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Servitization of products is becoming increasingly prevalent among manufacturing enterprises. Existing research has primarily focused on exploring whether the direct impact of servitization on manufacturer performance follows a linear or a curvilinear relationship. However, the understanding of the underlying mechanisms between servitization and manufacturer financial performance remains limited. This paper aims to examine the non-linear relationship between servitization and manufacturer performance as well as the mediating process and boundary condition associated with this relationship.

Drawing on resource-advantage theory, this paper proposes a theoretical model of the U-shaped relationship between servitization and the financial performance of equipment manufacturers. Panel data of 248 listed equipment manufacturers in China during the period of 2010–2020 are used to test each hypothesis through the ordinary least square method.

The empirical results indicate that servitization follows a U-shaped relationship with service business focus and the financial performance of equipment manufacturers. Service business focus mediates this U-shaped relationship between servitization and financial performance, and digital technology application moderates this relationship.

This paper pioneers the unraveling of the potential mechanism that can explain the curvilinear relationship between servitization of manufacturers and financial performance. This mechanism is the focus of the service business, which is theoretically delineated and empirically tested. Furthermore, digital technology application enables manufacturers to achieve service business focus more effectively in the process of servitization. Thus, this study addresses the call for research on digital servitization.

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

To be green creative is to come up with fresh, original and practical ideas for green products, green services, green processes or green activities. The purpose of this study is to provide a comprehensive overview of green creativity (GC) research by tracing the development of important theories, contexts, characteristics and methodologies (TCCM), and to illustrate how they relate to one another based on the systematic review and analysis of the existing literature relevant to GC from 2013 to 2023.

The research takes a methodical, structured approach to its literature evaluation, identifying prior contributions and offering frameworks for future study.

This research aims to highlight the challenges associated with planning, developing and implementing GC to realize the firm’s strategic and operational goals. Comprehensive networks, important countries, notable authors, key TCCM are provided by a TCCM and bibliographic analysis of the current GC literature.

The research addresses the concerns of managers across all types of entities and fills in the gaps, such as the skewed focus on GC’s applicability in large businesses and developing countries, as well as the limitations of a single-level analysis.

The research as a whole provides the taxonomy, utilization and mapping of logical concepts that strengthen GC. The study also highlights areas where more research is needed and where gaps and unresolved tensions remain. By delving into the nature of knowledge, the authors can better understand the factors that will ultimately shape the scope of future studies.