Mobile technologies provide a unique opportunity for practitioners to identify users’ real-time context and provide personalized interventions to influence their behaviors. However, less is known about a way to improve the effectiveness of mobile health intervention by using context information. This study provides design guidelines on how to use weather information with messaging formats to spur exercise. Through a field experiment that each participant experience different weather conditions in two different treatment periods under the gain or loss interventions, we found that the effects of gain or loss interventions under different weather conditions are heterogeneous. Loss intervention leads to higher fulfillment of exercise goals than gain intervention in sunny weather, whereas gain interventions are more effective than loss interventions in cloudy weather. In addition, we found that weather-based intervention can be used repeatedly over time without losing its effectiveness. Furthermore, we reveal that weather-based intervention is effective toward at-risk populations such as inactive individuals or lower income groups, serving as an mhealth solution that closes the health gap between the haves and have nots. Our findings provide useful guidelines for health service providers and health policymakers regarding how to effectively leverage contextual cues into mobile health intervention.

The rapid, widespread adoption of cloud computing over the last decade has sparked debates on its environmental impacts. Given that cloud computing alters the dynamics of energy consumption between service providers and users, a complete understanding of the environmental impacts of cloud computing requires an investigation of its impact on the user side, which can be weighed against its impact on the vendor side. Drawing on production theory and using a stochastic frontier analysis, this study examines the impact of cloud computing on users’ energy efficiency. To this end, we develop a novel industry-level measure of cloud computing based on cloud-based information technology (IT) services. Using U.S. economy-wide data from 57 industries during 1997–2017, our findings suggest that cloud-based IT services improve users’ energy efficiency. This effect is found to be significant only after 2006, when cloud computing started to be commercialized, and becomes even stronger after 2010. Moreover, we find heterogeneous impacts of cloud computing, depending on the cloud service models, energy types, and internal IT hardware intensity, which jointly assist in teasing out the underlying mechanisms. Although software-as-a-service (SaaS) is significantly associated with both electric and nonelectric energy efficiency improvement across all industries, infrastructure-as-a-service (IaaS) is positively associated only with electric energy efficiency for industries with high IT hardware intensity. To illuminate the mechanisms more clearly, we conduct a firm-level survey analysis, which demonstrates that SaaS confers operational benefits by facilitating energy-efficient production, whereas the primary role of IaaS is to mitigate the energy consumption of internal IT equipment and infrastructure. According to our industry-level analysis, the total user-side energy cost savings from cloud computing in the overall U.S. economy are estimated to be USD 2.8–12.6 billion in 2017 alone, equivalent to a reduction in electricity use by 31.8–143.8 billion kilowatt-hours. This estimate exceeds the total energy expenditure in the cloud service vendor industries and is comparable to the total electricity consumption in U.S. data centers. This paper was accepted by Chris Forman, information systems. Supplemental Material: Data files and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4442 .

Sexual assault is one of the most repellant and costly crimes, which inflicts irrecoverable harms on victims and society. This study examines the effect of information technology (IT)-enabled ride-sharing platforms on sexual assaults. Drawing upon routine activity theory from the criminology literature, we posit that ride-sharing can reduce a passenger's risk of being a suitable target of sexual assault by providing a more reliable and timely transportation option for traveling to a safer place. By exploiting the nationwide quasi-experimental setting of Uber's city-by-city roilouts in the United States during 2005-2017, we demonstrate that Uber's entry into a city is negatively associated with the number of rape incidents. To zoom into the effects of ride-sharing at a more granular level, we employ precinct-hour-level data on Uber pickups and rape occurrences in New York City in 2015 and conduct spatiotemporal analyses. Our results from the spatiotemporal analyses corroborate those of the quasi-experiment and further reveal situational contingencies in the deterrent effect of ride-sharing. Specifically, ride-sharing contributes to a more significant reduction in the likelihood of rape occurrences in neighborhoods with limited transportation accessibility, and ride-sharing is more effective in deterring sexual crime in riskier circumstances, such as around alcohol-serving places on weekend nights or when the probability of crime occurrences increases. This study sheds new light on the potential of IT-enabled platforms to improve social well-being beyond their economic contributions and offers a new theoretical insight on the distinct role of digital platforms in public safety.

Purpose Past literature offered competing predictions of the effect of broadband Internet on suicide. The Internet facilitates suicide by providing suicide-related information and ruining mental health. In contrast, Internet prevents suicide by offering social interaction and online mental treatment. This study aims to solve this tension by empirically examining the effect of broadband Internet on suicide with large-scale panel set. Design/methodology/approach This study takes instrument approach with the US county-level panel set for the period 2013–17. This study uses the number of household broadband Internet subscriptions as the measure of broadband and leverages the number of telecommunication carriers as an instrument to address concern for endogenous relationship. Findings There exists a positive and significant association between broadband Internet adoption and suicide on average. This study provides empirical evidence that this association is attributable to the Internet's role in leading to a general decline in the mental well-being and in providing suicide-relevant information. This association is more evident in areas with high poverty and low social capital. Originality/value This study contributes to literatures that address the dark side of information systems in general and that address how Internet adoption can influence public health and well-being in particular. Results of underlying mechanisms why Internet affects suicide, and heterogeneous effect of Internet by poverty and social capital provide insight for governments to enact proactive regulations to address continuing rise of suicide.

Due to the increasing significance of ebooks and concerns regarding their potential cannibalization effect, prior research has examined the impact of ebook distribution on print sales. However, the primary focus has been on estimating the overall average effect of ebook availability; therefore, we still have a limited understanding of the factors that can explain the heterogeneity in ebook effects. As a result, little guidance has been offered to publishers in terms of how they can minimize the cannibalization effect of ebooks and maximize the combined profits from digital and print channels. This study aims to extend the literature and offer insights and actionable guidance to managers in publishing industries by examining the moderating role of both supply-side and demand-side factors in ebook effects. Specifically, we address the following research questions: (1) Do ebook distribution strategies (i.e., ebook discount rates and delays in ebook releases) affect the degree of cannibalization of print sales by ebooks? (2) Do book characteristics pertaining to portability benefits (i.e., book size and reading time) affect the degree of cannibalization? By employing a synthetic control approach to analyze actual book sales data, we find that ebook releases led to a 10.7% reduction in print sales over the eight-week estimation period. More importantly, the results suggest that (1) a higher discount rate for an ebook has no statistically significant impact on the degree of cannibalization, while delaying ebook releases mitigates the cannibalization effect; and (2) books with a longer reading time tend to experience more severe cannibalization, whereas the book size (i.e., number of pages) does not influence the degree of cannibalization. Our findings are robust to a series of sensitivity analyses. The contributions and managerial implications of these findings are discussed.

Due to the increasing significance of ebooks and concerns regarding their potential cannibalization effect, prior research has examined the impact of ebook distribution on print sales. However, the primary focus has been on estimating the overall average effect of ebook availability; therefore, we still have a limited understanding of the factors that can explain the heterogeneity in ebook effects. As a result, little guidance has been offered to publishers in terms of how they can minimize the cannibalization effect of ebooks and maximize the combined profits from digital and print channels. This study aims to extend the literature and offer insights and actionable guidance to managers in publishing industries by examining the moderating role of both supply-side and demand-side factors in ebook effects. Specifically, we address the following research questions: (1) Do ebook distribution strategies (i.e., ebook discount rates and delays in ebook releases) affect the degree of cannibalization of print sales by ebooks? (2) Do book characteristics pertaining to portability benefits (i.e., book size and reading time) affect the degree of cannibalization? By employing a synthetic control approach to analyze actual book sales data, we find that ebook releases led to a 10.7% reduction in print sales over the eight-week estimation period. More importantly, the results suggest that (1) a higher discount rate for an ebook has no statistically significant impact on the degree of cannibalization, while delaying ebook releases mitigates the cannibalization effect; and (2) books with a longer reading time tend to experience more severe cannibalization, whereas the book size (i.e., number of pages) does not influence the degree of cannibalization. Our findings are robust to a series of sensitivity analyses. The contributions and managerial implications of these findings are discussed.

Although a significant amount of research has examined the effect of broadband on the rise of employment, the majority of this work has been focused on general population's employment, with little attention paid to the effect of broadband may have on social minority employment, i.e., disabled. Motivated from this research gap, we empirically examine the effect of the broadband use on disabled employment in the United States during 2013–2016 using a county level panel data set. We find evidence that, on average, broadband use increases the disabled employment. This research contributes to the literature addressing the positive effect of Information Systems (IS) on labor market, by addressing how the broadband reshapes the disabled employment. We hope our findings help researchers, governments, and law enforcement entities that endeavor to include the disabled in labor market and our society.

Under what conditions is the Internet more likely to be used maliciously for criminal activity? This study examines the conditions under which the Internet is associated with cybercriminal offenses. Using comprehensive state-level data in the United States during 2004–2010, our findings show that there is no clear empirical evidence that the Internet penetration rate is related to the number of Internet crime perpetrators; however, cybercriminal activities are contingent upon socioeconomic factors and connection speed. Specifically, a higher income, more education, a lower poverty rate, and a higher inequality are likely to make the Internet penetration be more positively related with cybercrime perpetrators, which are indeed different from the conditions of terrestrial crime in the real world. In addition, as opposed to narrowband, the broadband connections are significantly and positively associated with the number of Internet crime perpetrators, and it amplifies the aforementioned moderating effects of socioeconomic status on Internet crime offenses. Taken together, cybercrime requires more than just a skilled perpetrator, and it requires an infrastructure to facilitate profiteering from the act.

Despite the growing adoption of the mobile health (mHealth) applications (apps), few studies address concerns with low retention rates. This study aimed to investigate how the usage patterns of mHealth app functions affect user retention. We collected individual usage logs for 1,439 users of single tethered personal health record app, which spanned an 18-months period from August 2011 to January 2013. The user logs contained timestamps whenever an individual uses each function, which enables us to identify the usage patterns based on the intensity of using a particular function in the app. We then estimated how these patterns were related to 1) the app usage over time (using the random effect model) and 2) the probability of stopping the use of the application (using the Cox proportional hazard model). The analyses suggested that the users utilize the app most at the time of the adoption and gradually reduce their usage over time. The average duration of use after starting the app was 25.62 weeks (SD: 18.41). The degree of the usage reduction, however, decreases as the self-monitoring function is more frequently used (coefficient = 0.002, P = 0.013); none of the other functions has this effect. Moreover, engaging with the self-monitoring function frequently (coefficient = -0.18, P = 0.003) and regularly (coefficient = 0.10, P = 0.001) significantly also reduces the probability of abandoning the application. Specifically, the estimated survival rate indicates that, after 40 weeks since the adoption, the probability of the regular users of self-monitoring to stay in use was about 80% while that of non-user was about 60%. This study provides the empirical evidence that sustained use of mHealth app is closely linked to the regular usage on self-monitoring function. The implications can be extended to the education of users and physicians to produce better outcomes as well as application development for effective user interfaces.

With the rapid growth of sharing economy, there has been a bitter controversy on the disruptive nature of sharing economy to threaten traditional industry. This study examines the impact of ride-sharing services, which is one of the most successful business models in sharing economy, on taxi industry. Using comprehensive data on Uber and taxi transactions in New York City from April to September 2014, we find that ride-sharing is negatively associated with the demand for taxis. Interestingly, this effect is contingent upon market- and customer-segments. The negative effect of Uber on taxis is mostly driven in Manhattan and high-income areas, where most taxis are concentrated. Furthermore, our analyses reveal that ride-sharing services take more demand of taxi customers who pay by cash and who are price-sensitive, by providing relative advantages of ride-sharing platforms. In addition, taxi customers in groups appear to more switch to ride-sharing services. Relevant implications for both research and practice are discussed.

Smartphone applications have recently been used as a breakthrough technology for monitoring mental health conditions in cancer outpatient settings. However, the use of electronic patient-reported outcomes (ePROs) on mental conditions through smartphone applications raises new concerns, which includes the question of the accuracy of depression screening. Thus, research is essential for improving the depression-screening performance. This study aims to (1) test whether deep-learning-based algorithms can overcome the limitations of traditional statistical methods in terms of depression screening accuracy. In addition, the study aims to (2) explore ePRO patterns that adversely affect depression screening accuracy. As a deep learning-based algorithm, a feedforward neural network algorithm was used. As a traditional statistical method, a random intercept logistic regression was employed. To explore the ePRO patterns that negatively impact model accuracy, mental fluctuations, missing data, and compounding effects between mental fluctuations and missing data were tested. The performances of the algorithms and the effects of the ePRO patterns were measured through the receiver operating characteristic comparison test. The results of the study show that the performance of the deep-learning-based models was superior to that of the traditional statistical approach. The study found that mental fluctuations statistically reduced the accuracy of depression-screening models. A weak association between ePRO omissions and screening accuracy was found. Moreover, the compounding effects that had a negative effect on the depression screening accuracy were statistically significant. Although well-trained deep-learning-based models exhibit excellent performance, they still have some limitations. Thus, it is very important to focus on data quality to predict health outcomes when using data that is difficult to quantify, such as mental conditions.

Background: There has been a lack of understanding on what types of specific clinical information are most valuable for doctors to access through mobile-based electronic medical records (m-EMRs) and when they access such information. Furthermore, it has not been clearly discussed why the value of such information is high.Objective: The goal of this study was to investigate the types of clinical information that are most valuable to doctors to access through an m-EMR and when such information is accessed.Methods: Since 2010, an m-EMR has been used in a tertiary hospital in Seoul, South Korea. The usage logs of the m-EMR by doctors were gathered from March to December 2015. Descriptive analyses were conducted to explore the overall usage patterns of the m-EMR. To assess the value of the clinical information provided, the usage patterns of both the m-EMR and a hospital information system (HIS) were compared on an hourly basis. The peak usage times of the m-EMR were defined as continuous intervals having normalized usage values that are greater than 0.5. The usage logs were processed as an indicator representing specific clinical information using factor analysis. Random intercept logistic regression was used to explore the type of clinical information that is frequently accessed during the peak usage times.Results: A total of 524,929 usage logs from 653 doctors (229 professors, 161 fellows, and 263 residents; mean age: 37.55 years; males: 415 [63.6%]) were analyzed. The highest average number of m-EMR usage logs (897) was by medical residents, whereas the lowest (292) was by surgical residents. The usage amount for three menus, namely inpatient list (47,096), lab results (38,508), and investigation list (25,336), accounted for 60.1% of the peak time usage. The HIS was used most frequently during regular hours (9: 00 AM to 5: 00 PM). The peak usage time of the m-EMR was early in the morning (6: 00 AM to 10: 00 AM), and the use of the m-EMR from early evening (5: 00 PM) to midnight was higher than during regular business hours. Four factors representing the types of clinical information were extracted through factor analysis. Factors related to patient investigation status and patient conditions were associated with the peak usage times of the m-EMR (P<.01).Conclusions: Access to information regarding patient investigation status and patient conditions is crucial for decision making during morning activities, including ward rounds. The m-EMRs allow doctors to maintain the continuity of their clinical information regardless of the time and location constraints. Thus, m-EMRs will best evolve in a manner that enhances the accessibility of clinical information helpful to the decision-making process under such constraints.

Herding in open platform adoption decisions appears to prevail even when the systems adopted represent high risks with enterprise-wide impact. We present a model of organizational open platform adoption that integrates six different theoretical mechanisms based on a technology-organization-environment framework that drives herding: network effect benefits, new platform benefits, new platform risk, organizational learning, mimetic pressures, and competitive pressures. To find which mechanisms work significantly in organizational IT decision-making in accordance with IT diffusion and rival precedence, we empirically test the model by splitting the samples according to the proportion of rivals that have already adopted innovations. The empirical results demonstrate that among the six herding mechanisms, new platform risk and organizational learning drives herding in the earlier stage of diffusion and new platform benefits and competitive pressure drives herding in the later stage of diffusion. The results imply that organizations react conservatively to new platforms when they perceive less platform diffusion. However, as diffusion increases, organizations react more strategically to maintain competitive parity with rivals by imitating rival decisions.

Sexual assault is one of the most repellant and costly crimes, which inflicts irrecoverable harms on victims and society. This study examines the effect of IT-enabled ride-sharing platforms on sexual assaults. Drawing upon routine activity theory from the criminology literature, we posit that ride-sharing can reduce a passenger’s risk of being a suitable target of sexual assault by providing a more reliable and timely transportation option for traveling to a safer place. By exploiting the nationwide quasi-experimental setting of Uber’s city-by-city rollouts in the United States during 2005–2017, we demonstrate that Uber’s entry into a city is negatively associated with the number of rape incidents. To zoom into the effects of ride-sharing at a more granular level, we employ precinct-hour level data on Uber pickups and rape occurrences in New York City in 2015 and conduct spatiotemporal analyses. Our results from the spatiotemporal analyses corroborate those of the quasi-experiment and further reveal situational contingencies in the deterrent effect of ride-sharing. Specifically, ride-sharing contributes to a more significant reduction in the likelihood of rape occurrences in neighborhoods with limited transportation accessibility, and ride-sharing is more effective in deterring sexual crime in riskier circumstances, such as around alcohol-serving places on weekend nights or when the probability of crime occurrences increases. This study sheds new light on the potential of IT-enabled platforms to improve social well-being beyond their economic contributions and offers a new theoretical insight on the distinct role of digital platforms in public safety.

본 연구는 텍스트마이닝 기법 중 토픽 분석을 활용하여 관련 업계 국내 1위 S社의 최고경영자 대상 온라인 교육 콘텐츠 강의 중심으로 원문 스크립트를 분석했다. 지난 5년간(2011~2015)년 서비스된 총 4,824개 콘텐츠를 바탕으로 핵심 키워드를 추출한 다음 주제별 22가지 토픽으로 분류한 후 동향 분석을 수행했다. 이를 통해 최근 콘텐츠 비중이 급증하고 있는 토픽 주제를 확인할 수 있었다. 다음으로 토픽 분석을 통해 분류한 토픽 및 카테고리를 바탕으로 회원 평가 요인을 적용해 카테고리 및 각 토픽별 지적 관심도를 체계화 할 수 있었다. 경영․경제 분야에서는 마케팅전략, 인사/조직, 커뮤니케이션 분야 등이 높은 관심도와 만족도를 나타냈다. 인문 분야에서는 철학, 전쟁사, 역사(서양) 라이프스타일에서는 마음건강 분야가 관심도와 만족도 둘 다 높은 것으로 나타났다. 이와 함께 교육용 콘텐츠가 시대 변화에 민감하게 반응할지라도 회원의 관심과 만족도 제고에는 실패할 수 있다는 사실을 확인할 수 있었다. 최근 콘텐츠 비중은 급증했지만 평균 이하의 만족도를 기록한 IT기술 토픽이 대표적 사례라 할 수 있다. 이를 통해 최고경영자 대상 콘텐츠 제작 시 단순히 기술적 측면의 정보전달에서 끝나는 것이 아닌 기술 적용을 통한 가치혁신에 대한 깊이 있는 시사점을 도출하거나 풍부한 영상 자료를 바탕으로 다양한 볼거리를 제공하는 등 양적인 측면과 함께 질적인 측면을 고려해야 한다는 교훈을 얻을 수 있었다. 본 연구는 포털 사이트 혹은 SNS 자료가 아닌 국내 가장 영향력 있는 이러닝 기업 데이터를 토대로 분석을 진행했기에 보다 심도 있고 실용적인 결과를 도출했다. 또한 이러닝 관련 연구 분야에서 지금까지는 드물었지만 기술의 발달로 점점 연구 조사 방법론으로 기대가 높아진 텍스트마이닝 방법에 대하여 그 적용 가능성을 성공적으로 탐색해 보았다. 기존에는 콘텐츠 운영 현황 분석 시 콘텐츠 프로그램명에 입각, 표면적인 방식으로 분류할 수밖에 없는 한계가 존재했다면 텍스트마이닝 방법론을 활용하면 비정형 데이터 콘텐츠 스크립트를 바탕으로 분석하여 내용을 바탕으로 한 보다 심도 있는 콘텐츠 분류 및 주제 분류를 이끌어 낼 수 있다. 이를 바탕으로 연도에 따른 주제별 콘텐츠 서비스 현황을 도식화한다면 현재 부족한 분야와 필요한 분야에 대한 보다 심도 있는 고찰이 가능하다. 본 연구는 다양한 텍스트마이닝 기법 중에서 이러닝의 상황에서 효과적으로 연구하기 위한 새로운 방법론을 제시했으며 향후 최고경영자 교육 관련 분야별 지적 관심도에 대한 분석에 도움이 될 것으로 기대된다.

Crowdfunding has emerged alongside the IT development. It is believed that overwhelmingly successful projects, blockbusters, would have significant impacts on the overall crowdfunding platform. However, there are notable limitations in previous studies. First, we consider how the advent of blockbusters impact according to the projects’ similarity with inside and outside clusters, rather than pre-determined category. Second, we examine the blockbusters’ heterogeneity with the type of backers that bring different effects. We use project-level dataset and apply novel clustering method to analyze blockbuster effects. We find empirical evidence that blockbusters have a spillover effect on same categories, especially inside clusters experience larger effects than outside clusters. In the long run, these spillover effects decay faster in outside clusters, but last long for inside cluster. Furthermore, this result changes according to the composition of backers. Our study presents a promising avenue for the application of semantic network analysis to the crowdfunding context.