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Digitalization is becoming one of the most debated technological trends radically changing how enterprises run and affect entire value chains across organizations. Factors such as globalization, increased data flow, complexity in supply chain operations, and more demanding and critical customer requirements are changing the nature of modern supply chains. The current research reveals how digitalization is reshaping supply chains and how different regions and industries are adapting to digitalization by investigating: the emerging digitalization trends in supply chain strategies, the global geographical impact of digitalization trends, and the impact on different types of business industries. The research is based on a comprehensive statistical analysis conducted on 119 scientific and management consulting studies. The study reveals that digitalization is becoming a revolutionary trend with an increasing pattern of interest from governments, organizations, and academic researchers in the form of nine main digitalization trends applied in various business industries and regions.

Technological innovations are an important part of the everyday life of people, industries, public sectors, etc., thus, it is important to understand the factors influencing individuals' decisions of technology acceptance and adoption. This paper contributes to the existing literature by providing an overview of the development of the Technology Acceptance Model (TAM). An extensive literature review was carried out utilizing scientific databases. Moreover, to ensure the relevance of the content in terms of the inclusion criteria, title, abstract, methods and text of the selected literature were screened. Inclusion criteria were as follows: (1) original and peer-reviewed research, (2) applied empirical research approach such as qualitative, quantitative or mixed methods research, and (3) articles on influential factors to technology acceptance and adoption that examined Technology Acceptance model and its updated and comprehensives versions Fifty-four (54) relevant studies of TAM applications to various technologies across many countries between 2011 and 2020 are used. This study is important as it provides an overview of the likelihood of acceptance for the individual technologies, and further highlights ten influential factors affecting user's decision to accept and adopt technology namely knowledge, awareness, policy, social influence, demographics, self-efficacy, trust, enjoyment, perceived risk and compatibility. The influential factors are discussed in relation to some of the remarkable technological trends. Finally, the outcome of the research should help businesses and governments to develop an effective strategy and policy towards more successful technology adoption.

Abstract
The industrial cold stores can act as thermal energy stores that can store the energy as passive thermal energy. The cold stores have intentions to contribute with flexible consumption but need some knowledge about the potential. By cooling the cold stores and the goods further down when the energy is cheaper, there is a potential of an attractive business case, especially if the elspot price can be predicted further into the future. The cold stores can provide flexibility by load shifting to the energy grid by moving their extensive energy use to off-peak hours. To fulfill the potential, it is necessary to measure some data in the cold stores to be able to control them and ensure food safety. A case study was tested and revealed that by cooling further in periods of low-cost electricity could results in 30% savings. With proper elspot price forecasting this percentage could reach up to 40%. Theoretically, by utilizing the full capacity of cold stores in Denmark for thermal energy storage, it is possible to use 2% of the average wind electricity production.

Reliable empirical data on the siting characteristics and operational performance of wind farms are scarce. Knowing more about the technical characteristics of wind farms provides insight into the business mindset of wind farm developers, which can be useful for policymakers or researchers who are intent on designing policy in a way to optimize wind farm investment by creating better alignment between the investment patterns sought by developers and government support designed to attract investment. This study draws on a unique dataset from 32 wind farms, 20 onshore and 12 in forested areas with a total of more than 2.5 GW installed wind capacity to explore development patterns. The paper examines four hypotheses related to characteristics of wind farms in emerging markets and investigating how project delays and progressive technological enhancements shape wind farm development. In this paper, we explain these results and conclude by extracting lessons from this analysis for creating wind power policy better aligned with developers' interests.

The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelized cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as "Grand Challenges" in the digitalisation of wind energy. They are, first, the need to create reusable data frameworks; secondly, the need to connect people to data to foster innovation; and finally, the need to enable collaboration and competition between organisations. The Grand Challenges thus include a mix of technical and cultural aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.