A study on the impact of entrepreneurial bricolage on enterprise performance management using the BPNN-DEMATEL method and social network analysis

IF 10.1 1区社会学 Q1 SOCIAL ISSUES

Technology in Society Pub Date : 2025-03-26 DOI:10.1016/j.techsoc.2025.102883

Xi Kang , Saiyong Li , Kanchaya Chaivirutnukul

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引用次数: 0

Abstract

With the advancement of artificial intelligence (AI), intelligent methods have become increasingly important for optimizing enterprise development strategies. This study applies the Back Propagation Neural Network-Decision Making Trial and Evaluation Laboratory (BPNN-DEMATEL) method and social network analysis to improve strategic decision-making for emerging enterprises. First, the BPNN-DEMATEL method is developed based on Back Propagation Neural Network (BPNN) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL). Then, it is refined using insights from social network analysis. Finally, the model is evaluated to assess its effectiveness in analyzing performance management strategies. Results indicate that the BPNN-DEMATEL model improves calculation accuracy by approximately 23 %–47 % and reduces reaction time by 20 %–50 % compared to existing models. After optimization, integrating social network analysis further enhances accuracy, increasing it by 38 %–70 %. Additionally, the model effectively examines the impact of entrepreneurial bricolage on performance management, providing insights that support new venture development. These findings contribute to the optimization and practical application of AI in enterprise strategy, offering both technical and theoretical foundations for business growth in the digital era.

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基于BPNN-DEMATEL方法和社会网络分析的创业拼凑对企业绩效管理的影响研究

随着人工智能（AI）的发展，智能方法对于优化企业发展战略变得越来越重要。本研究运用反向传播神经网络决策试验与评估实验室（BPNN-DEMATEL）方法和社会网络分析来改善新兴企业的战略决策。首先，基于反向传播神经网络（BPNN）和决策试验与评估实验室（DEMATEL）建立了BPNN-DEMATEL方法；然后，使用来自社交网络分析的见解对其进行细化。最后，对模型进行了评估，以评估其在分析绩效管理策略方面的有效性。结果表明，与现有模型相比，BPNN-DEMATEL模型的计算精度提高了约23% - 47%，反应时间缩短了20% - 50%。优化后，整合社会网络分析进一步提高准确率，提高38% - 70%。此外，该模型有效地考察了创业拼凑对绩效管理的影响，提供了支持新企业发展的见解。这些发现有助于人工智能在企业战略中的优化和实际应用，为数字时代的企业增长提供技术和理论基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technology in Society Multiple-

CiteScore

17.90

自引率

14.10%

发文量

316

审稿时长

60 days

期刊介绍： Technology in Society is a global journal dedicated to fostering discourse at the crossroads of technological change and the social, economic, business, and philosophical transformation of our world. The journal aims to provide scholarly contributions that empower decision-makers to thoughtfully and intentionally navigate the decisions shaping this dynamic landscape. A common thread across these fields is the role of technology in society, influencing economic, political, and cultural dynamics. Scholarly work in Technology in Society delves into the social forces shaping technological decisions and the societal choices regarding technology use. This encompasses scholarly and theoretical approaches (history and philosophy of science and technology, technology forecasting, economic growth, and policy, ethics), applied approaches (business innovation, technology management, legal and engineering), and developmental perspectives (technology transfer, technology assessment, and economic development). Detailed information about the journal's aims and scope on specific topics can be found in Technology in Society Briefings, accessible via our Special Issues and Article Collections.