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With the growing influence of online social media, firms increasingly take an active role in interacting with consumers in social media. For many firms, their first step in online social media is management responses, where the management responds to customers' comments about the firm or its products and services. In this article, we measure the impact of management responses on customer satisfaction using data retrieved from a major online travel agency in China. Applying a panel data model that controls for regression toward the mean and heterogeneity in individual preference for hotels, we find that online management responses are highly effective among low satisfaction customers but have limited influence on other customers. Moreover, we show that the public nature of online management responses introduces a new dynamic among customers. Although online management responses increase future satisfaction of the complaining customers who receive the responses, they decrease future satisfaction of complaining customers who observe but do not receive management responses. The result is consistent with the peer‐induced fairness theory.

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Background Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas. Methods This single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The Tree-based Pipeline Optimization Tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomics features. We compared the performance of 10 independent TPOT-generated models based on training and testing cohorts using the area under the curve (AUC) and average precision to obtain the final model. An independent cohort of 22 patients was used to validate the best model. Results Ten prediction models were generated by TPOT, and the accuracy obtained with the best pipeline ranged from 0.788 to 0.867 for the training cohort and from 0.60 to 0.84 for the testing cohort. After comparison, the AUC value and average precision of the final model were 0.903 and 0.911 in the testing cohort, respectively. In the validation set, the AUC was 0.85, and the average precision was 0.855 for the best model. Conclusions The autoML classifier using radiomics features of conventional MR images provides high discriminatory accuracy in predicting the H3 K27M mutation status of midline glioma.

Purpose -This study aims to examine whether and how hotel class, attributes of the room, quality, location, cleanliness, and service influence room rates in hotels. Design/methodology/approach -Regression models were developed for the hotel industry and for various grades of hotels. Findings -Using data from New York, empirical findings suggest that room quality and location are important determinants of room price for the industry, but attributes that can influence room rates differ greatly among hotel segments. Practical implications -Hotels can reap benefits from understanding customers' specific expectation of a market segment and seeking to provide amenities accordingly. Originality/value -The quality of hotel attributes is evidenced through customer reviews on a travel advice website. The theory of the hierarchy of needs is supported in the hotel industry, namely, the ascending order of accommodation needs are the quality of a room, location, and service.

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Diffuse midline glioma, H3 K27M-mutant (H3 K27Mmt DMG), is a rare and highly aggressive tumor that is more common in children than in adults. Few studies have compared the differences between pediatric and adult patients with this rare tumor. We here report our retrospective study of 94 adult and 70 pediatric cases of diffuse midline glioma. Surgical tumor samples were analyzed by routine histopathology and immunohistochemistry for H3 K27M, IDH1 R132H, ATRX, p53, OLIG2, glial fibrillary acidic protein, and Ki-67; Sanger sequencing for hot mutation spots in genes including H3F3A, HIST1H3B, IDH1, IDH2, TERT, and BRAF; and methylation-specific polymerase chain reaction for O 6 -methylguanine DNA methyltransferase promoter methylation. The most frequent anatomic locations in adult and pediatric patients were the thalamus and brainstem, respectively. Molecular profiling revealed higher frequencies of ATRX loss and H3.3 mutation in adult than in pediatric H3 K27M-mt DMGs. TERT promoter mutations and O 6methylguanine DNA methyltransferase promoter methylation were not detected in pediatric patients but were present in a few adult patients. During the follow-up period, 93/122 patients (70.1%) died from the disease, with a median survival time of 10.5 months (range: 1 to 104 mo). Kaplan-Meier analyses demonstrated that the prognosis was better for adult patients than the pediatric cohort (P = 0.0003). Multivariate analyses indicated that patient age, primary tumor size, status of ATRX expression, and Ki-67 index were independent prognosticators. The present study showed that there were differences between adult and pediatric H3 K27M-mt DMGs in terms of the anatomic location of tumor, molecular changes, and prognosis.