Sustainable Business Practices in Technology Start-ups: A Qualitative Inquiry into Environmental and Social Strategies
Bibliographic record
Abstract
The objective of this study was to explore the sustainable business practices in technology start-ups, focusing on their environmental and social strategies. The research aimed to understand the motivations behind adopting sustainability, the specific practices implemented, the challenges faced during implementation, and the impact of these practices on business performance. This qualitative study employed semi-structured interviews to collect data from key personnel in technology start-ups, including founders, CEOs, sustainability managers, and other strategic decision-makers. A total of 19 participants were selected using purposive sampling. The interviews were transcribed and analyzed using NVivo software, following an inductive approach to identify themes. Theoretical saturation was achieved when no new themes emerged from the data. The analysis revealed five main themes: motivation for sustainability, environmental strategies, social strategies, challenges in implementation, and impact on business performance. Motivations included environmental concerns, market differentiation, regulatory compliance, ethical considerations, and economic benefits. Environmental strategies encompassed renewable energy use, waste management, sustainable product design, carbon footprint reduction, water conservation, and green procurement. Social strategies focused on employee well-being, community engagement, fair labor practices, stakeholder collaboration, customer education, social innovation, and inclusive hiring practices. Challenges included financial constraints, technological barriers, organizational resistance, supply chain complexities, regulatory hurdles, and market perceptions. The impact on business performance was positive, enhancing financial outcomes, operational efficiency, brand loyalty, innovation, and employee satisfaction. This study provides a comprehensive understanding of the sustainable business practices in technology start-ups, highlighting the multifaceted benefits and challenges of sustainability. The findings offer valuable insights for practitioners, policymakers, and researchers, emphasizing the importance of integrating environmental and social strategies into business operations to achieve long-term success and societal impact.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".