The frontline employees of the service industry are the first connection between enterprises and consumers. Therefore, their performance often represents the image of the company. This study intended to discuss employees’ sustained work behavior through the perceived organizational climate, from the point of view of direct supervisors’ leadership. Employees of chain convenience stores in Taiwan were used as the research samples for the questionnaire survey. A total of 473 valid questionnaires were considered using structural equation analyses. The results showed that authoritarian leadership and employees’ turnover intentions had a significant positive relationship; moreover, there were negative relations between moral leadership, benevolent leadership, and employees’ turnover intention. Thus, employees’ perceived playfulness can decrease turnover intention when under paternalistic leadership. This study provides valuable insights for managers to understand the work value of playfulness.

Instance is important in data analysis and mining; it filters out unrepresentative, redundant, or noisy data from a given training set to obtain effective model learning. Various instance selection algorithms are proposed in the literature, and their potential and applicability in data cleaning and preprocessing steps are demonstrated. For multiclass classification datasets, the existing instance selection algorithms must deal with all the instances across the different classes simultaneously to produce a reduced training set.Generally, every multiclass classification dataset can be regarded as a complex domain problem, which can be effectively solved using the divide-and-conquer principle. In this study, the one-versus-all (OVA) and one-versus-one (OVO) decomposition approaches were used to decompose a multiclass dataset into multiple binary class datasets. These approaches have been widely employed when constructing the classifier but have never been considered in instance selection. The results of instance selection performance obtained with the OVA, OVO, and baseline approaches were assessed and compared for 20 different domain multiclass datasets as the first study and five medical domain datasets as the validation study. Furthermore, three instance selection algorithms were compared, including IB3, DROP3, and GA. The results demonstrate that using the OVO approach to perform instance selection can make the support vector machine (SVM) and k-nearest neighbour (k-NN) classifiers perform significantly better than the OVA and baseline approaches in terms of the area under the ROC curve (AUC) rate, regardless of the instance selection algorithm used. Moreover, the OVO approach can provide reasonably good data reduction rates and processing times, which are all better than those of the OVA approach.