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http://dx.doi.org/10.15207/JKCS.2019.10.10.153

The Analysis of the Relationship between the Review Scale and Posting Information of Company and Purchasing Patterns -Focusing on Amazon and Google Users  

Kim, Dong-Il (Dept. of Business Administration, Pusan National University)
Choi, Seung-Il (Dept. of International Trade, Changwon National University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.10, 2019 , pp. 153-160 More about this Journal
Abstract
In this study, The purpose of this study is to analyze how the rating scale and review contents attributes of social network-based services and products affect consumer purchasing patterns. information provided by screening the main factors. These analyzes are closely and quickly integrated between individuals and businesses, and enable to analyze the transaction that the impact of changing consumers on consumption and purchasing through the usefulness and a priori estimates of reviews and ratings at this time when networks and smart technologies are involved in a wide range of consumer activities. For this study, hierarchical analysis (AHP) and delphi (Delphi) methods applied to classify the high end variables into usefulness, technicality and value, Each subvariable was grouped into three factors and analyzed for importance through evaluation weights. As a result, we could analyze the importance of durability, usefulness, technological innovation, and cost and quality of value. Therefore, this study is expected to provide supplementary and additional useful information to consumers and companies participating in economic activities in various ways by simultaneously analyzing the review score and the reliability of posting information provided by verifying the main factors.
Keywords
Reliability; Convenience; Durability; Quality; Price;
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Times Cited By KSCI : 16  (Citation Analysis)
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1 W. Jongen. (2016). from Retail to Consumer Innovation, www.wijnandjongen.com.
2 S. K. Park, D. S. Kim & H. Y. Ahn. (2018). An Exploratory Study on the Repurchase Intentions of Start-Ups' Products in the Online Shopping Mall, Korean Journal of Entrepreneurship Studies, 13(2), 197-217.   DOI
3 H. Zhang, H. E. Kim & B. S. Kim. (2017). Effects of Application Attributes of Coffee Chains on Consumer's Repurchase Decision-Making Processes, Journal of Digital Convergence, 15(9), 137-246. DOI: 10.14400/JDC.2017.15.9.137   DOI
4 S. J. Park & K. T. Hwang. (2016). A Study on the Repurchase Intention of Customers in the Foreign Direct Sales Internet Shopping Mall - Focused on the Japanese Customers, Journal of Digital Convergence, 14(6), 199-218. DOI: 10.14400/JDC.2016.14.6.199   DOI
5 Y. W. Lee, J. R. Kim & S. B. Kim. (2019). A Study on The Influnene of Consulting Paticipation of The Consulted Firm on Consulting Repurchase Intention, Journal of Digital Convergence, 17(1), 111-122. DOI: 10.14400/JDC.2019.17.1.111   DOI
6 E. S. Jin, J. J. Youn, H. J. Kim & H. S. Lee. (2014). A study on films selection of education content development for promoting pre-service early childhood teacher's creativity and personality using delphi technique, Journal of Digital Convergence, 12(5), 403-413. DOI: 10.14400/JDC.2014.12.5.403   DOI
7 K. M. Kim, M. J. Kang & H. J. Kim. (2018). Development of Indicators for the National GHG Reduction Technology Selection Based on Delphi Method, Journal of Digital Convergence, 16(10), 11-26. DOI: 10.14400/JDC.2018.16.10.011   DOI
8 S. J. Park & C. A. Koh. (2012). ChanAn Analysis of Decision Making Factor by Delphi and DEMATEL Model for Decision Support Information System development - Wartime Operational Control Transition approach, Journal of Digital Convergence, 10(11), 47-58. DOI: 10.14400/JDPM.2012.10.11.047   DOI
9 D. Norman & O. Helmer, (1963) An experimental application of the Delphi method to the use of experts, Management science, 9, 458-467.   DOI
10 N. Shrestha & F. Nasos. (2019). Deep Learning Sentiment Analysis of Amazon.Com Reviews And Ratings, International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 8(1), 1-15.
11 M. D. Koerniawan, Munawir & B. J. Koerniawan, (2019) Visitor Perceptions and Effectiveness of Place Branding Strategies in Thematic Parks in Bandung City Using Text Mining Based on Google Maps User Reviews, Journal of Sustainability, 11(2123), 2-20.
12 T. J. Gu, Y. D. Cha, T. H. Nam & G. T. Yeo. (2017). A Study on the Analysis of Success Factors about Frozen and Refrigerated Warehouses using Fuzzy-AHP, Journal of Digital Convergence, 15(11), 121-131. DOI: 10.14400/JDC.2017.15.11.121   DOI
13 B. J. Park, T. H. Nam & G. T. Yeo. (2019). Study on location selection of integrated depot of warehouse stores utilizing AHP method, Journal of Digital Convergence, 17(7), 135-144.
14 J. T. Oh & S. Y. Lee. (2019). A Movie Recommendation System processing High-Dimensional Data with Fuzzy-AHP and Fuzzy Association Rules, Journal of Digital Convergence, 17(2), 347-353. DOI: 10.14400/JDC.2019.17.2.347   DOI
15 B. H. Kim, S. H. Park, J. M. Gong & G. T. Yeo. (2018). A Study on the Safety Factor Analysis of Bulk Cargo Handling Using Fuzzy-AHP : Focused on steel cargo, Journal of Digital Convergence, 16(2), 179-188. DOI: 10.14400/JDC.2018.16.2.179   DOI
16 S. J. Kwon, Y. D. Cha & G. T. Yeo. (2017). A Study on the Improvement of Apparel Logistics Warehouse Using Fuzzy-AHP, Journal of Digital Convergence, 15(9), 23-33. DOI: 10.14400/JDC.2017.15.7.97   DOI
17 J. T. Oh & S. Y. Lee. (2017). Fuzzy-AHP Based Mobile Games Recommendation System Using Bayesian Network, Journal of Digital Convergence, 15(4), 461-468. DOI: 10.14400/JDC.2017.15.4.461   DOI
18 H. S. Joun & S. Y. Lee. (2016). Fuzzy-AHP-Based Technology Evaluation Model for venture firms, Journal of Digital Convergence, 14(7), 363-371. DOI: 10.14400/JDC.2017.15.4.461   DOI
19 T. L. Saaty. (1990). The Analytic Hierarchy Process, RWS Publication, Pittsburgh, PA
20 G. Rowe & G. Wright. (2001). Expert opinions in forecasting: The role of the Delphi technique," Principles of Forecasting, Springer, 12(4), 125-144.
21 T. L. Saaty. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process, RWS Publication, Pittsburgh.
22 S. W. Lee & S. H. Kim. (2016). Finding Industries for Big Data Usage on the Basis of AHP, Journal of Digital Convergence, 14(7), 21-27. DOI: 10.14400/JDC.2016.14.7.21   DOI
23 Y. S. Jeong. (2015). An Efficiency Management Scheme using Big Data of Healthcare Patients using Puzzy AHP, Journal of Digital Convergence, 13(4), 227-233 DOI: 10.14400/JDC.2015.13.4.227   DOI
24 S. J. Park & D. M. Kim. (2014). A Study on fairness of broadcasting by AHP, Journal of Digital Convergence, 12(11), 171-181. DOI: 10.14400/JDC.2014.12.11.171   DOI
25 N. Divyasharee, K. L. Kumar & L. S. Majumdar. (2017). Opinion Mining and Sentiment Analysis of TripAdvisor in for Hotel Reviews, Int. Res. J. Eng. Technology. 4, 1462-1467.
26 H. G. Jang & M. J. Noh. (2019). Exploring the Structural Relationship among Online Reviews and WOM Intentions and Repurchase Intentions Focusing on the Moderating Effects of Commodity Characteristics and Promotional Marketing in Mobile Apps, Journal of Marketing Management Research, 24(3), 47-64.   DOI
27 S. J. Ahn. (2018). A study on the effect of selection attributes in HMR over satisfaction and intention for recommendation and repurchase, Korean Journal of Food Service Industry, 14(2), 137-149.
28 F. Bronner & R. De Hoog. (2011). Vacationers and eWOM: who posts, and why, where, and what?. Journal of Travel Research, 50(1), 15-26.   DOI
29 H. R. Lee & J. J. Kim. (2015). The influence of social distance and the expertise of the informant on brand attitude and purchase intention formed by online word of mouth. Journal of Distribution Business Administration, 18(4), 51-74.
30 Vidusi & S. S. Gurjot, . (2017). Sentiment Mining of Online Reviews Using Machine Learning Algorithms. Int. J. Engineering Technology, 5(2), 1321-1334.
31 C. Dellarocas, X. M. Zhang & N. F. Awad. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive marketing, 21(4), 23-45.   DOI
32 T. Reimer & M. Benkenstein. (2016). When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews, Journal of Business Research, 69(12), 5993-6001.   DOI
33 M. H. Lee. (2019). The Effects of Non Face-to-face Channel Marketing on Relational Benefits and Repurchase Intention in Food Service Company, Hotel Resort Research, 18(1), 293-318.