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This study aims to examine the blockchain landscape in supply chain management by drawing insights from academic and industry literature. It identifies the key drivers, categorizes the products involved and highlights the business values achieved by early adopters of blockchain technology within the supply chain domain. Additionally, it explores fingerprinting techniques to establish a robust connection between physical products and the blockchain ledger.

The authors combined the interpretive sensemaking systematic literature review to offer insights into how organizations interpreted their business challenges and adopted blockchain technology in their specific supply chain context; content analysis (using Leximancer automated text mining software) for concept mapping visualization, facilitating the identification of key themes, trends and relationships, and qualitative thematic analysis (NVivo) for data organization, coding and enhancing the depth and efficiency of analysis.

The findings highlight the transformative potential of blockchain technology and offer valuable insights into its implementation in optimizing supply chain operations. Furthermore, it emphasizes the importance of product provenance information to consumers, with blockchain technology offering certainty and increasing customer loyalty toward brands that prioritize transparency.

This research has several limitations that should be acknowledged. First, there is a possibility that some relevant investigations may have been missed or omitted, which could impact the findings. In addition, the limited availability of literature on blockchain adoption in supply chains may restrict the scope of the conclusions. The evolving nature of blockchain adoption in supply chains also poses a limitation. As the technology is in its infancy, the authors expect that a rapidly emerging body of literature will provide more extensive evidence-based general conclusions in the future. Another limitation is the lack of information contrasting academic and industry research, which could have provided more balanced insights into the technology’s advancement. The authors attributed this limitation to the narrow collaborations between academia and industry in the field of blockchain for supply chain management.

Practitioners recognize the potential of blockchain in addressing industry-specific challenges, such as ensuring transparency and data provenance. Understanding the benefits achieved by early adopters can serve as a starting point for companies considering blockchain adoption. Blockchain technology can verify product origin, enable truthful certifications and comply with established standards, reinforcing trust among stakeholders and customers. Thus, implementing blockchain solutions can enhance brand reputation and consumer confidence by ensuring product authenticity and quality. Based on the results, companies can align their strategies and initiatives with their needs and expectations.

In essence, the integration of blockchain technology within supply chain provenance initiatives not only influences economic aspects but also brings substantial social impacts by reinforcing consumer trust, encouraging sustainable and ethical practices, combating product counterfeiting, empowering stakeholders and contributing to a more responsible, transparent and progressive socioeconomic environment.

This study consolidates current knowledge on blockchain’s capacity and identifies the specific drivers and business values associated with early blockchain adoption in supply chain provenance. Furthermore, it underscores the critical role of product fingerprinting techniques in supporting blockchain for supply chain provenance, facilitating more robust and efficient supply chain operations.

Despite the popularity of the entrepreneurial ecosystem (EE) concept, research on its value-adding activities receives less attention. Thus, in this article, the authors investigate the role of EEs in supporting global value chain (GVC) activities.

The authors employ the fuzzy-set qualitative comparative analysis (fsQCA) technique to identify practical configurations of EE’s framework and systemic conditions spurring GVC activities in 80 countries.

The findings suggest different configurations of EE`s framework and systemic conditions necessary for various GVC activities regarding input-output structure, geographical scope, upgrading, and forward and backward participation.

This study contributes to the extant literature by pioneering the EE approach to explaining GVC development. Moreover, the findings provide novel insights for understanding the EE – GVC interplay. As a result, the study offers a more nuanced understanding of how the EE supports GVC activities.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

Bangladesh's ready-made garment (RMG) industry plays a vital role in the economic growth of this country. As the global trend in the fashion market has introduced a high-mix, low-volume ordering style, manufacturers are facing an increased number of changeovers in their production systems. However, most of the Bangladeshi RMG manufacturers are not yet ready to respond to such small orders and to improve the flexibility of their production systems. Consequently, the industry is falling behind in global market competition. Thus, this study aims to advance the current performance of RMG manufacturing operations to respond to the fast-fashion industry's challenges effectively using quick changeover.

In this study, a Single-Minute Exchange of Dies (SMED) is applied to attain quick changeover following the best practices of lean manufacturing.

This study examined the performance of the SMED technique to reduce changeover time in two case organisations. The changeover time was reduced by 70.76% from 434.56 min to 127.08 min and 42.12% from 2,664 min to 1,542 min for the case organisations, respectively. The results of this study show that companies require improved changeover times to address the demand for high-mix, low-volume orders.

This study will certainly guide practitioners of the RMG industry to adopt SMED to reduce changeover time to meet small batch production.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

The public distribution system (PDS) is an Indian food security system established to manage the distribution of food grains at affordable prices. As a result of the population explosion, the long-established PDS system finds it challenging to maintain operational efficiency, quality, trust and transparency. This paper explores the possibility of leveraging blockchain technology to overcome these operational hurdles.

Through a literature review and expert interactions, the present research identifies critical success factors in terms of enablers and barriers that influence the adoption of blockchain technology in PDS. Furthermore, we propose two independent interpretive structural models (ISM) and MICMAC to characterize these attributes.

The research identifies 15 distinct enablers and ten barriers that influence the diffusion of the latest technology in the sector at focus. The analyses disclose the interrelationships/dependencies among these enablers and between barriers, along with their individual driving power and dependence power.

The research showcases the importance of automating the system and illustrates how the features of blockchain technology can assist in augmenting stakeholder satisfaction levels. However, poor or nonexistent government regulations and patronage are found to be the major impediments to adoption. The research also delineates the cost implications of this barrier through its interrelationships with other barriers.

Interesting inferences are drawn from the models that offer actionable insights for the industry, government and technologists for improving PDS performance. Such interventions will ensure national food security through enhanced trust and transparency, which can further improve efficiency and effectiveness.

Companies avoid taxes in a variety of ways and use different methods to do that, one of the most common being earnings management. The purpose of this paper is to investigate whether companies manipulate their financial data in order to reduce taxes paid.

We considered a sample of 63 listed Romanian companies for the period 2016–2021. The Beneish model was used for estimating earnings management, and the effective tax rate was used to measure tax avoidance. The analysis was carried out using regression analysis in Stata13 software.

The findings of the research indicate a negative and statistically significant association between effective tax rate and earnings management, implying that one of the main reasons why companies manipulate their earnings to reduce tax burden and avoid taxes. Moreover, our results show that return on assets (ROA) has a statistically significant negative influence on the effective tax rate. Furthermore, our analysis reveals that firm size, growth, and Big4 audit have no effect on effective tax rate.

Because it analyzes concrete cases using financial data and provides some recommendations for addressing the issue of tax avoidance, this work is useful in advancing both quantitative and qualitative research on this topic. This research is relevant for businesses, governments, regulators, audit professionals and investors.

The study, by analyzing concrete cases using reported financial data, contributes in filling the gap within the literature that results from a lack of scientific research on the relationship between tax avoidance and earnings management, and then it clarifies the nature of the causal connection between them. Moreover, it considers a combination of firm related variables including performance, size and also audit quality.