Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning Jiaheng Xie , Zhu Zhang, Xiao Liu & Daniel Zeng Pages 166-195 ABSTRACT Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.

Controlling Technical Debt Remediation in Outsourced Enterprise Systems Maintenance: An Empirical Analysis Narayan Ramasubbu & Chris F. Kemerer Pages 4-28 ABSTRACT Technical debt refers to maintenance obligations that stem from violations of established standards during the development and subsequent maintenance of enterprise systems. Technical debt remediation is particularly challenging in the outsourcing context due to information asymmetry between client and vendor teams. Control balancing-the periodic adjustments to the control configurations of outsourced projects-has been proposed as a process to help mitigate these information asymmetry challenges. Using data collected from 1,824 real-world projects, we tested to what extent control balancing improves the remediation of technical debt. After controlling for a number of technical and environmental factors, including system size, system lifespan, and contract parameters, we find that control balancing can benefit technical debt remediation, but primarily when processes for migrating technical debt-laden systems to new technological platforms have been identified. We highlight the role of technical debt in influencing the effects of relational flexibility in inter-firm engagements.

Handling the Efficiency-Personalization Trade-Off in Service Robotics: A Machine-Learning Approach Schahin Tofangchi, André Hanelt, David Marz & Lutz M. Kolbe Pages 246-276 ABSTRACT While multiple mechanisms for value creation from big data analytics (BDA) exist, their application in everyday life can create trade-offs, particularly in the context of service robotics where the dispersion of autonomous digital technologies creates potentials for data-driven efficiency gains. Attending to personal preferences in such contexts, increasingly vital for customer acceptance, may run counter to efficiency, thus constraining value creation and rendering efficiency-personalization trade-offs a key managerial challenge. For the case of autonomous vehicles (AVs), we formalize this trade-off and design a machine-learning approach to handle it by drawing on a unique dataset comprising 35,000 drives by 1,850 users. We consider the real-time dynamics and user interactions that affect the decisions of AVs and develop a model extension that allows for leveraging the properties of sharing business models to make better-informed decisions. Our study contributes to information systems (IS) AV research by providing an artifact that targets both efficiency and personalization of AV operations as well as the dynamic balance between the two. With the focus on everyday life contexts, our study points to the value of incorporating trade-offs between competing goals as well as human-centered perspectives in information systems designs for research on BDA value creation. For practitioners, our work provides a practical and generalizable approach to realize the potentials of service robots without risking customer acceptance.

How Firms Make Information Technology Investment Decisions: Toward a Behavioral Agency Theory John Qi Dong, Prasanna P. Karhade, Arun Rai & Sean Xin Xu Pages 29-58 ABSTRACT While prior research has established that information technology (IT) investment has a significant impact on firm performance, relatively few studies have provided insights into the antecedents of IT investment decisions. By integrating the behavioral theory of the firm and agency theory, we propose a behavioral agency theory to explain performance shortfalls and corporate governance, which monitors and controls managers' tendency of overinvestment or underinvestment in IT, as key drivers that jointly determine IT investment. As such, IT investment facilitates a firm's problemistic search that generates innovation in response to performance gaps. We further examine the role of innovation outputs as a mediating mechanism linking IT investment to firm performance. Our econometric analysis of a large-scale panel dataset provides empirical evidence corroborating our theory. Overall, this study contributes a behavioral agency theory to deepen our understanding about performance drivers and outcomes of IT investment decisions.

How Information and Communication Technology Shapes the Influence of Culture on Innovation: A Country-level Analysis Terence J.V. Saldanha, Babu John-Mariadoss, Michelle Xiao Wu & Sunil Mithas Pages 108-139 ABSTRACT Advances in Information and Communication Technology (ICT) and the distributed nature of innovation in a culturally heterogeneous world raise an important question about how ICT and culture jointly influence innovation. We argue that ICT shapes the influence of culture dimensions on national innovation in nuanced ways, depending on the nature of the dimensions. In particular, cultural dimensions that pertain to norms, formality, and structure (e.g., power distance and uncertainty avoidance) are often considered detrimental to innovation, whereas culture dimensions that pertain to the pursuit of goals and achievement (e.g., individualism, masculinity, and long-term orientation) are often considered conducive for innovation. We hypothesize that ICT amplifies the positive effects of individualism, masculinity, and long-term orientation on national innovation-and, attenuates the negative effects of power distance and uncertainty avoidance on national innovation. Our analyses of data on 66 countries from 2007 to 2015 broadly support our hypotheses about ICT amplifying the positive effects of individualism and long-term orientation, as well as our hypotheses about ICT attenuating the negative effects of power distance and uncertainty avoidance on national innovation. The findings suggest that ICT amplifies the effects of culture dimensions related to the pursuit of goals and helps overcome barriers to innovation that arise from a culture's focus on norms, formality, and structure. In turn, these findings can help managers and policy makers to make judicious decisions regarding ICT investments to transcend or leverage the country's cultural characteristics for innovation.

Impact of Incentive Mechanism in Online Referral Programs: Evidence from Randomized Field Experiments Jaehwuen Jung, Ravi Bapna, Alok Gupta & Soumya Sen Pages 59-81 ABSTRACT Despite the growing popularity of online referral programs, a minimal amount is known regarding the theoretical foundations that drive the key actions associated with successful referrals. In this paper, we study which type of referral reward structure is most effective in maximizing word-of-mouth by conducting two randomized experiments in mobile gaming context. Specifically, we examine the effect of three incentive schemes: selfish reward (inviter gets all the reward), equal-split reward (50-50 split), and generous reward (invitee gets all the reward). Consistent across the two experiments, we find that pro-social referral incentive schemes, namely the equal-split and generous schemes, tend to dominate purely selfish schemes in creating WOM. Our mechanism-level analysis shows that both equal-split and generous schemes result in higher number of conversions by significantly increasing the invitee's likelihood to accept referrals, which we further show that is partially due to selective and better targeted referrals. Our results contribute to the understanding of the optimal design of online referral programs and provide important implications for designing effective referral reward schemes in the digital world.

Mitigating the Adverse Effect of Monetary Incentives on Voluntary Contributions Online Dandan Qiao, Shun-Yang Lee, Andrew B. Whinston & Qiang Wei Pages 82-107 ABSTRACT Numerous online information systems (e.g., question and answer [Q&A] forums, citizen science communities, and review websites) rely heavily on volunteer contributions. Managers have used monetary incentives to induce individuals to increase their contribution level. However, monetary incentives could also generate adverse effects, which could dampen individuals' intrinsic motivation and lead to lower contribution quality when incentives are small. To address this issue, we propose two intervention strategies, goal-setting and challenge-seeking, and conduct a series of randomized experiments. We find that small monetary incentives, when combined with the appropriate intervention strategies, can motivate users to increase contribution quantity while simultaneously sustaining high quality. Thus, integrating such intervention strategies with small incentives can be a cost-effective way to encourage voluntary contributions. Our research contributes to the literature on incentive provision and provides theoretical and practical implications for platforms relying on voluntary contributions.

The Effectiveness of Social Norms in Fighting Fake News on Social Media Henner Gimpel,Sebastian Heger, Christian Olenberger & Lena Utz Pages 196-221 ABSTRACT Fake news poses a substantial threat to society, with serious negative consequences. Therefore, we investigate how people can be encouraged to report fake news and support social media platform providers in their actions against misinformation. Based on social psychology, we hypothesize that social norms encourage social media users to report fake news. In two experiments, we present participants a news feed which contains multiple real and fake news stories while at the same time exposing them to injunctive and descriptive social norm messages. Injunctive social norms describe what behavior most people approve or disapprove. Descriptive social norms refer to what other people do in certain situations. The results reveal, among other things, that highlighting the socially desired behavior of reporting fake news using an injunctive social norm leads to higher reporting rates for fake news. In contrast, descriptive social norms do not have such an effect. Additionally, we observe that the combined application of injunctive and descriptive social norms results in the most substantial reporting behavior improvement. Thus, social norms are a promising socio-technical remedy against fake news.

The Empowering and Competition Effects of the Platform-Based Sharing Economy on the Supply and Demand Sides of the Labor Market Ziru Li, Yili Hong & Zhongju Zhang Pages 140-165 ABSTRACT The sharing economy has fundamentally changed the way many individuals work. In this paper, we study the impact of the entry of a major ridesharing platform into U.S. Metropolitan Statistical Areas (MSAs), on the supply and demand sides of the labor market. Leveraging the difference-in-differences (DID) research design and a data set combining multiple U.S. Census archival sources, we exploit the variation in labor market metrics before and after Uber's entry into the MSAs. Our empirical findings reveal that the introduction of the ridesharing platform has an empowering effect on workers (the supply side of the labor market) and a competition effect on traditional jobs (the demand side of the labor market). Specifically, Uber's entry into the MSAs increases labor force participation, decreases the unemployment rate of residents living below the poverty level, and improves the employment and financial status of low-income workers. In addition, Uber's entry reduces the employment number and increases wages of conventional low-skill and/or low-wage jobs. This paper provides empirical evidence of the impact of a digital sharing economy platform on the labor market and suggests that policymakers and platform operators should account for this broader impact when they devise policies and make strategic decisions.

The Roles of IT Strategies and Security Investments in Reducing Organizational Security Breaches He Li, Sungjin Yoo & William J. Kettinger Pages 222-245 ABSTRACT This research examines the joint effects of information technology (IT) strategies and security investments on organizational security breaches. We focus on two forms of IT strategies: digitalization and embeddedness in IT outsourcing networks. Our longitudinal analysis of U.S. hospitals demonstrates that IT security investments reduce security breaches in less digitalized organizations but increase security breaches for highly digitalized organizations. Investing in technical network control security systems such as anti-virus and intrusion detection systems reduces external breaches. Implementing identity and access management security systems such as biometric scanning and user authentication decreases internal breaches but increases external breaches. However, organizations' embeddedness in IT outsourcing networks weakens the impacts of these technologies investments on external breaches but amplifies the negative relationship between identity and access management security systems and internal breaches. Our results offer an alternative understanding of organizational IT security investments and explain contrary results found in prior studies. Practical guidelines on organizational IT security strategies are discussed.