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Sports marketing and sponsorship research is located at the intersection of behavioral and design research, which means that it analyzes the current world and shapes a future world. This dual focus poses challenges for formulating and testing theories of sports marketing.

This article develops criteria for categorizing theoretical concepts as either behavioral or formed as different ways of expressing ideas of sports marketing research. It emphasizes the need for clear concept categorization for proper operationalization and applies these criteria to selected theoretical concepts of sports marketing and sponsorship research.

The study defines three criteria to categorize theoretical concepts, namely (1) the guiding idea of research, (2) the role of observed variables, and (3) the relationship among observed variables. Applying these criteria to concepts of sports marketing research manifests the relevance of categorizing theoretical concepts as either behavioral or formed to operationalize concepts correctly.

This study is the first in sports marketing to clearly categorize theoretical concepts as either behavioral or formed, and to formulate guidelines on how to differentiate behavioral concepts from formed concepts.

Nobody concerned with political economy can neglect the history of economic doctrines. Structural changes in the economy and society influence economic thinking and, conversely, innovative thought structures and attitudes have almost always forced economic institutions and modes of behaviour to adjust. We learn from the history of economic doctrines how a particular theory emerged and whether, and in which environment, it could take root. We can see how a school evolves out of a common methodological perception and similar techniques of analysis, and how it has to establish itself. The interaction between unresolved problems on the one hand, and the search for better solutions or explanations on the other, leads to a change in paradigma and to the formation of new lines of reasoning. As long as the real world is subject to progress and change scientific search for explanation must out of necessity continue.

Digital design technologies play a significant role in assisting the designer through conceptual architectural design. Computer supported design systems can generate various images at the early design phase and can contribute to seeking alternative architectural forms. Currently, different design approaches are being employed in the formation of architectural products. Examples of architecture that produce unusual forms are often encountered within unique conceptual approaches. The development of new design examples is supported by the digital production of forms, and three-dimensional models through varying geometric approaches. In this study, a design approach that uses computer aided architectural design to produce architectural forms will be suggested. This approach utilizes principles existing in the unique fractal dimension of elements based on a vocabulary relevant to a specific architectural language. By relying on the fractal dimension and features of an existing architectural pattern, this generative design approach supports creativity in the production of new forms. The proposed approach is evaluated as a creative tool in architectural design. The subject of architecture; buildings, spaces, surroundings, symbols of that particular society are also the elements of a meta-language which creates a fractal geometry based relation. It is possible to analyse this relation through a fractal geometry-based principle. In short, a fractal geometrical generative method is suggested. Also, recently-surfaced discussions about "Chaos Theory" and its effects on the design process via "Chaos and Self - Similarity" are studied. The significance of these different phenomena and disciplines upon architectural design are also studied for developing a possible creative tool.

The Fully Automatic Syntactically‐based Indexing of Text (FASIT) system represents the contents of a document without a full parse or semantic analysis of the text. Content‐bearing units are isolated and then grouped into quasi‐synonymous classes whose main term is used to index the document. Previous experiments with FASIT demonstrated its usefulness in an associational retrieval environment; the experiment described here explores FASIT's value as a book‐indexing system. It is difficult to avoid the conclusion that this indexing approach offers the promise of being practical and effective.

This study aims to identify the effective components in object interpretation based on Pierce’s triadic sign model to determine how meaning is created and identify the components affecting conceptualization in ontology.

A qualitative approach was adopted, and 6320 comments posted on the Instagram pages of five groups forming the research population were analyzed by qualitative content analysis.

Concepts are dynamic and context-oriented. Over time, a person’s concepts and mental patterns about an object are altered or eliminated. Social, political and economic conditions, governance, technology, interactions, language, environment, culture and social habits contribute to object interpretation and meaning creation.

In designing ontologies, especially in a context-dependent field, attention should be paid to users’ mental preconceptions, mental patterns, context and personal characteristics. Instead of using words, researchers can use visual and multimedia signs. In addition to logic, statistics and computer science, personal and anthropological characteristics should be considered in the ontology design.

This study dealt with conceptualization, which is a principal component of ontology. Using Pierce’s triadic model sign and unstructured Instagram data, ontology was studied from a user and pragmatic viewpoint, independently of technical aspects.

This article addresses the functional links between knowledge and performance in human activity. Starting with the evolutionary roots of knowledge and activity, it shows how the combination of adaptive behavior and knowledge storage has formed over various stages of evolution. The cognitive architecture of human actions is discussed against this background, and it is shown how knowledge is integrated into action control. Then, methodological issues in the study of action knowledge are considered, and an experimental method is presented that can be used to assess the structure of action knowledge in long‐term memory. This method is applied in studies on the relation between object knowledge and performance in mechanics and between movement knowledge and performance in high‐performance sportswomen. These studies show how experts’ knowledge systems can be assessed, and how this may contribute to the optimization of human performance. In high‐level experts, these representational frameworks were organized in a highly hierarchical tree‐like structure, were remarkably similar between individuals, and matched well the functional demands of the task. In comparison, the action representations in low‐level performers were organized less hierarchically, were more variable between persons, and were not so well in accordance with functional demands. These results support the hypothesis that voluntary actions are planned, executed, and stored in memory directly by way of representations of their anticipated perceptual effects. The method offers new possibilities to investigate knowledge structures. Based on such results it is possible to improve performance via special training‐techniques. This paper fulfils an identified research need concerning the interaction of knowledge and performance and offers new perspectives for future forms of knowledge management.

The purpose of this study was to construct a mind counseling ontology to efficiently facilitate the diagnosis of the diseases of mind. To determine the structure of mind counseling ontology, this study conducted analysis on structural forms available in counseling books and other related fields and adopted essential ones in the explanation of counseling. The processing of the diseases of mind was divided into three stages: cause, symptoms and counseling. The stages were analyzed one by one in terms of process, functional elements and relevant technique necessary at each stage.

In the mind counseling list, there are 12 different diagnoses of diseases of mind that are classified into four classes. Thus, the causes, symptoms, prescription and medical history for 12 diseases of mind are defined as a higher rank concept of mind counseling ontology. The causes, symptoms, prescription and medical history consist of definition, affective characteristics and related factors, while the potential diagnosis consists of definition and risk factor. This information does specify detailed notions in the diagnosis of diseases of mind, but considering the limitation of not being able to represent all the diseases, this study enables a counseling center to give and use individual definitions of diagnostic terminology of their own.

This study adopted the top-down approach, in which mind counseling ontology defines a higher rank concept, the terminology in diagnosing diseases of mind, based on the list of terms from the counseling record that specifies the abstract concepts of the diagnosis. The bottom-up approach was also incorporated, which defines the diagnostic terms extracted from the counseling record as a subordinate concept of the mind counseling ontology. Thus, the development of the mind counseling ontology involves the combination of top-down and bottom-up approaches to the construction of ontology.

This research has significance in that it deals with the fundamental problem of the mind aiming for a true change and healing of it, which is the ultimate purpose of this ontology, especially in the circumstances where research on ontology in diagnosing the diseases of mind is unprecedented.

The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.

The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.

In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.

The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.

This study aims to focus on the interpretation component of Peirce’s triadic model of sign to identify: the components affecting the interpretation of an object and the components affecting the meaning creation by people for use in ontologies. Ashura was selected because it is a historical event and a meaningful sign in Iranian religious culture.

This qualitative study was conducted in two steps. In the first step, the Instagram pages of five groups of people were chosen as the sample and examined using qualitative content analysis. In the second step, 18 creators of Ashura works were selected, their lived experience in creating these works was collected using in-depth semi-structured interviews and analysed through phenomenology method.

Based on the results, the concept is context-oriented; one cannot interpret the signs according to one’s personal preferences because the context, social rules and habits limit this interpretation. Accordingly, the use of reference systems and rules of logic, as well as artificial intelligence and computer science tools alone cannot help represent meaning in ontologies. Rather, individual characteristics, contexts, presuppositions and components existing in personal, social and technological worlds are effective on concept formation and meaning creation. Thus, these factors must be considered in ontology, especially for multi-faceted and context-oriented concepts, such as Ashura.

This research adopted a new approach and method to study ontology. This study focused on interpretation and adopted Peirce’s sign model for ontology design.

Although the ideas of F.W. Taylor have profoundly marked the twentieth century, they do not seem to have been understood in the same way by the people who have studied them. Aims to enrich our understanding of the ideas of this remarkable author. Proposes a graphic representation of Taylor’s thinking in the form of a cognitive map. Then analyses the structure and content of the map using the Decision Explorer software package. Most of the concepts and links shown in the map were drawn from “Shop management”, and the remainder were taken from The Principles of Scientific Management. The results highlight the relative importance of the concepts used by Taylor, the dimensions on which he more or less consciously structured his thinking, together with the characteristics of the concepts he considered basically as “explanations” or “consequences”, and the more or less systemic or circular logic that guided him in the organization of his thinking. Discusses the limitations of the results and some future avenues for research.