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Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

This chapter first reviews some of the background concepts on central bank digital currency (CBDC) to provide a broad context, before diving into wholesale CBDC often a starting point for central banks to build CBDC prototypes based on distributed ledger technology (DLT), as it involves less complexity in experimentation. This chapter also examines cross-border CBDC, often an extension of wholesale CBDC prototypes based on DLT. The next chapter will then discuss retail CBDC as well as the prospects of economy-wide roll out of CBDC going forward.

Smart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries.

This study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method.

The empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries.

Enhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.

This chapter examines mobile payments and digital banking services. The past decade has seen a rapid increase in the use of alternative payment systems, away from cash to electronic payments. The digitalization of payments includes business-to-business (B2B), customer-to-business (C2B), and government-to-business and consumers (G2B/C), whether the payments are by computer, wire transfers, and point of sale (POS) systems. POS systems have become a standard in many retail outlets. Mobile payments use a smart device for contactless pay. Consumers see the increasing prevalence of payment systems when they go to the retail checkout or service counter. Worldwide, mobile payments are approaching 50% of digital. Digitalized payment systems are becoming more secure, decreasing concerns over mistaken payments, fraud, and errors. Consumers' confidence in value and usage decreases with age. Most fraud is due to scams and not hacking. Greater access to improved infrastructure and affordable smart devices will expand the usage of digitalized payment systems worldwide.

The purpose of this review paper is to investigate the consequences of tourism development and economic growth within the Association of Southeast Asian Nations (ASEAN) countries, focusing specifically on Lao PDR post-pandemic. The adverse effect of COVID-19 on tourism and economic sectors has been pervasive across the ASEAN region, with varying degrees of impact. Some of these difficulties are set to continue, though there are positive signs of recovery and of the resilience of the tourism industry. Utilising case material from Lao PDR in Southeast Asia – an area frequently neglected in tourism studies – the paper sheds light on the post-pandemic landscape to address existing gaps in the current literature.

A case study approach was taken in this review paper, utilising secondary data such as media reports, official reports from Tourism Laos and international governing bodies like United Nations and the World Bank to form a viewpoint discussion in the Lao PDR post-pandemic condition.

This paper reveals that contrary to a long period of recovery post-pandemic, there has been a degree of continuity from the pre-pandemic period. Considerable numbers of backpackers have returned to Vang Vieng, along with Vientiane and Luang Prabang. While the pre-pandemic emphasis on mass tourism persists, there is also an increased focus on regional and domestic markets. Laos, with its strategic location and cross-border connections, aims to take advantage of this shift.

The paper highlights a detailed exploration of the Lao tourism industry post-pandemic. It goes beyond the initial expectations in literature of a complete transformation post-pandemic, highlighting the continuity in visitor sources and traditional tourist attractions. It emphasises the Lao PDR strategic position for market reorientation, providing insight into the nation’s adaptive strategies and a nuanced perspective on the evolving landscape of Lao tourism.

The construction industry has arrived at a crossroads of rapid technological progress. While it is foreseen that the advent of new construction technologies will disrupt the construction industry’s future, such disruptions often create the ideal environment for innovation. As poor payment practices continue to plague the construction industry, the advent of smart contracts has created an opportunity to rectify the inherent flaws in the mitigation of payment problems in traditional construction contracts. Given the intrinsic resistance of construction firms to such revolutionary changes, this study aims to understand the various factors influencing the adoption of smart contracts in the Singapore construction industry.

A mixed method was adopted involving quantifying respondents’ perceptions of the factors influencing smart contract adoption, and validation from a group of interviewees on the matter. Out of 461 registered quantity surveyor members contacted via the Singapore institute of surveyors and valuers website, 55 respondents took part in the survey. This is followed by semi-structured interviews to validate the survey results.

The findings indicate that construction firms have neither a significant knowledge of nor willingness to adopt smart contracts. A total of 29 institutional factors were also identified that significantly influence the adoption of smart contracts. The quantitative findings were further reinforced by qualitative interviews with five industry experts.

With recognition of and the successful formulation of the significant institutional drivers and barriers, the key findings of this study will be integral in driving the commercial adoption of smart contracts within the construction industry.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.

The purpose of this study synthesises the body of research revolving around blockchain technology (BCT) whilst drawing on the technology-organization-environment framework, resource-based theory and theory of constraints, to conceptualize capabilities (enablers) and constraints (barriers) of BCT in the hospitality and tourism (H&T) industry.

A systematic literature review of BCT in the hotel and tourism industry has been achieved through two databases, i.e. Scopus and Web of Science. From 544 articles selected between the years 2008 and 2023 (first quarter), a sample of 49 articles was used to structure existing research on this subject.

The findings of this systematic literature review of BCT in the H&T literature establish a solid groundwork for assessing the evolution of this research area over time. Findings are classified into two groups: capabilities (enablers) and constraints (barriers) of BCT based on publication year, different research methods, theoretical underpinnings and applicable contexts.

To the best of the authors’ knowledge, this is one of the first attempts to synthesize studies related to BCT in H&T research by combining three theoretical approaches. It serves as a foundation to evaluate the development of BCT studies in this field.

This chapter first reviews the current stages of retail CBDC development before examining some of the common characteristics of retail CBDC projects being proposed in various jurisdictions. This chapter then examines the possible future of retail CBDC going forward.