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Ho HF. A novel approach for exploring channel dependence of consumers' latent shopping intent and the related behaviors by visualizing browsing patterns. DATA TECHNOLOGIES AND APPLICATIONS 2021. [DOI: 10.1108/dta-11-2020-0295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThis study is to propose a more effective and efficient analytic methodology based on within-site clickstream associated with path visualization to explore the channel dependence of consumers' latent shopping intent and the related behaviors, with which in turn to gain insight concerning the interactivity between webpages.Design/methodology/approachThe primary intention of the research is to design and develop a more effective and efficient approach for exploring the consumers' latent shopping intent and the related behaviors from the clickstream data. The proposed methodology is to use text-mining package, consisting of the combination of hierarchical recurrent neural networks and Hopfield-like neural network equipped with Laplacian-based graph visualization to visualize the consumers' browsing patterns. Based on the observed interactivity between webpages, consumers' latent shopping intent and the related behaviors can be understood.FindingsThe key finding is to evidence that consumers' latent shopping intent and related behaviors within website depend on channels the consumers click through. The accessing consumers through channels of paid search and display advertising are identified and categorized as goal-directed and exploratory modes, respectively. The results also indicate that the effect of the content of webpage on the consumer's purchase intent varies with channels. This implies that website optimization and attribution of online advertising should also be channel-dependent.Practical implicationsThis is important for the managerial and theoretical implications: First, to uncover the channel dependence of consumer's latent shopping intent and browsing behaviors would be helpful to the attribution of the online advertising for the sales promotion. Second, in the past, webmasters did not understand users' preferences and make decisions of reorganization purely on the user's browsing path (sequential page view) without appraising psychological perspective, that is, user's latent shopping intent.Originality/valueThis study is the first to explore the channel dependences of consumer's latent shopping intent and the related browsing behaviors through within-site clickstream associated with path visualization. The findings are helpful to the attribution of the online advertising for the sales promotion and useful for webmasters to optimize the effectiveness and usability of their websites and in turn promote the purchase decision.
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Levi-Bliech M, Pliskin N, Fink L. Implementing A Sales Support app to Complement Face-to-Face Interaction: An Empirical Investigation of Business Value. JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE 2020. [DOI: 10.1080/10919392.2020.1750932] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Michal Levi-Bliech
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Nava Pliskin
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lior Fink
- Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Wu X, Yuan X, Duan C, Wu J. A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3509-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features. Neural Comput Appl 2016. [DOI: 10.1007/s00521-015-2157-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Hyper-Spherical Search (HSS) algorithm: a novel meta-heuristic algorithm to optimize nonlinear functions. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1636-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Wu X, Wen X, Li J, Yao L. A new dynamic Bayesian network approach for determining effective connectivity from fMRI data. Neural Comput Appl 2013. [DOI: 10.1007/s00521-013-1465-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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