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3892 items in total found

Journal Articles | 2026

Men in Beauty Work and Feminization of Digital Labor Platforms

Sai Amulya Komarraju, Manisha Pathak-Shelat, Payal Arora, Usha Raman

Extant research on the gendered dynamics on digital labor platforms and care work is divided in terms of focus: (migrant) men involved in supposedly “masculine” work such as driving and delivery, and home-based repair work, and the feminized invisible work performed by women in home-based care-work such as domestic work and beauty work. While such scholarship has merit, it completely dismisses the particularities of the South Asian context where beauty work, considered to be ritually impure work, has historically been performed by men from the marginalized Nai caste. Foregrounding the views of men in beauty work, particularly Nai-barbers (on and off platform), our findings reveal that Nai-barbers find the relocation of work from barbershop to customer’s home by platforms particularly humiliating. The transition from being entrepreneurs, in charge of their barbershops, to mere workers supervised by both platforms and customers, evokes memories of the servitude their ancestors endured. The humiliation and degradation of work they experience are rooted in caste and colonial histories. Our findings underscore the need to go beyond the immediate temporal context to identify the conditions of work that workers find degrading, and situate the feminization of platform economy within the context of coloniality and casticization of power, thus bringing a necessary intersectionality that recognizes but goes beyond gender.

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Journal Articles | 2026

Protection of capacitated hubs under demand uncertainty: a robust optimization approach

Sneha Dhyani Bhatt, Sachin Jayaswal, Ankur Sinha

Existing hub protection problems primarily consider deterministic demand and accordingly allocate protection resources to the most vulnerable hubs in the network that are at risk of attack. We study the protection problem of a capacitated hub-and-spoke network under the risk of an attack when the demand is uncertain. To model this, we propose a multi-level capacitated u-hub protection problem, with the network operator’s protection decision at the first level. At the second level, we model the interdiction problem of the network evader who intends to attack r hubs to maximise the post-interdiction re-routing cost of the network operator. At the third level, the network operator minimises the re-routing cost through the surviving hubs under the worst-case realisation of the demand, which is drawn from different robust uncertainty sets, namely, column, ellipsoidal, hose, and hybrid. A dual-based single-level reduction is proposed for the interdiction problem, which is then used within an implicit enumeration algorithm to solve the overall protection problem. We also propose tight values for bigM that are introduced due to complementary slackness conditions upon single-level reduction. Based on extensive experiments on the well-known CAB Dataset, we discuss several managerial and computational insights under different network parameter settings and uncertain scenarios.

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Journal Articles | 2026

High-dimensional regularized additive matrix autoregressive mode

Debika Ghosh, Samrat Roy, Nilanjana Chakraborty

High-dimensional time series has diverse applications in econometrics and finance. Recent models for capturing temporal dependence have employed a bilinear representation for matrix time series, or the Tucker-decomposition based representation in case of tensor time series. A bilinear or Tucker-decomposition based temporal effect is difficult to interpret on many occasions, along with its computational complexity due to the non-convex nature of the underlying optimization problem. Moreover, the existing matrix case models have not sufficiently explored the possibilities of imposing any lower-dimensional pattern on the transition matrices. In this work, we propose a regularized additive matrix autoregressive model with additive interaction of row-wise and column-wise temporal dependence, that offers more interpretability, less computational burden due to its convex nature and estimation of the underlying low rank plus sparse pattern of its transition matrices. We address the issue of identifiability of the various components in our model and subsequently develop a scalable Alternating Block Minimization algorithm for estimating the parameters. We provide a finite sample error bound under high-dimensional scaling for the model parameters. Finally, the efficacy of the proposed model is demonstrated on synthetic and real data.

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Journal Articles | 2026

Mandatory CSR and its impact on audit fees

Mehul Raithatha, Tara Shankar Shaw

Designing a corporation’s policy for meeting corporate social responsibility (CSR) has become an important strategic decision for the firms. Studies have investigated the role of CSR in firms’ financial reporting process, but a few papers, like Chen et al. (2016)LópezPuertas‐Lamy et al. (2017)Du et al. (2020)Yuan (2025) and Li et al. (2025) have looked at how the auditors have reacted to the firm’s CSR/Environmental, Social, and Governance (ESG) policy by changing its audit pricing. However, the above stream of research examines firms that voluntarily engage in CSR activities. In 2013, India implemented mandatory CSR regulation under their new Companies Act 2013 (CA2013), requiring companies that are above a certain profit, net worth and turnover threshold to spend two percent of their average past three years’ profits before taxes, on CSR activities scheduled under Section 7 of Clause 135 of CA2013 [1]. The objective of this study is to examine the effect of a firm’s CSR compliance on its audit fees and the factors that are likely to drive the effect.

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Journal Articles | 2026

The Legitimacy Lie as Dark Institutional Work: Rhetoric and Reality in India's Platform Economy

Dharma Raju Bathini, Shalini Parth, George Kandathil

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.

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Journal Articles | 2026

Dynamic capacity allocation under service-dependent demand and market exit risk

Govind Lal Kumawat, Felix Papier, Debjit Roy

Journal Articles | 2026

Capacity planning for platform services: Agent availability, compensation, and dual sourcing

Arulanantha Prabu, Ponnachiyur Maruthasalam, Debjit Roy, Prahalad Venkateshan, Asoo J. Vakharia

One of the key decisions for an on-demand service platform is to plan capacity to meet uncertain demand. This problem is also compounded by the operating environment and multiple stakeholder perspectives. For example, capacity is typically determined not only by multiple supply sources but also by the platform’s compensation scheme, as this affects labor pool availability. In addition, since on-demand platforms do not service demand using permanent (e.g., full-time) employees, it is likely that the employee pool is heterogeneous in their income preferences. In this paper, we analytically characterize the capacity planning problem for an e-hailing platform offering transportation service to customers (such as Uber and Lyft) using independent agents (or drivers). In the presence of uncertain demand, the unique features incorporated into our analysis include sources of supply (single/dual), driver absenteeism rates, platform compensation schemes, labor pool constraints, and heterogeneity in drivers’ income-earning orientation. Interestingly, one of our major findings is that labor pool constraints determine the types of drivers that the platform should recruit. In the absence of such constraints, the platform should use only “unreliable” drivers, whereas both reliable and unreliable drivers should be employed when the labor pool is constrained. From a platform perspective, a lower compensation fraction should be offered under a post-paid scheme than under a pre-paid compensation scheme. The model and its results are validated using empirical data from different markets. A sensitivity analysis is performed to assess the robustness of this approach across various demand, payment, and driver-type scenarios.

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Journal Articles | 2026

E-Commerce Middle-Mile Network Design with Delivery Speed Choices and Service Level Constraints

Aditya Malik, Shuvabrata Chakraborty, Sachin Jayaswal

The increasing demand for expedited e-commerce deliveries, with delivery times of one to three days, highlights the importance of optimizing the middle-mile network. Most retailers store a considerable portion of their inventory at the regional distribution centers (RDCs) outside urban areas, from where it is moved to the customer zones equipped with last-mile distribution facilities as required. Thus, RDC locations become critical in middle-mile operations, directly impacting the transit times to customer zones and, ultimately, the delivery times in the last mile. This paper presents a middle-mile network design problem arising in the context of e-commerce companies in the presence of customers with different delivery time preferences. Specifically, it allows RDCs to satisfy demands from customer zones using delivery times longer than requested, albeit with penalties, if that helps reduce cost without violating the service level requirements of fulfilling at least a given threshold of the demands within the requested delivery times. The problem is formulated as a mixed-integer linear program, for which an exact Lagrangian relaxation-based branch-and-bound algorithm is proposed. Several enhancements to the algorithm are provided, including an efficient Lagrangian heuristic for the primal-bound, a Benders decomposition framework to solve one of the Lagrangian subproblems efficiently, an analytical approach for obtaining Benders optimality cuts, and a partial analytical characterization of Pareto-optimal Benders cuts. With these enhancements, our final algorithm substantially outperforms the state-of-the-art commercial solver, as highlighted by our computational experiments on an extensive set of 220 instances with up to 80 potential RDC locations and 1,000 customer zones. Our best algorithm solves 204 of the 220 instances to 0.50% duality gap compared with only 108 that CPLEX could solve to the same gap within an allowed 10-hour CPU time limit. Furthermore, it achieves an average time savings of 63.24% compared with CPLEX across all the instances.

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Journal Articles | 2026

Predictive Hotspot Mapping for Data-Driven Crime Prediction

Karthik Sriram, Ankur Sinha, Suvashis Choudhary

Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving toward predictive approaches relying on historical data. In this paper, we create a nonparametric model using a spatiotemporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.

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Journal Articles | 2026

FinBloom: Knowledge-Grounding Large Language Model with Real-Time Financial Data

Ankur Sinha, Chaitanya Agarwal, Pekka Malo

Large language models (LLMs) excel at generating human-like responses but often struggle with interactive tasks that require access to real-time information. This limitation poses challenges in finance, where models must access up-to-date information, such as recent news or price movements, to support decision-making. To address this, we introduce Financial Agent, a knowledge-grounding approach for LLMs to handle financial queries using real-time text and tabular data. Our contributions are threefold: First, we develop a Financial Context Dataset of over 50,000 financial queries paired with the required context. Second, we develop FinBloom 7B, a custom 7 billion parameter LLM, by fine-tuning Bloom 7B on 14 million financial news articles from Reuters and Deutsche Presse-Agentur (DPA), alongside a random sample of 25% from 12 million Securities and Exchange Commission (SEC) filings. Third, we fine-tune FinBloom 7B using the Financial Context Dataset to serve as a Financial Agent. This agent generates relevant financial context, enabling efficient real-time data retrieval to answer user queries. By reducing latency and eliminating the need for users to manually provide accurate data, our approach significantly enhances the capability of LLMs to handle dynamic financial tasks. Our proposed approach makes real-time financial decisions, algorithmic trading and other related tasks streamlined, and is valuable in contexts with high-velocity data flows.

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