Corporate foresight organizational learning and performance: The moderating role of digital transformation and mediating role of innovativeness in SMEs
Bibliographic record
Abstract
Corporate foresight is a capability that includes any structural or cultural element that enables the company to detect discontinuous change early. The purpose of the present study is to examine the direct impact of corporate foresight, and organizational learning on the performance dynamic of SMEs working in the United Arab Emirates (UAE). In addition, the study tries to analyze the moderating as well as mediating effect of digital transformation and innovativeness on the relationship between exogenous and endogenous constructs. A sample of 576 questionnaires were distributed among the owners/managers of different SMEs working in the region of UAE. However, a final sample of 354 respondents was empirically tested. The data was analyzed through a two-step approach where structural equation modelling (SEM) under SmartPLS was found to be very helpful to examine the direct and indirect relationship between the study variables. The study findings show that there is an insignificant but positive impact of corporate foresight on organizational performance whereas significant impact of organizational learning on organizational performance. Furthermore, the study found evidence for the moderating effect of digital transformation between organizational learning and innovation. Additionally, it is observed that innovativeness mediates the relationship between corporate foresight and performance dynamics. The study findings suggest that for exploring the relationship between corporate foresight, digital transformation, and organization the role of innovation and digital transformation is quite significant. The study findings suggest that both owners and managers at SMEs of UAE should attach more importance to innovative capabilities and digital transformation for achieving higher levels of organizational performance. Policy makers should reasonably consider the direct and indirect effect of study variables while considering high performance at the workplace.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".