The Lueders Postulate and the Distinguishability of Observables

The Lueders postulate is reviewed and implications for the distinguishability of observables are discussed. As an example the distinguishability of two similar observables for spin-1/2 particles is described. Implementation issues are briefly analyzed.Comment: Submitted to the proceedings of ICFNCS, Hong Kong, 200

Understanding the Role of Social Media in the Assessment of Retailer-Hosted Consumer Reviews

Agile methods incorporate many techniques that support coordination in co-located software development teams. However, these benefits do not necessarily transfer to a distributed context. Even though research on coordination in distributed agile software development is growing, there is limited rigorous research on its application in context. Further the extant literature is fragmented, with little cohesive building of cumulative knowledge on coordination in distributed agile software development. This study investigates the scientific evidence between 2006 and 2016 by conducting a systematic review of the literature on coordination in distributed agile software development. The search strategy resulted in 178 studies, of which 50 were identified as primary studies relevant to this research. The studies were classified using three high-level categories: (i) theoretical foundation and application, (ii) tools and techniques, and (iii) challenges. This study provides a structured overview of the current state of knowledge on coordination in distributed agile development, and identifies opportunities for future research

Information Overload in Processing Consumer Reviews: The Role of Argumentation Changes

Information overload theory suggests that consumers can only processa certain amount and complexity of information. In this study, we analyze whetherinformation overload can also occur while processing individual product reviewswith a high rate of argumentation changes. An argumentation change denotes achange from positive to negative arguments, and vice versa.We propose a NeuroISexperiment in which participants are presented a given set of product reviews witha low or high rate of argumentation changes. The participants are asked about theirperceived helpfulness of the product review, their purchase intention for the product,and self-reported information overload. During the experiment, we measurecognitive activity based on electroencephalogram (EEG) and eye-tracking. Weexpect that a higher rate of argumentation changes is linked to greater cognitive activity,and, in particular, lower perceived review helpfulness and purchase intention

The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews

Review helpfulness serves as focal point in understanding customers’ purchase decision-making process on online retailer platforms. An overwhelming majority of previous works find longer reviews to be more helpful than short reviews. In this paper, we propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the line of argumentation in the review text. To test this idea, we use a large dataset of Amazon customer reviews in combination with a state-of-the-art approach from natural language processing that allows us to study the line of argumentation at sentence level. Our empirical analysis suggests that the frequency of argumentation changes moderates the effect of review length on helpfulness. Altogether, we disprove the prevailing narrative that longer reviews are uniformly perceived as more helpful. Retailer platforms can utilize our results to optimize their customer feedback system and to feature more useful reviews

Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning

Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred. As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis. To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level. Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability. Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90 %, exceeding the performance of alternative approaches by at least 3.80 percentage points. Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended