The more the better? Service transition for shaping sustainable development in manufacturing firms and the role of top management team attributes

Abstract

An increasing number of manufacturing firms are actively embracing service transition in pursuit of sustainable competitive advantage. However, the relationship between service transition and sustainability performance (SP) remains unclear. Drawing on resource‐based theory and resource orchestration theory, this study investigates the impact of service transition (i.e., product‐related and product‐unrelated service transition) on SP and examines the moderating role of the top management team (TMT) attributes (i.e., stability and faultlines). We conduct OLS and 2SLS analyses using a unique longitudinal dataset from 522 listed Chinese manufacturing firms. Empirical evidence indicates that the two dimensions of service transition have distinct effects on SP. Product‐related service transition positively impacts SP, whereas product‐unrelated service transition shows an inverted U‐shaped relationship with SP. In addition, TMT stability strengthens the impact of product‐related service transition on SP, whereas TMT faultlines flatten the inverted U‐shaped relationship between product‐unrelated service transition and SP. This study addresses the research inconsistency concerning the implications of service transition on SP by elucidating the heterogeneous effects of different types of service transition on SP and the boundary role of TMT attributes. These findings also help firms revisit their service strategies and their TMT membership structure to enhance sustainability practices.

Differentiation strategies and firms' environmental, social and governance: The different moderating effects of historical and social performance shortfalls

“…To ensure data accuracy, the samples were screened according to the following principles. We excluded the financial sector sample (Huang et al, 2023) because there are significant differences between their accounting treatment and other industries in China (Ren et al, 2023). In addition, we excluded samples with abnormal data, and samples with missing key variables (Yuan et al, 2020; Zhong, Ren, & Song, 2022).…”