The Influence of Network Position on Executives’ Preference for Imitation over Innovation: A Scenario-Based Experimental Approach

This study investigates how the different dimensions of a firm’s network position influence executives’ preference for imitation over innovation in new venture development, thus reflecting their preference for uncertainty. Through a scenario-based experimental approach involving 1024 responses from 64 senior executives, we examine how network centrality, structural equivalence, and structural autonomy shape these strategic preferences. The findings reveal that firms with higher network centrality tend to favor innovation over imitation more strongly. At the same time, those in structurally equivalent positions show a greater preference for imitation. Structural autonomy does not significantly affect these preferences. This study contributes to the literature by revealing how different network structures uniquely influence strategic decision-making in uncertain environments, offering valuable insights for scholars and practitioners. The scenario-based experimental method enhances the precision of the findings and provides actionable implications for real-world decision-making in venture development.

Authors

Institutions

Publication Details

Journal
Systems
Published
2024-12-25
DOI
https://doi.org/10.3390/systems13010004
Citations
1
Primary Topic
Digital Platforms and Economics
Type
article
Field-Weighted Citation Impact
0.55

Funders

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

The Influence of Network Position on Executives’ Preference for Imitation over Innovation: A Scenario-Based Experimental Approach

Guojian Zhang, Francis Boadu, Yu Xie
1 citations
Systems
Digital Platforms and Economics
0.55
article

The Influence of Network Position on Executives’ Preference for Imitation over Innovation: A Scenario-Based Experimental Approach

Guojian Zhang, Francis Boadu, Yu Xie
article en
1 citations

Abstract

This study investigates how the different dimensions of a firm’s network position influence executives’ preference for imitation over innovation in new venture development, thus reflecting their preference for uncertainty. Through a scenario-based experimental approach involving 1024 responses from 64 senior executives, we examine how network centrality, structural equivalence, and structural autonomy shape these strategic preferences. The findings reveal that firms with higher network centrality tend to favor innovation over imitation more strongly. At the same time, those in structurally equivalent positions show a greater preference for imitation. Structural autonomy does not significantly affect these preferences. This study contributes to the literature by revealing how different network structures uniquely influence strategic decision-making in uncertain environments, offering valuable insights for scholars and practitioners. The scenario-based experimental method enhances the precision of the findings and provides actionable implications for real-world decision-making in venture development.

SystemsVol. 13(1)
Chongqing Technology and Business University (CN), University of Electronic Science and Technology of China (CN), Kumasi Technical University (GH)
National Natural Science Foundation of China
Industry, innovation and infrastructure
Openalex Percentile: Top 19%
Digital Platforms and Economics
0.55
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.

The Influence of Network Position on Executives’ Preference for Imitation over Innovation: A Scenario-Based Experimental Approach — Guojian Zhang, Francis Boadu, et al. · Systems (2024) | TGRS Research Map | TGRS