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Researchers have shown increased interest in open innovation – that is, the inflow and outflow of ideas, or the collaborative efforts of innovating – while previous research on acquisitions of innovative firms has foremost focused on the inflow only. Open innovation, however, introduces several new challenges related to acquisitions of such firms, not the least related to intellectual property rights and innovative skills that may be distributed among several parties. This paper explores what issues the literature on open innovation and acquisitions deals with related to acquisitions in open innovation environments.

A systematic literature review is conducted to achieve the purpose of the paper. Two main questions are addressed. First, how can acquisitions be understood in relation to open innovation? Second, what does the open innovation literature say on matters of distributed innovations in relation to acquisitions?

The paper concludes that there is a quite limited amount of research concerning itself with open innovation and acquisitions combined. Furthermore, acquisitions are for the most part seen as a means to reach innovation in transaction-based transfers between parties.

With acquisitions of innovative firms, in general, being seen as an important means to reach new ideas, while open innovation is on the rise, the juxtaposing of these phenomena would be of high practical and theoretical relevance to study further.

Following the triple helix (TH) model and the way knowledge is transferred into the industry domain, this chapter aims to define features interface that should be implemented in order to facilitate the University–Industry (UI) relationship and thus encourage the spin-off creation.

In order to support this relationship, a new business model configuration of an entrepreneurial ecosystem is proposed, aiming at creating a sustainable environment, where business entities can grow. The field of the Governance of Entrepreneurial Ecosystems is also investigated in order to define a framework for launching, developing, and sustaining a company over time.

This chapter presents a case study developed within the University of Salento (Italy). It capitalizes results from three different research analyses, based on questionnaires and interviews with actors of the spin-off network (professors and researchers, graduating students, admin-tech staff of the Technology Transfer Office, spin-offs’ CEOs/Associates, and R&D managers of external companies) and on results coming from scientific publications and regional/national reports in the innovation context.

A research methodology based on semantic network analysis and sentiment analysis has been applied in order to identify which features an interface should implement in order to facilitate the UI relationship and encourage the spin-off creation.

To support the start-up overcoming the “death valley,” the creation of a link between the strategy used to transfer value to the market and the phase of innovation is proposed inside the business model configuration. Some aspects of a governance model of an entrepreneurial ecosystem were also presented in order to support the business evolution of a single business entity and assuring sustainability over time.

The team members’ expertise has been shown to increase team effectiveness when it is actively coordinated. While in face-to-face teams such expertise coordination unfolds through direct interaction, expertise coordination in dispersed teams is unlikely to evolve automatically. In this context, shared leadership, that is, the distribution of leadership influence across multiple team members is argued to serve as initiating mechanism for expertise coordination.

Purpose: There have been continuous developments in the production industry to meet the increasing customer demand from the past to the present. At this point, supply chain management (SCM) systems emerge as an important topic. SCM is a set of systems that manages the entire process from the production of a product to its delivery to the end user. Industry 4.0 aims to improve the production industry by increasing the quality, efficiency, and performance of the production process. Therefore, in this chapter, the authors highlight the challenges, benefits, and future trends of the combination of Industry 4.0 and SCM systems.

Methodology: In this chapter, the integration of Industry 4.0 and SCM systems was investigated. For this purpose, the Industry 4.0 position of the countries and the current status of SCM systems have been examined. In addition, the key technologies in the Industry 4.0 transformation, the possible problems encountered in the transformation, the deficiencies encountered in SCM systems, and how these deficiencies can be solved with Industry 4.0 were investigated.

Findings: The results of this study show that companies that use an SCM system can separate themselves from their competitors by using Industry 4.0 technologies.

Significance: This can allow them to achieve their strategic goals and to ensure the maintenance of their competitive advantage.

Amid rising environmental concerns, Industry 4.0 and blockchain technology (BCT) are transforming circular economy practices and prevailing business models. Recognizing the same, the current study examines the role of advanced technology in circular practices and their impact on eco-environmental performance, which influences organizational performance. The study collects data from 185 food processing enterprises that are located in Malaysian territories. By employing CB-SEM modeling, the study provides three key findings. First, Industry 4.0 significantly improves the circular economy practices. Second, circular economy practices help to improve firms' environmental performance but did not stimulate operational performance. Third, higher eco-environmental performance significantly boosts organizational performance. This study set out the foundations for participating countries/firms that help to achieve sustainable goals through the integration of blockchain technology in circular economy practices.

This chapter explores the role of nodes in knowledge networks. The authors characterize knowledge nodes by the type of actors they represent, including individual human agents, collective human groups and teams, explicit non-human objects and resources, and non-human agents and machines. The authors define knowledge nodes by their role in the network, including producer, consumer, or broker of knowledge, and in terms of the stock of knowledge they represent and their capacity to absorb knowledge made available in the network.

An issue that is becoming yet more relevant to modern manufacturers is that of flexibility. As life cycles become shorter a manufacturing firm can easily be left with redundant stock and dated processes. In Taiwan this issue has been addressed at several levels, this case study describes one such project. A Taiwanese academic conducted a study, gaining business acceptance of a hierarchical set of theoretical flexibility factors, then rearranging these via pictorial representations of fuzzy logic-derived plane surfaces, and finally re-presenting them to business as a set of ordered propositions designed to identify the key factors contributing to flexibility. The learning points relate, first, to the empirical facts uncovered about the specific factors that have a major bearing on manufacturing flexibility. These factors are, of course, specific to Taiwan and the current environment there. Second, though, is the more enduring illustration of a mixed-method case approach; where interviews, fuzzy logic analytical methods, and pictorial representation of the fuzzy logic output all combine to give clear guidance to managers of an industrial sector under stress, and to the policy makers who exert significant control over their environment.

This chapter uses multiple research methods, including quantitative science mapping analysis (SciMat) and a qualitative literature review, to provide insight into the academic debate unfolding at the intersection of big data and business processes. SciMat analysis based on keyword co-occurrence enabled identifying 12 of the most productive research themes, as reflected in a poll of 301 articles about big data and business processes. The three most important themes are: firm performance, Industry 4.0, and innovation. The traditional literature review on firm performance indicated that big data analytics (BDA) positively influence business process performance and have a beneficial impact on a firm’s performance, that is, the role of big data is viewed as critical in the context of Industry 4.0 because it enhances productivity and improves business processes. The benefits of BDA can be achieved only if the organizational obstacles related to planning, workforce attitude, and alignment with strategy are overcome. Moreover, big data is perceived as a significant source of innovation in an organization and can be conceptualized with the use of a resource-based view (RBV) of the firm. BDA positively influence business processes, which is strengthened by adequate implementation and openness to innovation.