دانلود مقاله ارزیابی کیفیت خدمات و بهبود خدمات با بررسی آنلاین
چکیده
مقدمه
مروری بر مطالعات
چارچوب و روش تحقیق
توضیحات و پیش پردازش داده ها
آزمایش
نتیجه گیری
رعایت استانداردهای اخلاقی
منابع
Abstract
Introduction
Literature review
Research framework and methodology
Data description and preprocessing
Experiments
Conclusions
Compliance with ethical standards
References
چکیده
در عصر داده های بزرگ، ارزیابی کیفیت خدمات با استفاده از بررسی های آنلاین به یک موضوع محبوب تبدیل شده است. با این حال، مطالعات بسیار کمی به طور همزمان بر ارزیابی کیفیت خدمات و بهبود خدمات تمرکز می کنند. در این مطالعه، یک چارچوب تحقیقاتی برای ارزیابی کیفیت خدمات و بهبود خدمات پیشنهاد شده است، از تحلیل احساسات برای استخراج امتیازهای زمانی ویژگی‌های خدمات هر زیربعد از مدل کیفیت خدمات از بررسی‌های آنلاین، و یک شبکه حافظه کوتاه‌مدت استفاده می‌شود. برای پیش بینی نمرات ارائه دهنده کیفیت خدمات استفاده می شود. علاوه بر این، یک تحلیل حساسیت مبتنی بر حافظه کوتاه‌مدت شبکه، همراه با هزینه‌های بهبود، برای رتبه‌بندی ابعاد فرعی در مدل کیفیت خدمات استفاده می‌شود. سپس، استراتژی های بهبود خدمات با توجه به رتبه بندی ویژگی های خدمات تعیین می شود. بررسی آنلاین هتل ها برای بررسی اثربخشی چارچوب پیشنهادی استفاده شد. مجموعه ای از استراتژی های بهبود خدمات برای ویژگی های خدمات خاص ارائه شده است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
In the era of big data, service quality evaluation using online reviews has become a popular topic. However, very few studies focus simultaneously on service quality evaluation and service improvement. In this study, a research framework for service quality evaluation and service improvement is proposed, sentiment analysis is used to extract the temporal scores of the service attributes of each subdimension of the service quality model from online reviews, and a long short-term memory network is used to predict the scores for the service quality provider. Furthermore, a long short-term memory network-based sensitivity analysis, in conjunction with improvement costs, is used to rank the subdimensions in the service quality model. Then, service improvement strategies are determined according to the rankings of the service attributes. Hotels’ online reviews were used to investigate the effectiveness of the proposed framework. A series of service improvement strategies for the specific service attributes are provided.
Introduction
Compared with a physical product, a service has the characteristics of intangibility, heterogeneity and inseparability, which make it difficult to evaluate service quality (Parasuraman et al., 1985). Comprehensive evaluation methods are usually applied to aggregate the scores for each dimension of a service quality model to obtain the overall service quality evaluation result. Representative comprehensive evaluation methods include the analytic hierarchy process (Yucesan and Gul, 2020), fuzzy comprehensive evaluation (Wei et al. 2015), the technique of order preference by similarity to the ideal solution (TOPSIS) (Yucesan and Gul, 2020), the decisions making trial and evaluation laboratory (DEMATEL) (Tseng, 2009), and data envelopment analysis (DEA) (Lee and Kim, 2014). These methods of service quality evaluation have the shortcoming of relying on experts’ subjective scoring data and often lack sufficient samples (Wei et al. 2015). In addition, the Kano model (Hsu et al., 2018; Bi et al., 2019; Qi et al., 2016) and importance-performance analysis (IPA) (Deng et al., 2008) are often used for service quality evaluation. In addition to comprehensive evaluation methods, multivariate regression is applied to rank the importance of service quality dimensions (Palese and Usai, 2018).
Conclusions
In this study, a research framework for service quality evaluation and service improvement using online reviews and the hierarchical service quality model is developed. The hierarchical service quality model is combined with online reviews and text mining technology to effectively obtain the scores of the subdimensions in the service quality model. LSTM is used for service quality evaluation, and LSTM-based sensitivity analysis is used to rank the subdimensions in the service quality model. Then, service improvement strategies are obtained by considering the score measures of subdimensions in the service quality model, the interest degree of the service attributes in each subdimension and the reasons for dissatisfaction mined from online reviews. The results of the online reviews of hotels show that LSTM obtained better prediction results than the RNN and ANN. Moreover, the rankings of the primary dimensions and the subdimensions in the service quality model and the service attributes extracted from the online reviews were reported, and a series of service improvement strategies for the specific service attributes were provided. From the results, emerging service attributes such as the Wi-Fi, food and methods of payment are the most important attributes to be improved, and the sub-dimensions of “design” and “waiting time” reflect the customer’s requirements and should also be improved; in contrast, the “tangibles” subdimension has a low improvement score.