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The contribution of women toward entrepreneurial activities has gained significant attention in recent years because of economic and social concerns, government support and initiatives and increased education and awareness. Women’s entrepreneurial activity has increased and women-owned businesses can today be found in every sector of the economy, irrespective of region. In India, government bodies such as the Ministry of Micro, Small and Medium Enterprises (MSMEs) and several other organizations (private and NGOs) have adopted considerable measures to promote women entrepreneurship (WE). To improve WE, the critical factors of WE adoption need to be identified and evaluated. The purpose of this paper is to identify, prioritize and evaluate the critical success factors of WE adoption in Indian MSMEs.

This paper proposes a methodology based on fuzzy analytical hierarchal process to prioritize the critical success factors of WE adoption. A numerical analysis of Indian MSMEs is presented to demonstrate the use of the proposed method. This proposed method considered fuzzy framework, which can handle impreciseness and uncertainty. Sensitivity analysis is also performed to test the robustness of the proposed model.

Potential critical success factors are identified from relevant literature and validated by industry experts. This research finalize the critical success factors of WE adoption in Indian MSMEs under seven dimensions, so prioritization of identified critical success factors can be developed and insights relationship of factors would be explored. The results of the study found that individual, management and government dimensions take paramount importance while women aim to become entrepreneurs in Indian MSMEs.

This study is limited to identifying evaluation factors; other factors have not been identified and categorized. Evaluation is one by experts in this area so it is natural that views of decision-makers may be subjective and vary with regard to industry-type, priorities, resources, etc.

This study will help industry to identify, evaluate and prioritize factors for successful implementation of women entrepreneurship. MSMEs could device these factors by applying the outcome of the study in their decisions with higher priority to implement women entrepreneurship culture.

Potential factors are identified from relevant literature and validated by industry experts. Indian MSMEs could device these factors by applying the outcome of the study in their decisions with higher priority to adopt women entrepreneurship.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

This study aims to broaden the understanding of the blockchain for human resource (HR) managers through use cases. The study presents a plausible solution for HR professionals to effectively manage some of the core processes to focus on more strategic work and be a true HR business partner for the organization.

The study adopted a case research strategy. The case research strategy is well-suited to capture the practitioner’s knowledge, mainly when focusing on contemporary events (such as COVID-19). Data collected from 12 tech organizations through telephonic conversations and the interviews were recorded and transcribed using NoNotes call recording.

This study identifies five use cases to streamline the critical processes, helping HR professionals such as certificates verification, skill mapping, payroll processing, data protection and performance management. These early use cases offer a plausibly superior alternative in managing critical HR functions and associated business processes with blockchain technology.

Despite the growing number of blockchain applications, its usage in HR activities is limited. By extensive qualitative case study and data triangulation, the study integrates a resource-based view and unified theory of acceptance by explaining how blockchain adoption helps organizations use their internal resources and capabilities to gain a competitive advantage. The study presents five use cases and propositions that can act as building blocks for the HR department in adopting blockchain applications. Lack of empirical validation (quantitative rigor) of the propositions is the limitation and can be a future research scope.

Adopting new technologies is not new for HR managers. However, most of the technologies are disjointed applications, and therefore, the need for an all-pervasive solution assumes significance. Several of the blockchain concepts are still in the nascent stage. Thus, the study highlights the need for HR leaders to work alongside technical architects to create blockchain applications. Unlike other HR applications, blockchain can integrate all the employees, clients, vendors and businesses seamlessly. This study proposes research propositions that provide research directions for future research.

Academic literature on connecting blockchain technology with HR functions and applications is notably absent. This research can be considered one of the first academic articles connecting blockchain and HR processes.

The purpose of this study is to analyse user-generated content and firm-generated content on perceived quality and brand trust, and eventually how it impacted brand loyalty with pandemic fear as the moderator.

This study employed an online survey questionnaire method in the Facebook online shopping groups to collect the data. The filter question technique was adopted to verify the respondent's validity. A total of 434 samples was collected using purposive sampling. The data were then analysed using SmartPLS 3.0.

The analysis showed that firm-generated content appeared to have a stronger positive relationship on perceived quality and brand trust than on user-generated content. Brand trust and perceived quality are found to influence brand loyalty positively. However, pandemic fears only moderate the relationship between firm-generated content and brand trust and perceived quality. This study revealed that the main components in social media communication do not influence perceived quality and brand trust to the same extent.

This study contributes to the knowledge of social media communication during the pandemic period that has not been studied empirically in the Malaysian context. The main components in social media communication were delineated and the impact of pandemic fears on how they would possibly affect the established relationships in the literature were examined. This study enables the researchers and practitioners to take a closer look at how the pandemic crisis could provide a shift on what has been understood so far.

The primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.

This study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.

It was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.

This study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

To help alleviate the immense suffering caused by humanitarian crises worldwide, organisations are forming relationships for effective coordination and resource sharing. However, organisations can struggle to build trust because of the uncertain context, varying institutional mandates and socio-cultural differences. Thus, this paper aims to better understand how humanitarian groups can leverage formal mechanisms to produce greater trust.

This paper adopts a logical–positivist research paradigm to formulate and test its hypotheses. This paper answered this study’s research question using structural equation modelling from survey data of 180 humanitarian managers.

In inter-organisational humanitarian relationships, formal mechanisms indirectly foster trust through two mediators: distributive justice and information sharing.

This research presents the perspective of only one partner in inter-organisational relationships. Moreover, the operationalisations of formal mechanisms and trust were not comprehensive (i.e. only contracts and integrity-based trust, respectively).

To the best of the author’s knowledge, this research is a first attempt to empirically link the widely discussed idea of formal mechanisms, distributive justice, information sharing and trust in inter-organisational humanitarian relationships. Further, this research is the first attempt to present and empirically validate a theoretical model that addresses how formal mechanisms foster trust in inter-organisational relationships.

In conventional discourses on sustainability, the relationship between economics and ecology is central. A number of nations' economies benefited from responsible tourism following these conferences. By supporting local businesses and attractions, ‘green’ tourism helps communities achieve their natural and cultural objectives while also preserving their limited resources. In terms of sustainable travel, Kerala was an early leader. This study looks at RT initiatives in various stages, with an emphasis on green tourism's sustainable responsible travel practises. The green economic development bottom line method was used for this descriptive research. These results highlight the difficulties inherent with RT implementation. Our review of secondary data shows that the first rollout of RT was unsuccessful, but that subsequent stages showed great promise. In order to create sustainable tourism on a worldwide scale, the study also highlights the necessity for more research in other culturally distant places.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

Blockchain technology is spreading its roots and growing rapidly. With the advancement of blockchain technology, government organizations are focusing toward adopting blockchain technology. The purpose of this study is to identify the factors that affect the adoption of blockchain technology and provide a conceptual framework to adopt blockchain technology in government organizations.

To answer the research questions, this study has used systematic literature review (SLR). For achieving the quality and transparent process, this study has applied the PRISMA framework in the SLR.

With the help of SLR, this study has identified 72 factors that influence the adoption of blockchain technology. After critically examining the factors, this study has developed the conceptual framework for the adoption of blockchain technology in government organizations with four factors of the unified theory of acceptance and use of technology model and four additional factors, i.e. facilitating conditions, social influence, effort expectancy, performance expectancy, trust, transparency, cost and security.

To perform SLR, this study has used two databases: Web of Science and Google Scholar; future studies can consider other databases. This study has provided the framework but not validated it; future studies can use structural equation modeling and artificial neural network to validate the framework.

This study helps to provide insights about blockchain technology and help government and policymakers to take decisions for the adoption of blockchain technology and in determining the future action plan.

Few studies are available in the literature that have used SLR to identify blockchain technology adoption in government organizations.

The purpose of this paper is to apply an efficient hybrid computational numerical technique, namely, q-homotopy analysis Sumudu transform method (q-HASTM) and residual power series method (RPSM) for finding the analytical solution of the non-linear time-fractional Hirota–Satsuma coupled KdV (HS-cKdV) equations.

The proposed technique q-HASTM is the graceful amalgamations of q-homotopy analysis method with Sumudu transform via Caputo fractional derivative, whereas RPSM depend on generalized formula of Taylors series along with residual error function.

To illustrate and validate the efficiency of the proposed technique, the authors analyzed the projected non-linear coupled equations in terms of fractional order. Moreover, the physical behavior of the attained solution has been captured in terms of plots and by examining the L₂ and L_∞ error norm for diverse value of fractional order.

The authors implemented two technique, q-HASTM and RPSM to obtain the solution of non-linear time-fractional HS-cKdV equations. The obtained results and comparison between q-HASTM and RPSM, shows that the proposed methods provide the solution of non-linear models in form of a convergent series, without using any restrictive assumption. Also, the proposed algorithm is easy to implement and highly efficient to analyze the behavior of non-linear coupled fractional differential equation arisen in various area of science and engineering.