Explore the vast range of titles available.

Information systems based on artificial intelligence (AI) have increasingly spurred controversies among medical professionals as they start to outperform medical experts in tasks that previously required complex human reasoning. Prior research in other contexts has shown that such a technological disruption can result in professional identity threats and provoke negative attitudes and resistance to using technology. However, little is known about how AI systems evoke professional identity threats in medical professionals and under which conditions they actually provoke negative attitudes and resistance. The aim of this study is to investigate how medical professionals' resistance to AI can be understood because of professional identity threats and temporal perceptions of AI systems. It examines the following two dimensions of medical professional identity threat: threats to physicians' expert status (professional recognition) and threats to physicians' role as an autonomous care provider (professional capabilities). This paper assesses whether these professional identity threats predict resistance to AI systems and change in importance under the conditions of varying professional experience and varying perceived temporal relevance of AI systems. We conducted 2 web-based surveys with 164 medical students and 42 experienced physicians across different specialties. The participants were provided with a vignette of a general medical AI system. We measured the experienced identity threats, resistance attitudes, and perceived temporal distance of AI. In a subsample, we collected additional data on the perceived identity enhancement to gain a better understanding of how the participants perceived the upcoming technological change as beyond a mere threat. Qualitative data were coded in a content analysis. Quantitative data were analyzed in regression analyses. Both threats to professional recognition and threats to professional capabilities contributed to perceived self-threat and resistance to AI. Self-threat was negatively associated with resistance. Threats to professional capabilities directly affected resistance to AI, whereas the effect of threats to professional recognition was fully mediated through self-threat. Medical students experienced stronger identity threats and resistance to AI than medical professionals. The temporal distance of AI changed the importance of professional identity threats. If AI systems were perceived as relevant only in the distant future, the effect of threats to professional capabilities was weaker, whereas the effect of threats to professional recognition was stronger. The effect of threats remained robust after including perceived identity enhancement. The results show that the distinct dimensions of medical professional identity are affected by the upcoming technological change through AI. Our findings demonstrate that AI systems can be perceived as a threat to medical professional identity. Both threats to professional recognition and threats to professional capabilities contribute to resistance attitudes toward AI and need to be considered in the implementation of AI systems in clinical practice.

We study how platform owners’ decision to enter complementary markets affects innovation in the ecosystem surrounding the platform. Despite heated debates on the behavior of platform owners toward complementors, relatively little is known about the mechanisms linking platform owners’ entry and complementary innovation. We exploit Google’s 2015 entry into the market for photography apps on its own Android platform as a quasi-experiment. We conclude based on our analyses of a time-series panel of 6,620 apps that Google’s entry was associated with a substantial increase in complementary innovation. We estimate that the entry caused a 9.6% increase in the likelihood of major updates for apps affected by Google’s entry, compared to similar but not affected apps. Further analyses suggest that Google’s entry triggered complementary innovation because of the increased consumer attention for photography apps, instead of competitive “racing” or “Red Queen” effects. This attention spillover effect was particularly pronounced for larger and more diversified complementors. The study advances our understanding of the effects of platform owner’s entry, explicates the complex mechanisms that shape complementary innovation, and adds empirical evidence to the debate on regulating platforms. The online appendix is available at https://doi.org/10.1287/isre.2018.0787 .

Conversational agents (CAs) are natural language user interfaces that emulate human-to-human communication. Because of this emulation, research on CAs is inseparably linked to questions about anthropomorphism—the attribution of human qualities, including consciousness, intentions, and emotions, to nonhuman agents. Past research has demonstrated that anthropomorphism affects human perception and behavior in human-computer interactions by, for example, increasing trust and connectedness or stimulating social response behaviors. Based on the psychological theory of anthropomorphism and related research on computer interface design, we develop a theoretical framework for designing anthropomorphic CAs. We identify three groups of factors that stimulate anthropomorphism: technology design-related factors, task-related factors, and individual factors. Our findings from an online experiment support the derived framework but also reveal novel yet counterintuitive insights. In particular, we demonstrate that not all combinations of anthropomorphic technology design cues increase perceived anthropomorphism. For example, we find that using only nonverbal cues harms anthropomorphism; however, this effect becomes positive when nonverbal cues are complemented with verbal or human identity cues. We also find that CAs’ disposition to complete computerlike versus humanlike tasks and individuals’ disposition to anthropomorphize greatly affect perceived anthropomorphism. This work advances our understanding of anthropomorphism and contextualizes the theory of anthropomorphism within the IS discipline. We advise on the directions that research and practice should take to find the sweet spot for anthropomorphic CA design.

Gaining economic benefits from substantially lower labor costs has been reported as a major reason for offshoring labor-intensive information systems services to low-wage countries. However, if wage differences are so high, why is there such a high level of variation in the economic success between offshored IS projects? This study argues that offshore outsourcing involves a number of extra costs for the client organization that account for the economic failure of offshore projects. The objective is to disaggregate these extra costs into their constituent parts and to explain why they differ between offshored software projects. The focus is on software development and maintenance projects that are offshored to Indian vendors. A theoretical framework is developed a priori based on transaction cost economics (TCE) and the knowledge-based view of the firm, complemented by factors that acknowledge the specific offshore context. The framework is empirically explored using a multiple case study design including six offshored software projects in a large German financial service institution. The results of our analysis indicate that the client incurs post-contractual extra costs for four types of activities: (1) requirements specification and design, (2) knowledge transfer, (3) control, and (4) coordination. In projects that require a high level of client-specific knowledge about idiosyncratic business processes and software systems, these extra costs were found to be substantially higher than in projects where more general knowledge was needed. Notably, these costs most often arose independently from the threat of opportunistic behavior, challenging the predominant TCE logic of market failure. Rather, the client extra costs were particularly high in client-specific projects because the effort for managing the consequences of the knowledge asymmetries between client and vendor was particularly high in these projects. Prior experiences of the vendor with related client projects were found to reduce the level of extra costs but could not fully offset the increase in extra costs in highly client-specific projects. Moreover, cultural and geographic distance between client and vendor as well as personnel turnover were found to increase client extra costs. Slight evidence was found, however, that the cost-increasing impact of these factors was also leveraged in projects with a high level of required client-specific knowledge (moderator effect).

Many organizations and software firms these days use pair programming to meet the challenges posed by digital transformations and to meet customer needs. They use pair programming to become more agile and responsive to their competitive environment. Although some studies suggest that pair programming can potentially increase the quality of developed software code and the satisfaction and confidence of developers, very little is known as to how pair programming affects team performance. Therefore, we conducted an empirical study at one of the largest enterprise software firms that serves a large number of Fortune 500 companies, and we collected data from the software developers, Scrum masters, and product owners of 62 software development teams. Our findings show that pair programming affects team performance positively by changing cognitive structures and behavioral patterns in software teams. Pair programming helps team members develop shared mental models and, as a consequence, increases backup behavior among team members. Backup behavior is particularly valuable for teams facing high task novelty. Our findings have critical implications for organizations and senior managers as they lead their digital transformation efforts where they often rely on autonomous teams for providing digital innovation. We show that pair programming can be a key ingredient for high-performance teams. Establishing the practice of pair programming promises to be particularly valuable for those teams that lack shared mental models and teams whose members fail to provide each other backup. This study examines the team-level effects of pair programming by developing a research model that accounts for mediators and moderators of the relationship between pair programming and team performance. We hypothesize that pair programming helps software development teams establish backup behavior by strengthening the shared mental models among developers. In turn, backup behavior attenuates the negative effect of task novelty on team performance. We collect data from the software developers, Scrum masters, and product owners of 62 software development teams in a global enterprise software firm and find broad support for our research model. The study makes important contributions by shifting attention to the team-level effects of pair programming and by explicating mediating and moderating mechanisms related to the roles of shared mental models, backup behavior, and task novelty. The results underline the importance of viewing pair programming as a context-specific practice that helps establish backup behavior in teams. In terms of implications for practitioners, our results show that pair programming can be a valuable element of team governance to create shared mental models and backup behavior and to achieve high team performance when teams face high levels of task novelty.

While diagnostic AI systems are implemented in medical practice, it is still unclear how physicians embed them in diagnostic decision making. This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists’ own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. In deriving six propositions, the account of sensemaking and usage of diagnostic AI systems in medical practice paves the way for future research.

Process mining is a set of analytical techniques aimed at gaining insights into business processes in organizations. Recently, information systems scholars have recognized its potential for analyzing human behavior through digital trace data. In this paper, we draw on the conceptual and technical analogies between business processes and human behavior to thoroughly investigate the application and transfer of process mining techniques to the analysis of human behavior. This analysis, called human behavior mining (HBM), is conceptualized as a four-part framework. To illustrate HBM’s research potential, we apply this framework in an mHealth scenario. We explore dynamic concepts proposed by social cognitive theory to analyze changes in physical activity behavior through digital trace data collected through a dedicated app. This exemplary application demonstrates that HBM can be used to empirically test previously unspecified and uncontested dynamic concepts in human behavior. It also highlights HBM’s suitability for health analytics, given the vast amount of health-related behavior data available through apps and wearables, and the direct connection between behavior and health-related outcomes. Our research provides a dynamic and temporal perspective on human behavior, showcasing the potential of HBM to enrich theoretical frameworks in IS research.

Background and Purpose Investigating the cost‐effectiveness of future mobile stroke unit (MSU) services with respect to local idiosyncrasies is essential for enabling large‐scale implementation of MSU services. The aim of this study was to assess the cost‐effectiveness for varying urban German settings and modes of operation. Methods Costs of different operating times together with different personnel configurations were simulated. Different possible catchment zones, ischemic stroke incidence, circadian distribution, rates of alternative diagnoses, as well as missed cases were incorporated to model case coverage and patient numbers. Based on internationally reported clinical outcomes of MSUs, a 5‐year Markov model was applied to analyze the cost‐effectiveness for the different program setups. Results Compared with standard stroke care, MSUs achieved an additional 0.06 quality‐adjusted life years (QALYs) over a 5‐year time horizon. Assuming a catchment zone of 750,000 inhabitants and 8 h/7 day operation resulted in an incremental cost‐effectiveness ratio (ICER) of €37,182 per QALY from a societal perspective and €45,104 per QALY from a healthcare perspective. Lower ICERs were possible when coverage was expanded to 16 h service on 7 days per week and larger populations. Sensitivity analyses revealed that missing ischemic strokes significantly deteriorated economic performance of MSU. Conclusions Major determinants of cost‐effectiveness should be addressed when setting up novel MSU programs. Catchment zones of more than 500,000–700,000 inhabitants and operating times of at least 12–16 h per day, 7 days per week could enable the most cost‐effective MSU services in the German healthcare system. The analysis explored the long‐term economic effects of mobile stroke unit (MSU)‐based care in the German healthcare system. When setting up novel MSU programs, catchment zones of >500,000–700,000 inhabitants and operating times of at least 12–16 h per day on 7 days per week are preferable and could enable the most cost‐effective MSU services.