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The purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature.

A focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers.

This study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities.

This study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling.

The outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.

In the era of multiple global disruptions, firms are finding it to continue their business. MSMEs are impacted more as they have constrained resources. Organizational flexibility has emerged as an organizational and management principle that would help firms stay competitive even in volatile markets. This study aims to present a set of guidelines and insights for MSME managers to implement organizational flexibility in their organizations.

This study uses total interpretive structural modelling to study how the various factors contributing to organizational flexibility behave together. Behavioural theory is used to explain why organizations need to incorporate flexibility, and systems theory of organization is used to explain why an organization needs to have open boundaries.

Organizational flexibility is a principle that may be supported by the systems theory of organization. The study has shown that it is important for MSMEs to have supply chain collaborations to be more flexible. The study also shows pressure from competitors as the key driver that would make a firm more flexible, and that adequate support from management and technological skills are required to drive flexibility in an organization.

Single respondent bias may have occurred in this study. This can be eliminated by interviewing multiple people from the same organization. Further research around the reasoning for linkages can be explored with theory-driven grounded studies.

This study attempts to use a multi-criteria decision-making technique to present insights to managers to help them make their organizations flexible.

Applied a hybrid approach using genetic algorithms (GAs) for a case-based retrieval process in order to increase the overall improved cost accuracy for a case-based library. The paper aims to discuss this issue.

A weight optimization approach using case-based reasoning (CBR) with proposed GAs for developing the CBR model. GAs are used to investigate optimized weight generation with an application to real project cases.

The proposed CBR model can reduce errors consistently, and be potentially useful in the early financial planning stage. The authors suggest the developed CBR model can provide decision-makers with accurate cost information for assessing and comparing multiple alternatives in order to obtain the optimal solution while controlling cost.

The system can operate with more accuracy or less cost, and CBR can be used to better understand the effects of factor interaction and variation during the developed system’s process.

Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.

This paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.

The R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.

The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

This study aims to examine the influence of digital financial literacy (DFL) on the financial well-being (FWB) of students in Andhra Pradesh, specifically exploring the factors of impulsivity and self-control. Both DFL and FWB are treated as multi-dimensional constructs in the study. The research delves into the impact of DFL dimensions, viz. digital financial knowledge, digital financial experience and digital financial skills, on both impulsivity and self-control. Subsequently, the study assesses the effects of impulsivity and self-control on financial well-being.

To gather data, a questionnaire-based survey method was employed, reaching 475 university students through purposive sampling. The study utilizes confirmatory factor analysis for scale validation and structural equation modeling for hypothesis testing.

The results reveal a significantly negative influence of digital financial knowledge (DFK), digital financial experience (DFE) and digital financial skills (DFS) on impulsivity, while demonstrating a significantly positive impact on self-control. Additionally, the study finds that impulsivity negatively affects financial well-being, whereas self-control has a positive impact. Focusing on higher education institutions in Andhra Pradesh, the research highlights students’ limited concern for long-term financial planning.

This study underscores the relevance of understanding the crucial role of digital financial literacy in enhancing their financial well-being. The implications of these research findings are substantial and can be utilized to shape educational programs for students in higher education institutions. Such programs can guide institutions in imparting knowledge and skills related to personal finance management, particularly in the context of the increasing digitalization of financial transactions.

This paper aims to investigate critical change management factors in small and medium enterprises (SMEs), shedding light on key aspects of resilient organizational strategies.

The research adopted a mixed-methods approach, including inductive content analysis to identify the drivers and the best-worst method with data collection from eight subject matter experts.

The drivers selected by the experts as most important were “the existence of a plan with clear guidelines and steps for organizational change” and “leadership engagement to motivate and support employees” (with weights of 0.31 and 0.25, respectively). Disparities in experts’ opinions arise concerning perceptions of management evolution, leadership styles and communication efficacy. The findings suggest that leadership needs to adopt a more inclusive and participatory approach to drive better outcomes in SMEs. Furthermore, flexibility and adaptability were identified as fundamental traits for maintaining competitiveness in the market.

The research’s originality lies in its focused and data-driven approach applied to the Brazilian context. This study offers a new methodological contribution by integrating the best-worst method with qualitative content analysis to prioritize and contextualize critical change management factors specific to SMEs. Unlike previous research that has mainly addressed general challenges or descriptive analyses, this study provides applied results for managers and policymakers. Also, it presents a replicable framework for exploring similar organizational challenges in other contexts.

Inefficient waste disposal technique and cement production methodology caused significant environmental impacts, leading to global warming. The purpose of the research was to invent an effective, sustainable technology to use the wastes and alternate for cement in concrete. Geopolymer technology could be the most desirable solution to use the wastes into an effective product.

The wood waste ash derived from nearby tea shops was used as an alternate binder for fly ash. The replacement of WWA with FA was varied from 0 to 100% at 10% intervals. In this research, setting and mechanical features of Geopolymer Concrete (GPC) along with Waste wood ash (WWA) was carried out. The influence of wood waste ash in the microstructure of the GPC was also assessed using scanning electron microscope and X-ray diffraction analysis.

The findings revealed that 30% replacement of wood waste ash was performed higher in all measured features. Besides, the formation of different phases was also observed with the inclusion of wood waste ash.

The demand for fly ash was increased in recent years, and the fly-based GPC has required more alkaline solution and temperature curing. Hence, there was a research gap on finding an alternative binder for fly ash.

The research novelty was to use the wood waste ash, which has inbuilt alkaline compounds on the production of sustainable geopolymer. The finding showed that the wood waste ash could be alternate fly ash that eliminates the environmental impacts and economic thrust.

Working capital efficiency (WCE) is crucial for the sustainability of both large and small firms. This study aims to use the sample of micro, small and medium-sized enterprises (MSMEs) in India and tries to understand the critical determinants of WCE.

Using a fixed effect panel data model on a sample of 578 MSMEs (59 micro, 226 medium and 296 small firms), this study explores the relationship between the predictors of WCE. Additionally, the study adopted two metrics for measuring WCE among each type of firm (micro, small and medium).

Several firm-specific variables, including leverage (lever), firm age (AGE), firm size (Fsiz), profitability (Prof), extended payment terms (EPT), human capital (HCap), asset turnover ratio (ATR), reverse factoring (RF) and firm growth (FG), have a significant effect on working capital management efficiency (WCE). In contrast, tangibility (Tangib) and salary expenses (Sal) had an insignificant effect on working capital management efficiency.

The study is based on secondary data. Future studies may incorporate some primary data, which will facilitate qualitative analysis.

The studies explore the relationship between WCE and expenses in HCap, EPT, RF and Sal as the predictors for WCE, which was not studied earlier in MSMEs scenario, especially in case of developing nation.

Demand for Geopolymer concrete (GPC) has increased recently because of its many benefits, including being environmentally sustainable, extremely tolerant to high temperature and chemical attacks in more dangerous environments. Like standard concrete, GPC also has low tensile strength and deformation capacity. This paper aims to analyse the utilization of incinerated bio-medical waste ash (IBWA) combined with ground granulated blast furnace slag (GGBS) in reinforced GPC beams and columns. Medical waste was produced in the health-care industry, specifically in hospitals and diagnostic laboratories. GGBS is a form of industrial waste generated by steel factories. The best option to address global warming is to reduce the consumption of Portland cement production and promote other types of cement that were not a pollutant to the environment. Therefore, the replacement in ordinary Portland cement construction with GPC is a promising way of reducing carbon dioxide emissions. GPC was produced due to an alkali-activated polymeric reaction between alumina-silicate source materials and unreacted aggregates and other materials. Industrial pollutants such as fly ash and slag were used as raw materials.

Laboratory experiments were performed on three different proportions (reinforced cement concrete [RCC], 100% GGBS as an aluminosilicate source material in reinforced geopolymer concrete [GRGPC] and 30% replacement of IBWA as an aluminosilicate source material for GGBS in reinforced geopolymer concrete [IGRGPC]). The cubes and cylinders for these proportions were tested to find their compressive strength and split tensile strength. In addition, beams (deflection factor, ductility factor, flexural strength, degradation of stiffness and toughness index) and columns (load-carrying ability, stress-strain behaviour and load-deflection behaviours) of reinforced geopolymer concrete (RGPC) were studied.

As shown by the results, compared to Reinforced Cement Concrete (RCC) and 100% GGBS based Reinforced Geopolymer Concrete (GRGPC), 30% IBWA and 70% GGBS based Reinforced Geopolymer Concrete (IGRGPC) (30% IBWA–70% GGBS reinforced geo-polymer concrete) cubes, cylinders, beams and columns exhibit high compressive strength, tensile strength, flexural strength, load-carrying ability, ultimate strength, stiffness, ductility and deformation capacity.

All the results were based on the experiments done in this research. All the result values obtained in this research are higher than the theoretical values.