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Service Innovation in the IT Service Industry: Social Influence and Relationship Exchange Perspectives




               their performance, determine the level of effort to exert, and decide how long they will
               persist in the face of obstacles or challenges during the service creation and delivery
               processes (e.g., Tierney and Farmer, 2002). Gong et al. (2009) argue that employees’
               service innovation self-efficacy reflects their intrinsic motivation to engage in creative
               activities, which in turn influences their innovation performance. In addition, group
               and organizational level influences are also important for the innovation outcomes of
               individuals (Chen et al., 2013). We suggest that future research should examine team-level

               or contextual factors (e.g., team climate, team-efficacy, or team-level leadership), which
               may also affect individual innovation performance. Third, while this study offers valuable
               insights to deepen our understanding of engineers’ innovative behavior, our focus remains
               on the relational resources within firms. It is important to acknowledge the existence of
               other relationship resources in the workplace, such as the role of customer relationships in
               innovation performance and the impact of varying degrees of customer participation. We
               also recognize that the impact of EML on TMX warrants deeper investigation. In response

               to the editor’s recommendation and inspired by Cheong et al. (2019), we incorporated a
               new path into the original model (EML→TMX). After adding this path, we found that
               the structural model evaluation displayed a satisfactory fit with the observed data, and we
               identified a significant association between EML and TMX, suggesting that this area merits
               further exploration in future research. Finally, our Taiwanese research context may limit
               the generalizability of our findings: Farmer et al. (2003) argue that individuals’ thoughts
               and behaviors diverge across different cultures, regions, and countries. It would be useful
               to understand the effects of proximal factors on individual innovation performance in
               different cultures. Thus, future researchers are encouraged to examine these linkages in

               different cultural contexts.























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