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Building Information Modeling (BIM) is a revolutionary innovation in the construction industry to virtually design and mange projects throughout the building lifecycle. Although Estonia is one of the foremost countries in the Information and Communications Technology (ICT) sector, BIM adoption in the Estonian construction industry is still lagging behind other countries. This paper is part of doctoral research that aims to determine the barriers to BIM adoption and develop a framework for effective implementation of BIM in the Estonian construction industry. The purpose of this paper is to examine the status of BIM adoption, BIM benefits and common barriers to BIM adoption in the construction industry worldwide.

The methodology used in this study is a literature review of journal articles, conference proceedings and published reports from various sources.

This study showed BIM benefits through building lifecycle phases and explored the BIM adoption rate in the construction industry of various countries. Eighteen barriers to BIM adoption were also identified.

The study presented is limited to a literature review – some related literature may have been missed.

The main practical significance of this study is that the findings can be used to inform a further survey to model the barriers to BIM adoption in the Estonian construction industry.

This study offers information on BIM adoption in the construction industry and will form the basis of further research.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation – artificial intelligence (AI) – for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

Over the past 20 years, the European Union has developed a comprehensive policy on gender equality (GE) in the fields of research, innovation and higher education. While North European countries have actively implemented policies in this direction, South and East European countries have been far less active and made limited progress, resulting in widening policy gaps across countries. Drawing from the experience of a capacity-building project (TARGET), this chapter explores the factors that impede the implementation of gender equality plans (GEPs) in research and higher education institutions across five countries – Greece, Cyprus, Romania, Italy and Serbia. It argues that the lack of a coherent GE discourse in research and innovation policies that sheds light on structural barriers and implicit bias is a central impediment: it severely limits the potential of GEPs and the power of change agents in research and higher education organisations in Southeast Europe to stimulate institutional change.

This chapter establishes the conceptual and analytic framework for the book. It relates not only to much of the existing work in evolutionary and institutional economics, but also to work in cultural science and cultural semiotics domains as well as in media convergence and transmedia studies. The central concept it first deploys is ‘innovation systems’ as applied in national, regional, international and sectoral contexts. It then builds on the general theory of economic evolution by Kurt Dopfer and Jason Potts and reviews the tools this theory provides to carry out a meso-level analysis of industries co-innovating and converging. It then proposes a new concept – ‘cross-innovation’ – to refer to the emergence of new structures and ‘rules’ at the boundaries of existing industries.