diff --git a/CHANGELOG.md b/CHANGELOG.md
index 97c06affac..586b65652c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Improved performance of `tensor.sort` and `tensor.argsort` for short arrays in the range [16, 64] elements [gh-1866](https://github.com/IntelPython/dpctl/pull/1866)
 
 ### Fixed
+* Fix for `tensor.result_type` when all inputs are Python built-in scalars [gh-1877](https://github.com/IntelPython/dpctl/pull/1877)
 
 ### Maintenance
 
diff --git a/dpctl/tensor/_type_utils.py b/dpctl/tensor/_type_utils.py
index 5defd154df..f279052f94 100644
--- a/dpctl/tensor/_type_utils.py
+++ b/dpctl/tensor/_type_utils.py
@@ -767,6 +767,9 @@ def result_type(*arrays_and_dtypes):
                 target_dev = d
                 inspected = True
 
+    if not dtypes and weak_dtypes:
+        dtypes.append(weak_dtypes[0].get())
+
     if not (has_fp16 and has_fp64):
         for dt in dtypes:
             if not _dtype_supported_by_device_impl(dt, has_fp16, has_fp64):
diff --git a/dpctl/tests/test_usm_ndarray_manipulation.py b/dpctl/tests/test_usm_ndarray_manipulation.py
index 882a001827..4bfd6dab9f 100644
--- a/dpctl/tests/test_usm_ndarray_manipulation.py
+++ b/dpctl/tests/test_usm_ndarray_manipulation.py
@@ -15,6 +15,8 @@
 # limitations under the License.
 
 
+import itertools
+
 import numpy as np
 import pytest
 from numpy.testing import assert_, assert_array_equal, assert_raises_regex
@@ -1555,3 +1557,26 @@ def test_repeat_0_size():
     res = dpt.repeat(x, repetitions, axis=1)
     axis_sz = 2 * x.shape[1]
     assert res.shape == (0, axis_sz, 0)
+
+
+def test_result_type_bug_1874():
+    py_sc = True
+    np_sc = np.asarray([py_sc])[0]
+    dts_bool = [py_sc, np_sc]
+    py_sc = int(1)
+    np_sc = np.asarray([py_sc])[0]
+    dts_ints = [py_sc, np_sc]
+    dts_floats = [float(1), np.float64(1)]
+    dts_complexes = [complex(1), np.complex128(1)]
+
+    # iterate over two categories
+    for dts1, dts2 in itertools.product(
+        [dts_bool, dts_ints, dts_floats, dts_complexes], repeat=2
+    ):
+        res_dts = []
+        # iterate over Python scalar/NumPy scalar choices within categories
+        for dt1, dt2 in itertools.product(dts1, dts2):
+            res_dt = dpt.result_type(dt1, dt2)
+            res_dts.append(res_dt)
+        # check that all results are the same
+        assert res_dts and all(res_dts[0] == el for el in res_dts[1:])