|
on Information and Communication Technologies |
| By: | Scholl, Lynn; Sabogal-Cardona, Orlando; Oviedo, Daniel; Arellana, Julián; Cantillo, Víctor; Ojeda-Diaz, Alfredo J. |
| Abstract: | Microtransit services are a midpoint between standard ride-hailing services and conventional bus fleets from public transit. Microtransit utilizes small buses or vans to provide on-demand shared transport, allowing users to reserve seats, track their trips, and receive real-time estimates of pick-up and drop-off times. While public transit systems in the Latin American and the Caribbean (LAC) region are the subject of critical and often contentious policy debates with frequent references to user discontent and an overall decline in quality, microtransit is emerging as an alternative that could improve existing transit systems. Microtransit is argued to be an effective means to extend the coverage of transit services in transit deserts, operating in areas without transit routes and where investments in stations and infrastructure might not be cost effective. Despite its potential benefits, microtransit remains under studied in the LAC region. Based on survey data gathered for Barranquilla, Colombia, and Mexico City, Mexico, this research examines the added value of digital technology features in microtransit. This paper explores individuals' perceptions of the Information and Communication Technology (ICT) features present in microtransit and different variables mediating such perceptions. Employing factor analysis, and Structural Equation Models (SEM), ICT features are considered as latent variables and placed as the main outcome of the SEM. Other latent variables encompassing perceptions, such as the quality and safety of public transit, are also included in the model. Results indicate that individuals with pro-car attitudes and those who own cars are more likely to prefer ICT features in microtransit, suggesting a potential for modal shift. Similarly, insecurity in public transit also explains favorable perceptions about the ICT features in microtransit. We also found that higher levels of technological savviness and being a ride-hailing adopter are related to increased valuations of microtransit. |
| Keywords: | Microtransit |
| JEL: | O14 R42 R58 Z18 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14219 |
| By: | Vidya Sagar G; Shifat Ali; Siddhartha P. Chakrabarty |
| Abstract: | This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing through dimensionality reduction and latent factor modeling via Principal Component Analysis and Autoencoders. Building on this, we extend the methodology using Variational Autoencoders, which introduces a probabilistic structure to the latent space. This enables Monte Carlo-based scenario generation, allowing for more nuanced, distribution-aware simulation of stressed market conditions. The proposed framework captures complex non-linear dependencies and supports risk estimation through Value-at-Risk and Expected Shortfall. Together, these pipelines demonstrate the potential of Machine Learning approaches to improve the flexibility, robustness, and realism of financial stress testing. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02011 |
| By: | Bernardi, Marta; Lindlacher, Valentin |
| Abstract: | For the developing world, instant connectivity was first established through the expansion of second-generation (2G) mobile networks. Leveraging exogenous variation in network disruptions induced by lightning strikes as an instrumental variable, we analyze panel data from 2, 040 regions across 13 developing countries between 1990 and 2015. Our findings reveal economically meaningful increases in employment (approximately 32-43%), driven primarily by a shift from self-employment toward formal wage employment, notably within agriculture, coupled with substantial rises in unpaid labor among women. |
| Keywords: | Mobile Coverage, Structural Transformation, Panel Inference, Technological Development, Micro Interventions, Aggregate Implications |
| JEL: | O33 O12 O14 E24 E27 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:tudcep:324641 |
| By: | Hossain, Sabiba |
| Abstract: | This study investigates the relationship between adolescents' perceptions of social belonging and their internet and phone dependency, using data from the Pew Research Center’s 2018 Teens and Technology survey. The research aims to determine whether adolescents who feel they fit in with their peers exhibit different levels of internet and phone dependency compared to those who do not. A latent variable for internet and phone dependency was constructed using factor analysis, and a regression model was employed to analyze the relationship between social belonging and dependency, controlling for variables such as gender, age, and socioeconomic status. Contrary to initial hypotheses, the study found no significant relationship between adolescents' perceptions of fitting in and their internet and phone dependency. However, gender was found to be a significant predictor, with female adolescents more likely to exhibit higher levels of dependency compared to males. The study also found that self-imposed screen time restrictions were not reliable indicators of dependency. These findings suggest that interventions aimed at reducing internet and phone dependency should focus on psychological factors such as social anxiety and self-esteem, rather than peer integration. The study contributes to the growing body of literature on adolescent internet dependency and offers practical recommendations for stakeholders, including the need for gender-specific interventions and universal programs that address underlying psychological factors. |
| Date: | 2025–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:5gynm_v1 |
| By: | Claudia van der Vorst (University of Applied Science Kufstein) |
| Abstract: | Generation Z (born 1997-2012) is transforming the landscape of higher education through distinct cognitive, digital, and behavioural traits. Characterized by nonlinear information processing, immediacy-oriented learning, and deep integration of digital tools, Gen Z students are both technologically fluent and pedagogically demanding (Hammad, 2023; Nuttall, 2025). While highly familiar with generative AI, many Gen Z learners lack the metacognitive strategies needed to apply these tools critically (Chardonnens, 2025). The distinct characteristics of Generation Z?such as their need for instant access to information, preference for visual and interactive content, and high levels of digital multitasking?profoundly shape a learning style that values autonomy, personalization, and technology-integrated instruction.This study addresses the central research question: How does increasing AI fluency among Gen Z students reshape their expectations toward instructional design and the role of educators in higher education?Drawing on the Scholarship of Teaching and Learning (SoTL) principles, the paper analyses qualitative data from two student cohorts (2024 & 2025, N=63/56). The findings show a measurable increase in AI tool use and digital confidence, alongside a consistent desire for structured, human-centred learning. Project-Based Learning (PjBL) and Problem-Oriented Learning (POL) emerge as pedagogical formats particularly suited to Gen Z?s values of collaboration, application, and feedback (Weimer, 2021; Hmelo-Silver & Barrows, 2020). Simultaneously, students report a continued need for structured learning environments and emotionally intelligent instruction.Notably, the study highlights a role inversion: students often surpass instructors in AI fluency, creating a ?digital competence gap? that challenges traditional hierarchies (Chan & Lee, 2023; Selwyn, 2023). Students increasingly view faculty not as content authorities, but as facilitators, tool mentors, and guides in reflective practice (Tang & Saade, 2023)What distinguishes this study is its time-sensitive comparison across cohorts and its linkage of behavioural data with instructional design. It confirms earlier calls for AI-integrated, learner-centred ecosystems that balance autonomy with supportive structure (Zawacki-Richter et al., 2021; Chardonnens, 2025). The paper concludes with evidence-based recommendations for repositioning faculty, redesigning assessments, and aligning future-proof pedagogy with the cognitive and technological profile of Generation Z. |
| Keywords: | Learning Methods, GenZ Learner Profile, Digital Learning Habits, Higher Education, Education 4.0, ChatGPT, GenAI, AI, Academic Integrity, Project-Based Learning (PbL), Problem-Oriented Learning (PoL) |
| JEL: | I21 I23 D83 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iacpro:15216833 |
| By: | Ekpeyong, Paul |
| Abstract: | This paper focuses on analyzing the implications of adopting generative artificial intelligence (AI) at the macroeconomic level in Nigeria through a task-based method of analysis informed by Acemoglu in 2018. In breaking the production process into individual tasks that are carried out either by labor or capital, the study then examines the impact of automation and task complementarities resulting due to AI, on productivity, gross domestic product (GDP), wages, and inequality due to a 10-year time frame. Based on the empirical estimates recorded by some related literatures regarding the effects of capital stock on the total factor productivity (TFP) of three economies, the paper is likely to improve by 0.51% to 0.66% depending on the growth of the capital stock; this translates to an increment in GDP of about 0.93 to 1.16 per cent. Every 10, 000 when capital investment is higher by an upper scenario, GDP will increase by up to 1.56 percent. Nonetheless, the welfare issues arise due to the occurrence of bad jobs like misinformation and digital manipulation, which may have the potential to negate up to 0.072 percent gain in the GDP. Demographic and education-based impacts differ as the workers with low educational skills have a slight advantage, whereas those with high skills remain unaffected. Income share held by capital also will increase boosting inequality. The paper highlights the importance of focus on inclusive AI approaches, ethical governance and investments in digital infrastructure in Nigeria. Generative AI is promising in its economic development but will depend on the institutional decisions on its usage, their regulatory rules, and deliberate integration with national development plans. |
| Keywords: | Artificial intelligence, Labor, Automation, Economic growth, Total factor productivity |
| JEL: | E6 J6 J7 |
| Date: | 2025–07–15 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125347 |