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“Social hesitancy” is a reluctance by people to purchase products, or engage in activities, that may benefit themselves and society. This paper aims to review and assess a visual marketing approach to this significant social marketing problem.

The authors use data visualization technology as an informational tool, visual sentiment analysis as a social text mining tool and Latent Dirichlet Allocation visual (LDAvis) modelling as a topic modelling tool to measure, assess and address social attitudes inherent in hesitancy. The paper’s hypothesis is that these technologies can help society understand the reasons for, and barriers to, hesitancy, and that visual marketing is an extremely effective approach to the hesitancy problem.

Using extensive vaccination data and results from the COVID-19 pandemic, the authors found that the visual marketing technologies were successful informational and motivational tools for social hesitancy.

Hesitancy is a social marketing concern that can have an impact on product or service promotional and motivational campaigns during a crisis. The LDA visual model, for example, can quantitatively extract and measure the social attitudes of people and identify and segment these people based on their feelings. These tools can be valuable to social marketers by helping to establish strategies for any product or service exhibiting hesitant consumer behaviour.

Using advanced visual technology, the paper contributes to social hesitancy by addressing the following question: does a visual marketing approach help social marketers understand the underlying reasons for, and help to mitigate, social hesitancy?

The study explores the factors affecting the use of smart human resource management 4.0 (SHRM 4.0) practices and its effect on dynamic capabilities and, consequently, on organizational performance.

The authors used socio-technical and dynamic capabilities theory to propose the notable research model. The authors explored the factors driving the use of SHRM 4.0 practices and their contribution to organizational performance through the development of dynamic capabilities. The authors collected data from 383 senior HR managers using a structured questionnaire, and PLS-SEM was used to analyze the data.

The results show that socio-technical factors such as top management support, HR readiness, competitive pressure, technology readiness and perceived usefulness influence the use of SHRM 4.0 practices, whereas security and privacy concerns negatively influence them. Furthermore, the authors also found the use of SHRM 4.0 practices influencing the dynamic capacities (build (learning), integration and reconfiguration) and, subsequently, its impact on organizational performance.

Its novelty lies in developing a model using dynamic capabilities and socio-technical theory to explore how SHRM 4.0 practices influence organizational performance through dynamic capabilities. This study extends the literature on SHRM 4.0 practices, HR technology use, HR and dynamic capabilities by contributing to socio-technical theory and dynamic capabilities and expanding the scope of these theories in the area of HRM. It provides crucial insights into HR and top managers to benchmark SHRM 4.0 practices for improved organizational performance.

Fish farmers in Africa often operate on small-scale culture units, primarily due to poor access to funding and low technology adoption. Digital innovation platforms seek to enhance farmers’ access to finance, production and farmers’ income. However, there is a lack of empirical evidence to support these claims. Therefore, this study investigated the factors influencing fish farmers’ access to microcredit from digital innovation platforms and the impact of this microcredit on fish farms’ yield and income in Nigeria.

A mixed-methods approach was adopted, and data were gathered from 387 fish farmers through a well-structured questionnaire and focus group discussion. The data were analyzed using probit regression and instrumental variable two-stage least squares regression.

The results revealed that ownership of smartphones, awareness of digital agricultural innovation platforms, farmers’ education, income, fish farming as a primary occupation, cooperative society and extension contacts positively influenced farmers’ access to microcredit from digital innovation platforms. The age of farmers and household size negatively influenced their access to digital microcredit. Digital microcredit positively and significantly impacted fish farms’ yield and farmers’ income.

Digital microcredit significantly increased fish farm yield and income. Therefore, digital innovation platforms should be encouraged and promoted through the creation of awareness about their ability to solve inadequate financing in agriculture by agricultural extension agents.

This study contributes to our understanding of the influencing factors for farmers accessing digital microcredit and how digital microcredit enhances farm yield and income.

The objective of this paper is to investigate the effect of working capital efficiency (WCE) and its components on the composite financial performance of a sample of Indian firms.

Our sample includes 796 non-financial listed firms from 2015–16 to 2021–22. Sample firms’ profitability, liquidity, solvency, cash flow management, and financial and operational leverage have been used to classify them into companies with high composite financial performance (HCFP) and with low composite financial performance (LCFP) by using K-Means Clustering technique. A composite financial performance score (CFPS) of 1 has been assigned to HCFP and 0 to LCFP. We have used logistic regression models with fixed effect to estimate the effect of cash conversion cycle (CCC) and its components, i.e. inventory days, accounts receivable days and accounts payable days on CFPS in the presence of control variables such as growth, leverage, firm size, and age.

The study finds that CCC and inventory days are inversely associated with CFPS. This finding shows that the firms’ WCE leads to superior financial performance on a composite basis.

The research findings are based on samples drawn from the population of the listed Indian non-financial companies. Since the operation, financial practices, working capital policies, and management styles of firms vary greatly among nations, the results of this study should be extended to firms in other countries after taking into account the degree of resemblance to the sample firms.

The findings of this study hold significant value for industry practitioners, as they provide guidance in determining the optimal allocation of funds for working capital and devising strategies for effectively managing inventory levels, credit sales, and vendor payments in order to increase the overall value of the company. This study aims to help investors in building their investment portfolios by identifying companies with superior composite financial performance. Investors can enhance the construction of their investment portfolios by strategically selecting companies that demonstrate superior overall performance.

The results of our study will help companies improve their WCM strategies to enhance their overall value, and their significance increases manifold during economic downturns. Business firms that perform well by efficiently managing their working capital have a multiplier effect on the economy and society at large in the form of GDP contribution, labor income, taxes to the government, investment in capital assets, and payments to suppliers.

To understand the impact of WCE on firms’ performance, the extant working capital literature focuses on some specific characteristics such as profitability, valuation, solvency, and liquidity. The limitation of employing a single parameter is its inability to present the comprehensive performance evaluation of firms. This study is among the earliest studies that focus on the holistic evaluation of WCE's impact on the composite performance of a company.

The purpose of this paper is to examine whether increased labour productivity could reduce the impact of output growth on the unemployment rate in India over the period 1991–2019 through Okun’s law and its expanded form.

The study uses Okun’s law and its expanded form, with the inclusion of labour productivity in the actual model. Further, the relationship between output growth, unemployment rate, and labour productivity is analysed by using the gap model, the difference model, the dynamic model, the error correction model (ECM), and the vector autoregressive (VAR) approach.

The empirical results from the applied models do not confirm an inverse relationship between output growth and the unemployment rate with an unexpected positive sign of Okun’s coefficient. The evidence of preference for more capital-intensive techniques in the Indian economy is also strongly supported by the results of the expanded form of Okun’s law with a statistically significant positive coefficient of GDP and labour productivity.

The study examined the proposed relationship using Okun’s law and its expanded form, which had not been employed in earlier studies in the context of India. The authors also show that a high economic growth rate is a necessary but not sufficient condition to solve the chronic and structural unemployment problem in India.

The purpose of this research is to identify and analyse the perceived risk factors affecting the safety of electric two-wheeler (E2W) riders in urban areas. Given the exponential growth of the global E2W market and the notable challenges offered by E2W vehicles as compared to electric cars, the study aims to propose a managerial framework, to increase the penetration of E2W in the emerging market, as a reliable, and sustainable mobility alternative.

The perceived risk factors of riding E2Ws are relatively scanty, especially in the context of emerging economies. A mixed-method research design is adopted to achieve the research objectives. Four expert groups are interviewed to identify crucial safety risk E2W factors. The grey-Delphi technique is used to confirm the applicability of the extracted risk factors in the Indian context. Next, the Grey-Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is employed to reveal the causal-prominence relationship among the perceived risk factors. The dominance and prominence scores are used to perform Cause and Effect analysis and estimate the triggering risk factors.

The finding of the study suggests that reckless adventurism, adverse road conditions, individual characteristics and distraction caused by using mobile phones, as the topmost triggering risk factors that impact the safety of E2Ws drivers. Similarly, reliability on battery performance low velocity and heavy traffic conditions are found to be some of the critical safety factors.

E2Ws are anticipated to represent the future of sustainable mobility in emerging nations. While they provide convenient and quick transportation for daily urban commutes, certain risk factors are contributing to increased accident rates. This research analyses these risk factors to offer a comprehensive view of driver and rider safety. Unlike conventional measures, it considers subjective quality and reliability parameters, such as battery performance and reckless adventurism. Identifying the most significant causal risk factors helps policymakers focus on the most prominent issues, thereby enhancing the adoption of E2Ws in emerging markets.

We have proposed an integrated framework that uses grey theory with Delphi and DEMATEL to analyse the safety risk factors of driving E2W vehicles considering the uncertainty. In addition, the amalgamation of Delphi and DEMATEL helps not only to identify the pertinent safety risk factors, but also bifurcate them into cause-and-effect groups considering the mutual relationship between them. The framework will enable practitioners and policymakers to design preventive strategies to minimize risk and boost the penetration of E2Ws in an emerging country, like India.

In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

Interconnections among banks are an essential feature of the banking system as it helps in an effective payment system and liquidity management. However, it can be a nightmare during a crisis when these interconnections can act as contagion channels. Therefore, it becomes essentially important to identify good links (non-contagious channels) and bad links (contagious channels).

The article estimated systemic risk using quantile regression through the ΔCoVaR approach. The interconnected phenomenon among banks has been analyzed through Granger causality, and the systemic network properties are evaluated. The authors have developed a fixed effect panel regression model to predict interconnectedness. Profitability-adjusted systemic index is framed to identify good (non-contagious) or bad (contagious) channels. The authors further developed a logit model to find the probability of a link being non-contagious. The study sample includes 36 listed Indian banks for the period 2012 to 2018.

The study indicated interconnections increased drastically during the Indian non-performing asset crisis. The study highlighted that contagion channels are higher than non-contagious channels for the studied periods. Interbank bad distance dominates good distance, highlighting the systemic importance of banking network. It is also found that network characteristics can act as an indicator of a crisis.

The study is the first to differentiate the systemic contagious and non-contagious channels in the interbank network. The uniqueness also lies in developing the normalized systemic index, where systemic risk is adjusted to profitability.

The purpose of this research contribution is to examine the thermoelastic interactions in a fiber-reinforced thermoelastic medium under the action of initial stress and gravity in which, the conventional Fourier’s law is modified, introducing a thermal relaxation parameter in the GN-III model of generalized thermoelasticity.

The work presented in this manuscript proposes a thermoelastic problem where the Moore–Gibson–Thompson equation is used to model the thermal law. The technique of normal mode is employed to derive the exact expressions of various physical fields under consideration.

Under the application of moving thermal load, normal displacement, temperature distribution and stress components are calculated numerically with the help of MATLAB software and then presented graphically to support the theoretical formulation.

Field variables are affected by initial stress, gravity, fiber reinforcement, velocity of thermal load and time duration, according to numerical simulations. Some particular cases of interest have also been inferred from the present problem.