Human–machine collaborative literacy and tourism SMEs’ perceived digital transformation performance: a configurational fsQCA

As artificial intelligence (AI) becomes embedded in tourism service work, employees increasingly need to interpret machine-generated outputs, evaluate their usefulness, and integrate them into organisational activities. Yet little is known about the firm-level resource configurations in which workforce human–machine collaborative literacy (HC) accompanies favourable digital transformation performance. Integrating organisational psychology perspectives on human–AI work with the technology–organisation–environment framework and a digital eco-embedding perspective, this study applied fuzzy-set qualitative comparative analysis (fsQCA) to 401 managerial questionnaires aggregated to 30 Chinese tourism small and medium-sized enterprises (SMEs). The analysis examined manager-perceived workforce HC alongside intelligent technology infrastructure (TI), digital niche (IO), policy support (PS), and open innovation networks (CN). No individual condition or its negation was necessary for high perceived performance (HC necessity consistency = 0.845). Two high-performance configurations shared core TI and IO: H1 included peripheral CN, with HC unspecified, whereas H2 included peripheral HC and PS. Seven firms satisfied the descriptive membership criterion for both high-performance configurations. Three non-high-performance configurations shared the absence of digital niche (~IO), with the absence of HC specified in two. The intermediate expressions were retained across the examined calibration and truth-table threshold settings. In the leave-one-firm-out analyses, the complete high-performance intermediate solution was retained in 28 of 30 deletions and the H2 conjunction in all 30, whereas the complete non-high-performance intermediate solution was retained in 20 of 30 deletions. These findings position manager-perceived workforce HC as a configuration-specific complement rather than a universally necessary condition for favourable perceived transformation performance. By situating a workforce-related capability within technological, ecosystem, and institutional resource configurations, the study provides an organisational-level contextual perspective on human–AI collaboration. The findings suggest that workforce capability development should be considered alongside technological readiness, digital ecosystem access, and institutional support.