Construction of Competitive Advantage Instrument in Jordanian SME Context Using Structural Equation Modelling
Bibliographic record
Abstract
Jordanian SMEs are becoming increasingly hard to endure and thrive in the aggressive entrepreneurial world economy due to weak capital structure, poor leadership and weak marketing strategy, as well as other legislative limitations. As a consequence, Jordan faces problems in achieving full advantage from the SME industry as well, making an inadequate contribution to the domestic GDP. Competitive advantage (CA) can, however, perform a crucial part in achieving fast economic development due to sustainability of SMEs and a coherent appropriate input to Jordan's GDP. But the construction of CA is still underdeveloped and ignores a unifying hypothesis, resulting in fragmented study attempts. In addition, there are now several frameworks for the evaluation and benchmarking of firm performance (FP), but none of these frameworks provide an approach for assessing the competitive advantage of the firm. Therefore, this study aims to explore and determine the dimensionality of items measuring CA construct. The issue has previously been discussed, but there is still no prevalent agreement between scholars as the number of dimensions and items to assess CA should be used. This research investigated the CA measurement items created by past researchers and tailored these items to accommodate the environment of SMEs in Jordan. In this regard, the Delphi technique has been combined by the Structural Equation Modeling (SEM) and also followed the steps of instrument creation developed by Churchill (1979). The primary objective of this research is to develop an instrument capable of assessing CA in Jordanian SME sector. The tool developed in this research provides a relevant and efficient tool that disclosed three aspects by satisfying all the socio-metric properties required by the measuring tool in the social science, namely dimensionality, reliability, and validity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".