The British enacted the Criminal Tribes Act in 1871 to control Indian society after the rebellion against colonial rule in 1857. By means of the Act, the British depicted entire communities and groups as hereditary criminals–without any substantive legal or incriminating evidence–using the concept of race, used in anthropology and anthropometry, and of caste. They termed the groups ‘tribes’ instead of ‘castes’ to evoke qualities of wildness and savagery in a way that the term ‘caste’ could not. The British also used the Act to term the tribes ‘criminal’. In ascribing criminality, they misinterpreted texts, folklore and proverbs, and they relied on the biased advice of upper caste elite native informants. This systematic sociopolitical and legal subjugation stigmatized, ostracized and impoverished many so-called lower-caste and tribal communities. Even 75 years after Independence and denotification of the ‘criminal’ status, these communities remain outcastes. This paper traces the making of the category of ‘criminal tribe’ in colonial times–by charting the discourses, practices, processes and legal landmarks that led to the enactment, subsequent amendments and, finally, repeal of the Criminal Tribes Act, 1871–and its afterlife in postcolonial India. The paper argues that although the category of ‘criminal caste’ did exist before colonial times, the British colonialists applied the category of ‘tribe’ to the criminal castes for rhetorical and administrative purposes, and that the socio-legal construction of criminal tribes happened through a concatenation of contradictory notions that made the label of ‘criminal tribe’ a patchwork of mutually untenable discourses. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Predicting doctor ratings is a critical task in the healthcare industry. A patient usually provides ratings to a few doctors only, leading to the data sparsity issue, which complicates the rating prediction task. The study attempts to improve the prediction methodologies used in the doctor rating prediction systems. The study proposes a novel deep learning (DL) model for online doctor rating prediction based on a hierarchical attention bidirectional long short-term memory (ODRP-HABiLSTM) network. A hierarchical self-attention bidirectional long short-term memory (HA-BiLSTM) network incorporates a textual review's word and sentence level information. A highway network is used to refine the representations learned by BiLSTM. The resulting latent patient and doctor representations are utilized to predict the online doctor ratings. Experimental findings based on real-world doctor reviews from Yelp.com across two medical specialties demonstrate the proposed model's superior performance over state-of-the-art benchmark models. In addition, robustness analysis is used to strengthen the findings. © 2023 John Wiley & Sons Ltd.

In the world of chaos, nothing is certain. In such an unpredictable world, measuring the effciency of any individual is inevitable. In a conventional data envelopment analysis (DEA) model, exact input and output quantity data are needed to measure the relative efficiencies of homogeneous decision-making units (DMUs). However, in many real-world applications, the exact knowledge of data might not be available. The rough set theory allows for handling this type of situation. This paper tries to construct a rough DEA model by combining conventional DEA and rough set theory using optimistic and pessimistic confidence values of rough variables, all of which help provide a way to quantify uncertainty. In the proposed method, the same set of constraints (production possibility sets) is employed to build a unified production frontier for all DMUs that can be used to properly assess each DMU's performance in the presence of rough input and output data. Besides, a ranking system is presented based on the approaches that have been proposed. In the presence of uncertain conditions, this article investigates the effciency of the Indian fertilizer supply chain for over a decade. The results of the proposed models are compared to the existing DEA models, demonstrating how decision-makers can increase the supply chain performance of Indian fertilizer industries. © 2023.

Purpose: The purpose of this study is to determine the factors that enable citizens to adopt e-government services at different maturity levels: information, two-way communication, transaction and political participation. Design/methodology/approach: The study uses a grounded approach by conducting semi-structured interviews. Findings: The study reveals that the factors influencing the adoption of e-government services vary at different levels. It identifies 27 influencing factors. In total, 13 of these factors influence adoption at the information level; 13 at the two-way communication level; 25 at the transactional level; and 16 at the political participation level. Auxiliary facilities, connectedness, corruption avoidance, transparency and fairness, customer support and forced adoption, not commonly discussed as influencing factors for e-government adoption in the extant literature, have been revealed in this study. Research limitations/implications: This study uses qualitative research and rather than generalization, the focus is explicitly on obtaining an in-depth understanding. Although the sampling used is sufficient for the purpose of this study and allows reasonable conclusions to be drawn; however, it cannot be considered representative of a vast country like India. Academicians and information systems researchers can use these findings for further research. Practical implications: This study advances the understanding of e-government adoption. The findings have potential implications for public administrators and policymakers in successfully designing, developing and implementing e-government services at different maturity levels. Originality/value: Existing e-government adoption theories are of limited scope and do not capture and specify the complete essence of citizens’ adoption characteristics at different levels of e-government services. Hence, a theoretical gap exists, which this study aims to fill. © 2022, Emerald Publishing Limited.

The growing concern for environment and pressing need for societal well-being require organizations to encompass sustainability in their projects. This study attempts to propose an integrated systems thinking approach to attain sustainability in project-based organizations (PBOs). An integrated approach consisting of viable system model (VSM), evolutionary game theory (EGT) and system dynamics (SD) is utilized to identify key organizational subsystems for achieving sustainability and analyse interaction patterns among various key personnel in PBOs. The proposed models are developed based on the data collected from two PBOs; thereafter, these models are validated through the data collected from eight PBOs (situated in Singapore and India). The outcomes of this study reveal key organizational subsystems that are required to achieve sustainability and differences in interaction patterns among various key personnel in PBOs, for these two countries. This study facilitates project professionals in identifying the best organizational designs to achieve sustainability in PBOs. © 2022 John Wiley & Sons Ltd.

The 2019 coronavirus disease (COVID-19) pandemic has seriously impacted the performance of all types of businesses. It has given a tremendous structural boost to e-commerce enterprises by forcing customers to online shopping over visiting physical stores. Moreover, customer expectations of the digital and operational capabilities of e-commerce firms are also increasing globally. Thus, it has become crucial for an e-commerce enterprise to reassess and realign its business practices to meet evolving customer needs and remain sustainable. This paper presents a comprehensive performance evaluation framework for e-commerce enterprises based on evolving customer expectations due to the COVID-19 pandemic. The framework comprises seven primary criteria, which are further divided into 25 sub-criteria, including two sustainability factors, namely, environmental sustainability and carbon emissions. The evaluation approach is then practically demonstrated by analyzing the case of three Indian e-commerce firms. The results are obtained using a multi-criteria decision-making (MCDM) method, namely, Fuzzy VIKOR, to capture the fuzziness of the inherent decision-making problem. Further, numerical analysis is conducted to evaluate and rank various e-commerce enterprises based on customer expectations and satisfaction benchmarks. The findings explain the most important criteria and sub-criteria for e-commerce businesses to ensure customer expectations along with their economic and environmental sustainability. © 2022 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd.

Publicly available information plays a crucial role in shaping cryptocurrency investor sentiments. Past literature has used various measures to proxy sentiment. In this study, we construct a cryptocurrency-specific news sentiment from daily cryptocurrency news headlines. The sentiments are classified into positive and negative news sentiments to understand their differential impact on the trading activity of the cryptocurrency market. The results show that positive news boosts investor confidence, thereby increasing returns, and negative news causes uncertainty. The most dominant cryptocurrency, Bitcoin, experiences a “negativity effect,” i.e., the impact of negative news on returns is higher than positive news. On the volatility and liquidity front, positive (negative) news increases (decreases) the volatility and liquidity. Positive news triggers the participation of uninformed traders who buy due to the “fear of missing out” phenomenon and pump-and-dump schemes. Negative news reduces liquidity by creating uncertainty among informed and uninformed traders. © 2023 Elsevier B.V.

Purpose: This study aims to investigate the impact of business strategy on the classification shifting practices of Indian firms. Design/methodology/approach: The study considered cost leadership and differentiation strategy. Two forms of classification shifting, namely, expense misclassification and revenue misclassification have been examined in this study. Panel data regression models are used to analyze the data for this study. Findings: The results show that managers of cost leadership strategy firms are more likely to be engaged in expense misclassification, whereas firms following differentiation strategy are likely to be engaged in revenue misclassification. Subsequent tests of this study suggest that firms following a hybrid strategy (mix of cost leadership and differentiation) prefer revenue misclassification over expense misclassification for reporting inflated operating performance. These results imply that firms prefer the shifting tool based on the ease and need of each shifting strategy. These results are consistent with several robustness measures. Practical implications: The results suggest that investors should understand business strategy before developing insights about the accounting quality of firms. Investors should conduct a comprehensive review of income statement items before using items for portfolio evaluation. Originality/value: To the best of the authors’ knowledge, this is the first study to examine the association between business strategy and classification shifting. © 2022, Emerald Publishing Limited.

Donor behavior and the act of charity have attracted evident attention globally in recent years primarily because of squeezed government funding to charities and Nonprofit Organizations (NPOs) post 2008 financial meltdown. This paper reviewed 148 articles on charity donor behavior and giving behavior, and provides a coherent and contemporary view with a classification scheme having various categories based on different attributes. The purpose of this study is twofold. First, it covers a systematic literature review (SLR) of charity donor behavior and its drivers. Second, it identifies the gaps in the current body of knowledge and highlights the future research direction. Using SLR Method, the present research identifies, meticulously evaluates, critically analyzes, and synthesizes findings of the relevant studies published in international journals in the fields of consumer behavior, information technology, services marketing, psychology, economics, and marketing management from 1980 onwards. Subsequently, various emerging themes in the area of Donor Behavior are identified based on gaps to facilitate the researchers and practitioners for their future research efforts. © 2021 Taylor & Francis.

This paper examines whether the Covid-19 pandemic has had a homogeneous or heterogeneous effect on stock returns in India. We consider panel data by using 1,318 companies that are listed on the National Stock Exchange of India. We find that the daily growth rate in Covid-19 cases and Covid-19 deaths are negatively associated with stock returns. Further, we observe that the average stock returns during Lockdown 2 are positive and highly significant, while the returns during Lockdowns 3 and 4 are negative. Moreover, our results show that the chemical, technology, and food and beverage industries earn higher returns. In contrast, the banking and finance, automotive, services, and cement and construction industries yield lower returns for the overall period. Interestingly, all industry groupings in this study earn a positive return during the lockdown period. In particular, the chemical, technology, automotive, metals and mining, and food and beverage industries provide higher returns during the lockdown period. Finally, this study supports the claim that the Covid-19 pandemic has had a heterogeneous effect in the Indian stock markets. © 2022, Academy of Economics and Finance.

This research develops a COVID-19 patient recovery prediction model using machine learning. A publicly available data of infected patients is taken and pre-processed to prepare 450 patients’ data for building a prediction model with 20.27% recovered cases and 79.73% not recovered/dead cases. An efficient logistic regression (ELR) model is built using the stacking of random forest (RF) and logistic regression (LR) classifiers. Further, the proposed model is compared with state-of-art models such as logistic regression (LR), support vector machine (SVM), decision tree (C5.0), and random forest (RF). All the models are evaluated with different metrics and statistical tests. The results show that the proposed ELR model is good in predicting not recovered/dead cases and handling imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Purpose: Drawing from socialization theory this study investigates the effect of financial socialization and mediating role of “attitude toward money” (ATM) and financial literacy on the financial behavior of young adults in an emerging economy. Design/methodology/approach: A cross-sectional survey of 302 young adults was conducted and responses were analyzed to determine the key antecedents of financial behavior. The model was tested using OLS regression. Parallel mediation was tested using Process Macro in SPSS. Findings: ATM, subjective financial literacy, objective financial literacy are positively associated with financial behavior. Furthermore, parallel mediation analysis establishes the role of ATM and subjective financial literacy as a mediator between financial socialization and financial behavior. Research limitations/implications: These findings have implications for both financial and academic institutions and policymakers. Academic institutions should introduce personal wealth management courses at early stages in their courses to help young adults make appropriate financial decisions. Policymakers should emphasize creating a habit of budgeting and managing expenses among young adults in addition to promoting financial literacy. Originality/value: This study focuses on determinants of financial behavior in young adults and specifically, argues that involving parents to financially socialize their children have a crucial impact on subjective financial literacy and ATM which has not been explored in previous literature. © 2023, Emerald Publishing Limited.

The goal of this paper is to explain why digital innovation is so important in business organizations in order to survive in Industry 4.0. The study helps to understand the new era of Industry 4.0 and the importance of introducing digital innovation into organizations. A systematic review of the literature and studies on Industry 4.0 and digital innovation were synthesized to find answers to the research questions. To improve their manufacturing industry, organizations have implemented digital technologies such as augmented reality, robotic sensing, artificial intelligence, cloud computing, cyber physical systems, and remote sensing technologies. These technologies focused on automating logistics and supply chain systems, improving manufacturing system performance, and simplifying automated production systems. Because digital innovations save time and energy, employees can devote more time and energy to creative and innovative activities. Organizations should plan to implement digital technology in order to keep the environment healthy and sustainable while meeting the demands of customers, consumers, and the Industry 4.0 dimension. © 2023 The Authors. Thunderbird International Business Review published by Wiley Periodicals LLC.

Purpose: As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns. Design/methodology/approach: The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase. Findings: The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume. Originality/value: This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division. © 2023, Emerald Publishing Limited.

Emerging markets with their imperfections, low liquidity, and dominance of naive retail investors are likely to provide attractive profit-making opportunities for sophisticated and resourceful institutional investors. The institutional investors traditionally had low participation in these markets, but it has grown tremendously during recent years. Therefore, these markets provide a natural experimental setting to understand the implications of their activities on asset pricing and to evaluate their market timing and stock-picking skills. In this study, we compare the performance of foreign institutional investors (FIIs) and domestic institutional investors (DIIs) in one of the important Asian emerging markets. We observe that the institutional investors (FIIs and DIIs) prefer to invest in low-risk (low beta, low book-to-market ratio, and high market capitalization) stocks. The portfolios of the stocks with higher FII holdings have lower returns than those with lower FII holdings, particularly after risk adjustments. Similarly, the portfolio of the stocks bought in a quarter by FIIs offers inferior returns in the next quarter compared to the portfolio of the stocks sold by them. Institutional investors are momentum traders. Their trading activities are negatively autocorrelated, indicating short-termism and cyclicality in their trading behavior. We do not observe any superior market-timing and security picking skills among them. © 2022 Elsevier Inc.

This study examines whether downside risk matters in the Indian equity market. We observe a strong negative relationship between standard variance-based risk measures (variance, beta, and idiosyncratic variance) and the expected stock returns. After controlling for traditional risk measures, analytically and statistically orthogonalized forms of downside risk measures present a positive risk–return relationship. In cross-sectional regressions, the downside beta shows a positive risk–return trade-off after controlling for the effect of traditional beta. It implies that investors avoid the “probability of loss” but look at the higher variance as a potential to earn higher returns. The desire to make speculative profits dominates over the need for safety while investing in the equity market. Investors seeking higher returns invest in high volatility or high beta stocks resulting in the overvaluation of these stocks. Preference for the lottery stocks (proxied by Max and idiosyncratic volatility) emerges as the strong determinant of cross-sectional variation of stock returns. After controlling for the lottery effect, the relationship between traditional beta and expected returns becomes flat. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.

An exponential demand increase for lithium-ion batteries (LIBs) has contributed to a looming waste crisis. Circular economy has been increasingly seen to counter this crisis, further scaled-up by operationalizing Industry 4.0 disruptions around the triple bottom line. As a core pillar of circular economy, this study addresses LIB recycling and explores its key drivers. The drivers are identified and evaluated using Delphi study and multi-criteria decision-making methods. The proposed integrated methodological framework is further tested with data involving multiple stakeholders. The findings underscore the most profound causal drivers: institutional incentives supporting LIB recycling, availability of reliable technology, and strengthening coordination among actors in a supply chain using Industry 4.0. Interestingly, the highest-ranking causal drivers of LIB recycling were retrieved not from the existing literature but from the expert pool. The findings also reveal some counterintuitive results, such as practitioners prioritizing environmental factors over economic ones instead of the widespread convention of their preferences toward economic goals. The study thereby offered some helpful policy levers, which when empirically strategized with Industry 4.0 should enable stakeholders make more informed decisions in shaping an emerging industry such as LIB recycling. © 2023 Elsevier Ltd

We conduct a mixed-method, multi-study investigation using a role congruity theory framework to examine prospective employees’ attitudes toward working for male- and female-led ventures. Results using data collected from business students, alumni, and executives in India reveal three insights: (1) prospective employees report similar evaluations for male- and female-led ventures under normal circumstances but evaluate female-led ventures more favorably than male-led ventures when the entrepreneur has a hierarchical workplace romance; (2) favorable attitudes toward female-led rather than male-led ventures when the entrepreneur is engaged in a hierarchical workplace romance are motivated by benevolent sexism; and (3) although respondents report a higher inclination to work for ventures without (vs with) a policy prohibiting workplace romance, the decline in willingness to work for an entrepreneur engaged in a workplace romance is higher for female- than male-led ventures. We discuss implications and directions for future research. © 2023 International Council for Small Business.

Literature has reported that store-level information is crucial in determining consumer engagement with green stores. This study examines the effects of in-store (green) information quality on consumer engagement behaviours and mediating effects of store credibility. Moreover, the study reveals whether consumer passion for the environment moderates the effect of in-store (green) information quality on store credibility. Statistical analyses of useable data from 763 respondents examined the relationships. Credibility emerged as a key mechanism to enhance the effects of information quality on consumer engagement. Thus, this study extends theoretical discussions on information quality, consumer passion, and consumer engagement for green retailing. © 2022 Elsevier Ltd

With the advent of fast computers, availability of data, and development of sophisticated algorithms, every sector is undergoing a process of rapid advancement. Industries utilize advanced technologies to digitize various phases of the supply chain (SC) for better production and enhanced customer experience. Supplier selection is a fundamental part of supply chain management (SCM) and has an immense scope of exploiting emerging technologies like IoT (Internet of Things), big data analytics, cloud computing (CC), etc. The present study provides brief research and a comparison of the conventional methods or models accessible in literature and the role of Industry 4.0. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.