Team Performance and the Development of Iranian Digital Start-ups: The Mediating Role of Employee Voice
Bibliographic record
Abstract
Team performance is one of the means to confront rapid change from human resource experts’ point of view. So constant change in the work environment and daily work-related activities requires sharing comments and ideas as well as an increase in the need for continuous learning. Thus, inattention to employee voice can bring serious disadvantages to firms, because without regarding employees’ expression of opinions, a firm cannot operate in a dynamic environment and will eventually lose its competitiveness in this contentious business environment. Therefore, this research aims to discuss the influence of team performance on the development of digital start-up companies with the mediating role of employee voice. In terms of objective, this is quantitative applied research. The statistical population of this research is the employees of 113 international Iranian digital start-up companies in the medical sector, covering 15% of the country’s aggregate exports in 2019 and 2020, estimated as 423 individuals. The sample size was calculated as 201 people using the Cochran formula. In order to collect the data, a standard questionnaire with 24 questions with a five-point Likert scale was used. Finally, the data were analysed using Smart PLS 3 software. The results showed that cognitive empowerment, emotional commitment, making innovation climate, and sharing knowledge, with the mediating role of employee voice, positively influence the success and survival of a firm. In other words, team performance of firms is an attitude towards employee loyalty and a continuous process that can lead to firm development by the contribution of individuals in decision-making processes. Team performance can be considered the main factor in learning and innovation, leading to a facilitation of trust between employees and creating new ideas through conversation. Performance at the team level helps members better comprehend how they work with each other and learn ways to improve self-management to earn high levels of efficiency and effectiveness.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".