Understanding the impact of frontline service technologies in healthcare on patient well-being

Abstract

Abstract The rise of frontline service technologies (FSTs) in healthcare is fueled by the ‘Triple Aim’, which is based on three interrelated aims: (1) improved population health, (2) reduced costs, and (3) better patient experiences (Bodenheimer & Sinsky, 2014). However, it remains ambiguous whether and how FSTs impact patients’ well-being. The importance of well-being can be explained in several ways. First, customers prefer service providers that treat them as individuals with unique needs instead of as part of the target group (Falter & Hadwich, 2020). Second, well-being is related to decreased illness, enhanced quality of life, and a higher level of profitability for organizations (Robertson & Flint-Taylor, 2010). Third, well-being can be actively managed (Chen et al., 2021). Fourth, it is a generalizable outcome measure across groups and over time (Lee et al., 2013). Therefore, and consistent with recent calls (Ostrom et al., 2021), this study aims to understand if and how FSTs impact patients’ well-being. Following the hedonic perspective, well-being involves positive and negative emotions and life satisfaction (Heady & Wearing, 1991). This well-being perspective is chosen for three key reasons. First, hedonic well-being is a malleable state rather than a trait (La Placa et al., 2013). Second, its measurement can be adapted to capture context-specific idiosyncrasies (Page & Vella-Brodrick, 2009). Third, hedonic well-being refers to the subjective experience of pleasure irrespective of its source (Waterman et al., 2010). Conceptually, this research starts from the Job Demands-Resources (JD-R) model (Demerouti et al., 2001), which hypothesizes that environmental characteristics (i.e., demands and resources) influence well-being via motivation and strain. The JD-R model’s relevance is four-fold. First, to capture the customer’s active role in services, Groth (2005) underscores the value of management theories. Second, it leads to a balanced understanding of well-being creation due to including positive and negative effects. Third, the model’s structure ensures that well-being is captured as an outcome and as a well-being co creation process (Chen et al., 2021). The empirical part consists of a mixed-method (i.e., interviews, diaries, storyboards, questionnaires) multi-sample (i.e., patients, caregivers) approach, which is currently being executed. This research provides at least four contributions. First, this study is the first to offer a rigorous empirical assessment of whether FST infusion impacts patient well-being. Second, the adopted balanced view on well-being leads to novel insights into key Transformative Service Research questions. Third, by studying technology usage from the perspective of the individual-level psychological process, we also extend existing theoretical models from the technology acceptance literature. Fourth, the results provide a basis for evidence-based, tailor-made FST decision-making.