A measure of Comparative Institutional Distance

Three varieties of institutionalism currently dominate International Business studies: new institutional economics, new organizational institutionalism, and comparative historical institutionalism. Yet currently applied measures of institutional country distance predominantly build on the thought of the first two strands of institutionalism. This paper sets out to address this underrepresentation of comparative historical institutional thought in currently available measures of institutional distance. Building on Whitley’s business systems framework, a measure of institutional distance is developed and validated which captures intrinsic, substantive institutional differences in economic organization, rather than differences in institutional effectiveness. The results of the two-stage cluster analysis used to validate the selected indicators closely approximate the business systems typology, which is both indicative of the validity of this measure and of the distinctiveness of the business system types that make up the business system framework

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape

Using Semantic Technologies in Digital Libraries- A Roadmap to Quality Evaluation

Abstract. In digital libraries semantic techniques are often deployed to reduce the expensive manual overhead for indexing documents, maintaining metadata, or caching for future search. However, using such techniques may cause a decrease in a collection’s quality due to their statistical nature. Since data quality is a major concern in digital libraries, it is important to be able to measure the (loss of) quality of metadata automatically generated by semantic techniques. In this paper we present a user study based on a typical semantic technique use

Between-group behaviour in health care: gaps, edges, boundaries, disconnections, weak ties, spaces and holes. A systematic review

<p>Abstract</p> <p>Background</p> <p>Gaps are typically regarded as a problem to be solved. People are stimulated to close or plug them. Researchers are moved to fill deficits in the literature in order to realise a more complete knowledge base, health authorities want to bridge policy-practice disconnections, managers to secure resources to remedy shortfalls between poor and idealised care, and clinicians to provide services to patients across the divides of organisational silos.</p> <p>Despite practical and policy work in many health systems to bridge gaps, it is valuable to study research examining them for the insights provided. Structural holes, spaces between social clusters and weak or absent ties represent fissures in networks, located in less densely populated parts of otherwise closely connected social structures. Such gaps are useful as they illustrate how communication potentially breaks down or interactivity fails. This paper discusses empirical and theoretical work on this phenomenon with the aim of analysing a specific exemplar, the structures of silos within health care organisations.</p> <p>Methods</p> <p>The research literature on social spaces, holes, gaps, boundaries and edges was searched systematically, and separated into health [n = 13] and non-health [n = 55] samples. The health literature was reviewed and synthesised in order to understand the circumstances between stakeholders and stakeholder groups that both provide threats to networked interactions and opportunities to strengthen the fabric of organisational and institutional inter-relationships.</p> <p>Results</p> <p>The research examples illuminate various network structure characteristics and group interactions. They explicate a range of opportunities for improved social and professional relations that understanding structural holes, social spaces and absent ties affords. A principal finding is that these kinds of gaps illustrate the conditions under which connections are strained or have been severed, where the limits of integration between groups occurs, the circumstances in which social spaces are or need to be negotiated and the way divides are bridged. The study's limitations are that it is bounded by the focus of attention and the search terms used and there is yet to be developed a probabilistic, predictive model for gaps and how to connect them.</p> <p>Conclusions</p> <p>Gaps offer insights into social structures, and how real world behaviours of participants in workplaces, organisations and institutions are fragile. The paper highlights the circumstances in which network disjunctures and group divides manifest. Knowledge of these phenomenon provides opportunities for working out ways to improve health sector organisational communications, knowledge transmission and relationships.</p

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-