Networks and Australian professional services in newly emerging markets of Asia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it