Network Centrality and Performance: Effects in the Automotive Industry
Bibliographic record
Abstract
Purpose – This paper seeks to assess and analyze the relationship between indegree centrality and organizational performance in the automotive industry. In other words, we look at whether the network position is related to the performance of the actors using production, revenue, and profit indicators. Theoretical framework – This research uses social network analysis as a method/theoretical approach allied with relational capital. Design/methodology/approach – A similarity assessment was carried out. Data were collected from 1359 relations across four specific governance structures between 2011 and 2013. Later, the same measures were implemented in subgroups detected with the Louvain method. The NodeXL, UCINET, and SPSS software were used for the graphs, metrics, and correlations, respectively. Findings – The results show a moderate to strong correlation between the actors and the subgroups formed by them, with their respective revenue and the indegree centrality for the three years selected. Our conclusions were that the centrality of an automotive manufacturer is positively related to its performance. Practical & social implications of research – This study might help organizations evaluate social network analysis usage as a tool for understanding their opportunities given their network. Originality/value – This paper contributes to the literature by indicating that in the automotive industry, formed by alliances between different governance structures, the structural position a manufacturer occupies in the network is related to its performance indicators. Keywords – automotive industry; interorganizational network; social network analysis; performance measures; similarity analyses.
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.
How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".