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This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

Administrative processes play a crucial role in various sectors, often contributing to cost overruns and delays. While lean concept has been extensively applied in many industries, there is a recognized need for further research on the integration of lean in administrative processes. To address this research gap, this study aims to systematically analyse the implementation of lean in administrative processes, providing a sector-wise comparison to explore its prospects for the construction industry.

A systematic literature review was conducted by adapting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. Eighty-six research papers are subjected to descriptive and content analysis on different tools applied in lean integrated administrative processes in health care, education, public administration and other sectors.

The research study reveals common lean tools irrespective of sector. Moreover, specific tools used for a particular sector are analysed. Out of the various lean tools and techniques, value stream mapping, 5S and Kaizen are highly acknowledged tools in every sector of lean intervention. Furthermore, ECRS is applied explicitly in the education sector. Furthermore, it is noted that Suppliers, Inputs, Process, Outputs and Customers (SIPOC), 5whys, load levelling and control charts are identical to administrative processes in lean health care. Furthermore, Spaghetti charts and Kaikaku are only adapted to administrative processes in the public administration sector, and causal loop diagrams are used explicitly in administrative processes in the construction sector.

This study contributes to research by developing a guideline to design lean intervention for a lean-integrated administrative process in any sector which still needs to be transformed into lean. Hence, this study will be a cornerstone for incorporating lean principles for administrative processes in construction sector.

This study focuses on (re-)introducing computer simulation as a part of the research paradigm. Simulation is a widely applied research method in supply chain and operations management. However, leading journals, such as the International Journal of Operations and Production Management, have often been reluctant to accept simulation studies. This study provides guidelines on how to conduct simulation research that advances theory, is relevant, and matters.

This study pooled the viewpoints of the editorial team of the International Journal of Operations and Production Management and authors of simulation studies. The authors debated their views and outlined why simulation is important and what a compelling simulation should look like.

There is an increasing importance of considering uncertainty, an increasing interest in dynamic phenomena, such as the transient response(s) to disruptions, and an increasing need to consider complementary outcomes, such as sustainability, which many researchers believe can be tackled by big data and modern analytical tools. But building, elaborating, and testing theory by purposeful experimentation is the strength of computer simulation. The authors therefore argue that simulation should play an important role in supply chain and operations management research, but for this, it also has to evolve away from simply generating and analyzing data. Four types of simulation research with much promise are outlined: empirical grounded simulation, simulation that establishes causality, simulation that supplements machine learning, artificial intelligence and analytics and simulation for sensitive environments.

This study identifies reasons why simulation is important for understanding and responding to today's business and societal challenges, it provides some guidance on how to design good simulation studies in this context and it links simulation to empirical research and theory going beyond multimethod studies.

Abuja, the capital city of Nigeria, is one of the fastest growing capital cities in sub-Saharan Africa. Recently, the city is experiencing an alarming rate of burglary and violent crimes, while the city planning management frameworks lacks adequate and effective crime mapping, monitoring and management techniques necessary for achieving liveable and safe environment for habitation despite its grandiose spatial planning and aesthetically appealing architectural design as a modern city. Based on police crime records (2007–2018) and geospatial analysis, this paper aims to provide adequate understanding on the interplay of land use configuration and burglary crime formation in residential neighbourhoods of Abuja, Nigeria.

The methods used for the purpose of data collection includes; field survey, Block Environmental Inventory, while inferential statistics and Geographic Information System tools was used for data analysis. The analysis established that Lagos, Nsukka and Enugu Streets are hotspots; while Chief Palace street, Ladoke Akintola and Oka-Akoko streets were found to be cold spots.

This study, however, established the applicability of crime pattern, opportunity theory and routine activity theory in understanding the rising burglary crime events in the study areas and the link between physical characteristics of street block typology and burglary crime pattern. The results of the analysis has in a way affirmed the positions of the theories, while disagreed with them in cases where the results indicated contrary outcome.

This paper concluded with inference drawn from the results that supported mixed-use development but with built-in crime prevention through environmental design strategies as effective burglary crime prevention mechanisms that contribute to crime rate reduction.

This study aims at investigating the effect of bolt hole-making processes on the post-fire behavior of S235 steel plates.

A total of nine steel plates with a single bolt hole are tested. The single bolt holes are fabricated using three different hole-making processes: drilling, waterjet and plasma. Among the nine steel plates, three fabricated specimens are control specimens and are tested at ambient temperature. The six remaining steel plates with a single bolt hole are subjected to a complete heating-cooling cycle and then monotonically loaded until failure. The six fabricated specimens are first heated up to two different temperatures 800 and 925 °C, and then cooled back to the ambient prior to loading.

The results show that after being exposed to post-fire temperatures (800 and 925 °C), the maximum decrease in strength of the S235 steel plate was 6% (at 925 °C), 14% (at 925 °C) and 22% (at 800 °C) when compared to the results of ambient specimens for waterjet, drilled and plasma bolt holes, respectively. For post-fire temperature tests, drilled and waterjet bolt hole-making processes result in having approximately the same load-displacement response, and both have larger strength and ductility than those obtained using plasma cutting.

This study provides preliminary data to guide the steel designers and fabricators in choosing the most suitable hole-making process for fire applications and to quantify the post-fire reduction in capacity of S235 plates.

The purpose of this paper is to understand the applications of metaverse-related technologies in US urban libraries, explore excellent cases of US urban libraries' practices in using metaverse-related technologies to serve patrons and try to find the factor that may affect the application of metaverse-related technologies in libraries at this stage.

To gather information about how and whether libraries use metaverse-related technologies such as three-dimensional (3D) technology, radio frequency identification (RFID), virtual reality augmented reality (AR) and artificial intelligence (AI) in their services. Firstly, the authors visit 150 US urban library websites that are members of the Urban Libraries Council. Secondly, the authors calculate the proportion of services provided by urban libraries that use metaverse-related technologies and introduce outstanding cases. Lastly, the authors discuss the factors that influence the application of metaverse-related technologies in urban libraries in the United States based on data published by the Institute of Museum and Library Services.

Metaverse-related technologies have been widely used in US urban libraries, but there are differences in the popularity of the applications of different technologies. In all, 84% of libraries use 3D technology, mainly in 3D printing services and 3D model building services; 76% of libraries use virtual and augmented reality technologies in their services, mainly concentrated in head-mounted VR device experiences, AR device experiences, virtual tours and virtual exhibitions; 62% of libraries use Internet of things (IoT) technology, mainly in self-checkout machines and book location services. However, AI technologies are less used in libraries, with 28% of libraries mentioning the applications of AI in their services, mainly focusing on intelligent search, virtual assistants and robot librarians. In addition, this study finds that library operating expenditures and population served do not affect the application of metaverse-related technologies in libraries.

This paper provides updated statistical data on the use of metaverse-related technologies in US urban libraries and aims to help library managers understand the overall applications and best practices. With this as an inspiration, they could formulate corresponding development plans to better serve their communities with metaverse-related technologies.

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.

Drawing upon a systematic literature review in new technology, innovation transfer and diffusion theories, and from interviews with technology leaders in digital transformation programs in the US Oil & Gas (O&G) industry, the authors explore the relationships among O&G industry dynamics, organization's absorptive capacity and resource commitment for new digital technology adoption-implementation process.

The authors employed the empirical survey method to gather the data (a sample size of 172) in the US O&G industry and used structural equation modeling (SEM) to test the measurement model for validity and reliability and the conceptual model for hypothesized structural relationships.

The results provide support for the study’s causal model of adoption and implementation with positive and direct relationships between the initiation and trial stages, between the trial stages and the evaluation of effective outcomes and between the effective outcomes and the effective implementation stages of digital technologies. The results also reveal partial mediating relationships of industry dynamics, absorptive capacity and resource commitment between respective stages.

Based on the current study's findings, managers are recommended to pay attention to the evolving industry dynamics during the initiation stage of new digital technology adoption, to utilize the organization's knowledge-based absorptive capacity during digital technology trial and selection stages and to support the digital technology implementation project when the adoption decision of a particular digital technology has been made.

The empirical research contributes literature on digital technology adoption and implementation by identifying and demonstrating the importance of industry dynamics, absorptive capacity and resource commitment factors as mediating variables at various stages of the adoption-implementation process and empirically validating a process-based causal model of digital technology adoption and a successful implementation project that has been missing in the current body of literature on digital transformation.

Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP.

Indian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software.

The findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP.

By developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.