Organizational identification and independent sales contractor performance in professional services
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the drivers of independent sales contractor (ISC) performance. As independently contracted sales agents, the ISC model is a growing method of non-permanent employment utilized in many sectors. Specifically, this study seeks to fill a gap in the literature related to the under-researched link between ISCs and organizational identification. Design/methodology/approach This study conducts an exploratory, mixed-methods study based on data collected from 189 ISCs from a professional services firm. Findings Results demonstrate that outcomes related to sales performance, retention and advocacy are influenced directly and indirectly by organizational identification. It also shows that tangible benefits related to financial and marketing values are the strongest predictors of ISC organizational identification. Intangible dimensions such as value congruence, management trust and embeddedness play a limited role in the model. Research limitations/implications Results show that ISC sales performance is enhanced when an ISC views their identity and the identity of the firm as highly interdependent. These findings suggest that organizational identification can be a key performance indicator when evaluating the return on marketing investment for a firm. Practical implications This study provides some important guidance to managers responsible for ISCs. First, the study identifies the primary drivers of organizational identification. Specifically, the study demonstrates that financial and marketing benefits are the primary relational antecedents of organizational identification. Both value congruence and operational benefits play relatively minor roles. Similarly, the results show that both organizational identification and historic sales performance are critical predictors of sales performance. Originality/value Few researchers have examined the link between ISCs and organizational identification. Organizational identification is of particular importance in the study of ISCs, as they possess the dual identity of an independent agent and that of a sales representative of the firm they are under contract. This study contributes to existing literature by extending previous studies that examine antecedents of sales performance.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".