Month: September 2017

Improving Multiple-Choice Assessments by Limiting Time

Standardized, multiple-choice assessments frequently come under fire because they test rote skills, rather than practical, real-world application.  Although this is a gross over-generalization failing to account for the cognitive-complexity the items (questions) are written to, standardized assessments are designed to evaluate what a person knows, not how well they can apply it.  If that were the end of the discussion, you could be forgiven in assuming standardized testing is poor at predicting real-world performance or differentiating between novices and more seasoned, experienced practitioners.  However, there is another component that, when added to standardized testing, can raise assessments to a higher level: time.  Time, or more precisely, control over the amount of time allowed to perform the exam, can be highly effective in differentiating between competence and non-competence.

The Science Bit

Research in the field of expertise and expert performance suggests experts not only have the capacity to know more, they also know in a way differently than non-experts; experts exhibit different mental models than novices (Feltovich, Prietula, & Ericsson, 2006).  Mental models represent how individuals organize and implement knowledge, instead of explicitly determining what that knowledge encompasses.  Novice practitioners start with mental models representing the most basic elements of the knowledge required within a domain, and their mental models gradually gain complexity and refinement as the novice gains practical experience applying those models in real world performance (Chase & Simon, 1973; Chi, Glaser, & Rees, 1982; Gogus, 2013; Insch, McIntyre, & Dawley, 2008; Schack, 2004).

While Chase and Simon (1973) first theorized that the way experts chunk and sequence information mediated their superior performance, Feltovich et al. (2006) suggested these changes facilitated experts processing more information faster and with less cognitive effort contributing to greater performance. 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.

For example, Chi et al. (1982) determined that the way novices and experts approach problem-solving in advanced physics was significantly different despite all subjects having the same actual knowledge necessary for the problem solution; novices focused on surface details while experts approached problems from a deeper, theoretical perspective.  Chi et al. also demonstrated the novice’s lack of experience and practical application contributed to errors in problem analysis requiring more time and effort to overcome. While the base knowledge of experts and novices may not differ significantly, experts appear to approach problem solving from a differentiated perspective allowing them more success in applying correct solutions the first time and recovering faster when initial solutions fail.

In that vein of thought, Gogus (2013) demonstrated that expert models were highly interconnected and complex in nature, representing how experience allowed experts the application of greater amounts of knowledge in problem solving.  The ability for applying existing knowledge with greater efficiency augments the difference in problem-solving strategy demonstrated by Chi et al. (1982).  Whereas novices apply problem-solving approaches linearly one at a time, experts evaluate multiple approaches simultaneously in determining the most appropriate course of action.

Achieving expertise is, therefore, not simply a matter of accumulating knowledge and skills, but a complex transformation of the way experts implement that knowledge and skill (Feltovich et al., 2006). This distinction provides clues into better implementing assessments to differentiate between expert and novice: the time it takes to complete an assessment.

Cool Real-World Example Using Football (Sorry. Soccer)

In an interesting twist on typical mental model assessment studies, Lex, Essig, Knoblauch, and Schack (2015) asked novice and experienced soccer players to quickly and accurately decide the best choice of tactics (either “a” or “b”) given a video image of a simulated game situation.  Lex et al. used eye-tracking systems to measure how the participants reviewed the image, as well as measuring their accuracy and response time.  As one would expect, the more experienced players were both more accurate in their responses, as well as quicker. Somewhat surprising was the reason experienced players performed faster.

While Lex et al. (2015) determined both sets of players fixated on individual pixels in the image for nearly the same amount of time, experienced players had less fixations and observed less pixels overall.   Less experienced players needed to review more of the image before deciding, and were still more likely to make incorrect decisions.  On the other hand, more experienced players, although not perfect, made more accurate decisions based on less information.  The difference in performance was not attributable to differences in basic understanding of tactics or playing soccer, but the ability of experienced players to make better decisions with less information and taking less time.

The Takeaway

Multiple-choice, standardized assessments are principally designed to differentiate what people know, with limited ability to differentiate how well they can apply that knowledge in the real world.  Yet, it is also well-established that competent performers have numerous advantages leading to better performance in less time.    If time constraints are actively and responsibly constructed as an integral component of these assessments, they may well achieve better predictive performance; they could do a much better job of evaluating not just what someone knows, but how well they can apply it.

 

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.

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.

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

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

Fear Not the AI Overlords! Humans Have Intrinsic Value.

There is significant hype about Artificial Intelligence (AI) and its potential to take over many jobs thought safe from Automation.  It has been suggested AI could replace accountants, lawyers, doctors, and even general management activities.  While it is true that advances in AI will certainly change many jobs, as so often happens, the fear is exaggerated.  First, there is no evidence to support the notion automation has ever eliminated more jobs than it has created.  Second, and more importantly, humans have intrinsic value that is unlikely to ever be replicated or replaced.

The Fear of Losing Jobs

Before anyone gets too excited, a recent Wall Street Journal article highlights the facts of mass automation in the past.  Technology from the cotton gin through AI has always eliminated some jobs, but historically it has also created far more and better paying jobs as a result.  Sure surrey drivers were put out of work with the advent of the automobile, but the auto industry created millions of jobs supporting the US GDP for decades.  AI is simply the latest in a long-line of technological advances feared to lead to the end of our society.  It has never happened before and is unlikely to happen anytime soon.  It is true that some jobs may cease to exist, but this will be accompanied by a growth of new jobs supporting the AI industry.  Even more remarkable will be the new jobs that don’t even exist today.

A recent report from the Institute for the Future estimates 85% of the jobs today’s students will perform by the year 2030 haven’t yet been invented.  This is a difficult prospect for today’s workers to imagine, but it is not without precedent.  Student’s graduating high school in the 1990’s could not have imagined careers working in web design, social media, or – for that matter — artificial intelligence, machine learning, and big data.  Another recent article from MIT Sloan Management Review hints at some of the new jobs AI technology may create.

On top of all of that, it is unlikely many of the jobs being predicted to succumb to AI will actually go away.  It is much more likely they will be augmented and changed than disappear entirely.  And the reason is simple: humans have innate value in performing jobs in a human society.

Humans Have Intrinsic Value

Although AI is redefining what is considered automata by allowing more variation in performance, it is still not human.  Human beings are defined by the irrational and emotional more than they are by cold, calculated precision.  While this may seem to be a negative aspect of humans, it is also the source of the innovation, creativity, and passion that simply cannot be replicated.  Just for sake of argument, let’s examine just one of the jobs proposed to be replaced in the future by AI: management.

Business management is an oft misunderstood discipline, which does not benefit from the HR moniker “people manager”.  You manage objects, but you lead people.  Objects are managed to gain efficiency, but they have finite limitations. You cannot encourage a robot to be more productive.  You cannot ignite passion in your inventory tracking software to go above and beyond.  Yet human beings have nearly limitless capability to “reach for a goal”, “put in extra effort”, or “embrace shared visions”.  While this can also work to reduce human performance (as discussed in this article from MIT Sloan Management Review), this is critical distinction when looking at the effects of AI in particular.

Management, in its truest sense, is absolutely ripe for AI replacement.  Eliminating the idiosyncracies of human performance can have significant value to organizations.  AI is simply better able to gather, process, and act on vast amounts of data where human input is less vital (although not necessarily irrelevant).  By offloading these tedious and taxing responsibilities, while also improving their performance, humans can spend more time doing the things where they have intrinsic, and irreplaceable value (See article from Swiss Cognitive).

Leadership, on the other hand, will no longer need to take backseat to management.  By focusing on leadership, organizations will not only gain the advantages of AI-based management efficiency, but also from the benefits of stronger human performance.  In essence, organizational leaders will be able to offload the tasks they don’t do very well anyway, and focus on the actions that lead to truly superior performance.

Fear Not!!

While the example above focuses on my area of expertise, the same can be said for many other jobs ripe for AI augmentation.  AI, like the cotton gin and automobile before it, are tools that will augment and improve the way we work.  Yes, some jobs may be significantly reduced or eliminated; however, they will be replaced by newer and better jobs.  The jobs getting augmented by AI will simply change, putting more focus on the human aspect.  It is not the end of the world.

 

 

“Zone to Win” Missing Critical Elements of Innovation Strategy

Although Zone to Win: Organizing to Compete In an Age of Disruption by Geoffrey Moore is a wonderful framework for the management of organizational resources to both lead, as well as survive, disruptive innovation, there are some missing elements that might prove useful to readers.  Specifically, Moore focuses on management, and although absolutely necessary, management does not supersede strategy or innovation in long-term success.  Zone to Win fails to encompass appropriate innovation strategy in two ways: the idea of disrupting your own organization, and gap between innovation ideation and implementation.

The Strategy of Disrupting Your Own Organization

Moore seems uncharacteristically blunt on the idea of organizations needing to disrupt their own business.  Moore suggests “all that stuff about how you have to learn to disrupt ourself–it’s baloney. It can’t be done.”  Moore’s contention is that it is impossible for an established business to replace one business model with another; yet, later in the book documents the success of organizations like Adobe and Microsoft that have radically changed their business models to deliver software via subscription instead of perpetual licensing.  It turns out Adobe and Microsoft are two excellent examples for exploring why it is absolutely important for organizations to engage in deliberate acts of self-disruption.  Adobe intentionally disrupted their existing business model (a highly profitable one) before anyone else could eat their lunch; Microsoft changed theirs only after Google started taking away their business.  Adobe was seen as a leader; Microsoft as a laggard.

From an organizational strategy standpoint, it is absolutely critical to develop strategic scenarios of how the future world may look, and how those changes may affect your existing business model.  Scenario planning provides context that is essential when disruption happens, but more importantly, can presage potential disruptions before they happen.  If scenario planning illuminates a significant threat, the organization has only two options: disrupt their own business in order to transfer old business to new business; or, let someone else start eating away your customer base.  It can be argued that cannibalizing your own business, if only to prevent someone else from doing so, makes sense in its own right.  If that new business also portends an era of growth, more the better.

Working to disrupt your own business should not be an organization’s only focus, but failure to even contemplate it, or act upon it, is a strategic failure.

Moore does cover how an organization can respond to disruptive innovations once the initial attacks begin and makes an excellent case for how to manage those attacks.  However, by dismissing, out of hand, the idea that an organization can lead those disruptions, and thus avoid the attack in the first place, Moore seems to completely discount the value of doing so.  While a brilliant theorist like Moore likely meant to dissuade companies from trying a wholesale rip-and-replace of their business model (not something any serious innovation strategist would propose), simply dismissing the strategic value of self-disruption seems cursory.  Moore’s stance is even more glaring considering the zone management framework he proposes actually makes it possible to disrupt your own business in a structured, well-managed way.

Working to disrupt your own business should not be an organization’s only focus, but failure to even contemplate it, or act upon it, is a strategic failure.

Mind the Gap

Another topic which gets too little attention in Zone to Win, is the gap between innovation ideation and the development of the Incubation Organizational Units (IOUs) suggested to incubate and develop promising innovation efforts.  Here again, Moore proposes management organization and governance brilliantly, but only once the innovation ideas get to the point of being a well-formed business proposals.  What is missing is the innovation strategy to get from ideation to the point of proposal.  Aside from a brief mention of internal R&D or other means, Moore fails to specify where this fits in the zone management framework, or how to appropriately fund it.

There is substantial research supporting the notion that “having ideas” is not the challenge for most established organizations.  On the contrary, the most commonly cited challenge is in identifying ideas with potential, and exploring them to the point where they can be taken as serious projects for the organization.   This process needs to be budgeted for, managed, and held accountable in order to be successful.  Without it, the only projects that move forward will be those lucky enough to have a champion with the authority, clout, and budget to develop them.  This creates a choke point to innovation.

Yet, it misses the fact most start-ups don’t start life with venture capital; they start with funding from the founders and personal investors until they have something they can pitch to those VCs.

Moore’s framework suggests treating IOUs as “start-up” companies and funding them just like venture capitalists (VCs) would, which is (again) a brilliant way to manage well-formed ideas.  Yet, it misses the fact most start-ups don’t start life with venture capital; they start with funding from the founders and personal investors until they have something they can pitch to those VCs.  This stage in the innovation cycle is critical and where most start-ups fail.  It is the large end of the funnel.  In such a well thought-out framework as Zone to Win presents, missing this critical element is disappointing.

Appropriately funding and managing the initial R&D neccessary to initiate innovation is just as critical to success as any other component.  However, the idea of funding pure R&D is not common among many of the organizations that would most benefit from taking Moore’s framework to heart.  Even technology organizations often do this as skunkworks or “off-the-books” projects with little organization, governance, or metrics.  Moore’s failure to address this misses a critical element in successfully leading disruptive innovation.

A Step in the Right Direction

Barring these two criticisms, Moore’s work has certainly cemented his place in the annals of business gurus and shows a continuing dedication to helping organizations overcome their own success.  The zone management framework provides a blueprint for overcoming the obstacles Clayton Christensen (and colleagues) frequently cites as the downfall of established organziations in light of disruptive innovation.  Most importantly, Moore adds significant credibility to the idea that innovation can only be truly successful if: a) it is done outside of the main business (incubation versus performance zones); and, b) it is treated with the same care and dedication used to manage any business process.   By addressing the importance of strategic planning, and formally defining the R&D component, zone management would be even better.

Lastly, one word of warning to devotees seeking to implement zone management. Remember the words of Michael Porter: “strategic positioning, means performing different activities from rivals’ or performing similar activities in different ways“ (1996, p. 62).  Frameworks like Moore’s are effective tools, but without making them your own, they lose their strategic value.  This is where leadership takes over from management.

References

Porter, M. E. (1996). What is strategy? Harvard Business Review, 74(6), 61–78. Retrieved from http://hbr.org/

 

The Effects of Positive Psychology on Organizational Success

Intent is a powerful mediator of outcomes.  An organization’s mission, vision, and values set the direction of future success simply by codifying the organization’s intent.  This intent flows through every aspect of the organization, affecting the choices people make, and the outcomes of those actions.  (Read Simon Sinek’s “Start with Why“).

For instance, suppose you start a company to develop a better mousetrap.  You might start this company simply because we all know that if you build a better mousetrap, the world will beat a path to your door and generate huge profits.  On the other hand, you might start your company because you have a passion for eradicating the scourge of mice and their negative effects on human society.   These are both valid rationales for starting a new company and developing a better mousetrap.  If you succeed in building a better mousetrap, both intents are likely to be successful, at least initially.  Intent, however, will begin to show over time.  The company whose intent is purely profit driven will make choices supporting that mission, while the company whose intent is based on a passion for improving the world through mouse eradication will make different choices supporting that mission.  These choices affect the long-term viability of the business.

Not only will these differences in intent affect things like pricing, marketing, customer experience, and other traditional business decisions, they will also affect the people in the organization.  A positive intent keeps employees engaged and helps them grow as people and employees.  These are the effects of positive psychology and should not be discounted when an organiation considers why they do what they do.

Understanding Positive Psychology

Positive psychology, in short, is simply a focus to understand and investigate the positive capabilities and achievements of the individual, community/organization and society as a whole (Fredrickson, 2001; Quinn, Dutton, & Cameron, 2003; Seligman & Csikszentmihalyi, 2000; Sheldon & King, 2001).  Seligman and Csikszentmihalyi (2000), suggest that, prior to WW II, psychology was normally associated with three purposes: treating mental illness, encouraging the development and growth of all people, and the identification and development of exceptional capabilities. Since WW II, however, the focus of psychology has been solely on mental illness, deficit and pathology.  The other, more positive implications of psychology withered. While positive psychology is not intended to supplant existing research and theories on mental illness (Sheldon & King, 2001), it suggests that the typical deficit models practiced for over half a decade fail to adequately describe the realities of the population as a whole, as well as be a reminder of the original tenets of psychology foundation to include the good, the exceptional and the positive.

This positive approach has spawned any number of new approaches to the understanding of human development and organizational development focused on examining the positive instead of the negative.  For instance, towards the understanding of human development, theories have evolved to demonstrate that positive emotions or moments not only broaden an individuals perceptions of the world, but also build capabilities and personal resources which help them mitigate the effects of negative experiences (Fredrickson & Losada, 2005).  Additional research suggests this broadening capability is not solely constrained to mental perceptions, but to physiological perceptions as well like visual attention and field of view (Fredrickson, 2013). The resources accumulated from positive moments may not be abstract resources related to resilience and adaptability, but more discrete resources like attention to detail and capacity to learn.

Towards understanding organizational development, positive psychological approaches have generated new ways at looking at the process of creating exceptional organizations, not by fixing what is wrong, but by amplifying what is right.  Appreciative Inquiry is a model for change practices making the assumption that all organizations have an essentially positive capability to succeed.  By examining the past moments of peak performance, achievement and success, the organization can create a vision of the future based on those positive aspects (Quinn et al., 2003).

The common element in all of these ideas is that there is benefit in focusing on what is good instead of what is bad.  Focusing on the positive aspects creates a upward spiral of reinforcement (Quinn et al., 2003) and is a self-perpetuating process under normal circumstances (Fredrickson, 2013).  The corollary is that a focus on the bad would promote a downward, self-perpetuating cycle.  Thus, attempting to make change by focusing on the negative aspects of  what not to do, while one appropriate response to challenges, creates a self-defeating process promoting blame, low self-worth and incompetence.  Positive psychology suggests that focusing on what to do, on what was successful, is a better alternative as it creates a self-fulfilling cycle promoting excellence, success and achievement.

Brining it back to Intent

One could argue that a profit intent is not inherently bad; without making money, companies cannot sustain themselves.  Yet, without a positive focus beyond profit, without an intent that can inspire and create positive feelings, the organization is likely to diminish in productivity, innovation, and, ultimately, profit.  The concepts of positive psychology push us to appreciate a focus on the existence of the extraordinary and exceptional instead of simply on what is broken and dysfunctional (Seligman & Csikszentmihalyi, 2000).  It is an attempt to look at the positive potential of the future instead of simply examining the future as an attempt to overcome deficit.  It tells us that our intent has significant influence on our long-term outcomes.

References

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology. The broaden-and-build theory of positive emotions. The American psychologist, 56(3), 218–226.

Fredrickson, B. L. (2013). Updated thinking on positivity ratios. The American psychologist, 68(9), 814–22. doi:10.1037/a0033584

Fredrickson, B. L., & Losada, M. F. (2005). Positive affect and the complex dynamics of human flourishing. The American psychologist, 60(7), 678–86. doi:10.1037/0003-066X.60.7.678

Quinn, R. E., Dutton, J. E., & Cameron, K. S. (2003). Positive Organizational Scholarship : Foundations of a New Discipline. San Francisco, CA: Berrett-Koehler. Retrieved from http://ezproxy.library.capella.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=260674&site=ehost-live&scope=site

Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5–14. doi:10.1037//0003-066X.55.1.5

Sheldon, K. M., & King, L. (2001). Why positive psychology is necessary. The American psychologist, 56(3), 216–7. doi:10.1037/0003-066X.56.3.216

The Three “People” Needed for Successful Innovation

Despite decades of research into the constructs of innovation, few practical sources of sustained innovation have proven causal to organizational success.  A cursory examination of innovation theory fails to provide concrete evidence that innovation, in itself, is key to long-term success; most innovation theories rely on post-hoc analysis of firm performance focused on successes, rather than failures (Buisson & Silberzahn, 2010; Burke, van Stel, & Thurik, 2010).  The term innovation is just as difficult to articulate being equally evaluated through ex post selection of successful innovation rather than innovative efforts in general.  While innovation may not guarantee firm success, it is clear that organizations failing to adopt to the pace of the modern, global marketplace will flounder (Reeves & Deimler, 2011); the chances of success increase dramatically if organizations are positioned to innovate and change in response.  As such, understanding the models, systems, and approaches improving an organization’s ability to innovate are increasingly important even if they are not proven to promote long-term success, or even successful innovation. This is the basis of the Innovation Strategy Framework, which attempts to combine multiple theories of innovation into a single construct.

InnovationModel

Figure 1. The Innovation Strategy Framework

Today, we are going to look at the importance of people to innovation strategy, what those people do, where they come from, and why they are important to innovation.

Human capital, or the knowledge, skills, abilities and other characteristics (KSAO’s) of the people associated with the organization are the source of innovation.  Human capital is a critical starting point and requirement for the development of organizational knowledge and innovation capabilities (Choong, 2008; Ployhart, Nyberg, Reilly, & Maltarich, 2014).  This is not just the employees of the organization, but also the knowledge resources of partners and other collaborators.  The greater the diversity and density of these knowledge resources, the greater the potential for organizations to achieve innovative outcomes (Dell’Era & Verganti, 2010; Phelps, 2010).   Clearly, the depth, breadth, and quality of the people in the organization are critical dimensions of innovation capability.

The three main groups of people necessary for successful, serial innovation capability are: the people with innovation ideas, the people nurturing and developing ideas into successful innovation, and the organizational leadership fostering innovation.

People as the Source of Ideation

The largest group of people involved with innovation is the infinite sources of innovation ideas (left side of Figure 1).  This group of people, including customers, partners, employees, and others, 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).  The composition of an organization’s knowledge ecosystem (employees, partners, customers, and others) significantly contributes to the ability of an organization to successfully innovate (Dell’Era & Verganti, 2010; Engel & Del-Palacio, 2011; Kim & Ployhart, 2014; Phelps, 2010; Rothaermel & Hess, 2010; Sandmeier, Morrison, & Gassmann, 2010; Wilson & Doz, 2011).

Interestingly, while this is the largest group of people directly involved with innovation efforts, they are not necessarily the most important.  Yet, many organizations embark on innovation efforts by encouraging their employees to “be more creative”, or “be innovative”.   To the contrary, research suggests most organizations have far more innovation ideas than they can possible deal with; the problem is selecting and developing ideas into real-world solutions.  While organizations need to encourage innovation and creativity, making it the primary focus of innovation efforts will fail more often than succeed. The belief that innovation stems from the rare, perfect idea is a pervasive myth.

Ideation is essential, but completely useless without the other people necessary for innovation success.

People Strategically Selecting and Developing Innovation

On the right side of Figure 1, people in the strategic domains represent the knowledge resources responsible for taking innovative ideas and developing them in alignment with organizational goals and strategy (Ramírez, Roodhart, & Manders, 2011). This group of people is arguably the most important innovation resource in the organization as they are often able to achieve innovation in the absence of well-defined innovation strategies or formally defined roles to direct innovation.  Unfortunately, in the absence of a strategic innovation practices, innovation success is less than assured, becoming the victim of conflicting responsibilities.

Instead of relying on happenstance, organizations should create specific job roles whose entire function is to surface, develop, and promote innovation ideas specific to one strategic organizational goal.  While the strategic goals themselves may change from year-to-year, or be longer term, these strategic innovation specialists are charged with all aspects of taking ideas aligned with their strategic focus from ideation all the way through market release.  These individuals are like innovation product managers, with a portfolio of potential innovation ideas.

The caveat here is that each individual (or team) should be focused solely on one strategic organizational goal, and must have the appropriate resources (outside of existing product management) to develop and mature their innovation portfolio, which leads us to the last group of people necessary for succesful innovation.

Innovation Leadership

Developing an innovation capability within an organization takes substantial effort (Barreto, 2010; Wilson & Doz, 2011).  Accumulating the vast knowledge resources to drive innovation and implementing the systems and processes to integrate knowledge into innovative execution takes significant resources, will, and commitment.   At the top of Figure 1, innovation 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).  Without dramatic changes in the way organizations are led, innovation cannot consistently take root (Hamel, 2009).

Innovative management strategies incorporate novel ways of interacting with customers, driving cultures of trust, and opening the organization to honest debate (Abele, 2011; Capozzi, Dye, & Howe, 2008; McGrath, 2011).  McGrath argues fear of failure inhibits organizations from achieving great innovation and an acceptance of potential failure can help organizations use failure to achieve success. Capozzi, Dye and Howe report the benefits of challenging the status quo of the organization often presents a springboard to innovation.  In much the same way reducing the fear of failure helps to spark responsible risk taking, reducing the fear of challenging organizational orthodoxies helps ensure that new ideas are not discarded simply because they are counter to the way things are currently done.  These cultural changes are more difficult than getting the right people or developing systems and processes; they require commitment at the highest levels of the organization.  Without leadership demonstrating this commitment to innovative practices, organizations are unlikely to truly capture their innovative capabilities.

Bridging the Divide

Having all the right people is critical to achieving long-term innovation capability.  Organizations already have an embarrassment of riches in terms of innovative, creative ideas, but without the appropriate people committed and dedicated to their development, innovation success is only a matter of chance.  It happens all the time, but rarely more than once or twice within the same organization.  Only the organizations designed and aligned to foster innovation and committed to the process achieve long-term, repeatable innovation success (think Shell, P&G, or 3M).  Simply telling your employees to be more innovative or offering a suggestion box is not sufficient.

Even having the right people is not enough.  Without the processes governing how these people work together to create successful innovation, success is possible but not guaranteed.  In the next Innovation Playbook, we will look at how to manage the innovation process for success. 

 

References

Abele, J. (2011). Bringing minds together. Harvard Business Review, 89(7–8). Retrieved from https://hbr.org/

Barreto, I. (2010). Dynamic capabilities: A review of past research and an agenda for the future. Journal of Management, 36(1), 256–280. http://doi.org/10.1177/0149206309350776

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/

Buisson, B., & Silberzahn, P. (2010). Blue ocean or fast-second innovation? A four-breakthrough model to explain successful market domination. International Journal of Innovation Management, 14(3), 359–378. http://doi.org/10.1142/S1363919610002684

Burke, A., van Stel, A., & Thurik, R. (2010). Blue ocean vs. five forces. Harvard Business Review, 88(5), 28. Retrieved from http://hbr.org/

Capozzi, M. M., Dye, R., & Howe, A. (2008). Sparking creativity in teams: An executive’s guide. McKinsey & Company, (April 2011), 1–8. Retrieved from http://www.mckinsey.com

Choong, K. K. (2008). Intellectual capital: definitions, categorization and reporting models. Journal of Intellectual Capital, 9(4), 609–638. http://doi.org/10.1108/14691930810913186

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

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

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

 

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

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

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

Reeves, M., & Deimler, M. (2011). Adaptability: The new competitive advantage. Harvard Business Review, 89(7/8), 134–141. Retrieved from http://hbr.org/

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

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

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

 

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