Cryptocurrencies have become a very popular topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Twitter for a wide variety of events and more specically for nancial markets. This paper studies to what extent public Twitter sentiment can be used to predict price returns for the nine largest cryptocurrencies: Bitcoin, Ethereum, XRP, Bitcoin Cash, EOS, Litecoin, Cardano, Stellar and TRON. By using a cryptocurrency-specic lexicon-based sentiment analysis approach, nancial data and bilateral Grangercausality testing, it was found that Twitter sentiment has predictive power for the returns of Bitcoin, Bitcoin Cash and Litecoin. Using a bullishness ratio, predictive power is found for EOS and TRON. Finally, a heuristic approach is developed to discover that at least 1-14% of the obtained Tweets were posted by Twitter bot accounts. This paper is the rst to cover the predictive power of Twitter sentiment in the setting of multiple cryptocurrencies and to explore the presence of cryptocurrency-related Twitter bots.

Until recently decisions were mostly modelled within the process. Such an approach was shown to impair the maintainability, scalability, and flexibility of both processes and decisions. Lately, literature is moving towards a separation of concerns between the process and decision model. Most notably, the introduction of the Decision Model and Notation (DMN) standard provides a suitable solution for filling the void of decision representation. This raises the question whether decisions and processes can easily be separated and consistently integrated. We introduce an integrated way of modelling the process, while providing a decision model which encompasses the process in its entirety, rather than focusing on local decision points only. Specifically, this paper contributes formal definitions for decision models and for the integration of processes and decisions. Additionally, inconsistencies between process and decision models are identified and we remedy those inconsistencies by establishing Five Principles for integrated Process and Decision Modelling (5PDM). The principles are subsequently illustrated and validated on a case of a Belgian accounting company.

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In the last few years, smart cities have attracted considerable attention because they are considered a response to the complex challenges that modern cities face. However, smart cities often do not optimally reach their objectives if the citizens, the end-users, are not involved in their design. The aim of this paper is to provide a framework to structure and evaluate citizen participation in smart cities. By means of a literature review from different research areas, the relevant enablers of citizen participation are summarized and bundled in the proposed CitiVoice framework. Then, following the design science methodology, the content and the utility of CitiVoice are validated through the application to different smart cities and through in-depth interviews with key Belgian smart city stakeholders. CitiVoice is used as an evaluation tool for several Belgian smart cities allowing drawbacks and flaws in citizens' participation to be discovered and analysed. It is also demonstrated how CitiVoice can act as a governance tool for the ongoing smart city design of Namur (Belgium) to help define the citizen participation strategy. Finally, it is used as a comparison and creativity tool to compare several cities and design new means of participation. Response to Reviewers: Dear Reviewers, We decided to include the reponse to reviewers in a separate file as a "Cover Letter".

Business process modeling often deals with the trade-off between comprehensibility and flexibility. Many languages have been proposed supporting different paradigms to tackle these characteristics. Well-known procedural, tokenbased languages such as Petri nets, BPMN, EPC, etc. have been used and extended to incorporate more flexible use cases, still the declarative workflow paradigm, most notably represented by the Declare framework, is widely accepted for modeling flexible processes. A real trade-off exists between the readable, rather inflexible procedural models, and the highly-expressive but cognitively demanding declarative models containing a lot of implicit behavior. This paper performs an in-depth study of the scenarios in which combining both approaches is useful, provides a scoring table for Declare constructs to capture their intricacies and similarities compared to procedural ones, and offers a step-wise approach to construct mixedparadigm models. Such models are especially useful in the case of environments with different layers of flexibility and go beyond using atomic subprocesses modeled according to either paradigm. The paper combines Petri nets and Declare to express the findings.