Impact of User Satisfaction with Mandated RM Use on Employee Service Quality

An increasing number of organizations are now implementing customer relationship management (CRM) systems to support front-line employees’ service tasks. With the belief that CRM can enhance employees’ service quality, management often mandates employees to use the implemented CRM. However, challenges emerge if/when employees are dissatisfied with using the system. To understand the role of front-line employee users’ satisfaction with their mandated use of CRM in determining their service quality, we conducted a field study in one of the largest telecommunications service organizations in China and gathered time-lagged data from self-reported employee surveys, as well as from the firm’s archival data sources. Our results suggest that employees’ overall user satisfaction (UserSat) with their mandated use of CRM has a positive impact on employee service quality (ESQ) above and beyond the expected positive impacts that job dedication (JD) and embodied service knowledge (ESK) have on ESQ. Interestingly, the positive effect of UserSat on ESQ is comparable to the positive effects of JD and ESK, respectively, on ESQ. Importantly, UserSat and ESK have a substitutive effect on ESQ, suggesting that the impact of UserSat on ESQ is stronger/weaker for employees with lower/higher levels of ESK. Finally, ESQ predicts customer satisfaction with customer service employees (CSWCSE); ESQ also fully mediates the impacts of UserSat and ESK, and partially mediates the impact of JD, on CSWCSE. The results of this study emphasize the importance of user satisfaction in determining employees’ task outcomes when use of an information system is mandated

PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%

Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images

Targeted color-dots with varying shapes and sizes in images are first exhaustively identified, and then their multiscale 2D geometric patterns are extracted for testing spatial uniformness in a progressive fashion. Based on color theory in physics, we develop a new color-identification algorithm relying on highly associative relations among the three color-coordinates: RGB or HSV. Such high associations critically imply low color-complexity of a color image, and renders potentials of exhaustive identification of targeted color-dots of all shapes and sizes. Via heterogeneous shaded regions and lighting conditions, our algorithm is shown being robust, practical and efficient comparing with the popular Contour and OpenCV approaches. Upon all identified color-pixels, we form color-dots as individually connected networks with shapes and sizes. We construct minimum spanning trees (MST) as spatial geometries of dot-collectives of various size-scales. Given a size-scale, the distribution of distances between immediate neighbors in the observed MST is extracted, so do many simulated MSTs under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. Our developments are illustrated on images obtained by mimicking chemical spraying via drone in Precision Agriculture.Comment: 21 pages, 21 figure

A Two-stage Architecture for Stock Price Forecasting by Integrating Self-Organizing Map and Support Vector Regression

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model

Advanced glycation end products (AGEs) in relation to atherosclerotic lipid profiles in middle-aged and elderly diabetic patients

<p>Abstract</p> <p>Objectives</p> <p>To evaluate the association between AGEs and atherosclerotic lipid profiles among aging diabetic patients in Taiwan.</p> <p>Design and Methods</p> <p>After age and gender matching, we selected 207 diabetic subjects and 174 diabetic subjects with proteinuria. Lipid profiles, including total cholesterol (TC), triglycerides (TG), high density cholesterol-lipoprotein (HDL-C) and low density lipoprotein-cholesterol (LDL-C) were measured using standard methods. AGEs were measured with the immunoassay method.</p> <p>Results</p> <p>In general, males were heavier; however, females had higher AGEs, fasting glucose (GLU), TC, HDL-C and LDL-C levels than males, and had higher TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C ratios compared to males. AGEs were more strongly correlated with TG levels and TCL/LDL-C, LDL-C/HDL-C and TG/HDL-C ratios when compared to glucose or hemoglobin A1c. Subjects had higher AGEs levels (≧ 2.0 AU) with more adverse lipid profiles.</p> <p>Conclusion</p> <p>AGEs seem to be a good biomarker to evaluate the association between diabetes and atherosclerotic disorders in aging diabetes.</p

The Association of Thyrotropin and Autoimmune Thyroid Disease in Developing Papillary Thyroid Cancer

Background. Papillary thyroid carcinoma (PTC) is the most common type of malignant thyroid neoplasm. However, the incidence of PTC with autoimmune thyroid disease (AITD) varies between studies. This study aims to investigate whether patients with AITD have increased incidence of PTC. We also analyzed the relationship of serum thyroid-stimulating hormone (TSH) levels and PTC in relation to AITD based on histopathological data. Methods. A total of 533 participants who underwent thyroidectomy were enrolled in this retrospective study based on clinicohistopathological data and known thyroid autoantibodies. Patients were divided into PTC and benign groups according to histopathologic diagnosis. Age, gender, body mass index, and serum TSH level before thyroidectomy were recorded. Results. Of the 533 enrolled patients, 159 (29.8%) were diagnosed with PTC, of which 38 (35.5%) had Hashimoto’s thyroiditis (HT). More patients with HT were female, and patients with HT, Graves’ disease, and thyroid nodules with higher TSH level had a higher incidence of PTC. Conclusions. A high proportion of the patients with PTC had HT. There was a trend that a higher serum TSH level was associated with a greater risk of thyroid cancer

Long working range light field microscope with fast scanning multifocal liquid crystal microlens array

The light field microscope has the potential of recording the 3D information of biological specimens in real time with a conventional light source. To further extend the depth of field to broaden its applications, in this paper, we proposed a multifocal high-resistance liquid crystal microlens array instead of the fixed microlens array. The developed multifocal liquid crystal microlens array can provide high quality point spread function in multiple focal lengths. By adjusting the focal length of the liquid crystal microlens array sequentially, the total working range of the light field microscope can be much extended. Furthermore, in our proposed system, the intermediate image was placed in the virtual image space of the microlens array, where the condition of the lenslets numerical aperture was considerably smaller. Consequently, a thin-cell-gap liquid crystal microlens array with fast response time can be implemented for time-multiplexed scanning