(I'm) Happy to Help (You): The Impact of Personal Pronoun Use in Customer–Firm Interactions
Bibliographic record
Abstract
In responding to customer questions or complaints, should marketing agents linguistically “put the customer first” by using certain personal pronouns? Customer orientation theory, managerial literature, and surveys of managers, customer service representatives, and consumers suggest that firm agents should emphasize how “we” (the firm) serve “you” (the customer), while de-emphasizing “I” (the agent) in these customer–firm interactions. The authors find evidence of this language pattern in use at over 40 firms. However, they theorize and demonstrate that these personal pronoun emphases are often suboptimal. Five studies using lab experiments and field data reveal that firm agents who refer to themselves using “I” rather than “we” pronouns increase customers’ perceptions that the agent feels and acts on their behalf. In turn, these positive perceptions of empathy and agency lead to increased customer satisfaction, purchase intentions, and purchase behavior. Furthermore, the authors find that customer-referencing “you” pronouns have little impact on these outcomes and can sometimes have negative consequences. These findings enhance understanding of how, when, and why language use affects social perception and behavior and provide valuable insights for marketers.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".