Remedies of low performance among Pakistani e-logistic companies: The role of firm’s IT capability and information communication technology (ICT)
Bibliographic record
Abstract
E-commerce market of Pakistan is instable which causes low performance of e-logistic industry.Thus, logistic industry of Pakistan is lacking as compared to other developing countries such as China, India, and Malaysia.Low performance is majorly based on low staff service quality, inappropriate website design and goods traceability system.As remedies of these issues, the current study introduced firm's IT capability and information communication technology (ICT).The primary objective of this study is to investigate the determinants of elogistic firm's performance in Pakistan.To achieve this objective, quantitative research approach along with cross-sectional research design was used.By using the survey method, 300 questionnaires were distributed among the managerial staff of e-logistic companies.Smart PLS 3 was used as a statistical tool.It is found that staff service quality, website design and etraceability had significant and positive relationships with firm's e-logistic performance.Moreover, firm's IT capability as a moderator enhanced the positive effect of staff service quality, website design and e-traceability.Nevertheless, information communication technology (ICT) positively mediated the relationship between e-traceability and firm's elogistic performance.Hence, firm's IT capability and information communication technology (ICT) are the key elements to decrease various issues of staff service quality, website design and e-traceability.The study is much significant for practitioners and e-logistic companies to enhance performance by focusing on firm's IT capability and information communication technology (ICT).
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".