Numerous articles in top IS journals note as a limitation and lack of generalizability that their findings are specific to a certain type of technology, culture, and so on. We argue that this generalizability concern is about limited scope (e.g., explanatory breadth). The IS literature notes this preference for generalizability as a characteristic of good science and it is sometimes confused with statistical generalizability We argue that such generalizability can be in conflict with explanation or prediction accuracy. An increase in scope (e.g., increasing explanatory breadth) can decrease explanation or prediction accuracy. Thus, in sciences such as cancer research, where explanation and prediction accuracy are highly valued, the cancer accounts (generally speaking) have become increasingly narrower (and less generalizable). IS thinking has not yet benefitted from these considerations. Whether generalizability is valued should be linked with the research aims. If the aim is practical applicability through explanation or prediction accuracy, then “limited” generalizability could be a strength rather than a weakness.