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Blockchain technology, renowned for its decentralization, security, reliability, and data integrity, has the potential to revolutionize businesses globally. However, its full potential remains unrealized due to adoption barriers, necessitating further studies to address these challenges. Identifying these barriers is crucial for businesses and practitioners to effectively tackle them. This systematic review analyzed 70 eligible studies out of 1944 gathered from various databases to understand and identify common blockchain adoption barriers. The Technology–Organization–Environment (TOE) framework was the most popular theory used in these studies. Despite differences in variable definitions, financial constraints, lack of stakeholder collaboration and coordination, and social influences like resistance to change and negative perceptions emerged as the top three barriers. The supply chain domain had the highest number of studies on blockchain adoption. Notably, there was a significant increase in studies addressing blockchain adoption in 2023, comprising 34.2% of the total reviewed studies. This review provides a comprehensive overview of identified barriers, serving as a valuable foundation for future research. Understanding these challenges allows researchers to design targeted studies aimed at developing solutions, strategies, and innovations to overcome obstacles hindering blockchain adoption.

Integrating digital technologies such as artificial intelligence and blockchain make the agri-food supply chain (ASCM) transparent, resilient and flexible. However, its adoption is quite complex due to various anticipated barriers. So, the presented research purposes to explore and investigate the barriers.

This study employs hybrid approach including Best-Worst Method (BWM) and Graph Theoretic Approach (GTA). Data were collected from industry experts employed in the agri-food sector and analyzed by means of standard operating procedures.

GTA results show that Technological barriers have the highest barrier intensity. Moreover, BWM results show that “Increased operational complexity” is the topmost barrier to adopting blockchain in ASCM. “Lack of interoperability” ranks second among the identified barriers.

The results benefit the managers, practitioners and researchers to understand the anticipated barriers so that necessary strategies can be developed, and organizations can become more resilient, agile, transparent and traceable.

The presented work is the first to develop a mathematical model and assess the industry’s eagerness to adopt blockchain in ASCM. The proposed framework will greatly benefit the stakeholders working in agri-food sector.

Recently, disruptive technologies (DTs) have proposed several innovative applications in managing logistics and promise to transform the entire logistics sector drastically. Often, this transformation is not successful due to the existence of adoption barriers to DTs. This study aims to identify the significant barriers that impede the successful adoption of DTs in the logistics sector and examine the interrelationships amongst them.

Initially, 12 critical barriers were identified through an extensive literature review on disruptive logistics management, and the barriers were screened to ten relevant barriers with the help of Fuzzy Delphi Method (FDM). Further, an Interpretive Structural Modelling (ISM) approach was built with the inputs from logistics experts working in the various departments of warehouses, inventory control, transportation, freight management and customer service management. ISM approach was then used to generate and examine the interrelationships amongst the critical barriers. Matrics d’Impacts Croises-Multiplication Applique a Classement (MICMAC) analysed the barriers based on the barriers' driving and dependence power.

Results from the ISM-based technique reveal that the lack of top management support (B6) was a critical barrier that can influence the adoption of DTs. Other significant barriers, such as legal and regulatory frameworks (B1), infrastructure (B3) and resistance to change (B2), were identified as the driving barriers, and industries need to pay more attention to them for the successful adoption of DTs in logistics. The MICMAC analysis shows that the legal and regulatory framework and lack of top management support have the highest driving powers. In contrast, lack of trust, reliability and privacy/security emerge as barriers with high dependence powers.

The authors' study has several implications in the light of DT substitution. First, this study successfully analyses the seven DTs using Adner and Kapoor's framework (2016a, b) and the Theory of Disruptive Innovation (Christensen, 1997; Christensen et al., 2011) based on the two parameters as follows: emergence challenge of new technology and extension opportunity of old technology. Second, this study categorises these seven DTs into four quadrants from the framework. Third, this study proposes the recommended paths that DTs might want to follow to be adopted quickly.

The authors' study has several managerial implications in light of the adoption of DTs. First, the authors' study identified no autonomous barriers to adopting DTs. Second, other barriers belonging to any lower level of the ISM model can influence the dependent barriers. Third, the linkage barriers are unstable, and any preventive action involving linkage barriers would subsequently affect linkage barriers and other barriers. Fourth, the independent barriers have high influencing powers over other barriers.

The contributions of this study are four-fold. First, the study identifies the different DTs in the logistics sector. Second, the study applies the theory of disruptive innovations and the ecosystems framework to rationalise the choice of these seven DTs. Third, the study identifies and critically assesses the barriers to the successful adoption of these DTs through a strategic evaluation procedure with the help of a framework built with inputs from logistics experts. Fourth, the study recognises DTs adoption barriers in logistics management and provides a foundation for future research to eliminate those barriers.

There have been significant developments in the field of retailing with digitalization. One of these developments is the emergence of omnichannel retailing. Although this has affected both firms and consumers considerably, the literature is dominated by the studies dealing with omnichannel retailing from the firms’ perspectives. The studies dealing with omnichannel retailing from the consumers’ perspectives have recently begun to attract the attention of researchers. For this reason, this study conducted a literature review to examine various consumer behaviors mentioned in the studies aimed at explaining consumer behaviors in the omnichannel retailing context. The distribution of these studies according to years and journals, research methods used, theories adopted, and the related five-stage consumer decision-making stages are summarized. Additionally, this review addresses future research avenues.

This study aims to synthesize extant literature on the concept of reverse knowledge transfer (RKT) built up over the past two decades with the help of bibliometric analyssis and also to suggest promising new areas for research in this field.

The study adopts a literature review methodology combined with bibliometric and network analysis based on 117 papers identified from the Scopus database. In particular, this study has tried to identify and capture themes not previously fully captured or evaluated by other reviews on this topic.

The authors have identified research themes and research gaps in the area of RKT. Overall, the review shows the main outlets that have published papers on RKT and the theoretical background this research is built on. This study exhibits core themes in this area that have persisted and grown consistently such as the subsidiary’s role in RKT. In addition, the review highlights less researched themes such as role of boundary spanning in RKT, which open exciting avenues for new research opportunities.

This study finds that RKT research has experienced remarkable growth from a complete viewpoint in recent years. There was a surge in publications in the area from 2008 onwards, and many of its influential papers seemed to have been published between 2013 and 2018. Prominent themes in this body of research have been identified and potential for future studies has been explored.

To the best of the authors’ knowledge, this is the first study to map, synthesize and discuss the literature concerning RKT.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

This study aims to identify the key drivers of consumer engagement in e-commerce among young consumers at bottom-of-pyramid (BoP) markets and their impact on continued usage intention.

A cross-sectional research design was used to understand low-income customers’ engagement in e-commerce, specifically online shopping. The data for this study were collected from BoP customers in the Indian market. A conceptual model was proposed, and hypotheses were developed using the stimulus–organism–response (S-O-R) framework. For analysis, structural equation modeling was performed using AMOS 20.0 software to test the structural model.

The results of the study highlight that perceived importance, technology and infrastructure and social influence are key drivers of e-commerce at BoP customers. Key drivers have shown a significant positive impact on customer engagement which leads to continue usage intention of e-commerce. Furthermore, customer engagement has shown a strong relationship with continue usage intention of e-commerce.

This study indicates that young consumers’ engagement is important for e-commerce service providers to gain a market share. BoP markets offer immense opportunities to create, develop and sustain e-commerce firms for a long time, especially in India. Managers should recognize the potential of BoP markets, which can generate a huge demand for products and services on e-commerce platforms.

This study contributes both theoretically and empirically. Theoretically, this adds to the existing knowledge of customer engagement, especially in e-commerce and BoP market segment. Empirically, it tested the conceptual research model of low-income customer engagement in the e-commerce marketplace using the S-O-R framework. The study recommended practical implications for e-retailers/e-commerce service providers engaging BoP customers in a digitally connected and intensively competitive era.

Managing differences is a difficult undertaking, especially considering the difficulties arising from the unconscious functions of our brains. Organisations should strive to counteract the potentially harmful effects of unconscious bias by implementing policies that support bias-aware management and decision-making. Although it is obvious that bias cannot be completely eliminated, there is enough data, as discussed in this work, to demonstrate that unconscious bias and stereotypes can be addressed and decreased with mindfulness-based interventions (MBIs) to some extent. Mindfulness involves the process of bringing non-judgemental awareness to experience by striving for full attention in the present moment. In this context, including mindfulness practises into training programmes for equality, diversity, and inclusion may serve as an accelerator for recognising hidden biases, reducing stereotypes, eliminating discrimination, and encouraging cognitive changes. This chapter explains the ways in which MBIs can be used to promote cognitive changes and comprehend the automatic and unconscious nature of emotions and thoughts in order to remove barriers between all differences in the workplace.

THIS paper describes a test method, developed on the Kirloskar AV‐1 (naturally aspirated) single‐cylinder diesel engine, for evaluating tendency towards detergency, ring sticking and accumulation of deposits in heavy‐duty type lubricating oils. The repeatability, reproducibility and the discriminating ability of the method is found to be good when the engine is run under the prescribed test method for 120 hours. This method is likely to be included in Indian Standard Specification for IC Engine Lubricating Oils, IS : 496. To finalize this test method, a correlation programme was also run between five laboratories. The results obtained correlate with the Petter AV‐1 test procedure when using a fuel containing 0–4% sulphur.