Academic

Postings based on research and academic support.

Follow the Leader – Followership vs. Leadership

There has been significant effort and press defining the properties and characteristics of leadership. There are probably hundreds of discrete classifications of leadership styles, each with its own unique perspective on what makes a great leader.  However, few of these approaches mention the most significant common factor: followers. A focus on followers, or why and how people follow a leader, is perhaps more instructive to the development of future leaders.

The Umwelt of Followership

Umwelt is a term used to describe the different perspectives individuals can have when viewing the same situation (Suderman, 2012).  Suderman documents the umwelton of followership as a means of understanding how perspectives of followership shape the success of those that would lead.  The umwelton documented include: position, power, situational, and partner.

Positional and power umwelton are somewhat traditional views of followership.  In each of these, the follower/leader dynamic is based on the perceptions of hierarchy and power typical of many organizations.  Leaders are defined by their roles within the organization and/or the authority they hold to induce followers to comply.  Conversely, followers perform simply because they do not have position or authority to do otherwise.  In this sense, leadership and followership have no relation to individual capabilities, only to circumstances.  Followers follow, but only through fear.

Situational umvelt defines leadership/followership based on the needs of the situation.  Much like the views of interdependent leadership (McCauley et al., 2008), this umwelt is characterized by leadership coming from the elevation of individuals based on expertise or idiosyncratic capabilities not found elsewhere. Interdependent leadership cultures are characterized as those treating leadership as a collective, collaborative process transcending any specific individual, while encompassing all individuals within the organization (McCauley et al., 2008). This idea of interdependent leadership, where leadership materializes through an adaptive, collaborative process is consistent with the descriptions of an actor-oriented scheme of organizational structure documented by Fjeldstad, Snow, Miles, & Lettl (2012). The concept of hierarchy, at least in terms of providing specific direction or work effort, is minimized. Both constructs suggest the complexity and challenges of modern markets/environments gives rise to the need for new methods of organization/leadership commensurate with these complexities.  This perspective has been famously followed by Netflix, as well as Zappos.  It’s long-term effectiveness for organizations has not been proven.

Finally, the partner umwelt is characterized, not by position, power, or situation, but by motives and intent (Suderman, 2012).  Followers choose to follow a leader because they believe in the purpose and goals the leader puts forth.  This umwelt encapsulates the idea that followers and leaders are co-dependent, but “. . . leaders can only lead when enabled by followers” (Suderman, 2012, p. 16).  Those in leadership roles may still have the position, power, or specific knowledge backing them in directing the actions of others, and still not be leaders in the minds of those they direct.  Followership is a state of mind, a true commitment to those that lead and the belief in where they are leading.  The umwelt of partner followership has a profound implication to what leadership truly is.

The Importance of Followership Perspective

The importance of understanding the umwelton of followership is critical towards becoming an effective leader.  Just being in a leadership role does not make you a leader.  Having position or power might result in people doing what you tell them, but does not necessarily make you a leader of people.  It also doesn’t mean that people will be engaged, perform their best, or go the extra mile to achieve superior outcomes.  They will simply do the least amount necessary.

True leaders focus more on the goals, the purpose, and the intent of where they are leading and convince others the destination is worth the effort.  True leaders understand that leadership is about harnessing the beliefs and desires of the entire organization towards a single goal, rather than building their own legacy. While putting the group first is not a natural tendency, it is a core requirement for building the trust necessary for true leadership (Collins, 2001; Collins & Porras, 2002; Sinek, 2014).   And, once a leader convinces others to follow, it doesn’t matter if they are in a leadership role or not.

Only followers can choose whom they follow; and, without true followers there are no true leaders.  Leaders never accomplish anything on their own.

References

Collins, J. C. (2001). Good to great: why some companies make the leap … and others don’t. New York, NY: HarperCollins.

Collins, J. C., & Porras, J. I. (2002). Built to last: Successful habits of visionary companies. New York, NY: HarperCollins.

Fjeldstad, Ø. D., Snow, C. C., Miles, R. E., & Lettl, C. (2012). The architecture of collaboration. Strategic Management Journal, 33(6), 734–750. Retrieved from 10.1002/smj.1968

McCauley, C. D., Palus, C. J., Drath, W. H., Hughes, R. L., McGuire, J. B., O’Connor, P. M. G., & Van Velsor, E. (2008). Interdependent leadership in organizations: Evidence from six case studies. A Center for Creative Leadership Report. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Interdependent+Leadership+in+Organizations:+Evidence+from+six+case+studies#0

Sinek, S. (2014). Leaders eat last: Why some teams pull together and others don’t (Kindle). New York, NY: Penquin Group.

Suderman, J. (2012). The umwelt of followership. Strategic Leadership Review, 1(1). Retrieved from http://scholar.google.com/

Advertisements

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

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

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

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

Carnevale, A. P., & Hanson, A. R. (2015). Learn & earn: Career pathways for youth in the 21st century. E-Journal of International and Comparative Labour Studies, 4(1). Retrieved from https://cew.georgetown.edu

Carnevale, A. P., Jayasundera, T., & Cheah, B. (2012). The college advantage: Weathering the economic storm. Retrieved from https://cew.georgetown.edu/

Carnevale, A. P., Jayasundera, T., & Hanson, A. R. (2012). Career and Technical Education: Five Ways that Pay. Retrieved from https://cew.georgetown.edu/

Claman, P. (2012). The skills gap that’s slowing down your career. Harvard Business Review. Retrieved from http://hbr.org

Ewert, S., & Kominski, R. (2014). Measuring Alternative Educational Credentials: 2012, (January), 14. Retrieved from https://www.census.gov/

Friedman, T. L. (2012, November 17). If You’ve Got the Skills, She’s Got the Job. The New York Times. New York, NY. Retrieved from http://www.nytimes.com/

Higher education: Is college worth it? (2014, April). The Economist. doi:Article

Rose, S. J. (2013). The Value of a college degree. Retrieved from http://cew.georgetown.edu/

Staton, M. (2014). The degree is doomed. Harvard Business Review. Retrieved from https://hbr.org/

Staton, M. (2015). When a fancy degree scares employers away. Harvard Business Review. Retrieved from http://hbr.org/

Swift, S. A., Moore, D. A., Sharek, Z. S., & Gino, F. (2013). Inflated applicants: Attribution errors in performance evaluation by professionals. PLoS One, 8(7). doi:http://dx.doi.org/10.1371/journal.pone.0069258