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Purpose: Entrepreneurship is one of the best ways towards self-sufficiency and poverty alleviation for women in a country without guaranteed employment. The research contributes and provides reasons for supporting women’s entrepreneurship, as although it is well-documented that women’s entrepreneurship increases economic growth, other approaches need attention, especially those related to sustainability.

Need for the study: Women have superior emotional intelligence, resilience, and capacity to maintain a healthy work–life balance; women make better business and entrepreneurial leaders. Many businesses see encouraging women to join the workforce as a key to achieving sustainability goals. This study will provide valuable insights into the role of female entrepreneurs in driving sustainable business practices in the USA and India – rural India and urban India.

Methodology: The chapter adopts a descriptive research design methodology in conducting a thorough literature review on the evolution of women entrepreneurship in India. The case-based approach has been adopted to depict women entrepreneurs and their growth stories. The analysis is based on qualitative analysis of secondary data and primary data. This research proposes exciting revelations regarding the trend of women entrepreneurship in India and, from a global perspective, challenges women entrepreneurs face in organised and unorganised sectors in India and policies in different countries that promote women entrepreneurship towards sustainability.

Findings: Women business owners in India help the economy thrive, reduce poverty, and have a greater chance of shaping India’s destiny. This chapter looks at how entrepreneurial actions affect cultural morals and values. Women’s business growth rates vary from country to country, and this variation is influenced by the fact that countries can be broadly categorised as either developed or developing.

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

The purpose of this paper is to investigate the free vibration response of a laminated honeycomb sandwich panels (LHSP) for aerospace applications. Higher order shear deformation theory (HSDT) was simplified for the dynamic analysis of LHSP. Furthermore, the effects of honeycomb parameters on the value of natural frequency (NF) of vibration were explored.

This paper applies HSDT to the analysis of composite LHSP to derive four vibration differential equations of motion and solve it to find the NF of vibration. Two analytical models (Nayak and Meunier models) were selected from literature for comparison of the NF of vibration. In addition, a numerical model was built by using ABAQUS and the results were compared. Furthermore, parametric studies were conducted to explore the effect of honeycomb parameters on the value of the NF of vibration.

The present model is successful in simplifying HSDT for the analysis of LHSP. The first five natural frequencies of vibration were calculated analytically and numerically. In the parametric study, increasing core height or young’s modulus or changing laminate layup will increase the value of NF of vibration. Furthermore, increasing plate constraint (using clamped edge boundary condition) will increase the value of NF of vibrations.

The current analysis is suitable for all-composite symmetric LHSP. However, for isotropic or non-symmetric materials, minor modifications might be adopted.

The application of simplified HSDT to the analysis of LHSP is one of the important values of this research. The other is the successful and complete dynamic analysis of all-composite LHSP.

Children’s sensory involvement refers to the degree to which children engage their senses, such as sight, touch, taste, smell and hearing, in their interactions with the environment. In the context of parents' purchase decisions, children’s sensory involvement pertains to how children's sensory involvement influences the purchasing decisions made by their parents. The aim of this study was to evaluate the effect of children's sensory involvement on parents’ purchase decisions considering the mediating role of the parent’s attitude.

In this study, a structured questionnaire survey was conducted with parents of children aged 7–12 in Isfahan, Iran. The sample consisted of 210 parents, aimed at elucidating the relationship between variables. Structural equation modeling (SEM) was employed to analyze the relationship between variables.

Results showed a significant relationship between children’s sensory involvement and parents’ purchase decisions, children’s sensory involvement and parents’ attitudes and parents’ attitudes and purchase decisions. It was concluded that children’s sensory involvement could indirectly influence the parents’ purchase decisions considering the mediating role of parents' attitudes.

In today's business landscape, it is imperative for organizations to discern the multitude of factors influencing consumers' purchasing decisions. Among these, family dynamics play a substantial role, with children often exerting a strong influence on their parents' buying choices. Despite the acknowledged importance of this dynamic in existing literature, the specific impact of children's sensory involvement on parental purchasing decisions remains largely unexplored. Therefore, this paper aims to fill this gap in the literature by shedding light on the role of children's sensory involvement in shaping parental buying behaviors.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.

In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance.

SL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments.

We verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively.

In this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.

The study attempts to estimate farm subsidies the governments can save by transitioning to a millet-based production system, replacing GHG emission-intensive crops.

It updates a 131 × 131 commodity input–output (IO) table of the year 2015–16 into 2021–22 using the RAS procedure and simulates the economy-wide impacts of replacing rice and wheat with pearl millet and sorghum using consumption and production approaches. It then quantifies fertilizer, electricity and credit subsidy expenses the government can save through this intervention. It also estimates the potential reduction in GHG emissions that the transition could bring about. India is taken as a case.

Results show pearl millet expansion brings greater benefits to the government. It is estimated that when households return to their pearl millet consumption rates that prevailed in the early-reform period, this could save the Indian government Rs. 622 crores (USD 75 m). The savings shall be reinvested in agriculture to finance climate adaptation/mitigation efforts, contributing to a sustainable food system. Net GHG emissions also decline by 3.3–3.6 MMT CO2e.

Indian government has been actively aiming to bring down paddy areas since 2013–14 through the Crop Diversification Program and promoting millets (and pulses and oilseeds) on these farms. The prime reason is to check rapidly declining groundwater irrigation in Green Revolution states. Regulations in the past in these states have not brought the intended results. Meanwhile, electricity and fertilizers are heavily subsidized for agriculture. A slight shift in the cropping system can help conserve these resources. Meanwhile, GHG emissions could also be brought down and subsidies could well be saved. The results of the study indicate the same.

A less warm society is what governments and nongovernment organizations across the world are aiming for at present. Financial implications affect actions against climate change to a greater extent, apart from technological innovations. The effects of policy strategies discussed in the study, taking a large country as a case, when implemented appropriately around the regions, could help move a step closer to action against climate change.

The paper addresses a key but rarely explored research issue – that how a climate-sensitive crop choice will help reduce the government’s fiscal burden to finance climate adaption/mitigation. It also offers a mechanism to estimate the benefits within an economy-wide framework.

Women entrepreneurs face several challenges in creating and running ventures, especially in emerging and developing countries. In this sense, by aiming to generate inputs capable of helping overcome them, this study aims to categorize the policy, managerial and practical implications of articles whose empirical research was in one or more of the 155 emerging and developing countries. Further, although scholars have addressed female entrepreneurship in developed economies, there is scant literature in the context explored here. This article provides suggestions for new studies, helping academics fill gaps in the literature.

This article adopts a systematic literature review approach, performing content analysis and bibliometric description for the sample. The study comprises 77 articles selected from the Scopus and Web of Science databases.

Research concentrates on Asian countries, with lower incidences in Latin America and Africa. The policy implications focus mainly on the executive rather than legislative spheres. The practical implications focus mainly on entrepreneurial development agencies and women entrepreneurs. Among the suggestions for novel studies, those focusing on methodological choices and female enterprises stand out.

This paper maps and categorizes the policy, managerial and practical implications, helping to raise governments’, policymakers’ and practitioners’ awareness of the preferred strategies to overcome the challenges of female entrepreneurship.

This paper emphasizes reflections of mutual interest to researchers, policymakers and practitioners, filling gaps in studies that prioritize an academic audience. Regarding the academic audience, this paper contributes to innovatively categorizing suggestions for future research and building an extensive research agenda capable of guiding research in this area.

Insufficient time allocation for the bidding period occurs, causing drawbacks to both parties, the client and the bidder. Hence, this study aims to evaluate the time allocated for preparing a bid proposal as per the National Competitive Bidding (NCB) in the Sri Lankan context.

The study has adopted a mixed method approach and expert interviews and document review to detect, analyse and validate the issues, and solutions based on NCB along with the adequacy of the allocated bidding period used as main data collection tools. Both qualitative and quantitative data were analysed through manual content analysis and inferential analysis respectively.

Overall, 24 local issues with the existing competitive bidding process and solutions for each were identified. Among the 24 local issues, it was unanimously agreed by all interviewees that three specific issues require attention and improvement. These issues are related to the standard and incompleteness of bidding documents, inaccurate BOQ quantities measured by the consultant or the main contractor, and the excessive number of bidding document amendments by the consultant. It was revealed that a maximum of 42 calendar days (6 weeks) is sufficient for the bidding process while a minimum of 21 calendar days (3 weeks) is insufficient.

The findings of this study would be recommended that Information and Communication Technology Agency (ICTA) understand the necessity of revising the NCB reference to the time allocated for the preparation of bids. By recognising the importance of sufficient time allocation for bid preparation, this research serves as a practical guide for authorities involved in policy formulation, aiding them in implementing revisions that align with the dynamic requirements of bidding procedures.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.