Article Figures & Data
Tables
- TABLE 1
Selected Empirical Research on Heuristics and Decision-Making Performance
Study and Context Research Goal Main Findings Survey of 93 MBA students and 185 marketing managers in the U.S. (Hoch, 1988). Ability of experts to predict the opinions of consumers compared with novices, with neither group having access to data. Experts were no better than novices at using their intuition. Wrong decisions involving intuition cannot be subject to feedback. Survey of 16 academics, 12 practitioners, and 43 high school students in the U.S. (Armstrong, 1991). Differences between predictions of experts and novices. No significant differences between any groups. Two experiments with 114 and 59 MBA students in the U.S. (Cripps and Meyer, 1994). Investigation of how consumers plan for the replacement of durable goods compared with optimal machine-replacement theory. Subjects made persistently suboptimal decisions, based upon a conservative heuristic, favoring obsolescence-motivated replacement of durables than better performing alternatives. Two experiments with 220 students and volunteers in Germany (Bröder, 2003). Is the use of heuristics in decision making adaptive, and does information acquisition correspond to decision strategies? Choosing the appropriate heuristic requires a meta-heuristic that integrates and evaluates cues from the environment that convey information about its payoff structure. Saving costs in the long run were traded off against not always choosing the best option, for example. Three experiments with 52 students and faculty in the U.K. (Newell and Shanks, 2003). Assessment of the parameters of the take-the-best (TTB) heuristic by offering the purchase of more information before the choice is finalized. Complex decisions processes can be performed in simple ways. Although TTB is powerful, it is not the universal decision-making tool. Survey of 561 projects containing 499 failures and 62 commercial successes in 1989–1994, including 1,143 entrepreneurs in Canada (Åstebro and Elhedhli, 2006). Understanding why Canadian Invention Assistance Program analysts correctly forecast the likelihood that an invention would reach the market as often as or more often than linear additive statistical models. Analysts use simple sums of counts, using significantly more cues than typically observed. The conjunctive model predicted 86 percent. Experts, however, correctly predicted 83 percent, significantly outperforming a log-linear additive statistical model (79 percent). Three experiments with 497 students in the U.S. (Saini and Monga, 2008). Investigation of whether consumer decision making is more heuristic when it comes to spending time rather than money. Time and money might be seen as the same by economists, but in practice, consumers more likely will use heuristics to reduce time, whereas they more likely will use algorithms when it comes to spending money. Analysis of three datasets including information on 2,330 apparel, 2,891 airline, and 2,357 CD customers in the U.S. (Wübben and Wangenheim, 2008). Comparison of the outcomes of stochastic models versus the heuristics used by firms to predict future purchases. Complex methods were only slightly superior to heuristics in terms of determining the (in)activity of customers, and there was no clear evidence that such models are superior to heuristics. Three experiments with 530 undergraduates in the U.S. (Hutchinson, Alba, and Eisenstein, 2010). Differences between optimal marketing-budget allocations and those predicted by heuristics. Data-based inferences are subject to strong, heuristic-based biases that are not reduced by graphical presentations of the data, “real world” experience, or explicit training. - TABLE 2
Heuristic Types Presented in the Survey
Typology Label Description of Decision Tools Algorithmic The creative work that proved best on the basis of analyzing the data Default The creative work most similar to what we normally choose to do Defer The creative work that we thought the client wanted Equality We didn't make one choice; we integrated creative works equally from all competing campaigns Experience The creative work that the most experienced person in our team wanted Fluency The creative work we recognized quickest Hierarchy The creative work that senior agency managers wanted Instinct We followed our instincts Majority The creative work most people wanted Recognition The creative work we most easily recognized Satisficing The first creative work that exceeded our objectives Take the best The creative work we thought would be best for the client Tallying The creative work with the highest number of favorable aspects to it - TABLE 3
Demographic Characteristics of the Sample
Demographic n (%) Age of office (n = 95) M 106 Mdn 136 Mode 150 Staff No. years at agency (n = 88) M 3 Mdn 2 Mode 1 Age (n = 90) 25–34 45 (50) 35–44 26 (29) 18–24 10 (11) 45–54 7 (8) 55–64 2 (2) Gender (n = 89) Female 43 (48) Male 46 (52) Job (n = 112) Account planner/researcher 21 (18.8) Account director 12 (10.7) Digital account director 11 (9.8) Digital media 11 (9.8) Digital account planner/researcher 11 (9.8) Community manager 6 (5.4) Creative director 5 (4.5) Copywriter/art director 3 (2.7) SEO specialist 3 (2.7) Designer/specialist 3 (2.7) Digital copywriter/art director 2 (1.8) Digital creative director 1 (0.9) Media 0 (0.0) Other 23 (20.5) Note: SEO = search engine optimization.
- TABLE 4
Decision-Making Techniques and Confidence
Heuristic Agreement (scored 1–7) M SD Take the best: The creative work we thought would be best for the client or sponsor 5.75 1.34 Tallying: The creative work with the highest number of favorable points about it 4.70 1.87 Instinct: We followed our instincts. 4.57 1.82 Satisficing: The first creative work that exceeded our objectives; we then ignored the rest. 4.02 2.03 Majority: The creative work most people wanted 3.93 1.84 Algorithmic: The creative work that proved best on the basis of analysis of the data 3.87 1.97 Defer: The creative work the client wanted 3.91 1.76 Experience: The creative work that the most experienced person in our team wanted 3.36 1.85 Hierarchy: We chose the creative work that senior managers wanted. 2.95 1.77 Recognition: The creative work we most recognized 2.86 1.70 Default: The creative work most similar to what we normally choose to do 2.84 1.53 Equality: We didn't make a choice; we allocated resources equally to all competing creative works. 2.88 1.81 Fluency: The creative work we recognized quickest 2.51 1.57 Note: N = 69.
- TABLE 5
Heuristic Type Factor Loadings
Item Component Communalities Acknowledge Top Know-How Breakdown Recognition .883 .813 Fluency .879 .875 Default .711 .602 Instinct .735 .631 Satisficing .720 .546 Take the best .667 .629 Tallying .521 .422 Experience .832 .793 Hierarchy .821 .741 Defer .516 .679 Equality .834 .721 Algorithmic .657 .489 Note: Kaiser-Mayer-Olkin = .749, Bartlett's test of sphericity = .000, total variance explained = 65.271.
- TABLE 6
Regression Estimates
Model B SE B β t p Constant 74.805 5.209 14.359 .000 Heuristic Acknowledge −3.997 2.545 −.192 −1.571 .123 Top 11.430 2.390 .555 4.784 .000 Know-How 0.127 2.609 .006 0.049 .961 Breakdown 2.214 2.739 .105 0.808 .423 Controls Years in advertising 0.482 0.697 .175 0.691 .493 Years in this agency −0.239 1.117 −.046 −0.214 .831 No. agencies −1.816 1.920 −.179 −0.945 .349 Creative/Planner 1.283 8.294 .020 0.155 .878 Note: The dependent variable was confidence. R2 = .374.
- TABLE 7
Correlations
Variable Acknowledge Top Know-How Breakdown Age −.253* .173 −.169 −.248 Years in this agency −.287* .067 −.093 −.267* No. agencies −.145 .087 −.111 −.265* Years in advertising −.185 .068 −.135 −.240 Note: * Correlation is significant at the 0.05 level.
Vol 58 Issue 2
How Do Heuristics Influence Creative Decisions at Advertising Agencies?
