The adoption of electronic records management system (ERMS) in the Yemeni oil and gas sector
Bibliographic record
Abstract
Purpose Identification of factors for electronic records management system (ERMS) adoption is important as it allows organizations to focus their efforts on these factors to ensure success. The purpose of this paper is to identify the factors that influence ERMS adoption in the Yemeni oil and gas (O&G) sector. Design/methodology/approach This paper conducts a systematic literature review (SLR) to extract the most common factors that could facilitate successful ERMS adoption. Information technology (IT) experts were asked to rank the extracted factors via an e-mail questionnaire and to recommend specific critical success factors that must be given extra attention to increasing the success of ERMS adoption. Essentially, the proposed methodology is technology-organization-environment (TOE) modeling to examine the important factors influencing decision-makers in the Yemeni O&G sector regarding ERMS adoption. Findings This paper identifies factors influencing ERMS adoption based on SLR and an expert-ranking survey. The data that were collected from IT experts were analyzed using the statistical package for the social sciences. The results showed that only 12 out of 20 factors were significant. The experts then added three new factors, resulting in 15 significant factors classified into the three dimensions as follows: technology, organization and environment. Originality/value Limited studies have been carried out in the context of the O&G sector, even among developed countries such as Canada, the UK and Australia. These studies have focused on a limited number of factors for ERMS adoption targeting better utilization of human resources, faster and more user-friendly system responses and suitability for organizational ease. This paper explores the factors that may prove useful in adopting of ERMS in the O&G sector of developing countries, similar to Yemen.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".