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research papers on industry

  • 13 May 2024
  • Research & Ideas

Picture This: Why Online Image Searches Drive Purchases

Smaller sellers' products often get lost on large online marketplaces. However, harnessing images in search can help consumers find these products faster, increasing sales and customer satisfaction, finds research by Chiara Farronato and colleagues.

research papers on industry

  • 01 Apr 2024
  • In Practice

Navigating the Mood of Customers Weary of Price Hikes

Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.

research papers on industry

  • 17 Jan 2024

Psychological Pricing Tactics to Fight the Inflation Blues

Inflation has slowed from the epic rates of 2021 and 2022, but many consumers still feel pinched. What will it take to encourage them to spend? Thoughtful pricing strategies that empower customers as they make purchasing decisions, says research by Elie Ofek.

research papers on industry

  • 05 Dec 2023
  • Cold Call Podcast

Tommy Hilfiger’s Adaptive Clothing Line: Making Fashion Inclusive

In 2017, Tommy Hilfiger launched its adaptive fashion line to provide fashion apparel that aims to make dressing easier. By 2020, it was still a relatively unknown line in the U.S. and the Tommy Hilfiger team was continuing to learn more about how to serve these new customers. Should the team make adaptive clothing available beyond the U.S., or is a global expansion premature? Assistant Professor Elizabeth Keenan discusses the opportunities and challenges that accompanied the introduction of a new product line that effectively serves an entirely new customer while simultaneously starting a movement to provide fashion for all in the case, “Tommy Hilfiger Adaptive: Fashion for All.”

research papers on industry

  • 05 Jul 2023

How Unilever Is Preparing for the Future of Work

Launched in 2016, Unilever’s Future of Work initiative aimed to accelerate the speed of change throughout the organization and prepare its workforce for a digitalized and highly automated era. But despite its success over the last three years, the program still faces significant challenges in its implementation. How should Unilever, one of the world's largest consumer goods companies, best prepare and upscale its workforce for the future? How should Unilever adapt and accelerate the speed of change throughout the organization? Is it even possible to lead a systematic, agile workforce transformation across several geographies while accounting for local context? Harvard Business School professor and faculty co-chair of the Managing the Future of Work Project William Kerr and Patrick Hull, Unilever’s vice president of global learning and future of work, discuss how rapid advances in artificial intelligence, machine learning, and automation are changing the nature of work in the case, “Unilever's Response to the Future of Work.”

research papers on industry

  • 25 Apr 2023

How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model

The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.

research papers on industry

  • 11 Apr 2023

Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide

More companies are bringing seemingly unrelated businesses together in new ways, challenging traditional stock categories. MarcAntonio Awada and Suraj Srinivasan discuss how applying machine learning to regulatory data could reveal new opportunities for investors.

research papers on industry

  • 04 Apr 2023

Two Centuries of Business Leaders Who Took a Stand on Social Issues

Executives going back to George Cadbury and J. N. Tata have been trying to improve life for their workers and communities, according to the book Deeply Responsible Business: A Global History of Values-Driven Leadership by Geoffrey Jones. He highlights three practices that deeply responsible companies share.

research papers on industry

  • 03 Mar 2023

When Showing Know-How Backfires for Women Managers

Women managers might think they need to roll up their sleeves and work alongside their teams to show their mettle. But research by Alexandra Feldberg shows how this strategy can work against them. How can employers provide more support?

research papers on industry

  • 27 Feb 2023

How One Late Employee Can Hurt Your Business: Data from 25 Million Timecards

Employees who clock in a few minutes late—or not at all—often dampen sales and productivity, says a study of 100,000 workers by Ananth Raman and Caleb Kwon. What can managers do to address chronic tardiness and absenteeism?

research papers on industry

  • 06 Dec 2022

Latest Isn’t Always Greatest: Why Product Updates Capture Consumers

Consumers can't pass up a product update—even if there's no improvement. Research by Leslie John, Michael Norton, and Ximena Garcia-Rada illustrates the powerful allure of change. Are we really that naïve?

research papers on industry

  • 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

research papers on industry

  • 21 Nov 2022

Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income Shoppers

More consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers.

research papers on industry

  • 18 Oct 2022

Chewy.com’s Make-or-Break Logistics Dilemma

In late 2013, Ryan Cohen, cofounder and then-CEO of online pet products retailer Chewy.com, was facing a decision that could determine his company’s future. Should he stay with a third-party logistics provider (3PL) for all of Chewy.com’s e-commerce fulfillment or take that function in house? Cohen was convinced that achieving scale would be essential to making the business work and he worried that the company’s current 3PL may not be able to scale with Chewy.com’s projected growth or maintain the company’s performance standards for service quality and fulfillment. But neither he nor his cofounders had any experience managing logistics, and the company’s board members were pressuring him to leave order fulfillment to the 3PL. They worried that any changes could destabilize the existing 3PL relationship and endanger the viability of the fast-growing business. What should Cohen do? Senior Lecturer Jeffrey Rayport discusses the options in his case, “Chewy.com (A).”

research papers on industry

  • 06 Sep 2022

Reinventing an Iconic Independent Bookstore

In 2020, Kwame Spearman (MBA 2011) made the career-shifting decision to leave a New York City-based consulting job to return to his hometown of Denver, Colorado, and take over an iconic independent bookstore, The Tattered Cover. Spearman saw an opportunity to reinvent the local business to build a sense of community after the pandemic. But he also had to find a way to meet the big challenges facing independent booksellers amid technological change and shifting business models. Professor Ryan Raffaelli and Spearman discuss Spearman’s vision for reinventing The Tattered Cover, as well as larger insights around how local businesses can successfully compete with online and big box retailers in the case, “Kwame Spearman at Tattered Cover: Reinventing Brick-and-Mortar Retail.”

research papers on industry

  • 26 Jul 2022

Burgers with Bugs? What Happens When Restaurants Ignore Online Reviews

Negative Yelp reviews hold more sway with consumers than restaurateurs might think. A machine learning study by Chiara Farronato reveals how online platforms amplify the customer voice, and why business owners should listen.

research papers on industry

  • 22 Mar 2022

How Etsy Found Its Purpose and Crafted a Turnaround

Etsy, the online seller of handmade goods, was founded in 2005 as an alternative to companies that sold mass-manufactured products. The company grew substantially, but remained unprofitable under the leadership of two early CEOs. Ten years later, Etsy went public and was forced into a new arena, where it was beholden to stakeholders who demanded financial success and accountability. Unable to contain costs, the company was almost bought out by private equity firms in 2017—until CEO Josh Silverman arrived with a mission to save the company financially and, in the process, save its soul. Harvard Business School professor Ranjay Gulati discusses the purpose-driven turnaround Silverman and his team led at Etsy—to make the company profitable and improve its social and environmental impact—in the case, “Etsy: Crafting a Turnaround to Save the Business and Its Soul.” Open for comment; 0 Comments.

research papers on industry

  • 05 Nov 2021

Is the Business World Finally Ready for the Wisdom of Shibusawa?

Legendary financier Eiichi Shibusawa advocated for business prosperity that would also benefit society. One hundred years after his death, his message is resonating with a new generation of leaders, say Geoffrey Jones and Rei Morimoto. Open for comment; 0 Comments.

  • 19 Oct 2021

Fed Up Workers and Supply Woes: What's Next for Dollar Stores?

Willy Shih discusses how higher costs, shipping delays, and worker shortages are putting the dollar store business model to the test ahead of the critical holiday shopping season. Open for comment; 0 Comments.

research papers on industry

  • 13 Jul 2021

Strategies for Underdogs: How Alibaba’s Taobao Beat eBay in China

In 2007, Alibaba’s Taobao became China’s leading consumer e-commerce marketplace, displacing the once dominant eBay. How did underdog Taobao do it? And will it be able to find a way to monetize its marketplace and ensure future success? Professor Felix Oberholzer-Gee discusses his case, “Alibaba’s Taobao,” and related strategy lessons from his new book, Better, Simpler Strategy: A Value-Based Guide to Exceptional Performance. Open for comment; 0 Comments.

Pulp, paper, and packaging in the next decade: Transformational change

From what you read in the press and hear on the street, you might be excused for believing the paper and forest-products industry is disappearing fast in the wake of digitization. The year 2015 saw worldwide demand for graphic paper decline for the first time ever, and the fall in demand for these products in North America and Europe over the past five years has been more pronounced than even the most pessimistic forecasts.

But the paper and forest-products industry as a whole is growing, albeit at a slower pace than before, as other products are filling the gap left by the shrinking graphic-paper 1 The graphic-paper segment includes newsprint, printing, and writing papers. market (Exhibit 1). Packaging is growing all over the world, along with tissue papers, and pulp for hygiene products. Although a relatively small market as yet, pulp for textile applications is growing. And a broad search for new applications and uses for wood and its components is taking place in numerous labs and development centers. The paper and forest-products industry is not disappearing—far from it. But it is changing, morphing, and developing. We would argue that the industry is going through the most substantial transformation it has seen in many decades.

In this article, we outline the changes we see happening across the industry and identify the challenges CEOs and their leadership teams will need to manage over the next decade.

Changing industry structure

The structure of the industry landscape is changing. The changes are not dramatic individually, but the accumulation of changes over the long term has now reached a point where they are making a difference.

Consolidation has been a major factor in many segments of the industry. The big have become bigger in their chosen areas of focus. At the aggregate level, the world’s largest paper and forest-products companies have not grown much, if at all, and several of them have reduced in size. What they have done is focus their efforts on fewer segments. As a result, concentration levels in specific segments have generally, if not universally, increased (Exhibit 2). In some segments such as North American containerboard and coated fine paper, ownership concentration as defined by traditional approaches to drawing segment boundaries may be reaching levels where it would be difficult for companies to find further acquisition opportunities that could be approved by competition authorities.

A grouping of companies has emerged that is not identical to, but partly overlaps with, the group of largest companies, and is drawn from various geographies and market segments. Companies in this group have positioned themselves for further growth through high margins and low debt (Exhibit 3). Our analysis suggests the financial resources available to some members of this group for strategic capital expenditure could be five to ten times greater than other top players in the industry. This potentially represents a powerful force for change in the industry, and over the next few years it will be interesting to see how these companies choose to spend their resources. Some of these companies with large war chests and sizable annual cash flows deployable for strategic capex might even face a challenge to find opportunities on a scale that matches these resources.

Where there are leaders, there are also laggards. We believe the pronounced differences in performance among companies across the industry continues to pique the interest of investors and private-equity players in an industry that is already undergoing substantial restructuring and M&A.

Changing market segments

Whether companies are well positioned for further growth or still needing to earn the right to grow, they can expect demand to grow for paper and board products over the next decade. The graphic-paper market will continue to face declining demand worldwide, and our research has yet to find credible arguments for a specific floor for future demand. But this decline should be balanced by the increase in demand for packaging—industrial as well as consumer—and tissue products. All in all, demand for fiber-based products is set to increase globally, with some segments growing faster than others (Exhibit 4).

That picture is not without its uncertainties. One hazy spot in the demand skies might be concerns over how fast demand will grow in China. Expectations of growth from only a few years ago have proved a bit too optimistic, not only in graphic papers but also in tissue papers and packaging. And recently, as a result of turmoil in the market for recycled fiber, Chinese users of corrugated packaging have reduced their consumption, through weight reductions and use of reusable plastic boxes. Given China’s weight in the global paper and board market, even relatively modest changes can have significant impact.

How these demand trends will translate into industry profitability will of course be heavily influenced by the industry’s supply actions. Supply movements are notoriously difficult to forecast more than a few years out, but we believe the following observations are relevant to this discussion.

  • Graphic papers, particularly newsprint and coated papers but also uncoated papers, will continue to face a severe decline in demand and significant pressure to restructure production capacity. We are likely to see continuing machine conversions into packaging and specialty papers, as well as more innovative structural moves that include innovations in distribution and the supply chain. Such structural changes are already having an impact and the profitability of graphic-paper companies has reemerged from several years in the doldrums. The turbulence in graphic papers has meanwhile spilled over to packaging and tissue segments, with capacity increases in segments that don’t really need it.
  • Consumer packaging and tissue will be driven largely by demographic shifts and consumer trends such as the demand for convenience and sustainability. It will grow roughly on par with GDP. We expect innovation to be a critical success factor, particularly in light of recent concerns over plastic packaging waste, which could harbor both opportunities and challenges for fiber-based consumer packaging. But we are uncertain how far packaging players can drive innovation by themselves. Clearly, they can take the lead on materials development, but they may need to follow the lead of—and cooperate with—retailers and consumer-goods companies in areas such as formats, use, and technology. At the same time, the inflow of capacity from the graphic-paper segment will need to be managed.
  • Transport and industrial packaging will also see opportunities for innovation and a certain amount of value-creating disruption in the intersection between sustainability requirements, e-commerce, and technology integration. We estimate that e-commerce will drive roughly half of the demand growth in transport packaging over the next several years. As packaging adapts to this particular channel, it will have to find new solutions to a variety of issues, such as how to handle last-mile deliveries, the sustainability choice between fiber-based and lightweight plastic packaging, and the potential merging of transport (secondary) and consumer (primary) packaging, to name but a few.
  • Fiber has gone through some turbulent times in the past two years, largely to the delight of pulp producers and to the chagrin of users. Hardwood and softwood prices alike have seen steady increases since 2017, due to some slow start-up of capacity (hardwood pulp), limited capacity additions, and a certain measure of industry psychology. In the past two years, prices globally went through what we would term a “fly-up regime,” whereby prices are significantly and unusually higher than the cost of the marginal producer. Such situations, seen from time to time in many other basic-materials industries, are rarely long lived. Indeed, since the beginning of 2019, prices have come down—in China drastically so.

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But even with a readjustment of the market, the midterm prospects are likely to be in favor of the producers, with little new capacity until 2021–22 and some softwood capacity that is likely to be converted to other products, such as pulp for textile applications. For softwood particularly, challenges in expanding the forest supply are constraining new supply. Also, the fact that much of the industry’s softwood-production assets are aging and need complete renewal or substantial upgrades could further contribute to scarcity, especially since the scale of the investments required is a potential roadblock to them being made.

The lingering question is whether such supply-side challenges can trigger an accelerated development of applications that are less dependent on wood-fiber pulp.

Challenges for the next decade

In such an environment, what are the key challenges senior executives will need to address? What are the key battles they will have to fight? The paper and forest-products industry is often labelled a “traditional” industry. Yet given the confluence of technological changes, demographic changes, and resource concerns that we anticipate over the next decade, we believe the industry will have to embrace change that is, in character, as well as pace, vastly different from what we have seen before—and anything but traditional. This will pose significant challenges for CEOs regarding how they manage their companies.

We argue that there are three broad themes that paper and forest-products CEOs will have to address through 2020 and beyond:

  • Managing short-to-medium-term “grade turbulence”

Finding the next level of cost optimization

  • Finding value-creating growth roles for forest products in a fundamentally changing business landscape

Managing short-to-medium-term ‘grade turbulence’

The past couple of years have seen increased instability in forest-products segments. The negative impact of digital communications on graphic paper has led many companies to steer away from the segment and into higher-growth areas, either through conversion of machines or through redirection of investment funds. This is leading to a higher level of uncertainty and overcapacity in, for example, packaging grades. The instability has also been exacerbated by the capacity additions that primarily Asian producers have made despite the slowing demand growth in that region.

A case in point is virgin-fiber cartonboard. Several producers in Europe have converted machines away from graphic paper and into this segment, creating further oversupply in Europe and leading producers to redouble their efforts to sell to export markets. This is happening just as increasing capacity in Asia, and particularly in China, looks set to displace imports that have traditionally come into the region, mainly from Europe and North America. Some of the new Asian capacity could even find its way into export markets.

This development is likely to persist for several years until markets again find more of an equilibrium, and it poses challenging questions for companies. What, if any, safe havens exist for my products? How do I protect home-market volumes? How do I protect my export volumes? What is the appropriate pricing strategy to use in the different regions?

For CEOs looking to move into a new market segment, it will be equally important to make the right assessment of which segments to enter as they shift their footing. Where will I be the most competitive? How will my entry change market dynamics, and will this matter to me?

On the raw-materials (fiber) side, we have already described the past couple of years’ turbulence in virgin pulp. If that might seem to trend toward stabilization, the situation in recycled fibers is still very uncertain. As China, and gradually other Asian countries, have increasingly restricted the import of recovered fiber (as well as plastics and other recovered materials), the dynamics have shifted. While prices of old corrugated containers (OCC) and other papers for recycling have plummeted in North America and Europe, prices of domestic Chinese OCC have increased drastically, challenging both the price and availability of recycled-based corrugated board. In response, companies have set up capacity to produce recycled-fiber pulp to export to China, while the country is jacking up its import of containerboard for corrugated packaging, as well as virgin fiber for strengthening purposes.

This of course affects how companies, in any country, think about their fiber-supply strategies as well as their product focus.

Even though we see new ways of creating value in the forest-products industry, low cost is, and will remain, a critical factor for high financial performance. One of the characteristics shared by companies with high margins and high returns is that they have access to low-cost raw materials, primarily fiber. This will continue to be a high-priority area, albeit with some twists compared with today.

Beyond the price increases of the past couple of years, fresh fiber is facing other, more long-term, cost issues. It is unclear whether plantation land in the southern hemisphere (primarily for short-fiber wood) will continue to be available at current low prices. And as companies go to more remote areas to acquire inexpensive land, such as in Brazil, their infrastructure and logistics costs increase. Will higher productivity and yield allow the global industry to add ever more low-cost capacity, or are we going to see a gradual increase in raw-material costs? For long-fiber products, the difficulties to expand long-fiber pulp capacity will make such assets very valuable over the next several years. But at what point will development of the material properties of short-fiber pulps make them rival more expensive long-fiber pulps in a number of major applications?

Operating costs for paper and board production are another area where companies need to get a tighter grip. Despite the fact that this area receives continual focus from management, our experience suggests there is still significant potential for cost reduction by using conventional approaches to work smarter and reduce waste in the production chain. This is particularly the case in areas that are less the focus of management attention, such as converting.

Many companies need to go beyond the conventional approaches to a next level of cost optimization—and many are ready to take this step. Most if not all paper and forest-products companies have completed large fixed-cost reduction programs. But there are often broader systemic issues that companies still need to address to be able to build sustainable operating models. In addition, in some segments many companies fail to reduce fixed costs as quickly as capacity disappears. By radically rethinking the operating model, companies can significantly shift their fixed-cost structure. By doing so, they can set a very different starting point in terms of flexibility and agility for when market volumes go through their normal cyclical swings.

The paper and forest-products industry has much to gain from embracing digital manufacturing : according to our estimates, this could reduce the total cost base of a producer by as much as 15 percent. New applications such as forestry monitoring using drones or remote mill automation present tremendous opportunities for increased efficiency and cost reductions. This is also the case in areas where big data can be applied, for instance, to solve variability and throughput-related issues in each step of the integrated production flows (Exhibit 5). The industry is well placed to join the digital revolution, as paper and pulp producers typically start from a strong position when it comes to collected or collectable data.

At the customer-facing end, the opportunity for innovation is huge and has the potential to transform existing industries and create new ones, especially in packaging segments. Digital developments will also help disrupt previous B2B2C value chains, paving the way for direct B2C relationships between paper-product makers and end consumers, for example, in tissue products.

The digital world is unfamiliar territory to most paper industry CEOs. To avoid too much doodling with small uncoordinated efforts, it is necessary to undertake a thought-through program, preferably guided by digitally experienced people either on the top-management team or board.

Finding value-creating growth roles for forest products

For any paper-company CEO who looks out ten years, the really different challenges will not be around cost containment. Global trends are moving the industry into a new landscape, where the challenges and opportunities for finding value-creating growth roles for forest products are changing radically. For example, the industry’s historic linear value chains are giving way to more collaborative structures with players in and outside the industry. We believe examples will include new producer and distributor collaborations; pulp players collaborating more innovatively with non-integrated players; paper and packaging companies collaborating more intensively with retailers, consumer-goods companies, and technological experts; and new products such as bio-refinery products requiring novel go-to-market partnerships. Here are some interesting examples of how these and other trends could play out.

Staying relevant (and increasing relevancy) in a fast-changing packaging world. The packaging market is multifaceted and continuously morphing. Digital developments influence it both by stimulating demand for packaging used in e-commerce and by enabling the integration into packaging of sensors and other technology. E-commerce has highlighted new packaging topics such as improved product safety, the “un-boxing” experience, counterfeiting measures, optimization for last-mile delivery , and a growing interest—at least from the large e-commerce-based retailers—in the possibility of merging primary and secondary packaging. At the same time, the packaging industry has to deal with increasing pressures around cost, resource conservancy, and sustainability. That last topic has gained huge momentum in the past couple of years as concerns over plastic waste have added to the concern over CO 2 emissions from fossil-based packaging materials. Consumer-goods companies, retailers, packagers, and policy makers alike are now exploring a wide range of possible solutions for what tomorrow’s packaging will look like.

The opportunity for forest-products companies to develop a differentiated and distinct customer value proposition in this landscape has never been greater. Packaging-materials CEOs will have to address a number of choices and trade-offs as they seek the appropriate strategic posture. Should you be a pure upstream player or a packaging-solutions provider? Should you focus on fiber-based packaging only or providing multi-substrate solutions? Should you be at the forefront of technology integration and application development in packaging or focus on materials development?

To stay relevant, many companies in packaging are trying to move closer to the brand owner or end user. Only a few companies are positioned to successfully make this move, however, and even they should be cautious. We are already seeing brand owners and leading customers challenging the benefits of packaging companies coming with consumer-facing ideas such as complete packaging concepts. Some of these players would prefer packaging companies to focus instead on core competencies such as materials development or interfaces with other substrates such as plastics.

How the paper and forest-products industry thrives in the digital age

How the paper and forest-products industry thrives in the digital age

Finding the right path in next-generation bio-products. Wood is a biomaterial with exciting properties, from the log on down to fibers, micro- and nanofibers, and sugar molecules. A healthy niche industry making bio-products has existed for many years alongside large-volume pulp, paper, and board products. We are in the midst of an explosion of research activity to develop new bio-products, ranging from applications for nanofibers to composite materials and lignin-based carbon fiber. New processes  are being designed to extract hemicellulose as feedstock for sugars and chemical production while still keeping the cellulose parts of the wood chip for pulp products.

We believe wood-based products will find new ways to enlarge their footprint in a more sustainable global economy. But the challenges are legion, particularly for finding cost-effective production methods that can withstand competition not only from oil-based materials but also from other biomaterials. Finding the right balance between developing the “new” and safeguarding the “old” will be a crucial undertaking for executives running companies with access to fresh fiber.

Finding growth in adjacent areas. Over the past decade or two we have seen the larger forest-products companies performing a focus adjustment. Most companies have moved from being fairly broad conglomerates present in various forest-products segments to focusing on a few core businesses. To find value-creating growth in the next two decades, we expect companies to start broadening their corporate portfolio again, but broadening it around the core businesses they have been working on, so as to create differentiated customer value propositions. Finding value-creating adjacencies to the core business will be a challenging exercise in creativity and business acumen for executive teams.

Finding new value-creating growth for forest products will also put the spotlight on a number of functional executive topics. We believe the following will be most important.

  • Innovation: The forest-products industry has not been known for a fast-paced innovation agenda. By and large it hasn’t been necessary, as markets and demand characteristics have changed relatively slowly. In the future, however, innovation in products, processes, organizational setup, and business models will be imperative. For many companies, getting efficient innovation practices and organization up to speed will be an important challenge.

Talent management: The different skills required over the next ten to 15 years, dictated by developments such as new business models in an online world, increased need for innovation and commercialization of products, and digitalization’s impact on everything from manufacturing processes to the content of work will put particular onus on the talent pool  of forest-products companies. Installing an executive team that is able to understand new demands across customer businesses, digital, bio-products that cater to completely different value chains, and cross-industry collaboration will be a major task for CEOs and boards.

One particular war-for-talent battle that can become a key differentiator is the content of work. Our research on the future of work  highlights that already today, around 60 percent of all tasks, that is, not entire jobs or roles but their components, can be automated. And looking to the coming ten to 15 years, more than 30 percent of physical and manual skills risk becoming obsolete while technological skills will continue to grow very quickly. This will provide a critical and likely success-defining reskilling challenge for companies in the industry.

  • Commercial excellence: Paper and forest-products companies will need to transform their commercial interface to stay relevant, particularly in packaging and downstream paper. They will need to put in place a more professionalized and skilled organization that focuses on value creation instead of focusing primarily on sales volumes.

We believe the paper and forest-products industry is moving into an interesting decade, one that will see nothing less than a transformation of large parts of the industry. There will be many barriers to overcome and metaphorical cliffs to fall off. But the companies that are able to navigate through successfully can look forward to an industry that has a new sense of purpose and an increasingly vital role to play.

This article was updated in August 2019; it was originally published in May 2017.

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Peter Berg  is a director of knowledge in McKinsey’s Stockholm office, where Oskar Lingqvist  is a senior partner. Together they lead McKinsey’s global Paper & Forest Products Practice.

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Robust optimization for a steel production planning problem with uncertain demand and product substitution

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Bibliometrics & citations, view options, recommendations, models and solution techniques for production planning problems with increasing byproducts.

We consider a production planning problem where the production process creates a mixture of desirable products and undesirable byproducts. In this production process, at any point in time the fraction of the mixture that is an undesirable byproduct ...

Optimal production planning in a petrochemical industry using multiple levels

Proposed mixed integer programming formulation for production planning.Proposed formulation incorporates production through multiple levels.Proposed formulation is expressive and can be solved to global optimality.Proposed formulation overcomes the ...

Production planning method for seru production systems under demand uncertainty

  • Seru production systems under demand uncertainty are addressed.

The seru production system is a new-type manufacturing system arising from Japanese production practices that can simultaneously achieve high efficiency, flexibility, and responsiveness. In this paper, a worker and production quantity ...


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How Are Insurance Markets Adapting to Climate Change? Risk Selection and Regulation in the Market for Homeowners Insurance

As climate risk escalates, property insurance is critical to reduce the risk exposure of households and firms and to aid recovery when disasters strike. To perform these functions efficiently, insurers need to access high quality information about disaster risk and set prices that accurately reflect the costs of insuring this risk. We use proprietary data on parcel-level wildfire risk, together with insurance premiums derived from insurers' regulatory filings, to investigate how insurance is priced and provided in a large market for homeowners insurance. We document striking variation in insurers' risk pricing strategies. Firms that rely on coarser measures of wildfire risk charge relatively high prices in high-risk market segments -- or choose not to serve these areas at all. Empirical results are consistent with a winner's curse, where firms with less granular pricing strategies face higher expected losses. A theoretical model of a market for natural hazard insurance that incorporates both price regulation and asymmetric information across insurers helps rationalize the empirical patterns we document. Our results highlight the underappreciated importance of the winner's curse as a driver of high prices and limited participation in insurance markets for large, hard-to-model risks.

We thank seminar participants at the NBER Summer Institute, NBER Insurance Working Group, the 2023 ASSA meetings, the 2024 AERE Summer Conference, TWEEDS, University of Chicago, Harvard University, MIT, Indiana University, Resources for the Future, the New York Federal Reserve, the San Francisco Federal Reserve, Stanford University, University of Alaska Anchorage, UC Berkeley, UC Davis, UC Santa Barbara, University of Southern California, University of Wisconsin, University of Illinois, University of Calgary, Georgia State University, and Oregon State University. We thank Josh Aarons, Kaleb Javier, and Kendra Marcoux for outstanding research assistance. We thank Benjamin Collier, Chris Costello, Mark Duggan, Penny Liao, Philip Mulder, and Joel Sobel for helpful discussions. This work received generous financial support from the University of California Office of the President Laboratory Fees Program (LFR-20-652467). Boomhower gratefully acknowledges funding via an Andrew Carnegie Fellowship from the Carnegie Corporation of New York. CoreLogic, Inc. is a source of data for this analysis. CoreLogic was not involved in the preparation of any aspect of the report. Any conclusions/interpretations of the CoreLogic data are those of the authors of the report, and not CoreLogic. CoreLogic is not responsible for any use of, nor any decisions based on or in reliance on, the CoreLogic data used in the report. CoreLogic does not represent or warrant that the CoreLogic data is complete or free from error and does not assume, and expressly disclaims, any liability to any person or entity for any loss or damage caused or resulting in whole or in part by any reliance on the CoreLogic data including, but not limited to, any loss or damage caused or resulting in whole or in part by errors or omissions in the CoreLogic data, whether such errors or omissions result from negligence, accident, or other cause. CoreLogic® is the registered trademark of CoreLogic, Inc. and/or its subsidiaries or affiliates. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.


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Measuring the impacts of university-industry R&D collaborations: a systematic literature review

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  • Published: 29 June 2024

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  • Maria Cohen   ORCID: orcid.org/0000-0002-4625-5258 1 ,
  • Gabriela Fernandes   ORCID: orcid.org/0000-0002-2715-9826 2 &
  • Pedro Godinho   ORCID: orcid.org/0000-0003-2247-7101 1  

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Measuring the impacts of collaborative projects between industry and academia raises significant challenges. It involves stakeholders with different outlooks and impact expectations. Moreover, the multidimensional nature of the impacts themselves means they are tangible and intangible, short- and long-term, direct and indirect, positive and negative, making their measurement process very complex. To gain a deeper understanding of how university-industry R&D collaborations (UICs) impact society, this study conducts a systematic review, using thematic analysis of 92 selected articles published between 2000 and 2022. The paper identifies and categorizes the impacts resulting from UICs, examines the challenges associated with measuring these impacts, and explores the strategies that can be employed to overcome such challenges. Finally, the paper integrates all such findings into a comprehensive framework. This study contributes to the theoretical advancement of impact measurement within the field of UICs, providing a foundation for the development of methodologies aimed at assessing impacts. Furthermore, it highlights important avenues for future research.

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1 Introduction

The measurement of the impact of research on society is an extremely relevant matter (Bornmann, 2013 ). When research is conducted with public funding, the measurement of these impacts is closely related to the need to demonstrate its value to funding entities, the opportunity to leverage funding for future research, and the ability to identify more efficient ways to generate greater impact (Penfield et al., 2014 ; Walsh et al., 2018 ). However, in the context of university-industry R&D collaborations (UICs), measuring impact is a complex task due to the heterogeneity of the institutions involved, the diversity of objectives and expected benefits, and different perspectives of each stakeholder since high-value impact for one group may not be the same for another (Fini et al., 2018 ).

Although a considerable body of scientific literature has addressed the socio-economic impacts of UICs in the past decade (Lima et al., 2021 ), comprehensive assessments of the broader impacts of collaborative research, known as ‘societal’ impacts, have remained limited (Bornmann, 2013 ; Galan-Muros & Davey, 2019 ; Siemieniako et al., 2021 ; Skute et al., 2019 ; Tijssen, 2012 ). These societal impacts are characterized by their macro-level nature, namely encompassing social aspects, as indicated by Siemieniako et al. ( 2021 ). The complexity of these impacts is exacerbated by a diffuse boundary that makes it challenging to clearly identify their relation to quality of life, health, or the environment, resulting in ambiguity when determining whether an impact is social, economic, or of another sort (Bornmann, 2013 ).

The challenge of conceptualizing the impacts of research is not new. In 2011, for example, the Health Economics Research Group organized an international workshop to gather academic and professional views on new pathways for assessing the social impact of research. Most participants agreed on the difficulty of finding a clear concept of social impact that could facilitate such evaluation (Donovan, 2011 ), an issue that continues to be mentioned in current studies on the impact of research in organizational contexts (Siemieniako et al., 2021 ).

In addition to the conceptual challenges associated with analyzing impacts, Galan-Muros and Davey ( 2019 ) characterized the field of UICs as fragmented, due to the limited linkages between its thematic domains. However, they have endeavored to integrate its elements into a conceptual framework, where UIC impacts are regarded as a central element within the UIC. In the same way, Skute et al. ( 2019 ) conducted a bibliometric study to map the research field of UIC and acknowledge the importance of analyzing the economic and social impacts generated by these collaborations at regional and national levels.

In the general context of UICs, the impact is defined as the outcome indirectly experienced by individuals, institutions, and society (Galan-Muros & Davey, 2019 ), or as the direct or indirect result that influences stakeholders, including society (Albats et al., 2018 ). In this study, impact is defined as a positive or negative change originated in the UIC context that can directly or indirectly affect individuals, organizations, communities and society in general (Siemieniako et al., 2021 ). The impacts caused by UICs can produce economic, environmental, health, cultural, political effects at the macro level and on the quality of life, stemming from the creation of new or improved products and services based on scientific knowledge (Fini et al., 2018 ).

UICs require support mechanisms, i.e. management (Fan et al., 2019 ), political, structural, operational, and strategic mechanisms to ensure that research is relevant to society (Galán-Muros et al., 2017 ), and is capable of creating monetary and non-monetary impacts that converge towards the boundaries collectively (Audretsch et al., 2019 ).

In fact, there are several key factors that may constrain or drive the impacts of UICs. The absence of shared objectives among universities, science, and businesses is a significant limiting factor (Issabekov et al., 2022 ), demanding sustainable strategies to maintain common interests over time (de Freitas et al., 2014 ). Factors such as company size, sector, commitment to digitization (Marra et al., 2022 ) and level of trust emerge as crucial drivers of innovation and future collaborative projects (Vega-González et al., 2012 ). Information asymmetry within the UIC is pointed out as a critical factor hindering the commercialization of university patents (Xiaojuan & Hongda, 2021 ).

The impacts of UICs are also influenced by the absorptive capacity of companies: companies with high absorptive capacity have a unique competitive advantage, adapting to changes in the environment and fostering innovation (Tian et al., 2021 ). In fact, when funding collaborative projects, governments tend to favor companies with high absorptive capacity, underscoring the relevance of this factor for the success of UICs (Cui et al., 2022 ). The synchrony between regional innovation and economic development fosters the correlation between basic research and market demand, leading to higher UIC impact (Cui & Li, 2022 ). Finally, institutional factors and structural conditions, such as economic cycles, impact UIC scientific production. For example, in a crisis, there may be an interest in signaling potential scientific areas that promote UICs and co-publication production (Azagra-Caro et al., 2018 ).

The present study identifies a gap in understanding how UICs impact society (Di Maria et al., 2019 ; Jones & Corral de Zubielqui, 2017 ; Nugent et al., 2022 ). To address this gap, a systematic literature review was conducted, by thematically analysing 92 studies published between 2000 and 2022. The current paper seeks chiefly to identify and categorize the types of impacts of UICs from the perspectives of universities, industry, and society. Additionally, it examines the challenges of measuring these impacts and identifies the strategies employed to overcome such challenges.

The literature review helped us identify a set of 25 impacts of UICs, which are subsequently classified into six categories. Some of the challenges in measuring these impacts are related to their intangible or transient nature, that is, their ability to appear, disappear, or transform from positive to negative across the collaborative lifecycle (Perkmann et al., 2011 ), as well as the complexity of dealing with the various causes that can explain their origin (Fini et al., 2018 ). Such intrinsic characteristics of impact make them hard to measure, the perspectives of the agents involved in the collaboration thus being a crucial element in the measurement process (Penfield et al., 2014 ). Finally, some strategies are presented to overcome the challenges of measuring impacts of UICs. Many of these strategies are utilized in empirical studies, while others are theoretical guidelines that can be implemented in future studies.

The main contribution of this article is to consolidate insights from the past two decades regarding the impacts of UICs, subsequently presenting key elements of the process in a single framework that can serve as a basis for the development of future impact measurement methodologies. Additionally, it provides thoughts on the need to advance in measuring more comprehensive impacts, considering not only the academic or industrial community but also other social groups that may be affected by the collaboration. It also encourages a deeper analysis of key factors that may restrict the realization of impacts.

The rest of this paper is structured as follows. Next, the context of UICs and their impact on society is presented. Then the research methodology employed is detailed. Subsequently, it delves into the findings, an unfolds in a comprehensive discussion, concluding with insights into potential future research directions.

2 Background

The literature defines UIC in general terms as a type of alliance that benefits innovation performance significantly (Wirsich et al., 2016 ), resulting in a positive impact on R&D participation and learning opportunities for the company involved (Scandura, 2016 ). UIC is also described as an interactive relationship that aims to enhance competitive advantages through trust, commitment, and access to each partner's resources, aimed at producing a social impact (Galan-Muros & Davey, 2019 ), which can be formal in nature when delivered by explicit contracts, or informal when focused on personal interactions using trust as a prerequisite for collaboration (Apa et al., 2021 ).

More specifically, UICs are defined as agreements between the university and the industry with the purpose of conducting joint research. Some of the R&D activities included in this type of collaboration are contract research projects, joint publications by industry and university or R&D consulting (Pinto & Fernandes, 2021 ). A good management system with appropriate mechanisms is thus a critical tool to influence the expected impacts and control the uncertainty underpinning this type of collaboration (Morandi, 2013 ).

The aforementioned definitions imply that UICs are aligned with the concept of 'Mode 2 of production.' This approach represents a different and interdisciplinary way of generating knowledge between the scientific community and other stakeholders, with the aim of impacting industry, government and society. In this context, knowledge production takes place through a continuous negotiation of interests among the various actors involved (Gibbons et al., 1994 ). In contrast to 'Mode 1' of production, which focuses on the interests of the academic community and aims to generate high-impact research, 'Mode 2' is considered more suitable for generating socially useful research, albeit with a lower impact factor (Nightingale & Scott, 2007 ).

Similarly, the literature related to UICs has long considered that the lack of complementarity between industry and academic activities undermines scientific production (Perkmann & Walsh, 2009 ) or produced low impact factor publications (Abramo et al., 2009 ). However, recent studies have shown that UICs built upon expected complementarities, such as resources (Zhang et al., 2022 ), skills, availability of equipment, and task distribution among academic and industry scientists, can increase scientific production and enhance the business activity in the industry (Bikard et al., 2019 ). Nevertheless, there is a need to understand the impacts of UICs on society (Di Maria et al., 2019 ; Jones & Corral de Zubielqui, 2017 ; Nugent et al., 2022 ).

On the one hand, academics' interest in translating the results of their research into broader benefits for society (Nugent et al., 2022 ) is justified by funding institutions’ focus on the real contribution of their investments (Penfield et al., 2014 ). On the other hand, the strong pressures experienced by companies and universities due to the speed of technological change, the quest for more advanced knowledge, the growing cost of research, and the need to address social and economic problems stimulate the creation of UICs worldwide and demand proof of their impact capacity (Ankrah & AL-Tabbaa, 2015 ).

2.2 UIC impacts

Literature published in the last two decades has chiefly focused its analysis on the industry perspective and generally agrees that business innovation is an important positive impact of UICs (e.g., Apa et al., 2021 ; Eom & Lee, 2010 ; Giannopoulou et al., 2019 ). Political agendas have evolved with the inclusion of science-based technological innovation. However, the absence of reliable quality indicators at the business and technological levels hinders effective guidance for policymakers, limiting broader impacts on society (Tijssen, 2012 ).

From the perspective of universities, there is a consensus regarding UICs affecting academic productivity, but there are different points of view as regards this effect and its positive or negative nature (Banal-Estañol et al., 2015 ; Bikard et al., 2019 ; Perkmann & Walsh, 2009 ). This lack of consensus has encouraged Garcia et al. ( 2020 ) to analyze the impact of UICs on the productivity of academic research in the long-term, due to the ease of managing contract rules between universities, companies and funding agencies. The results confirm that the long-term impact is positive; however, this occurs at decreasing rates, suggesting that the positive effects of UIC on scientific productivity may be constrained over time.

Faced with the complex task of identifying and developing a classification for the different types of impacts, the theoretical field of interorganizational relations offers interesting prospects for the analysis of the impact of research, categorizing it into three levels: micro, mezzo and macro (Siemieniako et al., 2021 ). The micro level is related to individual aspects in the organization, the mezzo level pertains to aspects that affect specific groups acting within the organization, and the macro level encompasses groups or communities outside of the organization, transcending interorganizational relationships (Siemieniako et al., 2021 ).

We believe that the interorganizational approach can be applied to the field of UICs, allowing for the evaluation of impact from both an internal and external perspective. At the micro level, it would be possible to consider the impacts experienced directly or indirectly by academics, researchers, students, entrepreneurs, or any other individual involved in the collaborative environment. At the mezzo level, impacts are experienced by research teams, industrial associations, and communities within the collaborative context. Finally, at the macro level, impacts would extend to external communities that are directly or indirectly affected by the collaboration. These communities can encompass various domains such as industry, academia, region or any other group in society (Galan-Muros & Davey, 2019 ).

Another relevant discussion in the literature addresses the pathways for UICs to generate greater impact on society. One perspective, as proposed by Bornmann ( 2013 ), argues that knowledge commercialization is a way to create broader impacts. In other words, when research outcomes are transformed into marketable products, such as consumer goods, medicines, devices, or services, broader impacts are achieved. For example, the Argus II device, an artificial retina resulting from a collaboration between academia, industry, and the government, materialized in socially important innovations (Walsh et al., 2018 ). However, it is important to mention that UICs generally pursue research objectives through joint R&D activities, and their results usually become intellectual property assets, such as patents, licenses, and sales, which are subsequently traded (Pinto & Fernandes, 2021 ).

Fini et al. ( 2018 ) argue that there is a lack of understanding of how research can impact through commercialization. In this regard, the authors suggest moving away from the emphasis on direct outcomes of commercialization (such as patents and licenses) and understanding commercialization as the process of turning knowledge into useful products or services available on the market. This new approach involves creating direct links between users and performers of R&D activities, which would give rise to collaborative projects targeting user needs and generating higher societal impact (Fini et al., 2018 ).

2.3 Previous reviews and research gap

Extant literature reviews have widely emphasized the analysis of crucial factors for technology transfer (Da Silva Florencio & De Oliveira, 2022 ) and collaborative innovation (Sjoo & Hellstrom, 2019 ). Reviews grounded in case studies have also explored aspects often overlooked in such relationships, such as the choice of partners and the management of stakeholder interactions (Marinho et al., 2020 ). These review studies frequently consider the analysis of each of these factors at different levels, spanning from the individual to the institutional and academic level (Puerta Sierra et al., 2017 ).

Other reviews highlight the challenges and motivations faced by universities at both individual and institutional levels, as outlined by Harryson et al. ( 2007 ) and Nsanzumuhire and Groot ( 2020 ). These aspects assume particular relevance as the academic community interested in collaborating with industry grapples with the challenge of legitimizing their activities within the academic sphere while balancing their responsibilities of teaching, research, and participation in industrial initiatives (Miller et al., 2018 ).

The relationship between academic engagement and commercialization has also undergone thorough analysis. As discussed by Perkmann et al. ( 2013 ) academic engagement is interpreted as a multi-level phenomenon influenced by both individual characteristics and the organizational and institutional context. It serves as a mechanism for resource acquisition by high-performing academics in institutions with limited resources (Perkmann et al., 2013 ).

Although the aforementioned studies do not specifically focus on analyzing UIC impacts, they recognize the need to address this theme in future research. In contrast, some authors have explored less extensively the impacts arising from knowledge-sharing collaboration (Mascarenhas et al., 2018 ), as well as the effects of the trilateral relationship between university, industry, and government in regional innovation systems (Lew & Park, 2021 ).

The systematic review conducted by Lima et al. ( 2021 ) leads to a conceptual model classifying the UIC impacts into three categories: economic, social, and financial. This study underscores the social impact of UICs as an emerging field characterized by predominantly exploratory and qualitative research, encompassing various theoretical approaches, albeit still lacking a more robust foundation (Lima et al., 2021 ). Similarly, qualitative analysis techniques have been employed in literature reviews to identify the economic, institutional, and social benefits of UICs (Ankrah & AL-Tabbaa, 2015 ), as well as the proposition of impact assessments of public–private partnerships, notably in the biomedical and pharmaceutical sectors (de Vrueh & Crommelin, 2017 ).

It is worth mentioning that a considerable number of studies have employed quantitative methods with a particular emphasis on the use of econometric models (Apa et al., 2021 ; Di Maria et al., 2019 ; Vega-Jurado et al., 2020 ), structural equations combined with narratives (De Silva et al., 2021 ) and case studies (Azagra-Caro et al., 2017 ) to measure the impact of UICs. This trend in the combined use of methods confirms that the type of methodological approach employed in measuring the impacts of research (Bornmann, 2013 ) remains relevant in the field of UIC. However, the current paper does not intend to delve into the specific methods used to measure the impacts of UICs, which would otherwise extend the length of the paper significantly.

Given that the literature review revealed a gap in measuring the impacts of UICs (Galan-Muros & Davey, 2019 ), and motivated by studies emphasizing the importance of broadening the scope of UIC impact analysis (Mascarenhas et al., 2018 ; Miller et al., 2018 ; Skute et al., 2019 ) and influenced by the remarkable proliferation of literature related to the emerging impacts of the UIC in recent years, we conduct a systematic literature review that identifies and categorizes the types of UIC impacts. Furthermore, we identify the challenges in measuring these impacts and the strategies that have been employed to address such challenges. We observe that these aspects have not been addressed simultaneously and comprehensively in previous reviews. Therefore, bringing this information into a single framework will serve as a foundation for future empirical research aimed at developing systematic methodologies for measuring the impact of UICs.

The UIC measurement impact framework here proposed aims to provide a structured approach to better understanding the categories, challenges, and strategies related to the measurement of impact across the UIC lifecycle. With the help of this framework, researchers will be able to be more rigorous, careful, and strategic in analyzing the impact in real contexts. Ultimately, our goal is to advance the knowledge and understanding of the impact of UICs on society at large.

3 Research methodology

To understand the process of measuring the impacts of UICs, we have identified (a) types of UIC impacts, (b) categorization of impacts, (c) challenges of measuring UIC impacts, and (d) strategies to overcome these challenges. Following the systematic review process presented by Tranfield et al. ( 2003 ), we have divided this process into three phases: phase I, review planning; phase II, identification, and selection of studies; phase III, evaluation of study quality, data extraction (thematic analysis) and presentation of results (Fig.  1 ).

figure 1

Systematic literature review phases

Each phase was preceded by periodic meetings held by three authors to discuss issues related to the application and follow-up of the protocol. After a cycle of preliminary readings, the keywords selected for the search in Scopus and Web of Science were as follows: ("UIC*" OR "university-industry" OR "industry-university" OR "UBC*" OR "university-business cooperation" OR “public–private” OR “private–public”); ("university*" OR "academic*" OR "higher education"); (“ industry *” OR “enterprise *” OR “company*” OR “firm*”); ("impact*" OR "benefit*"); (“R&D” OR “Innovation”). The expressions were searched in the title, abstract, and keywords of the articles.

The initial process resulted in 1.593 documents which four filters were applied to (type of document, language, year of publication, and fully completed articles). That is, only articles in English published between 2000 and 2022 were searched, considering that from 2000 onwards there were more studies focusing on measuring the impacts of UICs. However, interest in this thematic area began to grow in the last five years, as also evidenced by Lima et al. ( 2021 ). Therefore, the danger of omitting relevant studies can be minimized by analyzing recent articles that use previous studies as a basis (Ankrah & AL-Tabbaa, 2015 ). This filtering procedure eliminated 620 documents, leaving a total of 973 articles. After eliminating duplicates, 665 articles passed the selection and evaluation phase.

Later in the process, two inclusion criteria were applied to the aforementioned 665 articles, incorporating in the analysis those articles that, based on their abstract, provided a positive response to at least one of the following questions: 1) Does the study address the impacts of UIC? 2) Does the study address useful mechanisms or criteria for measuring the impacts of UIC? In some cases, it was necessary to go beyond the abstract to answer these questions. This process resulted in 172 articles being selected for further analysis.

Finally, as our interest was centered on studies that addressed the types of impacts, concepts, ways of measuring them, or key theoretical elements to consider in a measurement process, a detailed reading of each article allowed us to eliminate those whose contribution did not offer the degree of depth necessary for this research, resulting in a final set of 77 articles for the data extraction process. The snowballing strategy was employed in the literature review. The examination of the 77 key articles allowed for the identification of significant theoretical contributions, which guided the review of the relevant references. This process facilitated the inclusion of 15 additional articles, resulting in a more comprehensive understanding of the topic across a total of 92 analyzed articles.

The data analysis process for identifying and categorizing UIC impacts was carried out following two main steps. Firstly, the selected articles were carefully scanned to extract key data, which was organized in a matrix. This information encompassed the author and publication date of the article, the identified impacts, the impacted 'agent' (whether it was the university, society, or industry), and the impacted area (social, economic, technological, environmental, intellectual, or strategic). It is worth noting that most of the literature indicated, either explicitly or implicitly, whether an impact fell into the economic, environmental, technological, social, or intellectual categories. However, for impacts such as reputation, competitiveness, and new collaborations, it was less evident to classify them into a specific category. Nevertheless, given their direct association with an organization's image, they were found 'strategic.' Throughout this data analysis process, in-depth and comprehensive readings of the selected texts were conducted. This immersion in literature was crucial for extracting key data and thoroughly understanding the various impacts identified in the studies selected.

Secondly, we merged some impacts, due to terminological variations when referring to similar impacts. Therefore, a regrouping process was undertaken, resulting in a total of 25 identified UIC impacts. Categorization was carried out by linking the impact's meaning with the corresponding, most affected area, according to the authors' views. To ensure accuracy and consistency in the impact categorization process, researchers met several times to engage in meaningful discussions and achieve a consensus in the categorization of each impact.

The challenges of measuring the impact of UICs and the strategies to overcome such challenges were not explicit in most of the selected articles. We strategically applied a methodological approach of reflexive thematic analysis, which allows for the use of the researcher’s subjectivity in the data analysis process, and for being flexible and recursive, without having to follow a linear process (Braun et al., 2019 ). Our thematic analysis started with “focused familiarization”, i.e. the documents were analyzed by focusing on two central ideas, "impact measurement challenges" and "strategies to overcome impact measurement challenges". The first phase identified a set of topics connected to each predefined core idea, leading to a second phase that consisted of analyzing and discussing the relationships and interpreting the coherence of each topic. Finally, a third phase fostered a discussion tying up all topics in a comprehensive framework.

4.1 Identification and categorization of UIC impacts

Most of the studies looking into the impact of UICs from an industry perspective focused on how UICs affect business innovation performance (Al-Ashaab et al., 2011 ; Apa et al., 2021 ; Fan et al., 2019 ; Jones & Corral de Zubielqui, 2017 ). Even though the majority found a positive impact on innovation (Fan et al., 2019 ; Zhang et al., 2019 ), when the unit of analysis is small and medium-sized enterprises, empirical evidence showed that formal UICs do not necessarily induce positive innovation performance without the presence of informal relationships (Apa et al., 2021 ). Similarly, UICs, when consisting of companies with low absorption capacity, do not have a significant impact on innovation (Vega-Jurado et al., 2020 ). This heterogeneity in results is generally related to the type of company, type of relationship, partner, and absorptive capacity. Therefore, each of these factors should be considered with caution in the impact measurement process (Acebo et al., 2021 ).

Result heterogeneity is also evident in the measurement of the academic perspective. The literature reveals some concern about how commercialization objectives in the industry undermine scientific production (Perkmann & Walsh, 2009 ). A recent explanation relates this effect to the attention theory of firms, i.e., high levels of collaboration generate many ideas and low publication rates (Banal-Estañol et al., 2015 ). Other studies consider that academic institutions can indeed experience intellectual benefits (De Fuentes & Dutrenit, 2012 ), but with diminishing returns as the time spent in the industry increases (Banal-Estañol et al., 2015 ), or when an academic is involved in several collaborative projects (Di Maria et al., 2019 ).

In contrast to the previous argument, Bikard et al. ( 2019 ) state that the low scientific productivity is explained by the fact that the universities decide to collaborate in projects more oriented to commercial results than to scientific outputs, or UIC participants do not otherwise apply the advantages of specialization, i.e., delegating responsibilities according to the specialty of each participant. Thus, if the commercial activity is carried out by industry members and the scientific production by academics, the results would benefit all stakeholders (Bikard et al., 2019 ).

Although only a fraction of collaborative research results in co-authorship, sectors such as electronics, pharmaceuticals, and biotechnology tend to produce more scientific production(Tijssen, 2012 ). There will always be a risk that some academics may shift the focus of their concern, i.e., they will be more concerned with the commercial outcome of their product than with the content of their scientific output (Bornmann, 2017 ).

In any case, the trade-off between participating in collaborative projects with industry and the decrease in academic productivity with a high impact factor implies an opportunity cost that is worth thinking about when significant socioeconomic impacts are generated (Di Maria et al., 2019 ). Certain scholars emphasize the important role of incentives to engage academics in collaborative efforts with the industry (Puerta-Sierra et al., 2021 ; Skute et al., 2019 ). Other authors acknowledge the importance of delving deeper into the understanding of the factors that encourage academics to engage with the industry, aiming to enhance the effectiveness of policies promoting such collaborations (Abramo & D’Angelo, 2022 ).

Once it is acknowledged that long-term collaborations with industry has limitations due to diminishing returns in scientific production (Garcia et al., 2020 ), several studies have proposed measures, such as the institutionalization of interdisciplinary UIC. For instance, creating incentives distinct from common requirements for scientific production by adopting criteria related to social, human, and financial spheres (Galán-Muros et al., 2017 ). Accordingly, there are proposals to reformulate the assessment of scientific activity in a more equitable manner and realign certain policies, often conflicting due to their encouragement of high-impact scientific production and simultaneous encouragement of academic engagement in public–private collaborations (Abramo & D’Angelo, 2022 ).

From a social perspective, UICs have been widely recognized as a significant source of skills and specialized knowledge, playing a crucial role as intellectual capital that drives job creation and wealth generation (Guerrero et al., 2021 ). Moreover, when social needs are met through responsible innovation generated by UICs, regional economies grow and education systems improve (Acebo et al., 2021 ; Audretsch et al., 2019 ). Recent studies state that society imposes certain demands on universities, which are related to social aspects such as poverty relief, inequality reduction, and an enhanced quality of life for individuals. Thus, UICs, as innovation systems, provide a viable means to address each of these challenges (Puerta-Sierra et al., 2021 ).

One aspect worth mentioning is the relationship between societal impact and the type of country where the UIC is based. The literature points out that in developed countries UICs are driven by commercial, economic and reputational factors, while in developing countries UICs result from the very needs and challenges these countries face, which may explain why collaborations in developing countries generate higher societal impact (Roncancio-Marin et al., 2022 ).

After summarizing the analysis of the 92 selected articles, 25 UIC impacts were pointed out. Table 1 introduces the impacts and their descriptions. Additionally, we have proposed five impact categories: 'type', 'agent', 'time', 'incidence' and 'nature'. The 'type' category is directly related to the affected area. Consequently, the 25 impacts identified have been classified into six types: intellectual, economic, technological, environmental, social and strategic, as evidenced in Table  1 . The different types of impacts can be defined as follows:

Intellectual : Impacts that directly affect the academic and the industrial communities. These are closely linked to scientific production, the resolution of industrial issues, and opportunities to enhance the capabilities and experience of human capital (De Fuentes & Dutrenit, 2012 ), alongside the improvement of the educational system and learning processes (Zavale & Schneijderberg, 2021 );

Economic : Impacts related to the financial outcomes that arise from the development of new ventures, the commercialization of innovative products, and the optimization of resources. This type of impact can emerge after a series of interactions over time between the university and businesses (Azagra-Caro et al., 2017 ) and is often linked to the increase in anticipated capital and wealth generation (Audretsch et al., 2019 ). Economic impacts in the field of UIC have been extensively analyzed in the literature (Puerta-Sierra et al., 2021 ; Roncancio-Marin et al., 2022 ; Yeo, 2018 ) and from the university perspective, the economic impact becomes evident as the presence of financial resources allocated to research increases (De Fuentes & Dutrenit, 2012 );

Technological : Impacts that arise from the implementation of new technologies or innovative concepts in collaborative projects between academic institutions and industry. These innovations can lead to both positive and negative consequences in various spheres, such as productivity, quality of life, job creation, and the environment, among others. Consequently, the degree of efficiency with which these new ideas are transformed into marketable products and services becomes a crucial element in fostering the creation of new innovation mechanisms (Audretsch et al., 2019 ).

Environmental : Impacts related to the outcomes, whether positive or negative, arising from the activities conducted within the collaborative project that directly or indirectly influence the environment (Zhang et al., 2022 ). Among the environmental impacts documented in the analyzed literature, noteworthy examples include the mitigation of pollutants (Al-Ashaab et al., 2011 ; Albats et al., 2018 ) and the advancement of practices that promote the use of recyclable materials (Al-Ashaab et al., 2011 ).

Social: Impacts across several groups of society, encompassing crucial domains such as employment generation (Apa et al., 2021 ; Wong & Singh, 2013 ), quality of life enhancement (Zavale & Schneijderberg, 2021 ), and entrepreneurial endeavors aimed at meeting community demands (Acebo et al., 2021 ; Audretsch et al., 2019 ; Roncancio-Marin et al., 2022 ).

Strategic : Impacts that directly affect the image of an organization in its environment, particularly their reputation (Crespo & Dridi, 2007 ; De Fuentes & Dutrenit, 2012 ; Galan-Muros & Davey, 2019 ). Namely, strategic competitiveness (Acebo et al., 2021 ; Galan-Muros & Davey, 2019 ), and the organization’s ability to foster future collaborations (Al-Ashaab et al., 2011 ; De Silva et al., 2021 ; Zavale & Schneijderberg, 2021 ).

The second category, known as ‘agent’, represents the community directly or indirectly affected (Table  2 ). In literature, it is possible to observe that an impact can affect more than one agent, thus demonstrating that the knowledge generated within the UIC can have broader impacts on many areas, namely, industries, universities and society (Galán-Muros et al., 2017 ).

As for the third category, 'time', both the short- and long-term are considered. In this regard, the literature agrees that impacts on the collaborative lifecycle can occur in both timeframes (Maietta, 2015 ; Siemieniako et al., 2021 ; Yeo,  2018 ).

The fourth category, 'incidence', relates to the nature of the impact, whether direct or indirect (Maietta, 2015 ). In this research, we define direct impacts as those generated from the activities carried out within the collaborative lifecycle. These impacts are observable in the short-term and primarily affect the communities directly involved in the project. Conversely, indirect impacts are those that were not foreseen and can be considered an extension of the effects produced by the activities carried out under the collaborative lifecycle. These impacts occur in the medium and long term, affecting not only stakeholders, but also other social groups.

The fifth category, 'nature', is related to the tangible or intangible features of the impact (Bellini et al., 2019 ; Fernandes & O’Sullivan, 2021 ; Perkmann & Walsh, 2009 ). In this document, we establish a relationship between the concepts of tangibility and intangibility and the level of complexity associated with measuring their impact.

In other words, when there is an exact measure of the impact, it is considered measurable. Conversely, if the nature of the impact is intangible, it does not imply that it is impossible to measure; rather, it requires more sophisticated assessment approaches and tools. Figure  2 organizes the 25 UIC impacts under five impact categories.

figure 2

UIC impact categories

Finally, Fig.  3 illustrates the evolution over time of the six types of impact in literature. To achieve this, we have divided our period of analysis (2000–2022) into four specific periods (2000–2004, 2005–2009, 2010–2014, and 2015–2022) and counted the number of articles that address each impact type, thus providing a visual representation of how the analysis of each impact has evolved over time in the context of UICs.

figure 3

Temporal Evolution of the Six Types of Impact

Figure  3 shows a consistent and growing interest by economic impacts since the period 2000–2004. Similarly, intellectual impacts gained in prominence in the literature from the second period onward, alongside the economic impacts. This initial trend substantiates our findings regarding how academic and industrial perspectives have been scrutinized in the literature on UICs. Consequently, it underscores the need to analyze more comprehensive impacts that transcend both academic and industrial realms.

The significantly deeper analysis of social impacts in the last period analyzed suggests that the literature is responding to the call to address social needs. A plausible explanation for this trend could be linked to the fact that social impact is a great concern of policymakers and professionals involved in the commercialization of science (Fini et al., 2018 ).

Concerning the strategic and technological impacts, although not growing in the number of articles at the same pace as the others, these impacts still catch the interest of literature.

Regarding the environmental impacts resulting from UICs, few studies have delved into this subject. This result emphasizes the historical lack of additional research on environmental indicators, as indicated by Karatzoglou ( 2013 ) in his literature review on the university's role in sustainable development. After looking into 123 articles, Karatzoglou identified only three which focused exclusively on measurement systems, highlighting the urgent need for more in-depth studies in this field.

4.2 Challenges in measuring UIC impacts

The reflexive thematic analysis allowed us to identify four recurring methodological challenges in measuring the impact of UICs. The first challenge concerns the 'multidimensional nature of the impact', whether tangible or intangible (Bellini et al., 2019 ; Soh & Subramanian, 2014 ), due to the uncertainty of the impact appearing in the long-term or short-term (Maietta, 2015 ; Yeo, 2018 ), to its direct or indirect impact (Perkmann & Walsh, 2009 ) or to its positive or negative effect in a given area (Fini et al., 2018 ). All of these aspects increase the complexity of measuring any impact that takes time to materialize (Perkmann & Walsh, 2009 ).

The second challenge identified was 'causal attribution of effect'. A clear example thereof is the difficulty in establishing whether an increase in sales is the result of the UIC, since there may be other factors that influence its performance (Perkmann et al., 2011 ). Although some studies suggest that UICs positively influence the sales of innovative products (Arvanitis et al., 2008 ), or improve a company's market value (Crespo & Dridi, 2007 ), there is no clear understanding of the results derived from collaboration (Galan-Muros & Davey, 2019 ). So there is the risk of alternative causes explaining such effects (Fini et al., 2018 ). Therefore, knowing to what extent the collaboration was useful for achieving innovative results, or even knowing what would have happened if the collaboration had not taken place are still counterfactual issues to be considered (Lööf & Broström, 2008 ).

Formal collaboration in R&D encompasses a wide range of cooperative research and knowledge transfer activities, involving continuous interactions between stakeholders (Wong & Singh, 2013 ). When these continuous interactions are of high professional value, they tend to ensure the existence of societal impacts (Bornmann, 2017 ) However, there is a challenge related to the 'identification of impacts', both perceived and expected. Identifying perceived impacts requires addressing significant differences in individual and institutional perceptions that are subjectively correlated with affective evaluations. The perceived benefits, particularly those related to future collaborations, are thus positive (De Silva et al., 2021 ). Moreover, identifying expected impacts is a challenging exercise due to the risks and uncertainties inherent to UICs (Fernandes & O’Sullivan, 2021 ).

To measure the impact of research on society, it is often more convenient to compile data at the institutional level than at the individual level, since institutional data are more easily identified (Bornmann, 2017 ). However, more than identifying data, another frequent difficulty in measuring the impacts of UICs is having sufficient and appropriate information, especially when it comes to micro-level data (Yeo, 2018 ). Thus, we have identified a fourth challenge, which we have called 'data limitations', explained by some authors as the absence of a culture of periodic recording of information by organizations (Penfield et al., 2014 ).

The literature related to UICs showed that 'data limitations' can occur for different reasons, such as low stakeholder participation in surveys, as evidenced by a low response rate or a significant number of contradictory answers (Arvanitis et al., 2008 ). 'Data limitations' can also occur because of the short period in which the information remains available or because the available data do not reveal the specific realities of the context under analysis (Zavale & Schneijderberg, 2021 ). Particularly, in the context of co-financed R&D projects, data limitations are influenced by geographical, economic, scientific and cultural factors that often constrain the integrity and validity of the data (Tijssen, 2012 ). Whatever the reason, this problem may limit the scope of the study by having to exclude part of a sample, for example small companies, from a study (Maietta, 2015 ). In contrast, when data are recorded extensively in organizations, such impact is more likely to reveal itself (Yeo, 2018 ), which facilitates the study of longitudinal phenomena (Arvanitis et al., 2008 ).

4.3 Strategies used to measure UIC impacts

Our literature review allowed us to identify some strategies for UIC impact measurement. Given the 'multidimensional nature of impact' challenge and the tendency of traditional measurement methods to focus on the last stage of the collaboration lifecycle, i.e. the output phase of patents, licenses and joint publications, some authors have proposed the implementation of 'continuous monitoring throughout the UIC lifecycle', i.e., inputs, activities in process, outputs, and impacts (Albats et al., 2018 ). 'Continuous monitoring throughout the UIC lifecycle', accompanied by the 'interactive participation of stakeholders', is expected to foster the evaluation and balanced selection of the most appropriate indicators to measure impact, and to point out the appropriate direction for future collaborations (Albats et al., 2018 ).

A second strategy used to measure UIC impacts is the 'combination of data collection tools', namely by the use of primary data collection tools, such as case studies, interviews and narratives (Al-Ashaab et al., 2011 ; Borah et al., 2021 ; Morandi, 2013 ; Perkmann & Walsh, 2009 ) and the use of secondary databases (Scandura, 2016 ; Vega-Jurado et al., 2020 ; Wong & Singh, 2013 ; Zhang et al., 2019 ). We believe that the combined use of these data collection tools (e.g., Soh & Subramanian, 2014 ; Wirsich et al., 2016 ; Wong & Singh, 2013 ) can contribute to a more complete picture of the UIC context and capture the different views of academic, industry and society stakeholders (Albats et al., 2018 ), becoming an important strategy for dealing with 'data limitations' and facilitating the 'identification of impacts', both perceived and expected.

The literature also suggests the implementation of 'benefit management systems' that allow for a more precise identification of expected benefits and the allocation of responsibilities among participants in the UIC (Fernandes & O’Sullivan, 2021 ). A 'benefit management system' involves a set of interactive activities among UIC members, aimed at identifying, reviewing, executing and projecting future actions (Fernandes & O’Sullivan, 2021 ). Considering that benefits have a positive connotation, while the definition of impact used in this paper implies positive and negative effects derived from the UICs, henceforth we will use the term 'Impact Management System'. Thus, the implementation of this system could contribute to building an ideal context involving interactions between academics and entrepreneurs, which are fundamental in the creation of common benefits (Galan-Muros & Davey, 2019 ). However, it is an exogenous strategy, beyond the scope of the UIC impact evaluator, and its implementation depends on the UIC organizations involved.

Fini et al. ( 2018 ) guide impact evaluators to go beyond traditional measures and use 'digital and technological tools' to build more efficient databases. For instance, web-based metrics (altmetrics) can help map the broader impact of research by connecting interactions on social networks between scientific production and various groups, such as public policymakers (Bornmann, 2017 ). Furthermore, the 'use of multidisciplinary approaches' that consider ethical and moral issues is also considered relevant when the commercialization of innovation generates positive and negative impacts simultaneously. For example, important technological innovation may generate negative environmental impacts related to moral and ethical issues that must factored into impact measurement (Fini et al., 2018 ).

Impact causality in the field of UIC has been addressed with 'parametric/non-parametric and qualitative methods', such as estimators (e.g. instrumental variables) and nonparametric estimators (e.g. matching estimators) (Lööf & Broström, 2008 ). The latter impose a condition of independence to determine whether the impacts would be possible in the absence of collaboration (Scandura, 2016 ), and empirically addressed counter factuality, using regression models and propensity score matching for comparative analyses between groups of companies participating in the collaboration and a non-collaborative control group, aimed at estimating the impact of the UIC. Another strategy identified was qualitative approaches using questionnaires to obtain participants' views on what would have happened in the absence of the collaboration (Wooding et al., 2007 ).

5 Discussion

This research builds on knowledge of UICs by delivering a macro-level perspective on measuring broader impacts of UICs for which there is limited understanding (Bornmann, 2013 ; Di Maria et al., 2019 ; Jones & Corral de Zubielqui, 2017 ; Nugent et al., 2022 ). More specifically, the contribution of this paper is twofold. Firstly, we propose a framework that outlines the process of measuring the impacts of UICs, integrating impact categories, the challenges associated with their measurement and strategies to overcome them.

The framework introduces the UICs as a cyclical process through which useful knowledge of high social impact can be produced. This cycle is basically divided into four phases, inputs , in-process activities , outputs and outcomes (Galan-Muros & Davey, 2019 ; Perkmann & Walsh, 2009 ). In each of these phases there is a degree of interactivity among the participants and with it the probability of generating direct or indirect impacts at the individual or community level (Bornmann, 2017 ). These impacted groups or communities may belong to industry, academia, and other social groups, such as funding institutions or those responsible for public policies, etc. Consequently, the identification of impact and the diverse groups affected requires caution and a comprehensive understanding of the multidimensional nature of impact to avoid unidirectional effects or underestimation of the existence of bidirectional knowledge flows (Verre et al., 2021 ).

The framework proposes a categorization of the impacts identified in the literature. Six types of impacts related to intellectual, economic, technological, environmental, social, and strategic areas (see Table  2 and Fig.  2 ) are identified. It is worth mentioning that the impacts belonging to the strategic category, such as reputation, competitiveness, and future collaborations are clearly mentioned and recognized in the literature. However, they are not commonly included in empirical studies of UIC impact measurement. Therefore, since there is a broad consensus on the existence of these impacts, strategies must be devised for their inclusion in future impact measurement methodologies.

The framework also shows four types of challenges related to impact measurement, namely, 'multidimensional nature of impact', 'causal attribution of effect', 'identification of impact' and 'data limitations'. These challenges explain the scarcity of systematic studies attempting to measure the impacts of UICs, complementing the findings in the literature on conceptual issues in impact types (Bornmann, 2013 ; Donovan, 2011 ).

Secondly, this research contributes to drawing clear connections between the challenges of impact measurement and the strategies to overcome these challenges. We contend that identifying these challenges not only enhances the likelihood of advancing the measurement of UIC impacts but also contributes partially to strengthening the connection among the thematic elements comprising the UIC ecosystem (Galan-Muros & Davey, 2019 ). In Fig.  4 , we use the colors green, lilac and yellow to visualize these connections.

figure 4

UIC impact measurement framework

Thus, the challenge of the 'multidimensional nature of impact' can be addressed through continuous monitoring across the lifecycle of the collaboration and interactive participation of project stakeholders. The challenge of ‘causal attribution of effect’ has been addressed in the literature mainly through qualitative methods that need to be combined with quantitative tools to build more robust methods of measurement (Bornmann, 2013 ). However, theory points to new paths in future research, namely by using 'multidisciplinary approaches', 'parametric/non-parametric and qualitative methods' and 'Digital and technological tools' for collecting and managing information, and to the connection of different areas of knowledge to allow for a broader analysis of the impact (Fini et al., 2018 ). Nevertheless, no empirical evidence applying these strategies was found in the analyzed articles.

The literature also shows that challenges related to 'data limitations' and 'impact identification' can be addressed through the 'combination of data collection tools', which was a common strategy among the analyzed studies (e.g., Soh & Subramanian, 2014 ; Wirsich et al., 2016 ; Wong & Singh, 2013 ). Many of them went beyond a particular source and used various tools to gather information, such as narratives, interviews, focus groups, and existing secondary databases.

Another strategy mentioned in the literature was the implementation of an 'Impact management system' (Fernandes & O’Sullivan, 2021 ), which helps to identify the expected impacts. We emphasize this last strategy, because it is an external mechanism that falls outside the control of the impact evaluator. We believe that UICs that manage to adopt this type of system could develop the capacity to provide more complete information on the changes experienced during the project, which would benefit future empirical studies that seek to analyze its impacts.

The set of seven strategies discussed, shown in the last layer of Fig.  4 , contributes partially to each of the four identified challenges of measuring UIC impacts. While all these strategies converge towards a common goal, which is the continuous monitoring of impact through information captured from many sources, the identified challenges can be intricate and require multiple and complementary strategies. Nevertheless, the linkages between strategies and challenges discussed in this paper, while not unique, can serve as a basis for the process of measuring impact in the UIC context.

Finally, it is worth mentioning that the potential 25 impacts resulting from UICs can significantly vary in terms of their impact degree. For example, (I.7) New business opportunities (e.g., creation of spin-offs and start-ups), (I.9) New and improved products and services, (I.14) Development of new technologies, (I.19) Promotion of regional economic and social development, and (I.20) creation of jobs, may have a significant potential. However, attaining a significant degree of these impacts requires achieving a deep change, capable of expansion at local, regional, national, and international levels, and showing long-term sustainability (Scoble et al., 2010 ).

The challenge of attaining a more significant level of impact may be linked to various inhibiting factors, including the lack of shared objectives among universities, science, and businesses (Issabekov et al., 2022 ). The absence of these common objectives can significantly affect the level of commitment from the involved parties, which is a key factor in driving the innovative performance of companies collaborating with universities (Marra et al., 2022 ). Additionally, as highlighted in the literature, information asymmetry increases uncertainty levels between the university and industry (Xiaojuan & Hongda, 2021 ). These inhibiting factors require sustainable strategies to maintain common interests over time (de Freitas et al., 2014 ), and facilitate the occurrence of more comprehensive impacts.

6 Conclusions and future avenues of research

University-industry collaboration in R&D projects (UICs) are characterized by their ability to impact individuals or groups in a society. However, measuring and demonstrating such impacts is a complex task that requires thorough analysis. Resorting to systematic literature review, this study identifies different types of impacts in the context of UICs, as well as the challenges of measuring such impacts and the strategies that can be used to overcome them (see Fig.  4 ). We propose a categorization of the impacts of UICs based on the 'type' (intellectual, economic, technological, environmental, social and strategic), the ‘agent' (industry, academy and society), the 'time' the impacts take place (short- or long-term), the 'incidence' (direct or indirect) and the 'nature' (tangible or intangible).

The categorization of impacts by 'agent' emphasizes the need to conduct empirical studies that consider the viewpoint of each stakeholder, as their interactive participation facilitates the identification of the impact, the specific group and area that may be affected by the activities carried out at the UIC. In this regard, it is crucial to acknowledge that, apart from the university and industry, society itself plays a pivotal role, represented by various academics, industrials, regions and other communities that are part of society.

We believe that the remaining three UIC impact categories (i.e., 'time', 'incidence' and 'nature') reflect the 'multidimensional nature of impact', which represents an challenge inherent in its measurement process. Likewise, the literature review allowed us to identify additional challenges related to 'causal attribution of the effect', 'data limitations' and 'impact identification'. These challenges further muddle the measurement of impact and explain the scarcity of studies that have attempted to analyze the former.

Various methodological strategies were identified in the literature to address the aforementioned challenges. However, some of these strategies, such as 'continuous monitoring throughout the UIC lifecycle', the use of 'multidisciplinary approaches', or of 'digital and technological tools' were not empirically applied in the studies analyzed. Nevertheless, there are exceptions worthy of note, such as the ‘combination of data collection methods', i.e. quantitative and qualitative methods, as well as the integration of databases, which have been frequently used in impact measurement studies, in view of obtaining a more comprehensive understanding of organizational-level impacts (Al-Ashaab et al., 2011 ; Borah et al., 2021 ; Morandi, 2013 ; Perkmann & Walsh, 2009 ).

This research represents a significant theoretical advancement in our understanding of UICs by skillfully integrating diverse strategies aimed at tackling the intricate challenges associated with measuring their impacts. By summarizing these strategies, we are aiming at the development of forthcoming methodologies capable of effectively encompassing the various impacts mentioned in literature, which are currently not suitably addressed in measurement processes due to their intangible and complex nature.

The current study’s findings have some limitations. Although a systematic review process was employed, as pointed out by Xia et al. ( 2018 ), literature reviews are never exhaustive. Therefore, in this process some articles may have been left out of the analysis. Possible exclusions may be the result of various factors, such as the choice of keywords, the string used, the scope of the search, or methodological gaps that could potentially be identified by other researchers. These issues present opportunities for future research or extensions of the current study. Besides, in the literature analysis process, specifically in qualitative and reflexive thematic analysis, cognitive bias cannot be entirely eliminated. Thus, while the results obtained offer suggestions for the measurement of UIC impacts, they are not confined to the framework proposed herein.

For future investigations is critical to focus on the perspectives of all parties involved in the collaboration to comprehend the perceived and anticipated impacts of these UICs. This involves employing diverse methods of information gathering to ensure that socially recognized impacts theoretically acknowledged are adequately included in the measurement process.

Future empirical research is also needed to delve into the knowledge of new strategies that can be implemented to overcome the challenges of measuring the broader impacts of UICs. For instance, field studies addressing the perspective and experience of the actors directly involved in collaborative project management can deliver valuable inputs. Additionally, promoting the use of multidisciplinary approaches, the application of digital and technological tools, and the combination of quantitative and qualitative methods can enrich the assessment of impacts and provide a more comprehensive understanding of the effects generated.

Future research could also further explore the inhibiting factors influencing the impacts stemming from UICs and the strategies that can be implemented to attain a more comprehensive level of impact. We recognize that there are impacts with high potential, such as: new business opportunities (e.g., creation of spin-offs and start-ups), new and improved products and services, development of new technologies, promotion of regional economic and social development, and creation of jobs.

However, strategies must be implemented to overcome inhibiting factors of these impacts. For instance, instituting regular innovation meetings on-site, involving multidisciplinary teams within the company and university, can serve as an effective approach to overcome such inhibiting factors, providing a platform to explore and assess the impacts resulting from the collaborations (Penfield et al., 2014 ). Such meetings have the potential to stimulate the development of new products, processes, and technologies that promote regional economic development, as well as to increase the level of trust and the likelihood of future collaborations (Fernandes & O’Sullivan, 2021 ).

Furthermore, not all economic development contributes to regional innovation (Cui & Li, 2022 ). Therefore, there is a need for policies that facilitate and incentivize greater exploration of collaboration results within the region itself. This strategy can play a significant role in promoting sustainable economic and social development while also fostering the creation of new businesses and increasing local employment.

Finally, the majority of the impacts of UICs recognized and identified in the literature are positive in nature. While few studies acknowledge the existence of negative impacts stemming from UICs, these are not explicitly identified, except the impact related to potential reduced scientific productivity among academics. Therefore, additional empirical studies are needed to explore other specific negative impacts that may arise from collaborations with industry. Additionally, it will be worthy to analyze potential political or organizational hindrances to accessing information related to the negative impacts that could result from co-funded collaborations between industry and university.

Abramo, G., & D’Angelo, C. A. (2022). Drivers of academic engagement in public–private research collaboration: An empirical study. Journal of Technology Transfer, 47 (6), 1861–1884. https://doi.org/10.1007/s10961-021-09884-z

Article   Google Scholar  

Abramo, G., D’Angelo, C. A., Di Costa, F., & Solazzi, M. (2009). University-industry collaboration in Italy: A bibliometric examination. Technovation, 29 (6–7), 498–507. https://doi.org/10.1016/j.technovation.2008.11.003

Acebo, E., Miguel-Dávila, J.-A., & Nieto, M. (2021). The impact of university-industry relationships on Firms’ performance: A meta-regression analysis. Science and Public Policy, 48 (2), 276–293. https://doi.org/10.1093/scipol/scab025

Al-Ashaab, A., Flores, M., Doultsinou, A., & Magyar, A. (2011). A balanced scorecard for measuring the impact of industry-university collaboration. Production Planning and Control, 22 (5–6), 554–570. https://doi.org/10.1080/09537287.2010.536626

Albats, E., Fiegenbaum, I., & Cunningham, J. A. (2018). A micro level study of university industry collaborative lifecycle key performance indicators. Journal of Technology Transfer, 43 (2), 389–431. https://doi.org/10.1007/s10961-017-9555-2

Ankrah, S., & AL-Tabbaa, O. (2015). Universities-industry collaboration: A systematic review. Scandinavian Journal of Management, 31 (3), 387–408. https://doi.org/10.1016/j.scaman.2015.02.003

Apa, R., De Marchi, V., Grandinetti, R., & Sedita, S. R. (2021). University-SME collaboration and innovation performance: The role of informal relationships and absorptive capacity. Journal of Technology Transfer, 46 (4), 961–988. https://doi.org/10.1007/s10961-020-09802-9

Arvanitis, S., Sydow, N., & Woerter, M. (2008). Do specific forms of university-industry knowledge transfer have different impacts on the performance of private enterprises? An empirical analysis based on Swiss firm data. Journal of Technology Transfer, 33 (5), 504–533. https://doi.org/10.1007/s10961-007-9061-z

Audretsch, D. B., Cunningham, J. A., Kuratko, D. F., Lehmann, E. E., & Menter, M. (2019). Entrepreneurial ecosystems: Economic, technological, and societal impacts. Journal of Technology Transfer, 44 (2), 313–325. https://doi.org/10.1007/s10961-018-9690-4

Azagra-Caro, J. M., Barberá-Tomás, D., Edwards-Schachter, M., & Tur, E. M. (2017). Dynamic interactions between university-industry knowledge transfer channels: A case study of the most highly cited academic patent. Research Policy, 46 (2), 463–474. https://doi.org/10.1016/j.respol.2016.11.011

Azagra-Caro, J. M., Tijssen, R. J. W., Tur, E. M., & Yegros-Yegros, A. (2018). University-industry scientific production and the Great Recession. Technological Forecasting and Social Change, 139 , 210–220. https://doi.org/10.1016/j.techfore.2018.10.025

Banal-Estañol, A., Jofre-Bonet, M., & Lawson, C. (2015). The double-edged sword of industry collaboration: Evidence from engineering academics in the UK. Research Policy, 44 (6), 1160–1175. https://doi.org/10.1016/j.respol.2015.02.006

Bellini, E., Piroli, G., & Pennacchio, L. (2019). Collaborative know-how and trust in university–industry collaborations: Empirical evidence from ICT firms. Journal of Technology Transfer, 44 (6), 1939–1963. https://doi.org/10.1007/s10961-018-9655-7

Bikard, M., Vakili, K., & Teodoridis, F. (2019). When collaboration bridges institutions: The impact of university-industry collaboration on academic productivity. Organization Science, 30 (2), 426–445. https://doi.org/10.1287/orsc.2018.1235

Bishop, K., D’Este, P., & Neely, A. (2011). Gaining from interactions with universities: Multiple methods for nurturing absorptive capacity. Research Policy, 40 (1), 30–40. https://doi.org/10.1016/j.respol.2010.09.009

Borah, D., Malik, K., & Massini, S. (2021). Teaching-focused university–industry collaborations: Determinants and impact on graduates’ employability competencies. Research Policy . https://doi.org/10.1016/j.respol.2020.104172

Bornmann, L. (2013). What is societal impact of research and how can it be assessed? A literature survey. Journal of the American Society for Information Science and Technology, 64 (2), 217–233. https://doi.org/10.1002/asi.22803

Bornmann, L. (2017). Measuring impact in research evaluations: A thorough discussion of methods for, effects of and problems with impact measurements. Higher Education, 73 (5), 775–787. https://doi.org/10.1007/s10734-016-9995-x

Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Thematic Analysis. In P. Liamputtong (Ed.), Handbook of Research Methods in Health Social Sciences (pp. 843–860). Springer.

Chapter   Google Scholar  

Crespo, M., & Dridi, H. (2007). Intensification of university-industry relationships and its impact on academic research. Higher Education, 54 (1), 61–84. https://doi.org/10.1007/s10734-006-9046-0

Ćudić, B., Alešnik, P., & Hazemali, D. (2022). Factors impacting university–industry collaboration in European countries. Journal of Innovation and Entrepreneurship . https://doi.org/10.1186/s13731-022-00226-3

Cui, W., Li, L., & Chen, G. (2022). Market-value oriented or technology-value oriented? Location impacts of industry-university-research (IUR) cooperation bases on innovation performance. Technology in Society . https://doi.org/10.1016/j.techsoc.2022.102025

Cui, Z., & Li, E. (2022). Does Industry-University-Research Cooperation Matter? An Analysisof Its Coupling Effect on Regional Innovation and Economic Develop-ment. Chinese Geographical Science, 32 (5), 915–930. https://doi.org/10.1007/s11769-022

Da Silva Florencio, M. N., & De Oliveira, A. M. (2022). The importance of absorptive capacity in technology transfer and organisational performance: A systematic review. International Journal of Innovation Management, 26 (2), 63. https://doi.org/10.1142/S136391962230001X

de Freitas, S., Mayer, I., Arnab, S., & Marshall, I. (2014). Industrial and academic collaboration: Hybrid models for research and innovation diffusion. Journal of Higher Education Policy and Management, 36 (1), 2–14. https://doi.org/10.1080/1360080X.2013.825413

De Fuentes, C., & Dutrenit, G. (2012). Best channels of academia-industry interaction for long-term benefit. Research Policy, 41 (9), 1666–1682. https://doi.org/10.1016/j.respol.2012.03.026

De Silva, M., Rossi, F., Yip, N. K. T., & Rosli, A. (2021). Does affective evaluation matter for the success of university-industry collaborations? A sentiment analysis of university-industry collaborative project reports. Technological Forecasting and Social Change, 163 , 120473. https://doi.org/10.1016/j.techfore.2020.120473

de Vrueh, R. L. A., & Crommelin, D. J. A. (2017). Reflections on the future of pharmaceutical public-private partnerships: From input to impact. Pharmaceutical Research, 34 (10), 1985–1999. https://doi.org/10.1007/s11095-017-2192-5

Di Maria, E., De Marchi, V., & Spraul, K. (2019). Who benefits from university–industry collaboration for environmental sustainability? International Journal of Sustainability in Higher Education, 20 (6), 1022–1041. https://doi.org/10.1108/IJSHE-10-2018-0172

Donovan, C. (2011). State of the art in assessing research impact: Introduction to a special issue. Research Evaluation, 20 (3), 175–179. https://doi.org/10.3152/095820211X13118583635918

El-Ferik, S., & Al-Naser, M. (2021). University industry collaboration: A promising trilateral co-innovation approach. IEEE Access, 9 , 112761–112769. https://doi.org/10.1109/ACCESS.2021.3104096

Eom, B. Y., & Lee, K. (2010). Determinants of industry-academy linkages and their impact on firm performance: The case of Korea as a latecomer in knowledge industrialization. Research Policy, 39 (5), 625–639. https://doi.org/10.1016/j.respol.2010.01.015

Fan, H. L., Huang, M. H., & Chen, D. Z. (2019). Do funding sources matter?: The impact of university-industry collaboration funding sources on innovation performance of universities. Technology Analysis and Strategic Management, 31 (11), 1368–1380. https://doi.org/10.1080/09537325.2019.1614158

Fernandes, G., & O’Sullivan, D. (2021). Benefits management in university-industry collaboration programs. International Journal of Project Management, 39 (1), 71–84. https://doi.org/10.1016/j.ijproman.2020.10.002

Fini, R., Rasmussen, E., Siegel, D., & Wiklund, J. (2018). Rethinking the commercialization of public science: From entrepreneurial outcomes to societal impacts. Academy of Management Perspectives, 32 (1), 4–20. https://doi.org/10.5465/amp.2017.0206

Galan-Muros, V., & Davey, T. (2019). The UBC ecosystem: Putting together a comprehensive framework for university-business cooperation. Journal of Technology Transfer, 44 (4), 1311–1346. https://doi.org/10.1007/s10961-017-9562-3

Galán-Muros, V., van der Sijde, P., Groenewegen, P., & Baaken, T. (2017). Nurture over nature: How do European universities support their collaboration with business? Journal of Technology Transfer, 42 (1), 184–205. https://doi.org/10.1007/s10961-015-9451-6

Garcia, R., Araújo, V., Mascarini, S., Santos, E. G., & Costa, A. R. (2020). How long-term university-industry collaboration shapes the academic productivity of research groups. Innovation Organization and Management, 22 (1), 56–70. https://doi.org/10.1080/14479338.2019.1632711

Giannopoulou, E., Barlatier, P. J., & Pénin, J. (2019). Same but different? Research and technology organizations, universities and the innovation activities of firms. Research Policy, 48 (1), 223–233. https://doi.org/10.1016/j.respol.2018.08.008

Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The New Production of Knowledge The Dynamics of Science and Research in Contemporary Societies . SAGE Publications Ltd.

Gray, D. O., & Steenhuis, H. J. (2003). Quantifying the benefits of participating in an industry university research center: An examination of research cost avoidance. Scientometrics, 58 (2), 281–300. https://doi.org/10.1023/A:1026236626942

Guerrero, M., Herrera, F., & Urbano, D. (2021). Does policy enhance collaborative-opportunistic behaviours? Looking into the intellectual capital dynamics of subsidized industry–university partnerships. Journal of Intellectual Capital, 22 (6), 1055–1081. https://doi.org/10.1108/JIC-07-2020-0254

Harryson, S., Kliknaitė, S., & Dudkowski, R. (2007). Making innovative use of academic knowledge to enhance corporate technology innovation impact. Int. J. Technology Management, 39 (2), 131–157.

Issabekov, B., Bayanbayeva, A., Altynbassov, B., & Barlykov, Y. (2022). University-business cooperation as a key factor in innovative economic development in Kazakhstan. Theoretical and Practical Research in Economic Field, 13 (1), 86–101.

Jones, J., & Corral de Zubielqui, G. (2017). Doing well by doing good: A study of university-industry interactions, innovationess and firm performance in sustainability-oriented Australian SMEs. Technological Forecasting and Social Change, 123 , 262–270. https://doi.org/10.1016/j.techfore.2016.07.036

Karatzoglou, B. (2013). An in-depth literature review of the evolving roles and contributions of universities to Education for Sustainable Development. Journal of Cleaner Production, 49 , 44–53. https://doi.org/10.1016/j.jclepro.2012.07.043

Kelleher, L., & Zecharia, A. (2021). A Triple Helix systems perspective of UK drug discovery and development: A systematic review of REF impact case studies. Industry and Higher Education, 35 (6), 650–666. https://doi.org/10.1177/0950422220969349

Lee, K. J. (2011). From interpersonal networks to inter-organizational alliances for university-industry collaborations in Japan: The case of the Tokyo Institute of Technology. R and D Management, 41 (2), 190–201. https://doi.org/10.1111/j.1467-9310.2011.00633.x

Lew, Y. K., & Park, J. Y. (2021). The evolution of N-helix of the regional innovation system: Implications for sustainability. In Sustainable Development (Vol. 29, Issue 2, pp. 453–464). Wiley. https://doi.org/10.1002/sd.2143

Li, J., & Xing, J. (2020). Why Is Collaborative Agglomeration of Innovation so Important for Improving Regional Innovation Capabilities? A Perspective Based on Collaborative Agglomeration of Industry-University-Research Institution. Complexity , 2020 . https://doi.org/10.1155/2020/7049606

Lima, J. C. F., Torkomian, A. L. V., Pereira, S. C. F., Oprime, P. C., & Hashiba, L. H. (2021). Socioeconomic impacts of university–industry collaborations–a systematic review and conceptual model. In Journal of Open Innovation: Technology, Market, and Complexity (Vol. 7, Issue 2). MDPI AG. https://doi.org/10.3390/joitmc7020137

Lin, M. W., & Bozeman, B. (2006). Researchers’ industry experience and productivity in university-industry research centers: A “scientific and technical human capital” explanation. Journal of Technology Transfer, 31 (2), 269–290. https://doi.org/10.1007/s10961-005-6111-2

Lo, C. C., Cho, H. C., & Wang, P. W. (2020). Global R&D collaboration in the development of nanotechnology: The impact of R&D collaboration patterns on patent quality. Sustainability (Switzerland) . https://doi.org/10.3390/su12156055

Lööf, H., & Broström, A. (2008). Does knowledge diffusion between university and industry increase innovativeness? Journal of Technology Transfer, 33 (1), 73–90. https://doi.org/10.1007/s10961-006-9001-3

Lucia, Ó., Burdio, J. M., Acero, J., Barragán, L. A., & Garcia, J. R. (2012). Educational opportunities based on the university-industry synergies in an open innovation framework. European Journal of Engineering Education, 37 (1), 15–28. https://doi.org/10.1080/03043797.2011.644762

Maietta, O. W. (2015). Determinants of university-firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry. Research Policy, 44 (7), 1341–1359. https://doi.org/10.1016/j.respol.2015.03.006

Marinho, A., Silva, R. G., & Santos, G. (2020). Why most university-industry partnerships fail to endure and how to create value and gain competitive advantage through collaboration–a systematic review. Quality Innovation Prosperity, 24 (2), 34–50.

Marra, M., Alfano, V., & Celentano, R. M. (2022). Assessing university-business collaborations for moderate innovators: Implications for university-led innovation policy evaluation. Evaluation and Program Planning . https://doi.org/10.1016/j.evalprogplan.2022.102170

Mascarenhas, C., Ferreira, J. J., & Marques, C. (2018). University-industry cooperation: A systematic literature review and research agenda. Science and Public Policy, 45 (5), 708–718. https://doi.org/10.1093/SCIPOL/SCY003

Miller, K., Alexander, A., Cunningham, J. A., & Albats, E. (2018). Entrepreneurial academics and academic entrepreneurs: A systematic literature review. International Journal of Technology Management, 77 (1–3), 9–37. https://doi.org/10.1504/IJTM.2018.091710

Morandi, V. (2013). The management of industry-university joint research projects: How do partners coordinate and control R&D activities? Journal of Technology Transfer . https://doi.org/10.1007/s10961-011-9228-5

Nightingale, P., & Scott, A. (2007). Peer review and the relevance gap: Ten suggestions for policy-makers. Science and Public Policy, 34 (8), 543–553. https://doi.org/10.3152/030234207X254396

Nsanzumuhire, S. U., & Groot, W. (2020). Context perspective on University-Industry Collaboration processes: A systematic review of literature. Journal of Cleaner Production . https://doi.org/10.1016/j.jclepro.2020.120861

Nugent, A., Chan, H. F., & Dulleck, U. (2022). Government funding of university-industry collaboration: Exploring the impact of targeted funding on university patent activity. Scientometrics, 127 (1), 29–73. https://doi.org/10.1007/s11192-021-04153-0

Penfield, T., Baker, M. J., Scoble, R., & Wykes, M. C. (2014). Assessment, evaluations, and definitions of research impact: A review. Research Evaluation, 23 (1), 21–32. https://doi.org/10.1093/reseval/rvt021

Perkmann, M., Neely, A., & Walsh, K. (2011). How should firms evaluate success in university-industry alliances? A Performance Measurement System. r&d Management, 41 (2), 202–2015.

Google Scholar  

Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’Este, P., Fini, R., Geuna, A., Grimaldi, R., Hughes, A., Krabel, S., Kitson, M., Llerena, P., Lissoni, F., Salter, A., & Sobrero, M. (2013). Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy, 42 (2), 423–442. https://doi.org/10.1016/j.respol.2012.09.007

Perkmann, M., & Walsh, K. (2009). The two faces of collaboration: Impacts of university-industry relations on public research. Industrial and Corporate Change, 18 (6), 1033–1065. https://doi.org/10.1093/icc/dtp015

Pinto, E. B., & Fernandes, G. (2021). Collaborative R&D the key cooperation domain for university-industry partnerships sustainability-Position paper. Procedia Computer Science, 181 , 102–109. https://doi.org/10.1016/j.procs.2021.01.109

Puerta Sierra, L., Vargas, M., & Torres, V. (2017). An institutional framework to explain the university-industry technology transfer in a public University of Mexico. Journal Technology Management Innovation, 12 (1), 4–12.

Puerta-Sierra, L., Montalvo, C., & Angeles, A. (2021). University-industry collective actions framework: Societal challenges, entrepreneurial interactions and outcomes. Technology Analysis and Strategic Management . https://doi.org/10.1080/09537325.2021.1875129

Robin, S., & Schubert, T. (2013). Cooperation with public research institutions and success in innovation: Evidence from France and Germany. Research Policy, 42 (1), 149–166. https://doi.org/10.1016/j.respol.2012.06.002

Roncancio-Marin, J., Dentchev, N., Guerrero, M., Díaz-González, A., & Crispeels, T. (2022). University-Industry joint undertakings with high societal impact: A micro-processes approach. Technological Forecasting and Social Change . https://doi.org/10.1016/j.techfore.2021.121223

Scandura, A. (2016). University-industry collaboration and firms’ R&D effort. Research Policy, 45 (2016), 1907–1922.

Scoble, R., Dickson, K., Fisher, J., & Hanney, S. (2010). Research Impact Evaluation, a Wider Context: Findings from a Research Impact Pilot.

Siemieniako, D., Kubacki, K., & Mitręga, M. (2021). Inter-organisational relationships for social impact: A systematic literature review. Journal of Business Research, 132 , 453–469. https://doi.org/10.1016/j.jbusres.2021.04.026

Sjoo, K., & Hellstrom, T. (2019). University-industry collaboration: A literature review and synthesis. Industry and Higher Education, 33 (4), 275–285. https://doi.org/10.1177/0950422219829697

Skute, I., Zalewska-Kurek, K., Hatak, I., & de Weerd-Nederhof, P. (2019). Mapping the field: A bibliometric analysis of the literature on university–industry collaborations. Journal of Technology Transfer, 44 (3), 916–947. https://doi.org/10.1007/s10961-017-9637-1

Soh, P. H., & Subramanian, A. M. (2014). When do firms benefit from university-industry R&D collaborations? The implications of firm R&D focus on scientific research and technological recombination. Journal of Business Venturing, 29 (6), 807–821. https://doi.org/10.1016/j.jbusvent.2013.11.001

Tian, M. Y., Su, Y. W., & Yang, Z. (2021). University-industry collaboration and firm innovation: An empirical study of the biopharmaceutical industry. Journal of Technology Transfer . https://doi.org/10.1007/s10961-021-09877-y

Tijssen, R. J. W. (2012). Co-authored research publications and strategic analysis of public-private collaboration. Research Evaluation, 21 (3), 204–215. https://doi.org/10.1093/reseval/rvs013

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14 , 207–222.

Vega-González, L. R., Nairn, J., Stiles, B., & Ascanio, G. (2012). International private-public collaboration for technology development and knowledge generation: The development of an automatic moulding press. International Journal Technology Intelligence and Planning, 8 (3), 278–294.

Vega-Jurado, J., Manjarrés-Henríquez, L., Fernández-de-Lucio, I., & Naranjo-Africano, G. (2020). A virtuous circle? The effects of university-industry relationships in a region with low absorptive capacity. Science and Public Policy, 47 (4), 503–513. https://doi.org/10.1093/scipol/scaa030

Verre, V., Milesi, D., & Petelski, N. (2021). Science-industry cooperation: What are the benefits for the public part? Evidence from argentine biopharmaceutical sector. International Journal of Innovation and Technology Management . https://doi.org/10.1142/S0219877021500073

Walsh, A. C., Salem, M. E., Oliver, Z. T., & Clark-Sutton, K. (2018). Social and economic impact of the commercialization of the Argus II artificial retina in the United States. Journal of Technology Transfer, 43 (6), 1607–1630. https://doi.org/10.1007/s10961-017-9610-z

Wang, J., & Shapira, P. (2012). Partnering with universities: A good choice for nanotechnology start-up firms? Small Business Economics, 38 (2), 197–215. https://doi.org/10.1007/s11187-009-9248-9

Wirsich, A., Kock, A., Strumann, C., & Schultz, C. (2016). Effects of university-industry collaboration on technological newness of firms. Journal of Product Innovation Management, 33 (6), 708–725. https://doi.org/10.1111/jpim.12342

Wong, P. K., & Singh, A. (2013). Do co-publications with industry lead to higher levels of university technology commercialization activity? Scientometrics, 97 (2), 245–265. https://doi.org/10.1007/s11192-013-1029-1

Wooding, S., Nason, E., Klautzer, L., Rubin, J., Hanney, S., & Grant Jonathan. (2007). ‘RAND Europe’, Policy and Practice Impacts of Research Funded by the Economic Social Research Council. www.rand.org

Xia, N., Zou, P. X. W., Griffin, M. A., Wang, X., & Zhong, R. (2018). Towards integrating construction risk management and stakeholder management: A systematic literature review and future research agendas. International Journal of Project Management, 36 (5), 701–715. https://doi.org/10.1016/j.ijproman.2018.03.006

Xiaojuan, Z., & Hongda, C. (2021). Impact of university-industry collaborative research with different dimensions on university patent commercialisation. Technology Analysis and Strategic Management, 34 (11), 1235–1248. https://doi.org/10.1080/09537325.2021.1950677

Yeo, B. (2018). Societal impact of university innovation. Management Research Review, 41 (11), 1309–1335. https://doi.org/10.1108/MRR-12-2017-0430

Zavale, N. C., & Schneijderberg, C. (2021). Academics’ societal engagement in ecologies of knowledge: A case study from Mozambique. Science and Public Policy, 48 (1), 37–52. https://doi.org/10.1093/scipol/scaa055

Zhang, S., Xu, X., Wang, F., & Zhang, J. (2022). Does cooperation stimulate firms’ eco-innovation? Firm-level evidence from China. Environmental Science and Pollution Research, 29 (51), 78052–78068. https://doi.org/10.1007/s11356-022-21296-6

Zhang, S., Yuan, C., & Wang, Y. (2019). The impact of industry-university-research alliance portfolio diversity on firm innovation: Evidence from Chinese manufacturing firms. Sustainability (switzerland), 11 (8), 2–16. https://doi.org/10.3390/su11082321

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Cohen, M., Fernandes, G. & Godinho, P. Measuring the impacts of university-industry R&D collaborations: a systematic literature review. J Technol Transf (2024). https://doi.org/10.1007/s10961-024-10114-5

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  1. Industry 5.0

    This paper presents a tertiary study of thirty-two literature reviews on Industry 5.0, supported by a bibliometric analysis in the Scopus database. The results show three stages of Industry 5.0 research since 2018, starting with the Industry 4.0 separation.

  2. The Current Status and Developing Trends of Industry 4.0: a Review

    3.2 Descriptive Analysis 3.2.1 The Trend of Publication (2011 - 2021). Within the current decade, research on Industry 4.0 has been increasing tremendously; since 2018, the number of publications has increased by an exponential level (Fig. 2).For the year 2021 through the writing of this article, as many as 631 papers have been published.

  3. A systematic review of the implementation of industry 4.0 from the

    Industry 4.0 (I4.0) is a fast-evolving area of research, bringing together knowledge from multiple academic fields into creative solutions for manufacturing innovation. Despite the growing amount of published work covering a wide range of I4.0 areas, there has been relatively little research devoted to the organisational side of implementing I4.0.

  4. Sustainable manufacturing in Industry 4.0: an emerging research agenda

    This paper contributes to advances on Industry 4.0 research identifying that the concepts of sustainable manufacturing and the use of the new technologies can enable Industry 4.0 to have positive impacts on all the sustainability dimensions in an integrated way, and also supporting the implementation of the Industry 4.0 agenda in the following ...

  5. The growing influence of industry in AI research

    The Increasing Dominance of Industry in AI Research. Industry's dominance of AI inputs is now manifesting in an increasing prominence in AI outcomes as well—in particular, in publishing, in creating the largest models, and in beating key benchmarks. Research papers with one or more industry co-authors grew from 22% of the presentations at ...

  6. Market Research and Insight: Past, Present and Future

    Here, we briefly provide a background to the origins and development of market research before introducing the papers that comprise this special issue. Origins and Development of Market Research. Market research and insight has its origins in the social sciences and has undergone various stages of evolution and growth. In the 1840s, early ...

  7. Reskilling and Upskilling the Future-ready Workforce for Industry 4.0

    Industry 4.0 is revolutionizing manufacturing processes and has a powerful impact on globalization by changing the workforce and increasing access to new skills and knowledge. World Economic Forum estimates that, by 2025, 50% of all employees will need reskilling due to adopting new technology. Five years from now, over two-thirds of skills considered important in today's job requirements ...

  8. 10000 PDFs

    Explore the latest full-text research PDFs, articles, conference papers, preprints and more on MANUFACTURING INDUSTRY. Find methods information, sources, references or conduct a literature review ...

  9. Open innovation in the manufacturing industry: A review and research

    The open innovation (OI) paradigm has garnered increasing importance in academic research and industrial applications. Considering this interest, this paper aims to synthetize up-to-date findings, outline the intellectual structure of OI within the manufacturing research domain, and suggest a future research agenda.

  10. Industrial digitalization. A systematic literature review and research

    When applied to manufacturing industries, digitalization is sometimes thought of as a synonym of "Industry 4.0" (Negri et al., 2017), the "Industrial Internet", and the "Industrial Internet of Things" ... Most of the research papers are exploratory in nature (79 papers), while the remaining papers are either theory-building (42) ...

  11. State of Industry 5.0—Analysis and Identification of Current Research

    This serves as the motivation of this paper in identifying and analyzing the various themes and. research trends of what Industry 5.0 is using text mining tools and techniques. T oward this, the ...

  12. (PDF) The future of the automotive industry: dangerous challenges or

    and the US data on the automotive industry presented in two companion papers (Russo et al., 2020; Carreto, 2020). A complementary source of information, direct interviews with com-

  13. Sustainability trends and gaps in the textile, apparel and fashion

    Textile, apparel, and fashion (TAF) industries contribute significantly to global environmental pollution at every point of the supply chain. Clothing manufacturing and transportation produce a large volume of waste and high greenhouse gas emissions, often taking advantage of cheap labor in developing countries. As a result, stakeholders are becoming more aware of the effect of the textile ...

  14. Retail Industry Research from Harvard Business School

    Retail. New research on the retail industry from Harvard Business School faculty on issues including online and brick-and-mortar strategies, consumer behavior, and use of technology. Page 1 of 188 Results →. 13 May 2024.

  15. Journal of Marketing Research: Sage Journals

    Journal of Marketing Research (JMR) is a bimonthly, peer-reviewed journal that strives to publish the best manuscripts available that address research in marketing and marketing research practice.JMR is a scholarly and professional journal. It does not attempt to serve the generalist in marketing management, but it does strive to appeal to the professional in marketing research.

  16. The packaging, pulp and paper industry in the next decade

    Pulp, paper, and packaging in the next decade: Transformational change. From what you read in the press and hear on the street, you might be excused for believing the paper and forest-products industry is disappearing fast in the wake of digitization. The year 2015 saw worldwide demand for graphic paper decline for the first time ever, and the ...

  17. International Journal of Market Research: Sage Journals

    The International Journal of Market Research (IJMR) publishes original research addressing key challenges in market research and insight. Since its founding in 1958 IJMR has been at the forefront of the development of new research methods, … | View full journal description. This journal is a member of the Committee on Publication Ethics (COPE).

  18. Industry 4.0: A survey on technologies, applications and open research

    This paper conducts a comprehensive review on Industry 4.0 and presents an overview of the content, scope, and findings of Industry 4.0 by examining existing literature in all databases within Web of Science and Google Scholar. The selected 88 papers are grouped into five research categories and reviewed.

  19. Industrial microbiology

    Industrial microbiology is a branch of applied microbiology in which microorganisms are used in industrial processes; for example, in the production of high-value products such as drugs, chemicals ...

  20. Industry Research Reports

    Features of Industry Research Reports. Comprehensive data and analysis that is easy to read and digest. Reports available that cover industries in the US, Canada, Australia, Germany, UK and New Zealand. Copy & paste our charts, graphs and images for use in presentations and decks. Individual reports are 30-40 pages and all follow the same table ...

  21. (PDF) Industry 5.0: A Survey on Enabling Technologies ...

    To provide a very first discussion of Industry 5.0, in this paper, we aim to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0. We first ...

  22. Paper Products Market Share Insights

    The global paper products market size was estimated at USD 268.8 billion in 2018 and is expected to register a CAGR of 0.3% from 2019 to 2025. This growth is primarily attributed to the rising demand for packaging paper by major companies in the retail, FMCG, pharmaceutical, and hospitality industries. Increasing technological developments for ...

  23. Robust optimization for a steel production planning problem with

    This paper addresses a production planning problem in the steel industry, specifically focusing on determining production quantities and product-to-order assignment considering uncertain demand and product substitution.

  24. Clothing and Textiles Research Journal: Sage Journals

    Clothing and Textiles Research Journal (CTRJ), peer-reviewed and published quarterly, strives to strengthen the research base in clothing and textiles, facilitate scholarly interchange, demonstrate the interdisciplinary nature of the field, and inspire further research. It is the official publication of the International Textile & Apparel Association, Inc. View full journal description

  25. How Are Insurance Markets Adapting to Climate Change? Risk Selection

    A theoretical model of a market for natural hazard insurance that incorporates both price regulation and asymmetric information across insurers helps rationalize the empirical patterns we document. Our results highlight the underappreciated importance of the winner's curse as a driver of high prices and limited participation in insurance ...

  26. Measuring the impacts of university-industry R&D ...

    Measuring the impacts of collaborative projects between industry and academia raises significant challenges. It involves stakeholders with different outlooks and impact expectations. Moreover, the multidimensional nature of the impacts themselves means they are tangible and intangible, short- and long-term, direct and indirect, positive and negative, making their measurement process very ...

  27. Why Are Companies That Lose Money Still So Successful?

    In a well-functioning capital market, profits should be the sole criterion for firm survival; that is, firms reporting losses should disappear. ... The authors' series of new research papers ...

  28. The real cause of post-pandemic inflation was demand, not supply

    A new research paper argues that the driver of a surge in post-pandemic inflation on both sides of the Atlantic was demand, not supply. At the European Central Bank annual gathering in Sintra ...

  29. Crypto as a Marketplace for Capital Flight

    This paper shows how cryptocurrency markets can fuel cross-border capital flight by serving as marketplaces that match counterparts with and without (illicit) access to FX. In countries where international transactions are restricted, crypto exchanges effectively allow domestic agents to pay a premium to buy foreign currency. The counterparts to these transactions are agents with access to FX ...

  30. Scientist defeats J&J lawsuit over cancer research

    A New Jersey federal judge has dismissed a lawsuit brought by a Johnson & Johnson subsidiary against a scientist who published a paper linking talc-based consumer products to cancer, finding that ...