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This study investigates the media activities of firms issuing private equity placements and seasoned equity offerings in Taiwan, as firms have incentives to manage media coverage to influence their stock prices during private equity placement.

We collect a corpus of news stories and transform the news into term sets based on the part of speech. Then, we refer to Cecchini et al. (2010) to classify the news terms into positive, negative, and usual categories. Next, we employ the SVM algorithm to perform the classification tasks and the term frequency method to perform the text mining task. In last, we use a multiple regression model to verify the hypotheses.

We determine that issuing firms in a private placement have substantially more positive news stories and fewer negative news stories than those in public offerings. Furthermore, we evidence that the media management effects of postequity issues are more active than those of preequity issues. Finally, our results demonstrate that the timing and content of financial media coverage among different equity issuance methods may be biased by firm management. According to previous studies, they may attempt to manipulate stock prices to increase the number of highly profitable insider stakeholders.

To our knowledge, this is the first study to investigate that if private placement will associate with more active media management than the public offerings. According to our results of the difference-in-means test, the public offerings market may control news coverage; however, this result is inconsistent with that of the regression results. The private placements market may also exercise media management in the “before announcement day” and “after announcement day” periods by increasing positive news and reducing negative news.

Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly‐used hybrid models by classification+classification and clustering+classification hybrid approaches, respectively, in terms of customer value prediction.

To construct a hybrid model, multiple techniques are usually combined in a two‐stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre‐process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k‐means and self‐organizing maps for the clustering techniques to construct six different hybrid models.

The experimental results over a real case dataset show that the classification+classification hybrid approach performs the best. In particular, combining two‐stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/0.43 percent).

The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.

The purpose of this paper is to propose an integrated fuzzy approach to determine important universal usability problems (UUPs) by providing experts who behave like real users and to establish a work plan to correct the most important ones.

In this study, a fuzzy multi-criteria decision-making approach with three stages is proposed for the evaluation of universal usability. At the first stage, UUPs are identified by performing modified heuristic evaluation, and severity rating of each problem is determined by experts. At the second stage, critical problems are specified by applying the fuzzy Delphi considering these severity ratings. At the third stage, Fuzzy Decision Making Trial and Evaluation Laboratory approach is applied to prioritize critical problems as sub and main criteria. An illustrative example related to emergency service is performed to apply the proposed approach.

Results showed that the elevator button design, the elevator emergency button design and the position of the floor signboard are the first three problems that should be primarily improved as sub-criteria. In terms of main criteria, equitable use, simple and intuitive use, and perceptible information are the first three main criteria that should be improve in emergency service.

This study is original in terms of methodology and providing a new perspective for building design evaluation. The results can help the designers to see the UUPs in buildings, to focus the most important UUPs and to establish improvement ranking. These advantages provide time and cost-effective design improvement actions.

Because presentation formats, i.e. table v. graph, in shopping web sites may promote or inhibit deliberate consumer decision making, it is important to understand the effects of information presentation on deliberateness. This paper seeks to empirically test whether the table format enhances deliberate decision making, while the web map weakens the process. In addition, deliberateness can be influenced by the decision orientation, i.e. emotionally charged or accuracy oriented. Thus, the paper further examines the effect of presentations across these two decision orientations.

Objective and detailed description of the decision process is used to examine the effects. A two (decision orientation: positive emotion v. accuracy) by two (presentation: map v. table) eye‐tracking experiment is designed. Deliberateness is quantified with the information processing pattern summarized from eye movement data. Participants are required to make preferential choices from simple decision tasks.

The results confirm that the table strengthens while the map weakens deliberateness. In addition, this effect is mostly evident across the two decision orientations. An explorative factor analysis further reveals that there are two major attention distribution functions (global v. local) underlying the decision process.

Only simple decision tasks are used in the present study and therefore complex tasks should be introduced to examine the effects in the future.

For consumers, they should become aware that the table facilitates while the map diminishes deliberateness. For web businesses, they may try to strengthen the impulsivity in a web map filled with emotional stimuli.

This research is one of the first attempts to investigate the joint effects of presentations and decision orientations on decision deliberateness in the internet domain. The eye movement data are also valuable because previous studies seldom provided such detailed description of the decision process.

This paper aims to propose an enhanced algorithm and used to decision-making that specifically focuses on the choice of a domain in the calculation of degree of greyness according to the principle of grey numbers operation. The domain means the emerging background of interval grey numbers, it is vital for the operational mechanism of such interval grey numbers. However, the criteria of selection of domain always remain same that is not only for the calculated grey numbers but also for the resultant grey numbers, which can be assumed as unrealistic up to a certain extent.

The existence of interval grey number operation based on kernel and the degree of greyness containing two calculation aspects, which are kernel and the degree of greyness. For the degree of greyness, it includes concepts of domain and calculation of the domain. The concepts of a domain are defined. The enhanced algorithm is also comprised of four deductive theorems and eight rules that are linked to the properties of the enhanced algorithm of the interval grey numbers based on the kernel and the degree of greyness.

Aiming to improve the algorithm of the degree of greyness for interval grey numbers, based on the variation of domain in the operation process, the degree of greyness of the operation result is defined in this paper, and the specific expressions for algebraic operations are given, which is relevant to the kernel, the degree of greyness and the domain. Then, these expressions are used to the algorithm of interval grey numbers based on the kernel and the degree of greyness, improving the accuracy of the operation results.

The enhanced algorithm in this paper can effectively reduce the loss of information in the operation process, so as to avoid the situation where the decision values are the same and scientific decisions cannot be made during the grey evaluation and decision-making process.

Service innovation, ICT-enabled services, museum, cultural and creative industries.

Graduate-level courses of “Innovation Management,” “Service Innovation,” or “Cultural and Creative Industries”.

In 2006, the National Palace Museum (NPM) in Taipei, Taiwan, announced its new vision “Reviving the Charm of an Ancient Collection and Creating New values for Generations to Come”. In recent years, the NPM has been shifting its operational focus from being object-oriented to being public-centered, and the museum has held not only the physical forms of artifacts and documents but also their digital images and metadata. These changes would inject new life into historical artifacts. In addition, archives as its collections would be given a refreshingly new image to the public and become connected with people's daily lives. Among these endeavors for displaying historical artifacts online and prevailing Chinese culture in the modern age, the key issues are related to digital technology applications and service innovations. The service innovations would be further divided into information and communication technologies (ICT)-enabled ones and non-ICT-enabled ones. These shifts clearly claim that adopting digital technologies and innovative services can bring positive impacts to the museum. The NPM administrative team wants to keep infusing life into ancient artifacts and texts, sustaining curiosities of the public for Chinese culture and history, and invoking their interests to visit the NPM in person. However, to develop for the future while reviewing the past, the NPM administrative team has to meditate on the next steps in terms of implementation of service innovations.

Students will learn motivations of digital establishment and service innovations from the organization perspective and the necessities of technological implementation. Students will understand the difference in innovations between ICT-enabled services and non-ICT-enabled services. Students would be able to understand the process of developing a new service. Students will be aware of challenges the organization would face in developing a new service.

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The empirical modelling of major rapid prototyping (RP) processes such as fused deposition modelling (FDM), selective laser sintering (SLS) and stereolithography (SL) has attracted the attention of researchers in view of their contribution to the overall cost of the product. Empirical modelling techniques such as artificial neural network (ANN) and regression analysis have been paid considerable attention. In this paper, a powerful modelling technique using genetic programming (GP) for modelling the FDM process is introduced and the issues related to the empirical modelling of RP processes are discussed. The present work aims to investigate the performance of various potential empirical modelling techniques so that the choice of an appropriate modelling technique for a given RP process can be made. The paper aims to discuss these issues.

Apart from the study of applications of empirical modelling techniques on RP processes, a multigene GP is applied to predict the compressive strength of a FDM part based on five given input process parameters. The parameter setting for GP is determined using trial and experimental runs. The performance of the GP model is compared to those of neural networks and regression analysis.

The GP approach provides a model in the form of a mathematical equation reflecting the relationship between the compressive strength and five given input parameters. The performance of ANN is found to be better than those of GP and regression, showing the effectiveness of ANN in predicting the performance characteristics of the FDM part. The GP is able to identify the significant input parameters that comply with those of an earlier study. The distinct advantages of GP as compared to ANN and regression are highlighted. Several vital issues related to the empirical modelling of RP processes are also highlighted in the end.

For the first time, a review of the application of empirical modelling techniques on RP processes is undertaken and a new GP method for modelling the FDM process is introduced. The performance of potential empirical modelling techniques for modelling RP processes is evaluated. This is an important step in modernising the era of empirical modelling of RP processes.