Manufacturer's servitization level and financial performance: the role of risk management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The objective of this research is to investigate the impact of offering product-linked services on the effectiveness of risk management and, subsequently, on financial performance. Design/methodology/approach The investigation is based on an empirical analysis employing structural equation modeling (SEM) and cross-industry and multi-country survey data of 307 companies. The theorization is guided by the information processing theory (IPT). Findings Considering the basic and advanced classification of services, the analysis suggests that only the provision of advanced services influences the effectiveness of risk management. Specifically, the provision of advanced services strengthens the preventive dimension of risk management. Surprisingly, the analysis reveals a negative direct impact of preventive risk management on financial performance. Preventive risk management, however, indirectly enhances financial performance by supporting reactive risk management. Practical implications For practitioners, the research suggests a positive impact of servitization in a long term rather than in form of instant financial benefits. The research attempts to highlight the specific role of supply chain risk management (SCRM) through which servitization has a positive impact on financial performance. Originality/value Although there are assumptions about both reduction and increase in risk when manufacturers offer services, the extant literature lacks an empirical investigation on the association between servitization and the effectiveness of risk management. This study addresses the stated gap and offers novel insights into the role of SCRM in the performance consequences of servitization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it