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Economic costs and benefits are at the core while taking decision to adopt digitalization in the supply chain. The present chapter provides an in-depth exploration of the economic dimensions of digital supply chain management (DSCM) adoption in a firm. Drawing from a diverse source of literature, this chapter discusses the effect of economic outlook on DSCM adoption, the economic benefits of DSCM adoption and costs associated with it, and economic analysis and evaluation methodologies. The chapter also shares the case studies illustrating the real-world implications of economic considerations within DSCM initiatives. The chapter highlights how changing international socioeconomic and political dynamics can influence businesses across the globe. By analyzing the impacts of evolving market trends, changing consumer preferences, and geopolitical tensions, organizations can considerably forecast the possible impacts of these macroeconomic forces adeptly. The chapter also undertakes discussion on the economic cost and benefits associated with DSCM adoption. The economic analysis helps understand that the expected benefits outweigh economic costs, substantiating the economic viability of DSCM projects. The chapter concludes by discussing the examples of some real-world companies, highlighting how organizations have successfully applied economic analyses to their DSCM initiatives. This also highlights as to how showcasing how detailed economic assessments can justify substantial investments, deliver operational efficiencies, and reshape industries.

This study aims to analyse the literature published on ChatGPT and generative pre-trained transformer (GPT) available through Scopus to identify the top countries, institutions, authors, journals and titles in terms of publishing and citations in the area.The research also intends to determine the collaborative trends among countries and authors as well as top-used keywords on the topic identified from the analysed literature.

The researchers searched the Scopus database to collect and assess the literature on the topic. The paper used six applications such as Biblioshiny, VosViewer, Python, MS Access and Excel and Endnote to collect and analyse the literature.

It is found that European countries such as the USA, the UK and Germany took the lead in terms of publishing and impact in the area but the USA stood first with 90 publications and 1,720 citations in this connection. Likewise, the organization “Rheinisch-Westfälische Technische Hochschule Aachen” scored the top position regarding publishing, but Open AI remained on top for getting the highest citations (1,384). Apropos, the author “Choi, Y” produced the highest number of publications. The research also shares the collaborative patterns, top journals and publications in the area, as well as the top-used keywords on the topic.

To the best of the authors’ knowledge, this is the first study that shares a bibliometric analysis of literature published on GPT and ChatGPT. The research not only fills the research gap on the topic but also shares implications for relevant stakeholders and future research directions for future researchers.

Entrepreneurial ecosystems (EEs) research has focused on high-growth scale-up entrepreneurship, whereas the role of EEs in nurturing the ventures of marginalised groups like migrant women entrepreneurs (MWEs) has often been elided from extant discussions. This research explores how the EE's structure, policies and programmes advance diversity, equity and inclusion to foster MWEs, and MWEs' contribution to the dynamics and sustainability of the host country's EE based on EE actors' perspectives. We contribute to EEs' diversity, equity and inclusion, which are important but neglected social aspects of sustainable EEs.

The qualitative data was collected through thematic interviews with EE actors, including NGOs and entrepreneurial support-providing organizations based in Finland. The collected data was complemented by interviews with MWEs, archival data and published supplementary materials on ecosystem actors.

EE structure, policies, programmes and individual agency, coupled with MWEs' proactivity in lobbying the necessary actors in the required places for their interests, enhance their businesses' development. There were both impeding and fostering dynamics, which may have idiographic and contextual features. Evidently, by being occupied in various sectors, from science, technology, engineering and mathematics (STEM) to socially beneficial niche service sectors, MWEs contribute to the host country's EE dynamics not only through their productive entrepreneurship but by enriching the ecosystem's resource endowments and institutional arrangements.

We argue that exploring the gender and inclusivity aspects of EEs as the accommodating context is particularly relevant, given that the United Nation's sustainable development goals 5, 8 and 10 aim to improve women's empowerment at all levels, promoting sustained, inclusive and sustainable economic growth, and ensuring equal opportunities and reduced inequalities within the population. Inclusion and embeddedness in EEs positively affect diversity and sustainability in the host country. Theoretically, our contribution is twofold. First, by exploring female migrants' entrepreneurial experiences within the EE based on EE actors' perspectives, we broaden the research on inclusivity in EEs and gender aspects and enrich the research on their societal impact, which has received scant attention from scholars. More specifically, we contribute to EE research with (1) a novel understanding of MWEs and EE elements, their interconnections and dynamism, (2) identifying previously ignored elements shaping MWE and (3) providing EE actor insights into the co-creation of EE for MWE. Second, by analysing the impact of MWEs' businesses on the host country's EE, we contribute to calls for research on MWE contributions to its economic environment.

Through critically reflecting on existing research on information and communication technology (ICT) in hospitality, the purpose of this study is to propose recommendations for future research to further narrow the theory-practice gap.

Personal experiences along with evidence from the literature provide a foundation for discussion, which is further enriched by integrating industry practitioners’ points of view.

Single-perspective and technology adoption studies have dominated ICT research in the hospitality literature. Technology effectiveness has often been measured indirectly. Oversimplifying technological issues has limited the generalizability of research findings.

Future studies are suggested to go beyond examining technology adoption, embrace multi-perspective approaches and incorporate a wider range of situational and contextual factors.

Through a unique perspective, this study highlights the limitations of previous ICT research in the hospitality literature and provides suggestions for future research to better meet the needs of practitioners. The arguments presented are not purely from an academic standpoint, as they have been endorsed by senior industry executives.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

The green building (GB) market is maturing after years of practice; therefore, incentives for promoting GB should be adapted to reflect the market transformation. Adapting incentives can positively influence stakeholders' motives, which in turn change stakeholders' behavioural intention towards GBs. Hence, this study aims to examine the parameters influencing the decision to adopt GB and to ascertain how incentive mechanisms promote the uptake of GB in South Africa.

Using a scoping literature review and a sequential mixed research method, primary data were acquired from GB experts in four South African provinces. In analysing the quantitative data, the factors were ranked hierarchically using the mean ranking technique and factor analysis was computed to identify the underlying GB incentives. The qualitative data were analysed using content analysis.

The results indicate that incentive payment from a utility energy efficiency programme, rebates and discounts relating to environmentally friendly materials and products and providing grants to homeowners and developers to go towards certification were the most essential monetary incentives for promoting GB. In the category of non-monetary incentives, recognition of consultant team members and developers and free marketing/good publicity were the most important incentives. The incentive mechanisms were subsequently categorised as “Economic incentives” and “Reward scheme and technical support”.

This study was confined to four metropolitan areas in South Africa. Nevertheless, the outcomes have practical implications for GB projects in general and may serve as a good reference for other provinces in South Africa.

The findings of the study are a valuable resource for stakeholders such as the government, municipal assemblies, professional bodies and the Green Building Council of South Africa (GBCSA) in developing effective incentive mechanisms to promote GB adoption. This study adds to the body of knowledge relevant to GB incentivisation in South Africa.

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.

Increasing trade liberalization and financial integration with rise in foreign direct investment (FDI) flows have an intense impact on the labor market of both the developed as well as the developing world. The restructuring of economic activity has resulted in destruction of jobs in some parts of the economy and start-ups of new firms in the other part, growing import competition with foreign firms displacing local firms, relocation of jobs from high wage to low wage nations. It is claimed that rapid globalization has also the outcome of “race to the bottom” in terms of wages and the quality of employment. As per the latest International Labor Organization (ILO) Global Employment Trends, 2022, total global number of unemployed youths is estimated to reach 73 million in 2022 but still six million above the prepandemic level of 2019. The latest press release October 2022 points out that trade growth is likely to experience a slowdown in 2023 due to multiple shocks on global economy. High energy prices due to the Russia–Ukraine war is expected to contract household expenditure and raise the cost of manufacturing. The present chapter seeks to analyze the impact of trade flows on the labor market – job creation or job loss, wage inequality, movement toward informal economy, gender inequalities, and other related issues. The data are based on various reports available from the ILO, the World Development Indicators (WDI) and the United Nations Conference on Trade and Development (UNCTAD), and World Trade Oorganization (WTO) Trade Reports. The empirical analysis also confirms that there is a unidirectional causality from trade to employment generation.