Abstract
The decision to adopt new technologies is the most important stage in integrating a new technology into the ongoing processes of the organization and also to obtain benefits from its routine use. This paper proposes an integrated framework that combines Diffusion of Innovations (DOI) Theory, Technology-Organization-Environment (TOE) framework and Institutional Theory (INT) to characterize the critical factors influencing advanced technologies adoption in manufacturing context. This conceptual framework identifies three contextual environments - innovation, internal organizational and external environmental - that can influence the adoption decision along with some sub-contexts from the literature that may be considered. This framework can be used as starting point to explore in depth influential factors in advanced technologies in manufacturing contexts. Additionally, this framework can assist companies to develop adoption process plans as well as managerial practices that consider the role of these factors and thus lead to successful implementations.

Abstract
Today's manufacturing environment is increasingly pressured to higher flexibility induced by uncertain production volumes as well as uncertain product lifetime. A way to improve productivity in a flexible production system is by using a safe and flexible cooperation between robot and operator. Therefore, manufacturing companies are experiencing an increase in human-robot interactions and in the use of collaborative robots (cobots). To make full use of cobots, it is essential to understand the drivers for their adoption as well as how these drivers are aligned with the companies' strategic objectives. By means of in-depth interviews in six companies in Portugal and France, this study provides a comprehensive understanding of the drivers that influence the intent to adopt, or the effective adoption, of cobots and the alignment of these drivers with the strategic objectives of the company. Empirical results reveal "operational efficiency" and "ergonomics and human factors" concerns as important drivers in the adoption intent. In terms of strategic objectives, it was found that drivers are aligned with productivity and flexibility improvement as well as quality improvement strategic objectives. Understanding these drivers can help in motivating manufacturing companies to adopt cobots, in facilitating their adoption, and in reaping the benefits of this technology.

Abstract
The widespread use of internet is changing the way supply chain echelons interact with each other in order to respond to increasing customer requests of personalized products and services. Companies acquainted with the concept of industry 4.0 (i4.0) embrace the use of internet to improve their internal and external processes, delivering the dynamic and flexible response customers want. This chapter aims to discuss how supply chains may benefit from the adoption of i4.0 technologies by their partners and highlights some of its implementation challenges. Eight technologies cover most of i4.0 applications: additive manufacturing; big data & analytics; cloud computing; cyber-physical systems; cyber security; internet of things; collaborative robotics; and visual computing. At individual level, technologies such as additive manufacturing, collaborative robots, visual computing and cyber-physical systems establish the connectivity of a certain company. However, the integration of the whole supply chain, based on the principles of i4.0, demands that information provided by each company (Big Data) is shared through a collaborative system based on Cloud Computing and Internet of Things technologies. To safely share useful information, Cyber Security techniques must be implemented in individual systems and cloud solutions. Summing up, even though the adoption of i4.0 demands an individual initiative, it will only raise the supply chain’s competitive advantage if all companies adapt their manufacturing and supply chain processes. The main advantage foreseen here is based on an improved communication system of the whole supply chain, bringing consumers closer to the production process. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Governmental investments on the development of high-tech clusters are among the main policies for socioeconomic development, enabling countries to be part of global value networks. Our objective is to identify which are the strategies of countries that want to join global aerospace value networks, by means of an abductive case research. Countries were divided in 3 groups (A; B: C) according to their global aerospace exports share. The analytical framework used to identify the strategies has 3 dimensions: network structure, network governance, and network dynamics. Results show different strategies according to the country's global exports share. While for countries in group A (exports above 1%), a strategy focused on the dimension network structure indicated a sustained high-tech sector. Countries in group C tend to focus on specialization, taking advantage of shifts in technological paradigms to upgrade their development level. The dimension network governance is mainly related to governmental efforts toward the creation of clusters and associations, promoting specialization and collaborative work. Finally, the dimension network dynamics describes the attraction of foreign companies to qualify the clusters at countries who belong to group C, while countries at group A reinforce their research and development activities. The comparison between countries is helpful for governmental representatives who want to develop strategies toward increasing participation in an industrial global value network and for supply chain managers to help selecting the locations for their operations.

Abstract
Previous literature shows that there are different environmental factors with different impacts on the adoption of technologies in a supply chain context. Thus, the adoption of technologies in supply chains may vary according to different environmental factors. Despite the existence of several studies about adoption of technologies in supply chain contexts that include environmental factors, there is a gap in identifying which environmental factors influence the adoption of digitalization technologies in supply chains. The purpose of this study is therefore to identify and analyze the environmental factors that influence the adoption of digitalization technologies in the supply chain. An exploratory qualitative research was conducted using semi-structured interviews with Portuguese managers of companies at several tiers of the automotive supply chain. Environmental factors were pointed as particularly critical drivers to promote the adoption of digitalization technologies in the automotive supply chain. Such adoption is mainly driven by the Original Equipment Manufacturer ( OEM), through coercive and normative pressures over the other tiers of the supply chain. Relevant factors identified are: compliance with standards and legislation, market and industry pressures, and benchmark the evolution of supply chain partners. This study contributes to the literature with new knowledge concerning new specificities of the environmental factors that showed an important influence on the adoption decision.

Abstract
The concept of industry 4.0 (i4.0) encompasses the integration of different technologies into an autonomous, knowledge- and sensor-based, self-regulating production system. Our objective is to synthesize which are the challenges and opportunities of adopting i4.0 from the perspective of technology provider companies. A single-case research was conducted with ten companies at the Portuguese Production Technologies Cluster. Based on i4.0 technologies – Augmented reality; Additive Manufacturing; Big Data; Cloud Computing; Cyber-Physical Systems; Cybersecurity; Smart Robotics; Simulation; and System Integration – interviewees mentioned that the main adoption challenges are the analysis of data generated, integration of new technologies with available equipment and workforce, and computational limitations. The main opportunities are improvements in: efficiency; flexibility; productivity; cybersecurity; quality of products and services; and decision process due to data analysis. Interviewees have also foreseen changes in company's business model through the integration of internal resources with complementary activities of their partners and other cluster companies. © 2019 Elsevier Inc.