Using machine learning to determine factors affecting product and product–service innovation

IF 7.4 3区管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Journal of Enterprise Information Management Pub Date : 2024-02-27 DOI:10.1108/jeim-06-2023-0339

Oscar F. Bustinza, Luis M. Molina Fernandez, Marlene Mendoza Macías

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引用次数: 0

Abstract

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.

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利用机器学习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另一种定量方法。本文提出了一种揭示产品和产品服务创新（PSI）背后先决条件的新方法。研究结果分析表明，产品创新和产品服务创新的前因各不相同，但都植根于开放式创新和竞争优先原则。研究局限性/意义该分析以赤道地区的企业为样本，旨在说明 ML 技术如何适用于测试任何其他背景下的创新前因。原创性/价值与传统的定量分析相比，新颖的 ML 方法可以考虑到与所分析的每项创新相关的全套前因相互作用。

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

Journal of Enterprise Information Management Multiple-

CiteScore

14.80

自引率

6.20%

发文量

期刊介绍： The Journal of Enterprise Information Management (JEIM) is a significant contributor to the normative literature, offering both conceptual and practical insights supported by innovative discoveries that enrich the existing body of knowledge. Within its pages, JEIM presents research findings sourced from globally renowned experts. These contributions encompass scholarly examinations of cutting-edge theories and practices originating from leading research institutions. Additionally, the journal features inputs from senior business executives and consultants, who share their insights gleaned from specific enterprise case studies. Through these reports, readers benefit from a comparative analysis of different environmental contexts, facilitating valuable learning experiences. JEIM's distinctive blend of theoretical analysis and practical application fosters comprehensive discussions on commercial discoveries. This approach enhances the audience's comprehension of contemporary, applied, and rigorous information management practices, which extend across entire enterprises and their intricate supply chains.