Shuichiro Ogawa
日本語

Notes · updated 2026-11-12 · Markdown

Does Quantity Breed Quality? Hits and Hit Rates, Idea Counts and the Best Idea, How Prototypes Are Compared, Practice Time, and the Origin of the Ceramics Class Story

The saying that quantity breeds quality was examined separately for four kinds of quantity (number of works, number of ideas, prototype iteration, and practice time), using 35 academic sources and 5 documents on the origin of a well-known anecdote. People who produce more works also produce more hits.

Contents (11)
  1. What does the ceramics class story claim happened?
  2. Scope and method
  3. Do people who produce more also produce more hits?
  4. Are the works of prolific producers individually better?
  5. Does generating more ideas yield more good ideas?
  6. Can people select the good ideas from many?
  7. In prototyping, does the number matter, or the way prototypes are compared?
  8. Does practice time explain expertise?
  9. Where did the ceramics class story come from?
  10. Which forms of the claim are supported?
  11. Gaps

What does the ceramics class story claim happened?

On the first day, a ceramics teacher divided the class into two groups. Students on the left side would be graded only on the quantity of their work; at the end of the term, the teacher would weigh their pots, and fifty pounds would earn an A and forty pounds a B. Students on the right side would be graded only on quality, and one perfect pot would earn an A. At grading time, all of the works of highest quality had come from the quantity group. The quantity group had produced piles of work and learned from their mistakes, while the quality group had theorized about perfection and was left with little more than a pile of dead clay.

This story, from Art & Fear (1993) by David Bayles and Ted Orland, has been cited repeatedly as an example of quantity breeding quality (Kleon 2020; Kottke 2020). However, the story contains at least two different claims. The first claim is that producing more increases the probability that a good work appears. The second claim is that producing more improves skill, so that each work becomes better. Under the first claim, the average work of the quantity group need not differ from that of the quality group. Under the second claim, the works of the quantity group should be better on average as well.

This distinction holds across other kinds of quantity. The number of works over a career, the number of ideas in one session, the number of prototype iterations, and the hours of practice have each been studied in separate research traditions and linked to different measures of quality. This note examines for which quantity and which quality the saying holds, and to what extent. It also examines whether the class in the anecdote actually took place.

Scope and method

  • Scope: peer-reviewed articles, meta-analyses, reviews, and peer-reviewed conference papers that measured, or discussed how to measure, the relationship between quantity and quality. Four traditions were covered: lifetime creative output (the equal-odds rule and the random impact rule), brainstorming and idea selection, design prototyping, and expertise and practice. The period was 1977 to 2020.
  • Collection: 54 candidates were collected across scholarly databases (OpenAlex, Crossref, Europe PMC) and checked on Crossref for retractions, corrections, and predatory venues (0 retractions, 1 correction).
  • Origin of the anecdote: the bibliographic record of the book (Open Library) and 4 web articles reporting statements by an author and by the teacher were read in full. These documents are treated as a record of origin, not as academic evidence.
  • Verification: of the 40 references, 26 were obtained as full text (including the Open Library records of 2 books and 4 web articles), and numbers and claims were checked against the relevant passages. The other 14 were used within the scope of their abstracts and are attributed to their authors. The figures for Macnamara et al. (2014) are the values after the 2018 correction.
  • Reporting: for each study, this note distinguishes what was treated as quantity, what was treated as quality, and whether the comparison was between people or within the same person.

Do people who produce more also produce more hits?

Simonton (1977) divided the lives of 10 classical composers into consecutive 5-year periods and analyzed, as a time series, the number of works and the number of major works that were later frequently cited. According to the abstract, under both measures of productivity (works and themes), quality was a probabilistic consequence of quantity. Simonton (1997) adopted this relationship, as the equal-odds rule, as a principle of a model of lifetime creative output. The abstract states that two individual-difference parameters and two information-processing parameters, together with this single principle, predict the age curves of output and the timing of the first, best, and last contributions.

The equal-odds rule can be divided into two predictions. As summarized by Forthmann, Leveling, Dong, and Dumas (2020), the first prediction is that the number of hits increases as a linear function of total output, and the second is that the hit ratio is uncorrelated with total output. The second prediction conflicts with the second claim of the anecdote, namely that producing more makes each work better. If the equal-odds rule holds, prolific creators have more hits because they produce more, not because each of their works is better.

Forthmann et al. (2020) tested the two predictions in three large datasets. The datasets covered about 1.5 million inventors holding patents, 1,742 psychologists in German-speaking countries, and 20,296 scientists in multiple disciplines; quantity was the total number of patents or publications, and quality was citations. The correlation between total output and total citations was .59 to .77, so those who produced more were cited more. The correlation between total output and average citations per item was −.0004 to .07, and even the largest value, .07, explained only 0.5% of the variance in average citations. For structural equation models representing the equal-odds rule, the authors judged the fit to be good or excellent in all three datasets (with a slightly lower fit for the inventor data) and concluded that the equal-odds rule held.

Studies on the timing of hits show the same pattern. According to the abstract of Sinatra et al. (2016), the most influential paper of a scientist appears at a random position in the sequence of publications (the random impact rule). Janosov, Battiston, and Sinatra (2020) confirmed this rule in careers in film, music, books, and science, and treated the random position of the biggest hit as the equal-odds rule. If the best work is equally likely to appear anywhere in the sequence, a learning effect in which later works become better is not visible as a population average. Huber (1998), who examined the timing of inventors’ patents, also reported in the abstract that the timing of invention for most inventors fits a Poisson process (random arrivals).

Are the works of prolific producers individually better?

At this point, the results diverge. Larivière and Costas (2016) compared the number of papers with the share of papers in the top 1% by citations for 28,078,476 disambiguated researchers over 1980 to 2013. On average, researchers with more papers had a higher share of top 1% papers. This relationship was stronger in older cohorts, and decreasing returns to scale were observed in recent cohorts. Sandström and van den Besselaar (2016) also reported, for about 48,000 Swedish researchers, that more productive researchers had not only more but also a higher share of highly cited papers.

The apparent disagreement with Forthmann et al. (2020) may arise from different measures of quality. Forthmann et al. used average citations per item, whereas Larivière et al. and Sandström et al. used the share of papers in the top tier. Numerically, the average can remain unchanged while only the upper tail of the distribution becomes thicker. These three studies do not show which measure comes closer to answering whether quantity makes each work better. What they show is that the second prediction of the equal-odds rule, that the hit ratio is unrelated to quantity, does not hold under some measures.

Research on clusters of hits separated quantity from quality from another angle. Liu et al. (2018) found that, in the careers of artists, film directors, and scientists, high-impact works appear clustered in short periods (hot streaks). Hot streaks began at random times within a career, and no change in productivity was detected during them. The periods of consecutive hits were not periods of greater output. The authors explained that the random timing of hot streaks produces the appearance of the random impact rule.

For the number of works, therefore, the academic literature strongly supports the first claim: those who produce more have more hits. The second claim, that producing more makes each work better, is not supported by average citations, is partly supported by the share of top-tier works, and the clustering of hits was unrelated to quantity. In the abstract of Simonton (2003), scientific creativity is described as a quasi-random combinatorial process, that is, as constrained stochastic behavior. From this perspective, producing more increases the number of attempts at drawing a hit, which is a different function from improving skill.

Does generating more ideas yield more good ideas?

One brainstorming rule, to seek quantity, goes back to Osborn’s Applied Imagination (1953). In the abstract, Paulus, Kohn, and Arditti (2011) summarized the premise of this rule as follows: focusing on generating many ideas increases both the number of ideas and the number of good (original and useful) ideas. They also noted that earlier comparisons with conditions in which quantity was not emphasized had not clearly demonstrated this premise. They assigned four kinds of instructions: no specific focus, a quantity goal, a quality goal, and a combined quantity and quality goal. According to the abstract, the quantity goal condition outperformed the other three in both the number of ideas and the number of good ideas.

Within one session, later ideas are better. Beaty and Silvia (2012) gave 133 participants a 10-minute unusual uses task (listing uses for a common object), time-stamped each response, and had three raters score creativity. Creativity increased with time and flattened slightly near the end. Fluency, the number of responses per unit time, was highest in the first minute and then dropped. This serial order effect has been confirmed repeatedly since the first demonstration by Christensen et al. in 1957. Because the effect was smaller for participants with higher intelligence, Beaty and Silvia favored an account based on executive processes, in which knowledge is strategically retrieved and manipulated, over an account based on spreading activation toward remote concepts. Within one session, the better ideas came in the later part of continued generation.

Between people, those who generate more ideas also generate more good ideas. Jung et al. (2015) reported, for 246 participants, a correlation of r = 0.73 between the number of responses in a divergent thinking task and a creativity score given by three raters, and concluded that the result supported the equal-odds rule. However, the ratings were made by snapshot scoring, in which all responses of one participant receive a single holistic score. A higher rating for the whole output of someone who wrote more is different from each idea being better. In the study of product sketches by Kudrowitz and Wallace (2013), the abstract also reports a coefficient of determination of 0.82 between each participant’s number of ideas and overall creativity scores. The abstract does not describe how the overall score was constructed, so it cannot be confirmed whether the number of ideas entered the score.

When quality is measured as the average quality of each idea, the relationship becomes conditional. Friis-Olivarius and Christensen (2019) examined, for 154 graduate students, the relationship between the number of ideas and the average creative value per idea. This relationship became more positive as openness to experience increased and became significant at high levels of openness. The authors concluded that quantity does breed quality but that the effect is moderated by individual differences, and titled the paper “Not quite equal odds.”

Can people select the good ideas from many?

Even after many ideas are generated, only the selected ones are used. Girotra, Terwiesch, and Ulrich (2010) argued that organizations seek a few great ideas rather than good average ideas, and divided the quality of the best idea into four components: the average quality of the ideas generated, the number of ideas, the variance in quality, and the ability to discern quality. In a within-subjects experiment with 44 participants, the hybrid structure, in which individuals first generated ideas alone and then met, produced about three times as many ideas per unit time as the team structure, in which members worked together from the start. The ideas of the hybrid structure also had a higher average quality, and its best idea was better. The hybrid structure was also better at discerning the quality of its own ideas, but both structures were weak at this in absolute terms.

The weakness at the selection stage also appears in another experiment. According to the abstract of Rietzschel, Nijstad, and Stroebe (2010), participants strongly preferred feasible and desirable ideas over original ones, and this was the main reason for their poor selection performance. Explicitly instructing participants to select original ideas raised the originality of the selected ideas but lowered satisfaction with the selection and the rated effectiveness of the selected ideas. Even if generating more raises the probability that a good idea is included, quantity does not reach final quality unless that idea is selected.

Rare ideas are not necessarily more valuable. Kornish and Ulrich (2011) analyzed 1,368 ideas from five datasets and showed that redundancy among ideas generated in parallel was small. The ideas least similar to others were not generally the most valuable, and there was a positive relationship between the number of similar ideas and idea value.

When a group seeks quantity together, quantity itself decreases. In four experiments, Diehl and Stroebe (1987) examined why interacting groups produce fewer ideas than nominal groups, in which the same number of people work alone and their ideas are pooled; according to the abstract, most of the loss was explained by waiting for one’s turn to speak (production blocking). The meta-analysis by Mullen, Johnson, and Salas (1991) showed, according to the abstract, that interacting brainstorming groups were less productive than nominal groups in both quantity and quality. In the abstract, Nijstad and Stroebe (2006) presented a model in which turn taking interferes with both the knowledge activation stage and the idea production stage. The hybrid structure of Girotra et al., in which individuals generate ideas alone first, can be read as a design that avoids this turn taking.

In some settings, continuing to generate ideas narrows the range of ideas. In the abstract, Bayus (2013) reported from Dell’s IdeaStorm community that serial ideators were more likely than one-time contributors to generate an idea that was implemented, but that once their ideas were implemented, they were unlikely to repeat this success and proposed ideas similar to those already implemented.

In prototyping, does the number matter, or the way prototypes are compared?

Few design prototyping experiments have directly manipulated the quantity of prototypes. Dow, Heddleston, and Klemmer (2009) used a task of building a vessel that protects a raw egg from a fall, and compared a group that could drop, test, and revise its design during the design period with a group that could not test. The vessels of the testing group survived falls from greater heights, and inexperienced participants in the testing group performed as well as experienced participants in the non-testing group. What was manipulated was whether participants could test, not a graded number of tests.

Dow et al. (2010) held quantity constant and changed how prototypes were compared. According to the submitted version, 33 participants created web advertisements, and in both conditions they created five prototypes and one final design. The only difference was the timing of critique: the serial group received critique after each prototype, whereas the parallel group received critique after creating three prototypes and again after creating two more. According to the abstract, the parallel group’s advertisements outperformed those of the serial group in click-through data and expert ratings, were more diverse, and were accompanied by a larger increase in self-efficacy. The number of prototypes was the same. What separated the outcomes was whether several designs were placed side by side and compared.

Dow et al. (2011) showed the same point in a different form. In an experiment in which pairs created advertisements, three conditions were compared: designing three ads and sharing all three with a partner, designing three ads and sharing only the best one, and designing and sharing one ad. In both conditions in which three ads were designed, individual exploration was broader than in the one-ad condition. Outcome (click-through rate and ratings), integration of the partner’s ideas, and rapport with the partner were best when all three ads were shared. Producing more broadened individual exploration, and the difference in outcome arose when the designs were brought into a situation where they were compared. A broader treatment of the benefits and harms of prototyping is in Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design.

Does practice time explain expertise?

The study most often cited on practice and expertise is Ericsson, Krampe, and Tesch-Römer (1993). According to the abstract, expert performance is the result of prolonged engagement in effortful activities designed to optimize improvement (deliberate practice), and individual differences, even among elite performers, are closely related to the amount of deliberate practice.

Macnamara, Hambrick, and Oswald (2014) measured the relationship between deliberate practice and performance in a meta-analysis of 88 studies. A 2018 corrigendum corrected an error in the adjustment formula for dependent effect sizes and revised the figures, and the authors stated that the conclusions did not change. After the correction, the average correlation between deliberate practice and performance was .38, which explained 14% of the variance in performance. By domain, deliberate practice explained 24% in games, 23% in music, 20% in sports, 5% in education, and 1% in professions. In the sports meta-analysis by Macnamara, Moreau, and Hambrick (2016), deliberate practice explained 18% of the variance in performance, but only 1% among elite-level athletes. Athletes who reached a high level of skill had not begun their sport earlier than lower-skill athletes.

A replication of the original study also estimated a smaller effect. Macnamara and Maitra (2019) repeated the study of student violinists by Ericsson et al. with 39 participants and a double-blind procedure. When groups were compared on the amount of practice alone, the original study explained 48% of the variance in performance, whereas the replication explained 26%. The core finding that accumulated practice corresponded to each skill level was not replicated.

Ericsson’s side responded that what had been measured was not deliberate practice. Ericsson and Harwell (2019) stated that deliberate practice as defined in 1993 is individualized solitary practice whose goals and methods are set by a qualified teacher, and distinguished it from naïve practice, which merely accumulates domain-relevant activities such as playing games with friends. Reanalyzing only the effect sizes that met this criterion, they reported a correlation of r = .54 between accumulated practice time and performance, which explained 29% of the variance and 61% after correction for measurement reliability. In response, Hambrick, Macnamara, and Oswald (2020) pointed out that, across more than 25 years of writings by Ericsson and colleagues, the definition of deliberate practice had been inconsistent and the standard of evidence had shifted, and asked whether the view constitutes a falsifiable theory.

The two sides disagree on the size of the effect of practice time. However, neither side adopts the reading that merely accumulating hours explains expertise. Ericsson located the source of the effect in the design of practice rather than in time, and Macnamara and colleagues showed that the explanatory power of time is small and varies widely across domains. In the meta-analysis on music by Platz et al. (2014), the effect size between accumulated task-relevant practice and musical achievement was large, r_c = 0.61, but this value used task-relevant practice in general, which includes deliberate practice, as the predictor. In the abstract, Campitelli and Gobet (2011) reviewed studies of chess and concluded that abundant deliberate practice is necessary but not sufficient, estimating the minimum for reaching master level at about 3,000 hours. Even in a domain that requires a certain amount, quantity was not a sufficient condition.

Where did the ceramics class story come from?

According to the Open Library record, Art & Fear was published in 1993 by Capra Press and has 122 pages. James Clear, in an article published as an excerpt from a chapter of his book Atomic Habits, retold the anecdote as a photography class and cited page 29 of Art & Fear as the source in a footnote (Clear, article). The same footnote quotes Orland’s email of October 18, 2016, explaining the origin. According to Orland, the ceramics story is true “allowing for some literary license in the retelling,” and its real-world origin was a gambit used by the photographer Jerry Uelsmann to motivate his Beginning Photography students at the University of Florida. In Orland’s words, the book version “faithfully captures the scene as Jerry told it to me,” except that he replaced photography with ceramics. Orland wrote that both authors are photographers and that they replaced the medium because they were trying to broaden the range of media referenced in the book. Clear notes in the footnote that he combined the ceramics telling with facts from the original photography story, and the footnote does not show whether the grading details in Clear’s version (100 photos for an A, 90 for a B) are based on records of Uelsmann’s class.

Kleon (2020) noticed the footnote after reading Clear’s photography version and introduced the origin. Kottke (2020) wrote that, because it lacked specific details such as the teacher’s name, the story had always struck him as apocryphal.

Traced to its origin, the anecdote is a story that Orland heard from Uelsmann, who taught the class. The documents reviewed here do not state who evaluated the end-of-term works and how, or whether any record compared the two groups. Within the search conducted, no document recording this class and no study replicating the same assignment were found. The anecdote is a record of a method that a teacher used to motivate students and of the teacher’s impression of the result.

The mechanism within the anecdote is also inconsistent. The book version (as quoted by Kottke 2020 and Kleon 2020) explains the result by the quantity group having churned out piles of work and learned from their mistakes. This is the mechanism of the second claim, that producing more makes each work better. Uelsmann himself, in a 2013 interview, described as something students never like to hear that there is a way in which quantity produces quality, and explained it as producing enough images and then editing back to the ones that rise above (Florida Trend 2013). This is the mechanism of the first claim, that producing more and then selecting yields good works. The words of the teacher himself were on the side of the form that the academic literature supports most strongly.

Which forms of the claim are supported?

When the saying is divided by the four kinds of quantity, the strength of support differs by form.

Producing more yields more hits: strongly supported. The number of works and the number of hits were strongly correlated for composers, inventors, and scientists (Simonton 1977; Forthmann et al. 2020). A quantity goal also increased the number of good ideas (Paulus et al. 2011). The mechanism is that more attempts increase the chances of drawing a good one, which is consistent with the random position of the best work within a sequence (Sinatra et al. 2016; Janosov et al. 2020).

Producing more makes each work better: results diverge by measure and domain. Average citations were uncorrelated with quantity (Forthmann et al. 2020), whereas the share of top 1% papers increased with quantity (Larivière and Costas 2016; Sandström and van den Besselaar 2016). The average quality of ideas increased with quantity only among people high in openness (Friis-Olivarius and Christensen 2019). Within one session, later ideas were better (Beaty and Silvia 2012), but this is an effect of time within one task and a phenomenon different from improved skill.

Producing more improves skill: practice time explains part of expertise, but the share varies by domain and is very small among high-level performers (Macnamara et al. 2014, 2016). Even the side that advocated the effect of practice held that the design of practice, not time, is what matters (Ericsson and Harwell 2019). In design prototyping, the design of comparison changed outcomes even when the number of prototypes was held constant (Dow et al. 2010, 2011).

Producing more yields good results in hand: if the selection stage is weak, quantity does not reach final quality. People had difficulty selecting original ideas from their own output (Rietzschel et al. 2010), and the quality of the best idea also depended on the ability to discern quality (Girotra et al. 2010).

Returning to the ceramics class, what the academic literature supports is the probabilistic reading of the part in which the best works came from the quantity group. The book’s explanation, that each student in the quantity group improved, is supported in the literature only under certain conditions. The mechanism described by the teacher himself was not improvement but producing many and then selecting.

Gaps

  • No experiment was found that replicated the anecdote’s assignment (one group graded on quantity and one on quality) at the level of works. How changing the grading criterion affects the number of works, the quality of each work, the best work, and the skill learned has not been measured.
  • Within the search conducted, no study varied the number of prototype iterations in graded steps and measured the relationship between that number and outcomes (Dow et al. 2009 used two conditions, with and without testing).
  • Most tests of the equal-odds rule use citations as quality, and no test was found that used the quality of design works (ratings by users or experts).
  • When generative AI lowers the cost of producing ideas and prototypes, the constraint on quantity is removed. The weakness of the selection stage can then be expected to account for a larger share of outcomes. Studies directly measuring this were outside the scope of this review (for the literature on generative AI and prototyping, see Does Prototyping Widen Design Exploration? Fixation on Early Examples, Parallel Prototyping, Rapid Prototyping with Generative AI, and Documentation in Research through Design).

Unverified items

  • No major claim remains unverified.
  • Full texts could not be obtained for 14 sources, which were treated within the scope of their abstracts (Simonton 1977, 1997, 2003; Sinatra et al. 2016; Huber 1998; Diehl and Stroebe 1987; Mullen et al. 1991; Nijstad and Stroebe 2006; Paulus et al. 2011; Rietzschel et al. 2010; Bayus 2013; Kudrowitz and Wallace 2013; Ericsson et al. 1993; Campitelli and Gobet 2011). In the text, they are attributed to their authors with “according to the abstract” or “in the abstract.” For Osborn (1953), only the bibliographic record (Open Library) was checked, and the content of the rule was treated within the summary by Paulus et al. (2011).
  • For Dow et al. (2010), the design of the conditions (an equal number of prototypes) was checked in the full text of the submitted version, and the results were written within the scope of the abstract of the published version. Girotra et al. (2010) was read in the submitted version (third version, December 2009). Liu et al. (2018) was read in the arXiv author version, and Kornish and Ulrich (2011) in the SSRN working paper.
  • Page 29 of Art & Fear was not read. The wording of the anecdote was checked through the quotations in Kottke (2020) and Kleon (2020), and the page number through the footnote in Clear’s article. Orland’s email was read only as quoted in Clear’s footnote.

References

All sources were accessed on 2026-11-12.

Lifetime creative output (number of works and hits)

  • Simonton, D. K. (1977). Creative productivity, age, and stress: A biographical time-series analysis of 10 classical composers. Journal of Personality and Social Psychology, 35(11), 791–804. https://doi.org/10.1037/0022-3514.35.11.791
  • Simonton, D. K. (1997). Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104(1), 66–89. https://doi.org/10.1037/0033-295X.104.1.66
  • Simonton, D. K. (2003). Scientific creativity as constrained stochastic behavior: The integration of product, person, and process perspectives. Psychological Bulletin, 129(4), 475–494. https://doi.org/10.1037/0033-2909.129.4.475
  • Huber, J. C. (1998). Invention and inventivity is a random, Poisson process: A potential guide to analysis of general creativity. Creativity Research Journal, 11(3), 231–241. https://doi.org/10.1207/s15326934crj1103_3
  • Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239. https://doi.org/10.1126/science.aaf5239
  • Liu, L., Wang, Y., Sinatra, R., Giles, C. L., Song, C., & Wang, D. (2018). Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714), 396–399. https://doi.org/10.1038/s41586-018-0315-8
  • Janosov, M., Battiston, F., & Sinatra, R. (2020). Success and luck in creative careers. EPJ Data Science, 9, 9. https://doi.org/10.1140/epjds/s13688-020-00227-w
  • Forthmann, B., Leveling, M., Dong, Y., & Dumas, D. (2020). Investigating the quantity–quality relationship in scientific creativity: An empirical examination of expected residual variance and the tilted funnel hypothesis. Scientometrics, 124(3), 2497–2518. https://doi.org/10.1007/s11192-020-03571-w
  • Larivière, V., & Costas, R. (2016). How many is too many? On the relationship between research productivity and impact. PLoS ONE, 11(9), e0162709. https://doi.org/10.1371/journal.pone.0162709
  • Sandström, U., & van den Besselaar, P. (2016). Quantity and/or quality? The importance of publishing many papers. PLoS ONE, 11(11), e0166149. https://doi.org/10.1371/journal.pone.0166149

Quantity and quality of ideas

  • Osborn, A. F. (1953). Applied imagination: Principles and procedures of creative thinking. Scribner. https://openlibrary.org/works/OL161158W
  • Paulus, P. B., Kohn, N. W., & Arditti, L. E. (2011). Effects of quantity and quality instructions on brainstorming. The Journal of Creative Behavior, 45(1), 38–46. https://doi.org/10.1002/j.2162-6057.2011.tb01083.x
  • Beaty, R. E., & Silvia, P. J. (2012). Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks. Psychology of Aesthetics, Creativity, and the Arts, 6(4), 309–319. https://doi.org/10.1037/a0029171
  • Jung, R. E., Wertz, C. J., Meadows, C. A., Ryman, S. G., Vakhtin, A. A., & Flores, R. A. (2015). Quantity yields quality when it comes to creativity: A brain and behavioral test of the equal-odds rule. Frontiers in Psychology, 6, 864. https://doi.org/10.3389/fpsyg.2015.00864
  • Kudrowitz, B. M., & Wallace, D. (2013). Assessing the quality of ideas from prolific, early-stage product ideation. Journal of Engineering Design, 24(2), 120–139. https://doi.org/10.1080/09544828.2012.676633
  • Friis-Olivarius, M., & Christensen, B. T. (2019). Not quite equal odds: Openness to experience moderates the relation between quantity and quality of ideas in divergent production. Frontiers in Psychology, 10, 355. https://doi.org/10.3389/fpsyg.2019.00355

Idea selection and group brainstorming

  • Girotra, K., Terwiesch, C., & Ulrich, K. T. (2010). Idea generation and the quality of the best idea. Management Science, 56(4), 591–605. https://doi.org/10.1287/mnsc.1090.1144
  • Rietzschel, E. F., Nijstad, B. A., & Stroebe, W. (2010). The selection of creative ideas after individual idea generation: Choosing between creativity and impact. British Journal of Psychology, 101(1), 47–68. https://doi.org/10.1348/000712609x414204
  • Kornish, L. J., & Ulrich, K. T. (2011). Opportunity spaces in innovation: Empirical analysis of large samples of ideas. Management Science, 57(1), 107–128. https://doi.org/10.1287/mnsc.1100.1247
  • Diehl, M., & Stroebe, W. (1987). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology, 53(3), 497–509. https://doi.org/10.1037/0022-3514.53.3.497
  • Mullen, B., Johnson, C., & Salas, E. (1991). Productivity loss in brainstorming groups: A meta-analytic integration. Basic and Applied Social Psychology, 12(1), 3–23. https://doi.org/10.1207/s15324834basp1201_1
  • Nijstad, B. A., & Stroebe, W. (2006). How the group affects the mind: A cognitive model of idea generation in groups. Personality and Social Psychology Review, 10(3), 186–213. https://doi.org/10.1207/s15327957pspr1003_1
  • Bayus, B. L. (2013). Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community. Management Science, 59(1), 226–244. https://doi.org/10.1287/mnsc.1120.1599

Design prototyping

  • Dow, S. P., Heddleston, K., & Klemmer, S. R. (2009). The efficacy of prototyping under time constraints. In Proceedings of the Seventh ACM Conference on Creativity and Cognition (C&C ‘09) (pp. 165–174). ACM. https://doi.org/10.1145/1640233.1640260
  • Dow, S. P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D. L., & Klemmer, S. R. (2010). Parallel prototyping leads to better design results, more divergence, and increased self-efficacy. ACM Transactions on Computer-Human Interaction, 17(4), 1–24. https://doi.org/10.1145/1879831.1879836
  • Dow, S. P., Fortuna, J., Schwartz, D., Altringer, B., Schwartz, D. L., & Klemmer, S. R. (2011). Prototyping dynamics: Sharing multiple designs improves exploration, group rapport, and results. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ‘11) (pp. 2807–2816). ACM. https://doi.org/10.1145/1978942.1979359

Expertise and amount of practice

  • Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406. https://doi.org/10.1037/0033-295X.100.3.363
  • Macnamara, B. N., Hambrick, D. Z., & Oswald, F. L. (2014). Deliberate practice and performance in music, games, sports, education, and professions: A meta-analysis. Psychological Science, 25(8), 1608–1618. https://doi.org/10.1177/0956797614535810
  • Macnamara, B. N., Hambrick, D. Z., & Oswald, F. L. (2018). Corrigendum: Deliberate practice and performance in music, games, sports, education, and professions: A meta-analysis. Psychological Science, 29(7), 1202–1204. https://doi.org/10.1177/0956797618769891
  • Macnamara, B. N., Moreau, D., & Hambrick, D. Z. (2016). The relationship between deliberate practice and performance in sports: A meta-analysis. Perspectives on Psychological Science, 11(3), 333–350. https://doi.org/10.1177/1745691616635591
  • Macnamara, B. N., & Maitra, M. (2019). The role of deliberate practice in expert performance: Revisiting Ericsson, Krampe & Tesch-Römer (1993). Royal Society Open Science, 6(8), 190327. https://doi.org/10.1098/rsos.190327
  • Ericsson, K. A., & Harwell, K. W. (2019). Deliberate practice and proposed limits on the effects of practice on the acquisition of expert performance: Why the original definition matters and recommendations for future research. Frontiers in Psychology, 10, 2396. https://doi.org/10.3389/fpsyg.2019.02396
  • Hambrick, D. Z., Macnamara, B. N., & Oswald, F. L. (2020). Is the deliberate practice view defensible? A review of evidence and discussion of issues. Frontiers in Psychology, 11, 1134. https://doi.org/10.3389/fpsyg.2020.01134
  • Platz, F., Kopiez, R., Lehmann, A. C., & Wolf, A. (2014). The influence of deliberate practice on musical achievement: A meta-analysis. Frontiers in Psychology, 5, 646. https://doi.org/10.3389/fpsyg.2014.00646
  • Campitelli, G., & Gobet, F. (2011). Deliberate practice: Necessary but not sufficient. Current Directions in Psychological Science, 20(5), 280–285. https://doi.org/10.1177/0963721411421922

Origin of the ceramics class anecdote (non-academic documents)

Author: Shuichiro Ogawa (Design Researcher / Design Consultant) About me →