PurposeThe purpose of this paper is to detect predefined service attributes and their sentiments from online restaurant reviews, and then to measure the effects of customer sentiments toward service attributes on customer satisfaction (CS) and revisit intention (RVI) simultaneously.Design/methodology/approachThis study proposed a supervised framework to model CS and RVI simultaneously from restaurant reviews. Specifically, the authors detected the predefined service dimensions from online reviews based on random forest. Then, the sentiment polarities of the reviews toward each predefined dimension were identified using light-gradient boosting machine (LightGBM). Finally, the effects of attribute-specific sentiments on CS and RVI were evaluated by a bagged neural network-based model. The proposed framework was evaluated by 305,000 restaurant comments collected from DianPing.com, a Yelp-like website in China.FindingsThe authors obtained a hierarchal importance order of the investigated service themes (i.e. location, service, environment, price and food). The authors found that food played the most important role in affecting both CS and RVI. The most salient attribute with respect to each service theme was also identified.Originality/valueUnlike prior work relying on the data collected from surveys, this study is among the first to model the relationship among service attributes, CS and RVI simultaneously from real-world data. The authors established a hierarchal structure of eighteen attributes within five service themes and estimated their effects on both CS and RVI, which will broaden our understanding of customer perception and behavioral intention during service consumption.
In online education, the appropriate choice of means of knowledge visualization can reduce cognitive load and improve cognitive efficiency. However, no universal basis for selection can cause confusion in the pedagogical context. This study used the revised Bloom’s taxonomy to combine the types of knowledge with cognitive goals. We used a course on marketing research as an example to summarize the choices for visualizing factual knowledge (FK), conceptual knowledge (CK), procedural knowledge (PK), and metacognitive knowledge (MK) through four experiments. Visualized cognitive stages were used to determine the cognitive efficiencies of visualization for different knowledge types. In this stage, eye tracking is used for collecting eye movement indicators to measure cognitive load. The cognitive goals stage is used to get cognitive goals of the means of knowledge visualization. Combining the two stages, we get the conclusions as follows: Teachers and students can mostly benefit from presenting FK and CK points via mind maps. Using mind maps to teach FK online could be indirectly beneficial for improving students’ creativity. Concept maps may be chosen for this point if the linked knowledge points are PK and the achievement of the analytical objective is emphasized in the student’s knowledge points. The flowchart can be used to display PK, while timelines could be utilized if the PK point is to be presented in a temporal dimension. Teachers should choose the curve area chart to display MK. A pie chart might be chosen and added more instructions. The findings suggest that mind maps are very effective as a means of knowledge visualization in online education. In the meantime, it suggests that overly simplistic graphs increase cognitive load, while it also raises the possibility that redundant information in the text may increase cognitive load.
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