Supply chain performance of Indonesian SMES: The role of digital leadership, supply chain innovation and E-HRM
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between digital leadership variables and supply chain performance, supply chain innovation and supply chain performance and the relationship between electronic human resource management (E-HRM) and supply chain performance. The research method used in this study is a quantitative survey method research design. The technique used in selecting samples in this study is simple random sampling. Data collection in this study was done online by distributing questionnaires through the Google Form platform and obtaining direct responses from respondents. The number of respondents studied was 786 small and medium enterprises (SMEs) owners in Indonesia. The study uses path analysis techniques for data and hypothesis testing. Statistical testing on the path analysis model can be done using the partial least square method. The study uses a Likert scale, which is categorized into five categories: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree. Data analysis in this study used Smart Partial Least Square (SPLS) software version 3.00. Model evaluation in testing with SPLS consists of two stages, namely, evaluation of the outer model and inner model. The evaluation of the outer model consists of factor loading tests, Average Variance Extracted, cross-loading, Cronbach's alpha, and composite reliability, while the evaluation of the inner model consists of the coefficient of determination (R2), cross-validated redundancy (Q2), Goodness of Fit (GoF), and hypothesis testing. The results of this research analysis are that digital leadership has a positive and significant relationship with supply chain performance, supply chain innovation has a positive and significant relationship with supply chain performance and e-HRM has a positive and significant relationship with supply chain performance. Digital Leadership involves leaders who can drive digital transformation and implement innovative strategies to leverage digital technologies. This involves the ability to understand and leverage technologies such as artificial intelligence, etc. In addition, digital leadership also involves sensitivity to change and the ability to drive innovation, collaboration, and technology adoption across SMEs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".