Integration of information technology capabilities in generating small and medium enterprise performance
Bibliographic record
Abstract
The role of information technology (IT) during the Covid 19 pandemic has made everything in business easy. The role of technology in business during a pandemic also makes it easier for entrepreneurs to navigate buying and selling activities and services. Technology makes it easy to shorten time saving business costs, as in business financial records that make one financial report must record everything manually with technology done automatically with the help of accounting software. Research purposes to test resources-based view theory in relation to the implementation of IT to produce operational performance and financial performance of small and medium enterprises (SMEs). The research was conducted on SMEs in Bali. The research method used to answer the research objectives uses a quantitative test approach Partial Least Square. Based on data analysis, it was found that the development of SME IT Adoption had an effect positive on IT Assimilation, but directly IT Adoption is not able to improve operational performance and financial performance. IT assimilation can improve the operational performance and financial performance of SMEs. Operational performance is not able to mediate the effect of IT adoption on the financial performance of SMEs. IT assimilation is a fully mediating variable in the relationship between IT and the operational performance and financial performance of SMEs in Bali. The results of the study show that IT resource management through a technology-based business competency model can succeed in realizing Organizational Capability that can be used to build business competitiveness if the organization or business unit that adopts it pre-determines IT integration in accordance with the vision, mission, and goals of the organization. This is very important to implement in an effort to adapt to uncertain business world conditions, such as when Covid 19 occurred.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".