Over the years, one of the leading themes of the Intangible Economy has been the need for communities to build upon the local assets. So, I am always on the lookout for interesting examples.
One such example comes from Sparta, Greece. Petros Doukas, the Mayor of Sparta, describes what they are doing to build on local assets. They are deliberately not trying to become a “high tech” center. Instead, they are looking at areas where they have some type of existing advantage and have identified the sectors of agriculture, tourism, cultural activities, and sports. You can see his remarks describing what they are doing in each of these areas at this YouTube video starting at about minute 16:15
[FYI – This clip is part of an excellent discussion on local development as part of the Framing the Future series organized by the Global Federation of Competitiveness Councils (GFCC) (click here to see the links to previous sessions).]
That is not to say that efforts to create technology and innovation clusters are not important. They are. But as Mayor Doukas points out, many communities would be better off adopting a lower tech strategy. In any event, deciding which way to go starts with understanding your local assets.
One of the ongoing themes of my work has been the restructuring of the economy including the fusion of manufacturing and services. I have long argued that these categories are not useful in helping to understand economic activity. There are some cases where the categories are clear. For example, haircuts are clearly a service. But increasingly the boundaries are fuzzy. So-called manufacturing companies are more and more selling their products as a service and service activities are an increasing part of value-added.
And sometimes, services want to be seen as manufacturers. A story in today’s New York Times on the public-private revolving door for tax lawyers and accountants reminded me of how far and how creatively services can be considered “manufacturing.” Before it was repealed in 2017, Section 199 of the tax code provided a tax deduction for “domestic production” aka manufacturing. While revenues from retail food preparation were explicitly not allowed to take the deduction, creative interpretations of “production” were accepted including the notorious cheesecake-slicing-as-production claim mentioned in the Times story. And there was a florist who claimed the manufacturing deduction as they were using individual flowers to produce bouquets.
Interestingly, the law specifically included engineering and architectural services as qualifying activities. I assume these services were seen as important adjuncts to construction (itself a qualifying production activity).
I should note that the opposite classification of construction as services or manufacturing is used by the Institute of Supply Managers, which includes construction in its services index. I suspect that this is a legacy of the original formulation of this as the index of non-manufacturing, as opposed to their index of manufacturing (see earlier posting).
These stories about manufacturing versus services should give us pause. Admittedly, the cheesecake example is somewhat silly (of course not to those who got the tax break). But if assembling a car from various parts is considered manufacturing, isn’t assembling a bouquet from various parts also? If testing a computer chip is considered part of the manufacturing process, what about chip design services? And then there is the thorny question of these activities being carried out by outside firms (considered a service) rather than in-house (considered part of manufacturing).
We need a serious effort to rethink how we envision the economy. Part of that is embracing the idea of making value as opposed to making things (see earlier posting). But it also includes restructuring our industrial categories – possibly around types of end output (e.g., food, shelter, transportation, health, entertainment, etc.). All of the recent discussions about supply chains indicates how interconnected economic activities are and how these activities cluster into groups.
I’m not smart enough to come up with the best framework. I hope someone is. Otherwise, we will continue to make economic policy based on a view that is clearly out of date.
Studies of innovation usually focus on success stories – what worked and therefore what others should do. The National Science Foundation (NSF), specifically the National Center for Science and Engineering Statistics (NCSES), has recently released a study that takes the opposite approach: what can we learn from failures. Carried out by team of researchers from Center for Innovation Strategy and Policy at SRI International, the study (Understanding Unsuccessful Innovation) looks at 19 case studies of failures (see list below) and pinpointed the root cause of the failure.
Of course, it is somewhat problematic to draw conclusions from such limited sample of case studies. But they do provide an illustration of the various ways that an innovation can fail. And it should be noted that not all of these “failures” ultimately turned out to really fail. Some of these pivoted to a different market use that originally conceived (often much more specialized and narrower than the original target market).
Thus, the framework developed is probably more important a finding than the statistical evidence provided.
The framework identifies five root causes of innovation failure:
No market demand. A product or business process was developed and brought to market, but there was little market demand for it.
Poor performance. A product or business process was developed and brought to market, but it failed to function as intended.
Insufficient complementary assets. A product or business process was developed and brought to market, but the adjacent business inputs required for its successful customer use were not sufficiently available.
Poorly defendable position. A product or business process was developed and brought to market and met with positive market interest, but the innovator was unable to secure the innovation’s market position, for example, by way of intellectual property protection.
Regulatory restriction. A product or business process was developed and brought to market, but regulatory restrictions on its use limited its economic value.
Note that the study includes a breakdown of the “no market demand” category into innovations that are not useful, price point that are too high for the market, failure to meet investment expectation, and wrong market targets.
Those failures can manifest itself during one of the three time periods of innovation: launch, growth, or maturity. The root cause failure, however, occurs one of the earlier 5 stages: ideation, product development, launch, growth, and maturity. The model thus is as follows:
The model helps pinpoint where the fail occurs, rather than when it occurs.
For example, the paper concludes that iTunes Ping failed during launch due to poor performance that was the result of failure in the product development stage (specifically not having a data-sharing agreement with Facebook in place). Hoverboards failed in the transition to maturity due to a poorly defendable position in the growth stage. Google Wallet failed in the growth stage due to insufficient complementary assets that should have been addressed during product development.
The Google Wallet case study (along with the Wii U, Google Glass, Segway, Iridium Satellite Phone, and other cases) points out one of the limitations of the study. Each of these innovations are still in use – either as part of another innovation (Google Wallet as part of Google Pay; Wii U as part of Nintendo Switch) or as a scaled back version (Google Glass, Segway, and Iridium Satellite Phone). Over half of the cases resulted in this pivoting of the innovation to new markets and/or new uses. A more complete study would look in greater depth at the process of pivoting from apparent failure to at last limited success.
Another concern with the study is its focus on a sole root cause of failure. It seems to me that the root causes are not mutually exclusive. For example, the failure of Segway seems to be a combination of no market demand (due to a high price point) and regulatory restrictions. A look at the multiple causes, and their interactions, would strengthen the model. Especially important is how the lack of complementary assets can create low market demand and poor performance.
I come away from reading the case studies with three insights. [Note that in keeping with my concern over the small sample size, my comments are based more on the model and the insights from the case studies rather than statistical analysis of the case studies.]
The first insight concerns the failure to meet a market demand. It should come as no surprise that no market demand, especially not useful, is a root cause of an innovation failure. Yet, most of our public policy seems to ignore that point. All innovations are seen as beneficial. Innovations fail because of the lack of funding or some other barrier to adoption – not because they are not useful. I call this the “electronic swizzle stick” syndrome: just because we can build an internet-connected battery powered swizzle stick that can stir your cocktail in the perfect manner does not mean that there is a market need for such an item (although I keep watch to see if such a product appears in the airline in-flight catalogs). The Juicero case illustrates this point.
The second insight is that all of the causes of failure can be addressed somewhere in the innovation process. But the causes of failure need to be identified early and steps taken to avoid the failure. As the saying goes, an ounce of prevention is worth a pound of cure.
This maybe the hardest insight to operationalize. Such proactive measures do not come easily. The whole fail-fast ethos is built around the notion of rapid learning rather than pre-emptive action.
Fortunately for the rapid learning approach, the third insight from the case studies is that failure do not mean failure. As noted earlier, many of the innovations were at least partially implemented – just maybe not in the way or to the extent originally envisioned.
The study does not delve into policy proposals. As befitting its sponsor NCSES, the study looks at the implications of the model for statistics on innovation collected as part of the American Business Survey, and at issues for future research. Let me therefore speculate on some policy implications.
Let me suggest a more “rough and ready” approach. As part of any assistance to companies’ innovation activities a checklist could be consulted. It would a rudimentary checklist just to get people thinking about the possible problems just as the I-Corp process forces innovators to think realistically about the market needs. I realize that some may claim that this is just setting up another series of barriers to innovative activities – the creation of a way to say “no.” But it could be useful overcoming barriers by addressing the failure points proactively. And it could also force the naysayers to explain their objectives.
Obviously, such a checklist would not be a silver bullet to ensure that all innovations succeed. But it might be a small step toward helping prevent more innovations from failing due to a lack of foresight. Worth a try at least.
Breaking the pattern of the past few months, August’s employment data slows the clear effects of the COVID-19 Delta virus with a much smaller than expected increase of 235,000. As I have noted in an earlier posting, the labor market had been settling back into the pre-pandemic pattern of relatively equal growth in tangible-producing versus intangible-producing industries. August changed that.
In August, employment in intangible-producing industries rose much faster than in tangible-producing industries – admittedly at slower rate of 189,000 in intangible-producing industries versus 46,000 in tangible-producing industries. This compares to increases of around half a million in both tangible-producing and intangible-producing industries in the past few months.
The most dramatic shift was in Accommodation and Food Services which went from strong growth over the past few months to actually declining in August. But the slowdown in employment growth was generally across the board (see chart below). Only Professional and Business Services saw a real increase in employment growth in August.
I doubt the August data signals a return to the pre-2010 situation of rapid growth in employment in intangible-producing industries versus in tangible-producing (see earlier posting). But it does constitute a break in the parallel trajectories of the intangible-producing and the tangible-producing industries.
Recently, there has been a great deal of discussion about the effect of the pandemic on productivity. Has the shift in work, especially the increase in work from home (WFH), boosted productivity? Will a return to the office lead to more or less productivity? Or, to put it another way, will the return to the office enhance or degrade the key intangible asset of organizational competence?
The answer to the productivity question depends on the interaction between organizational changes and new technologies. Most of the productivity optimism is based on an expectation of continued deployment of advanced technologies, specifically artificial intelligence, machine learning, automation, and robotics. But experts such as Erik Brynjolfsson have long argued that concomitant organizational changes are need for deployment of these technologies (for example, see here and here).
For the most part, these changes in organizational structure are needed to take advantage of the productivity-increasing features of the new technology. The classic example is the change in factory layouts due to the shift from steam-driven belt and pulley system to individually electric engine driven machines.
But equally important is the role of a shift in work organization in facilitating the acceptance of new technologies. The pandemic-induced forced shift to WFH seems to have broken down barriers to the deployment of digital technologies. As a recent large survey of companies by Barrero, Bloom, and Davis concludes, “the pandemic created the conditions for coordinated experiments with WFH in networks comprised of firms, customers and suppliers, yielding lessons and know-how that were hard to acquire beforehand. In sum, the pandemic swept aside inertial forces related to experimentation costs, biased expectations, and coordination within networks that had previously inhibited remote work.”
The challenge facing companies now is how to harness these productivity drivers. Can they take advantage of the window of opportunity of lower barriers to technological change and use the WFH experience to create the new organizational structures needed to operationalize the new technologies? Or will forcing workers back to the office reimpose the pre-pandemic organizational structure and negate any organizational learning?
My guess is that we will see a mixed result. We are in the middle of a huge social experiment. Researchers, managers, and workers will need to monitor the experiment closely for lessons-learned and new “good practices.”
As a side note, I find the ongoing discussion on the impact of the elimination of the work commute on productivity to be somewhat confusing. It has been argued that the shift to work-from-home has raised productivity. For example, the Barrero et al. survey cited above concludes that WFH will increase productivity by 4.8%, half of which is due to reduced commute times.
But this is a case of working longer, not working smarter. Official productivity measures do not include commuting time as work time. Adding in time previously spent commuting increased the total number of hours worked. Working more hours presumably increases production (the amount of work you get done). This does not necessarily increase labor productivity (as measured by output per hour). And if the result of working from home doesn’t lead to a measurable increase in output (a distinct possibility for office work), then productivity actually goes down (more hours, same output). Also, any reduction in commuting time is a one-shot improvement (assuming that the number of people working from home does not continue to increase) and may be reversible (as workers are required to go back to the office at least part-time). And of course, it does increase company profits if workers are not paid more for those extra hours – and likely they are not (more output due to more hours for the same amount of expense).