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This paper aims to investigate the impact of perceived risk, ethical judgement, value consciousness, susceptibility to social influence and neutralisation on counterfeit clothes and accessories purchase intention in Egypt.

A single cross-sectional survey was conducted. Questionnaires were used to collect data from 361 counterfeit buyers in Egypt. To test the hypotheses, partial least squares-structural equation model was applied.

The results indicate that neutralisation, perceived risk and susceptibility to social influence significantly impact attitudes towards counterfeiting and purchase intentions, whereas value consciousness impacts counterfeit purchase intention. In addition, attitudes mediate the effects of perceived risk, susceptibility to social influence and neutralisation on purchase intention.

Brand producers/retailers and the government need to adhere to a number of practices to curb counterfeit demand, mainly by tackling the neutralisation’s impacts, demonstrating various risks of counterfeiting and developing a collective attitude against counterfeiting.

This study contributes to the ethical decision-making literature by empirically testing and quantifying the impact of neutralisation on shaping counterfeit buyers’ attitudes and purchase intention.

Product counterfeiting has been ubiquitously observed in various segments of the supply chain. The intrinsic values of brands create more opportunities for counterfeiting. The damaging reputation of such brands leaves them to deal with the fallouts of counterfeits. Hence, such companies address them mainly through legal action, price and quality strategy. However, consumer characteristics and the random distribution of counterfeit products to the consumer types affect the effectiveness of a counter strategy. This paper aims to generate insights on how to leverage digital technology to curb counterfeit entities with consideration of consumer characteristics and the random distribution of counterfeits to them.

The authors used game theory and vertical differentiation model to understand and encounter deceptive counterfeiting of brand products. The study understands the economic relationship between a brand product manufacturer and consumer types based on their awareness. Further, the authors have considered different cases in the model to gain useful insights.

The results reveal that when the consumers are proactive, informed and value-conscious brand product manufacturers take digital technology counterstrategy to earn the maximum revenue. Hence, this analysis highlights that the effectiveness of a counterstrategy critically depends on the consumer characteristics, whether they are proactive, informed or unaware.

The study outlines that brand product manufacturers must emphasize on the digital supply chain, product redesign and product tracking facility to empower informed and value-conscious and proactive consumers. Moreover, the government should take steps to create awareness among uninformed consumers via information campaigns.

This paper incorporates the role of consumers and brand product manufacturers to understand and address the deceptive counterfeiting issue.

The study's objective is to examine the consumers' intention to buy counterfeit brand shoes. A conceptual model is developed to test the risk-taking and word-of-mouth (WOM) as a moderator in the relationship between status consumption, brand image, and consumer intention to buy counterfeit shoes.

Based on the theory of reasoned action (TRA) and signaling theory (ST), this research was conducted in the Indian National Capital Region. Using a structured instrument, the data was collected from 240 respondents. After checking the psychometric properties of the survey instrument using the Lisrel package of structural equation modeling, Hayes's PROCESS macros were used for testing the hypotheses.

The findings from the study indicate that (1) status consumption and brand image are positively associated with purchase intention of counterfeit brand shoes, and (2) risk-taking moderates the relationship between (1) status consumption and purchase intention, and (2) brand image and purchase intension, (3) significant three-way interaction between WOM, risk-taking and status consumption on purchase intention, and (4) significant three-way interaction between brand image, WOM, and risk-taking on purchase intention of counterfeit brand shoes.

As with any survey research, this study has common method variance as a potential problem. However, through the latent variable method and Harman's single-factor analysis, the common method variance was checked. The study has several implications for managers, e-marketers, and consumers.

The study has several implications for marketers selling counterfeit products and managers intending to protect their branded products.

A conceptual model showing two-way and three-way interactions between status consumption, risk-taking, and WOM influencing the consumer purchase intention of counterfeit products was discussed. This is the first of its kind in India to explore such relationships.

In this age of extremely well-developed social media, it is necessary to detect any change in the corporate image of an enterprise immediately so as to take quick action to avoid the wide spread of a negative image. However, existing survey-based corporate image evaluation methods are costly, slow and static, and the results may quickly become outdated. User comments, news reports and we-media articles on the internet offer varied channels for enterprises to obtain public evaluations and feedback. The purpose of this study is to effectively use online information to timely and accurately measure enterprises’ corporate images.

A new corporate image evaluation method was built by first using a literature review to establish a corporate image evaluation index system. Next, an automatic text analysis of online public information was performed through a topic classification and sentiment analysis algorithm based on the dictionary. The accuracy of the topic classification and sentiment analysis algorithm is then calculated. Finally, three internet enterprises were chosen as cases, and their corporate image was evaluated.

The results show that the author’s corporate image evaluation method is effective.

First, in this study, a new corporate image evaluation index system is constructed. Second, a new corporate image evaluation method based on text mining is proposed that can support data-driven decision-making for managers with real-time corporate image evaluation results. Finally, this study improves the understanding of corporate image by generating business intelligence through online information. The findings provide researchers with specific and detailed suggestions that focus on the corporate image management of emerging internet enterprises.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.

To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.

The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.

Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.

This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.