The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Are Multichannel Customers Really more Valuable? The Moderating Role of Product Category Characteristics
Abstract
How does the monetary value of customer purchases vary by customer preference for purchase channels (e.g., traditional, electronic, multichannel) and product category? The authors develop a conceptual model and hypotheses on the moderating effects of two key product category characteristics—the utilitarian versus hedonic nature of the product category and perceived risk—on the channel preference–monetary value relationship. They test the hypotheses on a unique large-scale, empirically generalizable data set in the retailing context. Contrary to conventional wisdom that all multichannel customers are more valuable than single-channel customers, the results show that multichannel customers are the most valuable segment only for hedonic product categories. The findings reveal that traditional channel customers of low-risk categories provide higher monetary value than other customers. Moreover, for utilitarian product categories perceived as high (low) risk, web-only (catalog- or store-only) shoppers constitute the most valuable segment. The findings offer managers guidelines for targeting and migrating different types of customers for different product categories through different channels.
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Cites
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302
Measuring the hedonic and utilitarian dimensions of consumer attitude
Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003) · Journal of Marketing Research · doi:10.1509/jmkr.40.3.310.19238 · Cited by 2,151
Bolton, R. N. (1998) · Marketing Science · doi:10.1287/mksc.17.1.45 · Cited by 1,948
The transformation of poisson, binomial and negative-binomial data
ANSCOMBE, F. J. (1948) · Biometrika · doi:10.1093/biomet/35.3-4.246 · Cited by 1,300
On risk, convenience, and Internet shopping behavior
Bhatnagar, A., Misra, S., & Rao, H. R. (2000) · Communications of the ACM · doi:10.1145/353360.353371 · Cited by 1,089
Schlosser, A. E., White, T. B., & Lloyd, S. M. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.2.133 · Cited by 947
Brynjolfsson, E., Hu, Y. (., & Simester, D. (2011) · Management Science · doi:10.1287/mnsc.1110.1371 · Cited by 847
Adding Bricks to Clicks: Predicting the Patterns of Cross-Channel Elasticities over Time
Avery, J., Steenburgh, T. J., Deighton, J., & Caravella, M. (2012) · Journal of Marketing · doi:10.1509/jm.09.0081 · Cited by 533 · cited 1×
“We classify the store and catalog channels under the unifying banner of a traditional channel because they have a much longer history than electronic channels and are perceived as close substitutes (Avery et al. 2012).”
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Digital marketing: a framework, review, and research agenda
Kannan, P. K., & Li, H. “. (2017) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.11.006 · Cited by 1,707
Casidy, R., & Wymer, W. (2016) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.06.014 · Cited by 236
Arunachalam, S., Ramaswami, S. N., Herrmann, P., & Walker, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0574-1 · Cited by 123
A Review of Consumer-Facing Digital Technologies Across Different Types of Fashion Store Formats
Bonetti, F., & Perry, P. (2017) · Advances in Business Information Systems and Analytics · doi:10.4018/978-1-5225-1865-5.ch006 · Cited by 41
Bäckström, K., & Johansson, U. (2017) · The International Review of Retail, Distribution and Consumer Research · doi:10.1080/09593969.2017.1314865 · Cited by 39
Reimer, K., & Becker, J. U. (2015) · Management Review Quarterly · doi:10.1007/s11301-014-0110-z · Cited by 11
Mau, S., Cvijikj, I. P., & Wagner, J. (2015) · Zeitschrift für die gesamte Versicherungswissenschaft · doi:10.1007/s12297-015-0310-1 · Cited by 8
Metadata
- Authors
- Kushwaha, T., & Shankar, V.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2013-07
- Volume
- 77
- Issue
- 4
- Pages
- 67-85
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W2083301984
- Cited by
- 388
- References
- 8
- FWCI
- 72.57
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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