Artificial intelligence (AI) is a technology that can perform complex tasks that require human intelligence, and it holds the potential of exceeding human capabilities (Agarwal et al., 2020; Pandl et al., 2020). AI is one of the main drivers of industrial development as it promotes the integration of emerging technologies in the Fourth Industrial Revolution (IR 4.0) (Goodell et al., 2021; Lim, 2019; Zhang et al., 2021), such as blockchain (Ehrenberg & King, 2020), cryptocurrency (Li & Whinston, 2020), cloud computing (Hsu, 2020), and internet of things (IoT) (Ghaleb et al., 2021). Indeed, the massive amount of data generated by IoT devices, social media, and web applications has fueled the proliferation of AI, wherein the data is utilized in the training of machine learning algorithms (Dinh & Thai, 2018). However, some concerns associated with AI exists. Specifically, privacy has become a critical concern as a result of a series of leaks and misuse of personal data. The Facebook scandal in which millions of users were targeted without consent by Cambridge Analytica, a third-party political firm, is one such example. Other growing concerns with AI include explainability and trustworthiness as the technology does not interact or speak with human users and thus cannot be verified or trusted (Dinh & Thai, 2018).
Today, the language of business involves compound concepts such as dematerialization, disintermediation, and designing and producing goods on demand (Kumar, 2019). In this regard, success in the next industrial era requires companies to reconfigure their business models in ways where technology becomes central to their operations in order to address these changing demands in the future of work and marketplaces. AI and blockchain are powerful technologies that are well positioned for this endeavor as they hold the potential to reform existing processes for greater efficiency and seamlessness. Indeed, the paradigm of organizations today is transitioning from a hierarchical to a self-organizing model (Subic et al., 2020). While AI and blockchain have initially focused on the finance sector, companies today have come to realize its potential for other sectors, including agriculture, healthcare, logistics, manufacturing, and supply chains. (Pandl et al., 2020). Yet, no study, to date, have shed light on the peculiarities and opportunities for AI and blockchain integration specifically for business through a scientific consolidation of knowledge, which is arguably important for both future research (e.g., what else should we know) and practice (e.g., what should we do) in the field.
Electro-rheological fluid (ER) technology is an old newcomers coming to the market at high speed. Various industries including the automotive industry, production sector, and robotics are full of potential ER fluid applications. Electro-rheological fluid technology has been successfully employed already in various low and high volume applications. A structure based on ER fluids might be the next generation in design for products where power density, accuracy and dynamic performance are the key features. Additionally, for products where is a need to control fluid motion by varying the viscosity, a structure based on ER fluid might be an improvement in functionality and costs. Two aspects of this technology, direct shear mode (used in brakes and clutches) and valve mode (used in dampers) have been studied thoroughly and several applications are already present on the market. Excellent features like fast response, simple interface between electrical power input and mechanical power output, and precise controllability make ER fluid technology attractive for many applications. 2b1af7f3a8