Author: Aparajit

July 12, 2026

Governing Defensive Data Poisoning: Strategic Data Defense, Stakeholder Harm, and Ethical Legitimacy in the AI Scraping Economy

Artificial IntelligenceIn ProgressManagement

The growth of artificial intelligence has intensified the automated extraction of publicly accessible online data for model training, commercial analysis, and competitive intelligence. In response, organizations and content creators are increasingly considering defensive data-poisoning techniques that provide scrapers with misleading, synthetic, or strategically distorted information. While these measures may help protect valuable digital resources, they also raise ethical concerns involving deception, stakeholder harm, downstream data contamination, and damage to the wider information ecosystem. This review…

July 9, 2026

APPLICANT-SIDE AI AGENTS AND THE REDISTRIBUTION OF AGENCY IN ALGORITHMIC LABOR MARKETS: A DESIGN SCIENCE STUDY OF JOB SEARCH AUTOMATION AND CSR IN RECRUITMENT

Artificial IntelligenceHuman ResourcesIn ProgressManagement

This research develops and preliminarily evaluates a human-supervised applicant-side AI agent for job discovery, fit screening, application review, cover-letter generation, and tracking. Using Design Science Research and an Automation–Augmentation–Agency framework, the paper examines how job seekers can delegate routine tasks to AI while retaining control over consequential application decisions.

July 8, 2026

Built to Last? Corporate Social Responsibility and the Longevity of Firms

CSRIn ProgressManagement

This study examines whether corporate social responsibility contributes to firm longevity by strengthening organizational resilience, stakeholder support, and legitimacy. While prior research has linked firm survival to factors such as age, size, profitability, and financial condition, less is known about whether CSR helps organizations endure over time. The study will use a longitudinal panel constructed by merging Refinitiv CSR data with Compustat financial information and firm deletion records. The empirical analysis will distinguish among broad…

January 20, 2026

Identification of a Novel 19-bp Deletion Mutation in LTBP4 Using Exome Sequencing in Two Siblings with Autosomal Recessive Cutis Laxa Type 1C

GeneticsPublished

Autosomal recessive type I cutis laxa is genetically heterogeneous. Biallelic mutations in latent transforming growth factor β-binding protein 4 (LTBP4; MIM*604710) lead to type 1C cutis laxa due to nonsense, frameshift, single base pair indels, or duplication mutations. In this report, we describe the first Indian family with cutis laxa as a result of a novel 19 base pair homozygous deletion leading to premature termination of short isoform LTBP-4S. Download this published paper below: