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 This paper aims to document how AI has changed the way consumers make decisions and propose how that change impacts services marketing, service research and service management. Design/methodology/approach A review of the literature, documentation of sales and customer service experiences support the evolution of bot-driven consumer decision-making, proposing the bot-driven service platform as a key component of the service experience. Findings Today the focus is on convenience, the less time and effort, the better. The authors propose that AI has taken convenience to a new level for consumers. By using bots as their service of choice, consumers outsource their decisions to algorithms, hence give little attention to traditional consumer decision-making models and brand emphasis. At the moment, this is especially true for low involvement types of decisions, but high involvement decisions are on the cusp of delegating to AI. Therefore, management needs to change how they view consumers’ decision-making-processes and how services are being managed. Research limitations/implications In an AI-convenience driven service economy, the emphasis needs to be on search ranking or warehouse stock, rather than the traditional drivers of brand values such as service quality. Customer experience management will shift from interaction with products and services toward interactions with new service platforms such as AI, bots. Hence, service marketing, as the authors know it might be in decline and be replaced by an efficient complex attribute computer decision-making model. Originality/value The change in consumer behavior leads to a change in the service marketing approach needed in the world of AI. The bot, the new service platform is now in charge of search and choice for many purchase situations.
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 | 0.040 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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