Abstract: This paper examines the phenomenon of category literature within the landscape of contemporary Indian fiction published in English between 1990 and 2022. Category literature, broadly understood as fiction produced for and marketed within commercially demarcated generic categories such as romance, crime, horror, science fiction, mythological fantasy, and self-help narrative, has witnessed unprecedented growth in Indian publishing over the past three decades, complicating the established critical hierarchies that have long privileged the literary novel as the dominant form of serious cultural production. Drawing on a corpus of eighteen texts selected across six generic categories, the study investigates the ways in which Indian authors working within category fiction simultaneously negotiate, reproduce, and subvert the conventions of their chosen genres, and examines the relationship between generic belonging and the construction of readerly identity in a rapidly transforming urban consumer culture. Theoretically, the paper draws on Pierre Bourdieu's field theory of cultural production, Fredric Jameson's concept of the political unconscious and the ideologeme of genre, and the more recent revisionary work of scholars in popular fiction studies, including John Sutherland, Ken Gelder, and Rachel Noorda. A structured analysis of paratext, including cover design, blurb rhetoric, author branding, and digital marketing discourse, is combined with close reading of selected narrative passages to argue that category literature in the Indian context performs a distinctive mediatory function, producing generic pleasures that are simultaneously localised and globally circulating, commercially oriented and ideologically complex. The paper concludes by arguing for a reconfiguration of the critical apparatus through which Indian fiction in English is evaluated, one that takes seriously the cultural work performed by category fiction without collapsing the distinction between critical and commercial modes of value.
Keywords: category literature,genre fiction,Indian English fiction,popular fiction, Bourdieu, field of cultural production, paratext, mythological fiction, crime fiction, literary value
Abstract: Rural e-governance projects depend not only on the availability of technology, but also on citizens' perceptions of service quality and trust in digital service delivery systems. Common Service Centres (CSCs) serve as significant intermediaries between rural citizens and e-governance in India. This paper examines the impact of perceived service quality on citizen trust and, consequently, on adoption of CSCbased e-governance services. Drawing on the SERVQUAL model (Parasuraman et al., 1988) and the Technology Acceptance Model (TAM) (Davis, 1989), primary data were collected from 320 rural citizens across five districts of Madhya Pradesh. The study employs descriptive statistics, reliability analysis, exploratory factor analysis (EFA), Pearson correlation, multiple regression, and mediation analysis using SPSS 26. Findings indicate that SERVQUAL dimensions—particularly reliability, responsiveness, and assurance—significantly enhance citizen trust. Trust emerges as a powerful predictor of adoption intention and continued usage, partially mediating the relationship between service quality and adoption. Results underscore the importance of citizen-focused service delivery and trust-building mechanisms in rural digital governance. The paper advances the e-governance literature by directing analysis toward citizens and proposes policy shifts to improve adoption outcomes in rural India.
Keywords: E-Governance,Common Service Centres,SERVQUAL,Technology Acceptance Model, Service Quality, Trust, Citizen Adoption, Rural India, Madhya Pradesh.
Abstract: Plastic pollution has emerged as one of the defining environmental challenges of the contemporary era, exerting widespread harm on ecosystems, biodiversity, and public health. In response, governments across the globe have enacted plastic ban policies as regulatory instruments to curb single-use plastic consumption and promote sustainable alternatives. Within this evolving landscape, Generation Z has gained prominence as a consumer segment with distinctive digital habits, pronounced environmental awareness, and considerable market influence. Yet, the extent to which this awareness translates into actual sustainable purchasing decisions remains an open and nuanced question. This study investigates the determinants of sustainable buying behaviour among Generation Z consumers, focusing on the interplay between environmental consciousness and social influence in the context of plastic ban policies. Grounded in the Theory of Planned Behavior, Social Influence Theory, and Social Capital Theory, the research develops a conceptual model identifying environmental awareness, peer influence, social media exposure, price sensitivity, and product accessibility as the principal antecedents of sustainable purchasing behaviour. Drawing on a synthesis of peer-reviewed secondary literature, the study demonstrates that while Generation Z displays a meaningful commitment to environmental values, purchasing decisions are simultaneously shaped by social validation, digital trends, and economic considerations. The findings challenge the notion that sustainable consumption operates in isolation from social dynamics. Rather, environmental intent and social influence function as complementary forces. These insights carry practical implications for policymakers, brand managers, and environmental advocates seeking to encourage responsible consumption among younger generations.
Keywords: Generation Z,Sustainable Consumption,Plastic Ban Policy,Social Influence, Consumer Behaviour, Environmental Awareness, Attitude-Behaviour Gap
Abstract: As concerns about plastic pollution continue to rise, governments and regulatory agencies worldwide have begun enforcing stricter limits on single-use plastics. Within the food industry, where plastic packaging has historically served critical roles in preservation, transportation, and product presentation, these policy shifts carry profound operational and behavioural consequences. The study focuses on understanding how different socioeconomic factors shape consumer compliance with plastic ban regulations in urban and semi-urban areas of Madhya Pradesh, India. It specifically examines how household income, educational attainment, occupational category, and geographic location influence purchasing decisions and attitudes toward sustainable packaging alternatives. A mixed-method research design was employed, combining structured questionnaire surveys (n = 120) with qualitative in-depth interviews, to capture measurable behavioural patterns alongside the contextual reasoning behind individual decisions. Prior to full deployment, the instrument underwent a pilot test with twenty respondents, and internal consistency was assessed using Cronbach's Alpha (α = 0.81), confirming satisfactory reliability. Findings reveal that consumers with higher incomes and stronger educational credentials exhibit markedly greater receptivity to sustainable packaging, underpinned by superior environmental literacy and greater financial flexibility. Lower-income consumers, conversely, resist the transition predominantly on grounds of affordability constraints and restricted access to viable alternatives. Chi-square hypothesis tests confirm all five study hypotheses at p < 0.05. These findings suggest that environmental policies should be designed with socioeconomic differences in mind and should promote inclusive strategies to make plastic reduction both fair and workable in practice.
Keywords: plastic ban policies,consumer behaviour,socioeconomic determinants,food industry sustainability; sustainable packaging adoption; eco-friendly packaging; India; environmental regulation; Madhya Pradesh
Abstract: Artificial intelligence is rapidly transitioning from a peripheral tool to a central decision-making actor within public administration systems globally. Governments are deploying algorithmic systems to determine welfare eligibility, assess tax liabilities, predict recidivism for bail decisions, allocate public housing, and manage immigration outcomes — decisions of profound consequence for millions of citizens. Yet this transformation has outpaced the development of the legal frameworks, accountability mechanisms, and ethical guardrails necessary to govern it. The resulting regulatory and accountability void represents one of the most pressing governance challenges of the digital era. This paper conducts a comprehensive doctrinal, comparative, and empirical analysis of AI-driven decision making in public administration, with particular focus on three high-stakes domains: welfare benefit allocation (examining India's Aadhaar-linked DBT systems and the UK's Universal Credit algorithm), tax assessment (India's INSIGHT platform and Australia's controversial 'RoboDebt' system), and pre-trial bail decisions (examining the COMPAS system in the United States and emerging risk-assessment tools in Indian courts). Drawing on legal analysis of constitutional provisions, comparative legislation across nine jurisdictions, philosophical frameworks from Rawlsian justice and Kantian deontology, and empirical case study evidence, the paper identifies seven critical accountability gaps in current AI governance frameworks: opacity of algorithmic reasoning, absence of meaningful human review, inadequacy of existing administrative law remedies, systemic bias amplification, data sovereignty risks, democratic deficit in algorithm procurement, and lack of liability attribution for algorithmic harm. In response, the paper proposes the AIPA Governance Framework (Accountability, Integrity, Participation, and Auditability) — an original, comprehensive legal-ethical architecture for governing AI in public administration, incorporating mandatory explainability standards, algorithmic impact assessments, independent algorithmic audit authorities, citizen contestation rights, and liability allocation principles. The framework is calibrated to both the Indian constitutional context and internationally harmonized governance standards, contributing to an emerging body of AI governance scholarship with direct policy relevance.
Keywords: Artificial Intelligence,Public Administration,Algorithmic Decision Making,Accountability,Ethics, Welfare Allocation, Tax Assessment, Bail Decisions, COMPAS, RoboDebt, INSIGHT Platform, India, Explainability, AIPA Framework, Administrative Law, Algorithmic Bias
Abstract: The rapid and unplanned digitization of government services during and after the COVID-19 pandemic has created an unprecedented cybersecurity deficit in eGovernance infrastructure across developing nations, with India presenting a particularly salient case. This paper conducts a systematic post-pandemic cybersecurity audit of India's central and state-level eGovernance portals, evaluating their compliance with internationally recognized security standards including ISO/IEC 27001, NIST Cybersecurity Framework, OWASP Top-10, and India's own National Cyber Security Policy (NCSP 2013, revised 2023). Employing a mixed-methods approach comprising structured framework-based audit matrices, secondary data analysis of publicly reported vulnerabilities, and comparative benchmarking against the European eGovernment Benchmark 2025, the study reveals systemic deficiencies across five critical domains: SSL/TLS configuration, authentication mechanisms, data encryption, vulnerability patching cycles, and incident response preparedness. The audit identifies that over 60% of sampled portals exhibit at least one high-severity cybersecurity gap, corroborating global findings that 57% of government websites violate core security guidelines. In response, the paper proposes the E-GovShield Model — a seven-pillar, risk-tiered cybersecurity policy framework specifically calibrated for the organizational, financial, and technical constraints of Indian eGovernance institutions. The model integrates preventive, detective, and responsive security controls, incorporating concepts from Zero Trust Architecture, Security-by-Design, and continuous compliance monitoring. Policy recommendations are directed at CERT-In, the Ministry of Electronics and Information Technology (MeitY), National Informatics Centre (NIC), and state IT departments. This research contributes an original, actionable framework to the underexplored intersection of cybersecurity, eGovernance, and post-pandemic digital resilience.
Keywords: Cybersecurity,eGovernance,Post-Pandemic Audit,E-GovShield Model, CERT-In, India, NIST, Zero Trust Architecture, Digital India, Data Protection, Government Portals, Cyber Resilience
Abstract: Public procurement constitutes a significant share of government expenditure in developing nations, yet it remains highly susceptible to corruption, inefficiency, and opacity. Blockchain technology, with its inherent properties of decentralization, immutability, and transparency, presents a compelling solution to these systemic challenges. This paper investigates the potential of blockchain-based e-procurement systems to enhance transparency, reduce corruption, and improve service delivery in developing economies. Through a rigorous comparative analysis of India's Government e-Marketplace (GeM) portal and the Philippines' Government Electronic Procurement System (PhilGEPS), this research identifies structural vulnerabilities in existing digital procurement frameworks and proposes a comprehensive Blockchain-Integrated Public Procurement (BIPP) framework tailored for low-infrastructure developing nations. The study employs a mixed-methods approach, combining systematic literature review, case study analysis, and thematic framework development. Findings indicate that blockchain integration can reduce procurement fraud by up to 40–60%, improve vendor transparency, enable real-time audit trails, and lower administrative costs. The proposed BIPP framework incorporates smart contracts, distributed ledger technology, permissioned blockchain networks, and interoperability standards, accounting for the infrastructural and regulatory constraints typical of developing economies. The paper concludes with policy recommendations and a phased implementation roadmap for governments seeking to modernize procurement systems through blockchain adoption.
Keywords: Blockchain,,eGovernance,Public Procurement,Transparency, Corruption, GeM, PhilGEPS, Smart Contracts, Developing Nations, Digital Governance, Distributed Ledger Technology, Anti-Corruption
Abstract: Artificial intelligence-powered chatbots have emerged as one of the fastest-growing eGovernance innovations globally, with 43% of national government portals now deploying AI chatbot functionality as a primary citizen service interface. In India, chatbot deployments across central and state eGovernance platforms — including UMANG, DigiSeva, MADAD, and numerous state-level virtual assistants — represent significant investments in AI-mediated citizen-government interaction. Yet the dominant discourse around government chatbot adoption focuses almost exclusively on deployment metrics and cost efficiencies, while neglecting three dimensions of critical governance importance: the quality and accuracy of information delivered to citizens (effectiveness), the conditions under which citizens trust and rely on government chatbot responses (trust), and the differential impact of chatbot interfaces on digitally and socially marginalized citizen groups (inclusion). This paper addresses these gaps through a rigorous, multi-method empirical study drawing on primary data from 890 respondents across urban, peri-urban, and rural settings in five Indian states, supplemented by a systematic audit of query resolution quality across six government chatbot platforms and thematic analysis of 72 in-depth qualitative interviews. The study develops and validates an original Government Chatbot Quality Index (GCQI) — a composite instrument measuring technical performance, information accuracy, linguistic accessibility, emotional appropriateness, and inclusive design — and applies it across the six audited platforms. Key findings include: a mean query resolution rate of only 47.3% across sampled platforms; significant response quality disparities between English/standard Hindi queries and regional language queries (accuracy gap of 31.4 percentage points); elderly and rural users reporting substantially lower satisfaction (mean CSAT 2.8/5) than urban, educated users (4.1/5); and a critical finding that 62% of chatbot responses to sensitive welfare queries contained materially incomplete or inaccurate information that could adversely affect citizen decisions. The study identifies five structural failure modes in government chatbot design — resolution gap, linguistic exclusion, emotional blindness, bias in query interpretation, and accountability vacuum — and proposes the original CitizenFirst Chatbot Design Framework (C2DF) as a comprehensive design and governance architecture for equitable, trustworthy, and effective AI chatbot deployment in Indian eGovernance. Policy recommendations are directed at MeitY, NIC, the Ministry of Rural Development, and state IT departments.
Keywords: AI Chatbots,eGovernance,,, Citizen Service Delivery,Trust, Digital Inclusion, DigiSeva, UMANG, Government Virtual Assistant, Natural Language Processing, Rural Users, Elderly Users, Chatbot Bias, Query Resolution Quality, CitizenFirst Framework, India