Walmart’s international expansion: successes and miscalculations
Bibliographic record
Abstract
Purpose Walmart achieved extraordinary success and growth in its home country before embarking on a strategy of international expansion. While most of Walmart¹s international expansion efforts were successful, the retailer experienced some challenges in Germany and South Korea, exiting both less than ten years after initial entry. In 2016, Walmart announced the closure of 269 stores worldwide. Although most Walmart stores are now outside the USA, the performance of these stores lag their US counterparts. Walmart has not been able to simply export its “Everyday Low Price” approach. It is important to understand cultural differences in the way people shop in addition to understanding the market, economy and laws of various regions around the world. Design/methodology/approach Walmart’s successes and missteps in each country are analyzed. The studies looked at each country’s culture, shopping habits and discuss what worked and what did not in each country. The authors hope that managers planning international expansion will learn from the successes and failures of this giant retailer. Findings Walmart has a significant presence in Mexico, the UK, Brazil, China and Canada. It has been successful in countries where it has adapted the Walmart model to the local market. International expansion for Walmart, along with other retailers, is now being highly impacted by the growth in online shopping. However, the use of technology for shopping is not a homogenous global experience. The increased demand for online retailers suggests that firms slow down (but not stop) brick and mortar international expansion. Practical implications Considering the projected growth in online shopping, retailers with global aspirations need to have a strong and sustainable competitive advantage (e.g. products, operations, marketing and brand name reputation) in addition to a clear internationalization plan. The same factors critical to brick and mortar expansion are applicable to online growth. Having a successful, long-term presence in selected countries requires a clear understanding of each country’s infrastructure, demographics, political and economic systems, in addition to cultural awareness and an understanding of shopping practices. Social implications The growth of online shopping internationally will also fundamentally alter international expansion for Walmart and other retailers. Interestingly, Chinese shoppers may be leading the trend in online shopping, as nearly 65 percent of Chinese shoppers use their mobile phones for online shopping, are more likely to buy from off-shore online retailers and are more likely to use their mobile phones to compare prices than either Canadian or US shoppers (PWC, 2016). Walmart’s recent acquisition of Jet.com is sending a clear signal that brick and mortar shopping is not the only way to expand internationally. Originality/value This original work about Walmart’s growth strategy internationally is unique. This work will be of great value to managers thinking of expanding internationally. The non-embracing of local cultural habits and use of non-local managers is something that can be easily overlooked when thinking of expansion. Serious financial consequences can be easily avoided by being aware of the mistakes that others have made.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".