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Effects of Problem Scope and Creativity Instructions on Idea Generation and SelectionRietzschel, Eric; Nijstad, Bernard; Stroebe, Wolfgang

Published in:Creativity Research Journal

DOI:10.1080/10400419.2014.901084

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Running head: PROBLEM SCOPE AND CREATIVITY INSTRUCTIONS

Effects of Problem Scope and Creativity Instructions on Idea Generation and Selection

Eric F. Rietzschel

Department of Psychology, University of Groningen

Bernard A. Nijstad

Department of HRM & OB, University of Groningen

Wolfgang Stroebe

Department of Psychology, University of Groningen

Department of Psychology, Utrecht University

This research was supported by a grant (NWO grant 410-21-010) from The

Netherlands Organization for Scientific Research. The authors thank Benjamin de Boer for

his assistance in coding the data, and Anna Borleffs for her assistance in collecting the

data. The study reported in this paper was conducted while Dr Eric F. Rietzschel was at the

Department of Work and Organizational Psychology, University of Amsterdam, and is part

of his doctoral dissertation.

Correspondence and reprint requests should be addressed to Dr Eric F. Rietzschel,

Department of Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS

Groningen, The Netherlands (e-mail: [email protected]).

PROBLEM SCOPE AND CREATIVITY INSTRUCTIONS 2

Abstract

The basic assumption of brainstorming is that increased quantity of ideas results in

increased generation as well as selection of creative ideas. Although previous research

suggests idea quantity correlates strongly with the number of good ideas generated,

quantity has been found to be unrelated to the quality of selected ideas. This paper reports

the results of a brainstorming experiment aimed at increasing the average creativity of

ideas and creative idea selection (rather than idea quantity). Problem scope (narrow vs.

broad) and creativity instructions (emphasis on creativity vs. personal relevance) were

manipulated. Results show that both narrow (vs. broad) problems and creativity (vs.

relevance) instructions led to the generation of ideas that were more creative. However,

only under creativity instructions did participants select more creative ideas.

Keywords: creativity; brainstorming; idea selection


Effects of Problem Scope and Creativity Instructions on Idea Generation and Selection

Creativity, or the generation of ideas that are novel and useful (e.g., Barron, 1955;

Runco & Jaeger, 2012; Stein, 1953), is highly valued in many domains, and techniques that

aim to stimulate creativity are highly popular. One of the most popular is Osborn’s (1953)

brainstorming technique. Until a few years ago, brainstorming research focused almost

exclusively on idea quantity, with particular emphasis on the issue of productivity loss in

brainstorming groups (e.g., Diehl & Stroebe, 1987). However, recent studies have also

begun to study the way in which brainstorming contributes to idea quality, both during idea

generation and afterwards, i.e., in idea selection (see Stroebe, Nijstad, & Rietzschel, 2010,

for an overview). One reason for adopting this new perspective is that mere quantity (a

long list of ideas) cannot be the ultimate goal of a brainstorming session. Eventually, what

one needs are a few high-quality ideas that get selected for further development. Research

suggests that this idea selection stage leads to suboptimal results, and that the quality of

selected ideas is not related to idea quantity at all, but only to the average quality of

generated ideas (e.g., Rietzschel, Nijstad, & Stroebe, 2006). Thus, a closer look at idea

quality in brainstorming seems warranted. In the current paper, it is proposed that idea

quality can be enhanced in two ways: by enforcing a focused approach to the

brainstorming problem (during idea generation), and by stressing particular quality criteria

(during generation and selection).

Idea quality in idea generation

Brainstorming operates on the general principle that ‘quantity breeds quality’: The

more ideas are generated, the more high-quality ideas will be found among them. On the

whole, this reasoning is supported by the available research: a higher total output is

associated with higher availability of high-quality creative ideas (ideas that are both

original and feasible; e.g., Diehl & Stroebe, 1987). However, other studies show that even


under brainstorming instructions, people perform less creatively than they could, and that

interventions aimed at increasing idea quality (rather than quantity) can lead to significant

performance gains over and above the effects of the traditional brainstorming rules.

It has been theorized that an important obstacle to the generation of creative ideas is

people’s tendency to follow the ‘path of least resistance’ (Finke, Ward, & Smith, 1992;

Ward, 1994; Ward, Patterson, Sifonis, et al., 2002). According to this theory, people have a

tendency to only generate ideas that come to mind relatively easily, and give up when it

becomes harder to generate ideas. Unfortunately, the most accessible and easily generated

ideas often are the least creative ones, and, according to this perspective, people therefore

need to be induced to leave this path of least resistance. Although the brainstorming

instructions help them to do so by emphasizing quantity and by giving the goal to generate

as many ideas as possible, people nevertheless tend to fall back on ideas that are easily

generated and expressed, even within the context of a brainstorming session.

One way to force people to abandon this path of least resistance and make them

generate less accessible (and more creative) ideas may be to change how the brainstorming

problem is defined. Specifically, it might help to impose narrow boundaries on the

brainstorming problem. While creativity is commonly associated with flexibility (being

able to generate ideas within several semantic categories), the recent Dual Pathway Model

of Creativity (De Dreu, Baas, & Nijstad, 2008; Nijstad, De Dreu, Rietzschel, & Baas,

2010) suggests that creativity can benefit from a focused approach as well as from a

flexible one. That is, rather than stimulating people to come up with ideas across many

semantic categories, it may help to have them think more deeply within semantic

categories. Thus, it might be useful to define the brainstorming problem narrowly, focusing

on a subcategory of the overall problem, rather than on the overall problem in its entirety.

Interestingly, this was also recommended by Osborn (1953), and similar points have been


raised by other researchers. For example, Finke, Ward, and Smith (1992) noted that

“restricting domains of interpretation” in a creative task can stimulate creative

performance, because this “reduces the likelihood that a person will fall back on

conventional lines of thought” (p. 32). Pursuing this line of reasoning, Stokes and Fisher

(2005) argued that effective selection of constraints may even be central to the creative

process.

In line with these predictions, several studies have found that an artificially induced

focus on subproblems of the brainstorming topic can stimulate the generation of creative

ideas. For example, Dennis, Valacich, Connolly, and Wynne (1996) found that participants

generated more ideas when sequentially (as opposed to simultaneously) addressing

subproblems of a problem (also see Coskun, Paulus, Brown, & Sherwood, 2000; Dennis,

Aronson, Heninger, & Walker, 1999). In addition, Rietzschel, Nijstad, and Stroebe (2007)

found that participants who were induced to focus on a subcategory of the brainstorming

problem generated more original ideas within that subcategory.

In terms of the Dual Pathway Model, narrowing down the brainstorming problem

causes people to persist within a semantic subcategory, and hence should lead to the

generation of more original ideas. When people generate ideas about a broad topic, there

are many highly accessible but unoriginal ideas available; getting ‘past’ these ideas may be

very difficult (Jansson & Smith, 1991; Smith & Blankenship, 1991). When people generate

ideas about a narrow brainstorming problem, it requires less effort to deplete the pool of

unoriginal ideas, because there are, by definition, fewer unoriginal ideas to be found in a

narrow problem than in a broad problem. When people continue brainstorming after the

most accessible and least creative ideas have been generated, their subsequent ideas will be

more original. Thus, expending the same amount of cognitive effort, people are more likely

to generate creative ideas about a narrow topic than about a broad one.


Creativity versus Relevance

Another way to force people off the path of least resistance might be to stress the

importance of coming up with creative, rather than ordinary, ideas. Although the

brainstorming instructions encourage participants to refrain from self-censoring, people

find this difficult to do. This is shown by Rickards (1975), who found that organization

members refrained from freely speculating in brainstorming sessions. One reason for this

may be that brainstorming sessions in organizations usually concern a real (organizational)

problem that is highly relevant to the participants, rather than an artificial and unrealistic

problem that is not personally relevant to the participants. Indeed, research has shown that

coming up with creative ideas is more difficult with a ‘relevant’ or realistic problem than

with an irrelevant or unrealistic problem (Dillon, Graham & Aidells, 1972; Harari &

Graham, 1975). Other research shows that explicit instructions to be creative can enhance

idea quality. For example, Harrington (1975) found that participants generated more

creative ideas when explicitly instructed to do so, and Shalley (1991) found that whereas

productivity goals enhanced idea quantity, idea quality was enhanced most by explicit

creativity goals. Thus, stressing the importance of creativity may help people to generate

creative ideas, even when they are brainstorming about a realistic or relevant problem. In

contrast, stressing the personal relevance of the problem should lead to the generation of

ideas that are less original, because people will tend to stick with ‘safe’ ideas that come to

mind easily.

Thus, the generation of creative ideas should be facilitated by providing participants

with a narrow problem, and by stressing the importance of coming up with creative ideas.

The question is what happens when these two interventions are combined. One possibility

is that creativity instructions are sufficient to leave the path of least resistance, and that

problem scope will only affect idea quality in the absence of creativity instructions. When


working under creativity instructions, people may be less likely to rely on highly accessible

ideas, because the goal is to come up with novel ones. Another possibility is that, even

though creativity instructions enhance idea quality, there is still enough room for

improvement by narrowing problem scope; the two manipulations will then have an

additive effect, with participants working on a narrow topic with creativity instructions

generating the best ideas.

Does idea quality breed selection quality?

Although it is important to understand how creative ideas are generated, there is

more to creativity than that. The assumption that ‘quantity breeds quality’ consists of two

notions: a higher total output means higher output of creative ideas, and higher output of

high-quality ideas increases the likelihood of a creative idea being selected. While, as

described above, the first part of this reasoning is supported by research, the second part

has not fared so well (Stroebe, Nijstad, & Rietzschel, 2010). Faure (2004) found that

nominal brainstorming groups, although more productive than interactive groups, selected

ideas of similar quality. Rietzschel, Nijstad, and Stroebe (2006) also failed to find

significant differences in the quality of ideas selected by interactive and nominal groups,

despite the large differences in idea quantity. Moreover, selection performance was not

better than chance. Putman and Paulus (2009) observed that neither nominal nor

interactive groups selected their best ideas, although nominal groups generated and

selected more original ideas than interactive groups.

Rietzschel, Nijstad, and Stroebe (2010) found that the poor selection performance of

their participants was due to a strong tendency to select feasible ideas, at the cost of

originality. Further, Rietzschel et al. observed that participants could be induced to select

more creative ideas by instructing them to select ‘creative’ (rather than ‘the best’) ideas.

However, these instructions apparently did not change participants’ preferences for


practical, rather than creative ideas, since creativity instructions significantly decreased

participants’ satisfaction with their selection. In line with these results, Mueller, Melwani,

and Goncalo (2012) recently reported evidence for an implicit bias against creativity (and

in favor of practicality) under conditions of uncertainty (also see Blair & Mumford, 2007).

Further, Runco and Smith (1992) found that participants were not particularly good at

recognizing their most creative and most popular ideas.

All in all, the results on idea selection can be summarized as follows: in the absence

of specific selection instructions, selection effectiveness (the degree to which people

actually select their most creative ideas) appears to be rather low. As a consequence, the

selection outcome (the quality of the selected ideas) is not a function of idea quantity, but

of (average) idea quality. This raises the question what happens to idea selection when the

idea generation process is enhanced by changing the scope of the brainstorming problem

and by stressing creativity.

Rietzschel et al.’s (2010) results suggest that the idea selection will benefit from

creativity instructions, because these diminish participants’ reliance on feasibility as the

dominant selection criterion. For problem scope, a facilitating effect on idea selection

effectiveness is not likely. Participants using a narrow problem may select more creative

ideas, because they have more creative ideas to choose from, but problem scope does not

affect people’s preference for feasible and unoriginal ideas.

In order to address these issues, an experiment was conducted where individual

participants first generated, and then selected ideas. In this experiment, both problem scope

and creativity instructions were manipulated.

Method


Participants

Initially, the sample consisted of 105 participants. However, three participants did

not follow instructions and were excluded from the analyses. Hence, the analyses below

are based on 102 undergraduate university students (76 women and 26 men, mean age =

21.1 years), who were required to participate in psychology research as part of their

undergraduate curriculum. All participants signed up for this study voluntarily. Participants

received course credit or 7 Euros (about 9 US Dollars at the time).

Procedure

Upon arrival in the laboratory, all participants read a general introduction explaining

that they were about to participate in a brainstorming session, and that they would be

required to first generate ideas, and then make a selection from these ideas.

Participants were randomly assigned to conditions. In the creativity conditions, the

instructions then explained that the goal of the study was to find out how creative students

could be in generating and selecting ideas, and that it would be their task to generate ideas

that were as original (i.e., innovative and unusual) as possible. Alternatively, participants

in the relevance conditions were told that the goal of the study was to find out how

students generate and select ideas about a topic that they were personally involved in, and

that it would be their task to generate as many ideas as possible.

Participants in the conditions with a broad problem scope were then told that they

would be generating ideas about “possible improvements in the education at the

department of psychology”; in the conditions with a narrow problem scope, the topic was

“possible improvements in the lectures at the department of psychology”. Note that the

narrow topic essentially is a sub-topic of the broad one. Participants in the creativity

conditions were reminded that they were supposed to generate original ideas; participants


in the relevance conditions were reminded to keep their student experiences in mind while

generating ideas. Participants then generated ideas for 20 minutes.

After the brainstorming task, all participants received the instructions for the

selection task. All participants were instructed to select the best idea from the ideas they

had previously generated. Participants in the creativity conditions were instructed to base

their selection on originality, whereas participants in the relevance conditions were

instructed to base their selection on their experiences as students. A time limit of 15

minutes was set for this task.

After making their selection and completing a short post-experimental questionnaire,

participants were debriefed, thanked and paid.

Measures and Dependent Variables

Idea quantity. Idea quantity was measured as the number of unique (i.e., non-

redundant) ideas generated by each individual.

Idea quality. A trained rater who was blind to conditions rated all ideas for

originality and feasibility. The first author coded a random subset of 250 ideas on both

dimensions. Intraclass correlation coefficients (two-way random model) were .73 for

originality, and .63 for feasibility, which was considered good reliability (Cicchetti &

Sparrow, 1981).

Two originality and feasibility measures were for each participant: the average

originality (feasibility) of the ideas generated by that participant, and the originality

(feasibility) of the idea selected by that participant.

Satisfaction. Two items asked participants to indicate their satisfaction with the

number of ideas they had generated and with the quality of their ideas; one item asked

participants how satisfied they were with the quality of the idea they had selected. All

items were rated on 5-point scales (1 = “not at all”, 5 = “very much”).


Results

For clarity, results with regard to idea generation and idea selection are discussed

separately.

Idea Generation

Idea quantity. On average, participants generated 20.92 ideas (SD = 8.88), but a 2

(Instructions) x 2 (Problem Scope) ANOVA yielded no significant main or interaction

effects, indicating that neither originality instructions nor problem scope had an effect on

the number of ideas that participants generated.

Idea Quality. Means and standard deviations for idea quality are presented in Table

1. The average originality and feasibility of the generated ideas were negatively correlated

(r = -.57, p < .001). A 2 x 2 ANOVA on the originality of the generated ideas (M = 2.25,

SD = 0.34) showed that, as expected, a narrow problem led to the generation of more

original ideas (M = 2.33, SD = 0.41) than a broad problem (M = 2.18, SD = 0.23) (F (1, 98)

= 5.56, p = .02, partial η2 = .054). A marginally significant main effect of instructions (F(1,

98) = 3.81, p = .054, partial η2 = .037) indicated that participants with originality

instructions tended to generate more original ideas (M = 2.31, SD = 0.35) than participants

with relevance instructions (M = 2.19, SD = 0.32). The interaction, however, was not

significant (F(1, 98) = 1.14, p = .288).

An ANOVA on the feasibility of the generated ideas (M = 3.31, SD = 0.35) yielded

no significant main or interaction effects (ps > .1). Thus, neither problem scope nor

instructions affected the feasibility of the generated ideas.

Idea Selection

Selection outcome. The average originality and feasibility of the selected ideas were

significantly negatively correlated (r = -.25, p = .012), although this correlation was

significantly smaller than that for the generated ideas (Z = 2.76, p = .003).


A 2 x 2 ANOVA on the originality of the selected ideas showed that participants

with originality instructions selected more original ideas (M = 2.64, SD = 0.83) than

participants with relevance instructions (M = 2.12, SD = 0.83) (F (1, 98) = 9.88, p = .002,

partial η2 = .09); there was no effect of problem scope, nor was the interaction of problem

scope and instructions significant.

An ANOVA on the feasibility of the selected ideas yielded no significant effects (Fs

< 1).

Selection effectiveness. To analyze selection effectiveness, a 2 x 2 mixed model

ANOVA was conducted with the average originality of the generated ideas and the

selected idea as the within-subjects factor originality. This analysis revealed a significant

Originality x Instructions interaction (F (1, 98) = 6.25, p = .014, partial η2 = .06). Simple

effects analysis showed that participants with originality instructions selected ideas that

were more original (M = 2.64, SD = 0.83) than their generated ideas (M = 2.31, SD = 0.35;

F (1, 98) = 8.91, p = .004); this difference was not significant for participants with

relevance instructions (F < 1). In other words, participants with originality instructions

selected above chance level; participants with relevance instructions did not. There was no

effect of problem scope.

A repeated measures ANOVA with the feasibility of the generated and selected ideas

as the within-subjects factor feasibility yielded no significant effects (all ps > .1): the

feasibility of the selected ideas was not different from that of the generated ideas, and this

was the case in all conditions.

Satisfaction

The three items measuring participant satisfaction were analyzed with univariate 2 x

2 ANOVAs (see Table 2). For all three items, only a main effect of instructions was found:

participants with relevance instructions were significantly more satisfied with the number


of ideas they generated (F(1, 98) = 7.87, p = .006, partial η2 = .07), the quality of the ideas

they generated (F(1, 98) = 10.09, p = .002, partial η2 = .09) and the quality of the idea they

selected (F(1, 98) = 7.19, p = .009, partial η2 = .07) than participants with creativity

instructions.

Discussion

The results showed that problem scope and creativity instructions influenced idea

quality. Participants generated more creative ideas when the problem focus was narrow

rather than broad, and when they were instructed to come up with ideas that were original

and unusual rather to think of ideas that were relevant. Interestingly, there was no

interaction between these two manipulations; although the most creative ideas were

generated by participants working under creativity instructions on a narrow problem, this

effect was additive, rather than interactive. Thus, although both manipulations might be

taken to have been successful in forcing participants to abandon ‘the path of least

resistance’ (Finke et al., 1992), both also seem to have left sufficient room for the other

manipulation to improve idea quality as well.

With regard to idea selection, the results of this experiment replicate and extend

earlier findings (e.g., Rietzschel et al., 2010). Originality instructions enhanced idea

quality, and improved the effectiveness and outcome of the selection process. Specifically,

participants with originality instructions made a more effective selection from the ideas

that they had generated (selecting ideas of higher originality and equal feasibility), whereas

participants with relevance instructions performed at chance level. As found in other

research, originality instructions led to a lower satisfaction with the selection. Furthermore,

although participants who generated ideas about a narrow topic generated more original

ideas, this affected neither the selection outcome nor selection effectiveness.


Previous research suggests that the selection of creative ideas is not related to idea

quantity; instead, it is idea quality that seems to matter. The current study shows that

improving idea quality (e.g., by using a narrow topic) does not necessarily improve idea

selection either. Similarly to earlier research, in the current study participants only selected

more creative ideas when they were explicitly instructed to take originality into account.

The reason for this pattern is probably the pervasive bias against original ideas (Mueller et

al., 2012; Rietzschel et al., 2010), again indicated by the satisfaction ratings: Participants

with relevance instructions were more satisfied than those with originality instructions.

These results indicate an important discontinuity between idea generation and idea

selection: interventions aimed at improving idea generation (e.g., the use of nominal

groups, limiting problem scope) do not seem to carry over into the selection stage. The

findings thus underscore the role of idea selection as a bottleneck in the innovation

process. This point is exacerbated by findings that, when it comes to idea implementation,

creative ideas may actually be less likely to be implemented than mundane ones (e.g.,

Baer, 2012), and that the outcomes of idea evaluation may be interactively (rather than

additively) influenced by idea quality and evaluation criteria (Lonergan, Scott, &

Mumford, 2004). Future work should delve deeper into this issue; in particular, learning

more about the complex relation between ideational fluency, idea creativity, idea

evaluation, and willingness to invest in creative ideas (e.g., Rubenson & Runco, 1992) is

necessary to come to a more complete understanding of the entire creative process, rather

than single stages of it. What is clear at this moment, is that it is not useful to get people to

come up with more creative ideas in a brainstorming session, if the most creative options

are simply going to be rejected. Future research on group and individual brainstorming

should therefore focus on strategies that improve the process of idea selection, rather than

only idea generation.


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PRO

BLEM

SCO

PE AN

D C

REA

TIVITY

INSTR

UC

TION

S

19

Table 1

Means and Standard D

eviations for Idea Quality

B

road problem

N

arrow problem

Measure

Relevance

Originality

R

elevance O

riginality

Originality of generated ideas

2.15 (0.23) a c 2.21 (0.21) a d

2.23 (0.37) b c

2.43 (0.43) b d

Originality of selected idea

2.12 (0.83) a 2.52 (0.89) b

2.12 (0.85) a

2.77 (0.77) b

Feasibility of generated ideas 3.29 (0.34)

3.25 (0.23)

3.44 (0.33) 3.29 (0.45)

Feasibility of selected idea 3.24 (0.72)

3.19 (0.83)

3.33 (0.82) 3.15 (0.78)

Note. M

aximum

value = 5; N = 85; standard deviations are in parentheses.

a b Means w

ithin a row w

ith different superscripts are different from each other w

ith p < .05.

c d Means w

ithin a row w

ith different superscripts are different from each other w

ith p < .1.

PRO

BLEM

SCO

PE AN

D C

REA

TIVITY

INSTR

UC

TION

S

20

Table 2

Means and Standard D

eviations for Items C

oncerning Satisfaction

B

road problem

N

arrow problem

Measure

Relevance

Originality

R

elevance O

riginality

Satisfaction with num

ber of ideas generated 3.56 (0.92)

3.00 (0.96)

3.67 (0.64) 3.27 (0.87)

Satisfaction with quality of ideas generated

3.88 (0.78) 3.11 (1.19)

3.58 (0.78)

3.19 (0.85)

Satisfaction with quality of selected idea

4.44 (0.65) 3.85 (0.99)

4.21 (0.66)

3.96 (0.77)

Note. M

aximum

value = 5; N = 85; standard deviations are in parentheses.

University of Groningen Effects of Problem Scope and Creativity … · 2018-02-11 · ideas and...

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