How do multinational firms from emerging countries use acquisitions in advanced economies to upgrade their capabilities
Bibliographic record
Abstract
Debate is ongoing on whether firms from emerging economies are catching up technologically and will be ultimately able to produce new technology. Such debate has been primarily fed by the recent boom of acquisitions of firms located in advanced countries by emerging multinationals enterprises (EMNEs) (UNCTAD 2006). Extant research has indeed documented that EMNEs extensively use acquisitions to address their competitive disadvantage (Child and Rodrigues 2005) by furthering up the technological ladder and upgrading their resources and capabilities (Guillén and Garcıa-Canal 2009; Rui and Yip 2008). These scholars, however, have provided empirical evidence on and discussed upgrading via acquisitions primarily with reference to a generic strategy of capabilities upgrading (e.g. Luo and Tung 2007: Makino, Lau and Yeh 2002). \n\tWe seek to advance this literature by asking whether different technology-intensive EMNEs follow different capability upgrading strategies when acquiring advanced country targets. In particular, we distinguish between a dual capability upgrading strategy which encompasses the simultaneous upgrading of technological and complementary (e.g. managerial and organizational) capabilities, and a pure complementary capability upgrading strategy. To this end, we investigate whether manufacturing and services EMNEs operating in different technology-intensive sectors select advanced country firms in the same or higher technology-intensive sectors. We assume that within the same technology-intensive sector advanced country firms tend to have superior complementary assets as a result of their home country advantage (Erramilli, Agarwal and Kim 1997), while advanced country targets in higher technology-intensity sectors own both higher technological and complementary capabilities. Thus, EMNEs follow a dual capability upgrading strategy when they simultaneously upgrade their technological and complementary capability by acquiring higher technology-intensive advanced country firms, and a pure complementary capability upgrading strategy by acquiring advanced country firms at the same technology-intensive level. An EMNE acquiring an advanced country firm within the same technology-intensive sector may indeed acquire new technological knowledge without, however, technologically upgrading. \nWe rely on a large database of over 600 mergers and acquisitions undertaken by EMNEs from Brazil, Russia, India and China (BRIC) in Europe, North-America (USA and Canada) and Japan between 1985 and 2008, and classified according to the level of technology intensity of acquirer and target. \tOur findings suggest that medium technology-intensive EMNEs follow a dual capability upgrading strategy as they already have a critical mass of competences and resources. EMNEs that are at the top and bottom of the technological ladder pursue a pure complementary capability upgrading strategy. Low technology-intensive EMNEs are yet unable to climb up the technological ladder, while high technology-intensive EMNEs are willing to address their competitive disadvantage by gaining complementary capabilities and resources (Barney and Zajac 1994). We found that these patterns are consistent across manufacturing and services. \n\tThe study offers two contributions. First, it adds to the literature on EMNEs by providing a finely-grained analysis of different capability upgrading strategies via acquisitions based upon the level of technological-intensity of acquirer and target. To this literature, it also offers a comparative analysis of manufacturing and services acquisitions. Studies on EMNEs have indeed primarily focused on manufacturing (e.g. Knoerich 2010; Van-Hoesel 1999), while our knowledge on service EMNEs is still scant. Second, it extends the literature on international knowledge sourcing by pointing out the need to consider south-north patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".