University of Houston

About: University of Houston is a education organization based out in Houston, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 23074 authors who have published 53903 publications receiving 1641968 citations.

Consumer Cocreation in New Product Development

Abstract: The area of consumer cocreation is in its infancy and many aspects are not well understood. In this article, we outline and discuss a conceptual framework that focuses on the degree of consumer cocreation in new product development (NPD). The authors examine (a) the major stimulators and impediments to this process, (b) the impact of cocreation at each stage of the NPD process, and (c) the various firm-related and consumer-related outcomes. A number of areas for future research are suggested.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Extending the technology acceptance model: the influence of perceived user resources

Abstract: There has been considerable research on the factors that predict whether individuals will accept and voluntarily use information systems The technology acceptance model (TAM) has a base in psychological research, is parsimonious, explains usage behavior quite well, and can be operationalized with valid and reliable instruments A limitation of TAM is that it assumes usage is volitional, that is, there are no barriers that would prevent an individual from using an IS if he or she chose to do so This research extends TAM by adding perceived user resources to the model, with careful attention to placing the construct in TAM's existing nomological structure In contrast to measures of self-efficacy and perceived behavioral control that concentrate on how well individuals perceive they can execute specific courses of action, this paper examines perceptions of adequate resources that can facilitate or inhibit such behaviors The inclusion of both a formative and reflective set of measures provides the opportunity for the researcher and manager to decide whether to evaluate only the overall perceptions of adequate resources or also the specific underlying causes The extended model incorporating these measures was then tested in the field The results confirmed that perceived user resources is a valuable addition to the model

Training Transfer: An Integrative Literature Review:

Abstract: Given the proliferation of training transfer studies in various disciplines, we provide an integrative and analytical review of factors impacting transfer of training. Relevant empirical research for transfer across the management, human resource development (HRD), training, adult learning, performance improvement, and psychology literatures is integrated into the review. We synthesize the developing knowledge regarding the primary factors influencing transfer—learner characteristics, intervention design and delivery, and work environment influences—to identify variables with substantive support and to discern the most pressing gaps. Ultimately, a critique of the state of the transfer literature is provided and targeted suggestions are outlined to guide future empirical and theoretical work in a meaningful direction.