Indian Institute of Management Tiruchirappalli

The latest complexities of Big Data and Gen-AI are posing novel difficulties to Information Systems (IS) and Analytics researchers. The conventional mono-methods are inadequate to address this complexity. This chapter argues for methodological pluralism and suggests a synthesized approach, a combination of Mixed-Methods Research (MMR) and Computational Social Science (CSS). The chapter is grounded in a pragmatic philosophy. This approach leverages the interpretive richness of qualitative methods, the scale of the quantitative analysis, as well as the strength of computational research methods. The chapter identifies the MMR designs and emerging types of computational data, and provides examples of their combination, such as computational ethnography. The synthesized approach enables a more holistic and robust understanding of complex socio-technical phenomena in informatics, providing a direction for rigorous and relevant research.

For new ventures in contested fields, securing legitimacy is a paramount challenge. In the platform economy, firms often enter with a potent entrepreneurship rhetoric, but as they face contestation over their labor and regulatory practices, this rhetoric can devolve into a calculated misrepresentation—a legitimacy lie. Yet, the process by which such lies are performed and defended over time as a form of institutional work remains undertheorized, leaving a gap in our understanding of how platform firms achieve institutional entrenchment. Uncovering this process, we theorize the legitimacy lie as a form of dark institutional work. Using 47 driver interviews, extensive archival materials, court documents, and leaked internal communications (the Uber Files), we analyze the contested operations of Uber and Ola in India (2013–2020). Our analysis reveals a recursive two-phase process. In Phase 1 (Proactive Framing), platforms construct the legitimacy of the “micro-entrepreneur.” In Phase 2 (Reactive Escalation), legitimacy threats trigger a defensive escalation of dark work, including sharp increases in symbolic rhetoric, the material imposition of opaque algorithmic control, and the relational co-optation of powerful stakeholders. This dark work, in turn, provokes driver resistance, which constitutes a new legitimacy threat that refuels the cycle. This recursive process functions as the mechanism that enables the platform's institutional entrenchment. We advance institutional work scholarship by theorizing this process, explaining how the successful performance of a legitimacy lie allows a venture to cross a critical threshold from a fragile reliance on normative legitimacy to a state of durable structural power.

This study examines the drilling characteristics of crab carapace-impregnated coir polyester composite material. The chemical structure of the crab carapace offers better mechanical properties; however, optimizing drilling performance in a full factorial design under various drilling conditions, with the parameters of drill bit diameter, feed rate, and spindle speed. A reduction in feed rate combined with a small drill diameter and high spindle speed creates the optimal conditions to minimize the delamination value of 1.05 while maintaining structural strength and producing a strong thrust of 100 N and torque effects of 5.0 N-m on the composite material. The ANN was used to predict the drilling parameters, and the ANN model provides a precise prediction of drilling parameters within the defined boundaries for improved operation efficiency of this composite material, with average absolute percentage error of less than 3%. The ANN-based methodology shows efficient modeling capabilities for drilling operations in complex structured composite materials.

Current AI models process input through a single lens. The human brain never does this—it cross-references every sense against every other sense, flags conflicts, and only calls on expensive conscious reasoning when something doesn't add up. We present a complete architecture for an artificial brain that mirrors this design: specialized Small Language Models (SLMs) as parallel sensory cortices, a lightweight conflict detector as the anterior cingulate cortex, an expensive reasoning model as the prefrontal cortex activated only on demand, a streaming identity core as the default mode network, episodic memory via vector databases, slow knowledge consolidation via sleep-cycle fine-tuning, and a neuromodulator reward system that shapes all behavior over time. The system is born when started, develops personality through experience, sleeps to consolidate, and dies when stopped. Every component maps to a specific brain structure. Every design decision is grounded in neuroscience research. The architecture is implementable today on consumer hardware (RTX 4050, 6GB VRAM).

Affective states and how people see their partner’s responsiveness are important for understanding relationship quality, but these processes are not well studied in non-Western settings. Drawing on the affect-as-information framework and theories of perceived partner responsiveness, this study tested whether responsiveness helps explain the link between daily affect and marital satisfaction. Data came from 203 heterosexual married couples in Sikkim, India. Using the Actor–Partner Interdependence Model with mediation, we examined positive and negative affect as simultaneous predictors of perceived partner responsiveness and marital satisfaction. Positive affect was linked to greater marital satisfaction through one’s own perception of partner responsiveness. Negative affect also showed an asymmetrical pattern, with only one partner’s negative affect predicting lower marital satisfaction for both spouses, partly through reduced responsiveness. Partner-mediated indirect effects were minimal. These findings highlight the role of everyday affect and perceived responsiveness in shaping marital satisfaction in cultural contexts that are less often studied.

Purpose This paper aims to examine the impact of central bank independence (CBI), both de jure and de facto, on the fiscal deficit (FD) in the case of India from 1980–1981 to 2018–2019. With the level of financial development proxied by the ratio of private credit to government as a possible source of nonlinearity, this study explores the possibility of a non-linear association between the CBI and FD. Design/methodology/approach This study estimated a logistic smooth transit regression (LSTR) to uncover the possible nonlinearity. To quantify the de jure and de facto independence of the Central Bank of India, i.e. RBI, this study follows Jasmine et al. (2019) and Cukierman et al. (1992). Findings The results reported regime dependence of the effect of CBI on FD. In both the short and long run, FD and CBI are found to be positively related during a low financial development regime, and when the economy is in a high financial development regime, FD and CBI are found to be negatively related. Moreover, an increase in GDP growth rate is found to trim the FD, whereas an increase in trade openness, oil prices, and the central bank governor (TOR) turnover rate increases it. Practical implications The findings of this study highlight the significance of the level of financial development that enables the independent central bank to ensure discipline on the part of fiscal authorities and improve fiscal balances. Moreover, robust GDP growth, more de facto independence of the central bank, and less oil price inflation would be favourable to ensure improvements in fiscal balance. The results imply the significance of the level of financial development in making central bank independence a potent tool to ensure fiscal prudence. Originality/value To the best of authors’ knowledge, no study has been conducted to understand the non-linear impact of the independence of the Reserve Bank of India on the fiscal deficit in the case of the Indian economy, subject to the level of financial development. The non-linear results provide important input to policymakers regarding the relevance of the level of financial development to the effectiveness of central bank independence and fiscal prudence on the part of the government.

Short, information-dense texts such as news headlines, product blurbs, and mobile notifications require concise and factually accurate summaries, yet current abstractive summarization models often hallucinate when context is limited . Existing research largely focuses on long-form summarization or uses decoding-time heuristics, leaving a reproducible, training-based approach tailored to short-document factuality underexplored. We curate a stratified short-document corpus (inputs ≤128 tokens, summaries ≤32 tokens) and propose a transformer-based model that augments cross-entropy training with a coverage penalty and an NLI-derived factuality regularizer, evaluating systems with ROUGE, BERTScore, automated factuality proxies (NLI, QA-consistency ), and human judgments. The proposed model outperforms strong baselines (BART-base, T5-small) with ROUGE-1 = 39.1, ROUGE-2 = 16.9, ROUGE-L = 37.8 and a BERTScore of 0.88, and achieves higher automated factuality (NLI entailment 84%, QA-consistency 82%). Human evaluation corroborates these gains (mean Fluency 4.4, Relevance 4.2, Faithfulness 4.0) and shows smaller performance degradation under domain shift and input-length variation. These results indicate that integrating factuality-aware objectives into transformer fine-tuning yields more reliable short-document summaries and provides a reproducible path toward deployment in applications requiring concise, trustworthy text generation.