Do Performance Goals and Development, Feedback and Recognition, and a Climate of Trust Improve Employee Engagement in Small Businesses in the United States?
Bibliographic record
Abstract
Small businesses are the predominant contributors to the U.S. economy, yet they face many challenges to remain competitive and sustainable. There are several reasons a small business could fail, including a lack of human resources, limited financial resources, competition, technological advancements, disaster, and globalization. Improving employee performance by getting them engaged and productive in their work is an issue that cannot be overlooked for small businesses to function and remain competitive. There is limited empirical evidence that explains the dimensions of performance management and employee engagement in small businesses. However, how small businesses sustain their long-term performance remains uncertain. This study sought to bring together two previously distinct constructs: overall employee engagement and overall performance management, characterized by performance goals and development, a climate of trust, and feedback and recognition. The research was correlational in nature. A survey was conducted to generate and analyze data gathered from 121 employees of small businesses located in the United States. A series of Pearson correlation analyses confirmed the existence of statistically significant positive relationships between employee engagement and each variable of performance management, namely performance goals and development, feedback and recognition, and climate of trust. Notwithstanding these positive correlations, a multiple regression model with the three performance management variables as independent variables and employee engagement as the dependent variable suggested that there was a statistically significant regression model F(3, 117) = 32.34, p < .001, R2 = .453, explaining 45.3% of the variability in employee engagement. Nonetheless, this model confirmed that the variables performance goals and development and climate of trust were not statistically significant in the model (p > .05). In other words, only the feedback and recognition variable was statistically significant in the regression model, suggesting that it explained most of the variability in engagement, including that already explained by the other two variables. Overall, the outcome of this study suggests that small businesses implementing performance management processes have more engaged employees. The conclusions drawn from these findings suggest that overall performance management and overall employee engagement contribute to small business productivity and organizational success.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".