Networks and Australian professional services in newly emerging markets of Asia
Bibliographic record
Abstract
Purpose To understand better how professional services firms (PSFs) use networks to gain entry into newly emerging markets (NEMs), to analyze how such firms are assisted in this process by prior networks and to provide a framework of this process. Design/methodology/approach The methodology utilised in this study is qualitative and exploratory. Ten interviews across three large firms (legal, finance and media consulting) were used for the data gathering. Analysis incorporated open, axial and selective coding. Findings Prior networks provide impetus to the foreign entry aspirations of PSFs and are critical to the process. The specific functions of network actors in the entry process are to influence the firm and to provide intelligence‐gathering, arising from their participatory role in the foreign market. A framework is presented, supporting network theory as a key theoretical underpinning of strategy formulation, decision‐making and implementation by PSFs entering NEMs. Research limitations/implications The framework presented in this paper could be tested most appropriately by analysing an extended number of cases, still within a qualitative approach, prior to survey‐testing the extent of the phenomena. Within the scope of the current study, however, the framework is supported by these preliminary findings. Practical implications Networks are perceived by PSFs as a medium for capturing market knowledge and as a basis for strategic decision‐making in NEMs. Originality/value Network theory is posited as a key theoretical underpinning of core strategy formulation, decision‐making and implementation by professional services entering NEMs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".