شناسایی عناصر سازنده «ارزش پیشنهادی به مشتری» و تأثیر آنها بر رضایت مشتری با استفاده از تحلیل احساسات بر مبنای متن‌کاوی

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دکتری، گروه مدیریت بازرگانی، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران.

2 دانشیار، گروه مدیریت بازرگانی، دانشکده علـوم اداری و اقتصـاد، دانشگاه اصفهان، اصفهان، ایران.

چکیده

هدف: در سال‌های اخیر با توجه به گسترش کسب‌وکار‌های دیجیتال و حضور مشتریان در این محیط، امکان دسترسی به داده‌های مربوط به علایق و ارزش‌های مورد انتظار مشتریان فراهم شده است. هدف این پژوهش، شناسایی عناصر سازنده ارزش پیشنهادی به مشتری و بررسی تأثیر آنها بر رضایت مشتری با استفاده از روش تحلیل احساسات بر مبنای متن‌کاویِ محتوای تولیدشده توسط کاربران در فضای مجازی است.
روش: این پژوهش از نظر هدف، کاربردی، از نظر ماهیت، توصیفی ـ پیمایشی، از نظر اجرا، کیفی ـ کمی (آمیخته) با رویکرد استقرایی است. در پژوهش حاضر، کالای تلویزیون هوشمند، به‌عنوان مطالعه موردی انتخاب شد و 3865 نظر از خریداران این کالا از سایت فروشگاه آنلاین دیجی‌کالا جمع‌آوری و با استفاده از روش تحلیل احساسات تجزیه و تحلیل شد. برای طبقه‌بندی و جداسازی نظرهای مثبت و منفی، از روش‌های «یادگیری ماشین» استفاده شد و برای استخراج سازه‌های ارزش پیشنهادی، روش «تخصیص پنهان دریکله» به‌کار رفت.
یافته‌ها: بر اساس یافته‌های پژوهش، برای ارزش پیشنهادی به مشتری در رابطه با کالای تلویزیون هوشمند، 30 عنصر شناسایی شد. نتایج پژوهش نشان می‌دهد که 15 عنصر یا ویژگی محصول، بر رضایت مشتریان تأثیر مثبت دارد و 15 عنصر دیگر، به نارضایتی مشتریان منجر می‌شود.
نتیجه‌گیری: نتایج پژوهش نشان می‌دهد که تحلیل نظرهای مشتریان و به‌ بیانی «محتوای تولیدشده توسط کاربران»، برای شناخت و بررسی نگرش مشتری در رابطه با محصول، روشی کاربردی بوده و برای کسب‌وکارها در راستای ارائه محصول موفق با ویژگی‌های مورد علاقه مصرف‌کنندگان به بازار، ابزاری مؤثر است و با استفاده از آن توسط یادگیری ماشین، می‌توان در راستای تسهیل، افزایش دقت و سرعت شناسایی نیاز مشتری و خلق مشترک ارزش، گام‌های بزرگی برداشت.

کلیدواژه‌ها


عنوان مقاله [English]

Identifying the Constructive Elements of “Value Proposition” and their Impact on Customers’ Satisfaction using Sentiment Analysis based on Text Mining

نویسندگان [English]

  • Sayed Mohsen Mousavi 1
  • Seyed Fathollah Amiri Aghdaie 2
1 Ph.D., Department of Business Management, Faculty of Administrative Sciences & Economics, University of Isfahan, Isfahan, Iran.
2 Associate Prof., Department of Business Management, Faculty of Administrative Sciences & Economics, University of Isfahan, Isfahan, Iran.
چکیده [English]

Objective: Because of the expansion of digital businesses and the customers’ involvement in this environment in recent years, it is possible to access the data related to the interests and values ​​expected by customers. A review of the previous local and international studies in the field of "value proposition" using "sentiment analysis" based on text mining showed that no independent research has been conducted in this field. Although the study of value proposition plays an important role, this concept is still poorly understood and there is a gap accordignly. Finally, it can be said that using the potential capabilities of analyzing the data produced by the customers on the Internet can be an important step in advancing the goals of businesses in the present competitive market. The purpose of this study is to properly use the large volume of online users’ feedback in order to discover, analyze value dimensions, and examine their impact on customer satisfaction. In this study, the product of "smart TV" has been selected as a case study in order to identify and investigate the features and characteristics of a product and its effect on customer satisfaction. Therefore, the main questions of this research are: 1) what are the components of value proposition? and 2) what is the role of each value element in the feeling of satisfaction or dissatisfaction of the buyers?
 
Methodology: This research is applied in terms of purpose, descriptive-survey in nature, and is conducted based on qualitative-quantitative (mixed) method using an inductive approach. For this purpose, the smart TV product was selected as the case study and 3865 comments by the buyers were collected from the Digikala online store website and were then analyzed using the sentiment analysis method. "Machine learning" method was used in order to rank and classify the positive and negative comments; the "Drikle hidden allocation" method was also used to extract the value constructs.
 
Findings: Based on the research findings, 30 elements were identified for the value proposition in relation to smart TV products. The research results showed that 15 elements or product features had a positive effect on customer satisfaction and the other 15 elements lead to customer dissatisfaction. Positive constructs of value proposition included: positive feeling toward the product, screen size, product brand, reasonable price, excitement about the product, features and options, overall product quality, how to buy and when to receive the product, relative economic value, sound quality, overall product performance, other customrs' opinions, model and the appearance, installation services, and easy adjustments. Negative constructs of value proposition included: low economic value, low product efficiency, poor appearance, poor support for accessories, dissatisfaction with the price, low quality of the product, display problems, dissatisfaction with installation and operation, improper packaging, warranty problems, no guarantee for returning the goods, lack of accessories on the product, useless and additional options, improper remote control, as well as late and improper delivery of the product.
 
Conclusion: The results of the study showed that the analysis of the customers’ feedback (i.e. "content produced by users") is a practical way to identify and examine customers’ attitudes towards the products. It is also an effective tool for the businesses to propose a successful product tailored at the consumers’ characteristics. The implementation of machine learning can lead to great steps toward facilitating, increasing the accuracy and speed in identifying customers’ needs, and creating shared value.

کلیدواژه‌ها [English]

  • Proposition Value
  • Customer Satisfaction
  • Text mining
  • Sentiment analysis
  • Topic Extraction
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