This paper investigates how deeply entrenched offline gender norms and patriarchal power structures constrain and shape participation within Tech Aids, a large Facebook community in Pakistan focused on peer-to-peer technical help and e-commerce transactions. Through a qualitative study involving 14 months of participant observation and 20 semi-structured interviews (11 male, 9 female), we document significant gendered disparities: women exhibit lower public engagement, driven primarily by heightened privacy concerns, fear of harassment, and perceived male gatekeeping. Our analysis reveals that traditional socio-cultural restrictions on women’s mobility and constrained interactions with unfamiliar men in physical tech marketplaces are directly mirrored in the online Tech Aids environment. To manage these risks, women actively engage in practices of digital purdah, using workarounds like proxy-posting through male relatives to maintain both technical access and cultural modesty. By linking these offline barriers to online participation strategies, this study provides a vulnerability-centric framework and actionable design insights for creating more equitable online tech communities that explicitly address complex, deeply rooted socio-cultural constraints in non-WEIRD contexts.

The selection of a reliable and competent transportation company is a typical multi-criteria group decision-making (MCGDM) challenge in supply chain management. MCGDM has been widely used for decision support under ambiguity and uncertainty. This paper considers this problem in the setting of interval-valued q-rung orthopair fuzzy soft sets (IVq-ROFSS), a novel extension of fuzzy sets that presents an integrated approach to interpreting imperfect and ambiguous data. This study explores the novel Einstein aggregation operators (AOs) for this model, specifically the interval-valued q-rung orthopair fuzzy soft Einstein weighted average (IVq-ROFSEWA) and interval-valued q-rung orthopair fuzzy soft Einstein weighted geometric (IVq-ROFSEWG). These operators can consider large amounts of data that include all connections among parameters. Their fundamental properties (such as idempotency, boundedness, homogeneity, monotonicity, and shift invariance) are presented and proven. With the assistance of the new Einstein AOs, we design a novel MCGDM approach. A case study is presented to choose the most reliable transportation company that endorses the rationality and credibility of the proposed decision-making technique in supply chain management. Hence, this research helps with an innovative decision-support structure for assessing transport corporations.

We explore privacy perceptions, beliefs and practices of low-literate, low-income users in Pakistan, a patriarchal and religious context with a literacy rate of approx. 68% and where 59% of mobile users have less than 6 years of formal education. Through a qualitative study with 40 participants (17 male and 23 female) we examine the cultural, religious, and familial structures that impact users perceptions, management, and control of their personal privacy. We reveal significant gendered differences in privacy understandings, privacy preserving practices and the access to privacy related knowledge. Our work also highlights the seminal impact religious beliefs have on men and women’s understandings and management of privacy and the prolific use of after-market modified apps to support users specific privacy needs. The privacy concerns raised by our participants provide HCI researchers with valuable insights into designing privacy affordances for vulnerable and diverse populations beyond Western, educated, industrialized, rich and democratic contexts.