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Seaports seek to find innovative technological solutions to deal with the ever-increasing growth of transportation and increasing the intensity of competition through the use of emerging technology such as digital twin technology to improve the quality of their logistics operations. Despite the success of digital twins in many industries, there is still a lack of their application in the field of seaports where ports play a central role as part of global transportation chains. Seaports sustainability comprises three main aspects: the social aspect that encompasses more job opportunities, the economic aspect that enhances foreign trade, and the environmental aspect that refers to the process of managing and operating ports in a way that saves the environment. This chapter discusses how to apply digital twins’ technology on the imported Twenty equivalent foot unit (TEUs) taking into consideration the population growth, and the capacity of the storage area of the container terminals in an attempt to explore the impact on Kuwait’s seaports sustainability. The study provides a framework for capacity management in an attempt to initiate the next generation of smart seaports cities and consequently impact society, economy, and well-being in Kuwait and Gulf region. The results of the study showed that there is a strong correlation between population growth and imported TEUs growth during the essential stage of the study. The correlation factor was 0.97, and this correlation will support the prediction until Kuwait vision 2040.

This study aims to examine the cutting performance of a coated carbide tool during the boring of 1Cr17Ni2 martensitic stainless steel, with a focus on how the tool’s structural parameters, particularly the nose radius, affect the wear patterns, wear volume and lifetime of the cutting tool, and related mechanisms.

A full factorial boring experiment with three factors at two levels was conducted to analyze systematically the impact of cutting parameters on the tool wear behavior. The evolution of tool wear over the machining time was recorded, and the influences of the cutting parameters and nose radius on wear behavior of the tool were examined.

The results show that higher cutting parameters lead to significant wear or plastic deformation at the tool nose. When the cutting depth is less than the nose radius, the tool wear tends to be minimized. Larger nose radius tools have weaker chip-breaking but greater strength and wear resistance. Higher cutting parameters reduce wear for the tools with larger nose radius, maintaining their integrity. Wear mechanisms are primarily abrasive, adhesive and diffusion wear. Furthermore, the full-factorial analysis of variance revealed that for the tool with r_ε = 0.4 mm and 0.8 mm, the factors contributing the most to tool wear were cutting speed (38.76%) and cutting depth (86.43%), respectively.

This study is of great significance for selection of cutting tools and cutting parameters for boring 1Cr17Ni2 martensitic stainless-steel parts.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0266/

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

Know your customer (KYC), accounting standards, issuance, clearing, and trade settlement became the major barrier to implement accounting, accountability and assurance process in supply chain finance (SCF). Blockchain technology features have the potential to solve accounting problems. This research focuses on exploring how blockchain technology provides solutions to overcome the barriers of accounting process in SCF. The benefits, opportunities, costs and risks related to blockchain adoption are also explored.

Multi-case study and qualitative methods are used with a framework based on blockchain role to overcome the accounting process barriers. Ten blockchain projects in SCF and 29 interviews of participants as a unit of analysis are considered.

The findings indicate that blockchain technology offers solutions to solve accounting, accountability and assurance problems in SCF. Validity, verification, smart contracts, automation and enduring data on trade transactions potentially solve those barriers. However, it is also necessary to consider costs such as implementation, technology, education and integration costs. Then there are possible risks such as regulatory compliance, operational, code development and scalability risk. This finding reflects the current status of blockchain technology roles in SCF.

This study unveils blockchain's SCF accounting potential, emphasizing multi-case method limitations and future research prospects. Diverse contexts challenge findings' applicability, warranting cross-industry studies for deeper insights. Addressing selection bias and integrating quantitative measures can enhance understanding of blockchain's accounting impact.

Accounting professionals can get an idea of the future direction and impact of blockchain technology on accounting, accountability and assurance processes.

This study provides initial findings on the potential, costs and risks of blockchain that is beneficial for parties involved in SCF, especially for banks and insurance underwriters. In addition, the findings also provide direction for the contribution of blockchain technology to accounting theory in the future.

To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.

A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.

The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.

Construction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.

Influencers in physical activity and sports have gained significant prominence in promoting health and well-being via digital social networks. This research investigates the impact of athlete influencers on sports perceptions, with a focus on korfball. Using korfball as a representative sport minimizes biases, allowing a nuanced exploration of athlete-generated content’s direct influence on viewer attitudes.

A questionnaire survey with 319 respondents explores variables like athlete attitude, attractive appearance, social media profile attitude, emotional engagement toward korfball, korfball perception, korfball interest and general sports attitude. Structural equation modeling analysis examines relationships among these variables.

The study delves into the complex interplay among athlete attitude, social media attitude and emotional engagement toward sports, emphasizing the pivotal role of authenticity in fostering deeper connections. Contrary to expectations, athlete attractiveness did not significantly influence viewer priorities, signaling a notable shift towards valuing authenticity and performance. Furthermore, the research explores how general sports attitudes moderate relationships between sports perceptions and interest, offering critical insights for sports sociology and marketing.

This study innovates by focusing on korfball, emphasizing authenticity over attractiveness and providing insights into the evolving dynamics of sports marketing and consumer behavior in the digital age. This research evidences the profound impact of the digital age on sports engagement, laying a foundation for future studies and practical applications in sports sociology, marketing and consumer behavior.

This study measured influencer characteristics, consumer emotions, self-construal, and behavioural intentions of Gen Z consumers for selecting fast-food restaurants in India. A consumer behaviour model was conceptualized based on established theories.

A cross-sectional design was employed for hypothesis testing. Influencer characteristic perceptions, consumer emotions, self-construal, and behavioural intentions were measured for Gen Z consumers in Tier 1 cities in India using structural equation modelling.

Influencer characteristics significantly influenced behavioural intentions, consumer emotions, and self-construal in Gen Z consumers. Self-construal was also a significant predictor of behavioural intentions. Consumer emotions had a negative effect on behavioural intentions. Self-construal was a mediator between influencer characteristics and behavioural intentions and between consumer emotions and behavioural intentions. However, consumer emotions did not mediate the relationship between influencer characteristics and behavioural intentions.

Marketers can leverage these insights to design influencer campaigns that resonate with the emotions and self-construal of Gen Z consumers. Microinfluencers with characteristics that align with the target demographic’s emotions and self-perception can be strategically chosen.

Only a limited number of studies have investigated the influence of social media marketing on consumer behaviour within the fast-food industry, specifically with Gen Z consumers. This study sheds new light on the behavioural intention of Gen Z consumers predicted through influencer characteristics, consumer emotions, and self-construal through a conceptual model. The results support choosing microinfluencers and investing in them judiciously to promote fast-food businesses.

Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.

The purpose of the paper is to analyze the impact of involuntary frugality and deliberate frugality on the household intentions to adopt energy-efficient and energy-generating products. Additionally, the study aims to explore the role of motivation to save as a mediating factor between different types of frugality and the adoption of different kinds of energy products.

The study involved a survey of 413 households, gathering information through questionnaires from both tier I and tier II urban areas in India. The investigation used confirmatory factor analysis and structural equation modeling with Amos to explore the impact of frugality and also mediating impacts of motivation to save on the correlation between different forms of frugality (involuntary and deliberate) and the desire to acquire energy-efficient and energy-producing goods. This methodology facilitated a thorough examination of how various levels of frugality impact the uptake of sustainable energy solutions, with a specific emphasis on the fundamental motivational drivers behind these choices.

The study uncovers specific connections between various forms of frugality and the desire to embrace energy-efficient and energy-producing items. Unintentional frugality, characterized by sensitivity to prices, is shown to have a positive correlation with the adoption of energy-efficient devices but a negative association with the intention to adopt energy-generating products. Conversely, intentional frugality, distinguished by deliberate reduction actions, positively impacts the inclination to adopt both energy-efficient and energy-generating products. The results suggest that the mediating impact of motivation for savings varies depending on the type of frugality and the class of energy products being considered, emphasizing the subtle ways in which frugality influences sustainable consumption behaviors.

The contrasting effects of involuntary and voluntary frugality on the adoption of energy-efficient versus energy-generating products highlight the need to explore the underlying psychological and economic mechanisms. Future research should investigate the factors influencing the preferences of price-sensitive and deliberate frugal consumers towards this energy-efficient and energy-generating products.

Policymakers should develop specific subsidies and financial strategies for low-income households and incentive programs for conscientious consumers. Educational campaigns emphasizing the benefits of energy-generating goods and creating incentive structures with tax advantages, refunds and financial aid are essential. Companies should continue to emphasize cost savings for energy-efficient appliances and consider leasing or instalment plans for energy-generating products to appeal to price-sensitive consumers.

Literature shows that 82% of Indians prefer frugality to conserve energy through reduced consumption. However, consumer motivations for frugality vary. This study analyses the distinct impacts of involuntary and voluntary frugality on adopting energy-efficient and energy-generating products, offering a nuanced understanding of consumer behavior in sustainability—a topic underexplored in existing research. Additionally, this study investigates the role of the motivation to save as a mediator between frugality and energy product adoption, providing a novel perspective on how different frugality motivations influence different category of energy products.

There is consensus among scholars on how political institutional imprinting interprets the unique management and practice phenomenon of Chinese enterprises. However, little scholarly attention has been given to the different political institutional imprints that shape firms’ internationalization. Therefore, this study aims to investigate how communist and market logic political institutional imprintings influence firms’ initial ownership strategies in outward foreign direct investment.

Based on the propensity score matching difference in difference method and a sample of 464 foreign investments from 2009 to 2020 for 310 Chinese private firms.

The results show that private firms with market logic political institutional imprintings tend to adopt higher ownership and vice versa. As institutional differences increase, private firms with market logic imprintings are more risk-taking and adopt higher ownership, whereas private firms with communist imprintings are more conservative and choose lower ownership. When diplomatic relations are friendlier, private firms with market logic imprintings prefer higher ownership to grasp business opportunities and vice versa.

This study not only identifies the net effect of political institutional imprinting on private firms’ initial ownership strategy but also investigates the different moderating effects of current institutional forces to respond to the call for research on bringing history back into international business research and the fit between imprinting and the environment.