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Once introduced and conceptualized as a factor that causes erosion and decay of social institutions and subsequent deinstitutionalization, the notion of entropy is at odds with predictions of institutional isomorphism and seems to directly contradict the tendency toward ever-increasing institutionalization. The purpose of this paper is to offer a resolution of this theoretical inconsistency by revisiting the meaning of entropy and reconceptualizing institutionalization from an information-theoretic point of view.

It is a theoretical paper that offers an information perspective on institutionalization.

A mistaken understanding of the nature and role of entropy in the institutional theory is caused by conceptualizing it as a force that counteracts institutional tendencies and acts in opposite direction. Once institutionalization and homogeneity are seen as a product of natural tendencies in the organizational field, the role of entropy becomes clear. Entropy manifests itself at the level of information processing and corresponds with increasing uncertainty and the decrease of the value of information. Institutionalization thus can be seen as a special case of an increase in entropy and a decrease of knowledge. Institutionalization is a state of maximum entropy.

It is explained why institutionalization and institutional persistence are what to be expected in the long run and why information entropy contributes to this tendency. Contrary to the tenets of the institutional work perspective, no intentional efforts of individuals and collective actors are needed to maintain institutions. In this respect, the paper contributes to the view of institutional theory as a theory of self-organization.

By combining the subjective probabilistic viewpoint of fuzziness with the entropy of deterministic functions, it is possible to expand an information theory of fuzzy sets which is fully compatible and consistent with the classical Shannonian information theoretic framework. A model of transinformation between fuzzy sets, which could be of help in approximate reasoning can be obtained, an interesting feature of which is that it can be duplicated in the framework of fuzzy set theory.

The problem of expanding a meaningful entropic theory for fuzzy information cannot be thought of as being a mere (more or less formal) extension of Shannon theory. By using the information theory of deterministic functions, the present author had already obtained some results in this way, and he herein continues this approach. After a short background on the different entropies of deterministic functions and on membership entropy of fuzzy sets, successively mixed entropy of fuzzy sets, joint membership functions of independent fuzzy sets, and conditional entropy of fuzzy sets with respect to other fuzzy sets are considered; the problem of defining transinformation between fuzzy sets, as a generalisation of the well known Shannon concept, is then examined. One of the conclusions of the article is that it is possible to build up a meaningful information theory of fuzzy sets by using the entropy of deterministic functions.

Investigates the enigmatic phenomenon of information from the two major perspectives of modern cybernetics: the negentropic and the evolutionary. Discusses the meaning of this phenomenon, considering it, ultimately, as the means of combating entropy within cybernetic or control systems which create or receive, process, stock, and use information, eventually with the purpose of regulating their matter‐energy fluxes necessary for struggling against inner entropy. Discusses the sense of information as relating to thermodynamical entropy in a philosophical manner and not in some formal (mathematical) language.

The ideas of information theory which underlie cybernetics and its information‐processing aspects still has a solid reputation to be both extremely abstract and highly mathematical. Furthermore, the tract has been accused of a slow development and not accentuating the difference between information and communication theories. In this paper an attempt is made to demonstrate that principles and concepts used in information theory can be both understood and used from a psychological basis ‐ without the use of complicated mathematics. The difference between information and communication theory is considered as well as some new and revolutionary concepts. The study shows that the influences of the investigated area have grown considerably with the contributions from information physics.

The purpose of this manuscript is to quantify exactly how much information and/or predictive content is contained in business sentiment surveys.

This paper uses techniques drawn from information theory econometrics, and more specifically the theory of information entropy, to characterize the predictive content of business sentiment surveys. The authors apply these techniques to publicly available information obtained from various editions of the Federal Reserve Bank of New York's Empire State Manufacturing Survey, one of the most popular business sentiment surveys conducted in the USA. Parametric and non‐parametric statistical analyses are used to examine differences in the quantity of predictive content across various questions in the survey.

The results suggest that business sentiment surveys contain a reasonably high degree of informative content. However, the amount of informative content varies considerably from question to question in the survey. Questions that are more general in nature and ask about current perceptions (as opposed to future expectations) contain more informative content.

Business sentiment surveys are a practical, low‐cost method to assess the current and expected future state of local and regional economies. However, the value of these surveys is questionable if they do not contain much information. This research finds that such surveys do contain a large amount of information, and are worth administering. However, specific types of survey items convey more information that others, which also suggests that business sentiments surveys can be further revised to maximize the amount of content gained from respondents.

The purpose of this study was to explore the viability of transinformation analysis as a multimodal readability metric. A novel approach was called for, considering that existing and established readability metrics are strictly used to measure linguistic complexity. Yet, the corpus of multimodal literature continues to grow, along with the need to understand how non-linguistic modalities contribute to the complexity of the reading experience.

In this exploratory study, think aloud screen recordings of eighth-grade readers of the born-digital novel Inanimate Alice were analyzed for complexity, along with transcripts of post-oral retellings. Pixel-level entropy analysis served as both an objective measure of the document and a subjective measure of the amount of reader information attention. Post-oral retelling entropy was calculated at the unit level of the word, serving as an indication of complexity in recall.

Findings confirmed that transinformation analysis is a viable multimodal readability metric. Inanimate Alice is an objectively complex document, creating a subjectively complex reading experience for the participants. Readers largely attended to the linguistic mode of the story, effectively reducing the amount of information they processed. This was also evident in the brevity and below average complexity of their post-oral retellings, which relied on recall of the linguistic mode. There were no significant group differences among the readers.

This is the first study that uses entropy to analyze multimodal readability.

The purpose of this paper is to describe the way real estate data collection and dissemination can be improved.

The paper investigates that how information entropy fits into real estate research and data using Kelly betting as a framework.

Availability of real estate data is constrained by a host of impediments, paywalls and other archaic practices that limit the way it can be used to make worthwhile progress in the field.

Real-estate data will become available. Those who try to keep data close and restrict their common use will become irrelevant.

Entropy indicates uncertainty and chaos. Using this highly developed and respected area of science as context one can see how real estate research suffers from its present mindset.

This research fills a gap in process science by defining and explaining entropy and the increase of entropy in processes.

This is a theoretical treatment that begins with a conceptual understanding of entropy in thermodynamics and information theory and extends it to the study of degradation and improvement in a transformation process.

A transformation process with three inputs: demand volume, throughput and product design, utilizes a system composed of processors, stores, configuration, human actors, stored data and controllers to provide a product. Elements of the system are aligned with the inputs and each other with a purpose to raise standard of living. Lack of alignment is entropy. Primary causes of increased entropy are changes in inputs and disordering of the system components. Secondary causes result from changes made to cope with the primary causes. Improvement and innovation reduce entropy by providing better alignments and new ways of aligning resources.

This is the first detailed theoretical treatment of entropy in a process science context.