Satisfiability of cross product terms is complete for real nondeterministic polytime Blum-Shub-Smale machines

Nondeterministic polynomial-time Blum-Shub-Smale Machines over the reals give rise to a discrete complexity class between NP and PSPACE. Several problems, mostly from real algebraic geometry / polynomial systems, have been shown complete (under many-one reduction by polynomial-time Turing machines) for this class. We exhibit a new one based on questions about expressions built from cross products only.Comment: In Proceedings MCU 2013, arXiv:1309.104

An Attitude Strength and Self-Perception Framework Regarding the Bi-directional Relationship of Job Satisfaction with Extra-Role and In-Role Behavior: The Doubly Moderating Role of Work Centrality

Studies have identified variables either moderating the extent to which job satisfaction predicts work behavior or moderating the reverse impact of work behavior on job satisfaction. Based on an attitude strength and self-perception framework, we argue that certain variables may moderate both the predictive utility of job satisfaction for work behavior and the impact of work behavior on job satisfaction. Specifically focusing on work centrality, we hold that high work centrality renders job satisfaction a strong job attitude, whereas low work centrality renders job satisfaction a weak job attitude. Hence, the predictive utility of job satisfaction for both extra-role behavior and in-role behavior should be higher the more work is central to employees. In contrast, the influence of extra-role behavior, but not of in-role behavior, on job satisfaction should be higher the less work is central to employees. Results of a two-wave study (N = 176) were in line with these predictions. We discuss further variables that may play a similar role for the bi-directional relationship between job satisfaction and work behavior

Navigating the Research Landscape of Decentralized Autonomous Organizations: A Research Note and Agenda

This note and agenda serve as a cause for thought for scholars interested in researching Decentralized Autonomous Organizations (DAOs), addressing both the opportunities and challenges posed by this phenomenon. It covers key aspects of data retrieval, data selection criteria, issues in data reliability and validity such as governance token pricing complexities, discrepancy in treasuries, Mainnet and Testnet data, understanding the variety of DAO types and proposal categories, airdrops affecting governance, and the Sybil problem. The agenda aims to equip scholars with the essential knowledge required to conduct nuanced and rigorous academic studies on DAOs by illuminating these various aspects and proposing directions for future research

A Taxonomy of Decentralized Autonomous Organizations

Decentralized Autonomous Organizations (DAOs) are trustless organizations that automate transactions, operations, and decisions without a trusted third party (Wang et al. 2019). So far, this research area is missing a taxonomy that investigates the different dimensions and characteristics of DAOs and the many different forms they can take. This paper addresses this research gap by creating a data-driven taxonomy analyzing 72 DAOs. In doing so, we identify the three main categories treasury, community, and governance, seven sub-categories, 20 dimensions, and 53 characteristics. In addition, we provide dimensions with inadmissible characteristics DAOs cannot take, as well as dimensions used to assess DAOs. The results of our agglomerative clustering are five distinct DAO types: On-chain product and service DAOs, off-chain product and service DAOs with community focus or with investor focus, investment-focused DAOs, and networking-focused community DAOs