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This paper aims to examine the concept of standardization beyond its traditional use in generating and implementing standards and good practice guidelines (S&GPG) by looking at existing and emerging trends.

This paper utilizes two primary approaches to categorizing S&GPG for better comprehension: categorization based on provenance as well as based on subject matter.

A significant concern related to categorizing S&GPG based on provenance or subject is the constant proliferation of standards being developed and introduced every year. This rapid growth in standards requires frequent re-categorization to keep up with the dynamic nature of this field. To tackle this problem, this paper explores emerging concepts such as ontological representation and frameworks that offer archives and records management (ARM) professionals.

Standardization refers to establishing uniform rules through mutual agreement to ensure consistency. The study of standardization goes beyond the development of individual S&GPG, encompassing their practical application in work settings. Categorizing standards alone may not fully capture their actual use. However, abstraction mechanisms like ontological representations, models and frameworks can demonstrate how these standards have been leveraged. This paper provides illustrative examples rather than an exhaustive list to showcase how these mechanisms have been applied in research projects or as practical tools.

This paper explores the emerging topic of standardization from the perspective of ontological representations and models or frameworks. In addition, it also contributes to the discussion of the 2022 version of ARMA International’s Information Governance Implementation Model and the 2020 version of the World Bank Group's Records Management Roadmap, providing unique insights into these topics.

This chapter offers a critique of the affirmative forms of thought that attempt to ground the ontology of social being through subjective-idealist terms. Some recent examples came in the form of notion of truth grounded in subjects' experience and in rationality of language and discourse. The first part of the chapter demonstrates the perilous implications of such an approach for social theory tasked with ontology and for the conception of truth necessary for its task. The second part scrutinizes the paradigm of society that stems from this subjective-idealist notion of truth and social ontology that adopts discourse, language, and literary metaphors to comprehend social being. As an alternative, the final part of the chapter offers a preliminary sketch of the relation of ontology, normativity, and mediation, as well as the notion of critique necessary for social theory tasked with ontology.

The purpose of this paper was to develop a quantitative classroom observation method that is able to analyse the school day to identify Time-on-Task losses comprehensively and systematically, at a level of detail that can be used by teachers and principals to stimulate and focus practical improvement efforts.

The novel Time-on-Task Analysis (TOTA) model was developed by triangulating the conceptual framework of the Overall Equipment Effectiveness metric with the semantics and structure of the target domain. Once developed, the model was tested structurally against a time-series classroom observation data set, after which the resulting TOTA was presented to a sample of 52 education stakeholders, who then gave their perspectives of the analysis in a structured survey.

The ontological model was found to be accurate, complete and without conceptual incongruencies, and its output novel and useful by the sample of education stakeholders. Of the participants, 90.3% found the analysis to provide a new perspective, 94.2% reported that the analysis triggered improvement ideas and 80.8% thought that their school(s) could benefit from a TOTA study.

The TOTA model introduces a time-loss-focused perspective to the field of quantitative classroom observation studies, which is dominated by more sociologic- and pedagogic-focused topics. Its grounding in Overall Equipment Effectiveness also gives it a more detailed and systematic approach than the few Time-on-Task studies done to date, resulting in a model made for the “Gemba”: the school classroom.

This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.

This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.

Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.

This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

This study aims to develop a synthetic knowledge repository consisted of interrelated Web Ontology Language.

The ontology composes the main framework to categorize data of product life cycle with eco-design mode (PLC-EDM) and automatically infer specialists’ knowledge for data confirmation, eventually assisting the utilizations and generation of strategies toward decision-making

(i) utilization of a novel model with ontology mode for information reuse cross the different eco-design applications; (ii) generation of a sound platform toward life cycle evaluation; and (iii) implementation of the PLC-EDM model along the product generation process.

It cannot substitute an evaluation tool of life cycle. Certainly, this model does not predict the “target and range” and/or the depiction of the “utility module” that are basic activities in life cycle assessments as characterized through the international organization for standardization regulations.

As portion of this framework, a prototype Web application is presented which is applied to produce, reuse and verify knowledge of product life cycle.

By counting upon the ontology, the information conducted by the utilization is certainly semantically represented to promote the data sharing among various participants and tools. Besides, the data can be verified against possible faults by inferring over the ontology. Hence, a feasible way to a popular topic in the domain of eco-design applications extension in the industry.

The goals are: to lean on rigid modeling principles; and to promote the interoperability and diffusion of the ontology toward particular utilization demands.

This paper aims to present the findings of a government-initiated project that sought to explore the possibility of incorporating cultural connections to land within the federal national accounting system using the United Nations Systems of Environmental-Economic Accounting (UN-SEEA) framework as a basis.

Adopting a critical dialogic approach and responding to the calls for critical accountants to engage with stakeholders, the authors worked with two Indigenous groups of Australia to develop a system of accounts that incorporates their cultural connections to “Country”. The two groups were clans from the Mungguy Country in the Kakadu region of Northern Territory and the Ewamian Aboriginal Corporation of Northern Queensland. Conducting two-day workshops on separate occasions with both groups, the authors attempted to meld the Indigenous worldviews with the worldviews embodied within national accounting systems and the UN-SEEA framework.

The models developed highlight significant differences between the ontological foundations of Indigenous and Western-worldviews and the authors reflect on the tensions created between these competing worldviews. The authors also offer pragmatic solutions that could be implemented by the Indigenous Traditional Owners and the government in terms of developing such an accounting system that incorporates connections to Country.

The paper contributes to providing a contemporary case study of engagement with Indigenous peoples in the co-development of a system of accounting for and by Indigenous peoples; it also contributes to the ongoing debate on bridging the divide between critique and praxis; and finally, the paper delves into an area that is largely unexplored within accounting research which is national accounting.

This paper aims to review the literature on the use of the notion of performativity and its related concepts in accounting research. The literature uses the term performativity in almost diametrically different ways, yet most papers assume that the meaning of the term is self-evident. We build on recent reviews of the notion of performativity and explicate the implicit tensions in the accounting literature, discovering a need to clarify how the accounting literature has explored the processes – how accounting becomes performative – and effects – what is performed – of accounting performativity. The paper develops suggestions for future theoretical and empirical research.

The authors have searched in six leading accounting journals (Accounting, Organizations and Society, Accounting, Auditing and Accountability Journal, Management Accounting Research, Critical Perspectives on Accounting and Qualitative Research in Accounting and Management) for the terms “performativity” and/or “performative” and/or “performable”. This yielded 289 results from which we distilled a core sample of 92 papers which substantially draw on the concept and explicate their use of the term.

The authors find that the accounting literature has paid almost equal attention to the conforming and amplifying effects of performativity but has mostly explored how conditions of performativity are built. Less attention has been paid to how accounting generates multiple worlds and how differences in these worlds are coordinated by accounting. Building institutions and searching for accounting incompleteness have been developed as the two main processes where accounting is made performative.

The paper develops avenues for future research, highlighting the potential for a deeper understanding of how the notion of performativity can be used. We do not advocate homogenizing the literature, instead exploring its fruitful tensions to discover a renewed interest in how accounting is constitutive of existing and/or new worlds. We illustrate this potential by reflecting on the debates about accounting incompleteness and the boundaries of accounting. The authors also suggest the potentials for concepts of performativity in studying emerging phenomena such as big data and sustainability and revisiting the ethics of using accounting as a social and organizational practice.

The literature review explicates differences in the use of the term performativity, which usually remain implicit in the literature. The study develops a framework that attends to both the processes – problematizing the conditions for performativity or not – and effects – conforming and amplifying – of performativity accounting studies have drawn upon, which clarifies how the accounting literature has mobilized the notion of performativity and the contributions the accounting literature has added. Further, the authors extend Vosselman’s (2022) review both in scope and nuance.

The purpose of this paper is to review ontologies and data models currently in use for augmented reality (AR) applications, in the cultural heritage (CH) domain, specifically in an urban environment. The aim is to see the current trends in ontologies and data models used and investigate their applications in real world scenarios. Some special cases of applications or ontologies are also discussed, as being interesting enough to merit special consideration.

A search using Google Scholar, Scopus, ScienceDirect and IEEE Xplore was done in order to find articles that describe ontologies and data models in AR CH applications. The authors identified the articles that analyze the use of ontologies and/or data models, as well as articles that were deemed to be of special interest.

This review found that CIDOC-CRM is the most popular ontology closely followed by Historical Context Ontology (HiCO). Also, a combination of current ontologies seems to be the most complete way to fully describe a CH object or site. A layered ontology model is suggested, which can be expanded according to the specific project.

This study provides an overview of ontologies and data models for AR CH applications in urban environments. There are several ontologies currently in use in the CH domain, with none having been universally adopted, while new ontologies or extensions to existing ones are being created, in the attempt to fully describe a CH object or site. Also, this study suggests a combination of popular ontologies in a multi-layer model.

The study seeks to identify and document definitional challenges that hamper the delineation of the scope of real estate as a discipline and as an industry. Through literature review the article distils the perception of body of knowledge (BOK) of real estate within the academia. Two main issues are flagged up: the problem of undefined BOK and the collegiate dilemma. Later the study looks at the standard economic classification documents to capture the occupational domains of real estate professionals or real estate activities. These steps are necessary to help define an alternative academic, practical and social meaning of real estate that is sufficient and precise.

The study uses literature review and, as primary method, qualitative document analysis (QDA). The study has made a special appeal for the application of qualitative strategy in real estate research other than following the methodological orthodoxy of quantitative causal research designs. Further, it has argued for the recognition of QDA as a legitimate research method in the context of real estate studies. Consequently, the study performed QDA procedures on international economic classification standards.

From literature review and QDA, the study identified five definitional problems in the meanings or understandings of real estate: undefined body of knowledge, collegiate dilemma, inadequate classification of real estate occupations, inadequate industry classification and inadequate economic sector positioning. These are aspects that lead to misconceptions of the true boundary of knowledge in society and in the academia. The paper offers clarity and insights for the redrawing of these boundaries to give real estate its rightful place in the academia and in the real world.

The article follows up on the academic and social misconceptions on the BOK of real estate as a discipline and an economic activity domain to identify the contribution of real estate to the welfare of mankind. Ontology or the organization of academic or social knowledge is used to map out or catalogue real estate against competing domains and to show that the role of real estate is grossly understated and misunderstood. From the findings, the study makes recommendations to university curriculum developers, and international organizations like ILO, and UN-DESA to revise their conceptions of real estate to give the discipline its rightful position in society.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).