Skip to main content
SpringerLink
Log in

The effect of decision makers’ time perspective on intention–behavior consistency

  • Published:
Marketing Letters Aims and scope Submit manuscript

Abstract

This research demonstrates that decision makers’ time perspective—a cognitive, temporal bias that leads people to overemphasize the past, present, or future in their decision making—systematically influences self-reported behavioral intentions and thus intention–behavior consistency for distant-future behaviors. Whereas present-hedonistic individuals overstate their intentions, present-fatalistic individuals understate theirs, so both types exhibit low intention–behavior consistency. Future time-oriented individuals instead exhibit high intention–behavior consistency because they are less likely to overstate their intentions. The findings are contributed to decision makers’ time perspective influencing the construal of distant-future behavior when reporting behavioral intentions. Accounting for decision makers’ time perspectives helps improve predictive accuracy and may change insights obtained from causal models that use self-reported intentions as a proxy for actual, distant-future behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Although Zimbardo and Boyd (1999) measure time perspective with 56 items, 29 items led to factor loadings lower than 0.50, which qualified them for elimination (Hair et al. 2010). With a factor loading of greater than 0.60 as a cut-off value, we selected three items for each time perspective. The confirmatory factor analyses confirmed the five time perspectives: study 1 χ 2(80) = 109.54, p < 0.01, RMSEA = 0.071, GFI = 0.91, CFI = 0.92 and study 2 χ 2(80) = 131.73, p < 0.01, RMSEA = 0.079, GFI = 0.90, CFI = 0.91. The reliabilities of the individual scales also were good, ranging from 0.70 to 0.86 (see Table 1). All factor loadings were > 0.70.

  2. The correlations between the unweighted averages of the perspectives are considered low (r < 0.35, Cohen and Cohen 1983).

  3. This was also examined using a more restrictive approach by classifying individuals as a particular time perspective type when their factor score is at least 1 standard deviation above the sample mean for that factor and no greater than 1/2 standard deviation above the sample mean for any of the other factors. The conclusions remained unchanged.

  4. In predicting adoption, a 50% probability cut-off was used, with the assumption of a linear relationship between time and adoption likelihood; that is, if a participant reports a likelihood of 80% at t = 36 months, the inferred likelihood at 24 months is 53.3%. This approach yielded more favorable results than common nonlinear curves for the interpolation.

  5. The proposed approach worked, but is a conservative. More sophisticated models might increase the accuracy further.

References

  • Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Albarracin, D., Johnson, B. T., Fishbein, M., & Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127, 142–161.

    Article  Google Scholar 

  • Alexander, D. L., Lynch, J. G., Jr., & Wang, Q. (2008). As time goes by: Do cold feet follow warm intentions for really new versus incrementally new products? Journal of Marketing Research, 45(June), 307–319.

    Article  Google Scholar 

  • Armstrong, S. J., Morwitz, V. G., & Kumar, V. (2000). Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy? International Journal of Forecasting, 16(3), 383–397.

    Article  Google Scholar 

  • Castano, R., Sujan, M., Kacker, M., & Sujan, H. (2008). Managing consumer uncertainty in the adoption of new products: Temporal distance and mental simulation. Journal of Marketing Research, 45(June), 320–336.

    Article  Google Scholar 

  • Chandrashekaran, M., McNeilly, K., Russ, F. A., & Marinova, D. (2000). From uncertain intentions to actual behavior: A threshold model of whether and when salespeople quit. Journal of Marketing Research, 37(4), 463–479.

    Article  Google Scholar 

  • Chatzisarantis, N. L. D., & Hagger, M. S. (2007). Mindfulness and the intention-behavior relationship within the theory of planned behavior. Personality and Social Psychology, 33(5), 663–676.

    Article  Google Scholar 

  • Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Conner, M., Rodgers, W., & Murray, T. (2007). Conscientiousness and the intention-behavior relationship: Predicting exercise behavior. Journal of Sport & Exercise Psychology, 29(4), 518–533.

    Google Scholar 

  • Fennis, B. M., Adriaanse, M. A., Stroebe, W., & Pol, B. (2011). Bridging the intention-behavior gap: Inducing implementation intentions through persuasive appeals. Journal of Consumer Psychology, 21(3), 302–311.

    Article  Google Scholar 

  • Fitch, J. L., & Ravlin, E. C. (2005). Willpower and perceived behavioral control: Influences on the intention-behavior relationship and postbehavior attributions. Social Behavior and Personality, 33(2), 105–123.

    Article  Google Scholar 

  • Gollwitzer, P. M. (1999). Implementation intentions—Strong effects of simple plans. American Psychologist, 54(7), 493–503.

    Article  Google Scholar 

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Henson, J. M., Carey, M. P., Carey, K. B., & Maisto, S. A. (2006). Associations among health behavior and time perspective in young adults: Model testing with boot-strapping replication. Journal of Behavioral Medicine, 29, 127–137.

    Article  Google Scholar 

  • Hsiao, C., & Sun, B. (1999). Modeling survey response bias–With an analysis of the demand for an advanced electronic device. Journal of Econometrics, 89(1–2), 15–39.

    Google Scholar 

  • Juster, T. (1966). Consumer buying intentions and purchase probability: An experiment in survey design. Journal of the American Statistical Association, 61(315), 658–696.

    Article  Google Scholar 

  • Karniol, R., & Ross, M. (1996). The motivational impact of temporal focus: Thinking about the future and the past. Annual Review of Psychology, 47, 593–620.

    Article  Google Scholar 

  • Keough, K. A., Zimbardo, P. G., & Boyd, J. N. (1999). Who’s smoking, drinking, and using drugs? Time perspective as a predictor of substance use. Basic and Applied Social Psychology, 21(2), 149–164.

    Google Scholar 

  • Klapproth, F. (2008). Time and decision making in humans. Cognitive, Affective, & Behavioral Neuroscience, 8(4), 509–524.

    Article  Google Scholar 

  • Koehler, D. J., & Poon, C. S. K. (2006). Self-predictions overweight strength of current intentions. Journal of Experimental Social Psychology, 42, 517–524.

    Article  Google Scholar 

  • Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology, 75(1), 5–18.

    Article  Google Scholar 

  • Lusk, J. L., McLaughlin, L., & Jaeger, S. R. (2007). Strategy and response to purchase intention questions. Marketing Letters, 18, 31–44.

    Article  Google Scholar 

  • Morrison, D. G. (1979). Purchase intentions and purchase behavior. Journal of Marketing, 43(2), 65–74.

    Article  Google Scholar 

  • Morwitz, V. G. (1997). Why consumers don’t always accurately predict their own future behavior. Marketing Letters, 8(1), 57–70.

    Article  Google Scholar 

  • Norman, P., Sheeran, P., & Orbell, S. (2003). Does state versus action orientation moderate the intention-behavior relationship? Journal of Applied Social Psychology, 33(3), 536–553.

    Article  Google Scholar 

  • Pieters, R. G. M., & Verplanken, B. (1995). Intention behavior consistency: Effects of consideration set size, involvement, and need for cognition. European Journal of Social Psychology, 25(5), 531–543.

    Article  Google Scholar 

  • Salisbury, L. C., & Feinberg, F. M. (2008). Future preference uncertainty and diversification: The role of temporal stochastic inflation. Journal of Consumer Research, 35(2), 349–359.

    Article  Google Scholar 

  • Sheeran, P., & Orbell, S. (1998). Do intentions predict condom use? Meta-analysis and examination of six moderator variables. British Journal of Social Psychology, 37(2), 231–250.

    Article  Google Scholar 

  • Strathman, A., Gleicher, F., Boninger, D. S., & Edwards, S. C. (1994). The consideration of future consequences: Weighing immediate and distant outcomes of behavior. Journal of Personality and Abnormal Psychology, 66(April), 742–752.

    Google Scholar 

  • Sun, B., & Morwitz, V. G. (2010). Stated intentions and purchase behavior: A unified model. International Journal of Research in Marketing, 27(4), 356–366.

    Article  Google Scholar 

  • Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110(3), 403–421.

    Article  Google Scholar 

  • Trope, Y., Liberman, N., & Wakslak, C. (2007). Construal levels and psychological distance: Effect on representation, prediction, evaluation, and behavior. Journal of Consumer Psychology, 17(2), 83–95.

    Article  Google Scholar 

  • Vallacher, R. R., & Wegner, D. M. (1987). Levels of personal agency: Individual variation in action identification. Journal of Personality and Social Psychology, 57, 660–671.

    Article  Google Scholar 

  • Van Ittersum, K., & Feinberg, F. M. (2010). Cumulative timed intent: A new predictive tool for technology adoption. Journal of Marketing Research, 47, 808–822.

    Article  Google Scholar 

  • Van Ittersum, K., Pennings, J. M. E., Wansink, B., & Van Trijp, J. C. M. (2007). The validity of attribute-importance measurement: A review. Journal of Business Research, 60(11), 1177–1190.

    Article  Google Scholar 

  • Young, M. R., DeSarbo, W. S., & Morwitz, V. G. (1998). The stochastic modeling of purchase intentions and behavior. Management Science, 44(2), 188–202.

    Article  Google Scholar 

  • Zimbardo, P. G., & Boyd, J. N. (1999). Putting time into perspective: A valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77(6), 1271–1288.

    Article  Google Scholar 

  • Zimbardo, P. G., Keough, K. A., & Boyd, J. N. (1997). Present time perspective as a predictor of risky driving. Personality and Individual Differences, 23(6), 1007–1023.

    Article  Google Scholar 

Download references

Acknowledgments

The author thanks Muge Capar for her help during the data collection process and Joost M. E. Pennings and Nancy Wong for their feedback on earlier drafts of this paper. The author is grateful for the meaningful and constructive support received from the editor Joe Urbany and the three anonymous reviewers.

Author information

Authors and Affiliations

  1. College of Management, Georgia Institute of Technology, 800 West Peachtree Street NW, Atlanta, GA, 30308-1149, USA

    Koert Van Ittersum

Authors
  1. Koert Van Ittersum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Koert Van Ittersum.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Van Ittersum, K. The effect of decision makers’ time perspective on intention–behavior consistency. Mark Lett 23, 263–277 (2012). https://doi.org/10.1007/s11002-011-9152-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11002-011-9152-3

Keywords

Search

Navigation