Artificial Intelligence, Marketing, and the History of Technology: Kranzberg’s Laws as a Conceptual Lens
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.
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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 | 0.005 | 0.007 |
| 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 | 0.053 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".