Social and Cultural Challenges in ERP Implementation
Bibliographic record
Abstract
This paper studies the differential practices of change management in organizations of western origin and compares it with the best practices prevalent in Indian organizations, with special emphasis on social and cultural challenges faced in these countries. Since Enterprise Resource Planning (ERP), as part of an information and communication technology (ICT) initiative, is frequently associated with organization change and transformation in relation to its adaptation, it has been used as the context in this study. The impact of social factors and cultural challenges on change management processes and elements are compared and contrasted using multiple case studies from USA, Canada, European (Western/Eastern) and Indian organizations who have adopted ERP technologies. The conceptual framework highlights cultural and social factors that affect ERP implementation, and offers suggestions to researchers to empirically test these influences using sophisticated analytical methods and develop change strategies and practices in response to these challenges. Further, it also draws attention to the need for a contemporary, result-oriented, quantitatively measurable framework of change management at the individual and enterprise levels. It is expected that such an approach would result in better buy-in from all stakeholders in terms of increased accountability.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".