The effectiveness of matching sales influence tactics to consumers’ avoidance versus approach shopping motivations
Bibliographic record
Abstract
Purpose Adaptive selling can help build positive relationships between salespeople and consumers. The literature shows that consumers respond positively to salespeople under approach but not avoidance motivations. This paper aims to demonstrate a circumstance under which consumers with avoidance motivations can also respond positively, something not previously shown in the literature. Design/methodology/approach This research paper uses three experimental between-subject designs to test hypotheses. Findings The current research identifies appropriate sales influence tactics (e.g. a customer-autonomy-oriented or a loss-avoidance-oriented influence tactic) where consumers with avoidance motivations can also respond to sales agents positively by the evidence of higher purchase intentions. In addition, this research shows that consumers with approach motivations may not always respond positively to salespeople. Further, goal facilitation appraisals of the salespeople serve as a mechanism between consumers’ shopping motivations and their behavioral responses (e.g. purchase intentions). Originality/value First, while the previous literature demonstrates that approach motivations generally lead to more positive effects (Elliot and Trash, 2002), this research indicates that avoidance motivations can also have positive effects, which is a finding that has not been demonstrated in the literature thus far. Second, this research identifies goal facilitation appraisals as one underlying process that explains the interactive effect between matching influence tactics and consumers’ approach/avoidance motivations when shopping. Third, the authors integrate regulatory focus theory by using gain- or loss-avoidance-oriented sales influence tactics to match approach and avoidance motivations.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".