innovation department case study

Ten Types of Innovation: 30 new case studies for 2019

If you’ve followed my work for a while, you’ll know that I’m a big fan of the Ten Types of Innovation, a framework developed by Doblin (now a part of Deloitte).

I previously listed it as the #2 innovation framework you should be using.

And with good reason. I have used it frequently with clients to get them to think beyond innovating their product , which becomes harder, more expensive and less differentiating over time.

However, what I have found in recent workshops is that since it was originally published in 2013, some of the case studies and examples in the book already come across as out of date. That’s how rapidly the world is changing.

So here, I present three new more recent case studies for each of the Ten Types of Innovation, along with an outline on what each of them represents. Try and see which of these examples you would also suggest touch on more than one of the Ten Types, and let me know in the comments below:

1) Profit Model: How you make money

Innovative profit models find a fresh way to convert a firm’s offerings and other sources of value into cash. Great ones reflect a deep understanding of what customers and users actually cherish and where new revenue or pricing opportunities might lie.

Innovative profit models often challenge an industry’s tired old assumptions about what to offer, what to charge, or how to collect revenues. This is a big part of their power: in most industries, the dominant profit model often goes unquestioned for decades.

Recent examples:

• Fortnite – Pay to customise: This Free-to-Play video game by Epic Game Studios is currently one of the most popular and profitable games in the world. Unlike other “freemium” games which incentivise people to spend money to speed up progression, Fortnite is completely free to progress and people only need pay if they want to unlock cosmetic items which don’t affect gameplay but act to personalise their characters.
• Deloitte – Value sharing: Professional Services firm Deloitte is the world’s largest Management Consulting firm and still growing. They noticed a desire from their clients for assurance that the advice they were being given and transformation projects which Deloitte was running would actually succeed. As a result, Deloitte has begun trialling projects where instead of their fee being based just on Time and Materials, they will also share in value delivery, where additional bonus payments are only activated if previously-agreed performance metrics are successfully met.
• Supreme – Limiting supply: While most companies want to get their products in to the hands of as many people as possible, Supreme has built a cult following through deliberately forcing scarcity of its products. The streetwear clothing retailer announces limited items which will only be available from a specific day when they “drop”, and once they are sold out, that’s it, unless you want to pay huge markups for a second-hand item on eBay. Their red box logo is now so collectible and desirable that the company is able to sell almost anything by putting the logo on it for a limited time only. Case in point: you can find official Supreme Bricks (yes, like the ones used to build houses) which are still selling on eBay for $500.

Supreme’s limited quantity releases often lead to people queuing overnight

2) Network: How you connect with others to create value

In today’s hyper-connected world, no company can or should do everything alone. Network innovations provide a way for firms to take advantage of other companies’ processes, technologies, offerings, channels, and brands—pretty much any and every component of a business.

These innovations mean a firm can capitalize on its own strengths while harnessing the capabilities and assets of others. Network innovations also help executives to share risk in developing new offers and ventures. These collaborations can be brief or enduring, and they can be formed between close allies or even staunch competitors.

Recent Examples:

• Ford & Volkswagen – Developing Self-driving cars: As two of the world’s largest car-makers, Ford and Volkswagen are competitors on the road. However, in 2019 they announced a partnership to work together to develop technology for self-driving cars and electric vehicles which would be used in both company’s fleets of the future. While Ford brings more advanced automated driving technology, Volkswagen was leading in electric vehicles. Through the combined venture called ARGO, both firms can spread their R&D spending across more cars, while both developing competing products.
• Microsoft – launching on competitors platforms: Since new Microsoft CEO Satya Nadella has taken over, he has changed the innovation ethos of the company. Whereas previously Microsoft was a product-first company who tried to eliminate competing products and customers should stay within the company’s ecosystem, Nadella has shifted the mindset to a service company where their products should be accessible to customers should be able to access the products in whichever way they prefer. As a result, products such as Office 365 are now available in any web browser, as well as on the mobile marketplaces of Google’s Android and Apple’s IOS, previously seen as competitors.
• Huawei – Leveraging celebrity endorsement: Until recently, “high-quality smartphone” made people think of companies like Apple (USA), Samsung and LG (South Korea). Brands from China were often seen as competing on price but suffering from lower build quality and a lack of innovation. So in order to raise their profile in Western markets, Huawei has invested heavily in celebrities to endorse their flagship phones, such as Scarlett Johanssen, Lionel Messi, Henry Cavill and Gal Gadot. This initial investment raised brand name recognition, to the stage where it is now focusing marketing more towards features and functionality.

Huawei has paid Lionel Messi millions to endorse their brand

3) Structure: How you organize and align your talent and assets

Structure innovations are focused on organizing company assets—hard, human, or intangible—in unique ways that create value. They can include everything from superior talent management systems to ingenious configurations of heavy capital equipment.

An enterprise’s fixed costs and corporate functions can also be improved through Structure innovations, including departments such as Human Resources, R&D, and IT. Ideally, such innovations also help attract talent to the organization by creating supremely productive working environments or fostering a level of performance that competitors can’t match.

• Perpetual Guardian – Four-day working week: This small financial advisory firm in New Zealand trialed moving to a four-day working week, giving their staff an additional free day each week as long as they got their outputs done. As a result, they found people adjusted their working rhythm to achieve the same outcomes in 20% less time , while also resulting in more satisfied employees.
• Netflix – Unlimited Vacations: In order to drive their breakneck growth, Netflix reviewed their formal HR policies to see what processes were getting in the way of people doing their best work. They discovered that most bureaucratic processes which slowed down high performing individuals were in place to only handle situations where a low-performance individual would do something wrong. As a result, they scrapped most formal HR policies to free people to work in their own ways to benefit the company, summarised in their “Freedom and Responsibility” culture document, including allowing staff to take as many vacation days as they felt they needed to produce their best work.
• WeWork – Leveraging other companies’ hard assets: WeWork’s business model revolves around providing affordable office rentals for entrepreneurs and companies, fitting a lot of tenants into the same space by offering co-working areas. In order to rapidly deploy new working spaces and attract customers, WeWork started using a system called rental arbitrage, where they would rent commercial space, create a ready-to-use coworking setup, and then rent this space to customers. By not having to spend CAPEX on purchasing the buildings themselves, they were able to rapidly expand with lower overhead.

Netflix allows staff to take unlimited vacation days

4) Process: How you use signature or superior methods to do your work

Process innovations involve the activities and operations that produce an enterprise’s primary offerings. Innovating here requires a dramatic change from “business as usual” that enables the company to use unique capabilities, function efficiently, adapt quickly, and build market–leading margins.

Process innovations often form the core competency of an enterprise, and may include patented or proprietary approaches that yield advantage for years or even decades. Ideally, they are the “special sauce” you use that competitors simply can’t replicate.

• Tesla – Vertically integrated supply chain: Tesla’s electric cars require huge packs of EV batteries, made of thousands of lithium-ion cells. Until recently, the lack of demand for electric vehicles meant that companies had not invested in battery technology development, resulting in prices remaining high and making the cost of cars prohibitively more expensive than their gasoline counterparts. Tesla invested in a massive gigafactory to produce the newest battery packs themselves, and the economies of scale, as well as not paying markups to manufacturers, are estimated to save them 30% of the cost of the batteries.
• Amazon Web Services – opening internal technology to third parties: When Amazon Web Services initially launched in 2006 , it effectively launched the cloud computing market, allowing external companies to not just host webpages but run code and calculations at a fraction of the cost of building their own server network. Since then, Amazon has continued to develop new technology it would use for its own services, such as artificial intelligence, image recognition, machine learning, and natural-language processing, and later make this technology available to their customers.
• AliExpress – Making everyone a Shop Owner: AliExpress is one of the world’s largest eCommerce sites, and serves as a commercial storefront for thousands of Chinese companies, allowing you to purchase everything to phone cases to forklifts. However, AliExpress also allows the platform to handle purchases as listed on external storefronts using a system called drop-shipping, where anyone can set up their own store, sell someone else’s products (but to customers it looks like they are coming from the seller) and then have those manufacturers send the product directly to the customer.

Tesla’s Gigafactory is the world’s largest building

5) Product Performance: How you develop distinguishing features and functionality

Product Performance innovations address the value, features, and quality of a company’s offering. This type of innovation involves both entirely new products as well as updates and line extensions that add substantial value. Too often, people mistake Product Performance for the sum of innovation. It’s certainly important, but it’s always worth remembering that it is only one of the Ten Types of Innovation, and it’s often the easiest for competitors to copy.

Think about any product or feature war you’ve witnessed—whether torque and toughness in trucks, toothbrushes that are easier to hold and use, even with baby strollers. Too quickly, it all devolves into an expensive mad dash to parity. Product Performance innovations that deliver long-term competitive advantage are the exception rather than the rule.

• Gorilla Glass – Changing chemistry to improve smartphone durability: Gorilla Glass by Corning was listed as one of the original Ten Types by becoming scratch resistant. I have included it again for how it has changed the properties of its glass based on customer feedback each year. In 2016, version 5 of the glass was designed to resist shattering when dropped from 5+ feet, dubbed “selfie height” drops. However, after discussing what properties their customers wanted, by 2018 version 6 was no longer trying to resist shattering when dropped from a height once, instead the chemistry and manufacturing process had been changed to make it resistant to cracking after 15 drops from a lower height (1 meter, or a “fumble drop from your pocket”). I love this example of innovation as the product performance doesn’t just try to become “ better ” by resisting one drop from a higher height than last year, instead figuring out what really matters to customers and delivering that.
• Raspberry Pi – full PC for $35: The original Rasperbby Pi was developed by a UK charity to make a simple yet expandable computer which was affordable enough for everyone. Their credit-card sized PC may look bare-bones (it comes without a case and is effectively an exposed circuit board), yet it contains everything which someone needs to run a Linux operating system, learn to program and even connect it with external sensors and peripherals to make all manner of machines. The latest version 4 is now powerful enough to serve as a dedicated PC, all for a price so low you can give it to a child to tinker with without fear of it being broken.
• Lush Cosmetics – Removing what people don’t want anymore: As people become more aware of their impact on the environment, customers are demanding that customers do more to reduce the amount of plastic packaging their products use which could end up in landfill or the ocean. Lush Cosmetics was an early pioneer in bringing packaging-free cosmetics to scale, offering some of their packaging-free products like shampoo bars and soaps in dedicated packaging-free stores .

Giving children a cheap PC like the Raspberry Pi to learn and experiment on

6) Product System: How you create complementary products and services

Product System innovations are rooted in how individual products and services connect or bundle together to create a robust and scalable system. This is fostered through interoperability, modularity, integration, and other ways of creating valuable connections between otherwise distinct and disparate offerings. Product System innovations help you build ecosystems that captivate and delight customers and defend against competitors.

• Ryobi – One battery to rule them all: While handheld tools have had rechargeable batteries for decades now, Ryobi’s innovation was designing the modular One+ battery which could be used with over 80 different tools. Not only was this convenient for customers who needed fewer batteries overall for multiple uses, it also encouraged someone to buy into the Ryobi tool ecosystem once they had previously purchased one tool and battery set.
• Zapier – making APIs easy: Many web-based applications nowadays have an Application Programming Interface (API) which allows them to share data with other services. However, this often requires complex coding from the developers, and repeated effort to integrate with multiple different APIs. Zapier acts as a middleman for data, providing ready-made actions and API integrations between popular web services, allowing customers to automate certain activities every time a specific event happens.
• Airbnb – Expanding into experiences: Airbnb built their business on allowing everyday people to sell accommodation in their homes to strangers. Now the company has begun offering complementary services to people visiting new places through Experiences . These experiences are also sold by local guides, and allow guests to try things they would otherwise not have known about in addition to staying somewhere new.

Ryobi One+ battery powers multiple different tools

7) Service: How you support and amplify the value of your offerings

Service innovations ensure and enhance the utility, performance, and apparent value of an offering. They make a product easier to try, use, and enjoy; they reveal features and functionality customers might otherwise overlook, and they fix problems and smooth rough patches in the customer journey. Done well, they elevate even bland and average products into compelling experiences that customers come back for again and again.

• Kroger – Smartphone grocery scanning: US retail giant Kroger has been trialing a new smartphone app which allows shoppers to scan items as they shop, and then skip checking out altogether. Using the Scan, Bag, Go app, a customer will scan each item as they pick them up and place them into whatever bag they want, and once they are done, they can simply pay using the app and leave. This prevents shoppers having to wait in checkout lines and gives them an overview of their running total as they go, and also allows the supermarket to entice shoppers by sending coupons and offers directly to them.
• PurpleBricks – bringing real estate online: Estate Agents have a poor reputation for treating both sellers and buyers, especially for the amount they charge relative to the service they provide. PurpleBricks was one of the first online-only estate agents , where they could charge a significantly lower fee if the seller chose to complete some of the service processes themselves, such as showing the home to potential buyers. The firm can provide additional services for additional charges.
• Meituan Dianping – providing one app for all the services you want: As Fast Company’s 2019 Most Innovative company , Meituan Dianping provides a platform for Chinese consumers to purchase a variety of services. Known as a transactional super-app, you can use the app to book and pay for food delivery, travel, movie tickets and more from over 5 million Chinese small and large merchants.

Scan your own groceries with the Scan-Bag-Go app

8) Channel: How you deliver your offerings to customers and users

Channel innovations encompass all the ways that you connect your company’s offerings with your customers and users. While e-commerce has emerged as a dominant force in recent years, traditional channels such as physical stores are still important — particularly when it comes to creating immersive experiences.

Skilled innovators in this type often find multiple but complementary ways to bring their products and services to customers. Their goal is to ensure that users can buy what they want, when and how they want it, with minimal friction and cost and maximum delight.

• Dollar Shave Club – Direct to your door: Razor Blades have always been high-margin products, and Gillette was one of the original innovators by giving away the razor handle to make money on the subsequent razor blade sales. Dollar Shave Club has taken a different approach, by reducing the cost of each set of blades, but having people join a subscription service where blades are delivered to them automatically. While the margin on each set of blades is lower than retail, the subscription model has provided steady, predictable revenue for the company, to the extend that subscription boxes can now be found for almost any consumable product.
• Zipline – Blood Delivery for remote areas: In hospital settings, getting fresh blood can a matter of life and death. Unfortunately, many Sub-Sharan African countries don’t have road infrastructure suitable for quickly delivering blood between hospitals or storage locations. This is why Zipline has developed a simple, reliable drone network where hospitals in Rwanda and Ghana can order fresh blood from a central processing area and receive it within an average of 15 minutes, rather than the hours or days it would take using conventional transportation.
• 3D Printers – produce whatever you need at home: Instead of a single company, the industry of 3D printers is slowly beginning to change the way in which consumers get simple tools and parts. By downloading schematics from the internet (or designing their own), people owning a 3D printer now no longer to go to a retail location or order the parts they need. In commercial settings, this is also speeding up how quickly companies are able to prototype new ideas and designs, waiting hours rather than days or weeks.

zipline blood drone innovation

9) Brand: How you represent your offerings and business

Brand innovations help to ensure that customers and users recognize, remember, and prefer your offerings to those of competitors or substitutes. Great ones distill a “promise” that attracts buyers and conveys a distinct identity.

They are typically the result of carefully crafted strategies that are implemented across many touchpoints between your company and your customers, including communications, advertising, service interactions, channel environments, and employee and business partner conduct. Brand innovations can transform commodities into prized products, and confer meaning, intent, and value to your offerings and your enterprise.

• Gillette / Nike – being willing to lose customers who don’t align with purpose: I have combined both Gillette and Nike into this example of brand innovation since they have both recently aligned their brands to a purpose (social and political), which has been positively welcomed by some people but has resulted in hatred from other groups. Nike began by making former NFL Quarterback Colin Kaepernick the face and voice of one of their advertising campaigns. Kaepernick rose in prominence when he refused to stand during the national anthem before his games, his way of protesting the police brutality and inequality towards his African American community. This led to some people claiming he was disrespecting the American Flag, and therefore what the flag stands for. When his advert launched, a vocal minority took to social media to upload videos of themselves saying that Nike no longer aligned with their values, and they burned their shoes, vowing to never buy Nike again. Similarily, Gillette came out with a commercial urging all men to be “The best a man can be”, by pushing aside previously ‘masculine’ traits like bullying, chauvinism or fighting, and showing children how a modern man should behave. As soon as the ad was released online, many media outlets praised its message, but it brought the wrath of angry men who claimed that the razor manufacturer shouldn’t tell them what to think or how to behave, how they would never buy the products again, and how the world was becoming too politically correct, with women and minorities getting preferential treatment over white men. The advert quickly became one of the most disliked videos on Youtube, and even my commentary about the innovative message (seen in the video below) had the comments section covered by hate-filled messages. What both Nike and Gillette realised was that if they wanted to align with positive, progressive messages and values (which align with their target demographic of the future), then they would risk upsetting and alienating the proportion of their current customer base who didn’t share those views. In both cases, these were decisions that would have been signed off by all levels in the company, through marketing, sales, legal and the board, and the brands will be stronger in the future because of it.
• Burberry – modernising a classic brand: Burberry had built its luxury fashion reputation by aligning itself with the British Aristocracy, and its famous chequer patterned fabric was iconic. However, when trying to modernise and make the brand “sexy” in the early 2000s, a misstep happened when the luxury house began to license the chequered fabric, resulting in it becoming a status symbol and desired motif for a different social group: the British “Chavs” (rough, lower class and sometimes aggressive). This poisoned the once iconic brand in the eyes of their intended luxury clientele. In order to survive, the company and brand embraced innovation , by becoming one of the first fashion houses to redesign their website to be mobile-optimised, aligning their store layout to mirror the website, highlighting young British talent and livestreaming content and fashion shows. Most importantly, they moved away from the iconic chequer pattern in their fashion designs, where it is now limited to less than 10% of products.

10) Customer Engagement: How you foster compelling interactions

Customer Engagement innovations are all about understanding the deep-seated aspirations of customers and users, and using those insights to develop meaningful connections between them and your company.

Great Customer Engagement innovations provide broad avenues for exploration and help people find ways to make parts of their lives more memorable, fulfilling, delightful — even magical.

• REI – closing their stores on the busiest shopping day: Outdoor equipment retailer REI had begun closing its doors on Black Friday , traditionally one of the busiest shopping days of the year. They claim they are doing this to Eddie their customers to actually get outdoors and use their equipment, rather than queuing for discounted material goods.
• Peloton – bringing the gym into the home: Many people benefit from going to joint gym classes because the sense of a group working toward is goals together with a coach is more powerful than trying to exercise by yourself. Peloton makes exercise equipment with built-in screens, powered by a subscription to live and on-demand classes. It’s like being part of a workout group with the benefits of being at home.
• NBA – bringing the fans into the action: The NBA had invested heavily in innovation to make their sport more immersive. From live analytics and player statistics, new ways to watch like VR video, and official video game players for each team, they are finding new ways to bring basketball to the next generation, while making it even more exciting for existing fans.

Peloton brings exercise classes into the home

There we go, a new set of 30 examples of the Ten Types of Innovation.

If you found some of these examples interesting, please share the article.

Can you think of any more good examples? Let me know in the comments below.

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great examples! I now feel inspired to innovate in my entrepreneurial project. Thank you ?

Greetings from Mexico

Excellent work!

They’s very interesting. Do you have the solutions of some of recent examples?

My university has taken pretty much everything from here, poorly rephrased a few things and have delivered it to us, the student, as an entire weeks worth of content. Maybe i should be paying my fees here…

Bachelor of business student Australia

Very interesting. Which course was it being used for?

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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A Case Study on DARPA: An Exemplar for Government Strategic Structuring to Foster Innovation?

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• First Online: 11 February 2024

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• Rodney H. Yerger Jr 6  

Part of the book series: International Studies in Entrepreneurship ((ISEN,volume 56))

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Advocates for a mission economy contend that government bureaucracy can be transformed through a strategic structuring that would improve upon the dynamic capabilities necessary to pursue and direct innovation. The Defense Advanced Research Projects Agency (DARPA) is touted as a model organization of strategic structuring for inducing public sector innovation of emerging technologies. Applying economic theory and employing empirical analysis, I objectively examine key factors that are attributed to DARPA’s success, such as the organization’s autonomy, small size, and limited tenure of its program managers, in order to assess the worthiness of the agency’s exemplar status of empowering a mission-oriented approach to innovation. I find that while DARPA undoubtedly provides value for national defense and has distinct advantages over other government organizations, it falls short in representing a sustainable and scalable source of strategic structuring that would befit the entrepreneurial state.

This chapter’s contents were mostly part of the author’s dissertation Analyzing the Effectiveness of State-Guided Innovation for the degree of Doctor of Philosophy at George Mason University, USA, 2023. Reused with permission.

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• Public goods
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Introduction

Advocates for a mission-oriented directionality to innovation tout the Defense Advanced Research Projects Agency (DARPA) as one model improvement within the public sector that provides the agility and flexibility to pioneer revolutionary technology advancement (Mazzucato 2021 ). The purpose of this chapter is to execute a case study analysis of the DARPA organization, exploring its origins from 1958 and detailing changes in its focus and processes over time and how those changes track with its effectiveness at Schumpeterian entrepreneurship. The bulk of the case study describes and documents the institutional mechanisms that DARPA possesses to promote innovation. Much has been espoused regarding the success of the DARPA model in the form of various attributes (Gallo 2021 and DARPA 2016 ), which I categorize by the following three key factors:

Trust and autonomy.

Small size and the externalization of research.

Limited tenure and urgency.

This research objectively analyzes each factor, applying economic theory to corroborate or counter the expected outcomes from DARPA’s purported strengths and defending these assessments empirically where possible. I find that the organization’s touted autonomy is unstable over time due to political transaction costs as evidenced by increased congressional oversight, shifting focus toward incremental technology advancement to fulfill short-term military priorities, and a transfer of expert power to established vendors. While DARPA has distinct advantages over other government organizations, it falls short in representing a sustainable and scalable source of strategic structuring.

DARPA’s History and Construct

Following the Soviet Union’s success in the space race with the launch of Sputnik, the Eisenhower administration established the Advanced Research Projects Agency (ARPA) in 1958, chartered with preventing “technological surprise” (Van Atta and Windham 2019a , pp. 3–4). The agency was initially focused on large missions such as missile defense and nuclear test detection with a brief foray in space-related technology development until that function was absorbed by the standup of the National Aeronautics and Space Administration (NASA). However, within a few years, ARPA assumed an additional role in pursuing a “set of smaller, technically focused programs” in areas such as materials science, information technology, and behavioral science (Van Atta and Windham 2019a , p. 4). These pursuits led to what is typically acclaimed the agency’s two greatest contributions to innovation: the precursor to the Internet and the foundation of personal computing.

In 1972, because of increased scrutiny on military spending for many reasons including the unpopularity of the Vietnam War, the agency encountered its most significant focus change when Congress limited research efforts to only those having direct military application. This not only resulted in the name change from ARPA to DARPA but also added increased process and oversight (Fong 2019 ). The effects of these process changes to DARPA’s purported strengths are explored in subsequent sections.

DARPA continued to evolve and shift focus throughout the decades following its renaming, primarily aligning with changes in national security priorities, such as the Global War on Terror in the 2000s. Despite these shifts, the underlying organizational mission has remained basically the same: “to prevent and create technological surprise” (Gallo 2021 , p. 5; DARPA 2016 , p. 4). DARPA asserts a commitment to achieve transformative research and development (R&D) with a stress on higher risks and higher rewards over incremental advances. To accomplish its mission, DARPA adheres to a process that externalizes research through an annual budget of approximately USD 3.5 billion to fund performers primarily from industry (62 percent in 2020), and secondarily from universities (18 percent in 2020), federal laboratories and research centers (15 percent in 2020), other nonprofits (4 percent in 2020), and foreign entities (1 percent in 2020) (Gallo 2021 , p. 10). DARPA’s funding levels have stayed fairly constant over time. So too has the agency’s manpower footprint, which is primarily composed of approximately 100 “empowered program managers coordinating high-risk high-reward external research” (Reinhardt 2020 ). This feature along with special hiring and contract authorities sets DARPA apart from other government agencies in terms of its independence, which advocates claim provides flexibility for both ideas generation and enhanced engagement opportunities with potential performers. These elements of the DARPA model frame my case study approach in analyzing the three key factors that purportedly promote innovation. The first factor is trust and autonomy.

Factor 1: Trust and Autonomy

DARPA’s autonomy stems from its explicit separation from the larger Department of Defense (DoD) to include the military services, which allows for disruptive technology pushes beyond the constraints levied by specific military requirements and missions (Gallo 2021 ). This uncoupling represents a mitigation of the institutional constraints that drive median results in the government domain and obstruct Schumpeterian entrepreneurship (Schnellenbach 2007 ). Less checks and balances, especially the avoidance of excessive oversight from Congress, provides DARPA a level of opacity that promotes speed and flexibility in decision-making, garners independence in problem-solving, and incentivizes risk-taking (Miller 1992 ; Reinhardt 2020 ). Ter Bogt ( 2003 , p. 151) connects the “autonomization” of a public organization to transaction cost economics (TCE); specifically, DARPA represents an “internally autonomized organization,” which stakes a claim in the lowering of economic transaction costs by limiting political influence.

The trust and autonomy bequeathed by the DoD and Congress to DARPA also extends to within the organization from the agency director to the aforementioned empowered program managers, who can select and terminate projects through their ability to deploy money rapidly and independently (Gallo 2021 ; Reinhardt 2020 ). Thus, DARPA’s organizational structure consists of a unique combination of centralized and distributed control mechanisms. Miller ( 1992 ) stresses that causes of market failure such as information asymmetry and team production externalities lead to hierarchical solutions for social dilemmas. Moreover, disadvantages of democracy such as preference instability and indecisiveness and/or manipulation in decision-making lend favor toward centralizing power (cf. Arrow 1963 ). In DARPA’s case, autonomy has been purposefully granted to the agency director, and other stakeholders like the military services and Congress are restrained in their decision-making authority as it pertains to DARPA’s purview.

Nonetheless, a hierarchy contains its own set of issues. Central planning efforts suffer from Hayek’s knowledge problem and what Tullock ( 2005 [1965], pp. 148–152) refers to as “whispering down the lane,” where agile coordination is constrained by the multiple levels of superior-subordinate interactions that impede knowledge diffusion and discourage entrepreneurship alertness and discovery. Miller ( 1992 , p. 80) argues that firms can address these issues by injecting an additional level of autonomy within the organization via delegation: “…a dictator who needs good information and good ideas must create the basis for independence inside the hierarchy.” The DARPA equivalency is delegating real shares of decision-making authority to program managers, who are hired from industry and academia and serve as experts in their specific domains of research within the fields of science and engineering (Gallo 2021 ).

Provided the strengths of DARPA’s unique form of independence through the combination of centralized and distributed control governance structures, theoretical counters exist to this organizational construct’s stability in maintaining autonomy, which also calls into question the appropriateness of possessing high levels of opacity for inherently governmental entities. The first counterpoint considers the overall agency level and its relationship to its external stakeholders. Because DARPA classifies as an “internally autonomized organization,” it is neither truly independent nor private; therefore, political influence can still erode efficiency, at least over time. In attempting to incorporate TCE into the public sector domain, Ter Bogt ( 2003 ) proffers a political transaction cost framework to account for the lack of emphasis placed on economic efficiency in government organizations. This framework analyzes each of the primary characteristics of TCE as promulgated by Williamson ( 1981 )—asset specificity, frequency and scale, and uncertainty—in order to assess the political willingness to increase or decrease an organization’s autonomy. According to Ter Bogt’s analysis, the willingness to “autonomize” will increase for basic government functions such as the provision of student loans or road maintenance. DARPA’s case is the opposite of basic functionality. Its product, innovation, involves high asset specificity in terms of uniqueness and importance and high uncertainty in terms of the frequency with which it can be produced and the ability to measure success.

Furthermore, Ter Bogt’s ( 2003 ) framework considers additional political transaction costs associated with maximizing electoral support, the influence of special interest groups, and political opportunism with a focus on increasing political efficiency for inherently governmental organizations. Applying these considerations, DARPA’s independence as an organization could be jeopardized by two key sources. The first source consists of special interest groups working through the larger DoD and military services, who might desire to control the shape and direction of DARPA-related technology development efforts. This source includes large public-private partnership companies that perform a huge proportion of defense-related R&D. The second source are the taxpayers, who typically demand the very checks and balances that have been removed through “autonomization” to ensure their money is being spent wisely and competently. The higher the level of opacity within an inherently governmental organization, the more difficult the challenge to safeguard against abuses. Given that DARPA explicitly regards each program manager as filling the role of a technical subject matter expert, this high level of opacity can result in what Koppl ( 2018 , pp. 189–200) refers to as a “rule of experts” scenario, where a monopoly of experts increases the likelihood of unreliability, which can lead to bad decision-making.

Another critical counterpoint involves DARPA’s autonomy internal to the organization residing with the individual program managers. Miller ( 1992 , pp. 86–89) highlights the downside of distributed control governance as explained through the Sen paradox: “…any organization that delegates decision-making authority to more than one subset of individuals must suffer from either incoherent behavior or inefficiency for some combinations of individual preferences.” The tradeoffs given the Sen paradox involve the individual self-interest of each DARPA program manager and the agency’s best interest. Thus, distributed control can evolve into a threatening construct to both the dictator and external stakeholders. However, the DARPA model exhibits additional strengths purported to combat inefficiency in outcomes and intransitivity in preferences. The second and third key factors of my case study analysis elaborates further on these strengths.

Factor 2: Small Size and Externalization of Research

To avoid the Sen paradox, Miller ( 1992 , pp. 94–95) contends that the hierarchy must “shape and mold individual preferences into patterns that are mutually consistent.” One way DARPA mitigates the threat of incoherent behavior and inefficient coordination is through its small manpower footprint. DARPA’s core staff size gravitates toward Dunbar’s number (~150), which is the suggested limit at which social relationships flourish as each member can get to know every other person in the organization. Knowing everyone creates peer pressure through scrutiny, which provides a check against abusing opacity and fosters an adherence to a common set of goals (Dunbar 1992 ; Reinhardt 2020 ). Remaining small in size may also help counter external threats to DARPA’s independence from special interest groups and the taxpayer. By staying below the radar, DARPA might avoid targeting for predation and regulation despite the higher political transactions costs associated with extremely uncertain and disruptive innovation efforts.

DARPA maintains its small footprint by externalizing research, which is promoted as another strength of its governance model. The agency avoids the high transaction costs involved in obtaining the unique knowledge and equipment required in pursuing groundbreaking research. DARPA does not establish its own labs or the bureaucracy involved in managing them (Cummings 2018 ; Reinhardt 2020 ). Instead, it outsources these assets through discrete project funding that yields a lower overhead and streamlines accountability by ensuring each project is responsible to one person, the program manager (Reinhardt 2020 ).

Despite the perceived advantages of DARPA’s small size and externalization of research, there may also exist associated drawbacks. Overcoming the Sen paradox by internally streamlining preferences might restrict a sense of competition among independent program managers and instead promote expert failure by enhancing synecological bias through motives that Koppl ( 2018 ) argues are inherent in maximizing expert utility. These motives include identification that is tied to a common mission as well as a sympathy for and a desire to please fellow experts.

Moreover, even though the organization’s small footprint might help to ward off threats to predation, it increases the detrimental effects of politicization should the willingness to decrease autonomy dominate as predicted by Ter Bogt’s political transaction framework. If all program managers are aligned tightly with DARPA’s director, absent bureaucracy, politicization of the director could lead to a prioritization of goals and efforts entirely dictated by external forces rather than the organization’s stated mission (Reinhardt 2020 ).

An intentional restriction in size also shapes broader ramifications for Mazzucato’s vision of strategic structuring that calls for a replication of the DARPA model to induce the entrepreneurial state. Breznitz and Ornston ( 2013 , p. 4) argue that bastions of successful public sector entrepreneurship will more likely “occur at the periphery of the public sector, in low-profile agencies with relatively few hard resources and limited political prestige.” They cite DARPA as a peripheral organization that does not suffer from the political interference found with a larger and “centrally positioned” agency. These strengths pose a significant challenge in attempting to scale the DARPA model in order to achieve a vision of transformational value creation by the public sector.

Finally, there are disadvantages in externalizing research that involve tradeoffs in transaction costs. While DARPA avoids the high overhead costs associated with providing its own labs and equipment, it incurs the costs of finding and establishing relationships with appropriate and competent performers and ensuring that these performers produce value on time and on budget. These costs involve large undertakings, which typically require hierarchical control to monitor and prevent shirking (Reinhardt 2020 ). Koppl ( 2018 ) argues that synecological redundancy is a key tenant in mitigating expert failure. Instead, the DARPA model relies on a lone program manager tasked with multiple ventures, which exacerbates the risk of unreliability due to expert error to include making unintentional or “honest” errors given the limited cognition of an expert’s bounded rationality. Therefore, by outsourcing its potentially transformative research efforts, DARPA might find it tempting or even necessary to outsource the centralized control mechanisms required to produce such results. Such requirements can limit research partnerships to larger, more mature companies and increase the likelihood of rent-seeking behavior. Nevertheless, the DARPA model provides a check against these alleged disadvantages by motivating active program management, which involves the third key factor of my case study analysis.

Factor 3: Limited Tenure and Urgency

Congress grants DARPA special privileges in hiring and contracting authority. Specifically, DARPA can directly and expeditiously hire science and engineering experts from industry and academia for term appointments, typically 3 to 5 years. DARPA’s special contracting authority lowers the transaction costs of the government acquisition process in not only bypassing burdensome procurement regulations to develop flexible agreements with R&D performers but also by empowering the program manager to reprioritize and reallocate funds based on performance (Gallo 2021 ; DARPA 2016 ). These authorities give DARPA distinct advantages through the motivation of active program management and ideas generation as well as in providing a counter to the Sen paradox.

Limited tenure encourages program managers to take risks in funding ideas for short-term durations but with a long-term view in mind, where both the need and value proposition are uncertain (Bonvillian et al. 2019 ; Gallo 2021 ). The hiring process sets expectations upfront that the program manager position is not career oriented. Excelling in the position will not result in a promotion within the organization, and funding unsuccessful long shot ideas will likely not adversely impact one’s career (Reinhardt 2020 ).

To achieve long-term impact, program managers seek ambitious project ideas and tolerate associated failures as “the cost of supporting potentially transformative or revolutionary R&D” (Gallo 2021 , p. 6). However, checks are inherent in the DARPA process that attenuate the effects of failure via the short-term funding of seedling projects, which allows the program manager to track progress and terminate and redeploy funding for those projects that underperform (Van Atta and Windham 2019a ). In this manner, while DARPA externalizes research, it bears the risk for the performer, which advocates insist is a major advantage over private sector venture capitalism. Furthermore, DARPA can also bear the risks for other funding mechanisms by signaling technology validation, which encourages larger industry performers to front their own money or other government entities like the National Science Foundation to provide grants to continue development (Reinhardt 2020 ).

In addition to incentivizing risk taking via active program management, limited tenure creates constant turnover of personnel (~25 percent per year) that should ideally result in a continued infusion of ideas. Not only does this turnover model help with new idea generation but also allows a revisiting of old ideas that might have been tried previously and failed. Subtle tweaks to an old idea or simply the timing and environment in which the idea reemerges may result in improved outcomes that would not have otherwise materialized had the organization preserved the memory of past naysayers (Gallo 2021 ).

A final advantage of limited tenure is that along with the aforementioned small manpower footprint, DARPA’s hiring flexibility provides a counter to the Sen paradox associated with distributed control governance mechanisms. The DARPA director can shape coherent behavior by hiring similarly minded and motivated subordinates with preferences that align to the DARPA mission of creating and preventing technological surprise.

As with the other key factors, there exist theoretical counterpoints to the purported benefits of DARPA’s limited tenure and flexible hiring policies. An obvious drawback to excessive risk taking is that associated failures are a cost to the taxpayer and moreover, could result in destructive entrepreneurial outcomes. While logic supports the need to tolerate failure when pioneering disruptive technology advancement, understanding the returns to such efforts via cost-benefit analysis remains an appropriate consideration. This includes taking into account the costs in revisiting or duplicating old ideas that simply will not work despite the fact that program manager turnover reinvigorates their appeal (Gallo 2021 ). Furthermore, while limited tenure may motivate risk-taking, it cannot completely displace familiarity bias, which influences agents to invest in and with those they trust (Reinhardt 2020 ). In the case of the DARPA program manager, this bias might result in allocating funding to those researchers with sound and stable reputations over less mature, smaller enterprises, which runs counter to Schumpeterian entrepreneurship.

With regard to flexible hiring practices, the methods DARPA uses to streamline preferences and foster coherent behavior do not fully embrace the theoretical underpinnings required in overcoming the Sen paradox. As government employees, neither DARPA program managers nor the director are residual claimants, which is a striking difference between public sector entrepreneurs and venture capitalists. The standard solution to address the agency problem caused by decision managers not being residual claimants is via compensation that accurately reflects performance in the overall market for management (Fama 1980 ). Miller ( 1992 , pp. 100–101) stresses that the streamlining of preferences via socialization is insufficient because adverse selection causes measurement error in determining the potential fit of a candidate for hire. Instead, the most effective means of “reconciling transitivity, efficiency, and delegation” is through the compensation system. While DARPA’s unique status allows for the authorization of higher salaries than compared to other government agencies, a pay gap certainly exists between similarly skilled private sector counterparts in the science and engineering communities. Consequently, DARPA must depend on the aforementioned personal gain incentives.

A final concern exists with the overall concept of active program management, which has sparked debate over the benefits of DARPA’s changes to process over time. In the days of ARPA (1958–1972), program managers exercised less control over the efforts of performers, while maintaining responsibility of overall vision and funding (Worrydream 2017 ; Kleinrock 2014 ). Tracking progress and performance via standard program management techniques can focus too much priority on near-term results and derail long-term vision (Cummings 2018 ). This focus is bureaucratic in nature, which ironically is what DARPA is chartered to avoid.

Empirical Analysis

The next step of my case study analysis explores quantitative and qualitative evidence that bolsters either the points or counterpoints described above regarding the three key factors of the DARPA model. First, regarding independence, ample evidence exists that DARPA has become less autonomous over time, which is an indication that political transaction costs have influenced the willingness of political actors to tolerate a high level of opacity. Starting with the transition of ARPA to DARPA in 1972, increased oversight has influenced how DARPA spends its money. Lump sum authorization of funding by Congress has shifted to demanding annual budgets for each program that include a description of the work to be performed. Despite DARPA’s streamlined processes over other government institutions, grants for seedling projects must still go through an open and involved solicitation process. As a result of orienting DARPA’s work more to the needs of the military to counter existing threats, DoD has shaped and dictated shorter-term areas of R&D efforts to support active conflicts such as the Vietnam War in the 1970s and Global War on Terror in the 2000s. Finally, and perhaps the biggest example of increased politicization, the appointment of DARPA directors is now aligned with presidential administrations (Reinhardt 2020 ).

Regarding the pros and cons of organizational size, DARPA has maintained a relatively small manpower footprint over time. In remaining small and flat, DARPA has successfully resisted Parkinson’s Law, a crucial contributor to bureaucratic inefficiency where success is measured by the growth in the number of subordinates under a director’s control (Tullock 2005 [1965]). However, evidence exists that DARPA’s externalization of research suffers from the high transaction costs involved in searching for competent researchers and monitoring performance. In 2001, DARPA started awarding prime contracts almost exclusively to “established vendors,” which relegated universities and start-up firms into a teaming concept that reports through the prime contractor (Fuchs 2010 , p. 1138).

Sound reasons exist for the shift in awarding prime awards to established vendors. Fuchs ( 2010 ) cites the decline of corporate R&D labs over time as responsible for raising the transactions costs. An established vendor can better perform the systems management necessary to see technology advancement through to production and thereby avoid “the Valley of Death.” Conversely, the relegation of start-ups to a supporting role in the DARPA process is concerning considering the view that newer entrepreneurial firms are the linchpin for breeding successful innovation because of ownership incentives and information advantages (Karlson et al. 2021 ). Furthermore, the dependence on larger, more mature companies to provide the hierarchal control mechanisms for the externalization of research increases DARPA’s vulnerability to rent seeking by special interests, which directly stunts productive entrepreneurial opportunities.

In a sense, DARPA’s arrangement with established vendors might represent a transfer of expert power from the program managers to the large industry R&D performers. Koppl ( 2018 ) proffers an information choice theory model of an epistemic system utilizing a sender-receiver game construct. As applied to DARPA following the shift in awarding prime contracts to established vendors, the program manager now represents the receiver (or nonexpert) beholden to a monopoly of senders (or experts) as represented by the large defense contractors. The receiver grows more powerless as rivalry among senders is reduced. Not only does this lack of rivalry increase synecological bias, but the intentional relegation of start-up companies also restricts free entry, which Koppl cites as a key contributor to expert failure: “‘Potential competition’ is more important than the number of incumbent competitors” (Koppl 2018 , p. 205; cf. Baumol 1982 ).

DARPA’s adherence to active program management might offset the increased likelihood of expert failure and vulnerability to rent seeking caused by the shift in contracting strategy. Anecdotal evidence supports the view that DARPA program managers have a healthy tolerance for failure. Over DARPA’s history, project losers ranging from research into paranormal activity to developing mechanical elephant transports to more recently, testing rapid space launch capabilities have showcased a willingness to try out challenging and quirky ideas (Gallo 2021 ). Of a more quantitative nature, Goldstein and Kearney ( 2017 , 2020 ) conducted studies measuring past project selection and performance for ARPA-E, the Department of Energy’s transformational R&D organization, which can serve as a proxy for DARPA. Goldstein and Kearney ( 2017 ) find that ARPA-E program managers exercise autonomy via their tendency to select projects for funding that receive less consensus from external peer reviews.

Furthermore, Goldstein and Kearney ( 2020 ) find that program managers do not shirk from playing an active role in the management of their portfolio by frequently redeploying money to increase funding for stronger performing projects and decreasing or terminating funds for those that perform weakly. In this manner, they are exercising real options similar to the way venture capitalists monitor their investments and unlike the hands-off approach that other public sector entrepreneurial mechanisms such as the Small Business Innovation Research (SBIR) program take via the provision of grants.

In terms of the effectiveness of DARPA’s flexible hiring practices, compensation gaps between program managers’ salaries and their private sector counterparts loom as a significant concern. Reinhardt ( 2020 ) estimates that experienced scientists and engineers at large tech companies receive at least twice as much compensation, whereas this gap was much less severe (~20 percent) in the 1960s during the days of ARPA. The commercial high-tech sector promises to be even more competitive going forward, which may not bode well for attracting top talent to a position that entails no promotion and requires relocation to Washington, DC.

In analyzing possible frictions between DARPA’s dual roles in executing transformative R&D and responding to threat-based time-sensitive challenges for the military, a review of DARPA’s history tells a tale of two different organizations. The first tale involves the ARPA years from 1958–1972, when Congress and DoD exercised much less oversight over the agency and the program managers exercised much less oversight over research performers. One of the earliest DARPA directors, Jack Ruina, “valued scientific and technical merit above immediate relevance to the military” and delegated a high level of autonomy to his program managers (Fuchs 2010 , p. 1137). The best example of this delegation involves one of the organization’s greatest successes, the R&D that led to the advent of the Internet and personal computing. J. C. R. Licklider, the program manager for these efforts, advanced an ambitious vision that foresaw computers serving as “interactive intellectual amplifiers for all humans, pervasively networked worldwide” (Worrydream 2017 , para 14; Kleinrock 2014 ). This vision was only loosely connected to solving command and control challenges for national defense, and it did not entail a specific set of goals nor a roadmap. Instead, Licklider leveraged the power of his vision to find and organize an impressive network of researchers and sustain investments in the underlying technologies to achieve success (Van Atta and Windham 2019b , pp. 39–40; Bonvillian 2019 , pp. 94–98).

It is important to note that ARPA’s considerable level of independence did not always result in productive entrepreneurial outcomes. Project AGILE supported combat operations in Vietnam and involved mismanaged efforts to improve weaponry, which included chemical agents. The project was an unmitigated disaster, which led to the conviction of the program manager, William Godel, for embezzlement. Yet, because of its covert nature, the project avoided scrutiny allowing it to survive for over a decade (Van Atta and Windham 2019a ; Reinhardt 2020 ). This example of a destructive entrepreneurial outcome calls into question the sustainability of unfettered independence for inherently governmental organizations, which provides a convenient segue to the second tale of DARPA.

The shift from ARPA to DARPA in 1972 increased oversight and focused the organization’s efforts more directly on military application. By 1975, DARPA’s new director, George Heilmeier, instituted what became known as the “Heilmeier Catechism,” which was the genesis of active program management. Heilmeier influenced more of a top-down and mission-oriented approach for the management of projects that involved setting intermediate and long-term goals, tracking progress, and estimating the costs and benefits of each research effort as it pertained to the customer (Van Atta and Windham 2019a , pp. 14–15; Fong 2019 , pp. 193–194; Cheney and Van Atta 2019 , pp. 233–234). Although active program management mitigates the risks of longer-term, highly uncertain technology advancement efforts and increases the success rate of technology transition, it also entails greater costs to autonomy and disincentives toward risk-taking over ARPA’s more vision-oriented approach.

The ultimate empirical evidence in evaluating the effectiveness of DARPA over time would be to accurately measure return on investment in terms of innovative output. Attempts at measuring patents per award and funding per patent illustrate that DARPA performs considerably well compared to other government agencies; however, these cannot be considered apples-to-apples comparisons given the varied charters and missions of these agencies, nor do these assessments address the more important question as to how well DARPA performs compared to the private sector (Piore et al. 2019 , pp. 49–52).

Reinhardt ( 2020 ) reviews the agency’s own advertised accomplishment timeline and bins what he refers to as “outlier successes” into two categories: pre-1972 (ARPA) and post-1972 (DARPA). An outlier success can be considered synonymous with architectural innovation, which disrupts and creates markets while also outmoding existing competencies (Abernathy and Clark 1985 ). The results of Reinhardt’s binning excursion reveal that the vast majority (over 70 percent) of DARPA’s architectural innovation occurred during the ARPA years. The ramifications of this revelation do not detract from the value DARPA has provided and continues to provide to its single customer, the military; albeit this value is harder to appreciate given its specific military utility and narrow applicability.

This chapter analyzed the institutional mechanisms of DARPA as a model for strategic structuring that fosters Schumpeterian public sector entrepreneurship. In reviewing three key factors that expound the DARPA model, I explored theoretical points and counterpoints that make for a complex and inconclusive assessment as to the potentiality of DARPA’s distinctive form of organizational governance in fulfilling the vision of an entrepreneurial state.

Through a unique combination of centralized and distributed control mechanisms, DARPA possesses a higher level of autonomy, at least compared to other government organizations; however, I find this autonomy to be unstable. Political transaction costs associated with state-guided innovation efforts decrease the willingness to autonomize, which erodes independence via three discrete sources. First, concerns from the taxpayer over abuses to opacity and expert failure have led to more congressional oversight over time. Second, vulnerability to rent seeking by special interests has increased, which is evidenced by a transfer of expert power to and a growing dependence on established vendors to provide the hierarchal control mechanisms for the externalization of research. Third, pressures from external stakeholders such as the military have influenced a greater focus on shorter-term military or administration priorities, which can incentivize technology transition over risk-taking. While DARPA is better equipped than others to ward off threats to its autonomy through such advantages as flexible hiring practices and special contract authorities, its model depends on employing highly competent and motivated program managers, and yet, subsequently cannot depend on compensation to overcome the residual claimant agency problem.

My research reveals that the vast majority of DARPA’s architectural innovation occurred prior to the critical shift from ARPA to DARPA in 1972, which was a time characterized by much less external oversight and a much lower pay gap between government and private sector high-tech labor. It is important to note, however, that this correlation between ARPA’s greater autonomy and innovation success should not imply causation. Another factor at play could be the characteristics of the post-World War II era, or perhaps more specifically, the height of the Cold War, which involved a level of crisis that dictated a demand for rapid and novel change and raised alertness to entrepreneurial opportunities. Indeed, DARPA’s founding is steeped in a collective mobilization across the public sector domain to counter the crisis of technological surprise. Since that time as the Cold War diminished, preparing for “system-level war” shifted toward a focus on responding to “shorter-term tactical missions.” Ruttan ( 2006 , pp. 183–184) contends that the absence of a major war, or at least the threat of one, diminishes the probability that our political system could generate the willpower and resources “required to initiate and sustain the development of major military and defense-related general-purpose commercial technologies of the past.”

Another crucial concern in assessing DARPA as a model for Mazzucato’s strategic structuring vision is its scalability. Even if DARPA can effectively sustain a resistance to political interference, this would be attributed to its small footprint and its existence as a peripheral organization. The fact that DARPA’s disruptive technology efforts can threaten status quo defense acquisition processes, which can drive opposition within the military, does not support the claim that the high-risk, high reward approach inherent in Schumpeterian entrepreneurship could expand to transform large areas of the government. Even attempts at cloning DARPA for the sake of establishing other peripheral organizations dedicated to long-term revolutionary R&D have met with resistance and limited success. For example, despite consultation on adopting the strengths and processes of the DARPA model, ARPA-E suffers from greater hierarchical control both internally and externally. Within the organization, the program managers are outnumbered by support staff, which entails a higher level of process-driven activity. External to the organization, ARPA-E is directly funded by the Department of Energy instead of Congress, which threatens independence of basic functions such as program selection and idea generation (Fuchs 2009 ; Reinhardt 2020 ).

In conclusion, DARPA undoubtedly provides value to the defense of the United States and has generated productive public sector entrepreneurial outcomes. However, the agency falls short in representing a sustainable and scalable source of strategic structuring that would befit the entrepreneurial state.

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Yerger, R.H. (2024). A Case Study on DARPA: An Exemplar for Government Strategic Structuring to Foster Innovation?. In: Henrekson, M., Sandström, C., Stenkula, M. (eds) Moonshots and the New Industrial Policy. International Studies in Entrepreneurship, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-031-49196-2_7

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Decision-making approaches in process innovations: an explorative case study

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 10 December 2020

Issue publication date: 17 December 2021

The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.

Design/methodology/approach

This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.

The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.

Research limitations/implications

This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.

Practical implications

Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.

Originality/value

Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

• Uncertainty
• Decision making
• Process innovation
• Case studies
• Production systems
• Manufacturing industry

Flores-Garcia, E. , Bruch, J. , Wiktorsson, M. and Jackson, M. (2021), "Decision-making approaches in process innovations: an explorative case study", Journal of Manufacturing Technology Management , Vol. 32 No. 9, pp. 1-25. https://doi.org/10.1108/JMTM-03-2019-0087

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Copyright © 2019, Erik Flores-Garcia, Jessica Bruch, Magnus Wiktorsson and Mats Jackson

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Process innovations, which involve new or significantly improved production processes or technologies, are essential for increasing manufacturing competitiveness ( Rönnberg, 2019 ; Yu et al. , 2017 ). The benefits of successfully implementing process innovations include reducing time to market, developing strong competitive barriers and increasing market share ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ). However, implementing process innovations does not always lead to desirable results ( Rönnberg et al. , 2016 ; Frishammar et al. , 2011 ). Instead, literature shows that staff frequently encounter difficulties when identifying decision-making approaches during the implementation of process innovations ( Eriksson et al. , 2016 ; Terjesen and Patel, 2017 ). These difficulties originate when staff responsible for implementing process innovations face unfamiliar circumstances ( Gaubinger et al. , 2014 ; Stevens, 2014 ; Jalonen, 2011 ). In particular, staff must deal with a lack of consensus and understanding (equivocality), and absence of rules or processes facilitating the analysis of information (analyzability) ( Piening and Salge, 2015 ; Milewski et al. , 2015 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ).

Operations management research offers diverse decision-making approaches useful for implementing process innovations ( Gino and Pisano, 2008 ; Hämäläinen et al. , 2013 ; Mardani et al. , 2015 ). This paper focuses on normative, intuitive and mixed-method decision-making approaches. Normative decision making involves quantitative analyses based on a systematic assessment of data ( Cochran et al. , 2017 ; Battaïa et al. , 2018 ; Dudas et al. , 2014 ). Intuitive decision making uses affectively charged judgments that arise through rapid, non-conscious, holistic associations ( Elbanna et al. , 2013 ; Dane and Pratt, 2007 ). The mixed-method approach considers both quantitative data and intuition ( Saaty, 2008 ; Thakur and Mangla, 2019 ; Kubler et al. , 2016 ; Hämäläinen et al. , 2013 ). It is vital to know when each decision-making approach is most suitable ( Zack, 2001 ; Eling et al. , 2014 ). Unless decision-making approaches are aligned with their conditions of use, the results could be disappointing ( Luoma, 2016 ).

Different decision-making approaches are used to solve problems when implementing process innovations ( Bellgran and Säfsten, 2010 ; Gershwin, 2018 ). However, it remains unclear when to select a particular decision-making approach ( Calabretta et al. , 2017 ; Dane et al. , 2012 ; Luoma, 2016 ; Matzler et al. , 2014 ). Recently, it is suggested that the degree of equivocality and analyzability of a decision, the structuredness of a decision, may constitute the main criteria for determining a decision-making approach ( Julmi, 2019 ). While this work provides novel insight, two salient issues require further research. First, there is a need for empirical understanding, as current findings remain purely conceptual. For example, manufacturing companies seldom experience a black-and-white divide between equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Eriksson et al. , 2016 ; Zack, 2007 ). Accordingly, it is necessary to remain open to unanticipated findings and the possibility that current explanations about selecting a decision-making approach require adjustments. Second, current findings give precedence to intuitive decision making over normative or mixed approaches. Identifying when and how to use normative and mixed decision making in addition to intuition is essential for implementing process innovations in the context of increasing computational capabilities and the interconnectedness of systems ( Mikalef and Krogstie, 2018 ; Liao et al. , 2017 ; Schneider, 2018 ; Rönnberg et al. , 2018 ). Thus, the purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. This study focuses on production system design, including conception and planning, because this stage contributes significantly to the performance of process innovations ( Andersen et al. , 2017 ; Rösiö and Bruch, 2018 ).

2. Frame of reference

2.1 understanding equivocality and analyzability in process innovations.

Equivocality is a central organizational challenge that negatively impacts the implementation of process innovations in manufacturing companies ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Parida et al. , 2017 ). The current understanding of equivocality is grounded on organization theory ( Galbraith, 1973 ). Equivocality refers to the existence of multiple and conflicting interpretations, and is associated with problems such as a lack of consensus, understanding and confusion ( Daft and Macintosh, 1981 ; Zack, 2007 ; Zack, 2001 ; Koufteros et al. , 2005 ). Equivocality originates when individuals face new or unfamiliar situations in which additional information will not help resolve misunderstandings ( Frishammar et al. , 2011 ). Individuals may experience equivocality of varying degrees ranging from high equivocality, ambiguous unclear events with no immediate suggestions about how to move forward, to low equivocality, clearly defined situations requiring additional information ( Daft and Lengel, 1986 ). The literature suggests that to reduce equivocality, staff must engage in information processing activities that exchange subjective interpretations, form consensus and enact shared understanding ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Daft and Lengel, 1986 ).

Staff responsible for implementing process innovations frequently encounter problems relating to lack of agreement or consensus, namely, equivocality ( Reichstein and Salter, 2006 ; Jalonen, 2011 ; Stevens, 2014 ). The way individuals respond to such problems is referred to as analyzability ( Daft and Lengel, 1986 ). Analyzability describes the extent to which problems or activities require objective procedures as opposed to personal judgment or experience to resolve a task ( Haußmann et al. , 2012 ; Zelt et al. , 2018 ). Similar to equivocality, analyzability is subject to varying degrees. For example, tasks lacking objectives rules and procedures are regarded as having low analyzability. Conversely, tasks including clear and objective procedures leading to a solution are considered as having high analyzability. The degree of analyzability of a task is associated with its degree of equivocality ( Daft and Lengel, 1986 ; Julmi, 2019 ; Byström, 2002 ). When a task is clear and analyzable, equivocality is low, and staff can rely on the acquisition of explicit information to answer questions. When a task is unclear and of low analyzability, equivocality is high, and staff must process information to generate consensus.

2.2 Decision-making approaches

Operations management literature offers distinct approaches to decision making relevant to implementing process innovations. A first approach involves normative decision making. Normative decision making involves a logical step-by-step analysis involving a quantitative assessment ( Mintzberg et al. , 1976 ) and requires information that is clear, objective and well defined ( Dean and Sharfman, 1996 ). Normative decision making is described as a slow and conscious process where information is logically decomposed and sequentially recombined to generate an output ( Jonassen, 2012 ; Swamidass, 1991 ; Papadakis et al. , 1998 ). The benefits of normative decision-making approaches include economizing cognitive effort, solving cognitively intractable problems, producing insight and integrating knowledge ( Liberatore and Luo, 2010 ). Criticism of the use of normative decision making extend from studies suggesting that individuals are intendedly rational, but only limitedly so ( Luoma, 2016 ; Simon, 1997 ). For example, decision makers may systematically deviate from recommendations produced by decision models ( Käki et al. , 2019 ). Normative decision making, despite its alleged drawbacks, continues to be used by organizations and has frequently led to good outcomes ( Metters et al. , 2008 ; Klein et al. , 2019 ).

A second approach includes intuitive decision making ( Bendoly et al. , 2006 ; Loch and Wu, 2007 ; Gino and Pisano, 2008 ; Elbanna et al. , 2013 ; White, 2016 ). Intuitive decision making involves affectively charged judgments that arise through rapid, non-conscious, holistic association of information ( Dane and Pratt, 2007 ). Intuitive decision making is associated with having a strong hunch or feeling of knowing what is going to occur, and can be advantageous when professionals are confronted with time pressure and possess experience in a field ( Gore and Sadler-Smith, 2011 ; Dane and Pratt, 2007 ; Bennett, 1998 ; Elbanna et al. , 2013 ; Hodgkinson et al. , 2009 ; Khatri and Ng, 2000 ). Intuitive decision making is not without drawbacks. Literature suggests that managers using intuition may ignore relevant facts, have a hard time explaining the reasons for making a choice, or produce gross misjudgments ( Dane et al. , 2012 ; Elbanna et al. , 2013 ; Dane and Pratt, 2007 ).

A third alternative includes the use of mixed decision-making approaches ( Tamura, 2005 ; Hämäläinen et al. , 2013 ). The main strength of this approach lies in reducing personal bias and allowing the comparison of dissimilar alternatives while integrating quantitative analysis ( Saaty, 2008 ). Mixed decision-making approaches provide solutions to problems involving conflicting objectives or criteria affected by uncertainty ( Kahraman et al. , 2015 ). Literature presents a variety of alternatives in relation to mixed decision-making approaches ( Mardani et al. , 2015 ), yet these have the common objective of helping deal with the evaluation, selection and prioritization of problems by imposing a disciplined methodology ( Kubler et al. , 2016 ).

2.3 Structuredness of decisions and decision making

In the past, decisions have been classified along a continuum according to their structure ( Shapiro and Spence, 1997 ). This argument maintains that a decision may range from well- to ill-structured depending on whether rules and processes can be unequivocally applied. Grounded on organization theory, recent studies propose that the structuredness of decisions may provide an indication for understanding the correspondence between the choice of a decision-making approach and its conditions of use ( Julmi, 2019 ).

Well-structured decisions include intellective tasks with a definite objective criterion of success within the definitions, rules, operations and relationships of a particular conceptual system ( Dane and Pratt, 2007 ). A well-structured decision involves rules or procedures and unequivocal interpretations that have developed over time ( March and Simon, 1993 ; Luoma, 2016 ). Therefore, it is argued that well-structured decisions relate to low equivocality and high analyzability, and that normative decision making is appropriate because of the structured rules and computable information involved.

Ill-structured decisions involve judgmental tasks where there are no objective criteria, or demonstrable solutions ( Dane and Pratt, 2007 ). Ill-structured decisions originate from novel situations that do not include widely accepted rules that may help determine the degree to which a decision is correct or biased ( Cyert and March, 1992 ; Luoma, 2016 ; Jacobides, 2007 ). Consequently, it is identified that ill-structured decisions correspond to high equivocality and low analyzability. It is suggested that staff facing ill-structured decisions adopt intuitive decision-making because intuition does not rely on rules to cope with a problem; rather, it relies on integrating information holistically into coherent patterns ( Dane and Pratt, 2007 ). Figure 1 illustrates the correspondence of decision-making approaches to the conditions of use based on the structuredness of decisions.

Conceptually, the structuredness of decisions provides a starting point to understand the correspondence of a decision-making approach to its conditions of use. However, there is a need to submit these conceptual arguments to empirical scrutiny and explore whether the degree of equivocality and analyzability provides guidance in selecting a decision-making approach when implementing process innovations. The empirical study to explore these issues is described in the following section.

3. Methodology

Prior studies have focused on explaining how to choose a decision-making approach; however, there is a need for further empirical insight. This casts doubt on the appropriateness of analysis-based research, which is better suited to evaluating well-developed hypotheses ( Johnson et al. , 2007 ; Mccutcheon and Meredith, 1993 ; Handfield and Melnyk, 1998 ). Accordingly, this study adopts a qualitative-based case study to elaborate on the current theory ( Ketokivi and Choi, 2014 ). Theory elaboration is well suited to explore an empirical context with more latitude, and conduct an in-depth investigation based on identified theoretical concepts ( Whetten, 1989 ). The choice of case study research is justified by prior studies which describe its advantages for observing and describing a complicated research phenomenon such that it conveys information in a way that quantitative data cannot ( Eisenhardt and Graebner, 2007 ; Handfield and Melnyk, 1998 ; Meredith, 1998 ; Mccutcheon and Meredith, 1993 ). In designing and conducting the case study, extant guidelines for qualitative case studies in Operations Management were followed ( Barratt et al. , 2011 ).

The focus of this study is the design of production systems. Decision making at this stage is important for achieving the desired level of competitiveness and the overall goals of implementing process innovations ( Bruch and Bellgran, 2012 ). Process innovations are frequently implemented in the form of projects ( Bellgran and Säfsten, 2010 ). Accordingly, the unit of analysis is the production system design project, and its embedded unit of analysis decisions within these projects. Given the research agenda, the decisions occurring in a production system design project are an appropriate unit of analysis. These decisions should adapt to the structure of the environment ( Gigerenzer and Gaissmaier, 2011 ), and are affected by the information processing capacities of an organization ( Matzler et al. , 2014 ).

This study uses empirical data from two production system design projects at one global manufacturing company, which we refer to as Projects A and B. While case study research at a single organization offers limited generalizability ( Ahlskog et al. , 2017 ), it allows an in-depth exploration of how decision making occurs at manufacturing companies beyond well-structured decisions ( Kihlander and Ritzén, 2012 ). The manufacturing company was selected based on theoretical sampling, with the aim of exploiting opportunities to explore a significant phenomenon under rare or extreme circumstances relevant to the study of single cases ( Yin, 2013 ; Eisenhardt and Graebner, 2007 ). In selecting a manufacturing company, the study focused on four factors associated with the competent implementation of process innovations including: large-sized firms of high capital intensity, established processes for developing production systems, continual design of new products and an emphasis on increasing flexibility of production systems ( Cabagnols and Le Bas, 2002 ; Pisano, 1997 ; Martinez-Ros, 1999 ).

Two aspects influenced the choice of projects. First, the focus was on projects implementing radical process innovations, namely, those projects involving new equipment and management practices and changes in the production processes ( Reichstein and Salter, 2006 ). These types of projects reportedly experience varying degrees of equivocality and analyzability ( Parida et al. , 2017 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ). In addition, radical process innovations depend on normative and intuitive decision-making approaches for their implementation ( Calabretta et al. , 2017 ), which are conditions essential to the focus of this study. Second, this study gave precedence to projects that included experienced staff responsible for implementing process innovations. Prior studies highlight that experience influences the capacity of staff to act under conditions of limited information and equivocality, and facilitates making rapid decisions in the absence of data ( Daft and Macintosh, 1981 ; Liu and Hart, 2011 ; Gershwin, 2018 ; Dane and Pratt, 2007 ). Accordingly, two projects in the heavy-vehicle industry focused on the transition from traditional production systems to multi-product production systems were considered.

One of the authors of this study is a researcher at the manufacturing company. Accordingly, this study adopts an interactive research approach ( Ellström, 2008 ), which is considered a variant of collaborative research. Interactive research is distinguished by the continuous joint learning and close collaboration between industry participants and researchers ( Svensson et al. , 2007 ; Ellström, 2008 ). Despite this close interaction, the primary focus of this study is to provide a theoretical contribution and relevant industrial results.

3.1 Description of Projects A and B

The manufacturing company is a leading producer of heavy-vehicle products with more than 14,000 employees and 13 manufacturing sites in Europe, Asia and North and Latin America. The heavy-vehicle industry is characterized by a high degree of product customization and specialized product families targeting specific markets. Manufacturers of this segment consider a wide offering of products to be a key competitive advantage. Production systems are distinguished by assembly lines that specialize in a single product family, and share little else other than the same manufacturing facility.

The manufacturing company initiated two projects, A and B, which originated from a common corporate goal of reducing time to market, manufacturing footprint, and lead time to customers, and increasing production flexibility. These projects focused on the transformation of traditional production systems to multi-product production systems. Projects A and B were considered process innovations because of their novel approach compared to traditional production in the heavy-vehicle industry, which included: standardizing product interfaces, utilizing new production processes and technologies for product assembly, redesigning facility layouts and developing internal logistic solutions. Projects A and B were considered successful because these upgraded outdated production processes and technologies increased production flexibility, reduced production unit labor cost per output, increased productivity and reduced the assembly area of the production systems. Table I describes Projects A and B, and Table II outlines the profiles of staff participating in these projects.

3.2 Data collection

Data collection took place between January 2014 and January 2016. This period comprised all activities and planning for Projects A and B. Different techniques for data collection were used including field notes, interviews and company documents to help obtain objective and reliable results ( Karlsson, 2010 ). The first author drafted field notes during 12 full-day workshops for Project A and 10 full-day workshops for B. Staff responsible for Projects A and B attended these workshops including project managers, production managers, production engineers, logistics developers, consultants and research and development personnel. These separately held workshops involved three themes. The first theme consisted of generating a common vision of the process innovations, identifying critical issues and proposing solutions to these issues. The second theme included designing, developing and deploying discrete event simulation models. The third theme focused on discussing the results of on-site tests for Projects A and B. In addition, the first author participated regularly as a passive observer in project meetings and drafted field notes, including 60 and 40 1-hour weekly meetings for Projects A and B, respectively.

The authors collected additional data based on five semi-structured interviews for Projects A and B. The interviews began with an explanation of the project, its background and goals. Staff described their professional experience and responsibilities in the project and identified the essential activities and decisions of each project. Next, they narrated the process of achieving agreement for each decision. Finally, they detailed how decisions were made including decision-making approaches, rules, processes, information and outcome. To gain a comprehensive understanding of decision making, the interviews involved staff members from different seniority levels, including project managers, production engineering managers, production engineers, logistics developers and consultants. The authors recorded and transcribed all interviews and sent all transcribed interviews to the interviewees for verification. Finally, data collection included company documents in the form of presentations, minutes and reports drafted during the projects. Table III lists the details of data collection.

3.3 Data analysis

Data analysis included an iterative comparison of the collected data and existing literature, as suggested by Yin (2013) . Following the recommendations of Miles et al. (2013) , data analysis occurred in four steps. First, collected data were concurrently selected, abbreviated and stored in a database during data collection. At this stage, salient decisions were identified for Projects A and B, and the focus was on decisions involving the commitment of resources (e.g. additional meetings, production experts or managerial discussions) leading to actions (selecting a layout, proposing a definition or selecting a group of products) as suggested in the literature ( Frishammar, 2003 ). Afterwards, staff participating in Projects A and B verified these decisions.

The second step involved systematically coding the collected data for Projects A and B. The authors jointly decided on three codes for analyzing data: equivocality, analyzability and decision-making approaches. The literature was heavily relied on to identify the equivocality and analyzability associated with a decision ( Daft and Lengel, 1986 ). High equivocality referred to multiple and conflicting interpretations and ambiguous information. Equivocal situations included partial agreement among the staff and ambiguous information. Low equivocality involved unequivocal interpretations and a lack of information. High analyzability concerned clear rules and processes, and low analyzability a lack of objective rules or rule based procedures. Staff of Projects A and B were left to operate freely when selecting decision-making approaches based on preferences or established processes operating at the manufacturing company. Importantly, no definitions of decision-making approaches were provided to the staff. Instead, the decision-making approach of each decision was identified a posteriori based on the characteristics of intuitive or normative decision making found in literature and shown in Table IV .

Third, the authors reassembled data according to the codes described above and analyzed data in two steps, as suggested by Eisenhardt (1989) . First, the author analyzed the projects separately to become acquainted with and identify patterns. Thereafter, the authors analyzed patterns across Projects A and B.

Fourth, the authors compared all the findings in a joint session with the aim of achieving a comprehensive interpretation of the study. The authors deliberated over differences of interpretation until an agreement was reached. Where there was no agreement, the authors contacted interviewees for further clarification. Finally, the authors drew conclusions and conceptualized the findings of the study. The findings were compared and related to existing theory concerning similarities, contradictions and explanations of differences ( Eisenhardt, 1989 ).

4. Empirical findings

4.1 equivocality.

What is a good assembly sequence for all these different products? You had to propose what to do, and then do it, and then show the results. It is not that you would have asked someone: Are we doing the right thing? Should we do it this way? No one really had an answer for that. (Project manager of Project A)

Each of us (project B) has worked with powertrains for a long time, but this was different. Originally we believed that it was necessary to include the vehicle transmission and an additional component in our scope. This choice was not simple because of the intrinsic differences and functionalities of each product family. In addition, we lacked experience on anything remotely similar, did not have enough information, and held different opinions on the matter. (Production engineer of Project B)

We based all the work on the assumption that there is one common assembly sequence. We regarded that as a backbone in the project. I strongly believe that if you have a common assembly sequence, it has an enormous impact on production. (Project manager of Project A)

First, we decided to do an extensive data collection. That drove the project into the wall. On a second attempt, we decided not to dig so deep into the details and focused on a holistic perspective. We went through our products looking for similarities. Based on discussions with our product and production experts, we identified 17 key components; based on these, we developed a common assembly sequence. (Production engineer of Project B)

After developing a shared understanding of a multi-product assembly, our activities focused on issues that could improve our concept. We collected and analyzed information, and compared alternatives. It was essential to know what choices brought our process innovation closer to objectives set up by management. (Logistics developer of Case A)

4.2 Analyzability

Staff experienced analyzability as a tension between two opposites. On the one hand, staff was subject to familiar circumstances, known problems or decisions encountered in the past. In these situations, they adopted standardized rules and procedures common in production system design projects at the manufacturing company: for example, processes for designing a production system, line balancing strategies or the classification of logistics parts.

On the other hand, staff faced new and unfamiliar decisions originating from the specification of the characteristics of a multi-product production system. For example, the manufacturing company possessed no procedures specifying the grouping of different product families for production in a multi-product production system. Similarly, the manufacturing company did not possess rules for identifying a best choice among alternative product groups. Staff considered both decision-making processes essential for multi-product production systems.

Once we developed a common perspective about a single assembly line, we mapped assembly times, figured out the number of stations, moved as much work as possible to sub-assembly lines, worked with logistics, material handling, kitting in line. With a common objective, it was easier for us to pinpoint what the production system would look like. (Production engineer of Project A)

4.3 Decision-making approaches

Staff of Projects A and B utilized three distinct decision-making approaches including intuitive, normative and a combination of intuitive and normative. Intuitive decision making was frequent at the start of Projects A and B, and relied on gut feeling, best knowledge and a holistic consideration of information. Intuitive decision making did not focus on detailed information. Instead, the staff integrated the results from different reports and argued for a solution based on experience or hunches. The staff utilized intuitive decision making in two distinct instances. First, staff relied on intuitive decision making during open and informal debates to achieve consensus. In these circumstances, they either generated a solution to a decision (e.g. agreeing on the importance of a common assembly sequence) or determined new rules or procedures (e.g. steps for grouping and ranking product groups). Second, they utilized intuitive decision making jointly with normative decision making, e.g. in identifying problems and proposing solutions to the production process. An additional example of the latter includes simulation models. Simulation models originally included rough assumptions and simplifications based on the intuition of experts and their general understanding of the production systems, which were increasingly completed with new information.

The results of the simulation analysis were very important to the outcome of the process innovation. This helped us understand how to eliminate variation in our production process. The simulation also helped us understand how the solutions we tested in the factory floor turned out over weeks or months across different areas. We could not have achieved this detail of understanding any other way. (Consultant of Project A)

Finally, staff jointly applied a combination of intuitive and normative decision-making approaches during Projects A and B. Joint intuitive and normative decision-making approaches were subject to the agreement of the staff, collection of data and clear rules or procedures which could be either new or established ones. Intuitive decision making could precede, follow or be used concurrently with normative decision making (e.g. when determining the advantages or trade-offs of a multi-product production system). In this example, staff utilized normative decision making (e.g. simulations) to compare the production systems of sites in North and Latin America to the multi-product production systems developed in Projects A and B. The results of this comparison were presented in workshops and face-to-face meetings. In these meetings, staff participating in Projects A and B and experts from sites in North and Latin America scrutinized the simulation results and compared them to demand forecasts, production reports and experience. This required several iterations, and the primary concern was that of earning trustworthiness from experts. Afterwards, the results of a decision were escalated to a managerial level. When determining the benefits and trade-offs of a multi-product production system, managers considered information from diverse sources – and not exclusively the results of a simulation analysis. Frequently, managers requested “what if” or sensitivity types of analysis from normative decision-making approaches. Accomplishing this required a new iteration of the steps described above. Finally, managers made decisions based on intuition, considering various sources of information holistically. Tables V and VI describe the salient decisions and equivocality, analyzability and decision making of each decision for Projects A and B.

An important observation is that decisions were subject to different degrees of equivocality and analyzability when implementing process innovations. The findings show that distinct decision-making approaches occur at different degrees of equivocality and analyzability. Understanding the correspondence of equivocality and analyzability to a decision-making choice is difficult to comprehend. Therefore, Figure 2 presents the correspondence and frequency of decision-making approaches to the degree of equivocality and analyzability in Projects A and B.

The correspondence between the degree of equivocality and analyzability of a decision and decision-making approaches is identified based on a synthesis of the choices of decision-making approaches in Projects A and B and extant literature. First, findings show that decision-making approaches were most frequently utilized in conditions of low equivocality and high analyzability. In this approach, the staff interpreted a problem unequivocally, possessed clear rules and procedures; however, they lacked information. The staff utilized three different decision-making approaches in conditions of low equivocality and high analyzability, including intuitive, normative and a combination of intuitive and normative decision making.

Our data show that staff found low equivocality and high analyzability as the only conditions suitable for normative decision making in this study. Normative decision making relied on explicit information, a sequential analysis and well-defined decisions. Staff from Projects A and B utilized normative decision making for detailed technical aspects such as evaluating layouts.

In addition, the staff made use of combined intuitive and normative decision making in conditions of low equivocality and high analyzability. Here, they utilized combined intuitive and normative decision making when facing new situations, having previously agreed on procedures for analysis (e.g. identifying vehicle modules). They utilized combined intuitive and normative decision making for high stake decisions involving an aggregation of prior activities and requiring managerial involvement (e.g. comparing a multi-product production system to existing multi-product production systems).

Finally, the staff utilized intuitive decision making in low equivocality and high analyzability when encountering situations perceived as similar to prior situations. In these instances, they relied on experience, quick decisions and a holistic association of information to produce a result (e.g. agreeing on the need for improving staff competence).

Second, Projects A and B faced conditions of low equivocality and low analyzability. Staff agreed on the nature of a problem; however, they lacked clear rules, procedures and relevant information. They judged that these conditions did not meet the criteria for the exclusive use of normative decision making. Instead, they utilized intuitive or a combination of intuitive and normative decision-making approaches. The staff applied intuitive decision making to decisions where the end goal was that of establishing rules or procedures. In these instances, they were not undecided about the goal of a decision, rather how to arrive at a solution (e.g. establishing the rules and procedures for modular assembly and performance indicators). When combining intuitive and normative decision making, they utilized intuition for agreeing on rules and procedures, associated decisions to those faced in the past, and devised steps that were understandable to others based on experience. Next, quantitative analyses were utilized to provide detailed insight, acquire information and logically decompose a problem (e.g. specifying an assembly sequence).

Third, staff of Projects A and B made decisions in a context of equivocality and high analyzability. This coincided with having clear rules and processes; however, with only a partial agreement about the information necessary to complete a task or the outcome of a decision. In these instances, the staff resorted to intuitive decision making for agreeing on the type of information necessary to complete a task. Next, they utilized normative decision making in the form of quantitative based analysis such as spread sheet calculations or simulations. Finally, they returned to intuitive decision making to arrive at a solution while considering holistic information from a variety of sources. Examples of this include proposing logistics solutions for multi-product production systems, and determining advantages and trade-offs of multi-product production systems. The findings of this study would suggest that the conditions of equivocality and high analyzability do not provide sufficient support for the use of an entirely normative decision-making approach. Empirical results suggest that applying purely intuitive decision-making approaches is undesirable. Actually, the staff recognized that decisions could not rest exclusively on hunches, experience or rapid decisions by acknowledging the need for additional information, and disputing the appropriateness of information to complete a task.

Fourth, staff of Projects A and B made decisions against a backdrop of equivocality and low analyzability. These decisions involved the lack of rules or processes and partial agreement about information necessary to complete a task. Decisions of equivocality and low analyzability were not like small differences of opinion resolved over the course of a meeting or workshop. Instead, these decisions required detailed investigation, resource commitment and weeks of deliberation. Staff in Projects A and B proceeded differently when encountering equivocality and low analyzability.

In Project A, the staff identified the logistics needs for a multi-product production system. They agreed on the need for adapting logistics capabilities; however, the information available did not correspond to the needs of a multi-product production system. They estimated logistics needs based on hunches, discussions and experience. They considered the outcome of this decision provisional and subject to increased knowledge about logistics in a multi-product production system. In Project B, the staff proposed a layout for a multi-product production system. To do so, they utilized intuitive decision making to set an initial direction. This was considered insufficient to finalize a decision, and additional information was acquired, and alternatives were judged based on normative decision making.

Findings suggest that these types of decisions are not readily solvable, and evidence a need for generating agreement about the purpose of the decision, information, rules and processes enabling a solution. Data suggest that intuitive decision making is important in enacting a shared understanding; nevertheless, committing to a decision may require the quantitative insight provided by normative decision making. Consequently, decisions experiencing equivocality and low analyzability were subject to a combined intuitive and normative decision-making approach. Examples include identifying logistics needs for multi-product production systems or proposing layouts for multi-product production systems.

Fifth, findings show that no decisions coincided with high equivocality and high analyzability, namely, multiple and conflicting interpretation, ambiguous information, and clear rules and processes. We argue that high equivocality and high analyzability present a contradiction and suggest that the incidence of decision making in these conditions may signal an error. This error may well indicate the inadequate interpretation of existing rules or processes by staff responsible for implementing process innovations.

Sixth, staff made exclusive use of intuitive decision making in decisions involving high equivocality and low analyzability. These type of decisions were characterized by the absence of objectives rules or processes, multiple and conflicting interpretations, and ambiguous information. These decisions were common in the beginning of Projects A and B, and when the staff faced decisions perceived as different from those encountered in the past. They relied on hunches, approximations or conjectures about the result of a decision to guide consensus. Additional information did not help resolve decisions in high equivocality and low analyzability: for instance, when agreeing on the definition of a powertrain across different product families. Figure 3 outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions.

5. Discussion and implications

The purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. In particular, this study focused on how the conditions of equivocality and analyzability provide guidance to the choice of a decision-making approach. Extant literature is compared to empirical findings from two projects implementing process innovations in the form of a multi-product production system in the heavy-vehicle industry. The findings of this study are particularly relevant in light of the interest from manufacturing managers and academics to better understand when and where a decision-making approach is most suitable during the implementation of process innovations.

5.1 Theoretical implications

Recent studies recommended decision-making approaches in extreme cases of problem structuredness, high equivocality and low analyzability or low equivocality and high analyzability ( Julmi, 2019 ). However, staff face varying degrees of equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Frishammar et al. , 2011 ). This study reveals additional combinations of equivocality and analyzability than those previously described in literature. This finding is important because it extends current understanding of decision structuredness, which thus far had been limited to presenting extreme cases, namely, well- and ill-structured decisions. In addition, this study provides empirical evidence that staff must respond to decisions at varying degrees of equivocality and analyzability when implementing process innovations. In particular, this study identified three degrees of equivocality and two of analyzability when implementing process innovations. This study highlights the need for increased understanding of equivocality and analyzability, which may help manufacturing companies avoid failed choice or erroneous approaches to decision making when implementing process innovations. This finding is important as it may help clarify the selection of decision-making approaches leading to an improved outcome ( Calabretta et al. , 2017 ; Luoma, 2016 ), a situation that is crucial for implementing process innovations ( Frishammar et al. , 2011 ; Milewski et al. , 2015 ).

Current understanding of decision structuredness argues that there are no superior decision-making approaches ( Julmi, 2019 ). Instead, a decision-making approach may be better suited to certain conditions and, under these conditions, lead to an effective outcome ( Gigerenzer and Gaissmaier, 2011 ). Our findings show that, consistent with the literature, well-structured and ill-structured decisions corresponded to normative and intuitive decision making. However, findings show differences with prior studies focused on decision structuredness and decision making. For example, staff applied intuitive decision making at varying degrees of equivocality and analyzability, combined normative and intuitive decision making not described in literature, and utilized more than one decision-making approach in three out of six combinations of equivocality and analyzability. The results of this study suggest that decision structuredness may not prescribe a decision-making approach, but may clarify the conditions in which decisions take place. This finding is important because it suggests that current understanding of decision-making choice based on extreme cases of problem structuredness, namely well- or ill-structured decisions, is insufficient to guide a choice of decision-making approach. Addressing this dearth of understanding, this study outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions. This findings is essential as it suggests that identifying the fit of a decision-making approach to the structuredness of a problem is as important as the technical acumen, resources and experience necessary for using a particular type of decision making ( Jonassen, 2012 ; Dean and Sharfman, 1996 ).

By classifying decisions in relation to their degree of equivocality, this study shows that decisions occur more frequently in situations involving low equivocality, followed by those of high equivocality, and finally by those involving partial agreement and ambiguous information or equivocal. A higher frequency of decisions in situations of low equivocality is expected when implementing process innovations. However, an intriguing finding of this study involves the frequency in which staff made decisions in situations including multiple and conflicting interpretations and ambiguous information (e.g. high equivocality). These decisions appeared when staff identified a problem (e.g. product, production process, tools and technology, layouts, logistics), were based on intuitive decision making and defined subsequent decisions of Projects A and B. This finding is disquieting as prior studies show that manufacturing companies frequently rely on ad hoc practices when making early decisions in production system design projects ( Rösiö and Bruch, 2018 ). Similarly, the literature highlights a limited understanding of equivocality at manufacturing companies when implementing process innovations ( Parida et al. , 2017 ). Therefore, our findings give credibility to the claim that comprehension of equivocality, its reduction and the effective use of intuition may harness a competitive edge for manufacturing companies implementing process innovations ( Rönnberg et al. , 2016 ; Frishammar et al. , 2012 ).

The literature advocates the use of structured processes for implementing process innovations ( Kurkkio et al. , 2011 ). Accordingly, the need for clear rules and procedures facilitating high analyzability is essential. The results of this study show no telling difference in the frequency of decisions involving high analyzability or low analyzability in Projects A and B. Importantly, data do not indicate that staff forwent rules and processes when these were lacking. Instead, staff developed rules and processes when facing decisions not previously experienced or described in established procedures. This result is significant and suggests that the ability of staff to develop rules and processes, or procedures when facing non-recurring situations ( Luoma, 2016 ), is as likely to be necessary as that of structured processes for implementing process innovations. The development of rules and processes during the implementation of process innovations is rarely discussed in literature, and therefore constitutes a venue for future research.

Mixed decision-making approaches constitute a well-established field that may help staff arrive at decisions under uncertainty ( Kubler et al. , 2016 ). This study showed that decisions were frequently reached as a result of combined intuitive and normative decision making. However, the process for arriving at these decisions was unlike the methods used in the literature. The findings of this study suggest both the need of mixed decision-making approaches when implementing process innovations, and increased efforts to bridge the gap between academic findings and manufacturing practice.

5.2 Practical implications

The findings of this study have direct practical implications that may benefit staff and managers responsible for implementing process innovations. First, this study underscores the importance of a structured process, experienced design teams and familiarity with normative, intuitive or mixed decision making that enable the implementation of process innovations ( Rösiö and Bruch, 2018 ). However, the analysis also shows that although these concepts are necessary, they are not sufficient to successfully implement process innovations. Instead, managers must be aware of the importance of determining a decision-making approach that corresponds to the conditions of a decision. Addressing this point, this study emphasized the importance of equivocality and analyzability when determining a decision-making approach during the implementation of process innovations. Accordingly, this study underscores the importance of information processing activities, which are under prioritized or neglected because of a lack of resources or competence ( Rönnberg et al. , 2016 ; Koufteros et al. , 2005 ).

5.3 Limitations and future research

Some key limitations circumscribe this study. Like all case studies, our contributions are limited by the idiosyncrasies of the context of study ( Eisenhardt, 1989 ). This study draws data from a global manufacturing company. Undoubtedly, smaller sized manufacturing companies may have different access to staff, resources and experienced personnel when implementing process innovations. Prior studies suggest that these elements affect decision-making approaches. Therefore, validating our results against cases from varying company sizes is important. Another limitation constitutes our focus on the production of heavy vehicles and their components. A suggestion for future research includes the investigation of cases in additional context: for example, the process industry or batch production.

Process innovations concern new production processes or technologies. This study, like many other process innovation studies ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ), focused on new material, equipment or reengineering of operational processes. In doing so, concern stemmed from the conditions that may determine the choice of a decision-making approach. Process innovation literature reflects increasing interest in the way artificial intelligence, automation and digital technologies connected to the Internet of Things affect decision making ( Rönnberg et al. , 2018 ). While the interplay of intuitive, normative and mixed decision-making approaches is a concern of this study, technological changes enabling decision making is not. Future research could focus on conceptualizing the domain of novel digital technologies and decision making when implementing process innovations.

Choice of intuitive or normative decision making based on decision structuredness

Correspondence of decision-making approaches to degree of equivocality and analyzability in Projects A and B

Choice of decision-making approaches when implementing process innovations according to the degree of equivocality and analyzability of decisions

Description of production system design Projects A and B focused on implementing a multi-product production system as a process innovation

Profiles of staff participating in Projects A and B

Details of data collection for Projects A and B

Characteristics of intuitive and normative decision-making approaches

Description of salient decisions, equivocality, analyzability and decision making in Project A

Notes: Equivocality (HE, high equivocality; E, equivocality; LE, low equivocality), Analyzability (HA, high analyzability; LA, low analyzability)

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Acknowledgements

The authors gratefully acknowledge the contributions of all the participants from the anonymous company used as a case study in this research. Financial support from the Knowledge Foundation (KKS), and the industrial graduate school “Innofacture” is also gratefully acknowledged.

Corresponding author

About the authors.

Erik Flores-Garcia is Doctoral Candidate at the Innofacture Industrial Graduate School, Mälardalen University, Sweden. His research interests include simulation, production decisions and process innovation.

Jessica Bruch is Professor in production systems at Mälardalen University, Sweden. Her research interest concerns various aspects of production development and addresses both technological and organizational aspects on the project, company and inter-organizational level.

Magnus Wiktorsson is Professor in production logistics at the Royal Institute of Technology (KTH), Sweden. His research interests include two ongoing major changes in production logistics: the digitization of all processes and the need for transformation into environmentally sustainable production.

Mats Jackson is Professor in innovative production at Jönköping University, Sweden. His research interests include flexibility of production systems, industrialization and innovation in production systems.

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This case study summarizes a multi-phase project the Institute completed for a customer service-oriented division at a state agency. The project focused on business intelligence analysis and process documentation. 

Before the project began, customers were experiencing long wait times. And the burden on employees coping with the overwhelming customer volume was made even more challenging because of unclear and undocumented processes.

After implementation of the recommendations we made, and other internal changes, customer wait times decreased nearly 60% from a median of 52 minutes to 21 minutes. The online process documentation we developed received 3,000 hits per day from division staff.

The agency had years of transactional data that had been developed and used for purposes other than gaining insights into division operations and how these operations could be improved. In fact, analysts at the agency had never had access to the data before our project. After this project, this data became a critical resource to inform and enhance decision-making.

Initially, there were major data quality issues because the transactions could not be associated with the office where they took place. They were only associated with the laptops used to complete them, and the laptops were taken to different offices throughout the week. Moreover, some office addresses were unverified. We began the project by verifying all office addresses and developing a script to assign each transaction to the correct location based on time of day and day of week.

We were able to summarize a billion records in ways the agency had never seen before. For example, we were able to identify peak transaction days of the week, hours of the day, and months of the year.

Based on these summary statistics and working with the agency, we identified two key areas in need of improvement:

• Resource allocation
• Process consistency

Resource Allocation

The agency had two critical resources to allocate across the state:

Allocating staff had two dimensions: time and place. This meant that we were looking at not only where the employees would be allocated, but also when. Scheduling was prioritized to identify staff deficits and surpluses. 

Many office managers suspected they needed more employees in the afternoon because that was when the offices were most crowded. In fact, our analysis revealed the afternoon crowds were caused by falling behind first thing in the morning. Counterintuitively, increasing employees in the morning  before  the office was packed would improve customer service throughout the day.

We were able to determine the ideal employee schedule for each office across the state using queuing theory. We then used the employee schedules to identify the minimum number of employees required at an office to ensure coverage throughout the day. If the office did not have enough employees to meet the schedule requirements, they were considered to have a negative employee gap. If they had more than enough, the office was considered to have a positive employee gap.

Using the employee gap analysis, we were able to identify where more staff was necessary office-by-office. In areas where existing office capacities had been reached, ideal new locations were identified.

We were able to identify ideal office locations using the k-means clustering algorithm. Further, we were able to correlate nearby population to an estimated number of employees necessary at each location to serve likley customers.

A clustering algorithm’s goal is to create groups which minimize the within-group variation and maximize the between-group variation. In other words, to create offices that are close to their customers and far from other offices. The k-means clustering algorithm follows 5 steps:

• Enter a number (k) of offices (means).
• Create (k) random office points in a specified area.
• Assign customers to the nearest office.
• Optimize office locations by placing them in the customer center of density.
• Repeat steps 2 – 4 until customer assignments do not change during step 3.

Before running the model, existing offices were used in the algorithm as constant means. In other words, during step 4 of the process, the existing offices would not move. Additionally, the customers we used were population points, but we weighted those points based on employee gaps where customers near offices with negative employee gaps were preferred by the model. Without this weighting, we might come to the wrong conclusion.

Instead, we see the model respond to these gaps and identify a reasonable ideal office location. Finally, a list of nearby ZIP codes was provided to the agency so that their staff could identify available properties. 

Process Consistency

The agency’s resource allocation issues were further complicated by outdated and complex policy and procedure manuals.

Policy and procedure documentation were printed and distributed in large binders and then maintained via a system of email memos and through employee tenure and experience. Formal manual updates were cumbersome and time consuming, and therefore, infrequent. This ad hoc policy documentation approach led to process inconsistency across the State and contributed to longer transaction and customer wait times. Moreover, when the most tenured employee at an office retired, it posed a significant risk to basic operations.  

To solve this problem, we developed an internal single-source online documentation system that enabled publishing to multiple formats with the click of a button.

By creating a single-source, formalized processes and procedures led to standardized training and reference materials increasing policy consistency while helping reduce transaction times across the State.

Additionally, because policy tends to change with new leadership and with legislative sessions, updating policies became more streamlined by having only one source of information rather than training, email, and other official communications.

The online structure also enabled employees to refresh their knowledge of an entire process or search for quick answers. After developing the initial online version, the Institute trained agency staff to assume responsibilities and keep the online document up to date.  

The project was award winning but more importantly, frontline employees and supervisors were more comfortable following agency policy and law knowing it would be consistently implemented at every office. 

Find out how the Institute for Government Innovation can help you

This website was created by the OECD Observatory of Public Sector Innovation (OPSI), part of the OECD Public Governance Directorate ( GOV ).

Validation that this is an official OECD website can be found on the Innovative Government page of the corporate OECD website.

Innovation labs through the looking glass: Experiences across the globe

• Innovation Labs

Governments are constantly searching for enablers to help them to keep up with changing times, move beyond the constraints of traditional approaches, and leverage innovation to improve public policies and services. Public innovation labs belong to this stream of change. They aim to boost the ability of governments to navigate emergent threats and opportunities and provide sustained, inclusive and proactive responses to people’s needs and expectations. The last 20 years have seen a surge on the emergence, spread and occasionally death of public innovation labs across the globe. While the lab hype cycle might be behind us, government labs continue being incubated across high, middle, and low-income countries.

So, what can we learn from these experiences? To inform the design of new and the evolution of existing labs, we lay out the primary uses of innovation labs and aggregated lessons from public innovation labs across the globe. Our analysis is founded on the initial review and the insights gathered from a public innovation lab directory covering 137 cases from 37 countries and four international organisations. This analysis was complemented with an extensive literature review of research on public innovation lab experiences across the globe.

Not starting from scratch, we build on our own experience and research to share our initial contributions to this topic. At the same time, we want to use this occasion to kick-start an open-ended crowdsourcing exercise to gather first-hand experiences in engaging, managing, and leading public innovation labs and keep building on our knowledge base.

This first blog presents then our initial systematisation of the purposes and learnings of public innovation labs. In a second post, we will put forward factors that may have led labs to fall short of expectations.

Unpacking the purposes of public innovation labs

A pivotal characteristic of public innovation labs is the multiple formats, approaches and labels they take. Laboratory, itself, can interchangeably refer to the concrete space, interdisciplinary teams, portfolios of methods and practices, or temporal slots that structure and materialise its activities. Public innovation labs can be embedded in diverse sectors of government, placed at the center of government, or operate across administrative boundaries. However, common to all, is their feature as purpose-driven enabler of change: public innovation labs exist to generate, scale and/or spread public sector innovation and associated reform initiatives through tangible and practical initiatives. While there are many potential purposes for a lab, recent research suggests that it is important to clearly spell out the purposes for design, study and evaluation reasons. In addition, the purpose needs to align with overarching government priorities and be communicated clearly to decision makers, stakeholders, beneficiaries, and citizens at large.

From the initial review, a set of purposes stands out as especially relevant to define the mission, format, and activities of public innovation labs:

• Question business as usual in governments: public innovation labs are created out of dissatisfaction with existing solutions, entrenched processes, or pre-set answers. For the renewal of prevalent practices, labs bring alternative approaches to the fore. These labs open outwards-in processes , which don’t assume government have all the answers to all the challenges (“governments knows best”) and, instead, reframe its initiatives from the standpoint of citizens (or users at large) to design, assess and implement innovative initiatives. Among others, public innovation labs also heavily rely on rapid and agile interventions for their initiatives, breaking the cascading procedures of traditional policymaking thanks to their relative autonomous status, small size, multidisciplinary skill sets, and/or flexible and agile ways of working. Public innovation labs help also to recognise approaches and activities that may need to be “ discontinued ” in government (and in the lab itself, of course).
• Foster problem-oriented, context-sensitive approaches: Context matters. Innovations cannot be transplanted and replicated across countries, regions and/or organisations in a dogmatic way – they often need to be re-framed and/or re-contextualised. Public innovation labs engage with public sector challenges and problems as they emerge, orienting themselves to cope with existing gaps and bottlenecks as well as to bring knowledge, circulate information, and improve capacity to steward changes. For those reasons, labs heavily rely on context-sensitive approaches, adapting processes and solutions to be understandable and usable in those circumstances.
• Open safe spaces for experimentation inside government : Public innovation labs enable governments to explore and experiment (usually at small-scale, limited costs and controlled regulation) with alternative and creative approaches, counteracting risk aversion and resistance to change. Using prototypes, pilots and testbeds, among many others, labs can test solutions before implementing a new policy or service – and do that acting “ quick and dirty ”. They can also boost capabilities to gather, synthesise and use evidence for decision making . This often entails rigorous experimental ways of working as well as sourcing data, including insights from citizens and those directly affected by the challenge at hand.   
• Facilitate co-creation, inclusiveness, and participation : Labs facilitate approaches that build on networking, stakeholder engagement and participation, and co-creation. Against the centralised vision that insulates governments from their surroundings, these labs are prone to establish networked approaches, both with “internal” and “ external stakeholders ”, such as civil society organisations, the private sector, universities and research centers. At the same time, against the command-and-order approaches to decision making, public innovation labs help to include diverse voices, bridge gaps between (knowledge) communities, and correct power asymmetries in the ideation, design and development of innovative approaches. Public innovation labs see inclusive processes as key, while acknowledging that they are a necessary but not sufficient condition for better outcomes.
• Promote cultural change and capacity-building in public administration : Public innovation labs nurture practical engagement, (self-)confidence and often empathy among public servants and managers in the exploration and use innovative approaches, methods and tools. As such, labs can contribute to promote cultural changes that transform both skills and attitudes, appearing as learning terrains that complement existing pedagogical strategies.
• Demonstrate value : Public innovation labs can provide tangible evidence of the value of new ways of working, regardless of whether these are rooted in technology. Public innovation labs often first work on making the case for a new way of working, before expanding a government’s toolbox and bringing what was once at the edge to the core of how business is done. There is an intent to empower everyone in the public sector to leverage an approach that has proven its value, thus facilitating the transition of innovative methods from the confines of the laboratory into extensive application.

12 lessons learned about public innovation labs

While there is no universal recipe for success, our research identified 12 elements that are especially relevant to steer and strengthen the action of public innovation labs:

• Top-level sponsorship and sustained support: Protection and sponsorship of high-level decision-makers is key , together with the existence of strong mandates (legal and institutional) to pursue their mission and the access to sustainable and continued sources of resources. In order for public innovation labs to endure , they need to be cautious of relying on only one sponsor or champion and be mindful of shifting political agendas. The Lieu de la transformation publique (France), the innovation lab of the Inter-Ministerial Division of Public Transformation (DITP), given its transversal and cross-sectoral nature, is especially suited to federate and steward the innovation labs that are disperse across different ministries, public organisations, regions and municipalities, providing support and visibility to their initiatives.
• Target citizens’ needs and expectations: Public innovation labs are oriented to solve concrete policy challenges and build on citizens needs and expectations. Labs target these needs and expectations not just as part of their narratives, but use them to set their strategic objectives and prove their significance and justify their existence. Participation of users in the ideation, design, development and test of potential solutions ensures that public innovation labs stay “ close to reality ”. The Bangladesh Government Innovation Lab has inscribed citizen-centric at the core of its definition of success: one of the initial principal drivers of change was the reduction of the “TCV”, i.e. the time (T), the costs (C) and the number of visits (V) that citizens need to access public services. The lab has achieved considerable impact on this metric and has since expanded its work as the ‘A spire to Innovate ’, a multinational digital transformation organization.
• Organisational embeddedness and autonomy : The integration of public innovation labs into public sector organisations ensures that their team is not isolated, but rather involved with the organic life of the public administration. Yet the format of the institutionalisation of public innovation labs can ensure its relative “ independence ”. While labs have to keep their autonomy protected in order to explore and experiment with innovative approaches, this embeddedness also brings access to pools of resources, support services (e.g. administrative or communication), and the ability to interact directly with government sectors and organisations. NIDO , the innovation lab of Belgium’s public administration, has built itself as a “safe environment” that allows public servants and managers to identify and analyze the challenges of their organisations as well as to reflect in an open way about innovative approaches. The lab even plays on words to present its mission: the lab wants to be the nest, “nid” in French, to “strong ideas and innovative solutions”.
• Skills and attitudes in core team: Public innovation lab teams rely on the right balance of skills, attitudes and mindsets to answer the challenges at hand and to preserve an environment favorable to innovative activities and initiatives. Labs with sustained activities seem to have a combination of diverse profiles and a multidisciplinary portfolio. GNova, the innovation lab from the National School of Public Administration (Brazil), has developed the “ CoLab ”, a capacity-building and mentorship programme to support lab teams across the country to access leadership, management and technical skills. After an open call for applications, the programme supported 10 units in its first edition (2022).
• Redistribution and capillarity: Labs that actively engage stakeholders and build up their initiatives through exchanges and partnerships across government and with the innovation ecosystem raise their visibility, circulate information and expertise more easily, and increase the mobilsation of resources. Networks and communities of practice are among the most recognisable initiatives of labs – including to connect labs with each other. In Peru, the “ national network of innovation labs ” connects 118 labs, incubators and accelerators to ensure the transference and circulation of experiences as well as to build shared agendas in a decentralised way.
• Free port policy: Gatekeepers and potential “ veto players ” have to be integrated early into the discussion of the initiative. Successful labs work proactively on identifying important players that might block their work and longevity. They define strategies to engage them and secure their support from an early stage. For ensuring a constant and direct connection with its beneficiaries and relevant stakeholders, GovLabAustria has established a “sounding board”. The board members’ meet to provide the lab with their knowledge and networking abilities, besides acting as “sparring partners” for the lab projects.
• Methodologically, not expertise, based : While there are labs that circumscribe their areas of intervention, diverse and adaptable portfolios and kits are assets that accelerate the capacity of labs to react and adapt to changing priorities and urgencies. Ultimately, the selection of methods and tools depend on the purposes of the lab. Often labs have developed and subscribed to process-based approaches to the resolution of problems , often through actionable, experimental and iterative logics. The innovation lab of the city of Bogotá (Colombia), iBO has adopted a methodology that rests on the “maker culture”, emphasizing guiding principles – such as citizen-centric design or systemic thinking – and combining multiple domains of knowledge.
• Experimentation, iteration and simulation: The adoption of robust research, design-driven and experimental methodologies in public innovation labs – that often appear as “ islands of experimentation ” – help to justify decision-making processes, improve the quality of delivery, and provide strong legitimacy. Portugal’s LabX has been adopting an extensive portfolio of experimental approaches in its projects, including the organisation of living labs in public services, the adoption of do-it-yourself approaches to design tools, or the use of gamification to solve public challenges.
• Transfer and dissemination of experiences and learnings : Public innovation labs are often invested in learning, from design to implementation. They seek out lessons from other contexts, either to prevent repeating errors or to profit from consolidated methods and solutions (“what works”). Sharing practices, products and methods also helps to strengthen the innovation culture in the public sector at large and builds a favorable environment for labs to thrive. The Japan+D is a team of volunteers in the Japanese Ministry of Economy, Trade and Industry that wants to spread design approaches and methods as a way to support the transformation of Government. The team gathers and shares knowledge, searches to implement cross-sectional initiatives, and connects with relevant experts and units at the national and international level.
• Time to thrive: Public innovation labs are able to provide fast actions and early results, while nonetheless giving enough time to build the necessary durable relationships and close-knit communities for impact to blossom. The Laboratorio de Gobierno (Chile) is its way to complete its 10 th anniversary in 2025. Since 2015, the lab has known changes in its team, its work priorities and streams, and even its institutional position (right now, it is located in the Ministry of Finance). This long journey was at the same time crucial to enable its initiatives and programmes to demonstrate their value and to be constantly improved, as happened with the successive iterations of the Innovation Index .
• Measurement and evaluation : Tracking, monitoring, and measuring the value-creation and impact of lab projects, outputs and practices is critical to sustain its mission. It is also part of their commitment to deliver societal value and conduct transparent, accountable, useful, and responsible activities. The existence of (self-) evaluation loops is also important to receive feedback, consolidate learnings, and fine-tune current activities to the ongoing changes. The iLab , the Northern Ireland Innovation Lab (United Kingdom), has commissioned an evaluation of its activities and governance (leadership, operating model, methods, capacity), including impact case studies (to provide tangible evidence on investments), to gather good practices and identify pitfalls.
• Storytelling : Public innovation labs that engage with their own communities, the government at large, and the public sphere need to tailor their messages to specific audiences, create or explore the most suitable channels, and find compelling formats and styles to convey their narrative. The Solutions Lab , in the city of Vancouver, Canada, has been communicating its mission, history and ambitions using a strongly visual journey – and emphasizing publicly that its existence is a vocation that answers a call “to respond to the root causes of these systemic challenges, not just apply incremental quick fixes”.

Tell us about your own experience!

We want to expand our perspective by including new and diverse examples and experiences of public innovation labs. In doing so, we will be able to present a more comprehensive and fine-grained account of the status, capacities, strategies and visions of labs. Share your contributions in this short questionnaire here . While the questionnaire is open-ended, we plan a first feedback review phase by late March 2024. 

This is the first in a series of blogs on public innovation labs. The second blog will be about why many labs fall short of expectations, followed by a blog that highlights the OECD Observatory of Public Sector Innovation’s experience in designing and piloting an innovation lab in the Government of Romania , the Laboratorul de Inovare. In a final blog, we will raise questions about the possibilities to re-invest public innovation labs in the face of the emerging and complex challenges faced by governments.

This blog is funded by the European Union. Its contents are the sole responsibility of the authors and do not necessarily reflect the views of the European Union.

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Streamlining processes and standing up HR operations with PwC’s Total Workforce Management solution

From acquisition to autonomy: how a tech company transformed its workforce

• May 29, 2024

A regional tech company faced the challenge of establishing a new company after an acquisition, while also scaling its workforce. To avoid costly transition services agreements (TSAs) and preserve deal value, it needed a rapid HR system separation. The company worked with PwC to swiftly move its enterprise-wide HR operations to SAP and stand up its own system. The solution provides unprecedented visibility across the organization and empowers leadership to make data-driven decisions that improve employee experience.

Regional Tech Company

time and pay accuracy after converting enterprise data from legacy systems over to SAP

faster than industry standard timeline to implement SAP SuccessFactors and Fieldglass for 6,000+ employees and contractors

HR TSAs required post-divestiture, despite accounting for HR and tax nuances in 35 states and 25+ employee unions, which helped preserve deal value

A human-led, tech-powered workforce transformation enables transparency and helps build trust with stakeholders

PwC shares the path to operational efficiency

What was the challenge.

The challenge was managing rapid change amid a complex acquisition . The client needed to physically separate the HR, payroll and operations systems of its newly acquired company to avoid relying on the former owner’s tech infrastructure via costly TSAs.

Speed was key. The goal was to stand up the new systems as quickly as possible without a significant impact on either company’s daily operations, which span 35 states. Simultaneously, the team also had to onboard thousands of employees overnight, causing a rapid scaling of the HR organization.

Describe the solution delivered by the PwC community of solvers

PwC’s Total Workforce Management solution powered by SAP was chosen to streamline HR processes and manage all related operations. This comprehensive, cloud-based HR suite integrates modules like S/4HANA, SuccessFactors and Fieldglass to efficiently handle talent management, learning, recruitment, timekeeping, finance (including financial planning and analysis) and contractor management. The automation tools and data cleansing enabled a smooth transition under a tight deadline, along with accurate financial data posting and streamlined payment processing for both contractors and over 25 employee unions across the business.

Transitions of this magnitude typically take at least 12 to 15 months, but PwC did it in 9 months. The client now has great operational efficiency and workforce management capabilities.

How does the solution blend the strengths of technology and people?

Despite the time constraints, PwC quickly implemented Total Workforce Management and the Experience Suite framework . This is a digital SuccessFactors-driven solution that provides tools to enhance employee upskilling, labor sourcing and localized people management. The solution simplified governance, improved visibility and empowered smarter decisions as the organization grew. Within the Experience Suite, you could see exactly what the system build would look like via a test environment, incorporating standardized practices to meet the deadline as an independent company.

Where or how did innovation and unexpected ways of thinking come into play?

PwC’s Experience Suite framework provided a practical and efficient approach to setting up a new system. This included leading practices and pre-built models based on PwC’s extensive experience with SAP SuccessFactors and Fieldglass implementations. It streamlined project management, reduced decision-making time and minimized complexities. PwC’s fit-to-standard approach also helped provide a standard system setup and HR enhancements to simplify the implementation process. The team’s innovative solutions truly made a difference in the workforce transformation journey.

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College of Agricultural and Environmental Sciences

Dan Potter Named Department of Plant Sciences Chair

Goals include streamlining curriculum, fostering diversity and unity.

• by Emily C. Dooley
• June 03, 2024

Dan Potter is the new chair of the Department of Plant Sciences, overseeing graduate and undergraduate programming, 60 faculty members, several emeriti professors and many greenhouses, labs and teaching facilities.  

Potter, who has been at UC Davis for nearly 28 years, researches the diversity and classification of flowering plants, focusing on horticultural crop plants and their wild relatives, the effects of humans on plant evolution and taxonomy of selected species native to California. He also studies ethnobotany, which examines the interactions of plants and people, and serves as director of the Center for Plant Diversity Herbarium. 

The department offers three undergraduate and two graduate degree programs centered on tackling agricultural, ecological and environmental issues. Professors teach and conduct research and Cooperative Extension specialists do outreach work and education with growers, industry and others. It’s among the largest department in the College of Agricultural and Environmental Sciences.

Diversity of interest, study area and experience

Courses cover a variety of topics, including biotechnology, plant breeding, genomics, crop quality, ecological management, environmental horticulture and international agricultural development.  

That diversity is something Potter wants to foster. 

“Diversity is really one of the greatest strengths of the department, if not the greatest, but it’s also one of the biggest challenges, because how do you keep it all going?” Potter said. “One of my priorities is maintaining that diversity and doing as much as possible to ensure that all the diverse perspectives and all the diverse interests get addressed and that people feel like they’re getting the attention they need.” 

The department’s classrooms, labs and facilities are spread out around campus, which can make it hard for students – graduate and undergraduate – to connect. Potter hopes to increase the sense of connectivity and unity among students and the department. “We’re so big and diverse and spread out over a lot of buildings,” he said. “That’s been a bit of a challenge.”

The department has been streamlining curriculum to ensure classes don’t overlap and Potter, who served as the department’s vice chair of teaching for seven years, said he would continue that effort, especially as long-time faculty retire. Increasing lab space is also a priority. 

“We want to continue providing really good experiential teaching,” he said. 

Potter replaces Professor Gail Taylor, who served as chair since 2017 and left UC Davis earlier this year to become a dean at University College London.

Media Resources

• Dan Potter, Department of Plant Sciences, [email protected]
• Emily C. Dooley, College of Agricultural and Environmental Sciences, [email protected]

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Study: 61% of the US to have cardiovascular disease by 2050

(CNN) – The American Heart Association is predicting about 61% of adults in the United States will have cardiovascular disease in the next two decades.

The group released research Tuesday, which found 45 million adults will have some form of cardiovascular disease or a stroke by 2050.

That number is up from 28 million in 2020.

Researchers said a big driver of the trend is the growing number of people likely to develop high blood pressure, which puts them more at risk for a heart attack or stroke.

An aging population is also a factor.

Heart disease is responsible for more than 800,000 deaths every year, making it the leading killer of Americans.

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