Developing a Pathway for the Adoption of Machine Learning Systems in Organizations: An Analysis of Drivers, Barriers, and Impacts with a Focus on the Healthcare Sector

The potential of machine learning (ML) and systems based thereon has grown steadily in recent years. The ability of ML systems to rapidly and systematically identify relationships in large volumes of data, which can be used to analyze new data to make meaningful predictions, enables organizations of all industries to make their processes more effective and efficient. Healthcare in particular may benefit greatly from ML systems in the future, as these systems’ capabilities could help to ensure adequate patient care despite many pressing issues, such as the acute shortage of specialists (e.g., through diagnostic support). However, many organizations are currently still failing to harness the potential of ML systems to their advantage, as implementing these systems is not a trivial task. Rather, the integration of ML systems requires the organization to identify and meet novel, multi-faceted preconditions that are unfamiliar as compared with previous, conventional technologies. This is mainly because ML systems exhibit unique characteristics. In particular, ML systems possess probabilistic properties due to their data-based learning approach, implying that their application can lead to erroneous results and that their functioning is often opaque. Particularly in healthcare, in which patients' lives depend on proper diagnoses and treatment, these characteristics result in ML systems not only being helpful, but – if introduced improperly – can also lead to severe detrimental consequences. Since previous research on the adoption of conventional technologies has not yet considered the characteristic properties of ML systems, the aim of this dissertation is to better understand the complex requirements for the successful adoption of ML systems in organizations in order for them to sustainably realize ML systems’ potential. The three qualitative, two experimental, and one simulation study included in this cumulative dissertation have been published in peer-reviewed journals and conference proceedings and are divided into three distinct parts with different focuses: The first part of this dissertation identifies the drivers of and barriers to the adoption of ML systems in organizations in general, and in healthcare organizations specifically. Drawing on an interview study with 14 experts from a variety of industries, an integrative overview of the factors influencing the adoption of ML systems is provided, structured according to technical, organizational, and environmental aspects. The interviews further reveal several problem areas where ML provider and ML user organizations’ perceptions diverge, which can lead to the flawed design of ML systems and thus delayed integration. In a second qualitative study, specific factors affecting the integration of ML systems in healthcare organizations are derived based on 22 expert interviews with physicians with ML expertise, and with health information technology providers. In a following step, these interviews are used to establish an operationalized maturity model, which allows for the analysis of the status quo in the adoption process of ML systems in healthcare organizations. How the identified requirements for the organizational introduction of ML systems can be fulfilled is subject of the second part of this dissertation. First, the concept of data donation is introduced as a potential mechanism for organizations, particularly in the healthcare sector, to achieve a valid database. More specifically, individuals’ donation behavior along with its antecedents, such as privacy risks and trust, and under different emotional states, is investigated based on an experimental study among 445 Internet users. Next, a design for rendering ML systems more transparent is proposed and evaluated using a questionnaire and an experiment among 223 Internet users. Thereby, the relevance of transparency for building trust among potential users and the resulting willingness to pay for transparent designs is highlighted. A qualitative study is further employed to reveal what motivates potential users, and especially the elderly, to accept health-related ML systems. The third part of this work includes a simulation study that presents the potential impact of adopting ML systems for organizational learning. The results suggest that an organization’s employees can be relieved of some of their learning burden through the application of ML systems, but the systems must be reconfigured appropriately over time. This holds especially true in case of rapid environmental changes, such as those caused by the COVID-19 pandemic. In summary, this dissertation assumes a socio-technical perspective to shed light on the integration of ML systems in organizations. It helps organizations better understand the complex interplay of technical, organizational, human, and environmental factors that are critical to the successful adoption of ML systems, enabling decision makers to target scarce corporate resources more effectively. Moreover, this work enables IS researchers to better grasp the specifics of ML systems, provide required adjustments to theoretical foundations, and sharpen their understanding of the contextual factors involved in the adoption of ML systems in organizations.