How Do Heuristics Influence Creative Decisions at Advertising Agencies?
=======================================================================

* Douglas C. West
* George Christodoulides
* Jennifer Bonhomme

## Factors that Affect Managerial Decision Making When Choosing Ideas to Show the Client

## ABSTRACT

This study focuses on individual choices made about what ideas to show clients in the absence of copy testing; it examines the decision-making heuristics employed by a global advertising agency. On the basis of an online survey, the results *inter alia* suggest that when it comes to deciding which ideas to show to clients, both analytic and pure heuristics are used in various combinations. The results provide insights about the nature of and factors that influence decision making among managers.

## MANAGEMENT SLANT

*   Even within the same agency, different combinations of heuristics are used in the choice of what creative solutions to show a client.

*   Both analytic and pure heuristics play a role in the decision-making process, which is in line with the dual nature of advertising creativity, where artistry meets business objectives.

*   A key element in the choice is a good understanding of the needs of the client or sponsor.

## INTRODUCTION

Place yourself in the role of a creative director in an advertising agency. You are reviewing the first-round creative ideas for one of your biggest accounts with a senior creative team assigned to the brief. The entire internal team is there, including the account planner, who wrote the brief; the project manager, who is eager to get moving on production; and the account director, who is thinking about whether the ideas will track with the client.

You can see that the creative team hardly can contain their excitement as they share their ideas and possible executions. After an hour of animated discussion, you disperse, reflecting on the proposed concepts. Guess what? The same team comes charging in the next morning with a number of new and supposedly even better ideas! They talk you through their latest creative routes.

You end up late for your next meeting, your head swirling with possible creative territories. The difficulty is that with so many different creative routes now on the table, which do you choose to show the client? Are any of the ideas any good? Will they achieve the desired creative impact? Do they make sense for the brand?

As always, feedback to the team needs to be quick, giving them clear directives for improvement, including thoughts on which ideas to keep and which to lose. When it comes to presentation time, you will need to go back to the client with a strong recommendation of which ideas you believe will make for the best work. As the creative director, you understand that the agency's, and your own, reputation depend on this decisiveness to push the boundaries of what makes for one-of-a-kind advertising ideas. What is more, you know that some of the best ideas in advertising history were received poorly in the first round of review.

Advertising is not unique in its subjectivity, however. Executives in other creative industries, including publishing, start-ups, film, music, and gaming, face similar challenges in decision making when it comes to new ideas. How can you tell whether an idea is a good one, when there is no sure answer?

Choices between competing ideas rarely are based on a rational, algorithmic solution. Advertising campaigns that challenge thinking and see the world in a different way often are not received well initially, but they can gain enormous traction over time, as people begin to understand and like the work.

Consider the Budweiser “Whassup?” campaign, for example. Developed by copywriters Charles Stone III and Vinny Warren for Group Creative Director Don Pogany at DDB Chicago, the campaign was spurned initially by distributors, who thought it was too urban for such a classic brand. Budweiser took the risk on DDB's concept, and it paid off handsomely, winning numerous creative awards. The campaign was linked to increasing unit sales by 2.4 million barrels, but it was an undoubted gamble (Winters Lauro, 2001).

The point is that there is no algorithm for creative teams to look up. There is no formula to work out. It is an unknown step into the dark each time. Even a campaign that is derivative takes a gamble, because the basic premise of the idea might have worn out.

As such, the subjective type of decision making inherent in choosing between competing creative work much more likely is in the realms of heuristics—that is, rules of thumb (Gigerenzer, 2008). Heuristics work when one is making choices between creative work, partly because they are easy to use, and partly because they provide customizable solutions to problems that can be adapted to many situations.

Contrary to conventional wisdom, heuristic decision making often has been found to outperform computer models (Brighton andGigerenzer, 2015; Czerlinksi, Gigerenzer, and Goldstein, 1999), especially in situations in which optimization often is difficult or impossible. When applied to creative choices, heuristics also may offer considerable insight into an advertising agency's organizational processes.

When one is choosing the best creative work, the ideal optimization technique is to specify or estimate a profit-advertising response function and choose the creative idea that might make the highest marginal profit return. Specifying response functions with this level of precision, however, is impossible in practice as well as in theory (Taylor, Kennedy, and Sharp, 2009). An account director or creative director cannot simply punch numbers into a spreadsheet and find out what creative work to pick. Given that no mind or machine has yet to solve the dilemma, heuristics begin to make sense.

Another reason for using heuristics is that they mitigate against the problem of overfitting. In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Studies indicate that relevant information often is merged with the irrelevant, which produces an overfit relative to a more robust, simplified model (Cosmides and Tooby, 1992).

Heuristics thinking is based on ordered cues that offer a means to reduce overfit by removing or minimizing noise in any decision (Hertwig and Todd, 2003). Heuristics enable decision makers to forget data and focus only on the pertinent issues. With a seemingly endless number of things to measure in the era of big data, this kind of convergent thinking can facilitate actionable and time-sensitive decisions.

Drawing on the paucity of research on the selection of competing creative work within an advertising agency, the present study sought to shed light on this process by investigating the heuristics used by advertising executives in the assessment of creative projects. What agency folks do and why they do it when choosing among competing creative ideas is shrouded in mystery. The focus in this study was on how agency executives make choices about what creative work to present and defend to clients.

There has been considerable research on the nature of creativity; how best to nurture and develop it; and its influence on the fortunes of clients, their agencies, and their team. The literature, however, is bereft of studies examining how such work is selected from a volume of initial ideas. To what extent is the decision eclectic or codified? Do agency executives apply set rules or take each case as it comes? The present study's results provide insights about the nature of and factors that influence decision making among managers when they are choosing among creative ideas—decisions that are pivotal to the retention of clients and the longevity of client relationships.

## CREATIVITY AND CREATIVE CHOICES

Advertising creativity has been described variously in terms of thinking, ability, problem solving, imagination, innovation, and effectiveness (Bell, 1992; Koslow, Sasser, and Riordan, 2003). Advertising practitioners encounter various viewpoints about their work, which, in turn, affect their views of what constitutes advertising creativity (Kelley, 1992; Smith and Yang, 2004). Career advancement in advertising requires that practitioners assimilate cultural codes of professionalism, which eventually become instinctive and habitual (Jenkins, 2002).

Much of this has to do with the correct use of the conventions and norms of the industry, rather than any rigid adherence to any creative concept or approaches. An advertising creative professional needs to appear to be both artistic and concurrently realistic, market oriented, and commercially driven (Dahlén, Rosengren, and Törn, 2008; Lehnert, Till, and Ospina, 2014). The agency creative staffer thus “is not a free-floating artist but…is one who works hard [to synthesize and apply] analysis and knowledge” to develop new and novel creative outcomes (Alvesson, 1994, p. 547).

The perceptions of what constitutes advertising creativity differ by role (Hirschman, 1989; Runco and Charles, 1993; White and Smith, 2001). Creative talent, known within the industry as “creatives,” thus tend to view advertisements as more appropriate if they are artistic, whereas account executives and account planners are inclined to view advertisements as more appropriate if they are strategic (Koslow, Sasser, and Riordan, 2003, 2006). With the increasing concentration of media planning and buying into large media agencies that provide specialist expertise and economies of scale, the role of creativity has become the core function of most advertising agencies (Dahlén et al., 2008; Nyilasy and Reid, 2009). Indeed, the place of creativity in advertising long has been recognized (Lehnert et al., 2014; West, Caruana, and Leelapanyalert, 2013), and the occurrence of “eureka moments” is documented well (Baas, De Dreu, and Nijstad, 2008; Michell, 1984; Stewart, Cheng, and Wan, 2008).

For the most part, nevertheless, creative team members are tasked with conjuring up a large volume of ideas for bringing to life the creative brief. The full volumes of these ideas rarely are presented directly to clients in their entirety. There is no set process, but typically a copywriter–art-director team might develop 50 rough ideas, which they then self-filter down to about 10 to share with the creative director on first review. The creative director generally makes a call on three or four to develop—or might ask the team to start over.

Once the creative director has a level of confidence in the ideas on the basis of the creative brief, the account-management and planning people working on the project are asked for their input. The cross-functional internal agency team vets the work and decides on the best ideas to take to the client. After several rounds of internal review, the client may be presented with perhaps three directions.

Aside from motivating and managing their creative teams, advertising executives have to make the final call, identifying a selective pool of client-worthy campaign ideas, often with a strong recommendation for a favorite concept. Most experienced international creative directors acknowledge that the creative brief is a very important part of the puzzle, and they are trained to refer back to the brief when doing an initial screen of the creative work. If that work provides a strategic fit with the brief, then that is the first key decision a creative director must make. Agency executives can make their reputation on the basis of selecting the work that best fits the clients' brief; otherwise, the relationship will flounder, and the account may be lost.

### Decision Making

The question of how creative work is selected among competing ideas is essentially a subset of the realm of logic, intuition, and heuristics—central concepts underlying decision making and problem solving. Logic has an emphasis on mental models and the use of cognition to solve problems and preserve the truth in well-structured problems. This perspective on decision making has more to do with probability than uncertainty, because logic uses information, which, although prone to error, necessitates risk about the future (Brooke, 2010; Knight, 1923).

By contrast, heuristics come into their own when the problem is ill defined and difficult to quantify, when time is limited, and when the probabilities are unclear (West, Ford, and Farris, 2014). According to a previous researcher, “the mind resembles an adaptive toolbox with various heuristics tailored for specific classes of problems—much like the hammers and screwdrivers in a handyman's toolbox” (Gigerenzer, 2008, p. 20). The literature broadly has identified 10 types of heuristic, as reported by investigators in specific tests and experiments, which are listed here alphabetically: 

*   Default, the most basic of heuristics, is when the choice made is most similar to what one normally would choose (Johnson and Raab, 2003). That could mean choosing a campaign idea that closely resembles what the agency usually offers a client.

*   Equality (Gigerenzer and Gaissmaier, 2011) might be termed a nonchoice. Here, a creative director attempts to integrate all the ideas across all competing choices rather than making any single decision. The advantage is that a mix of ideas are blended, but the danger is that the unity of a single idea might be lost.

*   Experience involves a more social process, whereby the choice is made by whoever is agreed to be the most experienced person (Boyd and Richerson, 2004). A creative director might favor the campaign idea of a well-respected creative team over a more junior one, whatever the work might be.

*   Fluency is making a choice on the basis of what is recognized quickest (Schooler and Hertwig, 2005). A creative director thus would go with the campaign idea that was appreciated most speedily.

*   “Imitate the majority” is another social heuristic, whereby the decision is based on what most people want (Boyd and Richerson, 2004). In this case, a creative director might opt for the idea that most people in the agency think is best, regardless of the idea itself.

*   Instinct often is seen as a separate aspect of decision making to heuristics (Wierenga, 2011). Such decision making relates to an internal and innate compulsive action. A campaign idea simply strikes an executive with an innate gut feeling as right or wrong, for example.

*   Recognition is when a choice is made on the basis of a previous encounter or knowledge (Goldstein and Gigerenzer, 2002), as with a campaign idea that is linked closely to a previous idea.

*   Satisficing involves making a decision on the basis of the first choice that exceeds set objectives. A creative director thus would choose the campaign idea that first meets the brief and ignore all the rest to save time and effort (Simon, 1955; Todd and Miller, 1999).

*   “Take the best” can be grouped and closely linked with recognition, whereby a choice is made on the basis of what is best (Gigerenzer and Goldstein, 1996). A creative director thus would choose an idea that strikes him or her as the best solution to the brief.

*   Tallying is a more demanding process (Dawes, 1979). A creative director evaluates each option and allocates several favorable points apiece. In a final comparison, he or she chooses the creative idea with the highest score.

It well might be that many decisions and industries—including the creative industries—have little choice other than to make decisions by heuristics. Is that a good thing, however? How well do heuristics stack up against algorithmic and more analytical decision making?

There is a large body of empirical work in the cognitive sciences focusing on this question. Decision-making studies have compared managers with statistical modeling and commercial databases. Many studies have compared managers with students, with a number suggesting that managers outperform such proxy novices.

Managers, for example, have been found to make decisions more quickly, compared with students (Day and Lord, 1992; Fredrickson, 1985; Isenberg, 1986); to be unaffected by context (Fredrickson, 1985); and to need less information (Isenberg, 1986). When it comes to statistical modeling, managers have been found to forecast more correctly the likelihood of an invention reaching the market (Åstebro and Elhedhli, 2006). Compared with commercial databases, managers have been found to be only slightly under par in identifying potential high-value lifetime customers (Wübben and Wangenheim, 2008).

Managers have not had it all their own way: marketing managers were found to be no better than students in predicting the opinions of consumers (Hoch, 1988). In another study, they were found to be no better at predicting the outcome of hypotheses published in the *Journal of Consumer Research* on the basis of academic research findings (Armstrong, 1991). (See Table 1 for a summary of the literature.)

Researchers have noted that the great advantage of heuristics is that they are fast (Gigerenzer and Goldstein, 1996; Kahneman, 2003). Heuristics also often lead to immensely satisfying and sometimes quite emotional outcomes. The defining nature of a decision based on heuristics is that it frequently involves affect and is accompanied often by excitement and harmony (Hayashi, 2001). Going through and crunching the numbers can be satisfying too, but such an approach rarely leads to any sense of euphoria. Considering the discussion above, the authors posed three central research questions in this exploratory study: 

*   RQ1: What techniques do agency teams employ in choosing campaign ideas to show their clients?

*   RQ2: How are choices made among competing ideas?

*   RQ3: In what way are the apparently preeminent solutions to the client's brief selected?

## METHODOLOGY

The research design the authors used was a single case study with embedded multiple units of analysis (Yin, 1983), primarily because the focus of this research was the decision making of advertising executives in selecting creative work, and this could not be considered without context. The decision-making heuristics were developed and applied in this setting, so it would have been impossible to have a true picture of advertising executives' decision making without employing such a context.

Access to decision makers within organizations was key to the study. Advertising executives are busy professionals, so establishing access to an organization enabled the authors to reach the target sample and enhanced participation in the study. Warm contacts were employed (per Maylor and Blackmon, 2005) in the selection of the agency, which is one of the leading advertising agencies worldwide and a frequent recipient of industry creativity awards. Note that the agency featured in this article is not Young & Rubicam, where one of this study's coauthors is employed.

### Measures

The authors developed a web questionnaire in liaison with five senior managers at this agency, in London. All study scales were used and validated in prior research, but because some were created in a nonadvertising setting, the authors assessed those items for appropriateness in an advertising context. The five executives scrutinized the questionnaire, and several additional refinements were implemented to enhance response over three versions.

The questions probed 

*   the demographics of the potential respondents and their agency office within the network;

*   the intensity of competition in their market;

*   the nature of the creative project they had worked on most recently;

*   the decision tools they used to pick the one creative solution to share with the client;

*   their confidence in the choice of project and the characteristics of the project;

*   in particular, how creative they regarded the chosen project to be and who was involved, by job function, in the development of the creative work.

View this table:
[TABLE 1](http://www.journalofadvertisingresearch.com/content/58/2/189/T1)

TABLE 1 
Selected Empirical Research on Heuristics and Decision-Making Performance

View this table:
[TABLE 2](http://www.journalofadvertisingresearch.com/content/58/2/189/T2)

TABLE 2 
Heuristic Types Presented in the Survey

 Respondents were prompted to think about a recent project they had worked on and to explain how they picked the one creative solution that they then presented to the client. To distinguish among decision-making tools, the authors selected 10 from the extant literature, with the addition of three new ones. The authors added “Algorithmic” decision making as a category to provide an alternative to using heuristics (See 

Table 2).

The authors added two additional heuristics to take account of the decision-making circumstance of an advertising agency, as advised by the agency executives. The first was “Defer,” in which the choice of the creative idea is based on knowledge of the expectations of the client. When they defer, executives place themselves in the shoes of the client, looking at the client's problem and seeing how well the creative idea fits.

The second heuristic added was hierarchy. “Hierarchy” recognizes the influence of senior executives at the agency and their prerogative to override other choices. Responses were measured on 7-point Likert scales.

### Pretest

The authors pretested the instrument to ensure that all questions were appropriate and clearly understood. They pretested the online questionnaire using a convenience sample of the same five advertising executives at the global agency who had guided the work, to ensure appropriateness of the various constructs and related scales. At this point, the questionnaire was deemed ready for mailing to the sample population.

### Survey

The questionnaire consisted of 24 questions. It began with the requisite instructions and statements of confidentiality. Respondents were asked to codify their chosen project as 

*   primarily using traditional media;

*   primarily using new media; or

*   using roughly an equal combination of traditional and new media.

Participants then were asked, “Looking at the project identified, more than likely you had to consider a number of alternative creative solutions for the execution. How did you pick the one creative solution to pitch to the client?” They then were presented with the 13 decision tools, without the typology label (See Table 2). For example, participants read, “The first creative work that exceeded our objectives,” without being provided with the label “Satisficing.” Respondents were able to choose more than one tool and used a 7-point scale to indicate the degree to which they employed (or not) each heuristic. Note that there was a natural bias for a professional to choose “Take the best: The creative work we thought would be best for the client or sponsor.” That option clearly had to be included, however.

### Sample

Senior managers at the London office of the agency sent out an explanatory e-mail with a URL link to the Qualtrics-hosted survey, with an explanation inviting responses to the European, North American, and Asian offices of the agency. The senior managers in turn sent out links encouraging their staff to participate. The head of creative talent in London, for example, sent the link to the head of creative talent in New York, who then forwarded it to all the creative staff in the New York office. The sample thus consisted of executives working on a range of accounts from a cross-national section of the agency. Recipients were directed to pass on the questionnaire link to the most senior marketing person in the company, if that was not them.

View this table:
[TABLE 3](http://www.journalofadvertisingresearch.com/content/58/2/189/T3)

TABLE 3 
Demographic Characteristics of the Sample

### Response

The database was administered through [Qualtrics.com](http://Qualtrics.com), and 144 responses were obtained from executives working across account management, creativity, media, and research. Because many of the recipients were involved in generating creative ideas rather than making the final choice as to which one to share with the client, the number of workable questions was 69. The internal nature of the sampling and lack of direct control by the researchers meant it was not possible to send out a second wave of the questionnaire, and many of the standard tests of potential for nonresponse bias were not applicable.

The researchers compared the first 25 percent of the responses with the last 25 percent of the responses (per Armstrong and Overton, 1977). They found no significant differences in the responses of early versus late respondents. The primary demographics of the sample who influenced the selection of ideas were account planners (29 percent), account directors (21 percent), and digital specialists (21 percent), with creative directors, art directors, and copywriters making up the next largest group (9 percent). Other roles included (senior) producer, creative technologist, digital strategist, strategic planner, and digital planner. The respondents had worked in advertising for an average of 8.6 years (*SD* = 6.9 years), 3.1 of those years at the present agency (*SD* = 3.6; See Table 3).

Self-reporting has several advantages, and the authors chose it over observation to enable participants to select phenomena closest to their own experiences. It must be noted, however, that any self-report method has the potential for bias, in particular the potential for social-desirability bias (Phillips and Clancy, 1972). Social-desirability bias has been associated with a wide range of topics that are measured commonly in surveys involving objective and subjective singularities, such as height and weight, the payment of taxes, beliefs in God, or voting intentions (Gittleman *et al.*, 2015). In the present study, for example, reporting that one had chosen the creative solution recognized quickest might seem unprofessional.

Such bias, however, mainly has been found when respondents are asked about potentially embarrassing attributes in the physical presence of an interviewer or over the phone. The evidence is clear that social-desirability bias does not affect greatly self-administered surveys by mail or Internet, given that the interviewer is absent, especially when participants are assured of full anonymity (Crutzen and Goritz, 2010; Holbrook and Krosnick, 2010).

Respondents were assured at the start of the survey that they could withdraw from the study at any point, that their data would be kept confidential, and that their data would be used solely by the researchers for academic purposes. As an additional check on potential bias, at the end of the survey respondents were asked to rate on a 7-point scale whether they “strongly disagreed” (1) to “strongly agreed” (7) with the following two statements: 

*   “I tried to answer this questionnaire to the best of my ability” (*M* = 6.3);

*   “I had great difficulty understanding most of the questions” (*M* = 2.1).

View this table:
[TABLE 4](http://www.journalofadvertisingresearch.com/content/58/2/189/T4)

TABLE 4 
Decision-Making Techniques and Confidence

 One person who scored 1 for the former question was removed from the sample (no one answered 2), and no one answered above 5 for the latter question.

## RESULTS

The top two heuristics (take the best and tallying) were at the analytical end of the decision-making spectrum, and all four analytic approaches (including satisficing and algorithmic) were in the top half (See Table 4). Instinct was the top pure heuristic, coming third in the ranking, with majority at fourth. All the other pure heuristics, from experience to fluency, were in the bottom half.

The researchers asked participants, “Looking at the project identified [for the survey], more than likely you had to consider a number of alternative creative solutions for the execution. How did you pick the one creative solution to pitch to the client? Please review the statements below and award stars to all options (1 star ‘strongly disagree’ to 7 stars ‘strongly agree’).” Respondents, therefore, were asked to reflect on their most recent decision on creative choices, a decision known by researchers to involve a complex mix of parameters. A scale, rather than a simple yes or no, enabled the degree of power between each heuristic to be illuminated.

The authors performed an exploratory factor analysis of principal components with a Varimax rotation, to examine the underlying structure of the decision-making heuristics used in selecting creative ideas for clients (See Table 5). Given the sample size, factor loadings less than .50 were suppressed from the analysis (Hair et al., 1998). A four-factor solution emerged from the analysis, which accounted for 65.3 percent of the variance. The factors were labeled acknowledge, top, know-how, and breakdown: 

*   “Acknowledge” (31 percent) consisted of default, recognition, and fluency—all decision-making techniques based on past experience and generally used in choices that often routinely were made.

*   “Top” (16 percent) included instinct, satisficing, take the best, and tallying. These were all decisions based around an assessment of what would work best, either through innate gut feeling or on the basis of some degree of assessment at a rudimentary level.

*   “Know-How” (10 percent) consisted of experience, hierarchy, and defer. These types of decisions were made by senior executives within the agency or people on the team who were deemed to have the most experience.

*   “Breakdown” (8 percent) consisted of those decisions based on a higher degree of analysis through the equality heuristic and through algorithmic decision making.

The authors saved factor scores as variables and used them in a multiple regression analysis to identify the factors of heuristics associated with the greatest level of confidence in the choice of the idea (See Table 6). Confidence was measured on a 100-point scale. The number of years that respondents had worked in advertising, in the specific agency, and in their job role (creative/not = coded as a dummy variable) were included in the regression as control variables.

View this table:
[TABLE 5](http://www.journalofadvertisingresearch.com/content/58/2/189/T5)

TABLE 5 
Heuristic Type Factor Loadings

View this table:
[TABLE 6](http://www.journalofadvertisingresearch.com/content/58/2/189/T6)

TABLE 6 
Regression Estimates

The prediction model was statistically significant, *F*(8, 48) = 3.588, *p* = .002, and accounted for approximately 37 percent of the variance of confidence. Decisions based on Top were found to be associated with higher levels of confidence (*β* = .555, *p* < .000), whereas decisions made on the basis of Acknowledge, Know-How, or Breakdown did not lead to statistically significant higher or lower levels of confidence in the choice of idea. None of the control variables was statistically significant.

The authors analyzed the correlations between the heuristics types and some key demographics, such as 

*   the age of the decision makers;

*   the number of years spent in the case-study agency;

*   the number of advertising agencies decision makers had worked for;

*   the number of years of experience in the field (See Table 7).

Acknowledge was correlated negatively with the age of decision makers, which suggests that younger respondents were less likely to adopt this heuristic type. Breakdown was correlated negatively with the number of agencies decision makers had worked for, which suggests that those with experience in a greater number of agencies were less likely to fall within this heuristic type. The findings also show that the number of years worked for the case-study agency was correlated negatively with both Acknowledge and Breakdown.

## DISCUSSION

What are the headlines in this exploratory study? There is a significant body of literature on creative decision making (Bergen, Dutta, and Walker, 1992; Buchanan and Michell, 1991; Hackley, 2003; Hotz, Ryans, and Shanklin, 1982; Johar, Holbrook, and Stern, 2001; Wackman, Salmon, and Salmon, 1986). Within that work, this article contributes to the field's understanding of the use of analysis and heuristics in determining what creative ideas to show the client. Little to nothing is known about how advertising practitioners make decisions in such situations when algorithms (*i.e.*, decisions based on logic and probability) have little or no role to play.

The first point to make is that advertising executives tend to be at the analytic (logical) end of the spectrum of heuristics. They consider their client's needs, choose solutions that have the most favorable points, set benchmarks, and assess any available data. Sitting amid these approaches are some pure heuristics—principally, instinct and majority. That is, executives combine these analytic heuristics with gut instincts and a majority vote.

View this table:
[TABLE 7](http://www.journalofadvertisingresearch.com/content/58/2/189/T7)

TABLE 7 
Correlations

This finding complements previous research highlighting the importance of incubating goal-directed creativity within agencies (West, Kover, and Caruana, 2008) and acknowledging the internal tensions of advertising creative talent between artistry and business (Alvesson, 1994). Given that this duality makes up the identity of agencies, and in the absence of solid analytic heuristics to inform the judgment of creative work, it is not surprising that the choice of creative work is based on combinations of heuristics.

There is no one-size-fits-all solution; advertising executives are prepared to combine decision-making types to reach a decision. This was demonstrated by principal-components analysis, which generated four categories of heuristics employed in the decision making and revealed the symbiotic nature of logical versus affective heuristics among the global agency. The dominant decision-making style was labeled Acknowledge, but it was not associated with higher levels of confidence. A possible explanation for this relates to the fact that the aforementioned decision-making style gravitates toward past experience and routine decisions made in the agency, which may, indeed, result in perceptually safe decisions that unlikely will thrill the client. Of course, this might be exactly what the client wants: something safe.

By contrast, decisions grounded in taking top choices by instinct or rudimentary assessment were associated with greater confidence than any other heuristic type. This result requires further analysis, and at this stage it is just conjecture. Perhaps because success was self-reported, however, managers attributed success to their own valuations and insights rather than to alternative logical processes. As one practitioner noted on reading the results, “The value of the findings to me, lies in their ability to help executives make swift and meaningful decisions in deciding creative. So often we overengineer or labor over which creative route is right, when actually instinct and swiftness of our decision are what make the difference.”

It is interesting to note that the Acknowledge decision-making style was correlated negatively with the respondents' age and years employed in the agency. This suggests that younger executives and those who had spent less time in the agency would less likely adopt this group of heuristics. Acknowledge probably is associated with the dominant decision-making culture in the case-study agency and draws on accumulated knowledge and routine practices.

One could argue that younger respondents who have spent less time in an agency lack the experience to make decisions comfortably using this group of heuristics. In follow-up probing, an experienced creative director replied, “Older creatives tend to be far more rigorous in their judgment, whereas younger creatives have an unconscious willingness to try new things that might, at first glance, seem off strategy.” Similarly, less experience within the agency as well as experience in fewer agencies were correlated negatively with the Breakdown style, which depends largely on analytic heuristics.

Counter to the authors' intuitive expectation, less experience led executives not to adopt analytic heuristics (to minimize the risk of their decisions) but rather to avoid them. This is in line with past research showing that, in fact, less-experienced employees are more comfortable taking risks, compared with their more experienced counterparts (Menkhoff, Schmidt, and Brozynski, 2006). When asked about the differences in decision making with respect to age and experience, one global-agency creative director responded, “I think newer creatives versus more experienced creatives choose work based on whether it is provocative versus persuasive.”

## CONCLUSION, LIMITATIONS, AND FUTURE RESEARCH

This exploratory study contributes to research on the heuristics employed by advertising executives in their selection of creative work and shows that even within the same agency, different combinations of heuristics are used. This study also highlights the role of both analytic and pure heuristics in the decision-making process, which is in line with the dual nature of advertising creativity: where artistry meets business objectives. From a management perspective, the study clearly demonstrates that the selection of creative solutions is not made in isolation.

A key element in the choice is a good understanding of the needs of the client or sponsor. With large professional clients and their large professional agencies, considerable research goes into the development of the creative brief. As such, judging the creative brief against the solution offered by the creative work inevitably has, built into it, a certain level of analytical judgment. The creative director must be able to make fast qualitative judgments regarding the following questions: 

*   “Is the desired message (the proposition) communicated?”

*   “Does the work have creative impact?”

*   “Is it the right brand personality?”

Despite the contribution of this study, the findings should be interpreted with caution. First, the research was undertaken within one agency, and the corporate culture of the agency plays a role in the decision-making styles and heuristics used by executives. Second, the sample size is such that further comparisons for subsamples—for example, based on specific job role—were not possible.

Future research could build on this work by investigating the choice of creative work within a range of agencies (specialist versus full-service agencies, small versus large, etc.) and by examining the impact of decision-making styles and heuristics on various campaign success metrics from different sources (experts, consumers). This would establish a more definitive list of the heuristics that lead to the best selection of creative work.

Another limitation of the study pertains to the memory bias resulting from asking respondents to recall a recent creative project and respond to the survey accordingly. An ethnographic approach that allows future researchers to take part in agency meetings and to observe the selection of creative work not only would increase the validity of the findings but also would yield additional insights into the group and personality dynamics of decision makers and how these might affect the heuristics employed. A final point to note is that because the study focused on the final stage of the creative choices, responses were biased toward account planners rather than creative people (copywriters, art directors, creative directors). This might have influenced the results compared with what might have been found at the earlier stages of creative development.

Future researchers may wish to examine a wider cross-sample of practitioners than the single global agency presented here, to establish greater reliability. They might investigate the wider topic of decision making within advertisers, agencies, and the media, for example. The effects of digitalization on the advertising business and decision making would be a rich topic to explore.

To end on a broader point, the authors note, “Managerial decision making in marketing is the heart of the field. Strangely enough, academic work on this topic is scarce. Existing work on marketing decision making is either descriptive or takes an optimization approach, with the role of the marketing decision maker practically disappearing” (Wierenga, 2011, p. 89). There are ample opportunities for researchers in the field to redress this imbalance.

## ABOUT THE AUTHORS

**Douglas C. West** is professor of marketing at King's College London. He has published widely on advertising and marketing. West has held several editorial posts, including at this journal where he was executive editor 2008–2014 and is now contributing editor, and sits on a variety of editorial boards He is a coauthor of *Marketing Strategy: Creating Competitive Advantage* (3rd ed., Oxford University Press, 2015).

**George Christodoulides** is a professor in marketing at Birkbeck, University of London. His research interests lie in the areas of brand management and digital marketing, particularly consumer-based brand equity conceptualization and measurement and the impact of interactive and social media on consumer–brand relationships. His work has appeared in international peer-reviewed journals such as the *Journal of Business Research, Psychology & Marketing*, and the *Journal of Advertising Research*.

**Jennifer Bonhomme** is a group planning director at Young & Rubicam. Her global experience includes creating award-winning strategies for a variety of sectors, including health care, consumer packaged goods, telecommunications, entertainment, and business to business. Her hybrid approach to strategy can be described as brand planning for the digital age.

*   Received December 5, 2015.
*   Received (in revised form) October 30, 2017.
*   Accepted November 14, 2016.


*   Copyright© 2018 ARF. All rights reserved.

## REFERENCES

1.  Alvesson M. “Talking in Organizations: Managing Identity and Impressions in an Advertising Agency.” Organization Studies 15, 4 (1994): 535–563. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1177/017084069401500403&link_type=DOI) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=A1994PX06800003&link_type=ISI) 

2.  Armstrong J. S. “Prediction of Consumer Behavior by Experts and Novices.” Journal of Consumer Research 18 (1991): 251–256. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1086/209257&link_type=DOI) 

3.  Armstrong J. S., Overton T. S. “Estimating Non-Response Bias in Mail Surveys.” Journal of Marketing Research 14 (1977): 396–402. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.2307/3150783&link_type=DOI) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=A1977DS66100020&link_type=ISI) 

4.  Åstebro T., Elhedhli S. “The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures.” Management Science 52, 3 (2006): 395–409. 
    
    

5.  Baas M., De Dreu C. K. W., Nijstad B. A. “A Meta-Analysis of 25 Years of Mood–Creativity Research: Hedonic Tone, Activation, or Regulatory Focus?” Psychological Bulletin 134, 6 (2008): 779–806. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/a0012815&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=18954157&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000260279500001&link_type=ISI) 

6.  Bell J. A. “Creativity, TV Commercial Popularity, and Advertising Expenditures.” International Journal of Advertising 11 (1992): 165–183. 
    
    

7.  Bergen M., Dutta S., Walker O. C. Jr.. “Agency Relationships in Marketing: A Review of the Implications and Applications of Agency and Related Theories.” Journal of Marketing, 56, 3 (1992): 1–24. 
    
    

8.  Boyd R., Richerson P. J. *The Origin and Evolution of Cultures*. Oxford, U.K.: Oxford University Press, 2004. 
    
    

9.  Brighton H., Gigerenzer G. “The Bias Bias.” Journal of Business Research 68, 8 (2015): 1772–1784. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1016/j.jbusres.2015.01.061&link_type=DOI) 

10. Bröder A. “Decision Making with the ‘Adaptive Toolbox’: Influence of Environmental Structure, Intelligence, and Working Memory Load.” Journal of Experimental Psychology: Learning, Memory, and Cognition 29, 4 (2003): 611–625. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/0278-7393.29.4.611&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=12924862&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000184436200010&link_type=ISI) 

11. Brooke G. T. F. “Uncertainty, Profit and Entrepreneurial Action: Frank Knight's Contribution Reconsidered.” Journal of the History of Economic Thought 32, 2 (2010): 221–235. 
    
    

12. Buchanan B., Michell P. C. “Using Structural Factors to Assess the Risk of Failure in Agency–Client Relations.” Journal of Advertising Research 31, 4 (1991): 68–75. 
    
    

13. 1.  Barkow J. H., 
    2.  Cosmides L., 
    3.  Tooby J.
    
    Cosmides L., Tooby J. “Cognitive Adaptations for Social Exchange.” In *The Adapted Mind: Evolutionary Psychology and the Generation of Culture*, Barkow J. H., Cosmides L., Tooby J., eds. New York: Oxford University Press, 1992. 
    
    

14. Cripps J. D., Meyer R. J. “Heuristics and Biases in Timing the Replacement of Durable Products.” Journal of Consumer Research 21, 2 (1994): 304–318. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1086/209399&link_type=DOI) 

15. Crutzen R., Goritz A. “Social Desirability and Self-Reported Health Risk Behaviors in Web-Based Research: Three Longitudinal Studies.” BMC Public Health 10, 1 (2010): 720–729. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1186/1471-2458-10-720&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=21092267&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 

16. 1.  Gigerenzer G., 
    2.  Todd P. M., 
    3.  the ABC Research Group
    
    Czerlinski J., Gigerenzer G., Goldstein D. G. “How Good are Simple Heuristics?” In *Simple Heuristics That Make Us Smart*, Gigerenzer G., Todd P. M., the ABC Research Group, New York: Oxford University Press, 1999. 
    
    

17. Dahlén M., Rosengren S., Törn F. “Advertising Creativity Matters.” Journal of Advertising Research 48, 3 (2008): 392–403. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImphZHZlcnRyZXMiO3M6NToicmVzaWQiO3M6ODoiNDgvMy8zOTIiO3M6NDoiYXRvbSI7czoyNToiL2phZHZlcnRyZXMvNTgvMi8xODkuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

18. Dawes R. M. “The Robust Beauty of Improper Linear Models in Decision Making.” American Psychologist 34, 7 (1979): 571–582. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/0003-066X.34.7.571&link_type=DOI) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=A1979HG14300001&link_type=ISI) 

19. Day D. V., Lord R. G. “Expertise and Problem Categorization: The Role of Expert Processing in Organizational Sense-Making.” Journal of Management Studies 29, 1 (1992): 35–47. 
    
    

20. Fredrickson J. W. “Effects of Decision Motive and Organizational Performance Level on Strategic Decision Processes.” Academy of Management Journal 28, 4 (1985): 821–843. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiYW1qIjtzOjU6InJlc2lkIjtzOjg6IjI4LzQvODIxIjtzOjQ6ImF0b20iO3M6MjU6Ii9qYWR2ZXJ0cmVzLzU4LzIvMTg5LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

21. Gigerenzer G. “Why Heuristics Work.” Perspectives on Psychological Science 3, 1 (2008): 20–29. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1111/j.1745-6916.2008.00058.x&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=26158666&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000261999600004&link_type=ISI) 

22. Gigerenzer G., Gaissmaier W. “Heuristic Decision Making.” Annual Review of Psychology, 62 (2011): 451–482. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1146/annurev-psych-120709-145346&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=21126183&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000287331200017&link_type=ISI) 

23. Gigerenzer G., Goldstein D. G. “Reasoning the Fast and Frugal Way: Models of Bounded Rationality.” Psychological Review 103, 4 (1996): 650–669. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/0033-295X.103.4.650&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=8888650&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=A1996VM90000002&link_type=ISI) 

24. Gittelman V., Lange V., Cook W. A., Frede S. M., et al. “Accounting for Social-Desirability Bias in Survey Sampling: A Model for Predicting and Calibrating the Direction and Magnitude of Social-Desirability Bias.” Journal of Advertising Research 55, 3 (2015): 242–254. 
    
    [FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiRlVMTCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImphZHZlcnRyZXMiO3M6NToicmVzaWQiO3M6ODoiNTUvMy8yNDIiO3M6NDoiYXRvbSI7czoyNToiL2phZHZlcnRyZXMvNTgvMi8xODkuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

25. Goldstein D. G., Gigerenzer G. “Models of Ecological Rationality: The Recognition Heuristic.” Psychological Review 109, 4 (2002): 75–90. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/0033-295X.109.1.75&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=11863042&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000173840400006&link_type=ISI) 

26. Hackley C. “Divergent Representational Practices in Advertising and Consumer Research: Some Thoughts on Integration.” Qualitative Market Research: An International Journal 6, 3 (2003): 175–183. 
    
    

27. Hair J. F., Anderson R. E., Tatham R. L., Black W. C. *Multivariate Data Analysis*, 5th ed. Upper Saddle River, NJ: Prentice Hall, 1998. 
    
    

28. Hayashi A. M. “When to Trust Your Gut.” Harvard Business Review 79, 2 (2001): 58–65. 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=11213698&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 

29. 1.  Hardman D., 
    2.  Macchi L.
    
    Hertwig R., Todd P. M. “More Is Not Always Better: The Benefits of Cognitive Limits.” In *The Psychology of Reasoning and Decision Making: A Handbook*, Hardman D., Macchi L., eds. Chichester, U.K.: Wiley, 2003. 
    
    

30. Hirschman E. C. “Role-Based Models of Advertising Creation and Production.” Journal of Advertising 18, 4 (1989): 42–54. 
    
    

31. Hoch S. J. “Who Do We Know: Predicting the Interests and Opinions of the American Consumer.” Journal of Consumer Research, 15 (1988): 315–324. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1086/209169&link_type=DOI) 

32. Holbrook A., Krosnick J. “Social Desirability Bias in Voter Turnout Reports: Tests Using the Item Count Technique.” Public Opinion Quarterly 74 (2010): 37–67. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1093/poq/nfp065&link_type=DOI) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000275270700002&link_type=ISI) 

33. Hotz M. R., Ryans J. K., Shanklin W. L. “Agency/Client Relationships as Seen by Influentials on Both Sides.” Journal of Advertising 11, 1 (1982): 37–44. 
    
    

34. Hutchinson J. W., Alba J. W., Eisenstein E. M. “Heuristics and Biases in Data-Based Decision Making: Effects of Experience, Training, and Graphical Data Displays.” Journal of Marketing Research 47, 4 (2010): 627–642. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1509/jmkr.47.4.627&link_type=DOI) 

35. Isenberg D. J. “Thinking and Managing: A Verbal Protocol Analysis of Managerial Problem Solving.” Academy of Management Journal 29, 4 (1986): 775–788. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiYW1qIjtzOjU6InJlc2lkIjtzOjg6IjI5LzQvNzc1IjtzOjQ6ImF0b20iO3M6MjU6Ii9qYWR2ZXJ0cmVzLzU4LzIvMTg5LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

36. Jenkins R. *Pierre Bourdieu*. New York: Routledge, 2002. 
    
    

37. Johar G. V., Holbrook M. B., Stern B. B. “The Role of Myth in Creative Advertising Design: Theory, Process and Outcome.” Journal of Advertising 30, 3 (2001): 1–25. 
    
    

38. Johnson J. G., Raab M. “Take the First: Option-Generation and Resulting Choices.” Organizational Behavior and Human Decision Processes 91, 2 (2003): 215–229. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1016/S0749-5978(03)00027-X&link_type=DOI) 

39. Kahneman D. “A Perspective on Judgment and Choice: Mapping Bounded Rationality.” American Psychologist 58, 9 (2003): 697–720. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/0003-066X.58.9.697&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=14584987&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000185941100001&link_type=ISI) 

40. Kelley H. H. “Common-Sense Psychology and Scientific Psychology.” Annual Review of Psychology 43 (1992): 1–23. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1146/annurev.ps.43.020192.000245&link_type=DOI) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=A1992HD03400001&link_type=ISI) 

41. Knight F. H. “The Ethics of Competition.” Quarterly Journal of Economics 37, 4 (1923): 579–624. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.2307/1884053&link_type=DOI) 

42. Koslow S., Sasser S. L., Riordan E. A. “What Is Creative to Whom and Why? Perceptions in Advertising Agencies.” Journal of Advertising Research 43, 1 (2003): 96–111. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImphZHZlcnRyZXMiO3M6NToicmVzaWQiO3M6NzoiNDMvMS85NiI7czo0OiJhdG9tIjtzOjI1OiIvamFkdmVydHJlcy81OC8yLzE4OS5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 

43. Koslow S., Sasser S. L., Riordan E. A. “Do Marketers Get the Advertising They Need or the Advertising They Deserve? Agency Views of How Clients Influence Creativity.” Journal of Advertising 35, 3 (2006): 81–101. 
    
    

44. Lehnert K., Till B. D., Ospina J. M. “Advertising Creativity: The Role of Divergence versus Meaningfulness.” Journal of Advertising 43, 2 (2014): 274–285. 
    
    

45. Maylor H., Blackmon K., *Researching Business and Management: A Roadmap for Success*. Basingstoke, U.K.: Palgrave Macmillan, 2005. 
    
    

46. Menkhoff L., Schmidt U., Brozynski T. “The Impact of Experience on Risk Taking, Over-confidence and Herding of Fund Managers: Complementary Survey Evidence.” European Economic Review 50, (2006): 1753–1766. 
    
    

47. Michell P. C. “Accord and Discord in Agency–Client Perceptions of Creativity.” Journal of Advertising Research 24, 5 (1984): 9–24. 
    
    

48. Newell B. R., Shanks D. R. “Take the Best or Look at the Rest? Factors Influencing ‘One-Reason’ Decision Making.” Journal of Experimental Psychology: Learning, Memory, and Cognition 29, 1 (2003): 82–96. 
    
    

49. Nyilasy G., Reid L. N. “Agency Practitioner Theories of How Advertising Works.” Journal of Advertising 38, 3 (2009): 81–96. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.2753/JOA0091-3367380306&link_type=DOI) 

50. Phillips D. L., Clancy K. J. “Some Effects of ‘Social Desirability’ in Survey Studies.” American Journal of Sociology 77, 5 (1972): 921–940. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1086/225231&link_type=DOI) 

51. Runco M. A., Charles R. E. “Judgments of Originality and Appropriateness as Predictors of Creativity.” Personality and Individual Differences 15, 5 (1993): 537–546. 
    
    

52. Saini R., Monga A. “How I Decide Depends on What I Spend: Use of Heuristics Is Greater for Time than for Money.” Journal of Consumer Research 34, 6 (2008): 914–922. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1086/525503&link_type=DOI) 

53. Schooler L. J., Hertwig R., “How Forgetting Aids Heuristic Inference.” Psychological Review 112, 3 (2005): 610–628. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1037/0033-295X.112.3.610&link_type=DOI) 
    
    [PubMed](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=16060753&link_type=MED&atom=%2Fjadvertres%2F58%2F2%2F189.atom) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000230914800004&link_type=ISI) 

54. Simon H. A. “A Behavioral Model of Rational Choice.” Quarterly Journal of Economics 69, 1 (1955): 99–118. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.2307/1884852&link_type=DOI) 
    
    [Web of Science](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=000205162300006&link_type=ISI) 

55. Smith R. E., Yang X. “Toward a General Theory of Creativity in Advertising: Examining the Role of Divergence.” Marketing Theory 4 (2004): 29–55. 
    
    

56. Stewart D. W., Cheng Y., Wan H. “Creative and Effective Advertising: Balancing Spontaneity and Discipline.” Journal of Advertising 37, 4 (2008): 135–139. 
    
    

57. Taylor J., Kennedy R., Sharp B. “Is Once Really Enough? Making Generalizations about Advertising's Convex Sales Response Function.” Journal of Advertising Research 2 (2009): 198–200. 
    
    

58. 1.  Gigerenzer G., 
    2.  Todd P. M., 
    3.  ABC Research Group
    
    Todd P. M., Miller G. F. “From Pride and Prejudice to Persuasion: Satisficing in Mate Search.” In *Simple Heuristics that Make Us Smart*, Gigerenzer G., Todd P. M., ABC Research Group, eds. New York: Oxford University Press, 1999. 
    
    

59. Wackman D. B., Salmon C. T., Salmon C. C. “Developing an Advertising Agency–Client Relationship.” Journal of Advertising Research 26, 6 (1986): 21–28. 
    
    

60. West D., Caruana A., Leelapanyalert K. “What Makes Win, Place, or Show? Judging Creativity in Advertising at Award Shows.” Journal of Advertising Research 53, 3 (2013): 324–338. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImphZHZlcnRyZXMiO3M6NToicmVzaWQiO3M6ODoiNTMvMy8zMjQiO3M6NDoiYXRvbSI7czoyNToiL2phZHZlcnRyZXMvNTgvMi8xODkuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

61. West D., Ford J., Farris P. “How Corporate Culture Drives Advertising and Promotion Budgets: Best Practices Combine Heuristics and Algorithmic Tools.” Journal of Advertising Research 54, 2 (2014): 149–162. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImphZHZlcnRyZXMiO3M6NToicmVzaWQiO3M6ODoiNTQvMi8xNDkiO3M6NDoiYXRvbSI7czoyNToiL2phZHZlcnRyZXMvNTgvMi8xODkuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

62. West D., Kover A. J., Caruana A. “Practitioner and Customer Views of Advertising Creativity: Same Concept, Different Meaning.” Journal of Advertising 37, 4 (2008): 35–45. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.2753/JOA0091-3367370403&link_type=DOI) 

63. White A., Smith B. L. “Assessing Advertising Creativity Using the Creative Product Semantic Scale.” Journal of Advertising Research 41, 6 (2001): 27–34. 
    
    [Abstract/FREE Full Text](http://www.journalofadvertisingresearch.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTA6ImphZHZlcnRyZXMiO3M6NToicmVzaWQiO3M6NzoiNDEvNi8yNyI7czo0OiJhdG9tIjtzOjI1OiIvamFkdmVydHJlcy81OC8yLzE4OS5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 

64. Wierenga B. “Managerial Decision Making in Marketing: The Next Research Frontier.” International Journal of Research in Marketing 28 (2011): 89–101. 
    
    [CrossRef](http://www.journalofadvertisingresearch.com/lookup/external-ref?access_num=10.1016/j.ijresmar.2011.03.001&link_type=DOI) 

65. Winters Lauro P. (2001, February 16) “Whassup? America's Asking.” *The New York Times*. Retrieved from [http://www.nytimes.com/2001/02/16/business/the-media-business-advertising-whassup-america-s-asking.html](http://www.nytimes.com/2001/02/16/business/the-media-business-advertising-whassup-america-s-asking.html) 
    
    

66. Wübben M., Wangenheim F. V. “Instant Customer Base Analysis: Managerial Heuristics Often ‘Get It Right.’” Journal of Marketing 72, 3 (2008): 82–93. 
    
    

67. Yin R. K. *Case Study Research: Design and Methods*, 3rd ed. Thousand Oaks, CA: Sage, 1983.