the innovator's dna - technical report

5/28/2018 The Innovator's DNA - Technical Report

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Innovators DNA

Assessment

TECHNICAL REPORT

INTRODUCTION

Innovation. Its the lifeblood of our global economy and a strategic priority for

virtually every CEO around the world. In fact, a recent IBM poll of 1,500 CEOsidentified creativity as the No. 1 leadership competency of the future. The powerof innovative ideas to revolutionize industries and generate wealth is evident fromhistory: Apple iPod out-plays Sony Walkman, Starbucks beans and atmospheredrowns traditional coffee shops, and Skype uses a strategy of free to beat AT&T.In every case, creative ideas generated by innovative entrepreneurs producedpowerful competitive advantages and tremendous wealth for the pioneeringcompany. Of course, the retrospective $1 million question is: why are some peoplemore capable than others at generating innovative business ideas?

One of the central questions addressed in the field of entrepreneurship is why someindividuals (entrepreneurs) recognize/discover opportunities for new businessesand products that non-entrepreneurs fail to recognize. (Baron, 2004, 2007; Kaish andGilad, 1991; Shane, 2003). Prior research designed to answer that question hasfocused largely on the personality traits of entrepreneurs. The surprising result?Successful entrepreneurs and successful business executives do not differsignificantly on personality traits (Brockhaus and Horwitz, 1986; Busenitz andBarney, 1997). According to Busenitz and Barney (1997: 11) After a great deal ofresearch it is now often concluded that most of the psychological differences betweenentrepreneurs and managers in large organizations are small or nonexistent(Brockhaus and Horwitz 1986; Low and MacMillan 1988).

Building on the more behavioral approach of social network researchers, the researchdescribed in this report focuses on the behaviors that individuals engage in thatenable them to recognize or discover innovations for new products or newbusinesses. Gartner (1989:57) has argued for a more behavioral approach to


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Innovators DNA Assessment Technical Report 2

entrepreneurship research claiming that research on the entrepreneur should focuson what the entrepreneur does, not who the entrepreneur is. Using a groundedtheory approach, Jeff Dyer, Hal Gregersen, and Clayton Christensen conducted aground theory study to inductively identify the behaviors of innovators that wererelevant to creative idea generation.1 The relevant behaviors identified in the

grounded theory study were: questioning, observing, networking, andexperimenting. They then developed The InnovatorsDNA Survey Assessment tocompare the extent to which a sample of innovative entrepreneurs displayed thesebehaviors compared to a sample of managers. Consistent with the grounded theorystudy, these behaviors are much more common and pronounced among innovativeentrepreneurs than among managers, including senior managers, in largeorganizations.

For an organization to be innovative, its leaders and workers must be innovative.The first step towards increasing ones innovative capacity is to assess theextent to

which he or she engages in behaviors that have been found to facilitate opportunityrecognitionand the generation of novel ideas. Then organizations, leaders, andindividuals can act on those assessments to target those behavioral habits that aremost likely to increase their innovative capacity.

Gaining better insights through Innovators DNA (iDNA) Assessments aboutindividuals and their Discovery skills is valuable to the individual and organization.

iDNA Assessment feedback to the individual can help them in several ways:

1. Identify relative weights between Discovery and Delivery (Execution)2skills, to enable individuals to pursue the ideal balance

2. Assess performance on specific Discovery (Execution) skills3. Facilitate individual efforts to strengthen Discovery skills in order to foster

innovation

iDNA Assessment feedback can help organizations in several ways as well:

1. Facilitate training and education effectiveness by better matching trainingand education to the needs of the individual

1For details on the grounded study, see: Dyer, Jeffrey H., Hal B. Gregersen, and Clayton Christensen (2008).Entrepreneur Behaviors, Opportunity Recognition, and the Origins of Innovative Ventures. Strategic

Entrepreneurship Journal, Vol 2, pp. 317-338.2Our measures of ones delivery skillsanalyzing, planning, detail oriented implementing, and self

disciplinedare drawn in part from survey items of Conscientiousness the Big 5 personality trait which has

found to be significantly correlated with an individuals job performance in organizations.


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2. Help managers allocate resources to enable employees unique innovationapproaches

3. Better determine the innovation capabilities of employeesTo accomplish these objectives, companies must ask several important questions

about any self-assessment tool measuring innovation capability: How do we define innovation success? What characteristics are essential for innovation success? How can we trust an assessment of these characteristics and critical

outcomes?

INNOVATION SUCCESS

Given the enormous potential rewards of innovation and the penalties for being

left behind, innovation success and failure are critical areas of concern. We defineinnovation success as the invention of a new product or launch of a new businessventure that differs from other products or businesses already on the market. Thenew product or business should offer a different value propositiondifferent featureset, convenience, pricing, etc. At the organization level, we measure innovationsuccess as the innovation premium that is built into the stock price of a particularcompany. We teamed up with HOLT (a division of Credit Suisse that had done asimilar analysis for The Innovators Solution) to develop a methodology fordetermining what percentage of a firms market value could be attributed to itsexisting products, services, and markets. If the firms market value was higher than

the cash flows attributed to its existing businesses, then the company shows anInnovation Premium. This is the proportion of a companys market value thatcannot be accounted for from cash flows of its current products and businesses in itscurrent markets. Investors give this premium because they expect companies tocome up with new products or markets (for details on how to calculate the premium,see endnote #1).1 It is a premium that every executive, and every company, wouldlike to have.

Strong innovation capabilities are valuable to companies in a variety of ways,including: helping recruit high quality employees and attract new customers due to areputation for innovation, creating new products or services for existing and newcustomers and markets, and creating new processes that deliver new products orservices more efficiently. Missing out on innovation may not always manifestimmediate or obvious consequences. But over time innovation failure can easily leadto a loss of market share, deteriorating morale, and deteriorating financialperformance as customers shift their allegiance to competitors with superiorinnovation capabilities.


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Discovery Skills as a Key Component of Creative Capacity

Four behaviors and one cognitive skill are the Discovery skills identified askey components of an individuals ability to think creatively in a business context .The Discovery skills are: ask provocative questions, observe the world like

anthropologists, network for new ideas especially with people very different from us,experiment and prototype and try new things, and associatively consider all of theideas and information youve asked about, observed, networked for, andexperimented with.

Questioning requires both curiosity and courage. The iDNA Assessment measuresa persons tendency to ask provocative questions, that challenge the status quo andapproach reality from different angles. Questioning is the key building block thatsupercharges the other Discovery skill behaviors. Through questioning how andwhy things are the way they are, innovative approaches can be discovered.

Observing involves seeing the world like anthropologists. When you observe as aDiscovery skill, you are taking nothing for granted. You are forcing yourself tobecome aware of the environment and details which normally are mere background.You can adopt a fresh perspective to observe your own environment to which youare desensitized. Or you can go observe seemingly unrelated customers, products,environments, and situations which can yield novel insights to apply back to yourcore questions.

Networking for ideas is a social process that is idea-focused. Idea-networking is

distinct from resource-networking, in that you are recruiting peoples ideas ratherthan assembling resources. Life experiences and perspectives vary widely betweenindividuals, but they typically vary most widely between those with the leastoverlapping types of experiences. Thus, the more diverse your idea networking poolis, the more likely you are to derive innovative value from it. Some dimensions toconsider varying your pool on are age, education, religion, gender, ethnicity,geography, industry, rank, and function.

Experimenting is the iterative process of trying things out; it also includes takingthings apart. In both prototypical methods of experimenting, the experiment is

interacting with the tangible world to see what might work, or to understand howthings already work. Experimenters often excel when they iterate more often andmore rapidly.


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Associating is a cognitive process whereby individuals connect previouslyunconnected ideas in new ways. New associations in the brain are more likely to betriggered when the brain is bombarded with novel knowledge, which is why newassociations are often triggered when individuals are in the behavioral act ofbringing in fresh knowledge inputs through questioning, observing, networking, and

experimenting.

Key Study Controls

Conscientiousness, a concept from psychological research, is typicallyconsidered the main personality trait that predicts job performance in abusiness context.Controlling for Conscientiousness from the Big 5 PersonalityTest allows us to focus on exploration rather than exploitation behaviors.In some studies we use standard Big 5 wording of the items (adapted slightly toa work, rather than general, context). In other studies, we have testedrespondents on four separate components of Conscientiousness, collectivelydubbed Delivery skills. Delivery skills include analyzing, planning, detail-oriented, and self-discipline. While these skills tend to be positively correlatedwith job performance, we found that certain dimensions of conscientiousness,notably planning and detail oriented implementing, have a significant, negativecorrelation with the discovery skills and with innovation outcomes. Analyzingand Self Disciplined have a non-significant effect on innovation outcomes.

Age was an important control to determine what amount of successfuloutcomes (e.g., innovation, promotion) was attributable to experience. Agewas significant statistically (i.e., the effect was not random) in predicting

whether someone had invented a new product or launched an innovativeventure, but the size of the effect was usually very small.

Education was another important control to test for the specific skills asdistinct from education levels generally. Education levels effect was notconsistently significant nor large in effect size.

Assessing Discovery Skills

Survey instruments (or questionnaires) can give individuals valuable insights intotheir Discovery skills. Individual characteristics such as conflict-resolution style orwillingness to communicate can be reliably assessed with standardized psychologicaltests. In their own academic research and work with thousands of individuals over a10 year period, Drs. Dyer, Gregersen, and Christensen have developed a


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standardized test that assesses several important behaviors vital to innovation. TheInnovators DNA (iDNA) Assessment is an assessment tool designed to giveindividuals feedback on their strengths and weaknesses relative to behaviors thatlead to innovation. It helps people assess themselves along the five criticaldimensions and two corollary sets of skills just outlined.

TRUSTWORTHINESS OF PSYCHOLOGICAL TESTS

Before examining the specifics of the iDNA Assessment, it is important to brieflyprovide an overview of how we in general can determine if a psychological test istrustworthy. Trustworthiness comes from two basic sources--reliability and validity.

Reliability

Reliability is essentially the degree to which the results of a measure are free from

error. For example, the odometer on your car would be considered reliable if itconsistently measured out a mile each time you actually drove a mile. If it measuredout 1.5 miles when you drove only one mile, it would not be completely reliable.There are three basic and widely used ways that reliability can be measured.

Test-retest methodThe first is called the test-retest method. Using this method, an individual

would take a test (such as an intelligence test) at one point in time and then retakethe test at another point in time. If the test is reliable, the scores between the twotests should be quite similar. The correlation between these two scores is typically

called a correlation coefficient. A high correlation coefficient (i.e., .60 to 1.0) wouldsuggest high reliability. A low correlation coefficient (i.e., .59 to 0.0) would suggestlow reliability, meaning the test or measure is basically not trustworthy.

The Innovators DNA Assessment will be re-administered in early 2012 tohundreds of participants from the past one to five years. The results of that studywill employ the test-retest method.

Split-half methodThe second method is called the split-half method. Using this method, an

individual would take a test and a score for half of the items would be compared tothe other half of the items designed to measure the same thing, such as motivation. Ifthe test is reliable, the scores between the two tests should be quite similar. Again,the correlation between these two scores is typically called a correlation coefficient.A high correlation coefficient (i.e., .60 to 1.0) would suggest high reliability. A lowcorrelation coefficient (i.e., .59 to 0.0) would suggest low reliability.


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Internal consistency methodThe third method is called the internal consistency method. Using this method,

several different items would be used to measure the same thing, such as conflictresolution style. If the test is reliable, the scores on all the items related to a givenconstruct (e.g., conflict resolution style) should be quite similar to each other. The

correlation among all these items is typically called a reliability coefficient. The mostcommonly used reliability coefficient is called Cronbach Alpha. A high alpha (i.e., .60to 1.0) would suggest high reliability. A low alpha (i.e., .59 to 0.0) would suggest lowreliability. In the scientific community, measures with internal reliability coefficientsof less than .60 are usually not acceptable for publication in academic journalsbecause the measures are considered untrustworthy. Innovators DNA internalconsistency tests regularly yield Cronbach Alpha measures well over the 0.6threshold, usually in the 0.76 0.86 range.

Validity

To be trustworthy, a psychological instrument must also be valid. Validityreflects the degree to which a measure actually measures what it purports tomeasure. There are a variety of measures of validity. Below we briefly describe thefour most common.

The first measure of validity is face validity. A measure has face validity to theextent that it appears to measure whatever it purports to measure. In the scientificcommunity this is often assessed by simply looking at the definition of the construct(e.g., idea networking) and then examining the specific items that are supposed tomeasure that construct (e.g., Actively seek out individuals from very different

backgrounds who can help find and evaluate new ideas.). Measures that lack facevalidity simply do not get published in recognized academic journals because theyare not measuring the construct of interest. The Innovators DNA measures havepassed the test of peer review as condition of being published in StrategicEntrepreneurship Journal. See Appendix A for measures of each construct.

The second measure of validity is construct validity. Things that are abstract,which almost all personal characteristics are, are known as constructs. The constructvalidity of a measure is established by showing that it is an appropriate operationaldefinition of the construct it purports to measure. An operational definition isessentially how the construct is measured. Construct validity is established in partby hypothesizing the relationship between the construct and some outcome and then

seeing if the measure of the construct is in fact related as hypothesized to anoutcome. For example, establishing the construct validity of experimenting behavior(as an antecedent to inventing a new product) would require creating a measure ofthe construct and then seeing if people who score high on experimenting are morelikely to invent a new product or launch an innovative new venture compared to


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people who score low on the experimenting measure. Each of the Discovery skills,even after controlling for alternate explanations (age, education, gender, nationality,conscientiousness or Delivery skills, etc.) have been demonstrated to correlate withinnovative product, process, intrapreneurial, and entrepreneurial outcomes.

The third measure of validity is concurrent validity. This is established by

measuring both the construct(s) and outcomes at the same point in time. Forexample, the concurrent validity of Discovery behaviors would be established bymeasuring the level of Discovery skills in a sample of businesspeople and then at thesame time measuring their level of (co-)founded innovative businesses. Theconcurrent validity would be established if Discovery skills were positively related tostarting new, innovative businesses. Typically correlation coefficients between .15and .90 are statistically significant.

Finally, the most rigorous measure of validity is predictive validity. Predictivevalidity is established by first measuring the construct (e.g., leadership style) at onepoint in time and then measuring the outcome (e.g., job performance) at another

point in time and examining the relationship. For example, if we could measure theDiscovery skills style of a sample of undergraduates before they entered theworkforce and then measure the amount of innovative ventures they created someyears later. If we found that those with higher Discovery skills were more likely tostart innovative new businesses, then Discovery skills would have predictivevalidity.

DEVELOPMENT AND VALIDATION OF THE INNOVATORS DNAASSESSMENT

Now that we have established the various definitions of innovative behaviors andhow the trustworthiness of an instrument can be determined, we can examine theextent to which The Innovators DNA Assessmentcan be considered a reliable andvalid measure of whether individuals are engaging in the behaviors that will lead toinnovation outcomes (e.g., ideas for novel products, services, processes, andbusinesses).

Instrument Background

Dr. Jeffrey H. Dyer at Brigham Young University, Dr. Dr. Hal B. Gregersen at

INSEAD, and Dr. Clayton M. Christensen at Harvard Business School haveconducted several studies on all aspects of innovators and the behaviors which leadto their innovations. Their individual-level innovator investigations are a follow-upto the groundbreaking book on disruptive innovation, The Innovators Dilemma, byDr. Christensen. Drs. Dyer, Gregersen, and Christensen wanted to figure out whyinnovative entrepreneurs recognize opportunitiesparticularly disruptive


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innovation opportunitiesthat non-entrepreneurs fail to recognize (Baron, 2004,2007; Kaish and Gilad, 1991; Shane, 2003). In short, what distinguishes businessinnovators from non-innovators? They interviewed dozens of high (e.g., Jeff Bezos,Michael Dell, Herb Kelleher) and low profile innovators to look for consistentpatterns of behavior. Over time, they developed and tested different survey

questions to measure the behaviors that consistently appeared in their research. Thestudies led up to articles in Strategic Entrepreneurship Journal, and Harvard BusinessReview, and a book The Innovators DNA. All together, Drs. Dyer, Gregersen, andChristensen have dozens of publications on innovation, including books by HarvardBusiness School Publishing and articles inMIT Sloan Management Reviewand HarvardBusiness Review.

The development of this assessment instrument began in 2001 with an exhaustivereview of the research literature over the last 40 years on personal factors that affectinnovation. Followed by interviews and iteratively refined survey research, Drs.Dyer, Gregersen, and Christensen identified four key behaviorsand one cognitive

skillthat play an important role in facilitating the generation of novel and usefulbusiness ideas.

Empirical Validation Study 1

This investigative research led to a complete survey to measure Discovery andDelivery skills. The Discovery skills were the construct of interest, and Drs. Dyer,Gregersen, and Christensen officially published their empirical measurement andvalidation of behaviors leading to innovation in Strategic Entrepreneurship Journal, apremier academic publication on entrepreneurship.

The initial sample included 72 innovative entrepreneurs and 310 executives. The72 innovative entrepreneurs had started a total of 137 innovative business ventures,based upon positive responses to How many new businesses (e.g., not franchises ora business where identical or close product substitutes were already on the market)have you started or cofounded as an entrepreneur based on your own, original (novel,unique) idea? The reliability alphas for each of the four Discovery behaviorsevaluated in this study were quite high:

Questioning Cronbach Alpha= .74Observing Cronbach Alpha= .78Networking Cronbach Alpha= .78Experimenting Cronbach Alpha= .78

The items each held together strongly as separate factors. See below for theexploratory factor analysis results.


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Table 2. Results of exploratory factor analysis of innovative entrepreneurbehavior items

Items / Factor 1 2 3 41. Experimenting

E1 0.71

E2 0.67E3 0.64E4 0.59E5 0.452. Questioning

Q1 0.77Q2 0.70Q3 0.70Q4 0.63Q5 0.60

Q6 0.42 0.523. Observing

O1 -0.72O2 -0.72O3 -0.70O4 -0.444.Ideanetworking

IN1 0.77IN2 0.76

IN3 0.70IN4 0.69Alpha 0.78 0.74 0.78 0.78Eigenvalue 6.05 2.13 1.21 1.16Percentage ofvariance

31.88 11.21 6.38 6.09

The cutoff point was 0.40.

The mean scores (on a 7 point scale) and standard deviations for each of themeasures are provided below. The concurrent validity was examined by measuring

the relationship between each of these characteristics and the number of innovativenew businesses (co-)founded. As the following table shows, the behaviors were allstatistically significant as correlated with starting innovative new businesses. Notealso that the Discovery behaviors were related to each other.


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Discovery Behavior Mean S.D. 1 2 3 4

1. Questioning 5.48 0.83 1.00

2. Observing 4.93 1.05 0.43** 1.00

3. Experimenting 4.93 0.94 0.39** 0.53** 1.00

4. Idea networking 4.29 1.26 0.30** 0.54** 0.51** 1.00

5. Innovativenew business

1.29 0.86 0.12** 0.22* 0.15** 0.24**

All correlations were statistically significant at the ** = .05 or * = .10 level.

Further, the discovery behavior constructs and concurrent validity weredemonstrated through a negative binomial regression (see Appendix B), includingimportant controls. The results from our quantitative study provide preliminary

support for the assertion that innovative entrepreneurs are more likely thanmanagers to engage in questioning, observing, experimenting, and idea networkingbehaviors. The regression results indicate that observing and experimenting were themost robust predictors of new venture creation, whereas questioning andnetworking were significant predictors of new venture creation when interacted witheach other or the other discovery behaviors. Questioning is only a consistentpredictor of new venture creation when interacted with each of the other behaviorsseparately. These results suggest that an individual who simply asks questionswithout actively observing, experimenting, or networking is unlikely to discover oract upon ideas for new venture creation.

Given that many Discovery skills are normatively sanctioned, it was important tocontrol for social desirability. While subsequent comparison of 360 versus individualresponses (e.g., June 2011) have confirmed that social desirability bias is not aproblem, in the initial study measures for social desirability were included (seeconstruct items in Appendix A) and the most biased respondents answers thrownout, without materially affecting the findings. Indeed, there was a slight (but notsignificant) negative correlation between responses to the social desirability itemsand new venture creation, suggesting that innovators are less likely to give sociallydesirable answers. While 360 and individual responses are typically different fromeach other (usually 0.3 0.5 point apart), there are not consistent differences betweenthose who are more or less prone to social desirability bias.

This validation study examined the robustness of Discovery behaviors leading toinnovation. That is, if the theory were sound, then these behaviors ought to be


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related to starting innovative business ventures. The results demonstrate that themeasures are highly reliable and have strong validity.

Empirical Validation Study 2

A follow-up study included a sample of 1075 respondents. Innovators, who hadcreated at least one of the following: innovative products (n = 296), innovativeintrapreneurial ventures (n = 362), or innovative entrepreneurial ventures (n = 296),comprised 537 members of the sample.

The alphas for each Discovery skill were even higher in this new sample than inthe SEJ study:

Questioning Cronbach Alpha= .83Observing Cronbach Alpha= .84

Networking Cronbach Alpha= .89Experimenting Cronbach Alpha= .79Associating Cronbach Alpha= .84

The means, standard deviations and correlations between variables were asshown above. All relationships shown were statistically significant at the 0.01 levelor stronger, except for those with Conscientiousness, where less significantcorrelations are specified in italics.

Variable Mean S.D. 1 2 3 4 5 6 7 8 91. Questioning 5.77 0.03 1.002. Observing 5.38 0.03 0.50 1.003. Idea

networking4.68 0.05 0.39 0.59 1.00

4. Experimenting 5.16 0.04 0.52 0.60 0.57 1.005.

Associating 5.59 0.03 0.60 0.58 0.53 0.55 1.006. InnovativeEntrepreneur

0.51 0.04 0.25 0.25 0.21 0.20 0.24 1.00

7. InnovativeIntrapreneur

0.81 0.05 0.21 0.21 0.24 0.22 0.24 0.46 1.00

8. InnovativeProducts

0.89 0.06 0.20 0.18 0.17 0.20 0.23 0.33 0.37 1.00

9. Position 3.78 0.05 0.19 0.19 0.27 0.16 0.25 0.27 0.23 0.23 0.16Conscientiousness 5.48 0.03 0.24 0.18 0.11 0.17 0.08

0.02-.060.04

-.050.10

-.10 0.030.35


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In addition to robustness checks on previous SEJ findings and those detailedabove, a strong motivation for the study was to test concurrent validity of Positionand Discovery skills. In psychological and organizational research (most famouslyBarrick & Mount, 1991), Position has been most strongly correlated withConscientiousness, of individual personality traits. We wanted to see how closely

correlated Position was related to Discovery, controlling for Conscientiousness (andindividual demographic differences).

Position was an ordered, categorical variable measured as shown below, inresponse to a dropdown selection from the item Current/Most Recent Position:

Title nIndividual Contributor 27Direct Supervisor 69Functional Manager 338

Business Unit Manager 156Senior Executive/VP 171CEO | or CEO Founder / Senior Exec 75

Total reported 836

An Ordered Logistic regression tested concurrent validity for OrganizationPosition as related primarily to Discovery skills, plus demographic controls, numberof intraprenuerial successes, and importantly, Conscientiousness. No variables wereimputed.

The results show that Discovery skills are much stronger for people at higherlevel positions (see appendix for ordered logistic regression). Higher level positionsdid not score higher on Conscientiousness, though generally individuals inmanagement positions score higher on conscientiousness than individualcontributors. However, those with higher level organizational positions did scorehigher on discovery skills. These results suggest that individuals at higher positionsare either selected for Discovery skills, or their responsibilities expect it. Regardlessof which interpretation is made, these results suggest that there may be a thresholdeffect with regard to Conscientiousness and management positions. One needs acertain proficiency at Conscientiousness to be considered for managerial positions.However, to rise to higher levels of the organization, one needs proficiency at the

Discovery skills.

Robustness checks (including individual Discovery skills, controlling forindustry, etc.) yielded similar strength of concurrent validity for Position ascorrelated with Discovery skill. Currently, a longitudinal study is in progress to test


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the extent to which Discovery skills lead to promotion into higher positions versusDiscovery skills are manifested more strongly in higher position duties. In themeantime, Study 2s results demonstrate a strong case for the importance ofDiscovery skills for those who wish to progress to higher levels of leadership.

Empirical Validation Studies for Social Sector

Business executives, MBAs, and business employees are not the only populationswho have been studied. We have tested these findings in the social sector (socialinnovators, NGOs, and Teachers) and find similar results. That is, the alphas areconsistently strong (all above 0.70) often even above earlier samples, and thecorrelation matrices demonstrate significant relationships between Discovery skillsand innovation success in non-business sectors.

For example, a sample of teachers yielded the following alphas:Questioning Cronbach Alpha= .86Observing Cronbach Alpha= .88Networking Cronbach Alpha= .91Experimenting Cronbach Alpha= .91

These studies bolster the case for the external validity of the Innovators DNAAssessment. Discovery skills are not merely applicable in a business context, butrather can help innovation efforts towards solving an even broader set of problemsand challenges.


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Further Empirical Validation- Testing Predictive Validity

Two additional studies designed to test the predictive validity of discovery skillsand a) innovation outcomes and b) organizational position are currently in progress.One study has already tested roughly 120 undergraduate business students on their

Discovery Skills over the past three years with plans for an even larger sample (500)this year and next. Our goal is to use the Discovery Skills scores to predict whichstudents will launch innovative new ventures.

A second study is in progress to test the predictive validity of the Discovery Skillswith regard to organizational position. This involves a follow up assessment of over2,000 individuals who have previously taken the assessment. The goal of this studyis to see whether Discovery Skills predicts which individuals were most likely to bepromoted to higher level management positions. Longitudinal data will allow us tomake appropriate causal inferences.

CONCLUSIONS

Innovation is crucial to organizational survival and advancement. TheInnovators DNA Assessment allows measurement of the Discovery skills which leadto innovation. Knowing scores and relative rankings allows better decisions on howto hone those Discovery skills. Further, knowing these metrics allows optimalresource and role allocation to take advantage of unique sets of Discovery skills. TheInnovators DNA aids in better application of existing innovation skills, and properlyaims future skill development. Firms, managers, and individuals all need

innovation. The Innovators DNA Assessment is the premier tool for diagnosis andprescription.


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APPENDIX A

Measures

Questioning (alpha=0.74)1. I am always asking questions.2. I am constantly asking questions to get at the root of the problem.3. Others are frustrated by the frequency of my questions.4. I often ask questions that challenge the status quo.5. I regularly ask questions that challenge others fundamental assumptions.6. I am constantly asking questions to understand why products and projectsunderperform.

Observing (alpha=0.78)1. New business ideas often come to me when directly observing how peopleinteract with products and services.

2. I have a continuous flow of new business ideas that comes through observingthe world.

3. I regularly observe customers use of our companys products and services toget new ideas.

4. By paying attention to everyday experiences, I often get new business ideas.Experimenting/exploring (alpha=0.78)

1. I love to experiment to understand how things work and to create new waysof doing things.

2. I frequently experiment to create new ways of doing things.3. I am adventurous, always looking for new experiences.4. I actively search for new ideas through experimenting.5. I have a history of taking things apart.

Idea networking (alpha=0.78)1. I have a network of individuals whom I trust to bring a new perspective and

refine new ideas.2. I attend many diverse professional and/or academic conferences outside of

my industry/profession.3. I initiate meetings with people outside of my industry to spark ideas for a new

product, service, or customer base.4. I have a large network of contacts with whom I frequently interact to get ideas

for new products, services, and customers


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NOTE: The above items are the property of Innovators DNA Inc. and are notavailable for private use without written permission from Innovators DNA Inc.

Conscientiousness items, adapted to work context (alpha=0.84)

1. Do not make rash decisions.2. Do careful analysis to make well-thought-out decisions at work.3. Do not jump into new projects and ventures or act quickly without careful

thinking and analysis.4. Do work according to an organized plan.5. Must have everything finished 'just right' when completing a work assignment.6. Pay attention to details at work.7. Am careful to avoid making mistakes.8. Consistently follow through on all commitments and finish what is started.9. Hold self and others strictly accountable for getting results.10.Don't need a push to get started on new tasks and assignments.

Social Desirability items1. I can remember playing sick to get out of something.2. There have been a few times when I felt like rebelling against people in

authority even when I knew they were right.3. On a few occasions I have given up doing something because I thought too

little of my ability.4. I am always willing to admit it when I make a mistake.5. No matter who Im talking to, Im always a good listener.6. There have been occasions when I took advantage of someone.7. When I dont know something I easily admit it.8. I sometimes try to get even rather than forgive and forget.9. I have never deliberately said something to hurt someones feelings.10.I am sometimes irritated by people who ask favors of me.


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Appendix B

Negative Binomial Regression (Study 1)

Column 1 Column 2 Column 3 Column 4

MethodNegativebinomial

Negativebinomial

Negativebinomial

Negativebinomial

Dependent variableInnovativeventuresfounded

Innovativeventuresfounded



Behavioral factorsQuestioning 0.008 (0.01) -0.013 (0.03) -0.102 (1.28) -0.113 (1.50)

Observing0.149(4.86)** 0.076 (1.00) 0.157 (5.39)**

0.149(4.88)**

Experimenting

0.436

(5.67)***

0.436

(5.72)*** 0.255 (1.62)

0.394

(4.62)**Networking -0.223 (1.67) -0.230 (1.80) -0.193 (1.27) -0.320 (2.32)

Question *observing

0.152(4.26)**

Question *experiment 0.278 (4.81)**

Question *networking

0.296(4.84)**

Control variablesAge 0.003 (0.39) 0.003 (0.54) 0.003 (0.51) 0.003 (0.38)

Education0.056(5.10)** -0.054 (4.68)**

-0.051(4.23)**

-0.052(4.32)**

Intercept0.950(19.93)***

0.928(19.08)***

0.955(20.32)***

0.970(20.91)***

Number ofobservations 382 382 382 382DF 375 374 374 374Pearson chi-square 381.5 379.1 378.7 348.5

Log likelihood -741.1 -739.1 -738.9 -738.7

Notes:All significant tests are two-tailed. * indicates p


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Appendix C

Ordered Logistic Regression (Study 2)Iteration 0: log likelihood = -1049.5775Iteration 1: log likelihood = -981.27738

Iteration 2: log likelihood = -979.77482Iteration 3: log likelihood = -979.77054Iteration 4: log likelihood = -979.77054

Ordered logistic regression Number of obs = 688LR chi2(7) = 139.61Prob > chi2 = 0.0000

Log likelihood = -979.77054 Pseudo R2 = 0.0665

------------------------------------------------------------------------------position | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------discovery_~g | .607676 .0965529 6.29 0.000 .4184357 .7969162

age | .0591422 .0092781 6.37 0.000 .0409575 .077327education | -.0898597 .0919575 -0.98 0.328 -.2700932 .0903737

female | .3519692 .1591954 2.21 0.027 .0399519 .6639865

us_nationa~y | -.1435725 .1451922 -0.99 0.323 -.428144 .140999newbiz_inc~s | .2843444 .0645449 4.41 0.000 .1578386 .4108501conscient_avg| -.0259025 .0087637 -2.96 0.003 -.0430791 -.0087259-------------+----------------------------------------------------------------

/cut1 | -.2949672 .7518539 -1.768574 1.178639/cut2 | .8490939 .7377928 -.5969534 2.295141/cut3 | 3.338204 .747348 1.873429 4.80298/cut4 | 4.286601 .7542044 2.808388 5.764815/cut5 | 5.969984 .7694917 4.461808 7.47816

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End Notes

1How the Innovation Premium is Calculated.

Step 1:

In assessing a companys current valuation, HOLT determines the next two years of cashgeneration from existing businesses for each firm based upon the consensus estimate of

earnings and revenues by analysts. The consensus estimate of earnings and revenues is based

on the median of the combined estimates of carefully screened analysts covering a public

company as selected by Institutional Brokers Estimate System [I/B/E/S]). Benchmarks for

historical periods (as are used in the Innovation Premium) use actual reported profitability and

reinvestment rates as the starting point for the cash flow forecasts.

Step 2:

HOLT then projects future free cash flows over the next 38 years from existing businesses

based uponfadealgorithms developed from an analysis of historical cash flows from over

45,000 firms and more than 500,000 data points. The concept of fade embodies the common-sense notion that competition is the one enduring constant in free markets (ala Schumpeters

creative destruction) and that technological change and changing market dynamics all

militate against the persistence of excessively high returns (this is consistent with prior

research that consistently shows a regression to the mean effect with regard to firm

profitability).

The fade algorithm for a given company is based upon the following:

a) The forward two year consensus estimate of ROI level. Firms with higher levels ofprofitability and ROI maintain higher returns into the future. However, the historical

experience of most firms shows a regression to the mean effect, meaning that high

ROIs will gradually fade towards the average ROI of firms in the economy. The

higher the current level of profit, the faster the expected decline. (Firms will tend to

maintain their rank order; however, the spread between the top and bottom performers

tends to narrow.)

b) Historical ROI volatility (over the previous five years). The greater the volatility ofROI historically, the faster the firms ROI tends to fade towards the average of all

firms going forward. Firms with consistent and stable ROI are more likely to maintain

a consistent ROI into the future.

c) A companys reinvestment rate. The faster a companys recent growth and the greaterthe amount of cash it has reinvested, the faster the firms ROI will fade towards the

mean profitability of firms in the economy. Its hard enough for a management team

to maintain high levels of financial performance; doing this while also growing rapidlyis even more difficult.

Step 3:


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The difference between the companys total enterprise value (market value of equity plus total

debt) and this value of existing businessconstitutes the Innovation Premium, expressed as a

percentage of the enterprise value,

While HOLTs fade algorithm is based specifically on the historical and future projectedperformance of the given firm, it may appear to reflect sector identification or industry

position. To the extent that firms in an industry or sector share the characteristics of ROI

level, variability, and reinvestment, the pattern of fade will also be similar. There is also an

apparent correlation between a companys fade expectations and its position in the industry,

since most industry leaders have higher and more stable rates of ROI and having been through

their growth phase in achieving their leadership position, no longer need to grow at above-

average rates.

Finally, we require at least 10 years of financial data for a given firm in order to be considered

on our list of most innovative companies. We also use a Research and Development screen

requiring that companies make some investment in research and development. Also, to

control for size differences we only include those with a market value greater than $10 billion.The innovation premium shown in the tables in this chapter reflects a weighted average

innovation premium over five years with the weighting as follows: most recent year (30%),

years 2-4 (20%), year 5 (10%).

the innovator's dna - technical report

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