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The purpose of this paper is to identify how project portfolio selection (PPS) methods have evolved and which approaches are more suitable for radical innovation projects. This paper addressed the following research question: how have the selection approaches evolved to better fit within radical innovation conditions? The current literature offers a number of selection approaches with different and, in some cases, conflicting nature. Therefore, there is a lack of understanding regarding when and how to use these approaches in order to select a specific type of innovation projects (from incremental to more radical ones).

Given the nature of the research question, the authors perform a systematic literature review method and analyze 48 portfolio selection approaches. The authors then classified and characterized these articles in order to identify techniques, tools, required data and types of examined projects, among other aspects.

The authors identify four key features related to the selection of radical innovation projects: dynamism, interdependency management, uncertainty treatment and required input data. Based on the content analysis, the authors identified that approaches based on different sources and nature of data are more appropriated for uncertain conditions, such as behavioral methods, information gap theory, real options and integrated approaches.

The research provides a comprehensive framework about PPS methods and how they have been evolving over time. This portfolio selection framework considers the particular aspects of incremental and radical innovation projects. The authors hope that the framework contributes to reinvigorating the literature on selection approaches for innovation projects.

The Internet of Things (IoT) real-time data collection can help to more efficiently optimize and control companies' internal processes. Prior research analyzed IoT benefits and potential applications. Nevertheless, there is little empirical evidence and theoretical understanding of how IoT impacts new product development (NPD). This article aims at narrowing this gap.

In total, 54 case studies were selected from an IoT database – IoT ONE. IoT ONE has a section on NPD. NPD was divided into three phases: discovery, development and commercialization. The adopted IoT technology maturity level was also analyzed. A content analysis was carried out to identify the impacts of IoT in NPD.

This study’s findings capture the emerging patterns of IoT adoption and its impact on NPD. Of the total, 33 IoT adoption cases in the sample were in the machinery and equipment sector. Adopted technologies were at least two years old in 85% of the sample. Only 15% adopted cutting edge technologies (less than 2 years old). Key actors (e.g. vendors) facilitate IoT adoption. By a small margin, the larger impacts of IoT were in the commercialization phase, where it was primarily applied to improve and optimize production processes, to better execute and synchronize new products launching, and to increase the factories' productive capacity. In the discovery phase, IoT was mainly used to identify new opportunities in the market and to collect customer data, to generate a better customer experience. In the development phase, IoT allows greater integration across departments, increasing internal collaboration and allowing more flexible NPD.

Many articles studied the impact of information technologies in NPD. Few address the impact of IoT in NPD. IT tells about the impact of better communications with relevant people. IoT tells about machine acquired information and knowledge. This is new, much broader and deals with quite different impacts on NPD.