BUG: Series.dot for arrow and nullable dtypes returns object-dtyped series

doc/source/whatsnew/v3.0.0.rst

@@ -710,6 +710,7 @@ Numeric
 ^^^^^^^
 - Bug in :meth:`DataFrame.corr` where numerical precision errors resulted in correlations above ``1.0`` (:issue:`61120`)
 - Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
+- Bug in :meth:`Series.dot` returning ``object`` dtype for :class:`ArrowDtype` and nullable-dtype data (:issue:`61375`)
 - Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)
 
 Conversion

pandas/core/series.py

@@ -2951,8 +2951,9 @@ def dot(self, other: AnyArrayLike | DataFrame) -> Series | np.ndarray:
                 )
 
         if isinstance(other, ABCDataFrame):
+            common_type = find_common_type([self.dtypes] + list(other.dtypes))
             return self._constructor(
-                np.dot(lvals, rvals), index=other.columns, copy=False
+                np.dot(lvals, rvals), index=other.columns, copy=False, dtype=common_type
             ).__finalize__(self, method="dot")
         elif isinstance(other, Series):
             return np.dot(lvals, rvals)

pandas/tests/frame/methods/test_dot.py

@@ -153,3 +153,19 @@ def test_arrow_dtype(dtype, exp_dtype):
     expected = DataFrame([[1, 2], [3, 4], [5, 6]], dtype=exp_dtype)
 
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype,exp_dtype",
+    [("Float32", "Float64"), ("Int16", "Int32"), ("float[pyarrow]", "double[pyarrow]")],
+)
+def test_arrow_dtype_series(dtype, exp_dtype):
+    pytest.importorskip("pyarrow")
+
+    cols = ["a", "b"]
+    series_a = Series([1, 2], index=cols, dtype="int32")
+    df_b = DataFrame([[1, 0], [0, 1]], index=cols, dtype=dtype)
+    result = series_a.dot(df_b)
+    expected = Series([1, 2], dtype=exp_dtype)
+
+    tm.assert_series_equal(result, expected)