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The purpose of this paper, conducted at Penn State University, is to inform a redesign of the library facility integrating a Collaboration Commons projected to cost over $20m.

A mixed-methods design comprised of observation, focus groups, conversations with students, interviews with Knowledge Commons personnel and a UX Café was employed. Researchers investigated the students’ need for workspaces and soft-seating.

Findings suggested that students generally come to the libraries with a goal of being productive and they value the productivity generated by spacious and well-designed workspaces over the comfort of soft-seating. Students desire an increase in the availability of workspaces.

These findings informed facility enhancement recommendations, and have been integrated into the program statement made available to design firms bidding on the renovation project.

Social networking services (SNS) empower users with a robust capability to connect with others and manage their social relationships. However, as the size of users’ social networks increases, coupled with the inherent boundary-spanning technical features of SNS, users are faced with unprecedented role stresses. This, in turn, leads to maladaptive lurking decisions. This study delves into the mechanism of this technology-induced decision-making process among SNS users.

Survey data were collected from 491 Chinese WeChat Moment users. The model and hypotheses testing were conducted using SmartPLS 4.0.

Our findings indicate that both social network size and boundary spanning have a positive influence on role conflict and role overload. Both role conflict and role overload significantly contribute to SNS fatigue, which further intensifies users’ lurking intention. Furthermore, SNS fatigue fully mediated the relationship between role conflict and lurking intention, and partially mediated the relationship between role overload and lurking intention.

Our study offers a fresh viewpoint for comprehending lurking behaviors on SNS, furnishing practical insights for platform providers. Additionally, it paves the way for future research into the deeper mechanisms driving SNS lurking behaviors, by providing a novel construct (i.e. boundary spanning) to distinguish and measure the unique social environment of SNS.

The purpose of this paper is to investigate the perceived role of library personnel in supporting first-generation students at Penn State University Libraries, and also how students’ perceptions of library personnel change over time, and the various experiences that influenced their changes in perception.

This study employed focus groups to solicit input from first-generation students. A four-step team-based approach to qualitative coding process was developed including the development of a codebook informed by common themes and concepts drawn from the literature.

Findings indicate that operating from a deficit of library cultural capital often results in low awareness of available services and changes in perception are more influenced by personal exploration than limited interactions with personnel. Further, while currently employed interventions are well targeted, opportunities exist for enhancing efforts.

As this is a case study, the findings are not generalizable. Per conducting only four focus groups, the experiences of participants may not represent the typical scope of personnel-related interactions.

This study adds to the limited body of evidence that first-generation students’ struggle from a deficit of library-related cultural capital.

Owing to some uncontrollable factors, only a portion of an experiment can be completed. Such incomplete data are generally referred to as censored data. Conventional approaches for analysis of censored data are computationally complicated. In this work an effective means of applying neural networks to analyze an experiment with singly‐censored data is presented. Two procedures are developed, which are simpler than conventional ones such as maximum likelihood estimation and Taguchi’s minute accumulating analysis. In addition, three numerical examples are presented to compare the proposed procedures with the conventional ones. Those comparisons reveal that proposed procedures are effective and feasible.

The relationships among multi‐dimensional data (such as medical examination data) with ambibuity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back‐propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi‐dimensional feature selection. The first one proposed is a feature selection procedure from the trained back‐propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis‐Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi’s fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD; it can deal with a reduced model, which is the focus of this paper. In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

Following the trend of global telecommunication liberalization, the telecommunication industry in Taiwan will be a star industry and grow up quickly in the future. This study uses questionnaires to survey the implementation issues of total quality management in Taiwan’s telecommunication industry. Most of the thirty‐nine telecommunication companies surveyed herein are medium‐sized with fairly weak quality performance. Advanced quality management training programs (such as experimental design) are seldom implemented and decision‐makers do not pay much attention to training programs related to TQM.

Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis‐Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach’s effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

Describes how a statistical Taguchi approach and a backpropagation neural network model were devised to evaluate the effect of various parameters and identify the optimal parameter setup values in a gas‐assisted injection moulding process. In applying the Taguchi approach, an L18 orthogonal array was employed to collect the observations, and the same collected data sets, with two additional inputs, were utilized to construct a neural network model to ascertain whether utilizing such a neural network would provide an improved generalization capability over a statistical method. The effect of the learning rate and the number of hidden nodes on the efficiency of the neural network learning algorithm was extensively studied to identify what provides the best forecasting of performance measure. In addition, to verify the generalization capability of the neural model, eight different parameter setups, which had not been included in the full factorial design, were constructed for network testing. The results revealed that the network is more efficient in identifying the real optimal parameter setup.

Customer satisfaction is one of the important goals of Total Quality Management (TQM). The outcome of a customer satisfaction activity has a decisive effect on an enterprise’s operational performance and its future development. For ordinary enterprises, their failure to establish a reasonable “Customer Satisfaction Evaluation System” that manages customers’ real voices and reflects the performance of a customer satisfaction activity has resulted in an unneccessary waste of their resources and a decline within their operational performance. This article will present a framework for an evaluation system for a customer satisfaction activity by elucidating the rationalization of the said framework through the analysis of a successful case model that demonstrates excellence in management. It will further illustrate the effectiveness of the “Evaluation System” operational process to elevate an enterprise’s willingness to develop a customer satisfaction activity and stimulate its progress toward success.