Bibliographic record
Abstract
How are new-age technologies transforming business operations? Why does it matter? The article suggests that a market management approach is conducive to understanding how new-age technologies can transform business operations. In this regard, the article defines market management approach as a holistic framework for managing transformative business operations that emphasizes the integration of emerging technologies with an organization’s operational processes. In this regard, the concept of transformative business operations is introduced and defined as the transformation of organizational systems, resources, and processes using new-age technologies to improve business functions that can generate superior value offerings to all stakeholders . The proposed transformative business operations approach identifies three triggers—the tension of uncertainty, adaptive capabilities, and operational elasticity—that drive the unique and synergistic impacts of these technologies. These triggers result in transformative changes through (a) foundational shifts in organizations, (b) strategy design, execution, and optimization, (c) unified ecosystem creation, and (d) pioneering business operations solutions. The actual transformation is observed in hyper-automation, augmented decision-making, and decentralized supply chains . The article also highlights barriers to adopting these technologies - cultural and workforce adjustments, data security and privacy concerns, and interoperability issues -, which moderate their potential impact, and guide organizations navigating these challenges. Finally, it outlines a market management agenda for exploring the implications of these developments.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".