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Three Essays on Big Data Analytics in Marketing


Three Essays on Big Data Analytics in Marketing

Shirdastian, Hamid (2022) Three Essays on Big Data Analytics in Marketing. PhD thesis, Concordia University.

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This thesis investigates how the immense amount of real-time and retrospective data can contribute to marketing theories and practices in branding, advertising, and communications.
In the first essay, brand management in social media is studied due to its vast potential as well as the companies’ interests in utilizing it for branding purposes. More specifically, the research examines the sentiments toward a brand, via brand authenticity, to identify both the reasons for positive or negative sentiments on social media and its polarity. Practically speaking, while firms need insights about users’ sentiment towards their brand, knowing just that it is a simple positive or negative sentiment does not provide them with enough information. They might ask themselves why users like us or what the reason is behind the negative sentiment. Using three qualitative studies, along with the latent semantic analysis (LSA) and the support vector machine (SVM), the findings illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis to predict both the brand authenticity dimensions and their level of sentiment.
The second essay is concerned with the immense amount of locational and destination-based data for advertising purposes. That comes from the fact that more and more apps have access to users’ locations and destinations. As the context, the sharing economy is selected to test if a ridesharing app, i.e. Uber, can alter one’s destination by providing relevant destination-based ads. Examining construal levels (i.e. spatial distance and cultural distance) show the effects on the relationships between attitudes towards destination-based advertising and redemption of marketing incentives as well as app reuse intention. Two experimental mock apps that mimic a well-known ridesharing app in North America are used. The findings provide implications for the construal level theory, the theory of planned behavior, and how practitioners can use it to alter planned behavior, i.e. planned destinations.
The third essay is also in the context of the sharing economy. Building on the speech act theory, the sale description impact on consumers’ social cognitions of service providers is investigated to find how they could be used to generate new content. Findings suggest the role of linguistic concreteness, service provider type, and sentiment analysis on the perceived level of warmth and competence of the service providers. Using two text mining methods (frequency analysis and modified LDA), the differences between the four kinds of property descriptions and their hosts are explained. Then, the findings are employed to better train the Natural language generation (NLG) algorithm. This shows how Long-Short-Term Memory (LSTM), as well as cleaned input, could help service providers generate more engaging content to communicate their service.
The research findings on each of these streams could contribute to several theories and facilitate further inquiries into big data analytics in marketing. This research also provides marketing practitioners with reliable and valid theories, models and decision support systems to gain insights and propose appropriate strategies to strengthen their firm.

Divisions:Concordia University > John Molson School of Business > Marketing
Item Type:Thesis (PhD)
Authors:Shirdastian, Hamid
Institution:Concordia University
Degree Name:Ph. D.
Program:Business Administration (Marketing specialization)
Date:15 March 2022
Thesis Supervisor(s):Laroche, Michel
Keywords:Big data, Big data analytics, AI, Brand sentiment, Brand authenticity, Location-based advertising, Destination-based advertising, Uber, Airbnb, Natural language processing, Natural language generation, Latent Dirichlet allocation, Communication
ID Code:990551
Deposited On:16 Jun 2022 15:26
Last Modified:16 Jun 2022 15:26


Aaker, J., Vohs, K. D., & Mogilner, C. (2010). Nonprofits are seen as warm and for-profits as competent: Firm stereotypes matter. Journal of Consumer Research, 37(2), 224-237.
Ahmad, S. N., & Laroche, M. (2017). Analyzing electronic word of mouth: A social commerce construct. International Journal of Information Management, (June), 37(3), 202-213..
Ailawadi, K. L., Lehmann, D. R., & Neslin, S. A. (2003). Revenue premium as an outcome measure of brand equity. Journal of Marketing, 67(4), 1-17.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior (pp. 11-39). Springer Berlin Heidelberg.
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79-95.
Assiouras, I., Liapati, G., Kouletsis, G., & Koniordos, M. (2015). The impact of brand authenticity on brand attachment in the food industry. British Food Journal, 117(2), 538-552.
Austin, J. L. (1975). How to do things with words. Oxford university press.
Barbier, G., & Liu, H. (2011). Data mining in social media. Social network data analytics (pp. 327-352). Springer.
Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research, 51(3) 270-285.
Bartikowski, B., & Singh, N. (2014). Should all firms adapt websites to international audiences? Journal of Business Research, 67(3), 246-252.
Bartikowski, B., Taieb, B., & Chandon, J. L. (2016). Targeting without alienating on the Internet: Ethnic minority and majority consumers. Journal of Business Research, 69(3) 1082-1089.
Bearden, W. O., Netemeyer, R. G., & Teel, J. E. (1989). Measurement of consumer susceptibility to interpersonal influence. Journal of Consumer Research, 15(4), 473-481.
Berger, J. Humphreys, A. Ludwig, S. Moe, W. W. Netzer, O. and Schweidel, D. A. (2020). Uniting the tribes: Using text for marketing insight. Journal of Marketing, 84(1), 1-25.
Bernritter, S. F. Verlegh, P. W. and Smit, E. G. (2016). Why nonprofits are easier to endorse on social media: The roles of warmth and brand symbolism. Journal of Interactive Marketing, 33, 27-42.
Beverland, M. B. (2005). Crafting brand authenticity: The case of luxury wines. Journal of Management Studies, 42(5), 1003-1029.
Beverland, M. B., & Farrelly, F. J. (2010). The quest for authenticity in consumption: Consumers’ purposive choice of authentic cues to shape experienced outcomes. Journal of Consumer Research, 36(5), 838-856.
Bifet, A., & Frank, E. (2010, October). Sentiment knowledge discovery in twitter streaming data. In: International Conference on Discovery Science (pp. 1-15). Springer Berlin Heidelberg.
Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological methods & research, 17(3), 303-316.
Bolton, R. N., Gustafsson, A., Tarasi, C. O., & Witell, L. (2021). Designing satisfying service encounters: website versus store touchpoints. Journal of the Academy of Marketing Science, 1-23.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679.
Brown, S., & Stayman, D. M. (1992). Antecedents and consequences of attitude toward the ad: A meta-analysis. Journal of Consumer Research 19(1), 34-51.
Brown, S., Kozinets, R. V., & Sherry Jr, J. F. (2003). Teaching old brands new tricks: Retro branding and the revival of brand meaning. Journal of Marketing, 67(3), 19-33.
Brumbaugh, A. M. (2002). Source and nonsource cues in advertising and their effects on the activation of cultural and subcultural knowledge on the route to persuasion. Journal of Consumer Research 29(2) 258-269.
Bruner, G. C., & Kumar, A. (2007). Attitude toward location-based advertising. Journal of Interactive Advertising, 7(2), 3-15.
Bulgarelli, D. and Molina, P. (2016). Social cognition in preschoolers: Effects of early experience and individual differences. Frontiers in Psychology, 7, 1762.
Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121-167.
Burke, R. R., & Srull, T. K. (1988). Competitive interference and consumer memory for advertising. Journal of Consumer Research, 15(1), 55-68.
Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116.
Chakraborty, I. Kim, M. and Sudhir, K. (2021). EXPRESS: Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes. Journal of Marketing Research, 00222437211052500.
Chan, K. W., Li, S. Y., & Zhu, J. J. (2015). Fostering customer ideation in crowdsourcing community: The role of peer-to-peer and peer-to-firm interactions. Journal of Interactive Marketing, 31, 42-62.
Chen, C. P., & Zhang, C. (2014). Data-intensive applications, challenges techniques and technologies: A survey on big data. Information Sciences, 275, 314-347.
Chen, C. Y. Mathur, P. and Maheswaran, D. (2014). The effects of country-related affect on product evaluations. Journal of Consumer Research, 41(4), 1033-1046.
Chen, Q., Clifford, S. J., & Wells, W. D. (2002). Attitude toward the site II: New information. Journal of Advertising Research, 42(2), 33-45.
Cho, C., & Cheon, H. J. (2004). Why do people avoid advertising on the internet? Journal of Advertising, 33(4), 89-97.
Choi, H., Ko, E., Kim, E. Y., & Mattila, P. (2015). The role of fashion brand authenticity in product management: A holistic marketing approach. Journal of Product Innovation Management, 32(2), 233-242.
Chu, S., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47-75.
Chung, J. Johar, G. V. Li, Y. Netzer, O. and Pearson, M. (2021). Mining Consumer Minds: Downstream Consequences of Host Motivations for Home Sharing Platforms. Journal of Consumer Research, 48(5), 817-838.
Cleveland, M., Rojas-Méndez, J. I., Laroche, M., & Papadopoulos, N. (2016). Identity, culture, dispositions and behavior: A cross-national examination of globalization and culture change. Journal of Business Research, 69(3) 1090-1102.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Danziger, S., Montal, R., & Barkan, R. (2012). Idealistic advice and pragmatic choice: A psychological distance account. Journal of Personality and Social Psychology, 102(6), 1105.
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391.
Del Chiappa, G. Pung, J. M. Atzeni, M., & Sini, L. (2021). What prevents consumers that are aware of Airbnb from using the platform? A mixed methods approach. International Journal of Hospitality Management, 93, 102775.
Donath, J. (2021). Commentary: The Ethical Use of Powerful Words and Persuasive Machines. Journal of Marketing, 85(1), 160-162.
Driscoll, K., & Walker, S. (2014). Big data, big questions working within a black box: Transparency in the collection and production of big twitter data. International Journal of Communication, 8, 20.
Drolet, A., & Frances Luce, M. (2004). The rationalizing effects of cognitive load on emotion- based trade-off avoidance. Journal of Consumer Research, 31(1), 63-77.
Ducoffe, R. H. (1995). How consumers assess the value of advertising. Journal of Current Issues and Research in Advertising 17(1) 1-18.
Eggers, F., O’Dwyer, M., Kraus, S., Vallaster, C., & Güldenberg, S. (2013). The impact of brand authenticity on brand trust and SME growth: A CEO perspective. Journal of World Business, 48(3), 340-348.
Eisend, M. (2008). Explaining the impact of scarcity appeals in advertising: The mediating role of perceptions of susceptibility. Journal of Advertising, 37(3), 33-40.
Fiske, S. T. Cuddy, A. J. and Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11(2), 77-83.
Fournier, S. and Alvarez, C. (2012). Brands as relationship partners: Warmth, competence, and in‐between. Journal of Consumer Psychology, 22(2), 177-185.
Fournier, S., & Avery, J. (2011). The uninvited brand. Business Horizons, 54(3), 193-207.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Gaspar, R., Pedro, C., Panagiotopoulos, P., & Seibt, B. (2016). Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Computers in Human Behavior, 56, 179-191.
Geiger, A. Horbel, C. and Germelmann, C. C. (2018). “Give and take”: how notions of sharing and context determine free peer-to-peer accommodation decisions. Journal of Travel and Tourism Marketing, 35(1), 5-15.
Gensler, S., Völckner, F., Liu-Thompkins, Y., & Wiertz, C. (2013). Managing brands in the social media environment. Journal of Interactive Marketing, 27(4), 242-256.
Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40(16), 6266-6282.
Ghiassi, M., Zimbra, D., & Lee, S. (2016). Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. Journal of Management Information Systems, 33(4), 1034-1058.
Ghose, A., Kwon, H. E., Lee, D., & Oh, W. (2019). Seizing the commuting moment: Contextual targeting based on mobile transportation apps. Information Systems Research, 30(1) 154-174.
Ghose, A., Li, B., & Liu, S. (2019). Mobile targeting using customer trajectory patterns. Management Science, 65(11), 5027-5049.
Gibbs, C. Guttentag, D. Gretzel, U. Morton, J. and Goodwill, A. (2018). Pricing in the sharing economy: A hedonic pricing model applied to Airbnb listings. Journal of Travel and Tourism Marketing, 35(1), 46-56.
Gilmore, J. H., & Pine, B. J. (2007). Authenticity: What consumers really want? Harvard Business Press.
Goldfarb, A., & Tucker, C. (2011). Online display advertising: Targeting and obtrusiveness. Marketing Science, 30(3), 389-404.
Gopaldas, A. (2014). Marketplace sentiments. Journal of Consumer Research, 41(4), 995-1014.
Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science 27(2) 184-206.
Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2) 193-225.
Grewal, D., Bart, Y., Spann, M., & Zubcsek, P. P. (2016). Mobile advertising: A framework and research agenda. Journal of Interactive Marketing, 34, 3-14.
Grewal, R. Gupta, S. and Hamilton, R. (2021). Marketing Insights from Multimedia Data: Text, Image, Audio, and Video. Journal of Marketing Research, 58(6), 1025-1033.
Habibi, M. R., Laroche, M., & Richard, M. (2014). Brand communities based in social media: How unique are they? Evidence from two exemplary brand communities. International Journal of Information Management, 34(2), 123-132.
Hair Jr, J. F. and Sarstedt, M. (2021). Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing. Journal of Marketing Theory and Practice, 29(1), 65-77.
Halkias, G. and Diamantopoulos, A. (2020). Universal dimensions of individuals' perception: Revisiting the operationalization of warmth and competence with a mixed-method approach. International Journal of Research in Marketing, 37(4), 714-736.
Harris, J., & Blair, E. A. (2006). Consumer preference for product bundles: The role of reduced search costs. Journal of the Academy of Marketing Science, 34(4), 506-513.
Haugtvedt, C. P., Petty, R. E., & Cacioppo, J. T. (1992). Need for cognition and advertising: Understanding the role of personality variables in consumer behavior. Journal of Consumer Psychology, 1(3), 239-260.
He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management, 52(7), 801-812.
Herrmann, L. K., & Kim, J. (2017). The fitness of apps: a theory-based examination of mobile fitness app usage over 5 months. Mhealth, 3(1), 1-9.
Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104-121.
Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016). Big data and consumer behavior: Imminent opportunities. Journal of Consumer Marketing, 33(2), 89-97.
Hovy, D. Melumad, S. and Inman, J. J. (2021). Wordify: A Tool for Discovering and Differentiating Consumer Vocabularies. Journal of Consumer Research, 48(3), 394-414.
Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42-53.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50.
Huberty, M. (2015). Can we vote with our tweet? On the perennial difficulty of election forecasting with social media. International Journal of Forecasting, 31(3), 992–1007.
Humphreys, A. and Wang, R. J. H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44(6), 1274-1306.
Hutto, C. and Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media, 8, 1, 216-225).
Internet live stats (2015). Twitter statistics. Retrieved July 14, 2015, from http://www.internetlivestats.com/twitter-statistics/

Izquierdo-Yusta, A., Olarte-Pascual, C., & Reinares-Lara, E. (2015). Attitudes toward mobile advertising among users versus non-users of the mobile internet. Telematics and Informatics, 32(2), 355-366.
Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188.
Johnson, A. R., Thomson, M., & Jeffrey, J. (2015). What does brand authenticity mean? Causes and consequences of consumer scrutiny toward a brand narrative. Brand Meaning Management, 12, 1-27.
Johnson-Laird, P. N., & Oatley, K. (1989). The language of emotions: An analysis of a semantic field. Cognition and Emotion, 3(2), 81-123.
Kadirov, D. (2015). Private labels ain’t bona fide! Perceived authenticity and willingness to pay a price premium for national brands over private labels. Journal of Marketing Management, 31(17-18), 1773-1798.
Kanemoto, E., & Dai, Z. (2015). Who cares about their names: Case study in Starbucks. Ray Browne Conference on Cultural and Critical Studies, 3
Kang, H., Hahn, M., Fortin, D. R., Hyun, Y. J., & Eom, Y. (2006). Effects of perceived behavioral control on the consumer usage intention of e‐coupons. Psychology & Marketing, 23(10), 841-864.
Kannan, P. K. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1) 22-45.
Kim, B., & Sullivan, M. W. (1998). The effect of parent brand experience online extension trial and repeat purchase. Marketing Letters, 9(2), 181-193.
Kim, C., Li, W., & Kim, D. J. (2015). An empirical analysis of factors influencing M-shopping use. International Journal of Human-Computer Interaction, 31(12), 974-994.
Kim, K. J. (2014). Can smartphones be specialists? Effects of specialization in mobile advertising. Telematics and Informatics, 31(4), 640-647.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Klein, J. G., Smith, N. C., & John, A. (2004). Why we boycott: Consumer motivations for boycott participation. Journal of Marketing, 68(3), 92-109.
Ko, D., Seo, Y., & Jung, S. U. (2015). Examining the effect of cultural congruence, processing fluency, and uncertainty avoidance in online purchase decisions in the US and Korea. Marketing Letters 26(3), 377-390.
Kumar, A., Bezawada, R., Rishika, R., Janakiraman, R., & Kannan, P. (2016). From social to sale: The effects of firm-generated content in social media on customer behavior. Journal of Marketing, 80(1), 7-25.
Laroche, M., Habibi, M. R., & Richard, M. (2013). To be or not to be in social media: How brand loyalty is affected by social media? International Journal of Information Management, 33(1), 76-82.
Lee, H., Han, J., & Suh, Y. (2014). Gift or threat? An examination of voice of the customer: The case of MyStarbucksIdea.com. Electronic Commerce Research and Applications, 13(3), 205-219.
Lee, J., & Hong, I. B. (2016). Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. International Journal of Information Management, 36(3), 360-373.
Lee, M. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3) 130-141.
Lee, S., Kim, K. J., & Sundar, S. S. (2015). Customization in location-based advertising: Effects of tailoring source, locational congruity, and product involvement on ad attitudes. Computers in Human Behavior, 51, Part A, 336-343.
Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881-894.
Li, H., Edwards, S. M., & Lee, J. (2002). Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising, 31(2), 37-47.
Li, J., Moreno, A., & Zhang, D. J. (2019). Agent pricing in the sharing economy: Evidence from Airbnb. In Sharing economy (pp. 485-503). Springer, Cham.
Liberman, N., & Trope, Y. (2014). Traversing psychological distance. Trends in cognitive sciences, 18(7), 364-369.
Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1995). Assessing the domain specificity of deal proneness: A field study. Journal of Consumer Research 22(3), 314-326.
Lin, T. T., Paragas, F., Goh, D., & Bautista, J. R. (2016). Developing location-based mobile advertising in Singapore: A socio-technical perspective. Technological Forecasting and Social Change 103, 334-349.
Liu, C., Sinkovics, R. R., Pezderka, N., & Haghirian, P. (2012). Determinants of consumer perceptions toward mobile advertising: A comparison between Japan and Austria. Journal of Interactive Marketing 26(1) 21-32.
Lu, H., & Yu-Jen Su, P. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research 19(4), 442-458.
Luo, X. Qin, M. S. Fang, Z. and Qu, Z. (2021). Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions. Journal of Marketing, 85(2), 14-32.
Luo, X., Andrews, M., Fang, Z., & Phang, C. W. (2014). Mobile targeting. Management Science, 60(7) 1738-1756.
Ma, S. Cui, X. Xiao, X. and Zhao, X. (2022). The impact of photo verification service on sales performance in the peer-to-peer economy: Moderating role of customer uncertainty. Journal of Business Research, 142, 45-55.
MacKenzie, S. B., & Lutz, R. J. (1989). An empirical examination of the structural antecedents of attitude toward the ad in an advertising pretesting context. Journal of Marketing, 53(2), 48-65.
Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media, 57(1), 20-33.
Marafino, B. J., Davies, J. M., Bardach, N. S., Dean, M. L., & Dudley, R. A. (2014). N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. Journal of the American Medical Informatics Association, 21(5), 871-875.
McGuire, W. (1968). Personality and susceptibility to social influence. In E. F. Borgatta, & W. W. Lambert (Eds.), Handbook of personality theory and research (pp. 1130–1187). Chicago: Rand McNally.
Merisavo, M., Kajalo, S., Karjaluoto, H., Virtanen, V., Salmenkivi, S., Raulas, M., & Leppäniemi, M. (2007). An empirical study of the drivers of consumer acceptance of mobile advertising. Journal of Interactive Advertising, 7(2), 41-50.
Miller, F. M. (2015). Ad authenticity: An alternative explanation of advertising’s effect on established brand attitudes. Journal of Current Issues and Research in Advertising, 36(2) 177-194.
Milstein, S., Lorica, B., Magoulas, R., Hochmuth, G., Chowdhury, A., & O'Reilly, T. (2008). Twitter and the micro-messaging revolution: Communication, connections, and immediacy--140 characters at a time. O'Reilly Media, Incorporated.
Moody, G. D., Galletta, D. F., & Dunn, B. K. (2017). Which phish get caught? An exploratory study of individuals′ susceptibility to phishing. European Journal of Information Systems, 26(6), 564-584.
Morhart, F., Malär, L., Guèvremont, A., Girardin, F., & Grohmann, B. (2015). Brand authenticity: An integrative framework and measurement scale. Journal of Consumer Psychology, 25(2), 200-218.
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251.
Muehling, D. D. (1987). Comparative advertising: The influence of attitude-toward-the-ad on brand evaluation. Journal of Advertising 16(4), 43-49.
Napoli, J., Dickinson, S. J., Beverland, M. B., & Farrelly, F. (2014). Measuring consumer-based brand authenticity. Journal of Business Research, 67(6), 1090-1098.
Naylor, R. W., Lamberton, C. P., & West, P. M. (2012). Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. Journal of Marketing, 76(6), 105-120.
Netemeyer, R. G., Krishnan, B., Pullig, C., Wang, G., Yagci, M., Dean, D., ... & Wirth, F. (2004). Developing and validating measures of facets of customer-based brand equity. Journal of Business Research, 57(2), 209-224.
Noguera, J. M., Barranco, M. J., Segura, R. J., & Martínez, L. (2012). A mobile 3D-GIS hybrid recommender system for tourism. Information Sciences 215, 37-52.
Nysveen, H., Pedersen, P. E., &Thorbjørnsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346.
Olah, C. (2015). Understanding LSTM Networks. Colah’s blog, Retrieved February 28, 2022, from http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31, 527-541.
Packard, G. and Berger, J. (2021). How Concrete Language Shapes Customer Satisfaction. Journal of Consumer Research, 47(5), 787-806.
Park, E. (2019). The role of satisfaction on customer reuse to airline services: An application of Big Data approaches. Journal of Retailing and Consumer Services, 47, 370-374.
Peng, L., Cui, G., Bao, Z., & Liu, S. (2021). Speaking the same language: the power of words in crowdfunding success and failure. Marketing Letters, 1-13.
Pine, B. J., Peppers, D., & Rogers, M. (1995). Do you want to keep your customers forever? Harvard Business Press.
Pitt, C., Plangger, K., & Eriksson, T. (2021). Accommodation eWOM in the sharing economy: automated text comparisons from a large sample. Journal of Hospitality Marketing and Management, 30(2), 258-275.
Plangger, K., & Montecchi, M. (2020). Thinking beyond privacy calculus: Investigating reactions to customer surveillance. Journal of Interactive Marketing, 50, 32-44.
Ranchordás, S. (2015). Does sharing mean caring: Regulating innovation in the sharing economy. Minnesota Journal of Law Science & Technology. 16, 413.
Richard, M. O. (2005). Modeling the impact of internet atmospherics on surfer behavior. Journal of Business Research, 58(12) 1632-1642.
Richard, M. O., & Habibi, M. R. (2016). Advanced modeling of online consumer behavior: The moderating roles of hedonism and culture. Journal of Business Research, 69(3) 1103-1119.
Rogers, T., & Bazerman, M. H. (2008). Future lock-in: Future implementation increases selection of ‘should’ choices. Organizational Behavior and Human Decision Processes, 106(1), 1-20.
San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350.
Saxena, S. (2020). Retrieved February 28, 2022, from https://medium.com/analytics-vidhya/understanding-embedding-layer-in-keras-bbe3ff1327ce
Schallehn, M., Burmann, C., & Riley, N. (2014). Brand authenticity: Model development and empirical testing. Journal of Product & Brand Management, 23(3), 192-199.
Scherer, K. R., Schorr, A., & Johnstone, T. (Eds.). (2001). Appraisal processes in emotion: Theory, methods, research. Oxford University Press.
Schmitt, B. (2019). From atoms to bits and back: A research curation on digital technology and agenda for future research. Journal of Consumer Research, 46(4), 825-832.
Sheth, J. (2021). New areas of research in marketing strategy, consumer behavior, and marketing analytics: the future is bright. Journal of Marketing Theory and Practice, 29(1), 3-12.
Shi, Z., Rui, H., & Whinston, A. B. (2013). Content sharing in a social broadcasting environment: evidence from twitter. MIS Quarterly, 38(1), 123-142.
Shirdastian, H., Laroche, M., & Richard, M. O. (2019). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48 291-307.
Shobeiri, S., Mazaheri, E., & Laroche, M. (2018). Creating the right customer experience online: The influence of culture. Journal of Marketing Communications 24(3) 270-290.
Shumanov, M. Cooper, H. and Ewing, M. (2021). Using AI predicted personality to enhance advertising effectiveness. European Journal of Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJM-12-2019-0941
Shusterman, A. (2020). Retrieved February 28, 2022, from https://datascience.stackexchange.com/questions/83984/how-to-choose-dimension-of-keras-embedding-layer
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., & Chanona-Hernández, L. (2014). Syntactic N-grams as machine learning features for natural language processing. Expert Systems with Applications, 41(3), 853-860.
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., & Chanona-Hernández, L. (2014). Syntactic n-grams as machine learning features for natural language processing. Expert Systems with Applications, 41(3), 853-860.
Smith, A. N., Fischer, E., & Yongjian, C. (2012). How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113.
Social bakers (2015), Starbucks statistics. Retrieved July 14, 2015, from http://www.socialbakers.com/statistics/twitter/profiles/detail/30973-starbucks
Song, R., Moon, S., Chen, H. A., & Houston, M. B. (2018). When marketing strategy meets culture: the role of culture in product evaluations. Journal of the Academy of Marketing Science, 46(3), 384-402.
Spiggle, S., Nguyen, H. T., & Caravella, M. (2012). More than fit: brand extension authenticity. Journal of Marketing Research, 49(6), 967-983.
Steenkamp, J. B. E., & Maydeu-Olivares, A. (2015). Stability and change in consumer traits: evidence from a 12-year longitudinal study 2002–2013. Journal of Marketing Research, 52(3) 287-308.
Steinhoff, L., Arli, D., Weaven, S., & Kozlenkova, I. V. (2019). Online relationship marketing. Journal of the Academy of Marketing Science, 47(3), 369-393.
Stryker, S., & Burke, P. J. (2000). The past, present, and future of an identity theory. Social Psychology Quarterly, 63(4), 284-297.
Sultan, F., Rohm, A. J., & Gao, T. (2009). Factors influencing consumer acceptance of mobile marketing: A two-country study of youth markets. Journal of Interactive Marketing 23(4), 308-320.
Tellis, G. J., Chandy, R. K., MacInnis, D., & Thaivanich, P. (2005). Modeling the microeffects of television advertising: Which ad works, when, where, for how long, and why? Marketing Science, 359-366.
Thompson, C. J., Rindfleisch, A., & Arsel, Z. (2006). Emotional branding and the strategic value of the doppelgänger brand image. Journal of Marketing, 70(1), 50-64.
Touré-Tillery, M. and McGill, A. L. (2015). Who or what to believe: Trust and the differential persuasiveness of human and anthropomorphized messengers. Journal of Marketing, 79(4), 94-110.
Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review 117(2), 440.
Tussyadiah, I. P. and Park, S. (2018). When guests trust hosts for their words: Host description and trust in sharing economy. Tourism Management, 67, 261-272.
Twitter counter (2015), Starbucks. Retrieved July 14, 2015, from http://twittercounter.com/Starbucks
Vasisht, D., Kumar, S., & Katabi, D. (2016). Decimeter-level localization with a single wifi access point. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16) (pp. 165-178).
Walsh, M. G. (2013). Protecting your brand against the heartbreak of genericide. Business Horizons, 56(2), 159-166.
Wang, X., He, J., Curry, D. J., & Ryoo, J. H. (2021). Attribute Embedding: Learning Hierarchical Representations of Product Attributes from Consumer Reviews. Journal of Marketing, 00222429211047822.
Wang, Z. Mao, H. Li, Y. J. and Liu, F. (2017). Smile big or not? Effects of smile intensity on perceptions of warmth and competence. Journal of Consumer Research, 43(5), 787-805.
Weinstein, N. D. (1984). Why it won't happen to me: perceptions of risk factors and susceptibility. Health psychology, 3(5), 431.
Wikipedia (2015). Starbucks. Retrieved July 14, 2015, from https://en.wikipedia.org/wiki/Starbucks
Wilson, J., Crisp, C. B., & Mortensen, M. (2013). Extending construal-level theory to distributed groups: Understanding the effects of virtuality. Organization Science 24(2), 629-644.
Wolin, L. D., Korgaonkar, P., & Lund, D. (2002). Beliefs, attitudes and behavior towards web advertising. International Journal of Advertising 21(1), 87-113.
Woolley, K. and Sharif, M. A. (2021). Incentives Increase Relative Positivity of Review Content and Enjoyment of Review Writing. Journal of Marketing Research, 58(3), 539-558.
Wright, S., & Calof, J. L. (2006). The quest for competitive, business and marketing intelligence: A country comparison of current practices. European Journal of Marketing, 40(5/6), 453-465.
Xu, D. J., Liao, S. S., & Li, Q. (2008). Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications. Decision Support Systems, 44(3), 710-724.
Xu, H., Teo, H., Tan, B. C., & Agarwal, R. (2009). The role of push-pull technology in privacy calculus: The case of location-based services. Journal of Management Information Systems 26(3) 135-174.
Xue, A. Y., Zhang, R., Zheng, Y., Xie, X., Huang, J., & Xu, Z. (2013, April). Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In 2013 IEEE 29th international conference on data engineering (ICDE) (pp. 254-265). IEEE.
Yang, K. (2010). Determinants of US consumer mobile shopping services adoption: Implications for designing mobile shopping services. Journal of Consumer Marketing 27(3) 262-270.
Yang, K. (2012). Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services 19(5), 484-491.
Yun, T., Sim, K., & Kim, H. (2000). Support vector machine-based inspection of solder joints using circular illumination. Electronics Letters, 36(11), 949-951.
Zhang, Y. (1996). Responses to humorous advertising: The moderating effect of need for cognition. Journal of Advertising, 25(1), 15-32.
Zhang, Y., & Buda, R. (1999). Moderating effects of need for cognition on responses to positively versus negatively framed advertising messages. Journal of Advertising, 28(2), 1-15.
Zhou, X., & Tuck, D. P. (2007). MSVM-RFE: Extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics (Oxford, England), 23(9), 1106-1114.
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