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This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This research aims to examine the performativity of corporate reports as an example of an accounting inscription that can frame the relationship between Aboriginal and Torres Strait Islander entities and their stakeholders. The framing and overflow effects of these reports have been explored to consider whether they strengthen or undermine the reputation and capability of these community-controlled entities.

Aligned with actor–network theory and a decolonising research protocol, qualitative interviews were conducted with senior managers and directors of Aboriginal and Torres Strait Islander entities and their key stakeholders to explore their experiences of corporate reporting. Additional analysis of these organisations' annual reports was conducted to corroborate key reporting themes.

This research has identified a dual role for corporate reporting, simultaneously framing performance against an expectation of failure, but with the potential for accounting inscriptions to highlight positive contributions to cultural and community priorities. It also indicates the need for sector specifics within the reporting frameworks and adequate resourcing for Aboriginal and Torres Strait Islander entities to meet reporting obligations.

This research makes policy-based recommendations in terms of user-driven and culturally informed performance measures. It also highlights the importance of adequate funding for Aboriginal and Torres Strait Islander entities to carry out meaningful performance evaluations beyond the preparation of financial statements.

One of the few empirical studies to capture the performativity of accounting inscriptions from the perspective of Aboriginal and Torres Strait Islander entities. This sector has received minimal attention within the accounting discipline, despite significantly contributing to community well-being and cultural protection. There is emancipatory potential via policy frameworks that resonate with Aboriginal and Torres Strait Islander cultural beliefs and practices.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

Information infrastructures can enable or constrain how companies pursue their visions of sustainability reporting and help address the urgent need to understand how corporate activity affects sustainability outcomes and how socio-ecological challenges affect corporate activity. The paper examines the relationship between sustainability reporting information infrastructures and sustainability reporting practice.

The paper mobilises a socio-technical perspective and the conception of infrastructure, the socio-technical arrangement of technical artifacts and social routines, to engage with a qualitative dataset comprised of interview and documentary evidence on the development and construction of sustainability reporting information.

The results detail how sustainability reporting information infrastructures are used by companies and depict the difficulties faced in generating reliable sustainability data. The findings illustrate the challenges and measures undertaken by entities to embed automation and integration, and to enhance sustainability data quality. The findings provide insight into how infrastructures constrain and support sustainability reporting practices.

The paper explains how infrastructures shape sustainability reporting practices, and how infrastructures are shaped by regulatory demands and costs. Companies have developed “uneven” infrastructures supporting legislative requirements, whilst infrastructures supporting non-legislative sustainability reporting remain underdeveloped. Consequently, infrastructures supporting specific legislation have developed along unitary pathways and are often poorly integrated with infrastructures supporting other sustainability reporting areas. Infrastructures developed around legislative requirements are not necessarily constrained by financial reporting norms and do not preclude specific sustainability reporting visions. On the contrary, due to regulation, infrastructure supporting disclosures that offer an “inside out” perspective on sustainability reporting is often comparatively well developed.

The aim of this paper is to systematically review the literature published in recognized journals focused on recognition-based heuristics and their effect on investment management activities and to ascertain some substantial gaps related to them.

For doing research synthesis, systematic literature review approach was applied considering research studies published within the time period, i.e. 1980–2020. This study attempted to accomplish a critical review of 59 studies out of 118 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioural finance domain-related explicitly to recognition-based heuristics and their effect on investment management activities.

The survey and analysis suggest investors consistently rely on the recognition-based heuristic-driven biases when trading stocks, resulting in irrational decisions, and an investment strategy constructed by implementing the recognition-based heuristics, would not result in better returns to investors on a consistent basis. Institutional investors are less likely to be affected by these name-based behavioural biases in comparison to individual investors. However, under the context of ecological rationality, recognition-based heuristics work better and sometimes dominate the classical methods. The research scholars from the behavioural finance community have highlighted that recognition-based heuristics and their impact on investment management activities are high profile areas, needed to be explored further in the field of behavioural finance. The study of recognition-based heuristic-driven biases has been found to be insufficient in the context of emerging economies like Pakistan.

The skilful understanding and knowledge of the recognition-based heuristic-driven biases will help the investors, financial institutions and policy-makers to overcome the adverse effect of these behavioural biases in the stock market. This article provides a detailed explanation of recognition-based heuristic-driven biases and their influence on investment management activities which could be very useful for finance practitioners’ such as investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/ broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making its financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on recognition-based heuristic-driven biases displayed by investors. The current study is original in discussing the role of recognition-based heuristic-driven biases in investment management activities by means of research synthesis. This paper is useful to researchers, academicians, and those working in the area of behavioural finance in understanding the role that recognition-based heuristics plays in investment management activities.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

The shift toward a circular economy (CE) represents a collaborative endeavor necessitating the presence of efficient frameworks, conducive contexts and a common comprehension. This research serves as a pivotal stride towards this goal, presenting an exclusive prospect for the investigation and fusion of these frameworks, with particular emphasis on the Quintuple Helix Model (5HM), into a unified theoretical framework that underscores the core principles of the CE. This study is centered on three pivotal questions aimed at decoding the CE transition in specific regional settings.

Adopting an abductive approach firmly anchored in a two-stage qualitative process, this study specifically merges the foundational principles from institutional theory, entrepreneurship literature and CE frameworks to provide insights into the dynamics of circular ecosystems, with a specific focus on the Huelva region in Spain.

The findings demonstrate significant potential in the CE, ranging from the integration of product and service systems to innovations in eco-industrial practices. Yet, a notable deficiency exists: the absence of institutional entrepreneurs, highlighting the essential role that universities can play. As recognized centers of innovation, universities are suggested to be key contributors to the transformation toward a CE, aligning with their societal and economic responsibilities.

This study highlights the importance of managing relationships with entities like SMEs and policymakers or academia for effective CE adoption. Policymakers can refine strategies based on the research’s insights, while the impact of university-driven circular ecosystems on sustainable societies is another crucial area for research.

The sustainability models cited in CE literature may not be comprehensive enough to prevent problem shifting, and it can be argued that they lack a sound theoretical and conceptual basis. Furthermore, the connections between sustainability objectives and the three levels of the CE operating system remain vague. Additionally, there is insufficient information on how regions foster the involvement of the environment in fivefold helix cooperation and how this impacts the CE.

This study aims to look at challenges that hinder small- and medium-sized enterprises (SMEs) from using open data (OD). The research gaps identified are then used to propose a next generation of OD platform (ODP+).

This study proposes a more effective platform for SMEs called ODP+. A proof of concept was implemented by using modern techniques and technologies, with a pilot conducted among selected SMEs and government employees to test the approach’s viability.

The findings identify current OD platforms generally, and in Gulf Cooperation Council (GCC) countries, they encounter several difficulties, including that the data sets are complex to understand and determine their potential for reuse. The application of big data analytics in mitigating the identified challenges is demonstrated through the artefacts that have been developed.

This paper discusses several challenges that must be addressed to ensure that OD is accessible, helpful and of high quality in the future when planning and implementing OD initiatives.

The proposed ODP+ integrates social network data, SME data sets and government databases. It will give SMEs a platform for combining data from government agencies, third parties and social networks to carry out complex analytical scenarios or build the needed application using artificial intelligence.

The findings promote the potential future utilisation of OD and suggest ways to give users access to knowledge and features.

To the best of the authors’ knowledge, no study provides extensive research about OD in Qatar or GCC. Further, the proposed ODP+ is a new platform that allows SMEs to run natural language data analytics queries.

This study aims to determine the contributing factors to technical knowledge valuation at the related quadruple levels of commercialisation, including the idea, benchtop technical knowledge, prototype technical knowledge and commercialised technical knowledge, and then classify the factors by the valuation objectives.

The study method is descriptive-causal, and documentation tools of published scientific research articles in authentic local and international journals were used to extract the contributing factors to technical knowledge valuation. Moreover, the Likert spectrum-based questionnaire is used to determine the weight of each determined component. On the other hand, hierarchical analysis is used based on the extracted results from the distributed classification questionnaire among scholars to determine the allocable weight of each component.

The results indicate that at the idea step, the highest ranks among the contributing factors to technical knowledge valuation are for the indicators of innovation rate enhancement, novelty, creation of new products, profitability growth and dependence decline. In the benchtop technical knowledge step, the indicators of profitability growth, product quality enhancement, novelty, production risk drop, innovation rate enhancement, production costs drop, product price competitiveness and independence from rare machinery have the highest impact coefficients on valuation. Moreover, the prioritisation of factors in prototype technical knowledge shows that the indicators of productive risk decline, infrastructure, decrease in product delivery time, productivity growth and profitability growth are the most critical factors in technical knowledge valuation. Finally, profitability growth factors, production cost drop, productive risk drop, creating a new product, product price competitiveness and dependence decline determine the most valuable technical knowledge in the commercialisation phase.

The most salient innovation of the study involves the development levels of technical knowledge in the commercialisation cycle for determining the contributing factors to technical knowledge valuation and using multivariate decision-making methods to classify the so-called factors. The major limitation can be the context of the study because the paper was carried out by Iranian assessors and specialists using the experiences, opinions and approaches of opinion leaders based on the dominant social, cultural and accounting background of a developing country, not a developed one.

This paper is applicable because it elucidates the technical knowledge valuation factors for managers and owners of technological and knowledge-based companies to facilitate value determination and register the technical knowledge of innovative products in financial statements for the logical presentation of available intangible assets in the economic unit. Besides, in the high-tech area, collecting information from the contributing factors to technical knowledge valuation provides an opportunity to support intellectual property rights and facilitate transaction processes. Finally, in legal areas, in cases of breaching intellectual property rights relative to technical knowledge, the determination of technical knowledge value provides a solid basis for estimating the damage rate.