The debate inside hospitality about how much to automate has often been framed as a binary: robots and AI versus human service. This research challenges that framing directly. Across a mixed-methods study — 21 interviews with senior hotel managers, analysis of 70 industry documents, and a survey of 800 Gen Z consumers across China and Australia — the answer that emerged consistently on both sides of the operator-guest divide was hybrid. Not robots replacing staff, not staff replacing robots, but a deliberate human-RAISA collaborative model where each element handles what it does best.
For operators, this reframes the technology decision. The question is no longer whether to invest in robots and AI, but how to design the collaboration between human staff and RAISA systems so that the result feels coherent and high-quality to a generation of guests who are digitally native, opinionated about technology, and making hotel choices based in part on the technological environment a property offers.
The cross-country comparison is one of the most practically useful elements of this research. Chinese hotels have substantially higher rates of AI and robot deployment, reflecting both government investment in hospitality technology and cultural openness to automated service interactions. Australian hotels, by contrast, remain more concentrated around mobile technology — apps, digital check-in, and mobile key — with robot and AI deployment lagging. This creates a divergence in what operators in each market know how to do and what Gen Z consumers in each market have come to expect.
On the demand side, Chinese Gen Z consumers show meaningfully stronger preferences for technological service elements and are more likely to select a hotel specifically because of its RAISA capabilities. This preference effect directly influences booking intent: technology preferences among Gen Z translate into intention to visit RAISA-equipped hotels, which has revenue implications that go beyond operational efficiency. Australian Gen Z consumers show similar directional preferences but at lower intensity, suggesting that the market is moving toward the Chinese pattern rather than diverging from it.
The supply-demand misalignment the researchers identify is the most actionable finding for strategy. In both markets, but more acutely in Australia, hotels are underestimating how much their Gen Z guests value RAISA-enhanced service. Properties that close this gap — not by automating everything, but by deliberately integrating AI and robot capabilities into a service model that still centers human interaction for complex and emotional service moments — are positioned to differentiate in a segment that will represent an increasing share of hospitality demand over the next decade.
The paper's methodological contribution — extending the Kano model from service quality refinement to technology preference exploration — also offers a practical tool for operators wanting to evaluate their own RAISA strategy. The Kano framework allows hotels to distinguish between RAISA features that are baseline expectations (failure to provide them damages satisfaction), performance features (more is better), and delight features (unexpected positive response). Applied to their specific market and guest profile, this gives operators a more structured way to prioritize technology investments than the common approach of benchmarking against competitors or following vendor recommendations.