Understanding the Success of Software-as-a-Service (SaaS) - The Perspective of Post-Adoption Use

Understanding the antecedents and consequences of trust in an online and on-demand outsourcing context is important. This study explores the effects of service quality on trust, which in turn affects a client firm’s post-adoption use in SaaS. A research model was developed based on the dedication-constraint framework of social exchange theory to measure service quality, trust, and SaaS post-adoption as multiple dimensions. We empirically examined the model by collecting data from 246 firms (key informants) that have adopted SaaS. Results show that while all three dimensions of service quality (client orientation quality, client response quality, environment quality) positively affect trust in service quality, client orientation and environment quality have positively influence on trust in provider. Both types of trust positively influence post-adoption intention. Finally, we discuss the theoretical and practical implications

Context-aware Document-clustering Technique

Document clustering is an intentional act that should reflect individuals’ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective documentclustering techniques need to take into account a user’s categorization context defined by or relevant to the target task under consideration. However, existing document-clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate context-aware document-clustering. In response, we propose a Context-Aware document-Clustering (CAC) technique that takes into consideration a user’s categorization preference (expressed as a list of anchoring terms) relevant to the context of a target task and subsequently generates a set of document clusters from this specific contextual perspective. Our empirical evaluation results suggest that our proposed CAC technique outperforms the pure content-based document-clustering technique

Collaborative Filtering-based Context-Aware Document-Clustering (CF-CAC) Technique

Document clustering is an intentional act that should reflect an individual\u27s preference with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user\u27s categorization context. In response, Yang & Wei (2007) propose a Context-Aware document Clustering (CAC) technique that takes into consideration a user\u27s categorization preference relevant to the context of a target task and subsequently generates a set of document clusters from this specific contextual perspective. However, the CAC technique encounters the problem of small-sized anchoring terms. To overcome this shortcoming, we extend the CAC technique and propose a Collaborative Filtering-based Context-Aware document-Clustering (CF-CAC) technique that considers not only a target user\u27s but also other users\u27 anchoring terms when approximating the categorization context of the target user. Our empirical evaluation results suggest that our proposed CF-CAC technique outperforms the CAC technique

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

Control of the nanoscale crystallinity and phase separation in polymer solar cells

Grazing-incidence x-ray diffraction and atomic force microscopy were performed on bulk heterojunction regioregular poly(3-hexylthiophene) (RR-P3HT) [6,6]-phenyl-C71-butyric acid methyl esters spin-cast films with different film processing conditions to correlate the crystalline nanostructure of P3HT with the corresponding solar cell performance. The increase in long wavelength absorption for solvent annealed films is related to highly conjugated crystal structure of RR-P3HT phase-separated in the active layer. Upon thermal annealing, the solvent annealed 50-nm-thick device shows high solar cell performance with fill factor up to 73% and power conversion efficiency of 3.80%

Control of the nanoscale crystallinity and phase separation in polymer solar cells

Grazing-incidence x-ray diffraction and atomic force microscopy were performed on bulk heterojunction regioregular poly(3-hexylthiophene) (RR-P3HT) [6,6]-phenyl-C71-butyric acid methyl esters spin-cast films with different film processing conditions to correlate the crystalline nanostructure of P3HT with the corresponding solar cell performance. The increase in long wavelength absorption for solvent annealed films is related to highly conjugated crystal structure of RR-P3HT phase-separated in the active layer. Upon thermal annealing, the solvent annealed 50-nm-thick device shows high solar cell performance with fill factor up to 73% and power conversion efficiency of 3.80%