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

Document Type : Research Paper

Authors

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.

Abstract

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.

Keywords

References

Abbasi, F., Khadivar, A., & Yazdinejad, M. (2019). A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis. Journal of Information Technology Management, 11(2), 59-78.
Adithya, H. (2013). Customer Satisfaction and Preference of Colour TV Brands–An Empirical Study in Bangalore City. IJSR-International Journal of Scientific Research, 2(2), 220-223.
Ahmadi, Z., PourAshraf, Y., Toulabi, Z. (2018). The Relationships between Sales Culture and Customers’ Perceived value and Performance (the Case of Employees and Customers of Banks and Financial Institutions in Poldokhtar City). Journal of Business Management, 9(4), 689-716. (in Persian)
Ahn, J., & Back, K. J. (2018). Antecedents and consequences of customer brand engagement in integrated resorts. International Journal of Hospitality Management, 75, 144-152.
Almutairi, Y., & Abdullah, M. (2020). IRHM: Inclusive Review Helpfulness Model for Review Helpfulness Prediction in E-commerce Platform. Journal of Information Technology Management, 12(2), 184-197.
Asgari, P. (2014). Fundamentals of Research Methods in the Humanities. Ahwaz: Islamic Azad University Press. (in Persian)
Baldassarre, B., Calabretta, G., Bocken, N. M. P., & Jaskiewicz, T. (2017). Bridging sustainable business model innovation and user-driven innovation: A process for sustainable value proposition design. Journal of Cleaner Production, 147, 175-186.
Ban, H. J., Choi, H., Choi, E. K., Lee, S., & Kim, H. S. (2019). Investigating key attributes in experience and satisfaction of hotel customer using online review data. Sustainability, 11(23), 6570.
Bazargan, A. (2016). Introduction to Qualitative and Mixed Research Methods: A Conventional Approach in the Behavioral Sciences. Tehran: Didar Press. (in Persian)
Bordbar, A., Abdolvand, N., Ghanbartehrani, N., Rajaee Harandi, S. (2019). Developing a Business Model for Social Commerce in Tourism Industry in Iran. Journal of Business Management, 11(4), 895-918. (in Persian)
Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68.
Chen, W., Hoyle, C., & Wassenaar, H. J. (2012). Decision-based design: Integrating consumer preferences into engineering design. Springer Science & Business Media.
Chiu, Y. J., Chen, H. C., Tzeng, G. H., & Shyu, J. Z. (2006). Marketing strategy based on customer behaviour for the LCD-TV. International journal of management and decision making, 7(2-3), 143-165.
Dianati Deilami, Z., Balooti, E., Darvand, R. (2018). Value-Driven Internet Shopping: The Mental Accounting Theory Perspective (case study: DigiKala). Management Accounting, 11(39), 49-65. (in Persian)
Dreisbach, C., Koleck, T. A., Bourne, P. E., & Bakken, S. (2019). A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International journal of medical informatics, 125, 37-46.
Duan, W., Yu, Y., Cao, Q., & Levy, S. (2016). Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly, 57(3), 282-296.
Edvardsson, B., Klaus, P., Payne, A., & Frow, P. (2014). Developing superior value propositions: a strategic marketing imperative. Journal of Service Management.
Eggert, A., Kleinaltenkamp, M., & Kashyap, V. (2019). Mapping value in business markets: An integrative framework. Industrial Marketing Management, 79, 13-20.
Eid, R., & El-Gohary, H. (2014). Muslim tourist perceived value in the hospitality and tourism industry. Journal of Travel Research, 54(6), 774-787.
Gholami, N., Aghaei, N., Mohammad Kazemi, R., Saffari, M. (2019). Value Proposition to the Customer in Sport Business Model. Sport Management Studies, 11(53), 83-98. (in Persian)
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.
Helander, N., Sillanpää, V., Vuori, V., & Uusitalo, O. (2017). Customer Perceived Value—A Key in Marketing of Integrated Solutions. In Strategic Innovative Marketing (pp. 37-42). Springer, Cham.
Itani, O. S., Kassar, A. N., & Loureiro, S. M. C. (2019). Value get, value give: The relationships among perceived value, relationship quality, customer engagement, and value consciousness. International Journal of Hospitality Management, 80, 78-90.
Jami Pour, M., Rahmati, E., Hosseinzadeh, M., Taheri, G. (2019). Desgining a Framework for Acquisition of Competitive Intelligence 0.2 Using Best Worst Method (BWM). Journal of Business Management, 11(3), 651-676. (in Persian)
Keiningham, T., Aksoy, L., Bruce, H. L., Cadet, F., Clennell, N., Hodgkinson, I. R., & Kearney, T. (2020). Customer experience driven business model innovation. Journal of Business Research, 116, 431-440.
Kim, H. S., & Noh, Y. (2019). Elicitation of design factors through big data analysis of online customer reviews for washing machines. Journal of Mechanical Science and Technology, 33(6), 2785-2795.
Kotler, P., & Keller, K. L. (2016). Marketing Management. Harlow, United Kingdom: Pearson.
Kotler, P., Armstrong, G., & Opresnik, M. (2018). Principles of Marketing. Harlow, England: Pearson.
Kumar, V., & Pansari, A. (2016). Competitive advantage through engagement. Journal of marketing research, 53(4), 497-514.
Kwon, W., Lee, M., & Back, K. J. (2020). Exploring the underlying factors of customer value in restaurants: A machine learning approach. International Journal of Hospitality Management, 91, 102643.
Kwong, C. K., Jiang, H., & Luo, X. G. (2016). AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products. Engineering Applications of Artificial Intelligence, 47, 49-60.
Leckie, C., Nyadzayo, M. W., & Johnson, L. W. (2018). Promoting brand engagement behaviors and loyalty through perceived service value and innovativeness. Journal of Services Marketing.
Lee, M., Jeong, M., & Lee, J. (2017). Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website. International Journal of Contemporary Hospitality Management.
Liu, C., Wang, S., & Jia, G. (2020). Exploring E-Commerce Big Data and Customer-Perceived Value: An Empirical Study on Chinese Online Customers. Sustainability, 12(20), 8649.
Liu, W. K., & Yen, C. C. (2016). Optimizing bus passenger complaint service through big data analysis: Systematized analysis for improved public sector management. Sustainability, 8(12), 1319.
Ma, E., Cheng, M., & Hsiao, A. (2018). Sentiment analysis–a review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management.
Ma, J., Kwak, M., & Kim, H. M. (2014). Demand trend mining for predictive life cycle design. Journal of Cleaner Production, 68, 189-199.
Masoudi, B. & Rahati Ghouchani, S. (2015). An LDA Topic Model for Farsi Word Sense Disambiguation. Signal and Data Processing. 36(4), 117-125. (in Persian)
Mimno, D., Wallach, H., Talley, E., Leenders, M., & McCallum, A. (2011, July). Optimizing semantic coherence in topic models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (pp. 262-272).
Mobini Dehkordi, A., Rezvani, M., Davari, A., Forozanfar, F. (2014). Innovative business model for B2C distribution's companies (Case Study: Golrang-pakhsh). Journal of Entrepreneurship Development, 7(3), 569-588. (in Persian)
Moorthi, Y. L. R., & Mohan, B. C. (2017). Brand value proposition for bank customers in India. International Journal of Bank Marketing, 35(1), 24-44.
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251.
Nam, S., & Lee, H. C. (2019). A text analytics-based importance performance analysis and its application to airline service. Sustainability, 11(21), 6153.
Nazari, M., Shah Hosseini, M., Tabatabaie Kalejahi, S. (2014). Impact of price perception factors on customer and price acceptance (Case study: MTN Irancell Company). Journal of Business Management, 6(3), 647-664. (in Persian)
Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2014). Value proposition design: How to create products and services customers want. John Wiley & Sons.
Payne, A., Frow, P., & Eggert, A. (2017). The customer value proposition: evolution, development, and application in marketing. Journal of the Academy of Marketing Science, 45(4), 467-489.
Rai, R. (2012, August). Identifying key product attributes and their importance levels from online customer reviews. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 45028, pp. 533-540). American Society of Mechanical Engineers.
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.
Sanayei, A., Mohammad Shafiee, M., & Karimian, M. (2019). Surveying of Criteria for Purchasing Television Set and Recognizing Customers' Decision Making Styles in Isfahan. Journal of BusinessManagement, 11(3), 631- 650. (in Persian)
Schmiedel, T., Müller, O., & vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941-968.
Shakeel, J., Mardani, A., Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2020). Anatomy of sustainable business model innovation. Journal of Cleaner Production, 121201.
Shekari, S. & Masumi, B. (2016). Semantic text clustering using “Latent Dirichlet allocation” and Genetic Algorithm. Proceeding of the 4th International Conference on Research in Science and Technology. Russia: Saint Petersburg. (in Persian)
Shin, H., Perdue, R. R., & Pandelaere, M. (2020). Managing customer reviews for value co-creation: An empowerment theory perspective. Journal of Travel Research, 59(5), 792-810.
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. " O'Reilly Media, Inc.".
Singh, A., Sharma, A., Singh, K. K., & Dhull, A. (2020). Sentiment Analysis of Social Networking Data Using Categorized Dictionary. Journal of Information Technology Management, 12(4), 105-120.
Stahl, F., Gaber, M. M., & Adedoyin-Olowe, M. (2014). A survey of data mining techniques for social media analysis. Journal of Data Mining & Digital Humanities, 2014.
Stone, T., & Choi, S. K. (2013, August). Extracting consumer preference from user-generated content sources using classification. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 55881, p. V03AT03A031). American Society of Mechanical Engineers.
Stone, T., & Choi, S. K. (2013, August). Extracting consumer preference from user-generated content sources using classification. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 55881, p. V03AT03A031). American Society of Mechanical Engineers.
Sun, S., Luo, C., & Chen, J. (2017). A review of natural language processing techniques for opinion mining systems. Information fusion, 36, 10-25.
Van Horn, D., Olewnik, A., & Lewis, K. (2012, August). Design analytics: capturing, understanding, and meeting customer needs using big data. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 45066, pp. 863-875). American Society of Mechanical Engineers.
Wang, L., & Alexander, C. A. (2015). Big data in design and manufacturing engineering. American Journal of Engineering and Applied Sciences, 8(2), 223.
Wang, X., White, L., Chen, X., Afshari, H., & Peng, Q. (2015). Modeling and quantifying uncertainty in the product design phase for effects of user preference changes. Industrial Management & Data Systems.
Yang, S., & Zhang, H. (2018). Text mining of Twitter data using a latent Dirichlet allocation topic model and sentiment analysis. International Journal of Computer and Information Engineering, 12(7), 525-529.
Zhang, T. C., Gu, H., & Jahromi, M. F. (2019). What makes the sharing economy successful? An empirical examination of competitive customer value propositions. Computers in Human Behavior, 95, 275-283.
Journal of Business Management
Volume 12, Issue 4
2021
Pages 1092-1116

Files

Share

How to cite

Statistics