Redemption Intention of Coupons: A Meta-Analytical Review and Future Directions

References

• Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control (pp. 11–39). Berlin, Heidelberg: Springer.
   Google Scholar
• Andrews, M., Goehring, J., Hui, S., Pancras, J., & Thornswood, L. (2016). Mobile promotions: A framework and research priorities. Journal of Interactive Marketing, 34, 15–24. doi:10.1016/j.intmar.2016.03.004
   Web of Science ®Google Scholar
• Argo, J. J., & Main, K. J. (2008). Stigma by association in coupon redemption: Looking cheap because of others. Journal of Consumer Research, 35(4), 559–572. doi:10.1086/591102
   Web of Science ®Google Scholar
• Ashworth, L., Darke, P. R., & Schaller, M. (2005). No one wants to look cheap: Trade‐offs between social disincentives and the economic and psychological incentives to redeem coupons. Journal of Consumer Psychology, 15(4), 295–306. doi:10.1207/s15327663jcp1504_4
   Web of Science ®Google Scholar
• Barwise, P., & Strong, C. (2002). Permission-based mobile advertising. Journal of Interactive Marketing, 16(1), 14–24. doi:10.1002/dir.10000
   Google Scholar
• Bawa, K., & Shoemaker, R. W. (1987). The effects of a direct mail coupon on brand choice behavior. Journal of Marketing Research, 24(4), 370–376. doi:10.2307/3151384
   Web of Science ®Google Scholar
• Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4), 1088–1101. doi:10.2307/2533446
   PubMed Web of Science ®Google Scholar
• Bhatnagar, A., & Ghose, S. (2004). Online information search termination patterns across product categories and consumer demographics. Journal of Retailing, 80(3), 221–228. doi:10.1016/j.jretai.2003.10.001
   Web of Science ®Google Scholar
• Biswas, D., & Burman, B. (2009). The effects of product digitalization and price dispersion on search intentions in offline versus online settings: The mediating effects of perceived risks. Journal of Product & Brand Management, 18(7), 477–486. doi:10.1108/10610420910998208
   Google Scholar
• Blattberg, R. C., & Neslin, S. A. (1990). Sales promotion: Concepts, methods, and strategies (pp. 349–350). Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to meta-analysis. New York, NY: John Wiley & Sons.
   Google Scholar
• Card, N. A. (2015). Basic computations: Computing mean effect size and heterogeneity around this mean. In Applied meta-analysis for social science research. New York; London: Guilford Publications.
   Google Scholar
• Clark, R. A., Zboja, J. J., & Goldsmith, R. E. (2013). Antecedents of coupon proneness: A key mediator of coupon redemption. Journal of Promotion Management, 19(2), 188–210. doi:10.1080/10496491.2013.769475
   Google Scholar
• Chakraborty, U., & Bhat, S. (2018). The effects of credible online reviews on brand equity dimensions and its consequence on consumer behavior. Journal of Promotion Management, 24(1), 57–82. doi:10.1080/10496491.2017.1346541
   Google Scholar
• Chen, M. F., & Lu, T. Y. (2011). Modeling e-coupon proneness as a mediator in the extended TPB model to predict consumers' usage intentions. Internet Research, 21(5), 508–526. doi:10.1108/10662241111176344
   Web of Science ®Google Scholar
• Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., & Sun, P. C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399–416. doi:10.1016/j.compedu.2004.06.001
   Web of Science ®Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. New York, NY: Routledge.
   Google Scholar
• Cornwell, J. M. (1993). Monte Carlo comparisons of three tests for homogeneity of independent correlations. Educational and Psychological Measurement, 53(3), 605–618. doi:10.1177/0013164493053003003
   Web of Science ®Google Scholar
• Cornwell, J. M., & Ladd, R. T. (1993). Power and accuracy of the Schmidt and Hunter meta-analytic procedures. Educational and Psychological Measurement, 53(4), 877–895. doi:10.1177/0013164493053004002
   Web of Science ®Google Scholar
• Danaher, P. J., Smith, M. S., Ranasinghe, K., & Danaher, T. S. (2015). Where, when, and how long: Factors that influence the redemption of mobile phone coupons. Journal of Marketing Research, 52(5), 710–725. doi:10.1509/jmr.13.0341
   Web of Science ®Google Scholar
• David, R. J., & Han, S. K. (2004). A systematic assessment of the empirical support for transaction cost economics. Strategic Management Journal, 25(1), 39–58. doi:10.1002/smj.359
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008
   Web of Science ®Google Scholar
• Dickinger, A., & Kleijnen, M. (2008). Coupons going wireless: Determinants of consumer intentions to redeem mobile coupons. Journal of Interactive Marketing, 22(3), 23–39. doi:10.1002/dir.20115
   Web of Science ®Google Scholar
• Edwards, S. M., Li, H., & Lee, J. H. (2002). Forced exposure and psychological reactance: Antecedents and consequences of the perceived intrusiveness of pop-up ads. Journal of Advertising, 31(3), 83–95. doi:10.1080/00913367.2002.10673678
   Web of Science ®Google Scholar
• Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical Research ed.), 315(7109), 629–634. doi:10.1136/bmj.315.7109.629
   PubMed Web of Science ®Google Scholar
• Farquhar, J. D., & Rowley, J. (2009). Convenience: A services perspective. Marketing Theory, 9(4), 425–438. doi:10.1177/1470593109346894
   Web of Science ®Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
   Google Scholar
• Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55–75. doi:10.1002/dir.20061
   Google Scholar
• Fortin, D. R. (2000). Clipping coupons in cyberspace: A proposed model of behavior for deal‐prone consumers. Psychology and Marketing, 17(6), 515–534. doi:10.1002/(SICI)1520-6793(200006)17:6<515::AID-MAR5>3.0.CO;2-B
   Web of Science ®Google Scholar
• Garretson, J. A., & Burton, S. (2003). Highly coupon and sale prone consumers: Benefits beyond price savings. Journal of Advertising Research, 43(2), 162–172. doi:10.2501/JAR-43-2-162-172
   Web of Science ®Google Scholar
• Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5(10), 3–8. doi:10.3102/0013189X005010003
   Google Scholar
• Guimond, L., Kim, C., & Laroche, M. (2001). An investigation of coupon-prone consumers: Their reactions to coupon feature manipulations. Journal of Business Research, 54(2), 131–137. doi:10.1016/S0148-2963(99)00091-0
   Web of Science ®Google Scholar
• Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414), 557. doi:10.1136/bmj.327.7414.557
   PubMed Web of Science ®Google Scholar
• Hsu, T. H., Wang, Y. S., & Wen, S. C. (2006). Using the decomposed theory of planning behavioural to analyse consumer behavioural intention towards mobile text message coupons. Journal of Targeting, Measurement and Analysis for Marketing, 14(4), 309–324. doi:10.1057/palgrave.jt.5740191
   Google Scholar
• Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods, 11(2), 193–206. doi:10.1037/1082-989X.11.2.193
   PubMed Web of Science ®Google Scholar
• Hui, S. K., Inman, J. J., Huang, Y., & Suher, J. (2013). The effect of in-store travel distance on unplanned spending: Applications to mobile promotion strategies. Journal of Marketing, 77(2), 1–16. doi:10.1509/jm.11.0436
   Web of Science ®Google Scholar
• Hunter, J. E., & Schmidt, F. L. (1990). Methods of meta-analysis: Correcting error and bias in research findings. Los Angeles, CA: Sage.
   Google Scholar
• Im, H., & Ha, Y. (2013). Enablers and inhibitors of permission-based marketing: A case of mobile coupons. Journal of Retailing and Consumer Services, 20(5), 495–503. doi:10.1016/j.jretconser.2013.05.002
   Google Scholar
• Jayasingh, S., & Eze, U. C. (2010). The role of moderating factors in mobile coupon adoption: An extended TAM perspective. Communications of the IBIMA, 1. doi:10.5171/2010.985461
   Google Scholar
• Jin, B., Sternquist, B., & Koh, A. (2003). Price as hedonic shopping. Family and Consumer Sciences Research Journal, 31(4), 378–402. doi:10.1177/1077727X03031004003
   Google Scholar
• 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 and Marketing, 23(10), 841–864. doi:10.1002/mar.20136
   Web of Science ®Google Scholar
• Keskar, M. Y., & Pandey, N. (2018). Internet banking: A review (2002–2016). Journal of Internet Commerce, 1–14. doi:10.1080/15332861.2018.1451969
   Web of Science ®Google Scholar
• Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322. doi:10.1016/j.chb.2009.10.013
   Web of Science ®Google Scholar
• Kim, H. W., Chan, H. C., & Chan, Y. P. (2007). A balanced thinking–feelings model of information systems continuance. International Journal of Human-Computer Studies, 65(6), 511–525. doi:10.1016/j.ijhcs.2006.11.009
   Web of Science ®Google Scholar
• Kim, N., Lee, M., & Kim, H. R. (2008). The effect of service coupons on the consumer trade-offs between price and perceived quality. Journal of Promotion Management, 14(1–2), 59–76. doi:10.1080/10496490802508716
   Google Scholar
• Lalwani, A. K., Wang, J. J., & Janiszewski, C. (2018). How do consumers’ cultural backgrounds and values influence their coupon proneness?: A multi-method investigation. Journal of Consumer Research, 45(5), 1037–1050. doi:10.1093/jcr/ucy033
   Web of Science ®Google Scholar
• Leppaniemi, M., & Karjaluoto, H. (2005). Factors influencing consumers' willingness to accept mobile advertising: A conceptual model. International Journal of Mobile Communications, 3(3), 197–213. doi:10.1504/IJMC.2005.006580
   Google Scholar
• Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1990). Distinguishing coupon proneness from value consciousness: An acquisition-transaction utility theory perspective. The Journal of Marketing, 54(3), 54. doi:10.2307/1251816
   Web of Science ®Google Scholar
• Light, R. J., & Pillemer, D. B. (1984). Summing up: The science of reviewing research. Cambridge, MA: Harvard University Press.
   Google Scholar
• Liu, F., Zhao, X., Chau, P. Y., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research, 25(3), 471–495. doi:10.1108/IntR-02-2014-0053
   Web of Science ®Google Scholar
• Mills, P., & Zamudio, C. (2018). Scanning for discounts: Examining the redemption of competing mobile coupons. Journal of the Academy of Marketing Science, 46(5), 964–982. doi:10.1007/s11747-018-0592-7
   Web of Science ®Google Scholar
• Muk, A. (2012). Perceptions of mobile coupons: A cross-national study. Journal of Direct, Data and Digital Marketing Practice, 13(4), 311–324. doi:10.1057/dddmp.2011.48
   Google Scholar
• Pandey, N., & Gudipudi, B. (2018). Understanding ‘what is privacy’ for millennials on Facebook in India. Journal of Data Protection and Privacy, 2, 39–49.
   Google Scholar
• Pandey, N., & Maheshwari, V. (2017). Four decades of coupon research in pricing: Evolution, development, and practice. Journal of Revenue and Pricing Management, 16(4), 397–416. doi:10.1057/s41272-016-0076-7
   Web of Science ®Google Scholar
• Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. doi:10.1177/109467050024001
   Google Scholar
• Patwardhan, A. A., Pandey, N., & Dhume, S. M. (2017). Integrated model for understanding Indian physicians’ internet usage pattern: An empirical approach. International Journal of Healthcare Management, 10(1), 19–33. doi:10.1080/20479700.2016.1270385
   Web of Science ®Google Scholar
• Paul, J., & Mas, E. (2016). The emergence of China and India in the global market. Journal of East-West Business, 22(1), 28–50. doi:10.1080/10669868.2015.1117034
   Web of Science ®Google Scholar
• Inmar. (2017). Promotion industry analysis reports for USA. Retrieved from http://couponpros.org/wp-content/uploads/Insight-Inmar-2017-Promotion-Industry-Analysis.pdf
   Google Scholar
• Raghubir, P. (2004). Free gift with purchase: Promoting or discounting the brand? Journal of Consumer Psychology, 14(1–2), 181–186. doi:10.1207/s15327663jcp1401&2_20
   Web of Science ®Google Scholar
• Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York, NY: The Free Press
   Google Scholar
• Scharl, A., Dickinger, A., & Murphy, J. (2005). Diffusion and success factors of mobile marketing. Electronic Commerce Research and Applications, 4(2), 159–173. doi:10.1016/j.elerap.2004.10.006
   Web of Science ®Google Scholar
• Spralls, S. A., III, Divine, R. L., & Garver, M. S. (2016). Have the mobile coupon preferences of millennials changed? Journal of Promotion Management, 22(6), 792–809. doi:10.1080/10496491.2016.1214204
   Google Scholar
• Sterne, J. A. C., Becker, B. J., & Egger, M. (2005). The funnel plots. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta- analysis: Prevention, assessment and adjustments (pp. 75–98). Hoboken, NJ: Wiley.
   Google Scholar
• Swaminathan, S., & Bawa, K. (2005). Category-specific coupon proneness: The impact of individual characteristics and category-specific variables. Journal of Retailing, 81(3), 205–214. doi:10.1016/j.jretai.2005.07.004
   Web of Science ®Google Scholar
• Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220. doi:10.1016/S0022-4359(01)00041-0
   Web of Science ®Google Scholar
• Tat, P. K. (1996). A motivation-based model of coupon usage. Journal of Promotion Management, 3(1–2), 1–14. doi:10.1300/J057v03n01_01
   Google Scholar
• To, P. L., & Sung, E. P. (2014). Hedonic motivations for online shopping. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 8(7), 2222–2224.
   Google Scholar
• Varaprasad, G., Sridharan, R., & Anandakuttan, B, U. (2013). Internet banking adoption in a developing country–an empirical study. International Journal of Services and Operations Management, 14(1), 54–66. doi:10.1504/IJSOM.2013.050561
   Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.
   Web of Science ®Google Scholar
• Wright, S. (1934). The method of path coefficients. The Annals of Mathematical Statistics, 5(3), 161–215. doi:10.1214/aoms/1177732676
   Google Scholar