Enablers for embedding big data solutions in smart factories: an empirical investigation

This study provides insight into the enablers that assist organizations in implementing big data solutions in their smart factory development, as well as the interrelationships between these enablers from an information system (IS) perspective. The research followed an inductive qualitative approach. Twenty-two in-depth semi-structured interviews were conducted with experienced consultants and IT managers from SAP Consultancy Company, and general managers and engineers from Xiamen Intretech Inc., a leading manufacturing company in adopting big data solutions in smart factory. Following thematic analysis approach, three sets of enablers including organization, technology and external environment were identified together with the interrelationships between them. This paper extends the current understanding of smart factory and big data solutions in information system research through offering an empirical investigation of different enablers in this context. The findings also provide recommendations for practitioners to increase the possibilities of success when implementing big data solutions in smart factory context

Can Touchscreen Devices be Used to Facilitate Young Children's Learning? A Meta-Analysis of Touchscreen Learning Effect

Because of the continuous stream of touchscreen apps that are claimed to be educational and the increasing use of touchscreen devices in early childhood, considerable attention is being paid to the effect of touchscreens on young children's learning. However, the existing empirical findings in young child samples are not consistent. In this meta-analysis we tested the overall effect of touchscreen devices on young children's (0- to 5-year-olds) learning performance, as well as moderators of this effect, based on 36 empirical articles (79 effect sizes) involving 4,206 participants. The overall analysis showed a significant touchscreen learning effect (d = 0.46), indicating that young children indeed benefited from touchscreen learning. Interestingly, age, learning material domain, comparison group, and experimental environment significantly moderated the effect of touchscreen devices on young children's learning outcome. These findings shed light on the role of touchscreen-related physical experience in early childhood education

Phase evolution of Ce-based heavy-fermion superconductors under pressure: a combined DFT+DMFT and effective-model description

In typical Ce-based heavy-fermion superconductors, superconducting (SC) phases emerge or can be tuned in proximity to the antiferromagnetic (AF) quantum critical point (QCP), but so far the explicit phase-evolution process and the coexistence of superconductivity and AF order near the QCP remain lack of understanding. Here, by combing DFT+DMFT with effective-model calculations, we provide a theoretical description for Ce-based SC compounds under pressure. Firstly, DFT+DMFT calculations for the normal states reveal that the Kondo hybridizations are significantly enhanced, while the initially localized $f$ electrons eventually become fully itinerant via a localized-itinerant crossover. In this context, we construct an effective model with tunable parameters under pressure, and show that the interplay of magnetic correlation and Kondo hybridization can drive successive transitions, from AF phase to AF+SC coexisting phase, then to paramagnetic SC phase via an AF transition which corresponds to the QCP, and finally to Kondo paramagnetic phase through a SC transition driven by localized-itinerant crossover. Our study gives a proper explanation for the pressure-induced magnetic QCP and SC transition, and for the phase-evolution process under pressure in typical Ce-based superconductors, and may also help to understand the SC states emerging around the ferromagnetic quantum transition points in uranium-based superconductors.Comment: 13 pages, 11 figure