From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior
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
Given the unprecedented reach of social media, firms are increasingly relying on it as a channel for marketing communication. The objective of this study is to examine the effect of firm-generated content (FGC) in social media on three key customer metrics: spending, cross-buying, and customer profitability. The authors further investigate the synergistic effects of FGC with television advertising and e-mail communication. To accomplish their objectives, the authors assemble a novel data set comprising customers’ social media participation data, transaction data, and attitudinal data obtained through surveys. The results indicate that after the authors account for the effects of television advertising and e-mail marketing, FGC has a positive and significant effect on customers’ behavior. The authors show that FGC works synergistically with both television advertising and e-mail marketing and also find that the effect of FGC is greater for more experienced, tech-savvy, and social media–prone customers. They propose and examine the effect of three characteristics of FGC: valence, receptivity, and customer susceptibility. The authors find that whereas all three components of FGC have a positive impact, the effect of FGC receptivity is the largest. The study offers critical managerial insights regarding how to leverage social media for better returns.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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