Purpose

Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).

Design/methodology/approach

The ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.

Findings

The analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.

Research limitations/implications

The analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.

Originality/value

The novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

中文翻译：

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使用机器学习来确定影响产品和产品服务创新的因素

目的

机器学习 (ML) 分析工具越来越多地被视为管理研究中的替代定量方法。本文提出了一种揭示产品和产品服务创新（PSI）背后的前因的新方法。

设计/方法论/途径

机器学习方法在国家层面的创新背景领域是新颖的。赤道国家技术与创新调查的样本由 6,000 多家公司组成，用于对创新的前因进行排名。

发现

分析表明，产品和 PSI 的前身是不同的，但都植根于开放创新和竞争优先的原则。

研究局限性/影响

该分析基于赤道公司的样本，目的是展示机器学习技术如何适合在任何其他背景下测试创新的前因。

原创性/价值

与传统的主题定量分析相比，新颖的机器学习方法可以考虑所分析的每个创新的全套先行交互。