Artificial Intelligence, Marketing, and the History of Technology: Kranzberg’s Laws as a Conceptual Lens
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
Killer applications, or killer apps, are technology applications that profoundly change the way any society thinks, works, and functions. This paper explores Artificial Intelligence (AI) as a killer app, with specific application to marketing. Specifically, this paper employs the lens of technology history to explore the relationship between marketing and AI. Using Kranzberg’s six laws of technology, this paper sheds light on all manner of innovations, how technologies have shaped and impacted society, and how marketers can respond to this. This inquiry offers two main contributions: First, it suggests a number of implications for marketing practice and scholars, derived from each of Kranzberg’s laws. These suggestions are intended to guide marketing practice when implementing or using AI. In addition, this article offers a number of research directions that might be fruitful and important areas for investigation in future scholarly work regarding technology’s impact among marketing scholars.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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