Expansion of service offering for startup company electrifying Africa: Made in Kenya, for Africa
Bibliographic record
Abstract
Whilst the global transport sector undergoes its seventh revolution, driven by electrification, decarbonization and advancements in mobility services, Kenya stands at a pivotal moment in its transition toward sustainable urban development, facing challenges such as unreliable electricity, high costs, and limited infrastructure. With transport responsible for nearly a quarter of global emissions, sustainable mobility is essential, not just a technological shift. While electric passenger cars gain traction in developed regions, Kenya is seeing rapid growth in electric two-wheelers due to their affordability, durability, and suitability for local conditions, where distances are longer and infrastructure less developed. This shift supports Kenya’s Vision 2030 goals of industrialization, improved infrastructure, and energy resilience. Within this context, companies like Roam, a Swedish-Kenyan electric motorcycle firm, play a crucial role by integrating renewable energy-powered charging stations, aligning with national efforts to reduce emissions and foster sustainable development. The purpose of this thesis is to describe and analyze how an electric two- wheeler company can expand its service offering to address pressing mobility challenges and promote the adoption of sustainable transportation solutions in Kenya. This study employs a qualitative single-case design with an abductive research approach, combining exploratory and problem-solving elements to generate both understanding and actionable findings. The case company, Roam, was selected through defined criteria to ensure relevance, and the research process was deliberately iterative. Moreover, data collection included literature review, secondary company sources, and semi-structured interviews with stakeholders across strategic and operational roles. Through using several established frameworks in tandem, a comprehensive understanding of Roam as a company was achieved, with an emphasis on their service offerings. Due to the nature of the frameworks, gaps could be identified and insights for further analysis were derived. In light of this, the key findings of this thesis highlight the importance of strengthening customer channels, ensuring effective partnerships, and leveraging the company’s service offering to differentiate itself in a homogenous market. Furthermore, this thesis contributes to the academia as well as industry application, particularly for emerging startup companies in East Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".