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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.

This article examines strategic management research across Latin America from 1990 to 2023, addressing four critical inquiries: the themes prevalent in strategic discussions, the leading countries in strategic management (SM) publications, the defining characteristics of strategic research in major Latin American economies and the reflection on whether Latin America is a region that generates or follows the knowledge of the Global North.

Utilizing co-occurrence analysis, this study maps the terrain of SM research in the region, analyzing 4,963 articles indexed in the Scopus database. The authors employed a co-occurrence analysis to map SM research in Latin America, analyzing 4,963 articles from the Scopus database.

Predominant themes include the theoretical underpinnings of strategy, sustainable development, innovation, tourism and international trade. Brazil, Mexico, Colombia and Chile have emerged as leaders in research volume and thematic diversity, particularly in sustainable development and innovation.

By identifying patterns, behaviors and trends in SM research, the authors uncover methods and tools that, once contextualized for the region, can significantly enhance organizational performance.

This investigation is a pioneering effort, providing a focused analysis on SM research within Latin America. It highlights significant contributions since 1990 across the region's main economies. This study represents one of the first comprehensive mappings of this academic field within Latin America. This is the first article, to the authors’ knowledge, developed to map the intellectual structure of the SM field in Latin America through an analysis of co-occurrences, with emphasis on the region's main economies.

The purpose of this paper is to consider the interface of research methodology paradigms (which are commonly conventionally perceived as being juxtaposed when conducting research) through the application of textual analytical software.

The argument develops matrices and applies these to the human resource (HR) context of individual and career group sense-making so as to better understand the inherent career dynamics and sense-making in a sample derived from a French media agency context.

The paper’s findings comprise a range of insights from the respondent managers (n = 26). The results suggest that a better understanding of methodologies facilitates the development of theories based on textual data analysis.

The limits of the approach are important. Indeed, it is necessary to make compromises and arrangements with epistemological orthodoxy. Indeed, at first and to be able to process the data with the Alceste© and Tropes©, the interviews were recorded, transcribed and considered as “definitive”. This potentially contradicts the methods of collecting data prescribed by the thematic analysis or the method of cognitive maps. These two interpretive approaches imply a process of co-construction of the results that would not theoretically rely solely on a recording.

It provides insights on how, and to what extent, can data analytical software packages facilitate a better understanding of paradigm (interpretive-positivistic) interoperation and commensurability.

Importantly, the study provides a novel means by which to study the important HR issue of career perception and trajectory.

The methodology-driven approach shows that there is potent scope to map and develop valuable and complementary data perspectives on research issues by interfacing paradigms and textual data analysis tools.

This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

The vulnerability of customers to malware attacks through weak supplier links has prompted a need for collaboration as a strategic alternative in improving supply chain cybersecurity (SCC). Current studies overlook the fact that the effectiveness of cybersecurity strategies is dependent on the form of interfirm relationship mechanisms within which supply chain digital assets are embedded. This paper analyses the association between interfirm collaborative cybersecurity management capabilities (ICCMC) and cybersecurity parameters across a supply chain and proposes an agenda for future research.

A systematic literature review (SLR) is conducted, employing text mining software to analyse content extracted from 137 scholarly articles on SCC from January 2013 to January 2022.

The co-occurrence analysis strongly confirms the potential of ICCMC to reinforce SCC. Furthermore, we establish that relational factors could have multiple roles: as antecedents for ICCMC, and as factors that directly affect SCC parameters. The analysis reveals knowledge gaps in SCC theory grounding, including a fragmented and sparse representation of SCC parameters and the potential presence of an omitted variable – SCC – that could improve subsequent testing of causal relationships for theory development.

The paper’s contribution is at the intersection of interfirm collaboration and mandating cybersecurity requirements across a supply chain. Our paper contributes to closing a social-technical gap by introducing social aspects such as the Relational View and the importance of developing ICCMC to reinforce SCC. We offer a method for testing co-occurrences in SLRs, a comprehensive definition of SCC, and a framework with propositions for future research on increasing the effectiveness of collaborative cybersecurity management. We position collaboration as a necessary condition for the transition from cybersecurity of a firm to cybersecurity across a supply chain, and its ecosystem.

This research aims to conduct a literature review of Lean Six Sigma (LSS) in the Indian context and related research publications and apply bibliometric analysis and the author's visualization to map research trends in this area.

This article conducts a bibliometric analysis of LSS-related research in the Indian context using data gathered from Scopus and Web of Science databases from 2011 to 2022. The review provides information on LSS-related research in the Indian context and evaluates performance based on primary sources, authors, keywords, countries, affiliations, and documents. The analysis employs the Biblioshiny app and Bibliometrix R-tool for data analysis and scientific mapping.

The results of the bibliometric analysis indicate that the LSS culture has widely spread in India. The International Journal of Lean Six Sigma and Production Planning and Control were found to be the most productive sources for publishing LSS-related research articles. Antony J. was identified as the most active author in this field, contributing the most over the years. Among all organizations, NITs have conducted the most comprehensive research on LSS, indicating their significant investment of resources and efforts in studying this methodology and its applications in India. Additionally, the study examined the intellectual, social, and conceptual structures to identify implicit gaps and future research opportunities.

The findings of this study can inform academicians, researchers, practitioners, and policymakers about the state-of-the-art and the specifics of the most prolific studies. This study will facilitate their exploration of emerging research areas in LSS.

To the best of the authors knowledge, this is the first bibliometric analysis of LSS in the Indian context, providing an overview of relevant publications published between 2011 and 2022. This study analyzed 194 articles on LSS in India, which can help researchers and academics identify emerging research areas, suitable collaborators, and relevant journals for future publications.

The growing interest on ecosystems on both managerial and academic side reflects a vibrant field of study, nevertheless, the subject is very heterogeneous and in particular concerning a particular form of ecosystems: collaborative ecosystems, the purpose of this study is to shed light on these.

The definition gap existent and the multiplicity of fields and composite studies on collaborative ecosystems have made it necessary to conduct a systematic literature review of the phenomenon, a bibliometric analysis and content analysis.

The research offers an overview of the phenomenon, three main cluster categories emerged: ecosystem, competition and open innovation; this cluster offers a comprehensive schematization of the research topics.

Limitations of this research are that the investigation has been confined to specific keywords related to collaborative ecosystems that led to a strict selection of papers to be analyzed and in having confined the investigation to publications in the managerial area. Further research will be able to expand the selection of keywords or use more strings and proceed to a comparative analysis of results. Another limitation is the small number of databases assessed (only Scopus and Web of Science) and have confined the analysis to specific clusters that emerged from the quantitative analysis of papers.

By proposing a taxonomy of collaborative ecosystems, the paper highlights what solutions and architectures an ecosystem manager must consider when developing collaborative ecosystems, what factors he or she must pay attention to in order to ensure their development and survival, and what elements determine the development of an ecosystem, in particular, the management of innovation and knowledge and the scarcity of resources in a hyper-competitive environment.

A combination of research methods was used to identify the intellectual structure of collaborative ecosystems. In addition, we propose a taxonomy for collaborative ecosystems, opening the study to new lines of investigation and providing a new research agenda.

Previous literature reviews mainly focus on green supply chain, however, there is surprisingly little effort made in the systematic literature review to focus on a specific function of green supply chain, i.e. green procurement (GP). Regarding research methodology, most reviews are qualitative and inductive generalizations. Quantitative methods such as bibliometrics have started to be applied in recent years. This paper responds to this trend by counting the largest amount of literature with 238 articles in the last 24 years from 2000 to 2023 to visually analyze different theoretical perspectives and research topics into a comprehensive framework and identify future research directions and opportunities in the GP field.

Based on CiteSpace and in-depth content analysis, this paper attempts to visually analyze 238 valid papers to identify key literature and analyze the evolution of research hot spots and frontier development trends.

Combined with the visualization results and the content analysis of key references, we identify key literature and examine the evolution of research hotspots and frontier development trends. The bibliometric analysis mainly includes knowledge mapping to identify key themes, co-occurrence and clustering analysis of keywords, cooperation networks in researchers' countries. An overall GP research model is presented and our research suggests future avenues for GP research, such as low carbon, digitalization, servitization and ecosystem perspectives.

Firstly, in terms of time and volume of literature, we have counted the most comprehensive and up-to-date literature in the field of GP research, which is conducive to further expanding and enriching GP research. Secondly, this study clearly describes the theoretical evolution of GP research from bilateral and multilateral to ecosystem perspectives, and constructs a new and comprehensive research framework of “driver-practice-outcome”. Thirdly, based on the bibliometric analysis results, our research suggests future avenues for GP research, such as low-carbon, digitalization, servitization, and ecosystem perspectives, which can contribute to expanding our understanding of GP development.

Large-scale text-based data increasingly poses methodological challenges due to its size, scope and nature, requiring sophisticated methods for managing, visualizing, analyzing and interpreting such data. This paper aims to propose semantic network analysis (SemNA) as one possible solution to these challenges, showcasing its potential for consumer and marketing researchers through three application areas in phygital contexts.

This paper outlines three general application areas for SemNA in phygital contexts and presents specific use cases, data collection methodologies, analyses, findings and discussions for each application area.

The paper uncovers three application areas and use cases where SemNA holds promise for providing valuable insights and driving further adoption of the method: (1) Investigating phygital experiences and consumption phenomena; (2) Exploring phygital consumer and market discourse, trends and practices; and (3) Capturing phygital social constructs.

The limitations section highlights the specific challenges of the qualitative, interpretivist approach to SemNA, along with general methodological constraints.

Practical implications highlight SemNA as a pragmatic tool for managers to analyze and visualize company-/brand-related data, supporting strategic decision-making in physical, digital and phygital spaces.

This paper contributes to the expanding body of computational, tool-based methods by providing an overview of application areas for the qualitative, interpretivist approach to SemNA in consumer and marketing research. It emphasizes the diversity of research contexts and data, where the boundaries between physical and digital spaces have become increasingly intertwined with physical and digital elements closely integrated – a phenomenon known as phygital.

This is a dual-focused study that anticipates qualitatively and quantitatively examining the literature on the recently initiated revolutionizing concept of ChatGPT in education by performing a Systematic Literature Review (SLR) and bibliometric analysis. Current study analyzed eight research questions: (1) the main information and annual scientific publications on ChatGPT in education, (2) the pioneer authors and collaborative authors exploring ChatGPT in education, (3) the authors' productivity through Lotka’s Law of Authors’ Scientific Productivity, (4) the most pertinent sources on ChatGPT in education and how are sources clustered through Bradford’s Law of Scattering, (5) the most related, cited countries and the nature of international collaborations exploring ChatGPT in education, (6) the most relevant publications exploring ChatGPT in education, (7) the most occurring and trending keywords in the empirical studies on ChatGPT in education, and (8) the themes and areas for future investigations on ChatGPT in education.

The current study was designed as a SLR and bibliometric analysis, extracting articles from the Scopus database and utilizing both Biblioshiny and VOSviewer software for advanced scientific mapping and visualizations via quantitative and qualitative analysis approaches.

The results indicated that ChatGPT in education is a progressively evolving worldwide concept generating 45 scientific publications from 2023 to 2024 (May). The USA, China, and Indonesia are the most productive countries that have published articles on ChatGPT in education. The education systems, AI, students, educational computing, human experiments, teaching, educational status, chatbots, generative AI, academic integrity, educational technology, worldwide education, and technology acceptance are the pertinent future directions in the field of ChatGPT in education.

The analysis’s outcomes will enhance the area of study with theoretical and practical implications and benefit students, teachers, policymakers, regulators of educational and higher educational sectors, government, and the general public worldwide with effective utilization of ChatGPT in education.