How Dynamic Capabilities and Ambidextrous Learning Shape SME Innovation Performance: The Moderating Role of Exploratory and Exploitative Learning

This study examines the differential effects of three dimensions of dynamic capabilities on innovation performance and investigates how ambidextrous learning (exploratory and exploitative learning) moderates these relationships. Drawing on survey data from 299 Chinese enterprises, we employ hierarchical regression analysis with interaction terms to test the proposed hypotheses. The results reveal that all three capability dimensions significantly enhance innovation performance, with innovative capacity exerting the strongest effect. Both exploratory and exploitative learning positively moderate the capability–performance relationships, though their effects vary across capability dimensions: exploitative learning more strongly reinforces the effect of absorptive capacity, while exploratory learning more effectively amplifies the influence of innovative capacity. These findings contribute to the dynamic capabilities literature by revealing the heterogeneous performance implications of distinct capability dimensions and demonstrating how different learning modes serve as boundary conditions that shape capability effectiveness.

Authors

Institutions

Publication Details

Journal
Systems
Published
2026-02-03
DOI
https://doi.org/10.3390/systems14020164
Primary Topic
Innovation and Knowledge Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How Dynamic Capabilities and Ambidextrous Learning Shape SME Innovation Performance: The Moderating Role of Exploratory and Exploitative Learning

Yonghong Ma, Zihui Wei
Systems
Innovation and Knowledge Management
article

How Dynamic Capabilities and Ambidextrous Learning Shape SME Innovation Performance: The Moderating Role of Exploratory and Exploitative Learning

Yonghong Ma, Zihui Wei
article en

Abstract

This study examines the differential effects of three dimensions of dynamic capabilities on innovation performance and investigates how ambidextrous learning (exploratory and exploitative learning) moderates these relationships. Drawing on survey data from 299 Chinese enterprises, we employ hierarchical regression analysis with interaction terms to test the proposed hypotheses. The results reveal that all three capability dimensions significantly enhance innovation performance, with innovative capacity exerting the strongest effect. Both exploratory and exploitative learning positively moderate the capability–performance relationships, though their effects vary across capability dimensions: exploitative learning more strongly reinforces the effect of absorptive capacity, while exploratory learning more effectively amplifies the influence of innovative capacity. These findings contribute to the dynamic capabilities literature by revealing the heterogeneous performance implications of distinct capability dimensions and demonstrating how different learning modes serve as boundary conditions that shape capability effectiveness.

SystemsVol. 14(2)
Harbin Engineering University (CN)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 70%
Innovation and Knowledge Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.