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Recent technological advancements, often linked to Industry 4.0, require organizations to be more agile and innovative. Blockchain technology (BT) holds immense potential in driving organizations to achieve efficiency and transparency in supply chains. However, there exist some insurmountable challenges associated with the adoption of BT in organizational supply chains (SC). This paper attempts to categorically identify and systematize the most influential challenges in the implementation of BT in SC.

This study resorts to an extensive literature review and consultations with experts in the field of supply chain management (SCM), information technology and academia to identify, categorize and prioritize the major challenges using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Combined Compromise Solution method (CoCoSo).

The top three classes of challenges revealed in this study are privacy challenges (PC), infrastructure challenges (IC) and transparency challenges (TC). Maintaining a balance between data openness and secrecy and rectification of incorrect/erroneous input are the top two challenges in the PC category, integration of BT with sustainable practices and ensuring legitimacy are the top two challenges in the IC category, and proper and correct information sharing in organizations was the top most challenge in the TC category.

Future scholars and industry professionals will be guided by the importance of the challenges identified in this study to develop an economical and logical approach for integrating BT to increase the efficiency and outcome of supply chains across several industrial sectors.

This study explored the impact of environmental awareness, knowledge, habits, attitudes, subjective norms and perceived behavioural control on purchase intention towards an eco-friendly hotel from a hotel guest perspective. The mediating role of habits and attitudes in the relationships was also examined.

Anchored on an extended theory of planned behaviour (TPB) model, the study employed a quantitative method through a self-administered questionnaire. Convenience and snowball sampling approaches were used to select 241 respondents. Structural equation modelling was adopted to examine relationships between constructs.

Results showed that hotel guests’ perceived environmental awareness positively influences their habits and that environmental knowledge positively affects their attitudes. Hotel guests’ habits, attitudes and perceived behavioural control also influence their purchase intention towards an eco-friendly hotel. In addition, habits and attitudes have a mediating effect on the relationship between environmental awareness and knowledge and purchase intention.

Hotel operators should implement marketing campaigns to arouse hotel guests’ eco-friendly habits and attitudes by promoting environmental awareness and knowledge such as energy saving initiatives and green activities, which can increase their purchase intention.

The findings extend the current hospitality and tourism literature advocating for the mediating role of habits and attitudes with the consequence of environmental awareness and knowledge about purchase intention. Moreover, this study increases the original TPB’s predictive power in the context of eco-friendly hotels by adding complementary constructs.

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/

Although visual prototypicality in fashion is an observed phenomenon, empirical examinations of the link between fashion products' design prototypicality and consumer evaluations still need to be included. The present study analyzes the influence of the visual prototypicality of fashion products on consumer-perceived product values and brand preference.

An online survey adopting the fashion product images with significantly differing levels of visual prototypicality was used to collect data from 456 US consumers. The hypothesized relationships among visual prototypicality, product values and brand preference were analyzed through multi-group analysis.

Perceived visual typicality of fashion product designs significantly increased the hedonic and utilitarian value of the product and only indirectly increase brand preference. The hypothesized positive relationship between visual prototypicality and the product’s social value was found to be significant only in the low-price levels but became insignificant in the high-price levels.

The findings of this study contribute to the extant literature by first providing an initial analysis of the mechanism of visual prototypicality in the fashion product design field. The results confirm that visual prototypicality indirectly influences consumers' brand evaluations by the product’s perceived value. This relationship was previously assumed but not empirically proven only in non-fashion product categories. The study also presents additional new points, further enriching the understanding of visual typicality. Additionally, the results show the complex relationship between the visual prototypicality of fashion product designs and the perceived social value of the product, which varies depending on the price range.

This paper analyzes the impacts of COVID-19 and related policies on airport short-run costs and decomposes the percentage changes in total and average variable costs between pre-COVID-19 and COVID-19 periods. Data for the analysis are a panel of 50 medium and large US airports from 2012 to 2021. COVID-19 measures include COVID-19 cases and deaths. COVID-19-related policies include state-level face mask and COVID-19 vaccine mandates. Based upon a short-term multi-output translog cost function with three positive outputs (departures, non-aeronautical revenue, and workload), three associated negative attributes (delay, congestion, and air pollution), COVID-19 measures and policies, the analysis has three main conclusions: (1) A 1% increase in COVID-19 cases leads to a 0.077% increase in total operating costs. State-level face mask and COVID-19 vaccine mandates increase total operating costs by 15.9% and 16.8%, respectively; (2) COVID-19 and related policies increase airport total operating costs through contractual services costs; and (3) the cost decomposition finds that a 1 million increase in COVID-19 cases results in a 109% increase in average variable costs, while the time/technological progress effect leads to a decrease of 87% compared to the pre-COVID-19 period. Face mask and vaccine mandates increase the average variable costs by 8.91% and 4.19%, respectively. The positive output total effects range from 3.46% to 7.99%. The effects of input prices and negative attributes are relatively small.

This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration. Consequently, a low-cost, data-driven approach was proposed for analyzing speed-related acceleration limits in metro systems.

A portable sensing terminal was developed to realize easy and efficient detection of car body acceleration. Further, field measurements were performed on a 51.95-km metro line. Data from 272 metro sections were tested as a case study, and a quantile regression method was proposed to fit the control limits of the car body acceleration at different speeds using the measured data.

First, the frequency statistics of the measured data in the speed-acceleration dimension indicated that the car body acceleration was primarily concentrated within the constant speed stage, particularly at speeds of 15.4, 18.3, and 20.9 m/s. Second, resampling was performed according to the probability density distribution of car body acceleration for different speed domains to achieve data balance. Finally, combined with the traditional linear relationship between speed and acceleration, the statistical relationships between the speed and car body acceleration under different quantiles were determined. We concluded the lateral/vertical quantiles of 0.8989/0.9895, 0.9942/0.997, and 0.9998/0.993 as being excellent, good, and qualified control limits, respectively, for the lateral and vertical acceleration of the car body. In addition, regression lines for the speed-related acceleration limits at other quantiles (0.5, 0.75, 2s, and 3s) were obtained.

The proposed method is expected to serve as a reference for further studies on speed-related acceleration limits in rail transit systems.

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.

Liquid-assisted laser surface texturing technology was used to create composite microtextures on triangular guide rail surfaces to enhance their tribological properties.

Numerical simulations were used to investigate the impact of various microtextures on fluid dynamic lubrication. Reciprocating friction and wear tests, followed by mechanistic analysis, examined the combined tribological effects of microtextured surfaces and lubricants.

The numerical simulation outcomes reveal a significant augmentation in the influence of fluid dynamic pressure due to composite microtextures, consequently amplifying the load-bearing capacity of the oil film. The average friction coefficient of composite microtextured samples was approximately 0.136 in reciprocating pin-on-disk friction tests, representing approximately 17% decrease compared to polished samples. Triangular guide rails with composite microtextures demonstrated the lowest average coefficient under conditions of high-speed and heavy-loading in the reciprocating friction and wear tests. Additionally, the presence of composite microtextures was found to promote the formation of adsorbed and friction films during friction, potentially contributing to the enhancement of tribological properties.

Triangular guide rails face high friction and wear, limiting their stability in demanding applications like machine tool guideways. This paper proposes a novel approach for steel triangular guide rails, involving composite microtexturing, numerical fluid simulations, liquid-assisted laser surface texturing and friction-wear testing. By implementing composite microtextures, the method aims to reduce friction coefficients and extend guideway service life, thereby saving energy and reducing maintenance costs. Enhancing the antifriction and antiwear properties of machine tool guideways is crucial for improving performance and longevity.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0183/

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.