Purpose

The paper attempts to address the following research questions (RQs): RQ1: What are the main research topics within PSS research? RQ2: What are future trends for PSS research?

Design/methodology/approach

Twenty years of research (1999–2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic. As the PSS field is relatively new and fragmented across different disciplines, a review of the prior and relevant literature is important in order to provide the necessary framework for understanding current developments and future perspectives. This paper aims to review and organize research contributions regarding PSS. A machine-learning algorithm, namely Latent Dirichlet Allocation, has been applied to the whole literature corpus on PSS in order to understand its structure.

Findings

The adopted approach resulted in the definition of eight distinct and representative topics able to deal adequately with the multidisciplinarity of the PSS. Furthermore, a systematic review of the literature is proposed to summarize the state-of-the-art and limitations in the identified PSS research topics. Based on this critical analysis, major gaps and future research challenges are presented and discussed.

Originality/value

On the basis of the results of the topic landscape, the paper presents some potential research opportunities on PSSs. In particular, challenges, transversal to the eight research topics and related to recent technology trends and digital transformation, have been discussed.

中文翻译：

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产品服务系统研究：主题格局与未来趋势

目的

本文试图解决以下研究问题 (RQ)： RQ1：PSS 研究中的主要研究课题是什么？RQ2：PSS 研究的未来趋势是什么？

设计/方法/方法

对产品服务系统 (PSS) 的二十年研究（1999-2018 年）产生了大量关于该主题的科学文献。由于 PSS 领域相对较新且分散在不同学科中，因此对先前和相关文献的回顾很重要，以便为理解当前的发展和未来的前景提供必要的框架。本文旨在回顾和组织有关 PSS 的研究贡献。机器学习算法，即潜在狄利克雷分配，已应用于 PSS 的整个文献语料库，以了解其结构。

发现

所采用的方法导致定义了八个不同且具有代表性的主题，能够充分处理 PSS 的多学科性。此外，建议对文献进行系统回顾，以总结已确定的 PSS 研究主题的最新技术和局限性。在此批判性分析的基础上，提出并讨论了主要差距和未来的研究挑战。

原创性/价值

基于主题景观的结果，本文提出了一些关于 PSS 的潜在研究机会。特别是讨论了八个研究主题的横向挑战以及与最近的技术趋势和数字化转型相关的挑战。