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Purpose: The purpose of this paper is to measure and investigate the determinants of corruption across Indian states. Design/methodology/approach: This research begins by developing a corruption index (CI) based on official data on corruption cases. Second, the authors also create an adjusted corruption index (ACI) using the stochastic frontier modelling approach to address corruption case under-reporting. Third, they use a panel feasible generalised least square (FGLS) technique to discover corruption determinants. Findings: The results show that approximately 77% of corruption cases in India go under-reported, which is a major concern. The results also show that per capita income, government spending, law and order and urbanisation are the important factors affecting corruption at the regional level. Practical implications: The findings of the study emphasise the need to address the huge under-reporting of corruption data. From a policy perspective, the governments need to emphasise factors like per capita income, government spending, law and order and urbanisation to tackle corruption in India. Originality/value: Corruption is a complex global phenomenon, and several studies have conducted detailed research on the causes of corruption at all levels (regional and cross national), but this study differs from previous studies in the following ways. First, the authors used a more objective measure of corruption by developing a CI at the state level. Second, the study uses stochastic frontier analysis, which is novel in the literature on corruption analysis, to address the most critical component of under-reporting in corruption data. Finally, the study attempts to examine the factors of corruption at the regional level, which is again innovative in nature. [ABSTRACT FROM AUTHOR]

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This article makes a novel contribution to the literature on Bhutan's International Relations (IR) by shifting the focus away from an exclusively India–China framing. First, it points out how small states are increasingly salient but under-studied in IR and how non-European non-island states like Bhutan are even more so, and why we gain by addressing this. Second, it shows how the conventional study of Bhutan has solely focused on its friendship with India and the threat to it/this from China, and why it is important that these conventional narratives be critically examined. In doing so, we perceive the endogenous drivers of Bhutan's foreign policies, for instance via Bhutan's stance on the Doklam issue. Third, going beyond the three typical determinants (economic factors, bilateral relations with India, and threats from China), the article provides two additional axes of understanding Bhutan's foreign policies – bridging of attributional distances and learning from experience. [ABSTRACT FROM AUTHOR]

This article attempts to combine the individual identity with politics and economy, using the current review of the relevant materials and literature of the two novels Adiga's "The White Tiger" (2008) and Adichie's "Americanah" (2013), to touch on the correct methods of contemporary criticism. The struggle of the individual in the third world has provided the study with a new way of learning and trying to break all the barriers. This study has spot the light on individuals in India and Nigeria to experience various oppressions throughout their lives, but due to social, economic and political changes, this problem has increased in the 21st century. The study can confirm their full understanding of the two novels by examining the importance of the personal anti-globalization and anti-hegemony experiences of the two novelists. In conclusion, post-colonial novelists have provided a rich background for the development of many novels, thus developing the economic, political, social and literary concepts. [ABSTRACT FROM AUTHOR]

Online reviews from the web are rich data sources that promote tourism analytics. Restaurants make a significant contribution to the growth of tourism in India. The literature studies on restaurant selection show that most decision frameworks do not handle uncertainty effectively and pay subtle attention to heterogeneous sources. Additionally, the extant models (i) cannot accept missing entries and its imputation; (ii) reliability of data source agents are not methodically determined; (iii) attributes' interactions are not properly considered; and (iv) personalized restaurant ranking is unavailable. The research problem considered in this study is the rational selection of restaurants based on online reviews from heterogeneous sources to support travelers in the tourism process. The main objective of this study is to circumvent the challenge in the literatures by proposing a novel integrated decision framework that collects data from heterogeneous rating sources and transforms them into 'probabilistic linguistic information (PLI)', which effectively handles uncertainty by relating occurrence probability to each linguistic term. Due to the uncertain nature of online reviews, missing data are inevitable. For rational imputation of data, a case-based method is proposed. Later, the relative significance of each attribute and the reliability of each rating source are determined using 'criteria importance through intercriteria correlation (CRITIC)' and Dempster–Shafer-based Bayesian approximation methods. Furthermore, the PLIs from each source are aggregated by using the newly proposed discriminative weighted Muirhead mean operator. Personalized prioritization of restaurants is achieved by using the newly proposed probabilistic linguistic comprehensive (PLC) method that acquires expectation queries from customers. Lastly, the practicality of the developed framework is testified by a real-case example of restaurant selection based on the data collected from online sources via web crawlers. Results infer that (i) the proposed framework is innovative/original, personalized, significant, and mitigates human intervention compared to the extant models, (ii) robust in terms of ranking of restaurants even after adequate weight alterations, and (iii) finally, supports stakeholders to effectively plan their tourism process and attain win-win conditions for effective growth of hospitality sector. • Web crawlers are used to extract data from online sources. • Web source data are transformed to probabilistic linguistic data. • Weights of experts and criteria are methodically calculated. • Missing values are imputed methodically via case-based approach. • Query-based ranking of restaurants is obtain via comprehensive method. [ABSTRACT FROM AUTHOR]