Topic Modelling of Management Research Assertions to Develop Insights on the Role of Artificial Intelligence in Enhancing the Value Propositions of New Companies
Bibliographic record
Abstract
The article suggests a Value Proposition (VP) framework that enables the analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP development activities. To develop such a framework, we examined existing management research publications to identify and extract assertions that could be used as a source of actionable insights for new growth-oriented companies. The extracted assertions were assembled into a corpus of texts that were subjected to topic modelling analysis – a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modeling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modeling results led to the identification of seven topics: 1) Value created; 2) Stakeholder value propositions; 3) Foreign market entry; 4) Customer base; 5) Continuous improvement; 6) cross-border operations; and 7) Company image. The uniqueness of the adopted topic modeling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e. in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance emerging the four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, Foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the VP development of new growth-oriented companies.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".