Leveraging Corporate Venture Capital for Customer-Centric Innovation in the Hospitality Industry
Bibliographic record
Abstract
In the current competitive hospitality landscape, corporate hotels are under increasing pressure to meet rising customer expectations while also facing competition from disruptive startups. To address these challenges, this paper explores the potential benefits of Corporate Venture Capital (CVC) as a strategic tool for corporate hotels to partner with startups and enhance their customer experience. It is believed that CVC offers unique advantages over traditional investment, such as early access to emerging technologies and innovative business models. By collaborating with startups, hotels can experiment with and integrate innovative solutions into their offerings before they become widely adopted. Furthermore, partnering with startups can provide hotels with valuable insights into evolving customer preferences and behaviors, enabling them to create personalized and data-driven experiences. Startups also offer hotels an agile and experimental approach, allowing them to test new concepts and adapt quickly to changing market trends. CVC collaborations can also create a unique value proposition for hotels, highlighting their commitment to innovation and customer-centricity. This paper proposes a framework for effective CVC-driven collaboration between corporate hotels and startups, which includes key considerations such as defining objectives and aligning on mutual goals, establishing clear communication channels, developing a flexible and adaptive partnership model, and managing risks and addressing potential conflicts of interest. The research aims to provide practical insights for corporate hotels to leverage the power of CVC to drive customer-centric innovation and gain a competitive edge in the dynamic hospitality landscape.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".