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For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

The argument that terror perception and individual time perspectives can affect behaviors has become increasingly relevant. This study analyzed the association of terror perception with financial risk and developed an integrated framework of the interaction between terror perception (mortality salience) and time perspective to determine risky investment. People with different time perspectives have different acknowledgments of risk. Two studies of terror perception leading to more or less risky investment choices supported this framework to demonstrate that individuals exhibit more (less) risky investment behavior when terror perception is salient and they have a high (low) present hedonic time perspective.

Two experiments were performed. Study 1 tested whether TP moderates the effects of terror priming (aging) on risky financial investment. Study 2 tested the effects of terror priming on risky financial investment decisions and the role of gender and the present hedonic TP (PH) in moderating these effects. The following hypotheses were then developed: H1: Participants with high PH primed with the aging condition will select riskier financial investments than those with low PH will (Study 1). H2: Male participants with higher PH primed in the death condition will select riskier financial investments than those in the control condition will (Study 2).

Because people with different TPs acknowledge risk differently, this article developed a new framework modeling MS on TP in risky investment selection. Two studies representative of MS affecting risky investment decision-making were conducted to test the framework. The results of the two studies indicated that individuals engaged in risky investment behavior more (less) when mortality was salient and the individual had high (low) PH. Moreover, gender influences these results. As anticipated, male participants with higher PH in the death-primed condition selected riskier financial investment than those in the control condition did. Additional research from a broader perspective is warranted.

Many psychological factors of risky decision-making have been discussed, such as motivational systems or situational motivation, self-framing, control orientation, sensation seeking, dishonesty, and ambiguity (Sekścińska et al., 2016). However, few studies have examined temporal framing, also known as time perspective (TP), the psychological concept of a person's relationship with time. This study mainly discusses the effects of terror perception on risky financial decisions by using a theoretical framework based on TP, which influences the effects of MS on risky financial decision-making. When mortality is made salient, individuals with present hedonic TP have behaviors characterized by risk perception. Those with present hedonic TP are more likely to make risky financial investments because they prefer present pleasure and risk over future outcomes (Zimbardo and Boyd, 2015).

The purpose of this paper is to study the breather waves, rogue waves and solitary waves of an extended (3 + 1)-dimensional Kadomtsev–Petviashvili (KP) equation, which can be used to depict many nonlinear phenomena in fluid dynamics and plasma physics.

The authors apply the Bell’s polynomial approach, the homoclinic test technique and Hirota’s bilinear method to find the breather waves, rogue waves and solitary waves of the extended (3 + 1)-dimensional KP equation.

The results imply that the extended (3 + 1)-dimensional KP equation has breather wave, rogue wave and solitary wave solutions. Meanwhile, the authors provide the graphical analysis of such solutions to better understand their dynamical behavior.

These results may help us to further study the local structure and the interaction of solutions in KP-type equations. The authors hope that the results provided in this work can help enrich the dynamic behavior of such equations.

This study aims to develop a new method to treat the numerical singularity at the critical nodes of two skew coordinates, and optimize the leakage of micro herringbone grooved journal bearings (MHGJBs) with this method.

A side leakage numerical algorithm is proposed by using the skew meshes with a virtual node (SMVN) method to evaluate the effects of groove angle, bank/groove ratio, groove depth and groove number on load capacity, friction and side leakage of MHGJB.

The SMVN method is effective in treating the numerical singularity at the critical nodes of two skew coordinates. Besides, a group of optimized parameters of micro herringbone groove is obtained which can not only minimize the side leakage but also improve the load capacity and friction force.

A virtual node method was proposed, which can significantly improve the calculation accuracy in the side leakage model.

Previous studies offer two contradictory propositions for the influence of customer participation on service failure attribution. The purpose of this paper is to solve this theoretical inconsistency by incorporating the concept of self-efficacy into its theoretical framework.

Two 2 (customer participation: high vs low) by 2 (self-efficacy: high vs low) experimental designs were employed under scenarios relating to education and haircut services.

The results show that customers with high self-efficacy attribute more responsibility to the firms for a service failure as their participation in service increases. In contrast, customers with low self-efficacy are less likely to blame firms for service failures in the high-participation condition than in the low-participation condition.

This study suggests that understanding customers’ self-efficacy could help firms improve recovery performance according to customers’ individual differences if service failure occurs.

The findings help resolve conflicting results reported in the literature and show that the impact of customer participation on service failure attribution differs according to customers’ self-efficacy. Therefore, this study provides a theoretical contribution by enhancing the knowledge of how customer participation influences causal attribution and satisfaction after a service failure.

The purpose of this paper is to find the key innovative principles for evaluating the long-term care (LTC) cloud system by exploring contradictory and complex points in its development.

The theory of inventive problem solving (TRIZ) and the decision-making trial and evaluation laboratory (DEMATEL) approaches are integrated to resolve complex contradictions in the system. The heuristic reasoning of TRIZ is applied to obtain innovation principles for an LTC cloud mining system. However, the importance and feasibility of these innovative principles require further assessment. In this study, DEMATEL is employed to clarify the complex relationships among the principles and evaluate their key influences.

This paper identifies six primary contradictions and derives 25 innovative principles for the resolution of these conflicts. Further analysis confirms three key innovative principles. First, the government should consider the overall planning of the cloud system platform, followed by the participation of other medical and LTC institutions. Second, the information capability of LTC institutions should be unified by recording the pathology data of care recipients to create an information exchange system. Third, LTC institutions should act in cooperation with medical institutions to provide professional medical capabilities.

The contributions of this paper are two-fold. First, this study provides an integrated methodology integrating the TRIZ and DEMATEL approaches to resolve LTC problems. Second, this research identifies the key innovative principles for developing an LTC cloud system in Taiwan.

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

A cross-platform paradigm (computing model), which combines the graphical user interface of MATLAB and parallel Fortran programming, for fluid-film lubrication analysis is proposed. The purpose of this paper is to take the advantages of effective multithreaded computing of OpenMP and MATLAB’s user-friendly interface and real-time display capability.

A validation of computing performance of MATLAB and Fortran coding for solving two simple sliders by iterative solution methods is conducted. The online display of the particles’ search process is incorporated in the MATLAB coding, and the execution of the air foil bearing optimum design is conducted by using OpenMP multithreaded computing in the background. The optimization analysis is conducted by particle swarm optimization method for an air foil bearing design.

It is found that the MATLAB programs require prolonged execution times than those by using Fortran computing in iterative methods. The execution time of the air foil bearing optimum design is significantly minimized by using the OpenMP computing. As a result, the cross-platform paradigm can provide a useful graphical user interface. And very little code rewritting of the original numerical models is required, which is usually optimized for either serial or parallel computing.

Iterative methods are commonly applied in fluid-film lubrication analyses. In this study, iterative methods are used as the solution methods, which may not be an effective way to compute in the MATLAB’s setting.

In this study, a cross-platform paradigm consisting of a standalone MATLAB and Fortran codes is proposed. The approach combines the best of the two paradigms and each coding can be modified or maintained independently for different applications.

In this study, the authors employ an intellectual-capital based view of the firm to examine the relationship between three bundles of human resource development (HRD) practices (i.e. developmental, constructive and collaborative HRD practices), three dimensions of intellectual capital (i.e. human capital, organizational capital and social capital), and organizational performance improvements. Specifically, the authors investigate the mediating role of intellectual capital in the relationship between HRD practices and changes in organizational performance.

The authors randomly distributed questionnaires to 1,000 HR executives of Taiwanese firms to assess the firms' HRD practices and intellectual capital. Firm performance data in terms of return on assets (ROA) were obtained from the Taiwan Economic Journal (TEJ). To test the model, the authors used the longitudinal data over three years from 213 firms in Taiwan.

The results show that human capital and social capital mediate the relationship between HRD practices (i.e. developmental and collaborative HRD practices) and organizational performance improvements in terms of return-on-assets growth.

This study adds to the empirical evidence regarding whether or not investment in HRD practices can lead to positive changes in financial performance.

The purpose of this paper is to propose a new group decision‐making approach, which can only use simple mathematical calculations to perform a group decision‐making task.

The large column and large row (LCLR) method is designed and applied.

The paper finds four propositions to support LCLR methods to be a simple and effectual means for group decision making.

The aggregated matrix constructed in LCLR methods should be generated from non‐contradictory circles.

Effective group decision‐making results can be obtained by easily used methods, not necessarily by using complex mathematics technology to conduct the task.

The new approach based on LCLR methods proposed in this paper may be one of the most easily used and effective means for group decision making.