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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.

In the face of information leakage, this study aims to demonstrate pathways to supply chain resilience (SCR) during information sharing by deploying organizational ethical climate (OEC) and information security culture (ISC) as non-punitive mitigation approaches.

This empirical study was conducted to verify the framework using a questionnaire distributed to Malaysian multinational corporations (MNCs) of the manufacturing sector. The data were analysed using structural equation modeling (SEM) techniques with the AMOS software.

This study has confirmed the adverse impact of intentional and unintentional leakages on information sharing effectiveness. The findings showed ISC could reduce the impact of information leakage, but an OCE could not. This study provides evidence that information sharing effectiveness could impact SCR. The former is a mediator between information leakage and SCR, with information leakage moderated by information security culture. These findings convey that multinationals should set up an ISC to reduce information leakage and enhance their SCR.

Prior studies lacked the explanation of the impact of mitigating factors on information leakage in information sharing effectiveness affecting SCR. A framework that explains the relationships add value to organizations making available strategic decisions to curb information leakage and manage SCR.

This paper's purpose is to suggest that manufacturers strategically place soft orders for assembly materials with suppliers in Silk Road Economic Belt countries who probably doubt the realization of the soft orders placed.

First, a two-stage Stackelberg competition is constructed, taking into account the supplier's trust level in formulating the decision process in the assembly supply chain. The authors then provide a buyback contract to coordinate the supply chain, in which the manufacturer obtains enough supplies by sharing some of the perceived risks of not fully trusted suppliers. Furthermore, the authors conduct a numerical study to investigate the influence of trust under a decentralized case and a buyback contract.

The authors found that all supply chain partners in Silk Road Economic Belt countries experience potential losses due to not fully trusting certain conditions. The study also shows that, in Silk Road Economic Belt countries, operating under a buyback contract is better than being without one in terms of assembly supply chain performance.

On the one hand, the authors only consider the asymmetry of demand information without considering that of cost structure information. On the other hand, a natural extension of the paper is to integrate single-period transactions into the multi-period transaction problem setting. As all these issues require substantial effort, the authors reserve them for future exploration.

Doing business with not-fully-trustworthy partners in Silk Road Economic Belt countries is risky, and this study reveals how trust works in global cooperation and with strategic reactions in situations of partial trust.

Since the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this paper is to propose a hybrid model ( KDJ–Markov chain) which integrates the advantages of the stochastic index (KDJ) and grey Markov chain methods and provide a useful decision support tool for investors participating in the digital currency market.

Taking Litecoin's closing price prediction as an example, the closing prices from May 2 to June 20, 2017, are used as the training set, while those from June 21 to August 9, 2017, are used as the test set. In addition, an adaptive KDJ–Markov chain is proposed to enhance the adaptability for dynamic transaction information. And the paper verifies the effectiveness of the KDJ–Markov chain method and adaptive KDJ–Markov chain method.

The results show that the proposed methods can provide a reliable foundation for market analysis and investment decisions. Under the circumstances the accuracy of the training set and the accuracy of the test set are 76% and 78%, respectively.

This study not only solves the problems that KDJ method cannot accurately predict the next day's state and the grey Markov chain method cannot divide the states very well, but it also provides two useful decision support tools for investors to make more scientific and reasonable decisions for digital currency where there are no existing methods to analyze the fluctuation.

A new approach to analyze the fluctuation of digital currency, in which there are no existing methods, is proposed based on the stochastic index (KDJ) and grey Markov chain methods. And both of these two models have high accuracy.

Nowadays, many firms are finding ways to enhance the survivability of sustainable supply chains (SUSSCs). The present study aims to develop a model for the SUSSCs of small and medium enterprises (SMEs) during the COVID-19 pandemic.

With the help of exhaustive literature review, constructs and items are identified to collect the responses from different SMEs. A total of 278 complete responses are received and 6 hypotheses are developed. Hypotheses testing have been done using structural equation modeling (SEM).

Major constructs identified for the study are supply chain (SC) performance measurement under uncertainty (SPMU), supply chain cooperation (SCCO), supply chain positioning (SCP), supply chain administration (SCA), supply chain feasibility (SCF) and the SUSSCs. From statistical analysis of the data collected, it can be concluded that the considered latent variables contribute significantly towardsthe model fit.

The present study contributes to the existing literature on disruptions and survivability. The study can be further carried out in context to different countries and sectors to generalize the findings.

The research findings will be fruitful for SMEs and other organizations in developing strategies to improve survivability during uncertain business environments.

The study has developed a model that shows that the identified latent variables and their indicators contribute significantly toward the dependent variable, i.e. survivability. It contributes significantly in bridging the research gaps existing in context to the survivability of SMEs.

Due to the introduction of new vaccines in the child immunization program and inefficient vaccine supply chain (VSC), the universal immunization program (UIP), India is struggling to provide a full schedule of vaccination to the targeted children. In this paper, the authors investigate the critical factors for improving the performance of the existing VSC system by implementing the next-generation vaccine supply chain (NGVSC) in India.

The authors design a fuzzy multi-criteria framework using a fuzzy analytical hierarchical process (FAHP) and fuzzy multi-objective optimization on the basis of ratio analysis (FMOORA) to identify and analyze the critical barriers and enablers for the implementation of NGVSC. Further, the authors carry out a numerical simulation to validate the model.

The outcome of the analysis contends that demand forecasting is the topmost supply chain barrier and sustainable financing is the most important/critical enabler to facilitate the implementation of the NGVSC. In addition, the simulation reveals that the results of the study are reliable.

The findings of the study can be useful for the child immunization policymakers of India and other developing countries to design appropriate strategies for improving existing VSC performance by implementing the NGVSC.

To the best of the authors’ knowledge, the study is the first empirical study to propose the improvement of VSC performance by designing the NGVSC.

Based on the analysis of the dissipative structure of the retail service supply chain (RSSC), this paper divides the system into two internal and external dissipative mechanisms, including the internal performance dissipation mechanism and the perceived quality dissipation mechanism outside the system. Based on the prediction of RSSC performance, this paper aims to discuss the application of Hidden Markov Model (HMM) in this field and puts forward a set of complete process of forecasting the service supply chain (SSC) performance based on HMM model.

Based on the theory of dissipative structure, this paper selects the RSSC as the research object, analyzes the system characteristics of the dissipation structure of RSSC from three aspects, such as system opening type, distance from equilibrium state and nonlinear order and describes the quality fluctuation process of RSSC as a Hidden Markov process. Taking the RSSC of J Company as an example, this paper makes use of the observed state value of customer perceived service quality from 1997 to 2016, predicts the performance status of the enterprise's RSSC.

The research results show that: RSSC is a dissipative structure system, and its performance is the internal entropy flow of the system, and the customer perceived service quality is external, their interaction determines the dynamic evolution of the system dissipation structure, and the Markov property between supply chain performance and perceived service quality. There is a Markov property between supply chain performance and perceived service quality. Using the perceived service quality observation state data of the external consumers of the system can effectively predict the implicit state of RSSC performance. Based on this prediction result, the strategy adjustment and optimization of the action mechanism of internal and external entropy flow in the dissipative structure system can be carried out to promote the sustainable development of the RSSC.

This paper thinks that RSSC is a dissipative structure system and the SSC performance and customer perceived service quality are the internal and external entropy flow of the system, which determines the dynamic evolution of the system dissipation structure. There is a Markov property between supply chain performance and perceived service quality. The hidden state of SSC performance can be predicted effectively by using a hidden Markov model and observing state data of perceived service quality from consumers outside the system.

The purpose of this study is to analyse various risks associated with third-party logistics (3PL) in the coffee supply chain and to present a framework that computes the influence of these risks on the critical success factors of the coffee supply chain.

The risks have been identified through a comprehensive literature review and validation by industry experts. The paper utilises an interpretive structural modelling (ISM) methodology for developing a hierarchical relationship among the CSFs. Furthermore, fuzzy MICMAC analysis is carried out to categorise these CSFs based on their driving power and dependence value. The fuzzy technique for order preferences by the similarity of an ideal solution (fuzzy-TOPSIS) approach has been applied to prioritise the risks associated with 3PL based on their ability to influence the CSFs of the coffee SC. Furthermore, we performed a sensitivity analysis to analyse the stability of the results obtained in this study.

This study illustrates ten risks associated with 3PL and five CSFs in the coffee supply chain. The analysis revealed that coffee enterprises need to develop a balanced pricing strategy to ensure a sustainable competitive advantage, whereas the lack of direct customer communication is the most dominant 3PL risk affecting the CSFs.

This research provides coffee enterprises with a generalised framework with set parameters that can be used to attain a successful coffee supply chain in any developing nation.

The study contributes to the literature by being the first kind of study, which has used fuzzy ISM-MICMAC to analyse the CSFs of the coffee supply chain and fuzzy-TOPSIS for analysing the impact of various risks associated with the 3PL in the coffee supply chain. Thus, this work can be considered a benchmark for future research and advancement in the coffee business field.

This study aims to examine factors affecting Chinese adolescents’ purchase intention for local brands; this study focuses on the effects of perceived social status value and materialistic values.

A theoretical model relating perceived social status value of brands to purchase intention, including materialistic values as a moderator, was developed and tested, using a sample of 587 Chinese adolescent respondents. Another experimental study examined the variability of the moderation of materialistic values across different levels of peer pressure in a product usage occasion.

Perceived social status value associated with local and foreign brands significantly influences purchase intention for local brands. Moreover, influence of perceived social status value of local versus foreign brands on local brand purchase intention is greater for materialistic adolescents. Additionally, the moderation of materialistic values is found in a product usage occasion with high peer pressure, but not in an occasion with low peer pressure.

The findings show that perceived social status value associated with brands shapes purchase intention for local brands. The moderating effect of materialistic values is complex and suggests further research. The study’s scope is limited to Chinese adolescents.

The findings provide understanding of the drivers of purchase intention, and thus serve as a guideline for Chinese firms and foreign marketers seeking to enter the growing Chinese market, as well as consumer educators in China.

This study contributes to the limited empirical research into the factors shaping country-of-origin effects. Moreover, the findings suggest the need to consider the moderating role of materialistic values on purchase intention for local brands.

This paper examines the impact of livestock environment regulations (LERs) on the location dynamics of the livestock farming industry. Specifically, it tests whether a “pollution haven effect” (PHE) exists in the Chinese livestock industry.

The authors manually collected LERs data based on the frequency of livestock-related vocabulary in government work reports from 279 prefecture-level cities from 2010 to 2017. Using China's implementation of LERs since 2014 as a natural experiment, the authors employed difference-in-difference (DID) to avoid the endogeneity problem.

LERs have led to a decline in livestock production in regulated areas. Moreover, compared with areas with more stringent LERs – southern China and developed areas, areas where LERs are less stringent – northern China and undeveloped areas, attract more livestock industry. As a result, people in northern China and undeveloped areas are exposed to higher livestock pollution.

First, most empirical studies on PHE focus on industrial firms. PHE in the livestock industry has received limited attention. This paper aims to fill this gap. Second, this paper regards LERs as an endogenous process and uses the DID method to generate unbiased results. Third, this paper introduces a novel measurement of LERs based on the frequency of livestock-related vocabulary in government work reports from each prefecture city. Fourth, this paper uses prefecture-level data to analyze the PHE of LERs, and thus obtains more reliable results.