Month: July 2017

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.


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.



Angelis, J., Macintyre, M., Dhaliwal, J., Parry, G., & Siraliova, J. (2011). Customer centered value creation. Issues of Business and Law, 3(1), 11–19.

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

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

Chen, G., & Muller, A. (2010). Measuring innovation from different perspectives. Employment Relations Today, 37(1), 1–8.

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

Costello, T., & Prohaska, B. (2013). Innovation. IT Professional, 15(3), 64–66. Retrieved from

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

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.

Hammon, L., & Hippner, H. (2012). Crowdsourcing. Business & Information Systems Engineering, 4(3), 1–166.

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

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.

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.

Ramírez, R., Roodhart, L., & Manders, W. (2011). How Shell’s domains link innovation and strategy. Long Range Planning, 44(4), 250–270.

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

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

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.

Schröder, A., & Hölzle, K. (2010). Virtual communities for innovation: Influence factors and impact on company innovation. Creativity and Innovation Management, 19(3), 257–268.

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

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.

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

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.



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

Bodley-scott, S. (2011). Linking innovation to strategy. Training Journal, (March), 64–67. Retrieved from

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

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

Chen, G., & Muller, A. (2010). Measuring innovation from different perspectives. Employment Relations Today, 37(1), 1–8.

Costello, T., & Prohaska, B. (2013). Innovation. IT Professional, 15(3), 64–66. Retrieved from

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

Hammon, L., & Hippner, H. (2012). Crowdsourcing. Business & Information Systems Engineering, 4(3), 1–166.

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.

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

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

Mullins, J., & Komisar, R. (2011). Measuring up: Dashboard for innovators. Business Strategy Review, 22(1), 7–16. Retrieved from

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.

Ramírez, R., Roodhart, L., & Manders, W. (2011). How Shell’s domains link innovation and strategy. Long Range Planning, 44(4), 250–270.

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

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

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.

Schröder, A., & Hölzle, K. (2010). Virtual communities for innovation: Influence factors and impact on company innovation. Creativity and Innovation Management, 19(3), 257–268.

Šebestová, J., & Rylková, Ž. (2011). Competencies and innovation within learning organization. Economics and Management, 16, 954–961. Retrieved from

Shepherd, H. (2012). Crowdsourcing. Contexts, 11(2), 10–11.

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

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


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.



Blair, D. C. (2002). Knowledge Management: Hype, Hope, or Help? Journal of the American Society for Information Science & Technology, 53(12), 1019–1028.

Drucker, P. F. (1992). The post-capitalist world. Public Interest, 109(Fall 1992), 89–101. Retrieved from

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.

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

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

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.

Kruger, J., & Dunning, D. (1999). Unskilled and Unaware of It : How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated. Journal of Personnality and Social Psychology, 77(6), 1121–1134.

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.

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

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

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

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.

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.


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).


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 (, 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.


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

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

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

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

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

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

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

Rose, S. J. (2013). The Value of a college degree. Retrieved from

Staton, M. (2014). The degree is doomed. Harvard Business Review. Retrieved from

Staton, M. (2015). When a fancy degree scares employers away. Harvard Business Review. Retrieved from

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:

Becoming the versatilist

A “versatilist” is someone who can be a specialist for a particular discipline, while at the same time be able to change to another role with the same ease (Wikipedia).  I first became aware of the term while researching my dissertation in the book The World is Flat: A Brief History of the Twenty-first Century (Friedman, 2005) and the term was first applied to me by a fellow scholar during doctoral studies.   I had always considered myself a generalist, not the guy you want to hire to do the job itself, but the guy you wanted to help plan, create, and innovate using a vast background of disparate knowledge and experience.

So, what is the difference between a “generalist” and a “versatilist”?  I think the difference is the degree of competency within each domain of knowledge.  A generalist has experience or is familiar with the various domains of knowledge they have, while a versatilist becomes an expert (albeit briefly) in each domain they pursue.  Generalists are philanderers in a sense, never truly committing to any particular domain of knowledge and content with superficial awareness; versatilist are serial monogamists, committing deeply to each domain with passion and intensity, but never staying for the long-haul to make a career of it.

This perspective certainly makes my varied career history a lot more understandable, explaining how I have achieved success in each of my roles, but never staying long enough to truly define what I do or give any clues as to what I want to be when I grow up.


  • computer nerd, basic/pascal programming for fun and profit
  • DOS expertise


  • DEC MINI administrator
  • Associates Degree in Electronics Technologies
  • Component-level repair of IBM mainframe logic boards and POS systems
  • Achieved Novell CNE and Microsoft MCSE
  • Independent Small Business technology consultant (design, sell, install hybrid LANs) specializing in “Internet Connectivity”
  • Started installing first wireless networks / fiber optic solutions
  • Achieved CCNA/P, Network+ certifications
  • Fortune 100 Retailer “eCommerce” team building “new” web architectures


  • Field Service Engineer for technology manufacturer
  • Achieved CISSP, C|EH and CCE security certifications
  • Independent computer forensics consultant
  • Security Architect for technology manufacturer
  • Technical Marketing Manager for technology manufacturer
  • Completed Bachelor’s in Information Technology
  • Manager Technical Marketing


  • Completed MBA (IT Management)
  • Corporate Press/Analyst Spokesperson
  • Manager of Professional Certification for technology manufacturer
  • Completed DBA (Strategy and Innovation)
  • Begin Data Science training

At each point in time, I was committed to being the best “whatever” I could, applying all my passion and effort towards achieving competence.  Yet, once I could consider myself an expert in that endeavor, I moved to the next thing, generally not looking back.  I’ve programmed, but am not a programmer.  I’ve been a systems administrator, but don’t do systems administration.  I’ve written numerous articles, whitepapers, and academic papers, but am not a writer.  I’ve done many things, but don’t feel that any of them define who I am or what I do.

The only moniker that makes sense is . . . I’m a Virsatilist.