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This study aims to explore the determinants of public debt in selected South Asian Association for Regional Cooperation (SAARC) countries for 19 years, from 2001 to 2019.

Using ordinary fixed and random effect models, the authors examine the role of internal and external factors in determining the composition of public debt. Furthermore, for robustness, they compare the results with two-stage least square (2SLS) regression estimates after considering the problem of endogeneity, overidentification, under-identification and weak instruments.

The findings show that among the selected macroeconomic variables, inflation, exchange rate and broad money have significant negative effects on the debt-GDP ratio. In contrast, military spending, corruption and interest rates appear to positively influence the same as per 2SLS results. From the policymaking perspective, SAARC countries should focus more on reducing military spending and make a concerted effort to augment investments in productive projects. Further, with strong fiscal consolidation and institutional quality, it is important to mitigate the frequent occurrence of corruption conundrums in emerging economies for the development of a transparent economic system.

The study is distinct from previous studies in two ways. First, to the best of the authors’ knowledge, there are no studies focusing on SAARC countries in the context of public debt. Second, the study expands the existing literature on public debt by taking into account both external and internal debts to decipher the within-country and cross-country determinants of debt accumulation. More specifically, this model considers accountability and transparency in the public sector, cross-border security challenges and benefits of globalization by including explanatory variables such as corruption, military expenditure spending and capital inflows.

There has been a substantial increase in the implementation of programmes that aim to enhance the livelihoods of rural people by organizing them into groups, emphasizing women towards achieving women empowerment, which is one of the key goals of the Sustainable Development Goals (SDGs). This study aims to assess the impact of participation in self-help groups (SHGs) under the National Rural Livelihood Mission (NRLM) on the political empowerment of rural tribal women in India.

This study is based on primary data collected using a multistage random sampling method. Field research is conducted in the state of Jharkhand, primarily focusing on women belonging to the scheduled tribe category. The propensity score matching technique derives the results using the psmatch2 command in STATA.

The results show a significant positive change in women's access to voting rights, awareness of various government schemes and entitlements, political awareness and participation, campaigning during elections and leadership positions after participation in SHGs under NRLM.

The existing literature indicates that research on the empowerment of tribal women through participation in SHGs is scant. This study makes a novel contribution by examining the effectiveness of participation in SHGs under NRLM on the political empowerment of tribal women in rural India. This study will provide significant insights to the government, policymakers, practitioners and researchers working on SHGs and tribal women’s empowerment.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0489

The paper presents the facts on the policy challenges and opportunities in the way forward of trade and economic co-operation in South Asia amid the coronavirus disease 2019, which comes to be the least economically integrated region worldwide. Due to tense geopolitics in South Asia, trade is heavily biased toward extra-regional markets despite of existing regional trade agreements (TAs) in the region.

Having tested the stationarity of data with structural break, the paper uses intra-regional trade in addition to other domestic economic variables as exogenous regressors in autoregressive distributed lag multivariate framework, hence raising the quality of statistical inference.

This paper highlights that intra-regional trade significantly affects the economic welfare as measured by Gross Domestic Product per capita of the people from the region, hence raising the need for higher regional trade openness. If trade barriers are overcome, all the South Asian countries will gain through effective implementation of regional TAs.

The study relies on the multivariate technique with regional trade share as the main exogenous variable. In addition, the regulatory and economic conditions of all countries are different which also tends to affect the mutual degree of trade relations.

Over the economic reasons, the manmade barriers owing to political differences are the root cause for the low intra-regional trade. Amid the pandemic, South Asian courtiers have the high time to leverage the bilateral trade for mutual benefits. India being the largest economy can play a decisive role in pushing forward the regional trade bloc – South Asian Association for Regional Cooperation (SAARC) – for achieving its objective through multilateral engagements in a wider perspective.

The present study makes pioneer efforts to examine the dynamic linkages between regional trade and economic growth. The results provide new insight into the dynamics of benefits driven by trade interdependency.

As fitness apps have integrated gamification elements into their design to transform exercise into a compelling and competitive experience, this study draws on the expectation confirmation model (ECM) and self-determination theory (SDT) to propose a comprehensive framework for sustained usage by satisfying the psychological needs of users.

A longitudinal study design is utilized where data from 598 fitness app users were gathered over two distinct phases separated by 24 weeks, where data were analyzed using structural equation modeling.

The findings until phase 1 depict that motivational affordances significantly affect mastery, autonomy and relatedness, which in turn exert a significant influence on flow. Identification and compliance have a more pronounced effect in phase 2 compared to stage 1 of the analysis.

Individuals are more prone to being swayed by fellow members of their social group who share an affiliation with fitness apps, leading them to sustain their usage of fitness apps. These insights can potentially guide app developers and marketers in formulating strategies based on modifiable factors to foster sustained growth for gamified fitness apps.

This study takes a novel stance by employing social impact theory (SIT) in a longitudinal setting to reveal how users react to identification and compliance effects during initial and post-adoption usage of gamified fitness apps.

The study aims to examine the influence of Unified Theory of Acceptance and Use of Technology (UTAUT) and anthropomorphic design cues in determining the level of satisfaction among banking chatbot users. It also tests the moderating impact of the localization of content on the relationship. The study also encompasses expectation confirmation, elucidating the significance of perceived trust in maintaining intention.

The study conducted a comprehensive online survey, collecting 667 questionnaires from users of conversational chatbots in both public and private sector banks. We analyse the data using Partial Least Squares Structural Equation Modelling and fuzzy-set Qualitative Comparative Analysis.

Performance and effort expectancy, perceived interestingness of interaction and perceived empathy were identified as significant indicators, whereas facilitating conditions, social influence and perceived intelligence were not significant in explaining satisfaction. Perceived trust was a significant mediator, while localization was a significant moderator in all the cases except social influence and satisfaction.

To improve perceived intelligence and empathy, tech developers should focus on improving the chatbot’s ability to maintain contextual understanding within a conversation where it can remember and reference previous interactions. Future studies might explore the development of banking chatbots that incorporate advanced levels of anthropomorphic characteristics, whether visual or intuitive.

The work is unique in that it integrates UTATUT, anthropomorphism and expectation confirmation model in the context of conversational banking chatbots, which is not achievable in a single theory-based model. The study also underlined the necessity of localizing chatbot content, recommending that banks engage localized native speakers to help with chatbot training and content creation, where specialists can fine-tune the conversational features.

The purpose of this paper is to determine the relationship between employee empowerment (structural empowerment [SE] and psychological empowerment [PE]), employee commitment (EC) and organizational citizenship behavior (OCB) within the unique context of a learning organization in the Indian retail sector.

The investigation was conducted using a survey-based questionnaire approach. Data were gathered from 222 frontline employees working in different retail stores in India. The researcher used AMOS 22.0 version and SPSS 22.0 version for the data analysis.

This study concluded that SE and PE have a positive and significant influence on OCB and EC. In addition, EC is a mediator between employee empowerment (SE and PE) and OCB.

This research is built on cross-sectional data with a limited sample size, that is, 222 sample size. This study also offers some practical insights to managers of retail stores.

To the best of authors’ knowledge, this study is the first to empirically investigate the mediating role of EC between SE and OCB. By focusing on the interplay of these variables within a learning organization, this study sheds light on the mechanisms driving employee behavior and organizational dynamics in this rapidly evolving retail sector.

The Indian government’s launch of Digital India initiative is critical to achieving a sustainable future by leveraging digital capabilities to promote social justice, economic prosperity and environmental stewardship. There are a lot of factors affecting digital India’s transformation to achieve sustainable development goals. This study aims to identify, examine and develop an empirical model estimating the relationship among enablers affecting and enhancing digital India’s transformation toward sustainability.

Following a thorough review of the literature, 10 key enablers were identified and analyzed using the total interpretive structural model (TISM). This novel approach has been adopted to analyze the hierarchical linkage between identified Enablers. Further Matrice d’impacts croisés multiplication appliquée à un classement (MICMAC) analysis technique is used to evaluate the driving and dependence power among enablers.

Findings show that the government’s supportive policy is the strategic enabler with the highest driving power, positioned at the bottom of the model. The government must support digital empowerment initiatives to ensure all individuals have access to the information they require to make proper decisions regarding their lives. This study examines the literature to generalize the findings to focus on the motivator of digital India transformation to achieve sustainable development goals.

This study suggests that TISM and MICMAC-based hierarchical models help practitioners, managers and other stakeholders to focus more on strategic factors to enhance the performance aspects of digital transformation. Government, business and academic cooperation promotes a comprehensive approach to environmental sustainability by stimulating innovation and digital approach implementation.

In addition, it offers novel insights into the enablers, such as how the identified enablers interact with one another depending on the drive and dependency power to achieve the goals of digital India’s transformation to achieve sustainable development goals.