Trust, Fairness, and Ethics in the Digital Age: A Systematic Literature Review on Their Impact on SME Performance
Bibliographic record
Abstract
This study aims to analyze the influence of the implementation of trust, fairness, and ethics on the performance of Micro, Small, and Medium Enterprises (MSMEs) in facing the challenges of digital transformation. Through a quantitative approach and cross-country comparative literature study, this study examines how moral and relational factors affect the sustainability and productivity of MSMEs. The results of this study are supported by global findings, including from Agarwal (2014) in India regarding the importance of trust in work relationships; Tlaiss et al. (2015) in Canada and Brown et al. (2015) in the UK regarding the role of fairness and organizational support in improving performance; and Abdullahi et al. (2016) in Nigeria which emphasizes ethics as the foundation of business. Other studies such as Engelbrecht et al. (2017) in South Africa and Afsar et al. (2018) in Pakistan show that fairness and ethics increase employee loyalty and performance. In the digital context, studies by Wong et al. (2020) in Hong Kong, Chamtitigul & Li (2021) in China, and Oh et al. (2022) in South Korea underlines the importance of digital ethics and trust in the modern business ecosystem. Research by Alpkan et al. (2020) in Turkey and Umar et al. (2024) in Nigeria confirms that organizational justice and ethical leadership can strengthen the competitiveness of MSMEs amidst digitalization. The findings of this study indicate that the synergy between trust, justice, and ethics is a crucial foundation in improving MSME performance, especially in facing digital disruption. Therefore, MSMEs need to internalize these values in managerial practices in order to survive and develop sustainably.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".