A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction

Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers.

EFL Undergraduate Students’ Perspective about the Characteristics of the Effective Instructor at Hashemite University in Jordan

Researchers in different educational fields regard the instructor as an important factor which influences students’ progress. Since students have a direct relationship with the instructor, the researcher has found it necessary to explore their perspectives about the instructors’ characteristics in the teaching-learning context. To achieve this, 190 students responded to a five-point Likert scale questionnaire and 25 responded to an open-end interview question. The researcher used Descriptive statistics, such as the t-test and ANOVA. He also categorized the data obtained from the open-end interview. Results of the study indicated that students attributed the most effective quality in the instructor to knowledge. Results also revealed significant differences in male and female students’ responses to the evaluation attribute category and to the five categories as a whole. With regard to the open-ended interview, results showed that the students’ views differed with their attitudes in terms of focus and agreed in general with students’ views in other research studies.

Research on the Employment of Normal University Students from the Perspective of Public Economics

From the perspective of public economics, this paper discusses the employment of normal college students in China, defines employment as the attribute category of mixed products, and analyzes the reasons for the failure of the employment market of normal college students. The government should strengthen the supply side structural reform of normal students' employment through the incentive mechanism and macro-control, and guide the employment supply of normal students with the actual demand of primary and secondary schools. Normal colleges and universities should strengthen the reform of normal education, improve the employment competitiveness of normal students through the construction of normal professional curriculum system, strengthening professional quality, and carrying out employment guidance.

Discriminating between the Effects of Valence and Salience in the Implicit Association Test

The Implicit Association Test (IAT) is the most widely used indirect measure of attitudes in social psychology. It has been suggested that artefacts such as salience asymmetries and familiarity can influence performance on the IAT. Chang and Mitchell (2009) proposed that the ease with which IAT stimuli are classified (classification fluency) is the common mechanism underlying both of these factors. In the current study, we investigated the effect of classification fluency on the IAT and trialled a measure—the split IAT—for dissociating between the effects of valence and salience in the IAT. Across six experiments, we examined the relationship between target classification fluency and salience asymmetries in the IAT. In the standard IAT, the more fluently classified target category was, all else being equal, compatible with pleasant attributes over unpleasant attributes. Furthermore, the more fluently classified target category was more easily classified with the more salient attribute category in the split IAT, independent of evaluative associations. This suggests that the more fluently classified category is also the more salient target category.

Intentional Faking of the Single Category Implicit Association Test and the Implicit Association Test

The Implicit Association Test (IAT) provides a relative measure of implicit association strengths between target and attribute categories. In contrast, the Single Category Implicit Association Test (SC–IAT) measures association strength with a single attribute category. This can be advantageous if a complementary category—as used in the IAT—cannot be composed or is undesired. If the SC–IAT is to be a meaningful supplement to the IAT, it should meet the same requirements. In an online experiment with a large and heterogeneous sample, the fakability of both implicit measures was investigated when measuring anxiety. Both measures were fakable through specific instruction (e.g., “Slow down your reactions”) but unfakable through nonspecific faking instruction even though nonspecific instruction was given immediately before the critical blocks (e.g., “Alter your reaction times”). When comparing the methodological quality of both implicit measures, the SC–IAT had lower internal consistency than the IAT. Moreover, with specific faking instructions, the SC–IAT was possible to fake to a larger extent than the IAT.

Single-Attribute Implicit Association Tests (SA-IAT) for the Assessment of Unipolar Constructs

A major problem with Implicit Association Tests (IATs) is that they require bipolar attributes (e.g., good-bad). Thus, IAT effects for an attribute category can be interpreted only relative to an opposite category. Problems arise if there is no clear opposite category; in this case, a neutral category can be used, although it induces systematic error variance and thus reduces validity. The present study suggests that this problem can be solved using single-attribute IATs (SA-IATs). Sociosexuality (the tendency to engage in uncommitted sex) was expected to be related at the implicit level to stronger stranger-sex associations relative to partner-sex associations. An IAT was constructed that used conversation as a neutral attribute; it showed satisfactory reliability but only low correlations with explicit sociosexuality. An alternative SA-IAT with sex as the only attribute showed a similar reliability but higher correlations with explicit sociosexuality.