NetFlix: Practical Examples of People, Process, and Culture in Creating Innovation

In keeping with the traditional analysis of innovative success, a post hoc examination of an organization known for innovation provides anecdotal evidence of the impact of people, process, and culture on organizational success.  By most accounts, Netflix, Inc. (Netflix) is considered a prime example of successful innovation.  Netflix is the leading Internet-based television network and counts some 44 million customers in 40 countries streaming more than one billion hours of content (Netflix, 2014).  Since its initial public offering in 2002, Netflix has moved from an innovative provider of DVD rentals-by-mail to become the dominant player in the pure-play Internet streaming market, a market they almost single-handedly pioneered (Netflix, 2014).  Netflix has enjoyed significant growth in market share, customers, and stock valuation over the years, and the stage has been set for further growth through international expansion, strategic partnerships, and the creation of their own content (Ramachandran, 2014a, 2014b, 2015; Ramachandran & Stynes, 2015; Schwartz, 2015). An examination of key elements of Netflix’s success suggest that business models is not the only thing that Netflix has innovated on the road to becoming a household name.

One of the better-known innovations of Netflix is the organizational culture playbook they developed.  Incorporating aspects of both people and culture, the Netflix employee handbook was presented in 127 presentation slides (McCord, 2014).  From a people perspective, Netflix purports to “hire, reward, and tolerate only fully formed adults” (2014, p. 4).  Among the corporate values are the courage to speak your mind, the ability to make sound independent judgments, being curious, and being innovative (Hastings, 2009).  Hastings also suggests “adequate performance gets a generous severance package” (slide 22).  Netflix not only states a goal to hire and retain only the best people, but also sets an upfront expectation of a culture that supports innovation.  A key element in the Netflix employee methodology is a management philosophy to create a culture of freedom and innovation with employee self-discipline and freedom eliminating many of typical corporate controls like performance reviews, bonuses, and managed vacation time (McCord, 2014).  According to the founder of Netflix, “we’ve had hundreds of years to work on managing industrial firms … we’re just beginning to learn how to run creative firms” (McCord, 2014, p. 6).  McCord reports the development of a culture conducive to innovation is seen as a primary responsibility of Netflix leadership.  Netflix, as an organization, demonstrates a commitment to attracting the right people (knowledge) and fostering a culture that fosters innovation.

Interestingly, the Netflix philosophy that supports the people and culture, eschews formalized process.  According to the Netflix culture definition, process is only required when the complexity of the business exceeds the capability of the people (Hastings, 2009, slide 47).  Hastings declares process, while useful for avoiding the chaos of increasingly large organizations, a limit to the flexibility of the organization to adapt as the business environment changes.  The Netflix response is to drive the percentage of high-performance employees faster than the rise of business complexity to maintain an informal and adaptable organization.   The apparent success of this approach calls into question whether process is independent of people and culture.  It is possible an innovative culture, or superior human capital, mediates the necessity of formalized processes for the diffusion of innovation throughout the organization. It is also possible that Netflix either lacks the need for existential knowledge that would benefit from more formalized approaches to knowledge development (Wilson & Doz, 2011), or that Netflix is simply ignoring the benefits of more formalized knowledge development approaches.  Whether superior knowledge resources and culture mediates the need for more formalized processes is a provocative notion that simply underscores how little is known about how to achieve successful innovation.

References

Hastings, R. (2009). Netflix culture: Freedom & responsibility. Retrieved August 8, 2015, from http://www.slideshare.net/reed2001/culture-1798664

McCord, P. (2014). How Netflix reinvented HR. Harvard Business Review, (JAN-FEB). Retrieved from http://hbr.org/

Netflix. (2014). 2013 annual report. Retrieved from http://ir.netflix.com/

Ramachandran, S. (2014a, November 18). Netflix sets its sights down under. Wall Street Journal (Online). Retrieved from http://www.wsj.com/

Ramachandran, S. (2014b, December 17). Dish Network to integrate Netflix app into its set-top boxes. Wall Street Journal (Online). Retrieved from http://www.wsj.com/

Ramachandran, S. (2015, February 4). Netflix to launch in Japan. Wall Street Journal (Online). Retrieved from http://www.wsj.com/

Ramachandran, S., & Stynes, T. (2015, January 20). Netflix steps up foreign expansion. Wall Street Journal (Online). Retrieved from http://www.wsj.com/

Schwartz, F. (2015, February 9). Netflix offers streaming video in Cuba. Wall Street Journal (Online). Retrieved from http://www.wsj.com/

Wilson, K., & Doz, Y. L. (2011). Agile innovation: A footprint balancing distance and immersion. California Management Review, 53(2), 6–26. http://doi.org/10.1525/cmr.2011.53.2.6

Is HR Sabotaging Your Innovation Efforts?

Re-post from LinkedIn from May 10, 2016

In today’s fast-paced, global economy, the traditional means of differentiation (land, capital and equipment) are becoming less differentiated and available equally to businesses large and small, old and new (Drucker, 1992; Friedman, 2006; Teece, 1998). This leaves the organization’s only means of differentiation the ability to combine undifferentiated resources together in unique ways … i.e. innovation (Lawson & Samson, 2001; Teece, 2011, 2012). Regardless of the approach to innovation you subscribe to (there are dozens, see Bowonder, Dambal, Kumar, & Shirodkar, 2010), the depth, breadth, and diversity of an organization’s people are significant antecedents to innovation success (Crook, Todd, Combs, Woehr, & Ketchen, 2011; Kim & Ployhart, 2014).  As a result, HR is a critical part of your innovation efforts.

Pre-employment assessments have been the principal tool used by HR to ensure the organization only hires the best and brightest. The use of pre-employment assessments by large U.S. organizations has increased from 26% in 2001, to 57% by 2013; eight of the top ten private employers use pre-employment testing for at least some of their positions (Weber, 2015). Unfortunately, as previously mentioned many of the traditional assessment tools have proven to be less reliable than flipping a coin when it comes to predicting future real-world performance. This realization has led to the increased use of psychological value assessments.  Value assessments attempt to match the values of prospective candidates with the value profiles of existing, high performing employees, essentially creating a way to find people who share the same values and perspectives of existing employees.  The idea is to find people who think and perform like existing top-performers. While these assessments may not be any better at predicting future performance, Weber reported on organizations reducing 90-day attrition rages from 41% to 12% in the span of only 3 years of use. Given the significant costs of hiring and training, this reduction in short-term attrition can be a significant savings for the organization. As the ease of utilizing these assessments continues to increase, the costs to utilize them decreases, they become increasingly difficult for HR organizations to ignore.

Surely, value assessments have benefit to the organization, but that value is no longer in the attracting and hiring “the best and the brightest”, and become the value of “culture” or “fit”. Even the validation of one of the most popular value assessments, the Hartman Value Profile (HVP), shows almost no correlation to real-world performance even when performance is subjectively evaluated by other members of the organization (Weathington & Roberts, 2005). Furthermore, making hiring decisions based on how well individuals “fit” within the existing organization seems at odds with the need for diverse knowledge and perspectives for effective innovation. While no one is arguing cultural “fit” is not an important aspect to collaboration and productivity, it is potentially dangerous to be seduced by the perceived benefits of values-based pre-employment assessments.

Innovation starts, and ends, with people. Decades of research demonstrate that successful innovation requires, not just the best and brightest, but also diversity in the perspectives and knowledge of those people. If everyone has the same perspective, values, and beliefs, it will be increasingly difficult to create anything “new”. If we compound this by not hiring the best and brightest (because we are more concerned with fit), the effects could be devastating.

Be wary about letting your HR practices sabotage your innovation efforts before they even get started.

References

Bowonder, B., Dambal, A., Kumar, S., & Shirodkar, A. (2010). Innovation strategies for creating competitive advantage. Research Technology Management, 53(3), 19–32. Retrieved from http://www.iriweb.org/

Crook, T. R., Todd, S. Y., Combs, J. G., Woehr, D. J., & Ketchen, D. J. J. (2011). Does human capital matter? A meta-analysis of the relationship between human capital and firm performance. Journal of Applied Psychology, 96(3), 443–456. doi:10.1037/a0022147

Drucker, P. F. (1992). The post-capitalist world. Public Interest, 109(Fall 1992), 89–101. Retrieved from http://www.nationalaffairs.com/

Friedman, T. L. (2006). The world is flat: A brief history of the twenty-first century. New York, NY: Farrar, Straus and Giroux.

Kim, Y., & Ployhart, R. E. (2014). The effects of staffing and training on firm productivity and profit growth before, during, and after the Great Recession. The Journal of Applied Psychology, 99(3), 361–89. doi:10.1037/a0035408

Lawson, B., & Samson, D. (2001). Developing innovation capability in organisations: A dynamic capabilities approach. International Journal of Innovation Management, 5(3), 377. doi:10.1142/s1363919601000427

Teece, D. J. (1998). Capturing value from knowledge assets: The new economy, markets for know-how, and intangible assets. California Management Review, 40(3), 55–79. doi:10.2307/41165943

Teece, D. J. (2011). Dynamic capabilities: A guide for managers. Ivey Business Journal Online, 1. Retrieved from http://search.proquest.com/

Teece, D. J. (2012). Dynamic Capabilities: Routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401. Retrieved from 10.1111/j.1467-6486.2012.01080.x

Weathington, B. L., & Roberts, D. P. (2005). Validation analysis of the Hartman Value Profile. Retrieved from http://www.hartmaninstitute.org/

Weber, L. (2015). Today’s personality tests raise the bar for job seekers. The Wall Street Journal. Retrieved from http://www.wsj.com

Diversity is not just a social issue, it is an economic one.

The problem with treating diversity as only a social-justice issue is that social issues rarely get solved without demonstrating how they indirectly affect all people, not just the disenfranchised.  All you must do is look at the history of social corporate responsibility (CSR) to see this effect (O’Toole & Vogel, 2011).  CSR was treated with mostly lip-service until two things were demonstrably clear in the marketplace: 1) consumer trends were changing to favor organizations demonstrating CSR principles; and, 2) CSR (or sustainable) practices made economic sense by reducing waste, improving operations, and elevating brand.  While the messaging is about being socially conscious, CSR business models can lead to competitive differentiation, which leading to profits. The only real academic argument against CSR is the implied altruistic nature of most CSR proponents; every corporation engaging in CSR generates either direct or indirect economic profit from those actions, meaning CSR is nothing more than “enlightened self-interest” (Smith, 2003).  As much as we should care about social issues, until they affect us directly, critical mass is not achieved towards solving them.

Diversity is no different.  We, as a collective species, should promote diversity (religious, nationality, sex, age, values, etc.) simply because it is ethical and just. However, because of the numerous permutations of bias, no single group of disenfranchised gains sufficient support to make true change solely on the basis of justice.   Unless we can demonstrate how biases affect everyone, progress will remain slow, or non-existent.  The best way to combat isses of diversity is through developing the enlightened self-interest of the greater society.

Fortunately, there is tremendous support that diversity is the key to economic profit and productivity benefiting everyone, including those who are not victims of bias.  It is no accident that our national headlines, business articles, and social commentary are inundated with stories about both diversity as well as innovation.  These two concepts are intimately conjoined. Without diversity of experience, thought, and perspective, innovation does not happen; without innovation, society will no longer grow and prosper, but decline.  This affects everyone.

Innovation is the only true competitive differentiator in today’s world economy (Drucker, 1992; Friedman, 2006; Salchow Jr., 2016; Teece, 1998, 2004). Whether the innovation is a means to increase organizational efficiency, develop new business models, or an innovative product, the days of competing solely on accumulated land, capital, equipment, or market dominance are long over.  Those who don’t innovate, fail in the long-run. This affects people, companies, communities, and countries not just certain individuals. The inability to adapt to the global economy has decimated entire regions in the U.S. from miners, to steal producers, to manufacturers.   The number of companies and individuals directly affected is miniscule to the number of companies, individuals, and communities that have collapsed indirectly from these failures.  Lack of innovation capability affects us all.

Yet, we know that diversity in perspective, knowledge, experience, and capabilities is a foundation of innovation (Gladis, 2017). We know that diversity drives innovation (Niebuhr, 2010; Parrotta, Pozzoli, & Pytlikova, 2014), and creates economic rents, productivity, and success (Beck & Walmsley, 2012; Crook, Todd, Combs, Woehr, & Ketchen, 2011; Kim & Ployhart, 2014). Without diversity, we cannot hope to innovate because innovation is all about seeing things from a different perspective, a different value structure, a different life experience, a different cognitive lens.  It is through exploring and evaluating these differences that we see new possibilities, new solutions, and new ways of moving forward as companies, communities, and societies.  Diversity forces us to challenge what we think we know, and that leads to innovation.

It is sad that at a time when collaboration and access to diverse perspectives is so easy, we have instead taken to divisiveness, to segregation. We seek the illusionary safety of the known and miss the forest for the trees that don’t look, act, talk, or believe like us.  However, if we fail to see how diversity is an asset, not a liability, we fail our society. We fail, not because we violate the social contract, but because we will bankrupt society.  We fail by succumbing to what we believe is, rather than seeing what could be.  Without innovation, driven by diversity, we become static and eventually decline (Second Law of Thermodynamics anyone?).

Diversity is an economic imperative, not just a social one. The best way to secure your own future, is to seek out and embrace diversity. It is in our own self-interest.

References

Beck, J. W., & Walmsley, P. T. (2012). Selection ratio and employee retention as antecedents of competitive advantage. Industrial and Organizational Psychology, 5(1), 92–95. http://doi.org/10.1111/j.1754-9434.2011.01410.x

Crook, T. R., Todd, S. Y., Combs, J. G., Woehr, D. J., & Ketchen, D. J. J. (2011). Does human capital matter? A meta-analysis of the relationship between human capital and firm performance. Journal of Applied Psychology, 96(3), 443–456. http://doi.org/10.1037/a0022147

Drucker, P. F. (1992). The post-capitalist world. Public Interest, 109(Fall 1992), 89–101. Retrieved from http://www.nationalaffairs.com/

Friedman, T. L. (2006). The world is flat: A brief history of the twenty-first century. New York, NY: Farrar, Straus and Giroux.

Gladis, S. (2017). The Formula for Achieving Innovation. TD: Talent Development, (February).

Kim, Y., & Ployhart, R. E. (2014). The effects of staffing and training on firm productivity and profit growth before, during, and after the Great Recession. The Journal of Applied Psychology, 99(3), 361–89. http://doi.org/10.1037/a0035408

Niebuhr, A. (2010). Migration and innovation: Does cultural diversity matter for regional R&D activity? Papers in Regional Science, 89(3), 563–585. http://doi.org/10.1111/j.1435-5957.2009.00271.x

O’Toole, J., & Vogel, D. (2011). Two and a half cheers for conscious capitalism. California Management Review, 53(3), 60–76. http://doi.org/10.1525/cmr.2011.53.3.60

Parrotta, P., Pozzoli, D., & Pytlikova, M. (2014). The nexus between labor diversity and firm’s innovation. Journal of Population Economics, 27(2), 303–364. http://doi.org/10.1007/s00148-013-0491-7

Smith, H. J. (2003). The shareholders vs. stakeholders debate. MIT Sloan Management Review, 44(4), 85–90. Retrieved from http://sloanreview.mit.edu/

Teece, D. J. (1998). Capturing value from knowledge assets: The new economy, markets for know-how, and intangible assets. California Management Review, 40(3), 55–79. http://doi.org/10.2307/41165943

Teece, D. J. (2004). Knowledge and competence as strategic assets. Handbook on Knowledge Management 1: Knowledge Matters, 40(3), 129–152. http://doi.org/http://dx.doi.org/10.1007/978-3-540-24746-3_7

Using Mental Models to Identify Expertise

Research in the field of expertise and expert performance suggest experts not only have the capacity to know more, they also know it differently than non-experts; experts employ different mental models than novices (Feltovich, Prietula, & Ericsson, 2006). While it remains unclear how antecedents directly affect the generation of mental models, the relationship between mental models and performance is demonstrated across multiple domains of research (Chi, Glaser, & Rees, 1982; Feltovich et al., 2006). Unlike attempts to directly elicit the antecedents of performance that may, or may not, contribute to future performance, the mental models of experts show stable and reliable differences in expert performance without requiring the artificial constructs of tacit knowledge measurements (Frank, Land, & Schack, 2013; Land, Frank, & Schack, 2014; Lex, Essig, Knoblauch, & Schack, 2015; Schack, 2004, 2012; Schack & Mechsner, 2006). The potential to accurately, easily, and quantifiably define job-related expertise is an organizational opportunity for both the accumulation as well as the management of talent.

What are Mental Models

Based on information processing and cognitive science theories, mental models are the cognitive organization of knowledge in long-term memory (LTM) developed through learning and experience (Chase & Simon, 1973; Chi et al., 1982; Gogus, 2013; Insch, McIntyre, & Dawley, 2008; Schack, 2004).  Mental models represent how individuals organize and implement knowledge, instead of explicitly determining what that knowledge encompasses.  Novice practitioners start with mental models consisting of the most basic elements of knowledge required, and their mental models gradually gain complexity and refinement as the novice gains practical experience applying those models in the real world (Chase & Simon, 1973; Chi et al., 1982; Gogus, 2013; Insch et al., 2008; Schack, 2004).  Consequently, achieving expertise is not simply a matter of accumulating knowledge and skills, but a complex transformation of the way knowledge and skill is implemented (Feltovich et al., 2006).  This distinction, between what the individual knows and how the individual applies that knowledge has theoretical as well as practical importance for use in human assessment.

Mental models capture important aspects that plagued prior attempts to assess human capital performance.  In contrast to prior assessment methods, differences in mental models propose to demonstrate differences in the way individuals apply knowledge cognitively, rather than differences in the knowledge itself  (Chi et al., 1982; Gogus, 2013; Insch et al., 2008).   The significance of these findings is the implication of a measurable basis for the difference in performance between expert and novice, substantiating mental models as the quintessential construct defining the difference between the knowledge an individual has versus how the individual applies that knowledge.

Evaluating mental models from a practical perspective, mental models clearly differentiate between expert and non-experts. Chase and Simon (1973) first theorized that the way experts chunk and sequence information mediated their superior performance. Simon and Chase found grand master chess players’ superior performance resulted from recalling more complex information chunks.  These authors demonstrated that both experts and novices could recall the same number of chunks, but the chunks of novices contained single chess pieces whereas the chunks of experts contained meaningful chess positions composed of numerous pieces.  Simon and Chase further showed this superior performance to be context sensitive and domain specific as grand masters were no better than novices at recalling random, non-game specific piece constellations and showed no better performance in non-chess related memory.  The domain dependency indicates mental models of performance are not universal predictors but have job-related specificity making them ideal for assessment.

The observation that expert and novices store and access domain-specific knowledge differently spawned research theorizing quantitative, measurable differences in knowledge representation and organization might differentiate expert performance from non-expert performance (Ericsson, 2006). This research continues to substantiate increased experience and practice as the driver in the development of larger, more complex cognitive chunks (Feltovich et al., 2006). Feltovich et al. (2006) noted this effect as one of the best-established characteristics of expertise and demonstrated in numerous knowledge domains including chess, bridge, electronics, physics problem solving, and medical applications.  Feltovich et al. suggested these changes facilitated experts processing more information faster, with less cognitive effort thus contributing to greater performance.

Evolution of Mental Model Evaluation

The conceptualization of evaluating expert performance in academic and business domains already indicates the importance of mental model differences (Chi et al., 1982; Insch et al., 2008; Jafari, Akhavan, & Nourizadeh, 2013).  The general acceptance of mental models as a critical discriminator of performance has driven a deeper focus on the nature and structure of these differences instead of the specific knowledge they represent (Gogus, 2013).  This evolution of mental model evaluation, from a theoretical construct to a quantitative measure, mirrors the evolution away from what individuals know, towards how individuals utilize that knowledge.

Studies of expertise and expert performance demonstrate the dramatic differences in the way experts and novices organize knowledge in complex physics problem solving a (Chi et al., 1982). Chi et al. (1982) utilized cluster analysis to show differences in the way experts and novices structure their knowledge; however, mental models were only one of several ways in which the authors analyzed expert and novice differences.

Acknowledgment of these differences in mental representations rationalized the use of mental models in constructing more traditional tacit knowledge measures (Insch et al., 2008). Insch et al. (2008) approached tacit knowledge measures through evaluation of the actions individuals performed, acknowledging tacit knowledge was inherently how individuals use knowledge, not necessarily what knowledge they had.  In taking this approach, the authors focused on the mental schemas that directed behavior instead of the antecedent values, beliefs and skills that contribute to performance.  The focus on schemas as the driving factor in performance is notable as divergent from prior tacit knowledge measures; however, Insch et al. did not attempt measuring and comparing resultant mental models explicitly.

More recently, Jafari et al. (2013) looked to elicit and visualize the tacit knowledge of Iranian autoworkers concerning their knowledge of organizational strengths.  The uniqueness of this study was the use of quantifiable measures of individual tacit knowledge for comparison between groups of individuals and purported experts, as well as the use of graphs to visualize the results for each group.  Jafari et al. stipulated differences in mental models as an indication of differences between novice and expert workers but focused on the content rather than the structure of the mental model.  The authors further operationalized the quantitative measures as differences in what the individuals knew, and not how they utilized or implemented the knowledge. This approach advanced the use of mental models in the identification of expert knowledge, yet failed to identify how these models differ regarding application or structure.

Other researchers focused more on the differences in comparative mental models than the specific knowledge represented within the models (Gogus, 2013).  In evaluating the applicability and reliability of different methods of eliciting and comparing mental models, Gogus (2013) suggested the theoretical and methodological approach to the analysis of mental models is independent of the domain of knowledge.  Gogus replicated and contrasted the use of two different methodologies for externalization and measurement of mental model differences.  Of particular note, the author focused on contrasting the features of mental models instead of on the specific knowledge, experience, attitude, beliefs, or values of participants.  These efforts further support differences in mental models as being more dependent on the tacit rather than explicit knowledge of the individual. Since mental models are inherently domain specific and often contain the same base explicit knowledge, structural differences in the mental models between experts and novices are more indicative of the differences in performance.

Research in the area of sports psychology has similarly focused on developing reliable means of differentiating mental models of individuals to differentiate performance and diagnose performance problems.  Distinct differences in the mental models between experts and novices have been documented across multiple action-oriented skills including tennis (Schack & Mechsner, 2006), soccer (Lex et al., 2015), volleyball (Schack, 2012), and golf (Frank et al., 2013; Land et al., 2014). Schack and Mechsner (2006) demonstrated how differences in the mental models of the tennis serve related to the level of expertise.  Lex et al. (2015) evaluated the differences in the mental models of team-specific tactics between players of varying levels of experience.  Less experienced players averaging 3.2 years of experience (n = 20, SD = 4.2) generated mental models viewing team-tactics broadly as either offensive or defensive.  More experienced players averaging 17.3 years of experience (n = 18, SD = 3.3), further differentiated offensive and defensive tactics into smaller groups of related actions.  For instance, more experienced players further segmented defensive tactics into actions for pressing the offense, and returning to standard defense.

Focus on the specific differences in the structure of mental models has not only proven effective in differentiating expert and novice performance but also provided insight into effective training regimens (Frank et al., 2013; Land et al., 2014; Weigelt, Ahlmeyer, Lex, & Schack, 2011).  Frank et al. (2013) compared the models of novice performers to those of experts prior to and following a training intervention.  The authors experimentally evaluated two randomly assigned groups of participants with no former experience in performing a golf putt.  With the exception of an initial training video provided to all participants, none received any training or feedback.  The experimental group participated in self-directed practice over a three-day period, while the control group did not practice at all.  Frank et al. found the mental models of participants subjected to practice evolved, becoming more similar to expert mental models than participants in the control group.  Since the formal knowledge of all participants remained the same, the outcome of this study further suggests the structure of individual mental models is dependent on the experience and tacit knowledge of the individual.

The Opportunity

The use of mental models to identify expertise shows great promise. Variations in mental model construction differentiate clearly between expert and novice performers across numerous domains of knowledge.  Furthermore, methodologies highlighting the structural differences between the mental models of experts and novices show promise in the development and evaluation of training regimens.  As a result, the development of human capital assessments based on the measurement of the structural differences between mental models represents a strategic opportunity for organizations to improve the quality of human capital selection as well as the development and assessment of existing human capital.

References

Chase, W. G., & Simon, H. A. (1973). The mind’s eye in chess. In Visual Information Processing (pp. 215–281). New York, NY: Academic Press, Inc. http://doi.org/10.1016/B978-0-12-170150-5.50011-1

Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7–75). Hillsdale: Lawrence Erlbaum Associates.

Ericsson, K. A. (2006). An introduction to Cambridge handbook of expertise and expert performance: Its development, organization, and content. In The Cambridge handbook of expertise and expert …. New York, NY: Cambridge University Press.

Feltovich, P. J., Prietula, M. J., & Ericsson, K. A. (2006). Studies of expertise from psychological perspectives. In The Cambridge handbook of expertise and expert …. New York, NY: Cambridge University Press.

Frank, C., Land, W., & Schack, T. (2013). Mental representation and learning: The influence of practice on the development of mental representation structure in complex action. Psychology of Sport and Exercise, 14(3), 353–361. http://doi.org/10.1016/j.psychsport.2012.12.001

Gogus, A. (2013). Evaluating mental models in mathematics: A comparison of methods. Educational Technology Research and Development, 61(2), 171–195. http://doi.org/10.1007/s11423-012-9281-2

Insch, G. S., McIntyre, N., & Dawley, D. (2008). Tacit Knowledge: A Refinement and Empirical Test of the Academic Tacit Knowledge Scale. The Journal of Psychology, 142(6), 561–579. http://doi.org/10.3200/jrlp.142.6.561-580

Jafari, M., Akhavan, P., & Nourizadeh, M. (2013). Classification of human resources based on measurement of tacit knowledge. The Journal of Management Development, 32(4), 376–403. http://doi.org/http://dx.doi.org/10.1108/02621711311326374

Land, W. M., Frank, C., & Schack, T. (2014). The influence of attentional focus on the development of skill representation in a complex action. Psychology of Sport and Exercise, 15(1), 30–38. http://doi.org/10.1016/j.psychsport.2013.09.006

Lex, H., Essig, K., Knoblauch, A., & Schack, T. (2015). Cognitive Representations and Cognitive Processing of Team-Specific Tactics in Soccer. PLoS ONE, 10(2), 1–19. http://doi.org/10.1371/journal.pone.0118219

Schack, T. (2004). Knowledge and performance in action. Journal of Knowledge Management, 8(4), 38–53. http://doi.org/10.1108/13673270410548478

Schack, T. (2012). Measuring mental representations. In G. Tenenbaum, R. Eklund, & A. Kamata (Eds.), Measurement in Sport and Exercise Psychology (pp. 203–214). Champaign, IL: Human Kinetics. Retrieved from http://www.uni-bielefeld.de/sport/arbeitsbereiche/ab_ii/publications/pub_pdf_archive/Schack (2012) Mental representation Handb

Schack, T., Essig, K., Frank, C., & Koester, D. (2014). Mental representation and motor imagery training. Frontiers in Human Neuroscience, 8(May), 328. http://doi.org/10.3389/fnhum.2014.00328

Schack, T., & Mechsner, F. (2006). Representation of motor skills in human long-term memory. Neuroscience Letters, 391(3), 77–81. http://doi.org/10.1016/j.neulet.2005.10.009

Weigelt, M., Ahlmeyer, T., Lex, H., & Schack, T. (2011). The cognitive representation of a throwing technique in judo experts – Technological ways for individual skill diagnostics in high-performance sports. Psychology of Sport and Exercise, 12(3), 231–235. http://doi.org/http://dx.doi.org/10.1016/j.psychsport.2010.11.001

Misconceptions about Certification

There seems to be wide misconceptions about what “Certification” and “Licensure” are all about.  Some see it as just the final part of an educational regimen.  Other’s see it as some kind of hurdle imposed by greedy organizations restricting access to some benefit.  These misconceptions and skewed perspectives, lead them to make demands affecting the very heart of what certification is all about and minimizing the value of the very certification they are working to achieve. First:

What Certification IS …

Certification is a legally defined classification stating a certifying body stands behind the capability of a certified individual to perform at some specific level of capability.  Certification is a very simple construct: it is the definition of a standard of performance, and a program assessing individuals to that standard.  That’s it, nothing more and nothing less.

Setting a standard is the first part of any certification.  Ideally, this standard is defined with help of people who perform the job.  In addition, a standard implies that everyone, no matter whether they have done the actual job for decades or participated in the development of the standard itself, must objectively demonstrate their capability in exactly the same manner.  Unless this standard is objectively applied equally to all individuals, it is not a standard.  Maintaining that standard is the foundation of certification value; it is what the certification stands for and how it should be used.

Creating an assessment of that standard is the second part of any certification.  This not only includes a mechanism assessing current capability, but also a program or process to ensure future capability.   Despite what many believe, creating an assessment is not a simple, ad hoc process where someone creates an assessment (test) and makes people take it to prove their ability.  It is, in fact, a highly rigours process backed by decades of scientific research on measuring cognitive ability (psychometrics), the sole purpose of which is ensuring the validity of the decisions made based on assessment performance.  It involves designing the assessment, job-task analysis of the job being assessed, formalized standards of item (question) writing, public beta-testing of items, psychometric evaluation of the item performance, assessment construction, and evaluation of appropriate scoring.  Done properly, it is time-consuming and costly (which is why some organizations don’t do it properly).

Finally, because knowledge and competence are perishable commodities, mechanisms must be put in place ensuring certified individuals remain capable in the future.  This is frequently done by limiting the length of time a certification is valid and requiring periodic re-certification (re-validation) of the individuals capability.  Other methods may include proving continued education and practice of the knowledge.  Regardless, the ongoing evaluation of certified individuals must adhere to the original standard with the same validity, or the standard no longer has value.

There is no hidden agenda to certification.  There is no conspiracy.  It is simply to establish a standard and assess individuals compared to that standard.

Misconceptions about Certification

Really, any belief beyond the design of a standard and the assessment to that standard, is a misconception about certification.  However, there are a number of misconceptions that often drive changes detrimental to the rationale of certification.  The most common ones relate to understanding what an assessment is for, training, and re-certification requirements.

Most people mistakenly assume the items within an assessment must represent the end-all-be-all of what someone should know.  They don’t understand that a psychometric assessment is not about the answers themselves, but the inferences we can make about performance based on those answers.  There is no way to develop an exhaustive exam of all the knowledge necessary to be competent and to deliver that exam efficiently.  However, we can survey a person’s knowledge and through statistical analysis infer whether they have all the knowledge necessary or not.  The answer to any specific question is less important than how answering that question correlates with competence.  Even a poorly written, incomplete, and inaccurate item can give us information about real world performance; in fact, evaluating how a candidate responds to such an item can be highly informative (although this is not a standard, intentional practice).  This focus on the items themselves, rather than the knowledge and competence the answers suggest, is what makes people incorrectly question the validity of the certification.

Similarly, many people think a certification should be able to be specifically taught.  As such, they believe a training course should be all that is necessary to achieve a certification.  However, this does not align with what we know about the development of human competence.  There is a big difference between knowing something, and knowing how to apply that knowledge competently.  Certification is an assessment of performance, not knowledge; and, as such, cannot be taught directly.  If someone can take a class and immediately achieve certification, either: A) the assessment does not evaluate actual performance; or, B) the course simply teaches the answers to the questions on the exam, rather than the full domain of knowledge.  In either case, you have biased the inferences made by the assessment.  Competence begins with knowledge, but must also have experience and practice.  This cannot be gained through a class, but only through concerted effort; you cannot buy competence.

Finally, many people also believe that once a certification has been achieved, it shouldn’t need to ever be evaluated again; or, that taking a course instead of an assessment should suffice.  The former belief simply ignores the fact performance capability is a perishable commodity: if you don’t use it, you lose it.  The latter once again confuses knowledge with performance.  How frequently this needs to happen, or whether continued education is sufficient to demonstrate continued performance is entirely dependent on the knowledge domain the certification attempts to assess.  In highly dynamic environments, this may need to be done much more frequently and rely more on assessments than in other domains; however, ongoing evidence of continued capability is a must if standards are to be maintained.

Leave Certification to the Professionals

The heart of the problem is that everyone seems to believe they are experts in the design of certification programs and assessments simply because they have participated in them.  The reality is that certification is a rigorous, research-based, and scientific endeavor.  The minimum requirement to be considered a psychometrician is a PhD; that’s a great deal of specialized knowledge most people do not have.  The decisions made are not arbitrary, nor are they made with the intention of anything other than maintaining the standard and making valid assessments of individuals according to that standard.

At the end of the day, the value of a certification is whether the people who achieve it can perform according to the standard the certification set forth.  If the certification cannot guarantee that, then it is not valid and has no value.  However, this requires people to actually understand what that standard is, what it means, and why it was created.   It requires people to accept there is a rigorous process accounting for all of the decisions and those decisions all support validity.  Finally, it requires people to understand that just because they may be experts in their field, they are not experts in certification.

 

 

 

 

 

What Makes an Expert, an Expert?

Re-post from LinkedIn April 28, 2016

Human beings have likely been trying to understand expertise since the first cave dweller wondered why Grog was so much better at hunting, or why Norg seemed to always know where the best berries were.   Efforts to identify, and more precisely to predict expertise have pretty much been ongoing ever since. It’s no wonder, since a McKinsey report showed that high-performers could generate significantly greater productivity (40%), profit (49%) and revenue (67%) depending on their role when compared to even average performers (Cornet, Rowland, Axelrod, Handfield-Jones, & Welsh, 2001). While we are still not very good at predicting future expertise, or even how to objectively quantify it, we have learned a few things along the way. Expertise is not necessarily an innate ability. Nor is expertise necessarily what you know but how you know it.

Scientific assessment of individual differences seems to have hit critical mass in the mid- to late- 19th century, culminating in the development of the general theory of intelligence (Spearman, 1904). Spearman was attempting to create a unified way of looking at and evaluating innate capability, sans training or experience. This idea that certain human beings were simply destined for greatness was the impetus for the intelligence testing that we still use today for assessing potential (e.g. IQ). While many people (including businesses) put a lot of stock into general measures of intelligence, it turns out that actual, real world performance is not simply a matter of innate ability. For instance, IQ measures proved to be useless in predicting the rankings of internationally ranked chess players. In fact, studies have shown intelligence measures to only account for between 4% and 30% of real world performance (Sternberg, Grigorenko, & Bundy, 2001). Even at the high-end of that range, more than two-thirds of the reason for an individual’s real world performance is unaccounted for by standard intelligence measures. Real world performance is more than innate ability, but the product of ability informed by experiential knowledge and skills.

The mid- 20th century ushered in the idea that, perhaps, expert performance was the result of specialized knowledge developed over time. Michael Polanyi famously defined tacit knowledge by suggesting we know more than we can tell (Peck, 2006; Polanyi, 1966). As opposed to explicit knowledge, which can be written down, easily expressed and taught, tacit knowledge remains elusive even to those who have it (Mahroeian & Forozia, 2012). While explicit knowledge is what we know, tacit knowledge is the ability to apply that knowledge successfully; experts exhibit some form of meta-knowledge enabling them to better apply their knowledge. Experts achieve automaticity in both their thoughts and actions, making complex processes appear effortless and simple. Yet, experts are generally unable to explain how they do this. The result is that experts appear to solve problems intuitively, not because they specifically know more, but because they know better.

One explanation of where tacit knowledge originates is through the development of superior mental models of domain knowledge. Research comparing the mental models of expert and novice practitioners show that experts organize their knowledge in ways uniquely different from novices (Chi, Glaser, & Rees, 1982; Gogus, 2013). This research substantiates that a principal difference between an expert and a novice is the structure of their mental models, not necessarily the contents of their knowledge. The mental models of expert practitioners appear to coalesce to a point of maximum efficiency regardless of how the skills develop (Schack, 2004). These efficient mental models allow experts immediate access to (more) knowledge and procedures relevant for efficient use in daily application (Feltovich, Prietula, & Ericsson, 2006). In short, experts generate the best solutions under time constraints, better perceive the relevant characteristics of problems, are more likely to apply appropriate problem solving strategies, are better at self-monitoring to detect mistakes and judgment errors, and perform with greater automaticity and minimal cognitive effort (Chi, 2006). Experts perform faster and more accurately with less effort.

A recent study comparing more-experienced and less-experienced soccer players utilized iris-scanning technology to make this point exceptionally salient (Lex, Essig, Knoblauch, & Schack, 2015). This study determined that while more-experienced and less-experienced players fixated on visuals of game situations for the same amount of time per pixel, more-experienced players focused on four specific aspects of the visual while less-experienced players fixated on many areas irrelevant to the decision-making process; the result was that more-experienced players made effective decisions much faster than their less-experienced counterparts. The point here is experts are capable of screening out extraneous information and focusing solely on the details that matter in order to make effective, efficient, and accurate choices. While all of the players had the same basic knowledge of the game, more-experienced players applied that knowledge more efficiently to make accurate decisions more quickly.

So, what makes an expert, an expert? Much like the number of licks to reach the center of a tootsie-pop, the world may never really know. Despite apocryphal notions, we don’t know how long it takes for someone to become an expert, or even if all individuals are capable of becoming experts. We don’t even have a universal means of determining if someone has truly become an expert or easily differentiating experts from novices objectively. What we do know is that expertise is not something you are born with and it is not something achieved simply by obtaining knowledge or training.  It is a metamorphosis from knowing what, to knowing how.

One might say that expertise is simply a state of mind.

References

Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7–75). Hillsdale: Lawrence Erlbaum Associates.

Cornet, A., Rowland, P. J., Axelrod, E. L., Handfield-Jones, H., & Welsh, T. A. (2001). War for talent, part two. McKinsey Quarterly, (2), 9–12. Retrieved from http://www.mckinsey.com/

Gogus, A. (2013). Evaluating mental models in mathematics: A comparison of methods. Educational Technology Research and Development, 61(2), 171–195. doi:10.1007/s11423-012-9281-2

Mahroeian, H., & Forozia, A. (2012). Challenges in managing tacit knowledge: A study on difficulties in diffusion of tacit knowledge in organizations. International Journal of Business and Social Science, 3(19), 303–308. Retrieved from http://ijbssnet.com/

Peck, D. A. (2006). Tacit knowledge and practical action: Polanyi, Hacking, Heidegger and the tacit dimension. ProQuest Dissertations and Theses. University of Guelph (Canada), Ann Arbor. Retrieved from http://search.proquest.com.library.capella.edu/docview/305337938?accountid=27965

Polanyi, M. (1966). The Tacit Dimension. Knowledge in Organizations. Butterworth-Heinemann. doi:10.1016/B978-0-7506-9718-7.50010-X

Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. doi:10.2307/1412107

Sternberg, R. J., Grigorenko, E. L., & Bundy, D. a. (2001). The predictive value of IQ. Merrill – Palmer Quarterly, 47(1), 1. doi:10.1353/mpq.2001.0005

The Innovation Strategy Framework

Innovation is critical to creating and maintaining a competitive advantage in the modern business environment. Organizational leaders must find ways to combine undifferentiated resources to create differentiated products and services (Lawson & Samson, 2001; Teece, 2011, 2012).  These dynamic capabilities require constant innovation to create new value for the organization as well as the organization’s customers.  Innovation is the driver of delivering sustained competitive advantage.

Innovation is not a simple construct. Innovation means multiple things depending on the context (Costello & Prohaska, 2013). Also, numerous, competing models have shown to be capable of creating successful innovation (Bowonder et al., 2010).  This plethora of conceptualizations and models leaves organizational leadership with little practical guidance and contributes to confusion on how to achieve competitive advantage through innovation. The reality is that innovation is a varied, complex concept that encompasses many components.  It is not even easy to identify whether innovation has taken place, because the ultimate litmus test to successful innovation is how it is received in the marketplace, not how it was conceived or executed.  Rather than focusing on specific definitions or models, organizational leaders require enumeration of the basic building blocks fostering innovative capabilities and guidelines on how to orchestrate them for success.

By studying organizations consistently demonstrating serial innovation success, we do know that successful innovation all relies on some basic building blocks.  Putting these building blocks together into an overarching framework allows for infinite variability in discovery, experimentation, failure, and success and is a good place to start understanding innovation as an organizational capability.

The Innovation Strategy Framework

The innovation strategy framework accounts for the key factors identified as critical to innovation success: knowledge resources, processes, metrics (monitoring), and culture (including leadership).   Figure 1 graphically depicts how the innovation success factors fit together as a composite framework.

InnovationModel

The Innovation Strategy Framework

Knowledge Resources:

Knowledge resources include the customers, ecosystem partners, and employees that generate innovative ideas, select appropriate ideas, promote the ideas, and ultimately create innovative solutions.  White boxes in Figure 1 represent the people involved in innovation.  On one side are the heterogeneous sources of knowledge providing innovative ideas and solutions.  These resources are both internal and external, creating the depth, breadth, and diversity of knowledge to supply the organization with innovative fuel (Dell’Era & Verganti, 2010; Phelps, 2010; Rothaermel & Hess, 2010).  On the other side, strategic domains are the knowledge resources responsible for taking innovative ideas and developing them in alignment with organizational goals and strategy (Ramírez, Roodhart, & Manders, 2011).  At the top, leadership develops knowledge networks, provides resources to create innovation processes, and the creation, funding, and direction of strategic domain groups (Brown & Anthony, 2011; Engel & Del-Palacio, 2011; Ramírez et al., 2011; Rufat-Latre, Muller, & Jones, 2010).

Innovation Processes:

Processes include both the processes used to integrate, promote, and develop innovative solutions, as well as the processes necessary to manage and monitor the innovation process.  Black boxes in Figure 1 represent the processes for the generation and development of innovation.  Following the definitions of agile innovation, the processes differ based on the type of knowledge necessary, including attracting, foraying, and experiencing (Wilson & Doz, 2011). Wilson and Doz recommended these be viewed as interactive and iterative depending on the innovation and the organizational need.  Ideation using VC’s (attracting), might require rapid prototyping (Sandmeier et al., 2010; Tuulenmäki & Välikangas, 2011) using direct engagement (foraying), or the development of dedicated innovation teams embedded in remote locations (experiencing). These well-defined approaches formalize the interaction of strategic domains and innovation contributors.  Processes designed to manage the innovation pipeline monitor these interactions.

Measuring Innovation Efforts:

The innovation pipeline in Figure 1 represents the process for managing and monitoring the innovation process.  While the specific measures implemented by any organization will be unique and should not be the same for every class of innovation project, organizational leaders must ensure every process and project has specific measures enabling appropriate management (Chen & Muller, 2010).  Chen and Muller also recommended measures related to the overall revenue and profit growth attributed to innovation, projected value of the innovation pipeline if all projects are successful, and evaluation of the pipeline status.  Measures of the actual profit growth and revenue promote accountability for overall innovation efforts, while the projected value of the innovation pipeline requires the evaluation of each project in terms of expected long-term benefit; project projections also allow for organizational prioritization.  Finally, measures of pipeline status, provide overall monitoring of organization innovation success by measuring the size of the innovation network, the number of ideas making it through each stage of the process, and how quickly innovative solutions reach the market.

Leadership and Innovation Culture:

Finally, effective leadership includes the support, development, and direction of innovation efforts to create an organizational culture built to achieve innovative success.  Gray arrows in Figure 1 represent the actions promoting innovation within the organization.  Knowledge resources are encouraged to participate in innovation development through the development of shared value (Hammon & Hippner, 2012; Lee, Olson, & Trimi, 2012; Schröder & Hölzle, 2010).  Strategic domain groups support the processes of attracting, foraying, and experiencing as a source for both innovative ideas, as well as the knowledge to develop ideas into marketable solutions promoting the organization’s strategic goals (Angelis, Macintyre, Dhaliwal, Parry, & Siraliova, 2011; Sandmeier et al., 2010; Tuulenmäki & Välikangas, 2011).  Leadership develops the organization’s knowledge network and provides the resources required by the strategic domains to engage those knowledge resources. (Brown & Anthony, 2011; Ramírez et al., 2011; Rufat-Latre et al., 2010).  These actions develop a culture where innovation supported, and embraced as a way of doing business.

Putting it all Together

The innovation strategy framework incorporates the principal factors identified to promote organizational innovation success.  Successful innovation requires depth, breadth, and diversity of the organization’s knowledge network, and the internal capabilities to identify, select, promote, and develop innovative solutions.  Organizations must have appropriate processes to integrate the knowledge from the knowledge network, as well as the capabilities to appropriately monitor and manage the innovation process.  The development of the knowledge network, the appropriate processes and the integration of innovation and strategy is the job of organizational leadership directly by example and indirectly through investment.  The innovation strategy framework represents a high-level approach to innovation strategy without making explicit definitions of innovation or requiring specific models for innovation.  The innovation strategy framework presents a holistic view of innovation, not as any specific innovation model, but as basic building blocks capable of delivering innovation in any dimension.

The value in a generic innovation strategy framework is in evaluating an organization’s overall capabilities and deficiencies for achieving innovation success as well as guiding how those critical innovation resources need to interact.  There are dozens of models to develop different types of innovative outcomes (Bowonder, Dambal, Kumar, & Shirodkar, 2010), but organizations lacking the basic building blocks of people, processes, and organizational commitment are unlikely to be successful applying any of them (Christensen & Overdorf, 2000).  Christensen and Overdorf specifically called out resources, processes, and organizational values as the principal factors keeping organizations from surviving disruptive innovation, not a lack of ideas or choice of innovative response.  Long before organizations choose the appropriate innovation approaches, organizations must be primed to be successful.  The innovation strategy framework provides a means of evaluating an organization’s readiness for innovation success and guidance for improving an organization’s chance for future success.

 

References

Angelis, J., Macintyre, M., Dhaliwal, J., Parry, G., & Siraliova, J. (2011). Customer centered value creation. Issues of Business and Law, 3(1), 11–19. http://doi.org/10.2478/v10088-011-0002-8

Bowonder, B., Dambal, A., Kumar, S., & Shirodkar, A. (2010). Innovation strategies for creating competitive advantage. Research Technology Management, 53(3), 19–32. Retrieved from http://www.iriweb.org/

Brown, B., & Anthony, S. D. (2011). How P&G tripled its innovation success rate. Harvard Business Review, 89(6), 64–72. Retrieved from http://hbr.org/

Chen, G., & Muller, A. (2010). Measuring innovation from different perspectives. Employment Relations Today, 37(1), 1–8. http://doi.org/10.1002/ert.20279

Christensen, C. M., & Overdorf, M. (2000). Meeting the challenge of disruptive change. Harvard Business Review, 78(2), 66–76. Retrieved from http://hbr.org/

Costello, T., & Prohaska, B. (2013). Innovation. IT Professional, 15(3), 64–66. Retrieved from http://www.computer.org/

Dell’Era, C., & Verganti, R. (2010). Collaborative strategies in design-intensive industries: Knowledge diversity and innovation. Long Range Planning, 43(1), 123–141. http://doi.org/10.1016/j.lrp.2009.10.006

Engel, J. S., & Del-Palacio, I. (2011). Global clusters of innovation: The case of Israel and Silicon Valley. California Management Review, 53(2), 27–49. http://doi.org/10.1525/cmr.2011.53.2.27

Hammon, L., & Hippner, H. (2012). Crowdsourcing. Business & Information Systems Engineering, 4(3), 1–166. http://doi.org/10.1007/s12599-012-0215-7

Lawson, B., & Samson, D. (2001). Developing innovation capability in organisations: A dynamic capabilities approach. International Journal of Innovation Management, 5(3), 377. http://doi.org/10.1142/s1363919601000427

Lee, S. M., Olson, D. L., & Trimi, S. (2012). Co-innovation: Convergenomics, collaboration, and co-creation for organizational values. Management Decision, 50(5), 817–831. http://doi.org/10.1108/00251741211227528

Phelps, C. C. (2010). A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation. Academy of Management Journal, 53(4), 890–913. http://doi.org/10.5465/amj.2010.52814627

Ramírez, R., Roodhart, L., & Manders, W. (2011). How Shell’s domains link innovation and strategy. Long Range Planning, 44(4), 250–270. http://doi.org/10.1016/j.lrp.2011.04.003

Rothaermel, F. T., & Hess, A. M. (2010). Innovation strategies combined. MIT Sloan Management Review, 51(3), 13–15. Retrieved from http://sloanreview.mit.edu/

Rufat-Latre, J., Muller, A., & Jones, D. (2010). Delivering on the promise of open innovation. Strategy & Leadership, 38(6), 23–28. http://doi.org/10.1108/10878571011088032

Sandmeier, P., Morrison, P. D., & Gassmann, O. (2010). Integrating customers in product innovation: Lessons from industrial development contractors and in-house contractors in rapidly changing customer markets. Creativity and Innovation Management, 19(2), 89–106. http://doi.org/10.1111/j.1467-8691.2010.00555.x

Schröder, A., & Hölzle, K. (2010). Virtual communities for innovation: Influence factors and impact on company innovation. Creativity and Innovation Management, 19(3), 257–268. http://doi.org/10.1111/j.1467-8691.2010.00567.x

Teece, D. J. (2011). Dynamic capabilities: A guide for managers. Ivey Business Journal Online, 1. Retrieved from http://search.proquest.com/

Teece, D. J. (2012). Dynamic Capabilities: Routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401. Retrieved from 10.1111/j.1467-6486.2012.01080.x

Tuulenmäki, A., & Välikangas, L. (2011). The art of rapid, hands-on execution innovation. Strategy & Leadership, 39(2), 28–35. http://doi.org/10.1108/10878571111114446

Wilson, K., & Doz, Y. L. (2011). Agile innovation: A footprint balancing distance and immersion. California Management Review, 53(2), 6–26. http://doi.org/10.1525/cmr.2011.53.2.6

The Building Blocks of Innovation

Innovation is not simple to achieve.  Not only is innovation difficult to distinctly define (Costello & Prohaska, 2013), there are numerous, competing frameworks proclaimed as being successful in creating effective innovation practices (Bowonder, Dambal, Kumar, & Shirodkar, 2010).  The litany of successful innovation conceptualizations leaves organizational leadership with little practical guidance in developing credible innovation strategy.  The result is the failure of most organizations in developing successful innovation practices (Rufat-Latre, Muller, & Jones, 2010).

What’s missing is an overarching framework integrating the various definitions of innovation, the different ways to achieve innovation, and the basic components necessary to achieve sustained innovation success.  While many authors have proposed innovation cookbooks purporting recipes for success, what is really needed is an innovation playbook: a set of resources that can be deployed in response to dynamic changes in organizational position and competitive reaction.  Here is an overview of the building blocks providing the foundation of the innovation playbook.

The Elements of Innovation Strategy

Successful innovation relies on the development of knowledge resources, processes, and an organizational commitment to innovation.  Knowledge resources comprise the employees, ecosystem partners, and customers that become the source of innovation ideation and development (Engel & Del-Palacio, 2011; Phelps, 2010; Rothaermel & Hess, 2010; Wilson & Doz, 2011).  In addition to the people necessary to generate innovation, organizations must have the processes to manage the integration of knowledge into the organization, the development of innovative ideas, and the means to manage innovation outcomes (Birkinshaw, Bouquet, & Barsoux, 2010; Rothaermel & Hess, 2010; Wilson & Doz, 2011).  Finally, organizational commitment towards developing knowledge resources, creating appropriate processes, and directing innovation efforts is necessary to create sustained innovation (Brown & Anthony, 2011; Engel & Del-Palacio, 2011; Ramírez, Roodhart, & Manders, 2011; Sandmeier, Morrison, & Gassmann, 2010).  Regardless of the innovation definition, or framework, the people, processes, and culture of the organization are critical requirements for building an innovation strategy.

Knowledge Wanted Here!

Successful innovation depends on the availability of vast, diverse knowledge resources to provide ideation and successful development of innovation.  The depth and breadth of the knowledge encapsulated in the employees, ecosystem partners, and customers of an organization have been linked to positive innovation outcomes (Dell’Era & Verganti, 2010; Kim & Ployhart, 2014; Phelps, 2010; Rothaermel & Hess, 2010; Sandmeier et al., 2010; Wilson & Doz, 2011).  Phelps reported the correlation between the depth and breadth of an organization’s knowledge network and innovation success; organizations with expansive knowledge networks achieved outsized innovative success.  Dell’Era and Verganti found similar correlations with the diversity of design knowledge and innovative success in design-intensive industries.  Sandmeier et al. identified the frequency and diversity of customer involvement in new product development as a catalyst to innovative outcomes.  Regardless of the perspective of innovation success, or the specific knowledge being integrated, successful innovation is consistently correlated with access to expansive knowledge resources through diverse sources.  Only Rothaermel and Hess suggested limits on the benefits of knowledge diversity and density and suggested the need to manage knowledge resources to achieve the greatest net effect.  In short, Rothaermel and Hess proposed the need for processes to identify the best knowledge resources, coordinate the development of innovation, and measure success effectively.

Some Processing Required

Processes for selecting, promoting, and executing innovative ideas are critical to innovative strategy.  Knowledge resources have differing value, and organizations must understand the differences to coordinate innovation effectively (Mahroeian & Forozia, 2012; Wilson & Doz, 2011).  Wilson and Doz identified a continuum of knowledge classifications from explicit to embedded, to existential (or tacit).  Each of these knowledge resources requires unique processes and systems for effective utilization by the organization.  Wilson and Doz also suggested the amount of effort required to utilize knowledge resources was directly proportional to the unique value of the knowledge gained.  Explicit knowledge, which can be easily codified and transferred via virtual communities (VCs) and crowdsourcing solutions, is also more easily acquired by competitors minimizing the unique value (Hammon & Hippner, 2012; Schröder & Hölzle, 2010; Shepherd, 2012).  On the other end of the spectrum, tacit knowledge requires significant effort to understand and experience, but prevents simple duplication (Mahroeian & Forozia, 2012; Wilson & Doz, 2011).  Organizational leaders must understand the use of varying processes appropriate to the knowledge needs of the organization and when to apply them throughout the process.  This knowledge contributes to an organization’s innovation competencies (Šebestová & Rylková, 2011).  Innovation management processes inform the development of these innovative competencies.  Yet, fully developing an organizations knowledge networks requires more than just process, it requires a culture ready to use it.

A Culture of Innovation

Successful innovation also requires an organizational commitment to the innovation process.  Failed innovation attempts are not only likely, that are inevitable (McGrath, 2011).  McGrath proposed developing an organizational approach embracing the inevitability of failure by building processes designed to learn from small failures to avoid large failures; i.e. fail small, fail fast. Proposing the acceptance of failure is a clear example of the import of organizational commitment to innovation and the need for leadership to build a culture that values the innovative process, including the inherent occurance of failure (Rufat-Latre et al., 2010).  Rufat-Latre et al. argued the development of successful innovation efforts was not a simple action, but an iterative process of developing a culture appreciative of, and committed to, developing innovative capabilities.  Organizational leadership is required to support initiatives inviting innovation from outside of the organization, implementing iterative innovation processes embracing failure, developing the organizational capabilities to innovate, and provide guidance on innovative efforts (Brown & Anthony, 2011; Ramírez et al., 2011).  Brown and Anthony, as well as Ramírez et al., highlighted the importance of effective leadership to financially support and direct innovation efforts as strategic and necessary practices within the organization.  Besides direct investment in the process of innovation, leadership is the critical link between organizational strategy and innovation (Bodley-scott, 2011; Ramírez et al., 2011).  Without this connection, innovation will not be directed towards the value that benefits the organization.

Measure for Success

Guiding an organization’s innovative process is a critical factor in developing innovative capabilities.  For innovative organizations, dashboards provide both the ability to gauge successful processes, as well as uncover unique opportunities (Mullins & Komisar, 2011).  Mullins and Komisar suggested dashboards, traditionally used to help keep an organization on track, could help innovators discover opportunities to innovate business processes.  When traditional metrics suggest existing methods are deviating, it could signal changes in the business environment and forewarn of shifts in market dynamics.  These warning signs provide leaders with better means to sense opportunities for capturing value before competitors (Teece, 2012).  At the same time, choosing appropriate measures to manage and measure overall innovation capabilities are also critical to building repeatable innovation practices (Brown & Anthony, 2011; Chen & Muller, 2010).  Chen and Muller presented a general approach to measuring innovation system performance using three primary criteria: innovation contribution to revenue and profit growth, the value of the innovation pipeline, and the quality of the innovation pipeline.  Brown and Anthony documented similar approaches used to increase the proportion of innovative successes.  Fully understanding the health of the innovation process is particularly important as, contrary to general belief, innovation is not stymied by lack of ideas, but an inability to select and promote good ideas (Birkinshaw et al., 2010).  Analyzing the innovation pipeline provides leadership the ability to prioritize innovation efforts, as well as pinpoint where innovation efforts are becoming restrained.

Innovation Building Blocks Summarized

The literature consistently highlights people, process, and organizational commitment as critical factors for successful innovation.  Broad, diverse knowledge resources increase the breadth of innovative solutions available to the organization.  Developing the processes appropriate to identify, integrate, and develop innovative ideas, as well as manage the innovation pipeline promote the development of an organization’s overall innovative capabilities.  Organizational commitment provides the resources, guidance, and culture required to innovate successfully, through the direct engagement of leadership in creating an organization valuing and promoting innovation.  People, processes, and effective innovation leadership constitute the building blocks for innovation strategy.

These building blocks are the foundation of the innovation playbook.

 

References

Birkinshaw, J., Bouquet, C., & Barsoux, J. (2010). The 5 myths of innovation. MITSloan Management Review, 52(2), 43–50. Retrieved from http://sloanreview.mit.edu/

Bodley-scott, S. (2011). Linking innovation to strategy. Training Journal, (March), 64–67. Retrieved from http://www.trainingjournal.com/

Bowonder, B., Dambal, A., Kumar, S., & Shirodkar, A. (2010). Innovation strategies for creating competitive advantage. Research Technology Management, 53(3), 19–32. Retrieved from http://www.iriweb.org/

Brown, B., & Anthony, S. D. (2011). How P&G tripled its innovation success rate. Harvard Business Review, 89(6), 64–72. Retrieved from http://hbr.org/

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McGrath, R. G. (2011). Failing by design. Harvard Business Review, 89(4), 76–83. Retrieved from http://hbr.org/

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Sandmeier, P., Morrison, P. D., & Gassmann, O. (2010). Integrating customers in product innovation: Lessons from industrial development contractors and in-house contractors in rapidly changing customer markets. Creativity and Innovation Management, 19(2), 89–106. http://doi.org/10.1111/j.1467-8691.2010.00555.x

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Why you want to, but won’t, hire a Versatilist

The quality of an organization’s human capital is more important today than at any time before.  Global, dynamic markets eradicate the competitive advantages of capital, equipment, and land (Drucker, 1992; Friedman, 2006; Hayton, 2005; Teece, 2011).  Today, differentiation comes from combining undifferentiated inputs and resources in unique ways (Dutta, 2012; Reeves & Deimler, 2011; Teece, 2007, 2011, 2012; Teece, Pisano, & Shuen, 1997). As such, the source of competitive differentiation and strategic value is not having superior resources, but the skill and knowledge necessary to innovate.  One way to describe this organizational ability is dynamic capabilities (Teece, 2012). Dynamic capabilities characterize the organizational ability to sense and seize new opportunities and transform the organization, maintaining a competitive position.  Organizations with strong dynamic capabilities change and adapt to dynamic markets, are strong innovators, and build lasting strategic differentiation.  The only place this knowledge and skill resides is within the individuals working for the organization: human capital (Blair, 2002; Ployhart, Nyberg, Reilly, & Maltarich, 2014).

If we take the notion of dynamic capabilities and apply it to a person, instead of an organization, you get versatilists.  Versatilists are wired to sense and seize new opportunities, leverage new skills and abilities, and innovate who and what they are.  They are always changing and adapting to the world around them to become experts in new areas.  They don’t have access to different knowledge or methods of learning than other people, but they combine them in new ways to create new versions of themselves.  If organizations need dynamic capabilities to innovate and be successful, who better than versatilists to drive that effort.  This is why organizations should identify and recruit versatilists as employees.

Unfortunately, current recruiting and hiring strategies are ill aligned to this goal. Just look at your average Sr. level job description: 5 -7 years doing one thing with 10+ years in the same industry, with the same focus; another: 10 years in this job role, plus 5 years in specific industry. The job descriptions go on to list several dozen areas of knowledge and experience necessary to be considered a good fit.  These descriptions will use terms like “successful track record of”, “expertise in”, and “demonstrated experience with”. While this likely doesn’t sound out of place to many, especially those in HR and recruiting, it puts the job in a nice, little box tied with a bow.  The versatilists will rarely look twice for a couple of reasons.

First off, after 5-7 years doing the same thing, most versatilists are ready for the next challenge, not the next opportunity to do the same thing. The industry experience is less of an issue (although it’s still a bad way to get new ideas into your organization).  Versatilists don’t just adapt and change because of external forces; we’re not forced to go down a different path. We choose to do new things in new ways. There is an internal drive to know more, to do more, and to do it better.  Once a versatilists has become an expert in a role, we see little opportunity for growth, either personal or professional, and are naturally attracted to the next opportunity.

Second, unlike a generalist who tends to oversell their experience, versatilists, having become experts, generally undersell.  This is the Dunning-Kruger Effect in action (Dunning, Johnson, Ehrlinger, & Kruger, 2003; Kruger & Dunning, 1999).  According to this research, people tend to estimate their knowledge on any topic as at, or slightly above average.  Those with the least amount of actual knowledge overestimate grossly what they know (and don’t know they are doing it).  However, this works with experts as well, who underestimate their knowledge by assuming it is also just at, or slightly above average (this is sometimes referred to as imposter syndrome).  Because versatilists become experts in each of their chosen areas, even if you ask for “expertise” in that specific area, they will not feel qualified generally. This is further compounded when the job description suggests the candidate should be competent in dozens of areas.

Consequently, organizations limit their ability to hire versatilists the minute they draft a job description, making themselves unattractive to the very human capital they should really want.  Organizations cannot become innovative or develop dynamic capabilities, and yet hire based on check boxes and job descriptions of what the job has always been.  Instead, organizations should be hiring the people that can adapt and change the job to what it needs to be tomorrow.  Unless you change the way you recruit and hire, you’re more likely to hire someone without the skills you thought you needed and no capacity to develop the skills you really need.

 

References

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Dunning, D., Johnson, K., Ehrlinger, J., & Kruger, J. (2003). Why people fail to recognize their own incompetence. Current Directions in Psychological Science, 12(3), 83–87. http://doi.org/10.1111/1467-8721.01235

Dutta, S. K. (2012). Dynamic capabilities: Fostering ambidexterity. SCMS Journal of Indian Management, 9(2), 81–91. Retrieved from http://search.proquest.com/

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Hayton, J. C. (2005). Competing in the new economy: the effect of intellectual capital on corporate entrepreneurship in high-technology new ventures. R&D Management, 35(2), 137–155. http://doi.org/10.1111/j.1467-9310.2005.00379.x

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Ployhart, R. E., Nyberg, A. J., Reilly, G., & Maltarich, M. a. (2014). Human capital Is dead; Long live human capital resources! Journal of Management, 40(2), 371–398. http://doi.org/10.1177/0149206313512152

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Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. http://doi.org/10.1002/smj.640

Teece, D. J. (2011). Dynamic capabilities: A guide for managers. Ivey Business Journal Online, 1. Retrieved from http://search.proquest.com/

Teece, D. J. (2012). Dynamic Capabilities: Routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401. Retrieved from 10.1111/j.1467-6486.2012.01080.x

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. http://doi.org/10.1016/b978-0-7506-7088-3.50009-7

Three Reasons Certification is Better than College.

Re-post from LinkedIn – April 8, 2016

Much of the national debate around the value of a college education seems to revolve around the cost of college, particularly in finding ways of making college more affordable and accessible to more people. This frame of reference assumes that a college education is the only means of post-secondary training and education that has value; this is simply not true. According to an analysis of employment during the Great Recession, 4 out of every 5 jobs lost were held by those without any formal education beyond high school; those without post-secondary training were more than three times as likely to lose there jobs than those with even “some college” (Carnevale, Jayasundera, & Cheah, 2012). This, by itself, suggests that even minimal post-secondary training can garner significant benefit; having a job is preferable to not having one. One way to accomplish this is through industry-based certifications (IBCs).

IBCs offer a number of advantages for improving an individual’s employability over simply making college more affordable and accessible. First, in regards to affordability and accessibility, IBCs offer greater return on investment (ROI) than traditional college education. In part because of their far lower time and money commitment, IBCs also provide a more flexible solution either to replace college, or to aid in preparation for later college education. Finally, the dynamic nature and industry relevancy of IBCs provide stronger signals to employers concerning an individual’s ability to actually do the job they need today, rather than months or years down the road. The strong case for IBCs begins with simple economics.

Direct ROI

Given the focus on ROI of post-secondary education, a comparison of the ROI of Certification Earnings Premiumcertification versus a Bachelor’s degree seems relevant. According to the U.S. Census Bureau (Ewert & Kominski, 2014), with the exception of a Master’s degree, there is an earnings premium for achieving certification or licensure regardless of education level. This premium predominantly benefits those with less post-secondary educational investment (see Figure 1). While it is true the earnings premium for having a Bachelor’s degree is much greater (see Figure 2), this is not a measure of return on investment. Return on investment is a measure of what you get for what you put in; i.e. ROI is the amount you can expect to get back for every dollar spent. This is where the ROI of certification is substantially better.

Assuming a cost of $40,000 for a Bachelor’s degree (probably a low estimate) and a cost Education Earnings Premiumof $5,000 to achieve certification (probably a high estimate), the ROI of achieving certification for someone with only a high school education is 2.3 times that of achieving a Bachelor’s degree (see Figure 3). Furthermore, this is just a starting point as it doesn’t account for differences in earning while those achieving a Bachelor’s degree remain in school or the cost of interest on student loans for college tuition. The fact is, certification provides individuals an extremely efficient mechanism to improve their earnings potential, and achieve the post-secondary credentials that improve their ability to get and keep a ROI of Certificationjob, even during tough economic times. The fact that IBCs add value to both those without other post-secondary education as well as those with, also demonstrates the greater flexibility of certifications.

Flexibility

One of the challenges to simply making a college education more affordable (or free), is that cost is not the sole factor contributing to non-participation or non-completion. An analysis of college completion statistics showed a 7% difference in achieving a Bachelor’s degree between students who complete high school with a 3.0 GPA versus those with a 3.5 GPA (Rose, 2013). Rose also reported that family and work responsibilities significantly affect the chances of completing a degree program. In other words, while the cost of a college education might inhibit individuals from starting a degree program, individual preparedness and the time commitment necessary to complete a degree are significant contributors to whether individuals ever actually graduate and garner the benefits. This is likely to be particularly true for low-income or disadvantaged students. IBCs provide more flexibility to address these challenges.

Firstly, IBCs have significantly lower time commitments associated with their completion, making it that much easier for students who must also maintain family and work obligations to complete the requirements for certification. Many IBCs do not even require formalized classes or specific training, allowing individuals to self-study as they are capable or as life permits. Finally, most IBCs award credentials, not based on having completed a regimented program of study, but upon the passing of competency-based exams. This means that students can take as much time, and as many attempts, as necessary without suffering negative consequences; it is not a one-time deal, thus providing greater chances of ultimate success. This applies to both those without any other post-secondary training as well as those with degrees who are simply looking for additional earnings potential.

Secondly, IBCs may provide students not ready for a formal degree program with the knowledge and skill to prepare them for a future degree. IBCs can provide students with exposure to a field of study without the time and financial commitment associated with a formal degree program, reducing the costs associated with choosing a career they ultimate find unsatisfactory or unfulfilling. In addition, this additional knowledge and skill may give students the confidence and ability necessary to complete degrees they would otherwise have been unprepared for.

Not everyone has the time, or capability to commit to formal degree programs. This has a much larger effect on educational outcomes than the cost; simply reducing the cost or providing universal access does not address either of these challenges. IBCs fill a gap between the demands of formalized postsecondary training, and the real world needs of students just trying to stay a head in a highly competitive marketplace while simultaneously making ends meet (Claman, 2012). In addition, IBCs are increasingly more valuable to employers.

Stronger Employability Signals

“The value of paper degrees lies in a common agreement to accept them as a proxy for competence and status, and that agreement is less rock solid than the higher education establishment would like to believe” (Staton, 2014, para. 3).

Despite the nearly $800 billion dollars spent each year in the United States for human capital development beyond primary and secondary education, nearly 70% takes place outside of four-year colleges and universities; of that, U.S. employers spent almost $460 billion on formal and informal employ training alone (Carnevale, Jayasundera, & Hanson, 2012). According to the Economist, only 39% of hiring managers feel college graduates are ready to be productive members of the workforce (“Higher education: Is college worth it?,” 2014).  The Economist further points out the skill gap between college degreed applicants and the needs of employers has left 4 million jobs unfilled. It is no wonder employers are beginning to question whether degrees are appropriate proxies for real world competence; and, some are even seeing advance degrees as a negative hiring signal requiring more cost with little benefit (Staton, 2015).

“The world no longer cares about what you know; they world only cares about what you can do with what you know” (Tony Wagner as quoted by Friedman, 2012, para. 11).

The hands-on, competency-based aspects of IBCs not only create value for individuals directly, but indirectly by providing stronger signals to employers about the actual competence of job candidates. The dynamic and flexible nature of IBCs make them a better reflection of current industry standards and competence even in rapidly changing industries (Carnevale, Jayasundera, & Hanson, 2012). Perhaps even more important, the standards and competency-based testing utilized in IBCs improves the ability to objectively compare applicants, something that has proven extremely unreliable for post-secondary metrics like GPA (Carnevale, Jayasundera, & Hanson, 2012; Swift, Moore, Sharek, & Gino, 2013). IBCs provide employers with highly credible evidence of applicant’s ability to actually do something with their knowledge, not just their ability to know something.

IBCs are increasingly embraced by employers as a more reliable and valid indicator of candidate competence and questioning the value of traditional post-secondary indicators (Carnevale & Hanson, 2015). Because IBCs are, by definition, industry-based, applicants holding IBCs are more likely to have relevant, up-to-date skills meeting national, or international standards. IBCs are not only easier to evaluate, but also provide strong indicators that a prospective applicant will not need additional employer-based training before becoming productive. This is likely why even holders of advanced professional degrees are paid premiums for also having IBCs (Figure 1).

Conclusion

The debate about the current state of education in the United States is a worthwhile discussion, perhaps even a critical discussion in light of the challenges facing us. The problem is the single means of post-secondary education (four-year degrees) that dominates the debate and a singular focus on the cost of educating to this level. This debate fails to account for the many other factors affecting student outcomes, and the actual needs of employers. The reality is that advanced economies are not dominated by high-volume, low-value production, but low-volume, high-value production (Friedman, 2012), and the demand for “middle-education” jobs is growing and will continue to grow for many years (Carnevale, Jayasundera, & Hanson, 2012).   Without addressing these realities, we are only perpetuating a divide between those with degrees and those without, while still failing to meet the needs of business. There will always be a need for formal degrees, but that does not make them the panacea for all people and for all jobs.

At the end of the day, credentialing is an attractive option for anyone looking to improve their employment options.  IBCs provide a greater ROI, in a shorter amount of time than formal degrees.  The flexibility and less structured design of IBCs  make them easier to obtain successfully, especially for students either unprepared for, or unable to commit to formal programs.  Furthermore, IBCs provide strong employment signals to potential employers about the individuals ability to contribute on day-one of employment.  In many cases, the ROI, the flexibility, and the strong employment signals attributed to IBCs may very well be a better option than college; in other cases, IBCs may be an essential stepping-stone to that first degree by providing the skills, and the additional income, necessary to commit to obtaining a formal degree.  AND, if you already have a bachelor’s degree, these same benefits await you compared to getting a graduate degree.  Certification may very well be better than college to many.

NOTE: Anyone interested in exploring how competency-based credentialing is a critical component of the future of higher education should investigate WorkCred (http://www.workcred.org/), a non-profit organization working to elevate the visibility of credentialing as an essential ingredient in the future of human capital development in the 21st century. The author is not affiliated with WordCred.

References

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