yi Yang and Ramanath Subramanyam
Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars’ collective toolkit to analyze ever-growing textual data.
#html-body [data- pbstyle =HT8IJA3]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat: not -repeat;给定文本语料库，例如消费者评论和在线讨论论坛，研究人员和业务实践者经常使用主题建模以无监督的方式探索数据，或者为随后的计量经济分析生成感兴趣的变量。然而，一个重要的问题源于这样一个事实，即主题模型可能因其不稳定性而臭名昭著，即生成的结果可能在不同时间不一致且不可复制，即使在相同的数据集上也是如此。因此，对于他们正在测试或发展的理论关系，研究人员可能会得出潜在不可靠的结果。在本文中，我们试图强调这一问题，并提出解决这一问题的潜在方法。首先，我们使用四个文本数据集对主题模型的稳定性问题进行了实证定义和评估。接下来，为了缓解这一问题，并以从文本数据中提取可操作的见解为目标，我们提出了一种新的方法——稳定LDA，它将主题词聚类纳入主题模型，以引导模型推理朝着一致的结果发展。我们证明了所提出的稳定LDA方法可以显著提高模型的稳定性，同时保持甚至提高主题模型的质量。此外，采用两个与在线知识社区和在线消费者评论相关的案例研究，我们证明了稳定LDA产生的变量可以在计量经济学分析中导致更一致的估计。我们相信，我们的工作可以进一步增强管理学者的集体工具包，以分析不断增长的文本数据。
{"title":"Extracting Actionable Insights from Text Data: A Stable Topic Model Approach","authors":"Yi Yang and Ramanath Subramanyam","doi":"10.25300/misq/2022/16957","DOIUrl":"https://doi.org/10.25300/misq/2022/16957","url":null,"abstract":"<style>#html-body [data-pb-style=HT8IJA3]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars’ collective toolkit to analyze ever-growing textual data.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 2","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.25300/misq/2022/17366
Yang Gao, Huaxia Rui, Shujing Sun
Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers’ perceived social presence on service outcomes and customers’ attitudes toward agents. Our identification strategy hinged on Southwest Airlines’ sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers’ perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers’ perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers’ emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues.
{"title":"The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter","authors":"Yang Gao, Huaxia Rui, Shujing Sun","doi":"10.25300/misq/2022/17366","DOIUrl":"https://doi.org/10.25300/misq/2022/17366","url":null,"abstract":"<style>#html-body [data-pb-style=UQ5I0O8]{justify-content:flex-start;display:flex;flex-direction:column;background-position:left top;background-size:cover;background-repeat:no-repeat;background-attachment:scroll}</style>Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers’ perceived social presence on service outcomes and customers’ attitudes toward agents. Our identification strategy hinged on Southwest Airlines’ sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers’ perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers’ perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers’ emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues.","PeriodicalId":49807,"journal":{"name":"Mis Quarterly","volume":"19 3","pages":""},"PeriodicalIF":7.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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