20,995 research outputs found
Mobile apps usage and dynamic capabilities: A structural equation model of SMEs in Lagos, Nigeria.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Significant knowledge exists regarding the application of dynamic capability (DC) frameworks in large firms, but their impact on smaller organisations is yet to be fully researched. This study surveyed 1162 small and medium sized enterprises (SMEs) in Lagos in an effort to understand how SMEs in developing country contexts use mobile apps to enhance their businesses through DCs. Through the use of the covariance-based structural equation modelling (SEM) technique, the study explored the fitness of a conceptual formative model for SMEs. The model assembled 7 latent variables namely: mobile app usage, adaptive capability, absorptive capability, innovative capability, opportunity sensing ability, opportunity shaping ability and opportunity seizing ability. Subsequently, 15 hypotheses aimed at testing the relationships between the latent variables were developed and tested. The findings revealed that mobile app usage increases the adaptive, absorptive and innovative capabilities of SMEs. Absorptive capabilities help SMEs to maximise opportunities, while innovative capabilities negatively influence SMEsâ tendency to maximise opportunities. The results failed to establish a direct relationship between mobile app usage and SMEsâ ability to maximise opportunities. The research outcomes indicate that SMEs in Lagos respond to opportunities innovatively but they seldom exhibit innovation in order to create opportunities. The heterogeneous nature of SMEs complicates any clear-cut narrative as to how SMEs in Lagos should employ mobile apps to create and maximise opportunities. However, mobile apps could induce innovation and, as such, impact significantly when developed and applied to the contextual requirements of SMEs. The research revealed the untapped potential of SMEsâ mobile app usage in Lagos
Trends in office internal gains and the impact on space heating and cooling demands
Internal gains from occupants, equipment and lighting contribute a significant proportion of the heat gains in an office space. Looking at trends in Generation-Y, it appears there are two diverging paths for future ICT demand: one where energy demand is carefully regulated and the other where productivity enhancers such as multiple monitors and media walls causes an explosion of energy demand within the space. These internal gains scenarios were simulated on a variety of different building archetypes to test their influence on the space heating and cooling demand. It was demonstrated that in offices with a high quality facade, internal gains are the dominant factor. As a case study, it was shown that natural ventilation is only possible when the ICT demand is carefully regulated
Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning
Traditional recommendation systems rely on past usage data in order to
generate new recommendations. Those approaches fail to generate sensible
recommendations for new users and items into the system due to missing
information about their past interactions. In this paper, we propose a solution
for successfully addressing item-cold start problem which uses model-based
approach and recent advances in deep learning. In particular, we use latent
factor model for recommendation, and predict the latent factors from item's
descriptions using convolutional neural network when they cannot be obtained
from usage data. Latent factors obtained by applying matrix factorization to
the available usage data are used as ground truth to train the convolutional
neural network. To create latent factor representations for the new items, the
convolutional neural network uses their textual description. The results from
the experiments reveal that the proposed approach significantly outperforms
several baseline estimators
Modeling Interdependent and Periodic Real-World Action Sequences
Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.Comment: Accepted at WWW 201
- âŠ