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This study aims to understand the relative intensity of the challenges and problems faced by small-scale entrepreneurs in Uttarakhand.

A survey methodology was used for this study. The judgement sampling method was used to select the sample for this study. The data were collected from 240 small-scale entrepreneurs using a self-structured questionnaire. Descriptive statistics, principal component analysis and confirmatory factor analysis were used to analyse the data.

The survey found that marketing, finance, taxation, human resource and government support–related problems are the major problems of small-scale entrepreneurs in the state.

This study was conducted in both rural and urban areas, but due to the unreachability of rural entrepreneurs, the representation of rural entrepreneurs is less, so the findings are more inclined towards urban entrepreneurs.

The research has highlighted the intensity of the major problems faced by small-scale entrepreneurs in Uttarakhand. Although many support schemes are operational in the state, small–scale entrepreneurs face many challenges, so this study provides solutions for those challenges.

This study is unique in that it measures the intensity of problems and challenges of small-scale entrepreneurs and provides insight into more serious issues prevalent in the state.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

In this paper, the author presents a hybrid method along with its error analysis to solve (1+2)-dimensional non-linear time-space fractional partial differential equations (FPDEs).

The proposed method is a combination of Sumudu transform and a semi-analytc technique Daftardar-Gejji and Jafari method (DGJM).

The author solves various non-trivial examples using the proposed method. Moreover, the author obtained the solutions either in exact form or in a series that converges to a closed-form solution. The proposed method is a very good tool to solve this type of equations.

The present work is original. To the best of the author's knowledge, this work is not done by anyone in the literature.

The study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels.

The study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework.

The results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention.

The study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario.

The study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels.

Although the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern.

Artificial intelligence (AI) has been touted as one of the viable solutions to address urban mobility issues. Despite a growing body of research on AI across various sectors, its use in the mobility sector remains underexplored. This study addresses this limitation by investigating AI applications and identifying the AI material properties and use cases that offer mobility-specific affordances.

Although AI applications in mobility are growing, academic research on the subject has yet to catch up. Therefore, we follow a systematic review and analysis of practitioner literature. We conducted a comprehensive search for relevant documents through Advanced Google and OECD databases and identified 173 sources. We selected 40 sources published between 2015 and 2022 and analysed the corpus of evidence through abductive qualitative analysis technique.

The analysis reveals that mobility organisations are implementing various AI technologies and systems such as cameras, sensors, IoT, computer vision, natural language processing, robotic process automation, machine learning, deep learning and neural networks. These technologies offer material properties for sensing mobility objects and events, comprehending mobility data, automating mobility activities and learning from mobility data. By exploiting these material properties, mobility organisations are integrating urban mobility management, personalising and automating urban mobility, enabling the smartification of infrastructure and asset management, developing better urban transport planning and management, and enabling automatic driving.

The study contributes a mid-range theory of the affordances of AI for mobility (AI4M) at the infrastructure, operation and service levels. This contribution extends the existing understanding of AI and offers an interconnected perspective of AI affordances for further research. For practitioners, the study provides insights on how to explore AI in alignment with organisational goals to collectively transform urban mobility to be affordable, efficient and sustainable.

Because of the significant differences in the features and requirements of specific products and the capabilities of various additive manufacturing (AM) solutions, selecting the most appropriate AM technology can be challenging. This study aims to propose a method to solve the complex process selection in 3D printing applications, especially by creating a new multicriteria decision-making tool that takes the direct certainty of each comparison to reflect the decision-maker’s desire effectively.

The methodology proposed includes five steps: defining the AM technology selection decision criteria and constraints, extracting available AM parameters from the database, evaluating the selected AM technology parameters based on the proposed decision-making methodology, improving the accuracy of the decision by adopting newly proposed weighting scheme and selecting optimal AM technologies by integrating information gathered from the whole decision-making process.

To demonstrate the feasibility and reliability of the proposed methodology, this case study describes a detailed industrial application in rapid investment casting that applies the weightings to a tailored AM technologies and materials database to determine the most suitable AM process. The results showed that the proposed methodology could solve complicated AM process selection problems at both the design and manufacturing stages.

This research proposes a unique multicriteria decision-making solution, which employs an exclusive weightings calculation algorithm that converts the decision-maker's subjective priority of the involved criteria into comparable values. The proposed framework can reduce decision-maker's comparison duty and potentially reduce errors in the pairwise comparisons used in other decision-making methodologies.

The present study is aimed at examining the antecedents of online shopping user behavior and customer satisfaction. More specifically, (1) the effect of social influence, variety seeking behavior, advertising and convenience on user behavior, and (2) the effect of user behavior on customer satisfaction are examined.

A conceptual model is developed and tested after verifying the psychometric properties of the survey instrument. Data were collected from 556 respondents from three major cities (Hyderabad, Chennai and Bangalore) in the southern part of India using structured instrument. Hierarchical regression is performed. Measurement model was checked using structural equation modeling (Lisrel package).

The results reveal that (1) social influence, (2) variety seeking, (3) advertising, (4) convenience, (5) trust and (6) product factors were positively related to online user behavior. Results also show that user behavior is significantly and positively related to customer satisfaction. The hierarchical regression results also showed moderating effects of (1) trust in the relationship between social influence, variety seeking and user behavior, and (2) product factors in the relationship between advertising, convenience and user behavior. Finally, results suggest that user behavior is partially mediating the relationship between trust and customer satisfaction, i.e. trust has both direct and indirect effect on customer satisfaction.

As with any survey-based research, the present study suffers from the problems associated with self-report measures viz., common method bias and social desirability bias. However, the authors attempted to minimize these limitations by following appropriate statistical techniques.

This study contributes to both practicing managers and the literature on advertising. The study suggests that trust and product play a major role in strengthening the relationship between antecedents and user behavior.

This study provides new insights about the importance of gaining trust in influencing consumer behavior. The conceptual model the authors developed is novel in the sense not many studies are available in India to empirically examine the moderating relationships of trust and product in consumer behavior.

The aim of this paper is to make a descriptive exploratory effort to discern the role of IT exports in India's macro-economic indicators, like national income, employment and balance of payment in the post-Liberalization, Privatization and Globalization strategy in the 1990s. The paper also explores the vital historical developments of various dimensions of IT, such as its export growth, major software and services exports destinations, compositions of IT exports and domestic growth in India.

This study is based on secondary data, which were collected from Balance of Payment Statistics Reserve Bank of India (RBI) and Handbook of Statistics on Indian Economy, National Association of Software and Service Companies (NASSCOM),rtd and Department of Electronics and Information Technology (DEITY). This study has used descriptive analysis and growth models for studying the objectives. Major IT sector dimensions, such as total output, exports revenue, domestic revenue, gross domestic product, employment and exports of the software and service industry, have been examined for the period 1991–2016.

The findings suggest that over the last 26 years, the information technology industry's economic footprint has extended by more than seven times. Over the same period, direct employment in the information technology sector increased at an average growth rate of around 17%. Software and services exports earn, on average, about three times greater than the other three major services of India's current account of the balance of payment.

This study focuses on originality in examining the role of IT exports in India's macro-economic indicators economic reforms of the 1990s and also explores the historical developments of various dimensions of IT exports and domestic growth in India. All the work has been done in original by the authors, and the work used has been acknowledged properly.

This study explores the role of human resource management (HRM) practices in advancing sustainable development goals (SDGs) within emerging markets. Specifically, we examine how HRM practices in financial institutions in the emerging markets of India and China promote SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). We also propose a framework integrating these key SDG goals with core HRM functions.

Secondary data analysis was employed using data from sustainability reports of the top five Indian and Chinese banks listed in Forbes – the Global (2000) ranking for 2022–2023. These sustainability reports were analysed based on their reporting of indicators from the Global Reporting Initiative GRI 400 series, aligned with the SDGs 8, 10 and 13.

The result of the comparative analysis indicates that both Indian and Chinese banks use HRM practices of recruitment and selection, rewards and payments, workplace health and safety, and training and development to meet SDGs 8 (decent work and economic growth), 10 (reduced inequalities) and 13 (climate action). Regarding the reporting and disclosure of HRM practices in diversity, equity and inclusion, Indian banks outperform Chinese banks, and these practices contribute significantly to SDGs 8, 10 and 13. The dominance of state-owned initiatives in China dictates the alignment of HRM strategies with economic priorities at the national level, highlighting the challenge of balancing global sustainability initiatives with a centralised management system.

The study provides a comprehensive examination of sustainability reports with a specific focus on HRM practices and their role in advancing SDGs. It applies institutional theory to understand the differences in the reporting and implementation of HRM practices that contribute to the achievement of SDGs.

This paper aims to find the best tools to influence the improvement of sustainability in food supply chains (FSCs) by conducting a systematic review of articles. The reader will learn how the different industry 4.0 tools (I4.0T) benefit the FSC and the limitations of each tool.

A review of 436 articles published during the period 2019 to 2022 referenced in the Scopus and Web of Science databases was performed. The review was limited to articles published in English and directly related to Industry 4.0, circular economy and sustainability in the food supply chain.

The results show different contributions of I4.0, with some being more influential than others in improving sustainability in FSCs; for example, Internet of Things and Blockchain have been shown to contribute more toward transparency, traceability, process optimization and waste reduction.

The paper's contribution consisted of ranking according to their importance and the I4.0T that affect sustainability in FSCs by classifying the aspects of each tool and the sustainability factors through a categorization by the Analysis Hierarchy Process.