Search results

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

This study aims to explore the determinants of public debt in selected South Asian Association for Regional Cooperation (SAARC) countries for 19 years, from 2001 to 2019.

Using ordinary fixed and random effect models, the authors examine the role of internal and external factors in determining the composition of public debt. Furthermore, for robustness, they compare the results with two-stage least square (2SLS) regression estimates after considering the problem of endogeneity, overidentification, under-identification and weak instruments.

The findings show that among the selected macroeconomic variables, inflation, exchange rate and broad money have significant negative effects on the debt-GDP ratio. In contrast, military spending, corruption and interest rates appear to positively influence the same as per 2SLS results. From the policymaking perspective, SAARC countries should focus more on reducing military spending and make a concerted effort to augment investments in productive projects. Further, with strong fiscal consolidation and institutional quality, it is important to mitigate the frequent occurrence of corruption conundrums in emerging economies for the development of a transparent economic system.

The study is distinct from previous studies in two ways. First, to the best of the authors’ knowledge, there are no studies focusing on SAARC countries in the context of public debt. Second, the study expands the existing literature on public debt by taking into account both external and internal debts to decipher the within-country and cross-country determinants of debt accumulation. More specifically, this model considers accountability and transparency in the public sector, cross-border security challenges and benefits of globalization by including explanatory variables such as corruption, military expenditure spending and capital inflows.

The purpose of this study is to explore the effect of backward supply chain information sharing (SCIS) on the performance of different parties along supply chains.

Using data collected from 617 Chinese manufacturers, this study examines the effect of information sharing from manufacturers to suppliers (ISMS) on supplier and manufacturer responsiveness in both two- and three-tier supply chains and the transferrable effect of information sharing from customers to manufacturers (ISCM) on supplier responsiveness. The authors use structural equation modeling and regression analysis to estimate the proposed relationships.

ISMS is positively related to supplier responsiveness in both two- and three-tier SCIS, whereas its effect on manufacturer responsiveness is conditional, indicating that upstream receiving parties benefit more from backward SCIS. ISCM is positively related to supplier responsiveness, demonstrating the transferable effect of backward SCIS.

When designing SCIS strategies, managers should take into account the unequal distribution of benefits from SCIS and the transferable effect of backward SCIS. Specifically, companies should always motivate their downstream partners to engage in SCIS, while they themselves need to be cautious with information sharing. They should also be aware that their information sharing behavior may benefit a third upstream party.

This study provides deep insights into the distribution of the benefits from SCIS among supply chain partners and suggests that the direction and the role supply chain partners play (as receiving or disclosing parties) in SCIS matter.

The organization of services can affect the adoption of sustainable practices within the relationship between a buyer (e.g. a shipper) and a supplier (e.g. a logistics service provider–LSP). The purpose of this paper is to analyse, within this relationship, the mechanisms affecting collaboration between shippers and LSPs towards adopting green logistics practices to reduce the negative environmental effects of logistics processes. The authors take the perspective of small and medium enterprises (SMEs), which represent – although less investigated than large enterprises – a relevant field of investigation given their impact on the environmental sustainability of logistics processes.

The authors conducted a multiple case-study investigation on a set of dyads involving shippers and LSPs. The authors explored the antecedents shaping the approach to sustainability in logistics and, adopting the absorptive capacity (AC) theory, the learning and knowledge transfer processes leading to the adoption of green practices.

Collaboration between shippers and LSPs for better sustainability in logistics seems not to work when relationships are limited to simple annual (or pluriannual) contracts, and when shippers do not show ambition to improve the level of sustainability of their logistics processes (regardless of whether they show an interest in general sustainability matters). On the other hand, successful cases show higher commitment in the dyadic relationship with respect to improving logistics sustainability, good levels of communication and a more structured process of knowledge sharing, enabled by IT integration, shared performance monitoring, and creation of inter-organizational teams.

While most of the existing research focuses on the perspective of shippers or LSPs, this work is original since it explores collaborative mechanisms within a buyer-supplier relationship simultaneously taking the perspective of both parties, according to the lens of the AC. It identifies directions for improving collaboration within the shipper-LSP relationship in the context of SMEs to foster the adoption of collaborative green logistics practices to impact sustainability positively.

eWOM is a very useful tool that is widely used nowadays to make a purchase decision. One of the sectors where this tool is very present is the gastronomic sector and Peru is no stranger to this gastronomic take-off. As a part of this take-off, Nikkei or Peruvian-Japanese cuisine have gained plenty of attention through digital social platforms, with a significant number of users relying on eWOM to determine which is their best purchase option; nevertheless, there is a lack of local investigations that addresses a correlation between eWOM and purchase intention in this type of restaurants. With the last statement, this study aims to determine the influence of eWOM on the purchase intention of consumers of Nikkei restaurants in Lima, Peru.

A quantitative descriptive and causal explanatory research was conducted, using the Information Acceptance Model (IACM) to test the purpose of the study. A total of 385 surveys were obtained and processed in SmartPLS.

The results show that consumers of Nikkei restaurants in Metropolitan Lima are mostly guided by the eWOM that is on social media to make a purchase decision. In addition, information usefulness is the eWOM dimension that most influences purchase intention. Finally, it was found that the credibility of the eWOM does not influence the information usefulness, which could be due to the distrust of the sources and the anonymity of the eWOM.

The present investigation identified that certain dimensions of the IACM does not influence their hypothesized counterparts, such as credibility and attitude towards information over information usefulness; circumstances that may conduct to other local studies based on the IACM applied to other type of restaurants to observe if there is some type of variation in the results obtained of both hypotheses.

From a practical point of view, the results obtained are valuable for social media marketers and generally digital marketing professionals, which by themselves provide a framework that assures a better understanding about the impact of eWOM in the purchase intention of Nikkei Restaurant consumers.

As the potential for this research to impact society in visible ways, it identifies how current individuals in a certain time and location, tend to act towards information obtained in social media or specialised forums; as well as how the characteristics of this information could affect their final purchase decision.

To the best of the authors’ knowledge, this is one of the first articles to study the influence of eWOM on purchase intention in the gastronomic sector in Peru, a country famous for its gastronomy. In this way, experts and professionals in this field will be able to generate knowledge of this tool, as well as generate strategies to improve sales of Nikkei restaurants and in general within the gastronomy industry in Peru.

This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.