Exploring the Impact of Customer Organizational Culture on Project Agility in ERP Implementation Projects
Bibliographic record
Abstract
Enterprise Resource Planning (ERP) projects have been the focus of extensive research in recent years. To overcome the challenges associated with these types of projects, one emerging and relatively unexplored stream of research has examined the application of agile project management (APM) in ERP implementation contexts. Despite its growing popularity, APM adoption remains complex, risky, and not yet fully understood. This study focuses on the critical role played by the customer in such projects, as it can either foster or hinder agility. A lack of customer collaboration can often be linked to the customer’s organizational culture (OC). Thus, this study aims to investigate the specific relationship between the customer’s OC and project agility in ERP implementation projects within small- and medium-sized enterprises (SMEs). To conceptualize OC, we adopted the Competing Values Framework (CVF), which distinguishes four cultural types: Clan, adhocracy, hierarchy, and market. Data were collected through an online questionnaire administered to 172 ERP end-users from Canadian SMEs who had participated in their organizations’ ERP implementation projects. The analysis was performed using SmartPLS version 4.1.0.9. The results confirm that customers characterized by a clan, adhocracy, or market culture positively influence project agility, while there was no significant effect of hierarchy culture on project agility. This study addresses several gaps in the literature and offers practical implications. The findings support the idea that vendors should better frame and justify introducing APM in ways that align with each customer’s cultural characteristics within ERP vendor–customer relationships.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".