Prior leadership research has largely focused on voluntary pro-environmental behaviour under inspirational leadership, leaving structured reward-based approaches to green service innovation underexplored. This study examines how green contingent reward transactional leadership (GCRTL) influences employees' green innovative service behaviour (GISB) in healthcare. Drawing on Cognitive Evaluation Theory (CET), we theorise green intrinsic motivation (GIM) as the motivational mechanism linking GCRTL to GISB and internal environmental locus of control (IELOC) as a boundary condition shaping this indirect effect. Using a two-source, time-lagged design, data were collected from 1,449 employee-supervisor dyads across 23 hospitals in Pakistan. The moderated mediation model was tested using SmartPLS 4. Results show that GCRTL positively predicts GISB and that GIM partially mediates this relationship. The indirect effect is significant at mean and high levels of IELOC but not at low IELOC. The study positions GCRTL as a domain-specific form of transactional green leadership in healthcare.
Augmented reality (AR) is increasingly used as a phygital touchpoint for introducing new products, yet its effectiveness in facilitating new product adoption remains unclear. Across four studies, we show that AR-based product presentation influences purchase intention through outcome-oriented mental simulation, which reflects consumers' consideration of the anticipated benefits and final utility of product use. This effect depends on product innovativeness. For really new products, AR-based presentation increases outcome simulation and purchase intention because consumers need greater support to understand and imagine unfamiliar product outcomes. In contrast, for incrementally new products, AR-based presentation decreases both responses, as consumers can more readily infer product outcomes from existing category knowledge. These findings identify AR-based presentation as an early-stage, information-oriented phygital touchpoint, extend AR research by highlighting its information-disclosure function, and show that firms should deploy AR selectively according to the innovativeness of the product being introduced.
With the increasing application of frontline robots (FLRs) in public service environments, their service failures are inevitably witnessed by observers. Although valuable explorations have been carried out in robotic failure and recovery research from the perspective of mistreated customers, little is known about how robotic failures and recovery influence observers' reactions. Based on attribution theory, this research examined the 'ripple effect' of FLR failures on observers' switching intention through three scenario-based experiments. The findings reveal that seeing FLR (vs. frontline employee) failures enhances observers' perceived stability, thus increasing their switching intention. Providing explanation-focused (vs. apology-focused) recovery, especially outcome-focused explanations, can mitigate the negative effect of FLR failures on observers' switching intention. These findings contribute to robotic failure and recovery, AI aversion and observer research, and provide practical guidance for better handling FLR failures and increasing observers' continued usage intention of failed FLRs. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)'(sic)(sic)(sic)(sic)', (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
