The Symbiosis of Distributed Ledger and Machine Learning as a Relevance for Autonomy in the Internet of Things

The Internet of Things (IoT) describes the fusion of the physical and digital world which enables assets on the edge to send data to a platform where it gets analyzed. Defined actions are then triggered to influence cross-functional edge activities. Furthermore, on the platform tier functionalities and relations need to be identified and implemented to realize assets operating autonomously and ubiquitously. The exploration of this paper results in the identification of autonomous characteristics and shows functional components to implement autonomous assets on the edge. Distributed Ledger Technology (DLT) and its fusion with Machine Learning (ML) as an area of Artificial Intelligence (AI) provides an integral part to realize the described outline. Thus, the recognition of DLT’s and ML’s usage in the IoT and the evaluation of the relevance as well as the synergies build the main focus of this paper

Blockchain Value Creation Logics and Financial Returns

With its complexities and portfolio-nature, the advent of blockchain technology presents several use cases to stakeholders for business value appropriation and financial gains. This 3-essay dissertation focuses on three exemplars and research approaches to understanding the value creation logics of blockchain technology for financial gains. The first essay is a conceptual piece that explores five main affordances of blockchain technology and how these can be actualized and assimilated for business value. Based on the analysis of literature findings, an Affordance-Experimentation-Actualization-Assimilation (AEAA) model is proposed. The model suggests five affordance-to-assimilation value chains and eight value interdependencies that firms can leverage to optimize their value creation and capture during blockchain technology implementation. The second essay empirically examines the financial returns of public firms\u27 blockchain adoption investments at the level of the three main blockchain archetypes (private-permissioned, public-permissioned and permissionless. Drawing upon Fichman\u27s model of the option value of innovative IT platform investments, the study examines business value creation through firm blockchain strategy (i.e., archetype instances, decentralization, and complementarity), learning (i.e., blockchain patents and event participation), and bandwagon effects using quarterly data of firm archetype investments from 2015 to 2020. The study\u27s propensity score matching utilization and fixed-effects modeling provide objective quantification of how blockchain adoption leads to increases in firm value (performance measured by Tobin\u27s q) at the archetype level (permissionless, public permissioned, and private permissioned). Surprisingly, a more decentralized archetype and a second different archetype implementation are associated with a lower Tobin\u27s q. In addition, IT-option proxy parameters such as blockchain patent originality, participation in blockchain events, and network externality positively impact firm performance, whereas the effect of blockchain patents is negative. As the foremost and more established use case of blockchain technology whose business value is accessed in either of the five affordances and exemplifies a permissionless archetype for financial gains, bitcoin cryptocurrency behavior is studied through the lens of opinion leaders on Twitter. The third essay this relationship understands the hourly price returns and volatility shocks that sentiments from opinion leaders generate and vice-versa. With a dynamic opinion leader identification strategy, lexicon and rule-based sentiment analytics, I extract sentiments of the top ten per cent bitcoin opinion leaders\u27 tweets. Controlling for various economic indices and contextual factors, the study estimates a vector autoregression model (VAR) and finds that finds that Bitcoin return granger cause Polarity but the influence of sentiment subjectivity is marginal and only stronger on bitcoin price volatility. Several key implications for blockchain practitioners and financial stakeholders and suggestions for future research are discussed