Showing papers in "Management Information Systems Quarterly in 2023"
TL;DR: In this paper , the authors illustrate the bidirectional dynamic relationship between information technology investment and data breaches moderated by threat and countermeasure security awareness using an eight-year panel of 311 U.S.-listed firms to provide empirical evidence that threat awareness broadens firms' scope for addressing data-breach issues by investing more in IT than in security.
Abstract: Data breaches can severely damage a firm’s reputation and its customers’ confidence. Firms must therefore continuously invest in security measures to prevent such breaches. However, the effectiveness of security investment has been questioned by both practitioners and academics. We illustrate the bidirectional dynamic relationship between information technology (IT) investment and data breaches moderated by threat and countermeasure security awareness using an eight-year panel of 311 U.S.-listed firms to provide empirical evidence that threat awareness broadens firms’ scope for addressing data-breach issues by investing more in IT than in security. Countermeasure awareness equips firms with sufficient knowledge and experience to ensure effective implementation of IT, which provides more comprehensive protection than security investment alone. Our results suggest that firms should evolve beyond the reactive mindset of solely upgrading security and begin nurturing both threat awareness and countermeasure awareness to address the underlying IT system issues that are the cause of data breaches.
4 citations
TL;DR: In this paper , the authors used a combination of a literature-based approach and a qualitative inquiry to develop a model of cyberslacking that includes a 2×2 typology of antecedents.
Abstract: Employees’ nonwork use of information technology (IT), or cyberslacking, is of growing concern due to its erosion of job performance and other negative organizational consequences. Research on cyberslacking antecedents has drawn on diverse theoretical perspectives, resulting in the lack of a cohesive explanation of cyberslacking. Further, prior studies have generally overlooked IT-specific variables. To address cyberslacking problems in organizations, as well as research gaps in the literature, we used a combination of a literature-based approach and a qualitative inquiry to develop a model of cyberslacking that includes a 2×2 typology of antecedents. The proposed model was tested and supported in a three-wave field study of 395 employees in a U.S. Fortune-100 organization. This study organizes antecedents from diverse research streams and validates their relative impact on cyberslacking, thus providing a cohesive theoretical explanation of cyberslacking. This study also incorporates contextualization (i.e., IT-specific factors) into theory development and enriches the IS literature by examining the nonwork aspects of IT use and their negative consequences to organizations. In addition, the results provide practitioners with insights into the nonwork use of IT in organizations, particularly regarding how they can take organizational action to mitigate cyberslacking and maintain employee productivity.
3 citations
TL;DR: In this paper , the authors investigate different mechanisms by which OHC content may affect patients' emotions and design a novel deep learning model to differentiate emotional support from auxiliary content, which is critical for identifying the negative effect of emotional support on unintended recipients.
Abstract: Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients’ emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect.
2 citations
TL;DR: In this article , a case study at a local manufacturing site that had struggled to replace its mission-critical legacy systems as part of the larger global company’s commercial-off-the-shelf (COTS) system implementation is presented.
Abstract: Organizations replace their legacy systems for technical, economic, and operational reasons. Replacement is a risky proposition, as high levels of technical and social inertia make these systems hard to withdraw. Failure to fully replace systems results in complex system architectures involving manifold hidden dependencies that carry technical debt. To understand how a process for replacing a complex legacy system unfolds and accumulates technical debt, we conducted an explanatory case study at a local manufacturing site that had struggled to replace its mission-critical legacy systems as part of the larger global company’s commercial-off-the-shelf (COTS) system implementation. We approach the replacement as a sociotechnical change and leverage the punctuated sociotechnical information system change model in combination with the design-moves framework to analyze how the site balanced creating digital options, countering social inertia, and managing (architectural) technical debt. The findings generalize to a two-level (local/global) system-dynamics model delineating how replacing a deeply entrenched mission-critical system generates positive and negative feedback loops within and between social and technical changes at local and global levels. The loops, unless addressed, accrue technical debt that hinders legacy system discontinuance and gradually locks the organization into a debt-constrained state. The model helps managers anticipate challenges that accompany replacing highly entrenched systems and formulate effective strategies to address them.
1 citations
TL;DR: In this article , the authors focus on the digital resilience of certified physicians who adopted an online healthcare community (OHC) to acquire patients and conduct telemedicine services during the COVID-19 pandemic.
Abstract: The COVID-19 pandemic has underscored the urgent need for healthcare entities to develop resilient strategies to cope with disruptions caused by the pandemic. This study focuses on the digital resilience of certified physicians who adopted an online healthcare community (OHC) to acquire patients and conduct telemedicine services during the pandemic. We synthesize the resilience literature and identify two effects of digital resilience—the resistance effect and the recovery effect. We use a proprietary dataset that matches online and offline data sources to study the digital resilience of physicians. A difference-in-differences (DID) analysis shows that physicians who adopted an OHC had strong resistance and recovery effects during the pandemic. Remarkably, after the COVID-19 outbreak, these physicians had 35.0% less reduction in medical consultations in the immediate period and 31.0% more bounce-back in the subsequent period as compared to physicians who did not adopt the OHC. We further analyze the sources of physicians’ digital resilience by distinguishing between new and existing patients from both online and offline channels. Our subgroup analysis shows that, in general, digital resilience is more pronounced when physicians have a higher online reputation rating or have more positive interactions with patients on the OHC platform, providing further support for the mechanisms underlying digital resilience. Our research has significant theoretical and managerial implications beyond the context of the pandemic.
1 citations
TL;DR: In this article , the authors conceptualize digital resilience as a dynamic capability (DC) and examine how the Commonwealth of Virginia (COVA) built DCs and rebounded from two shocks (the opioid crisis and the COVID-19 pandemic).
Abstract: During shocks, residents and businesses rely upon the government to ensure health, safety, and the continuity of services. The government’s ability to respond depends upon how well it utilizes its data resources and builds digital resilience. Yet governments often fail to integrate data from different agencies to respond effectively to shocks. We conceptualize digital resilience as a dynamic capability (DC). Although the DC framework provides a theoretical basis, it is unclear what actions managers can take to build DC. Through process tracing, we examine how the Commonwealth of Virginia (COVA) built DCs and rebounded from two shocks—the opioid crisis and the COVID-19 pandemic. COVA managers leveraged statewide data assets, built routines to disseminate data, and reconfigured operational capabilities to build three DCs—relationship building, intelligence creation, and value extraction. Data functioned as the “protein” to build the digital resilience “muscle.” We found that the relationship building DC leveraged the operational capabilities of data management, integration, and governance structure to foster data sharing, the intelligence creation DC leveraged analytics, and the value extraction DC converted analytics into cost savings, revenue generation, and new services. Whereas COVA built robust digital resilience by facilitating data sharing, the agencies exploited data assets to develop scalable solutions.
1 citations
TL;DR: In this article , the authors examine whether members in an OSS community increased or decreased their contributions to others' projects relative to their own in the face of the COVID-19 pandemic, a sudden and unexpected global health-related shock that has affected almost everyone.
Abstract: Contributions by individual open source software (OSS) community members are the lifeblood of the OSS projects that power today’s digital economy and are important for the very survival of such communities. Individual contributions by OSS community members to others’ projects and their own determine whether OSS communities are resilient in the face of major shocks. Arguably, if crises such as the COVID-19 pandemic prompt users to reduce their contributions to others’ projects relative to the contributions to their own projects, such behavior can have implications for the overall resilience of the OSS community. Therefore, whether and how individuals change their contributions in the face of a crisis is an important question. We examine whether members in an OSS community increased or decreased their contributions to others’ projects relative to their own in the face of the COVID-19 pandemic, a sudden and unexpected global health-related shock that has affected almost everyone. We also compare and contrast this behavior when the OSS community faced increasing unemployment, an economic cyclic shock that is arguably and relatively more personal. Drawing on the concept of prosocial behavior and conservation of resources (COR) theory, we hypothesize that the pandemic increased OSS community members’ contributions to others’ projects relative to their own; on the other hand, the threat of rising unemployment decreased OSS community members’ contributions to others’ projects relative to their own. Our empirical analyses of a longitudinal dataset of over 18,000 OSS community members on GitHub, with more than 1.4 million member-day observations, support our hypotheses. This study contributes by uncovering the differential effects of exogenous health-related and economic shocks on the resilience of the OSS community. We conclude with a discussion of our findings’ implications for OSS community resilience.
1 citations
TL;DR: In this article , the authors examine the impact of firm social media engagement on sales performance, answering "whether, what, and how" questions, and find that firms that sell low-involvement products benefit more from social media adoption, compared to those that sell high involvement products.
Abstract: We examine the impact of firm social media engagement on sales performance, answering “whether,” “what,” and “how” questions. The study uses a quasi-experimental design in a social e-commerce setting, for which propensity score matching and difference-in-differences methods quantify a mean 20.67% sales increase after firm social media adoption. We also find that firms that sell low-involvement products benefit more from social media adoption, compared to those that sell high-involvement products. Further, in terms of how to manage social media engagement, we find that informative content, in general, is effective for sales of high-involvement products, whereas promotional content, a new type of content discovered in this study, is more beneficial for sales of low-involvement products. Meanwhile, more social media followers generate better firm sales performance. We used instrumental variables and the control function method to address endogeneity issues and conducted robustness checks to support our conclusions. This study sheds light on the value of firm social media, particularly regarding industry differences and firm know-how.
TL;DR: In this article , a controlled diffusion process is developed to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time, considering a variety of factors such as profit margin and customer rating sensitivity, that influence a firm's effort to manage ratings and subsequently its sales and profits.
Abstract: We study how firms respond to online customer reviews in a competitive market where they jostle with one another for sales based on online ratings. The focus of this paper is on how firms can optimally manage their ratings through management response and how review ratings affect the sales and profits of competing firms. We develop a controlled diffusion process to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time. Our model considers a variety of factors, such as profit margin and customer rating sensitivity, that influence a firm’s effort to manage ratings and subsequently its sales and profits. More response effort needs to be exerted to manage ratings when either the profit margin of a tour is very high or customers are very sensitive to ratings. We estimate our model using data on Ctrip’s tours that include each tour’s sales, reviews, prices, and tour features. We find that consumers anchor their beliefs in the mean market rating and that their purchase decisions depend on the tour’s rating relative to this anchor. Thus, relative, rather than absolute, ratings matter. Our study informs firms on how competition and other primitives impact their efforts to manage ratings and hence profit. Our methodology allowed us to conduct “what-if” analyses, for example, to study what would happen to the review ratings, sales, and profits of a tour if a firm adopted a different response strategy. We were also able to provide turnaround strategies for struggling tours, i.e., factors that a loss-making tour should change if it wishes to make a positive profit. Ultimately, we conducted a competitive analysis that allowed us to modify certain parameters that affect the intensity of competition and hence the sales and the profits of competing tours. Finally, we demonstrate the flexibility of the model by extending it to incorporate multiple state variables that might affect the response strategy.
TL;DR: In this article , the heterogeneity in retail investor attention by comparing search conducted on weekdays vs. weekends and investigate the price pressure channel and information processing channel for stock return predictability.
Abstract: Existing studies have found that online search is a revealed measure for investor attention and a useful predictor of stock returns. We study the heterogeneity in retail investor attention by comparing search conducted on weekdays vs. weekends and investigate the price pressure channel and information processing channel for stock return predictability. According to the information processing channel, weekends afford retail investors more time for the intensive cognitive analysis necessary to make better predictions. Alternatively, weekend search might better capture the price pressure from retail investors’ trading activities. We provide empirical results that support the information processing channel. We first show that weekend search, rather than weekday search, predicts large-cap stock returns in both the cross-section and time series. Additionally, our findings on retail trading activity contradict the price pressure channel in that weekday search, rather than weekend search, leads to a subsequent retail order imbalance. Overall, our study contributes to the literature on the predictive power of online search on stock returns, which has mainly focused on the price pressure channel, which yields significant results for small-cap stocks only.
TL;DR: In this paper , the authors used a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls.
Abstract: Unstructured multimedia data (text and audio) provides unprecedented opportunities to derive actionable decision-making in the financial industry, in areas such as portfolio and risk management. However, due to formidable methodological challenges, the promise of business value from unstructured multimedia data has not materialized. In this study, we use a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls. DeepVoice forecasts financial risk by leveraging not only what managers say (verbal linguistic cues) but also how managers say it (vocal cues) during the earnings conference calls. The design of DeepVoice addresses several challenges associated with the analysis of nonverbal communication. We also propose a two-stage deep learning model to effectively integrate managers’ sequential vocal and verbal cues. Using a unique dataset of 6,047 earnings call samples (audio recordings and textual transcripts) of S&P 500 firms across four years, we show that DeepVoice yields remarkably lower risk forecast errors than that achieved by previous efforts. The improvement can also translate into nontrivial economic gains in options trading. The theoretical and practical implications of analyzing vocal cues are discussed.
TL;DR: In this article , the authors used a large-scale dataset of China's movie market from 2012 to 2018 and constructed and validated a domain-specific emotion lexicon and demonstrated the predictive power of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) in online reviews on box office sales.
Abstract: Emotion artificial intelligence, the algorithm that recognizes and interprets various human emotions beyond valence (positive and negative polarity), is still in its infancy but has attracted attention from industry and academia. Based on discrete emotion theory and statistical language modeling, this work proposes an algorithm to enable automatic domain-adaptive emotion lexicon construction and multidimensional emotion detection in texts. Using a large-scale dataset of China’s movie market from 2012 to 2018, we constructed and validated a domain-specific emotion lexicon and demonstrated the predictive power of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) in online reviews on box office sales. We found that representing overall emotions through discrete emotions yields higher prediction accuracy than valence or latent emotion variables generated by topic modeling. To understand the source of the predictive power from a theoretical perspective and to test the cross-culture generalizability of our prediction study, we further conducted an experiment in the U.S. movie market based on theories on emotion, judgment, and decision-making. We found that discrete emotions, mediated by perceived processing fluency, significantly affect the perceived review helpfulness, which further influences purchase intention. Our work shows the economic value of emotions in online reviews, generates insight into the mechanism of their effects, and has managerial implications for online review platform design, movie marketing, and cinema operations.
TL;DR: The authors examined the influence of a novel source of bias in online philanthropic lending, namely that associated with religious differences, and proposed a set of contextual moderators that characterize individuals' offline (local) and online social contexts, which combine to determine the influence on lending activity.
Abstract: Biases on online platforms pose a threat to social inclusion. We examine the influence of a novel source of bias in online philanthropic lending, namely that associated with religious differences. We first propose religion distance as a probabilistic measure of differences between pairs of individuals residing in different countries. We then incorporate this measure into a gravity model of trade to explain variation in country-to-country lending volumes. We further propose a set of contextual moderators that characterize individuals’ offline (local) and online social contexts, which we argue combine to determine the influence of religion distance on lending activity. We empirically estimate our gravity model using data from Kiva.org, reflecting all lending actions that took place between 2006 and 2017. We demonstrate the negative and significant effect of religion distance on lending activity, over and above other established factors in the literature. Further, we demonstrate the moderating role of lenders’ offline social context (diversity, social hostilities, and governmental favoritism of religion) on the aforementioned relationship to online lending behavior. Finally, we offer empirical evidence of the parallel role of online contextual factors, namely those related to community features offered by the Kiva platform (lending teams), which appear to amplify the role of religious bias. In particular, we show that religious team membership is a double-edged sword that has both favorable and unfavorable consequences, increasing lending in general but skewing said lending toward religiously similar borrowers. Our findings speak to the important frictions associated with religious differences in individual philanthropy; they point to the role of governmental policy vis-à-vis religious tolerance as a determinant of citizens’ global philanthropic behavior, and they highlight design implications for online platforms with an eye toward managing religious bias.
TL;DR: In this paper , the authors used simulations to understand the market characteristics and crowdfunding platform choices that influence experts' and nonexpert investors' returns, their return gap, and the extent to which nonexperts are better or worse off relative to a market without expert participation, and factors that may contribute to the small expert/nonexpert Prosper return gap.
Abstract: The growth of crowdfunding markets that include both expert and nonexpert investors will soon accelerate due to recent changes in Securities Exchange Commission (SEC) regulations. Prior work has suggested that nonexperts (1) may benefit from experts’ participation via mimicking their trades, but (2) will also face a cost, as experts crowding nonexperts out of the best opportunities will ensure that nonexperts will suffer lower returns than experts. Traditional economic theory holds that the crowding effect means that the relative importance of nonexperts in the market will decline over time until they become unimportant. Exploiting a unique period in one crowdfunding market (Prosper.com) that allowed us to directly estimate the net cost of competing with better-informed experts, we found that the net negative effects of expert participation on nonexperts are small. We used simulations to both better understand (1) the market characteristics and crowdfunding platform choices that influence experts’ and nonexperts’ returns, their return gap, and the extent to which nonexperts are better or worse off relative to a market without expert participation, and (2) the factors that may contribute to the small expert/nonexpert Prosper return gap.
TL;DR: In this article , the authors examined digital resilience in higher education institutions through the conceptual lens of disaster response management, by assessing the role played by the centralized governance of information technology investments, and found that centralized IT investments geared toward facilitating organizational coordination and providing instructional and technical support played a pivotal role in enabling ERT and improving student ratings during the crisis.
Abstract: The COVID-19 pandemic forced organizations, including higher education institutions, to rapidly adjust their operations. In the face of the pandemic, most higher education institutions shut down their campuses and transitioned to emergency remote teaching mode. This study examines digital resilience in higher education institutions through the conceptual lens of disaster response management, by assessing the role played by the centralized governance of information technology (IT) investments. We posit that centralized IT helps organizations maintain customer satisfaction with services during a crisis (e.g., student satisfaction with classes during COVID-19) by facilitating the organization-wide transition to an emergency operational mode and supporting its service operations. Consolidating data on IT investment, governance, and course evaluations from 463 U.S. higher education institutions from 2017-2020, we show that centralized IT helped organizations adapt better to the pandemic in terms of maintaining student satisfaction. Moreover, we found that centralized IT investments geared toward facilitating organizational coordination and providing instructional and technical support played a pivotal role in enabling ERT and improving student ratings during the crisis. These results are corroborated by interviews with CIOs of U.S. higher education institutions. Additional analyses also suggest that the effectiveness of centralized IT governance is contingent upon organizational size, dissimilarity of local units, and the strategic role of the CIO. We also discuss theoretical extensions toward digital resilience as well as practical implications.