As chatbots powered by Large Language Models (LLMs) are increasingly deployed across cultural contexts, evaluating whether they can reason about culturally conditioned norms becomes essential. We explore whether the eight workplace-culture dimensions of Erin Meyer’s The Culture Map can provide a practical foundation for such an evaluation. We operationalize these dimensions into 104 behavioral scenarios that test two complementary capabilities: classifying behaviors along a cultural dimension and judging the plausibility of nationality–behavior pairings. We evaluate nine open-weight LLMs spanning 7B to approximately 671B parameters. Nationality information helps some models classify behavior, but most still struggle with group-conditioned plausibility. Explicit cultural rules benefit the reasoning model most, possibly suggesting that this capability depends more on reasoning than on scale. These findings demonstrate both the diagnostic potential of the proposed framework and the limitations that a broader evaluation should capture. Our study is therefore a first step toward a large-scale, open, and multidimensional "Culture Map of Chatbots" benchmark for LLM cross-cultural understanding. Code is available at https://github.com/TH-77-oss/CMC-Benchmark.