diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4343f59df32..5639698d6fc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -104,6 +104,7 @@ In addition, this release completes implementation of `dpnp.fft` module and adds
 * `dpnp` uses pybind11 2.13.6 [#2041](https://github.com/IntelPython/dpnp/pull/2041)
 * Updated `dpnp.fft` backend to depend on `INTEL_MKL_VERSION` flag to ensures that the appropriate code segment is executed based on the version of OneMKL [#2035](https://github.com/IntelPython/dpnp/pull/2035)
 * Use `dpctl::tensor::alloc_utils::sycl_free_noexcept` instead of `sycl::free` in `host_task` tasks associated with life-time management of temporary USM allocations [#2058](https://github.com/IntelPython/dpnp/pull/2058)
+* Improved implementation of `dpnp.kron` to avoid unnecessary copy for non-contiguous arrays [#2059](https://github.com/IntelPython/dpnp/pull/2059)
 
 ### Fixed
 
diff --git a/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py b/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py
index 1da6c3f4400..e15bd93d7bb 100644
--- a/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py
+++ b/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py
@@ -676,10 +676,6 @@ def dpnp_kron(a, b, a_ndim, b_ndim):
 
     a_shape = a.shape
     b_shape = b.shape
-    if not a.flags.contiguous:
-        a = dpnp.reshape(a, a_shape)
-    if not b.flags.contiguous:
-        b = dpnp.reshape(b, b_shape)
 
     # Equalise the shapes by prepending smaller one with 1s
     a_shape = (1,) * max(0, b_ndim - a_ndim) + a_shape
@@ -693,7 +689,7 @@ def dpnp_kron(a, b, a_ndim, b_ndim):
     ndim = max(b_ndim, a_ndim)
     a_arr = dpnp.expand_dims(a_arr, axis=tuple(range(1, 2 * ndim, 2)))
     b_arr = dpnp.expand_dims(b_arr, axis=tuple(range(0, 2 * ndim, 2)))
-    result = dpnp.multiply(a_arr, b_arr, order="C")
+    result = dpnp.multiply(a_arr, b_arr)
 
     # Reshape back
     return result.reshape(tuple(numpy.multiply(a_shape, b_shape)))
diff --git a/tests/test_product.py b/tests/test_product.py
index a15e82f6d90..7fa26b13bc6 100644
--- a/tests/test_product.py
+++ b/tests/test_product.py
@@ -741,7 +741,7 @@ def test_kron_input_dtype_matrix(self, dtype1, dtype2):
     @pytest.mark.parametrize(
         "stride", [3, -1, -2, -4], ids=["3", "-1", "-2", "-4"]
     )
-    def test_kron_strided(self, dtype, stride):
+    def test_kron_strided1(self, dtype, stride):
         a = numpy.arange(20, dtype=dtype)
         b = numpy.arange(20, dtype=dtype)
         ia = dpnp.array(a)
@@ -751,6 +751,34 @@ def test_kron_strided(self, dtype, stride):
         expected = numpy.kron(a[::stride], b[::stride])
         assert_dtype_allclose(result, expected)
 
+    @pytest.mark.parametrize("stride", [2, -1, -2], ids=["2", "-1", "-2"])
+    def test_kron_strided2(self, stride):
+        a = numpy.arange(48).reshape(6, 8)
+        b = numpy.arange(480).reshape(6, 8, 10)
+        ia = dpnp.array(a)
+        ib = dpnp.array(b)
+
+        result = dpnp.kron(
+            ia[::stride, ::stride], ib[::stride, ::stride, ::stride]
+        )
+        expected = numpy.kron(
+            a[::stride, ::stride], b[::stride, ::stride, ::stride]
+        )
+        assert_dtype_allclose(result, expected)
+
+    @pytest.mark.parametrize("order", ["C", "F", "A"])
+    def test_kron_order(self, order):
+        a = numpy.arange(48).reshape(6, 8, order=order)
+        b = numpy.arange(480).reshape(6, 8, 10, order=order)
+        ia = dpnp.array(a)
+        ib = dpnp.array(b)
+
+        result = dpnp.kron(ia, ib)
+        expected = numpy.kron(a, b)
+        assert result.flags["C_CONTIGUOUS"] == expected.flags["C_CONTIGUOUS"]
+        assert result.flags["F_CONTIGUOUS"] == expected.flags["F_CONTIGUOUS"]
+        assert_dtype_allclose(result, expected)
+
 
 class TestMultiDot:
     def setup_method(self):