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Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

The purpose of this systematic narrative review is to discourse on vision and organizational performance. By analysing work-life and organization studies journals, the authors respond to a call to view the process of visioning more holistically.

The methodological approach is a discourse-oriented qualitative content analysis. The authors explore visioning through an epistemological lens, which emphasizes both the connections and differences between “traditional” philosophical approaches.

The findings show how the different interpretations of vision and related concepts are tied to the following themes: clarity, causality, embodiment and sensory experiences and actionability.

Through the frameworks of scientific realism and relativism, the authors illustrate novel insights into the ways in which visioning occupies a place in knowledge management.

This study was conducted to address the methodical shortcomings and high associated cost of understanding the use of new, poorly understood architectural spaces, such as makerspaces. The proposed quantified method of enhancing current post-occupancy evaluation (POE) practices aims to provide architects, engineers and building professionals with accessible and intuitive data that can be used to conduct comparative studies of spatial changes, understand changes over time (such as those resulting from COVID-19) and verify design intentions after construction through a quantified post-occupancy evaluation.

In this study, we demonstrate the use of ultra-wideband (UWB) technology to gather, analyze and visualize quantified data showing interactions between people, spaces and objects. The experiment was conducted in a makerspace over a four-day hackathon event with a team of four actively tracked participants.

The study shows that by moving beyond simply counting people in a space, a more nuanced pattern of interactions can be discovered, documented and analyzed. The ability to automatically visualize findings intuitively in 3D aids architects and visual thinkers to easily grasp the essence of interactions with minimal effort.

By providing a method for better understanding the spatial and temporal interactions between people, objects and spaces, our approach provides valuable feedback in POE. Specifically, our approach aids practitioners in comparing spaces, verifying design intent and speeding up knowledge building when developing new architectural spaces, such as makerspaces.

Higher education institutions and their lecturers are strategic agents and main drivers that contribute to circular economy transition. This requires them to understand the key circular economy competencies and how to integrate circular economy holistically into their curricula with the suitable teaching and learning approaches. This study aims to support them by providing an overview on the characteristics of education for the circular economy (ECE) and suggestions to lecturers to further develop their curricula.

The data consisted of scientific articles (n = 22) describing circular economy courses in higher education. Qualitative content analysis with quantitative features was performed on the selected articles to answer the research question.

The findings confirm that the system’s focus is the key issue in ECE. However, to integrate circular economy holistically into the curricula, ECE should be implemented more widely in the context of different industries and market contexts to find innovative teaching and learning approaches. The demand side needs to be incorporated in the courses, as systemic transformation is also about transforming consumption. All levels of implementation and circular economy objectives should be included in courses to promote systems thinking. In addition, innovative forms of real workplace interaction should be increased.

As ECE has started to emerge as a new field of study, this article provides the first integrated overview of the topic.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

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.

The competition in the academe has always been tough, but today, the academe seems to be more like an industry than an academic community as academics are evaluated through quantified and economic means.

This article leans on Heidegger’s thoughts on the essence of technology and his ontological view on being to show the dangers that lie in this quantification of researchers and research.

Despite the benefits that information systems (ISs) offer to people and research, it seems that technology has made it possible to objectify researchers and research. This has a negative impact on the academe and should thus be looked into especially by the IS field, which should note the problems that exist in its core. This phenomenon of quantified academics is clearly visible at academic quantification sites, where academics are evaluated using metrics that count their output. It seems that the essence of technology has disturbed the way research is valued by emphasising its quantifiable aspects. The study claims that it is important to look for other ways to evaluate researchers rather than trying to maximise research production, which has led to the flooding of articles that few have the time or interest to read.

This paper offers new insights into the current phenomenon of quantification of academics and underlines the need for critical changes if in order to achieve the academic culture that is desirable for future academics.

The study explores the relationship between the breadth of external pressures facing leaders of small and medium-sized enterprises (SMEs) and the entrepreneurial stance they adopt for their firm, that is, entrepreneurial orientation (EO).

Blending attention theory with EO literature, we argue that increasing breadth of external pressures will challenge leaders' attentions with implications for how they seek innovation, risk-taking and bold acts. We highlight an inflection point after which a negative relationship between the breadth of external pressure and EO will turn positive. We use data from a survey of 125 small-sized wineries in France to test this and capture a range of 15 external pressures on entrepreneurs.

The main tests and additional robustness tests provide support. It is the breadth of external pressures – as opposed to intensity of any one specific form of pressure – that plays a fundamental role in shaping leaders' adoption of EO in small enterprises over and above internal characteristics.

While the results may be context-dependent, they provide support for an attention-based view of entrepreneurial responses by leaders of SMEs under pressure.

SME leaders and entrepreneurs should be aware of how their attention is challenged by breadth of pressures from external sources, as this can influence the EO they adopt for their SME.

This nonlinear perspective on external pressures influencing the EO of small firms has not been taken in the EO literature to date, despite some recent work that considers only a small range of external pressures.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

The purpose of this case study was to complement existing weeding and retention criteria beyond the most used methods in academic libraries and to consider citation counts in the identification of important scholarly works.

Using a small sample of items chosen for withdrawal from a small liberal arts college library, this case study looks at the use of Google Scholar citation counts as a metric for identification of notable monographs in the social sciences and mathematics.

Google Scholar citation counts are a quick indicator of classic, foundational or discursive monographs in a particular field and should be given more consideration in weeding and retention analysis decisions that impact scholarly collections. Higher citation counts can be an indicator of higher circulation counts.

The authors found little indication in the literature that Google Scholar citation counts are being used as a metric for identification of notable works or for retention of monographs in academic libraries.