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This paper addresses the most important factors for the selection of additive manufacturing (AM) technology as a method of production of metal parts. AM creates objects by adding material layer by layer based on 3D models. At present, interest in AM is high as it is hoped that AM contributes to the competitiveness of Western manufacturing industries.

A literature study is conducted to identify the factors that affect the selection of AM technology. Expert interviews and the best–worst method are used to prioritize these factors based on relative factor weights.

Technology, demand, environment and supply-related factors are categorized and further mapped to offer a holistic picture of AM technology selection. According to expert assessments, market demand was ranked highest, although market demand is currently lacking.

The composition and size of the expert panel and the framing of some of the factors in light of previous literature cause validity limitations. Further research is encouraged to differentiate the selection factors for different AM implementation projects.

The paper presents a more complete framework of factors for innovation selection in general and the selection of AM technology specifically. This framework can serve as a basis for future studies on technology selection in the (additive) manufacturing sector and beyond. In addition to AM-specific factor weights, the paper explains why specific factors are important, reducing uncertainty for managers that have to choose between alternative manufacturing technologies.

The purpose of this paper is to examine high-tech small-to-medium-sized enterprises (SMEs) supply chain partnerships. Partnerships are considered at the level of business function rather than the entire organisation. Second, the drivers of SMEs to engage in partnerships are assessed to see whether functions engage in partnerships for different reasons. Third, performance per function is assessed to see the differential effect of partnerships on the function’s performance.

In this study, the relationship between the drivers of SMEs to engage in partnerships, four types of partnerships (marketing and sales, research and development (R&D), purchasing and logistics, and production) and four types of functional performances of firms (marketing and sales, R&D, purchasing and logistics, and production) are examined. The data have been collected from 279 SMEs. The proposed hypotheses are tested using structural equation modelling.

The results indicate that there are considerable differences between business functions in terms of the degree of involvement in partnerships and the effect of partnerships on the performance of these functions. This paper contributes to research by explaining the contradictory results of partnerships on SMEs performance.

This study helps firms understand which type of partnership should be established based on the firm’s drivers to engage in supply chain partnership; and which partnership has a significant effect on which type of business performance of the firm.

The originality of this study is to investigate the relationship between different drivers to engage in supply chain partnership and different types of partnerships and different functional performance of firm in a single model.

Earlier studies have generally shown a positive relationship between entrepreneurial orientation (EO) and the overall performance of the firm. The purpose of this paper is to understand in more detail how EO influences firm performance. It adds to the literature by distinguishing performances of different functions in a firm and by exploring how the dimensions of EO influence these functional performances and, in turn, overall firm performance.

This study examined the relationship between three dimensions of EO (innovativeness, proactiveness, risk-taking), three types of functional performances of firms (R&D performance, production performance, marketing and sales performance) and the overall performance of firms. The data are collected from 279 high-tech small-to-medium-sized enterprises (SMEs) using a postal survey. The proposed hypotheses are tested using structural equation modeling (SEM).

The results indicate that the dimensions of (EO) are related in different ways to the performance of functions in a firm. A positive relationship is observed between innovativeness and R&D performance and between proactiveness and marketing and sales performance. A negative relationship exists between risk-taking and production performance. The results also show a sequential positive relationship from R&D via production and marketing and sales to overall performance of firms. Therefore, it is concluded that the R&D, production and marketing and sales functions reinforce each other in a logic order and are complementary in their effect on overall firm performance.

The results imply that the three functions, R&D, production and marketing and sales, in a firm play different roles, both in the firm’s EO and in their contribution to overall performance. Managers can use the findings to monitor and influence the performance of different functions in a firm to increase overall firm performance.

The first contribution of this study is that it unravels (i) which dimensions of EO have an effect on the performance of separate functions in a firm, indicating that functions contribute in different ways to entrepreneurial orientation of the firm. A second contribution is assessing how the performance of these functions influence the firm’s overall performance. This paper fills a gap in the literature by exploring internal firm variables mediating the relationship between EO and overall firm performance and contributes to the discussion on the contradictory results regarding the relationship between risk-taking and firm performance.

The purpose of this paper is to introduce, summarize and combine the results of 11 articles in a special issue on the implementation of Industry 4.0. Industry 4.0 emerged as a phenomenon about a decade ago. That is why, it is interesting now to explore the implementation of the concept. In doing so, four research questions are addressed: (1) What is Industry 4.0? (2) How to implement Industry 4.0? (3) How to assess the implementation status of Industry 4.0? (4) What is the current implementation status of Industry 4.0?

Subgroups of articles are formed, around one or more research questions involving the implementation of Industry 4.0. The articles are carefully analyzed to provide comprehensive answers.

By comparing definitions systematically, the authors show important aspects for defining Industry 4.0. The articles in the special issue explore several cases of manufacturing companies that implemented Industry 4.0. In addition, systematic approaches to aid implementation are described: an approach to combine case-study results to solve new implementation problems, approaches to assess readiness or maturity of companies regarding Industry 4.0 and surveys showing the status of implementation in larger samples of companies as well as showing relationships between company characteristics and type of implementation. Small and large firms differ considerably in their process of implementing Industry 4.0, for example.

This special issue discusses implementation of Industry 4.0. The issue is limited to 11 articles, each of which with its own strengths and limitations.

The practical relevance of the issue is that it focuses on the implementation of Industry 4.0. Cases showing successful implementation, measurement instruments to assess degree of implementation and advice how to build a database with cases together with large-scale studies on the state of implementation do provide a wealth of information with a large managerial relevance.

The paper introduces an original take on Industry 4.0 by focusing on implementation. The special issue contains both literature reviews, articles describing case studies of implementation, articles developing systematic measurement instruments to assess degree of implementation and some articles reporting large-scale studies on the state of implementation of Industry 4.0 and thereby combine several perspectives on implementation of Industry 4.0.

For successful servitization, manufacturing firms must understand how their customers adopt new services. The purpose of this paper is to explore customers’ readiness for a manufacturer’s new services to complement its goods. The goal is to increase knowledge of the aspects that manufacturers should consider when bringing new kinds of services to market.

A qualitative case study design is used to analyze readiness for services and interest in service adoption in three customer firms of a manufacturer. The interview data were collected from 14 persons at customer sites and were content analyzed.

The results show that readiness – a concept that is often used in the field of technology – is relevant also for the service adoption process. In a business-to-business context, readiness for service adoption concerns the individual and organizational levels, and hence a new dimension of organizational culture and habits had to be added to the concept that originally focuses on individuals. People consider different factors when making consecutive decisions during the service adoption process and these factors can vary even within a company. The cornerstone for new service adoption is the customer firm’s actual need for the service.

The results offer new knowledge about service adoption in a business-to-business context by taking a customer firm’s perspective. They, thus, complement previous studies on the supplier perspective of servitization and service adoption in consumer business. The contributions help manufacturers focus their efforts when bringing new services to market.

The goal of this article is to show that memetics is particularly useful to predict the adoption of major innovations.

Describes how TNO Telecom, an applied research institute in The Netherlands, adopted the theory of memetics to develop an instrument that predicts the adoption of major innovations. Explains and defines relevant aspects of this focus.

Initial results are encouraging and suggest that the approach may provide qualitatively better results than the existing methods when applied to major innovations.

Describes for the first time how the theory of memetics can be used to gain a real insight into the market adoption of major innovations as well as to focus and optimise product development.

Diffusion of many successful communication technologies, like telephony and television technology, follows an almost perfect S‐shaped curve. This curve implies that, after their introduction, subsequent sales of products on the basis of these technologies can be predicted accurately. However, the diffusion of other breakthroughs in communication technologies, like interactive television, videotelephony or broadband mobile communication technology, shows a more erratic pattern. Introduction of these technologies is often postponed or, once introduced, they are quickly withdrawn from the market after the first disappointing results. Rather than distinguishing alternative patterns, this article shows that the S‐shaped curve and the more erratic patterns represent subsequent phases in one pattern of development and diffusion of breakthrough communication technologies. Three phases are distinguished in this pattern. Managerial implications of the differences between these phases are discussed. the paper shows that a company trying to introduce a new communication technology has to adopt different strategies in each phase.

The purpose of this paper is to describe a method of estimating the likelihood that a person with particular characteristics will imitate a particular new behaviour (i.e. the use of an innovation). This estimation can be used to provide a new form of forecast for the likely market demand for an innovation.

This method, termed imitation analysis, is based on imitation theories from the behavioural sciences and is applied in two recent case studies in The Netherlands: broadcast TV on mobile phones and a mobile friend‐network service.

These cases illustrate how: the market segments with the highest potential can be identified; marketing communication can be focused on specific issues important for each segment (e.g. based on the highest imitation potential); product design can be improved (by highlighting the characteristics with the most room for improving the imitation potential); and market demand can be modelled (the overall chance of imitation occurring).

Management implications for the two services, as well as the usefulness of imitation analysis in forecasting studies, are discussed.

The paper expands on original work published in this journal in 2005, showing the value of the approach in real‐world settings.