Journal of Korea Trade

Korea Trade Research Association (한국무역학회)
권호 표지
DOI QR코드

DOI QR Code

Global Big Data Analysis Exploring the Determinants of Application Ratings: Evidence from the Google Play Store

  • Seo, Min-Kyo (Department of Trade and Management, Daegu University) ;
  • Yang, Oh-Suk (Division of Business Administration & Accounting, Kangwon National University) ;
  • Yang, Yoon-Ho (School of Management, University College London)
  • Received : 2020.06.24
  • Accepted : 2020.10.07
  • Published : 2020.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

Keywords

References

  1. Aaker, D. A. (1995), Building Strong Brands, New York, NY: Free Press.
  2. Akaike, H. (1973), "Maximum Likelihood Identification of Gaussian Autoregressive Moving Average Models", Biometrika, 60(2), 255-265. https://doi.org/10.1093/biomet/60.2.255
  3. Anderson, D. J., T. A. Sweeney and T. A. Williams (1996), Statistics for Business and Economics, Minneapolis, MN: St. Paul, West Publishing.
  4. Anderson, E. W. and M. W. Sullivan (1993), "The Antecedents and Consequences of Customer Satisfaction for Firms", Marketing Science, 12(2), 125-212. https://doi.org/10.1287/mksc.12.2.125
  5. Babin, B. J., W. R. Darden and M. Griffin (1994), "Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value", Journal of Consumer Research, 20(4), 644-656. https://doi.org/10.1086/209376
  6. Baier, D. and E. Stuber (2010), "Acceptance of Recommendations to Buy in Online Retailing", Journal of Retailing and Consumer Services, 17(3), 173-180. https://doi.org/10.1016/j.jretconser.2010.03.005
  7. Baker, D. A. and G. P. Algorta (2016), "The Relationship between Online Social Networking and Depression: A Systematic Review of Quantitative Studies", Cyberpsychology, Behavior, and Social Networking, 19(11), 638-648. https://doi.org/10.1089/cyber.2016.0206
  8. Bardus, M., S. B. van Beurden, J. R. Smith and C. Abraham (2016), "A Review and Content Analysis of Engagement, Functionality, Aesthetics, Information Quality, and Change Techniques in The Most Popular Commercial Apps for Weight Management", International Journal of Behavioral Nutrition and Physical Activity, 13, 1-9. Available from https://doi.org/10.1186/s12966-016-0359-9
  9. Barron, A. R. (1993), "Universal Approximation Bounds for Super Positions of A Sigmoidal Function", IEEE Transactions on Information Theory, 39(3), 930-945. https://doi.org/10.1109/18.256500
  10. Berger, J. and E. M. Schwartz (2011), "What Drives Immediate and Ongoing Word of Mouth", Journal of Marketing Research, 48(5), 869-880. https://doi.org/10.1509/jmkr.48.5.869
  11. Berger, J., A. T. Sorensen and S. J. Rasmussen (2010), "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales", Marketing Science, 29(5), 815-827. https://doi.org/10.1287/mksc.1090.0557
  12. Berry, M. and G. Linoff (1997), Data Mining Techniques: For Marketing, Sales, and Customer Support, New York, NY: John Wiley & Sons.
  13. Boulding, W., A. Kalra, R. Staelin and V. A. Zeithaml (1993), "A Dynamic Process Model of Service Quality: From Expectations to Behavioral Intentions", Journal of Marketing Research, 30(1), 7-27. https://doi.org/10.2307/3172510
  14. Brown, J. J. and P. H. Reingen (1987), "Social Ties and Word-of-Mouth Referral Behavior", Journal of Consumer Research, 14(3), 350-362. https://doi.org/10.1086/209118
  15. Brown, S. R. (1980), Political Subjectivity, New Haven, CT: Yale University Press.
  16. Chatterjee, S. and B. Price (1991), Regression Analysis by Example, New York, NY: John Wiley & Sons.
  17. Chen, P. Y., S. Wu and J. Yoon (2004), "The Impact of Online Recommendations and Consumer Feedback on Sales", International Conference on Information Systems 2004 Proceedings. 711-724. Available from https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1146&context=icis2004
  18. Cheung, C. M. K. and M. K. O. Lee (2008), "The Impact of Electronic Word-of-Mouth: The Adoption of Online Opinions in Online Customer Communities", Applications and Policy, 18(3), 229-247.
  19. Chevalier, J. A. and D. Mayzlin (2006), "The Effect of Word of Mouth on Sales: Online Book Reviews", Journal of Marketing Research", 43(3), 345-354. https://doi.org/10.1509/jmkr.43.3.345
  20. Childer, T., C. Carr, J. Peck and S. Carson (2001), "Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior", Journal of Retailing, 77(4), 511-535. https://doi.org/10.1016/S0022-4359(01)00056-2
  21. Cho, Hyu-Kjun, Ju-Young Kang and Dae-Yong Jeong (2016), "An Exploratory Study on Mobile App Review through Comparative Analysis between South Korea and US", Journal of Information Technology Services, 15(2), 169-184. https://doi.org/10.9716/KITS.2016.15.2.169
  22. Choi, Jong-Hoo (2000), Analysis of Data Mining Decision Tree by AnswerTree, Seoul: SPSS Academy.
  23. Darley, W. K. and R. E. Smith (1995), "Gender Differences in Information Processing Strategies: An Empirical Test of The Selectivity Model in Advertising Response", Journal of Advertising, 24(1), 41-56. https://doi.org/10.1080/00913367.1995.10673467
  24. Dawson, S., P. Bloch and N. Ridgway (1990), "Shopping Motives, Emotional States, and Retail Outcomes", Journal of Retailing, 66(4), 408-427.
  25. Dellarocas, C., N. Awad and M. Zhang (2005), Using Online Ratings as a Proxy of Word-of-Mouth in Motion Picture Revenue Forecasting (SSRN Working Paper). Available from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.362.7819&rep=rep1&type=pdf
  26. Diamantopoulos, A. and J. A. Siguaw (2006), "Formative versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration", British Journal of Management, 17(4), 263-282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
  27. Donovan, R. J. and J. R. Rossiter (1982), "Store Atmosphere: An Environmental Psychology Approach", Journal of Retailing, 58(1), 34-57.
  28. Filieri, R. (2016), "What Makes An Online Consumer Review Trustworthy?", Annals of Tourism Research, 58(May), 46-64. https://doi.org/10.1016/j.annals.2015.12.019
  29. Fisher, J. D. and W. A. Fisher (2002), "The Information-Motivation-Behavioral Skills Model". In R. J. Diclemente, R. A. Crosby and M. C. Kegler (Eds.), Emerging Theories in Health Promotion Practice and Research, New York, NY: John Wiley & Sons, 40-70.
  30. Franses, P. H. and K. Van Griensven (1998), "Forecasting Exchange Rates Using Neural Networks for Technical Trading Rules", Studies in Nonlinear Dynamics & Econometrics, 2(4), 109-114.
  31. Frechtling, D. (2001), Forecasting Tourism Demand Methods and Strategies, Oxford, UK: Butterworth-Heinemann.
  32. Frie, K., J. Hartmann-Boyce, S. Jebb, C. Albury, R. Nourse and R. Aveyard (2017), "Insights From Google Play Store User Reviews for the Development of Weight Loss Apps: Mixed-Method Analysis", JMIR Mhealth Uhealth, 5(12), 1-14.
  33. Garson, D. G. (1991), "Interpreting Neural Network Connection Weights", AI Expert, 6(7), 47-51.
  34. Gefen, D., V. S. Rao and N. Tractinsky (2003), "The Conceptualization of Trust, Risk and Their Relationship in Electronic Commerce: The Need for Clarifications", Proceedings of the 36th Hawaii International Conference on System Sciences.
  35. Griffiths, W. E., R. C. Hill and G. G. Judge (1993), Learning and Practicing Econometrics, New York, NY: John Wiley & Sons.
  36. Gu, J., Y. C. Xu, H. Xu, C. Zhang and H. Ling (2017), "Privacy Concerns for Mobile App Download: An Elaboration Likelihood Model Perspective", Decision Support Systems, 94(February), 19-28. https://doi.org/10.1016/j.dss.2016.10.002
  37. Gupta, P. and J. Harris (2010), "How e-WOM Recommendations Influence Product Consideration and Quality of Choice: A Motivation to Process Information Perspective", Journal of Business Research, 63(9-10), 1041-1049. https://doi.org/10.1016/j.jbusres.2009.01.015
  38. Hair, J. F. J., R. E. Anderson, R. L. Tatham and W. C. Black (1998), Multivariate Data Analysis (5th ed.), Englewood Cliffs, NJ: Prentice-Hall.
  39. Hammond, K., G. McWIlliam and A. N. Diaz (1998), "Fun and Work on the Web: Differences in Attitudes between Novices and Experienced User". In J. W. Alba and J. W. Hutchinson (Eds.), NA-Advances in Consumer Research (Vol. 25), Provo, UT: Association for Consumer Research, 372-378.
  40. Harman, M., Y. Jia and Y. Zhang (2012), "App Store Mining and Analysis: MSR for App Stores", 2012 9th IEEE Working Conference on Mining Software Repositories (MSR). Available from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6224306
  41. Harrington, R., M. Ottenbacher and K. Kendall (2011), "Fine Dining Restaurant Selection: Direct and Moderating Effects of Customer Attributes", Journal of Foodservice Business Research, 14(3), 272-289. https://doi.org/10.1080/15378020.2011.594388
  42. Harvey, R. L. (1994), Neural Network Principles, Englewood Cliffs, NJ: Prentice-Hall.
  43. Hausman, A. V. and J. S. Siekpe (2009), "The Effect of Web Interface Features on Consumer Online Purchase Intentions", Journal of Business Research, 62(1), 5-13. https://doi.org/10.1016/j.jbusres.2008.01.018
  44. Helsel, D. R. and R. M. Hirsch (1992/2002), Statistical Methods in Water Resources, Amsterdam: Elsevier.
  45. Herr, P. M., F. R. Kardes and J. Kim (1991), "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective", Journal of Consumer Research, 17(4), 454-462. https://doi.org/10.1086/208570
  46. Higgins, J. R. (1996/2000), Sampling Theory in Fourier and Signal Analysis: Foundations, Oxford, UK: Clarendon Press.
  47. Hoffman, D. L. and T. P. Novak (1996), "Marketing in Hypermedia Computer-mediated Environments: Conceptual Foundations", Journal of Marketing, 60(3), 50-68. https://doi.org/10.2307/1251841
  48. Hoon, L., R. Vasa and J. G. Schneider (2013), An Analysis of the Mobile App Review Landscape: Trends and Implications (Unpublished Paper), Melbourne, Australia: Swinburne University of Technology. Available from https://researchbank.swinburne.edu.au/file/ba489b3c-a9bb-41f0-8b43-b6d1ff4d4829/1/PDF%20(23%20pages).pdf
  49. Lacob, C. and R. Harrison (2016), "Retrieving and Analyzing Mobile Apps Feature Requests from Online Reviews", 2013 10th Working Conference on International Mining Software Repositories, 41-44. Available from https://ieeexplore.ieee.org/xpl/conhome/6597024/proceeding
  50. James, G., D. Witten, T. Hasite and R. Tibshirani (2013), An Introduction to Statistical Learning: With Applications in R, New York, NY: Springer.
  51. Keller, K. L. (1993), "Conceptualizing, Measuring, and Managing Customer-Based Brand Equity", Journal of Marketing, 57(1), 1-22. https://doi.org/10.2307/1252054
  52. Kennedy, P. (1992), A Guide to Econometrics, Cambridge, MA: The MIT Press.
  53. Kidwell, B., D. M. Hardesty and T. L. Childers (2008), "Consumer Emotional Intelligence: Conceptualization, Measurement, and The Prediction of Consumer Decision Making", Journal of Consumer Research, 35(1), 154-166. https://doi.org/10.1086/524417
  54. Kim, D. J., D. L. Ferrin and H. R. Rao (2008), "A Trust-based Consumer Decision-making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents", Decision Support Systems, 44(1), 544-564. https://doi.org/10.1016/j.dss.2007.07.001
  55. Kim, Gi-Mun and Hoon-Young Koo (2016), "The Causal Relationship between Risk and Trust in The Online Marketplace: A Bidirectional Perspective", Computers in Human Behavior, 55(February), 1020-1029. https://doi.org/10.1016/j.chb.2015.11.005
  56. Kim, Myoung-Jong (2012), "Performance Comparison of Internal Accounting Control Assessment Models Applying Logistic Regression and Neural Networks", Korean International Accounting Review, 46, 1-30.
  57. Kim, Sang-Hwan (2000), "Establishing an Optimal Neural Network Model and Analyzing The Performance of Foreign Exchange Prediction", Financial Research, 14(1), 57-85.
  58. Kim, Tae-Hoon and Han-Kuk Hong (2004), "A Study on Apartment Price Models Using Regression Model and Neural Network Model", The Korea Spatial Planning Review, 43, 183-200.
  59. Kuan, C. M. and T. Liu (1995), "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks", Journal of Applied Econometrics, 10(4), 347-364. https://doi.org/10.1002/jae.3950100403
  60. Lee, Sang-Jae and Joon-Yeon Choeh (2014), "Predicting The Helpfulness of Online Reviews Using Multilayer Perceptron Neural Networks", Expert Systems with Applications, 41(6), 3041-3046. https://doi.org/10.1016/j.eswa.2013.10.034
  61. Lee, Dong-Il and Seung-Hoon Choi (2012), "The Impact of Consumer Review and Expert Review on the App Developer's Performance in The App Store", Journal of Korean Marketing Association, 27(2), 113-136.
  62. Lee, Eun-Ju and Soo-Yun Shin (2014), "When Do Consumers Buy Online Product Reviews? Effects of Review Quality, Product Type, and Reviewer's Photo", Computers in Human Behavior, 31(February), 356-366. https://doi.org/10.1016/j.chb.2013.10.050
  63. Lee, Kook-Yong (2017), "The Effects of E-WOM in Selecting the Mobile Application", The Journal of the Korea Contents Association, 17(1), 80-91. https://doi.org/10.5392/JKCA.2017.17.01.080
  64. Lee, Kun- Chang, In-Goo Han and Myoung-Jong Kim (1996), "A Study on The Credit Evaluation Model Integrating Statistical Model and Artificial Intelligence Model", Journal of Management Science, 21(1), 81-100.
  65. Lee, Kun-Chang, Myoung-Jong Kim and Hyuk Kim (1994), "An Inductive Learning-Assisted Neural Network Approach to Bankruptcy Prediction: Comparison with MDA, Inductive Learning, and Neural Network Models", Journal of Management Research, 23(3), 109-144.
  66. Leonard-Barton, D. (1985), "Experts as Negative Opinion Leaders in The Diffusion of a Technological Innovation", Journal of Consumer Research, 11(4), 914-926. https://doi.org/10.1086/209026
  67. Li, X. and L. M. Hitt (2010), "Price Effects in Online Product Reviews: An Analytical Model and Empirical Analysis", MIS Quarterly, 34(4), 809-831. https://doi.org/10.2307/25750706
  68. Liao, C., H. N. Lin, M. M. Luo and S. Chea (2016), "Factors Influencing Online Shoppers' Repurchase Intentions: The Roles of Satisfaction and Regret", Information & Management, 54(5), 651-668. https://doi.org/10.1016/j.im.2016.12.005
  69. Liu, B. (2010), "Sentiment Analysis and Subjectivity". In N. Indurkaya and F. J. Damerau (Eds.), Handbook of Natural Language Processing (2nd ed.), Cambridge, UK: Chapman & Hall Book, 627-665.
  70. Liu, Q. B., E. Karahanna and R. T. Watson (2011), "Unveiling User-generated Content: Designing Websites to Best Present Customer Reviews", Business Horizons, 54(3), 231-240. https://doi.org/10.1016/j.bushor.2011.01.004
  71. Liu, Y. (2006), "Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue", Journal of Marketing, 70(3), 74-89. https://doi.org/10.1509/jmkg.70.3.074
  72. Lizzeri, A. (1999), "Information Revelation and Certification Intermediaries", The RAND Journal of Economics, 30(2), 214-231. https://doi.org/10.2307/2556078
  73. Lovett, M. J., R. Peres and R. Shachar (2013), "On Brands and Word of Mouth", Journal of Marketing, 50(4), 427-444. https://doi.org/10.1509/jmr.11.0458
  74. Martens, D. and T. Johann (2017), "On the Emotion of Users in App Reviews", IEEE/ACM 2nd International Workshop on Emotion Awareness in Software Engineering. Available from https://ieeexplore.ieee.org/document/7961885
  75. Mehrabian, A. and J. A. Russell (1974), An Approach to Environmental Psychology, New York, NY: MIT Press.
  76. Moody, J. E. (1994), "Prediction Risk and Architecture Selection for Neural Networks". In V. Cherkassky, J. H. Friedman and H. Wechsler (Eds.), From Statistics to Neural Networks, NATO ASI Series (Series F: Computer and Systems Sciences, vol. 136), Berlin: Springer, 147-165. Available from https://doi.org/10.1007/978-3-642-79119-2_7
  77. Nayebi, Maleknaz, H. Cho and G. Ruhe (2018), "App Store Mining Is Not Enough for App Improvement", Empirical Software Engineering, 23(1), 2764-2794. https://doi.org/10.1007/s10664-018-9601-1
  78. Oliver, R. L. (1993), "A Conceptual Model of Service Quality and Service Satisfaction: Compatible Goals, Different Concepts", Advances in Services Marketing and Management, 2(1), 65-85.
  79. Oliver, R. L. (1997), Satisfaction: A Behavioral Perspective on The Consumer, New York, NY: McGraw-Hill.
  80. Oliver, R. L. and W. S. DeSarbo (1988), "Response Determinants in Satisfaction Judgments", Journal of Consumer Research, 14(4), 495-507. https://doi.org/10.1086/209131
  81. Pagano, D. and W. Maaleg (2013), "User Feedback in The Appstore: An Empirical Study", 2013 21st IEEE International Conference on Requirements Engineering. Available from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6636712
  82. Palomba, F. and M. Linares-Vasquez (2015), "User Reviews Matter! Tracking Crowdsourced Reviews to Support Evolution of Successful Apps", IEEE International Conference on Software Maintenance and Evolution. Available from https://ieeexplore.ieee.org/document/7332475
  83. Parasuraman, A., V. A. Zeithaml and L. L. Berry (1991), "Understanding Customer Expectations of Service", Sloan Management Review, 32(Spring), 39-48.
  84. Park, C. W., B. J. Jaworski and D. J. Maclnnis (1986), "Strategic Brand Concept-Image Management", Journal of Marketing, 50(4), 135-145. https://doi.org/10.2307/1251291
  85. Pavlou, P. A. (2003), "Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with The Technology Acceptance Model", International Journal of Electronic Commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275
  86. Pavlou, P. A. and D. Gefen (2004), "Building Effective Online Marketplaces with Institution-based Trust", Information Systems Research, 15(1), 37-59. https://doi.org/10.1287/isre.1040.0015
  87. Petter, S., D. Straub and A. Rai (2007), "Specifying Formative Constructs in IS Research", MIS Quarterly, 31(4), 623-656. https://doi.org/10.2307/25148814
  88. Phillips, D. M. (1999), The Role of Consumption Emotions in the Satisfaction Response, (Doctoral Dissertation), Philadelphia, PA: Pennsylvania State University.
  89. Phillips, P., K. Zigan, M. M. S. Silva and R. Schegg (2015), "The Interactive Effects of Online Reviews on The Determinants of Swiss Hotel Performance: A Neural Network Analysis", Tourism Management, 50(1), 130-141. https://doi.org/10.1016/j.tourman.2015.01.028
  90. Ponte, E. B., E. Carvajal-Trujillo and T. Escobar-Rodriguez (2015), "Influence of Trust and Perceived Value on the Intention to Purchase Travel Online: Integrating The Effects of Assurance on Trust Antecedents", Tourism Management, 47(April), 286-302. https://doi.org/10.1016/j.tourman.2014.10.009
  91. Putrevu, S. (2001), "Exploring The Origins and Information Processing Differences between Men and Women: Implications for Advertisers", Academy of Marketing Science Review, 10(1), 1-14.
  92. Ripley, B. D. (1993), "Statistical Aspects of Neural Networks". In O. E. Barndorff-Nielsen, J. L. Jensen and W. S. Kendall (Eds.), Networks and Chaos - Statistical and Probabilistic Aspects, London, UK: Chapman & Hall, 40-111.
  93. Schmitt, P., B. Skiera and C. V. den Bulte (2011), "Referral Programs and Customer Value", Journal of Marketing, 75(1), 46-59. https://doi.org/10.1509/jmkg.75.1.46
  94. Sher, P. J. and S. H. Lee (2009), "Consumer Skepticism and Online Reviews: An Elaboration Likelihood Model Perspective", Social Behavior and Personality: an International Journal, 37(1), 137-143. https://doi.org/10.2224/sbp.2009.37.1.137
  95. Swanson, N. R. and H. White (1995), "A Model-selection Approach to Assessing The Information in The Term Structure Using Linear Models and Artificial Neural Networks", Journal of Business & Economic Statistics, 13(3), 265-275. https://doi.org/10.2307/1392186
  96. Swinyard, W. R. (1993), "The Effects of Mood Involvement and Quality of Store Experience on Shopping Intentions", Journal of Consumer Research, 20(2), 271-280. https://doi.org/10.1086/209348
  97. Tauber, E. M. (1972), "Marketing Notes and Communications: Why Do People Shop?", Journal of Marketing, 36(4), 46-59. https://doi.org/10.2307/1250426
  98. Wakefield, K. L. and J. G. Blodgett (1999), "Customer Response to Intangible and Tangible Service Factors", Psychology & Marketing, 16(1), 51-68. https://doi.org/10.1002/(SICI)1520-6793(199901)16:1<51::AID-MAR4>3.0.CO;2-0
  99. Weiss, S. M. and C. A. Kulikowski (1991), Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, San Francisco, CA: Morgan Kaufmann Publishers Inc.
  100. Westbrook, R. A. and W. C. Black (1985), "A Motivation-based Shopper Typology", Journal of Retailing, 61(1), 78-103.
  101. Witt, S. F. and C. A. Witt (1995), "Forecasting Tourism Demand: A Review of Empirical Research", International Journal of Forecasting, 11(3), 447-475. https://doi.org/10.1016/0169-2070(95)00591-7
  102. Wooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT press.
  103. Xia, L. and N. N. Bechwati (2008), "Word of Mouse: the Role of Cognitive Personalization in Online Consumer Reviews", Journal of Interactive Advertising, 9(1), 3-13. https://doi.org/10.1080/15252019.2008.10722143
  104. Yoo, Chang-Jo, Jong-Hee Park and D. Maclnnis (1998), "Effects of Store Characteristics and In-Store Emotional Experiences on Store Attitude", Journal of Business Research, 42(3), 253-263. https://doi.org/10.1016/S0148-2963(97)00122-7
  105. Yuan, L. (2015), "Kingmakers of China's Internet: Baidu, Alibaba and Tencent", The Wall Street Journal. Available from http://www.wsj.com/articles/kingmakers-of-chinas-internet-baidu-alibaba-and-tencent-1445451143?mod=ST
  106. Zeelenberg, M. and R. Pieters (2004), "Beyond Polarity in Customer Dissatisfaction: A Review and New Findings on Behavioral Responses to Regret and Disappointment in Failed Services", Journal of Business Research, 57(4), 445-455. https://doi.org/10.1016/S0148-2963(02)00278-3
  107. Zhang, W. and S. Watts (2008), "Online Communities as Communities of Practice: A Case Study", Journal of Knowledge Management, 12(4), 55-71. https://doi.org/10.1108/13673270810884255
  108. Zhong, N. and F. Michahelles (2013), "Google Play Is Not A Long Tail Market: An Empirical Analysis of App Adoption on the Google Play App Market", Proceedings of the 28th annual ACM Symposium on Applied Computing, 499-504.
  109. Zhu, F. and X. Zhang (2010), "Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics", Journal of Marketing, 74(2), 133-148. https://doi.org/10.1509/jm.74.2.133