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Ten Simple Rules for Choosing between Industry and Academia

* E-mail: [email protected]

• David B. Searls

Published: June 26, 2009

• https://doi.org/10.1371/journal.pcbi.1000388
• Reader Comments

Citation: Searls DB (2009) Ten Simple Rules for Choosing between Industry and Academia. PLoS Comput Biol 5(6): e1000388. https://doi.org/10.1371/journal.pcbi.1000388

Copyright: © 2009 David B. Searls. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author received no specific funding for this article.

Competing interests: The author has declared that no competing interests exist.

One of the most significant decisions we face as scientists comes at the end of our formal education. Choosing between industry and academia is easy for some, incredibly fraught for others. The author has made two complete cycles between these career destinations, including on the one hand 16 years in academia, as grad student (twice, in biology and in computer science), post-doc, and faculty, and on the other hand 19 years in two different industries (computer and pharmaceutical). The following rules reflect that experience, and my own opinions.

Rule 1: Assess Your Qualifications

If you are a freshly minted Ph.D., you know that you will need a good post-doc or two before you can be seriously considered for a junior faculty position. If you're impatient, you might be thinking of industry as a way to short-circuit that long haul. You should be aware that companies will strongly consider your post-doctoral experience (or lack thereof) in determining your starting position and salary. While you may not relish extending your indentured servitude in academia, any disadvantage, financial and otherwise, can quickly be made up in the early years of your career in industry. In other words, trying to get off the mark quickly is not necessarily a good reason to choose industry over academia.

On the other hand, you may have completed an undergraduate or Master's program with a view to going to industry all along, with never a thought of an academic career. You should still consider the point of the previous paragraph. While abbreviated “practical” bioinformatics training programs can be excellent, a Ph.D. is a significant advantage in all but the most IT-oriented positions in industry, at least at the outset. This is not to discourage anyone from embarking on a fast-track-to-industry program if their heart is in it, but be aware that the further you climb the educational ladder, the higher and faster you can start when you step across to the business ladder, and the better you will compete for a job in the first place. The days are long past when bioinformaticists were in such short supply that any qualification would do.

If you are an old hand and have already notched up a post-doc or two, take stock of your star power. This unspoken but universally understood metric encompasses such factors as whom you've trained with, where you've published (and how much), and what recent results of yours are on everyone's lips. If you are fortunate enough to have significant capital in this department, then the world may be your oyster, but you still need to consider where you will get the greatest leverage. While your stardom may be less taken for granted in industry, my feeling is that academia is a better near-term choice in such circumstances. Consider that it was in academia that you achieved the success you own thus far, so you obviously “get it.” The simple fact is that academia is rather more of a star system (as in Hollywood) than is industry.

Finally, if you count among your qualifications a stint in industry already, as an intern or perhaps as part of a collaboration, you will not only be in a better position to compete for a permanent job, but you will be much better prepared to make the decision facing you. Stated another way, if you are seriously considering industry as a career path, you should probably have already taken advantage of the many opportunities out there to dip your toes in the water.

Rule 2: Assess Your Needs

In taking stock of your needs, and perhaps those of your family, a decent living is generally at or near the top of the list. Salaries are still higher in industry, though the gap is not nearly so wide as it once was. If you need a quick infusion of cash, companies may offer signing bonuses, though again these were more common when bioinformatics was a rarer commodity.

Industry offers forms of compensation unavailable in academia, and you will need to consider how to value them relative to your present and future needs. Despite recent bad press, bonus systems are often part of the equation, and depending on your entry point they may constitute a significant percentage of total compensation. There is a tendency among academics to discount bonus programs in their comparison shopping, sometimes to zero, and this is a mistake. Bonuses are considered core aspects of compensation in most companies, and though they always have a performance-based multiplier, the base levels have historically been fairly dependable. That said, these are tough times in industry, and there are no guarantees. Your best strategy is to understand the reward system thoroughly, ask for historical data, and avoid comparing only base salaries unless you are extraordinarily risk-averse.

Share options are another matter. While in the past these were very attractive, and fruitful in practice, most industry types will tell you frankly that any options they've received in the past decade are deep underwater and a deep disappointment. Many consider pharma shares (and therefore options) to be a bargain at the moment, but that's between you and your financial adviser to assess. In any case, it is not a short-term consideration, since options typically take several years to vest.

If you are looking at biotech, however, share options and similar ownership schemes need to be a key consideration, since these are a major rationale for assuming risk—more on that below.

Finally, you may have more specific needs to consider, such as a spouse also in need of a job. The two-body problem has always been tougher in academia than in industry, and probably always will be. If you are both academics, note that industry often has good contacts with local universities, and can facilitate interviews. Being a star certainly helps, so don't be afraid to negotiate. In fact, a general rule of thumb is that it never hurts to make your specific needs known, within reason. Academia will try to accommodate them as a community, while on the other hand business (particularly large, diversified companies) may have resources to address them that you wouldn't have expected. Nobody wants to hear a peremptory demand, but if a company wants you, be sure to let them know anything that might offer them a way to attract you.

Rule 3: Assess Your Desires

There are needs, and then there are desires. Do you want riches? Fame? A life at the frontiers of knowledge? The hurly-burly of the business world? How do you really feel about teaching, publishing, managing, interacting, traveling, negotiating, collaborating, presenting, reporting, reviewing, fundraising, deal-making, and on and on? Though it may seem obvious, this is a good time to decide what really drives you.

First, the obvious. Do you want to teach? If lecturing is in your blood, your decision is made, although if a smattering will suffice you may have the option from within industry of an adjunct academic appointment. (By the same token, if you are not so enchanted with lecturing, grading, tutoring, etc., there are often options for research track professorships that minimize teaching duties.) Do you want to publish? While it will always be “publish or perish” in academia, it is certainly possible to grow your CV in industry, and it can even enhance your career, depending on the company. However, it might be largely on your own time, and you will likely encounter restrictions in proprietary matters, though in practice you can generally find ways to work within them. Ask about publication at the interview, both policies and attitudes, and watch out for any defensiveness.

An important question, surprisingly often overlooked, is how you want to actually spend your time, day by day and hour by hour. In academia, you will immediately be plunged into hands-on science, and your drivers will be to start out on your career by getting results, publishing, networking, and building your reputation with a view to impressing your tenure committee. A career in industry may put more of an early emphasis on your organizational aptitude, people skills, powers of persuasion, ability to strategize and execute to plan, etc.; in terms of growing your reputation, your audience will be the rather narrower community of your immediate management. A somewhat more cynical view would be that in business you will spend seemingly endless hours in meetings and writing plans and reports, while in academia you will spend all that time and more in grantsmanship—in this regard, you must pick your poison.

Finally there is the elephant-in-the-room question: Do you want to make money, or to help people? This is, of course, a false dichotomy, but many people consciously or unconsciously frame the decision in just this way, and you had best deal with it. Try thinking of it not so much in terms of the profit motives of the respective institutions, but in terms of the people with whom you would spend your career. You should have encountered a good sampling of scientists from industry during meetings, internships, collaborations, interviews, etc. (or in any case you should certainly try to do so before making judgments). If you are left in any doubt as to their ethics or sincere desire to relieve human suffering as efficiently as possible, or if you feel these are somehow trumped by the corporate milieu, then by all means choose academia—but only after applying analogous tests to the academics you already know well. In my experience, business doesn't have a monopoly on greed, nor are humanitarian impulses restricted to academia. That said, in the final analysis you must be comfortable with your role in the social order and not finesse the question.

Rule 4: Assess Your Personality

Not surprisingly, some personality types are better-suited to one environment or the other. Raw ambition can be viewed as unseemly in either case, but there is more latitude for it in industry, and greater likelihood of being recognized and rewarded sooner if you are “on the go.” In fact, one of the clearest differences between academia and industry are their respective time constants. Although the pace of academia may have quickened of late, it is still stately by comparison with industry, and much more scheduled (so many years to tenure, so many months to a funding decision, etc.). If you are impatient, industry offers relatively fast-paced decision-making and constant change. If you thrive more under structured expectations, academia would be better for you, for although industry has all the trappings of long-range strategies and career planning, the highly reactive environment means these are more honored in the breach. For one thing, reorganizations are common, and in the extreme case mergers (I have experienced two) can reset everything, for good or ill, and devour many months.

This is not to say that all is chaos—industry certainly favors a goal-directed personality, but with plenty of flexibility. On the other hand, flexibility is more the hallmark of academic research, where you will have the opportunity to follow wherever the science leads, once you are running your own shop. In industry, the flexibility is more of the conforming sort, since you won't be able to investigate every promising lead and change your research direction at will. In academia, diverging from the Specific Aims of a grant may be a problem when the time comes to renew, but the risk is yours, as is the reward. In industry, you can make the case for a new program of research, but the decision is management's and will be guided by business considerations. The “lone wolf” or “one-person band” may be increasingly rare in academia in an age of collaboration, but it is unheard of in industry, where being able to work in teams with specialized division of labor is essential. It should be apparent, as well, that mavericks and quirky personalities tend to do better in academia.

The pecking order in industry is deeper and more pyramidal than in academia, and you might end up languishing in a pay grade (or feel like you are), but there are usually plenty of opportunities for lateral moves and a variety of experiences—not to mention that it's easier to switch companies than colleges. In industry, one does need to be able to thrive in a hierarchy; you will always answer to someone, though the degree to which you are monitored will vary. By the same token, if your personality is such that climbing a management ladder and assuming steadily greater responsibility suits you, industry is built for that, and plenty of management training is on offer in larger companies. Learning to manage is much more hit-or-miss in academia; opportunities to lead large organizations are rare (and to manage them actively rather than by consensus, rarer still).

If your personality type is that of a risk-taker, biotechs and/or startups may fit you to a tee. These are the wild and wooly end of the industry spectrum, and the risks and rewards are well-known. You will work longer hours than in large pharma, and maybe even more than in academia. You will most likely share more in ownership, and learn entrepreneurial skills that will serve you well, once the bug has bitten. Bear in mind the very common pattern of faculty spinning off startups or otherwise participating in boards and the like, not to mention staking out intellectual property (shared with their university); thus, you may well be able to scratch this itch from the vantage of academia as well.

A final word about politics. Whether you are an enthusiastically political animal, or abhor this aspect of the human condition, you will encounter plenty of politics in both academia and industry. The flavors differ, to be sure. As a student you doubtless heard the clichés about tedious academic committees and underhanded deans, but you have probably had more exposure to the realities behind those stories than the corresponding ones about the dog-eat-dog corporate world. Company politics, I would hazard to say, are more transparent—the maneuvering more open and the motives more apparent. The results are often more life-altering, unbuffered by tenure and academic convention. Again, it is a matter of taste, but in my opinion the differences are overblown, for the simple reason that people are the same everywhere, in both environments governed by an underlying sense of fair play, but also occasional opportunism.

Rule 5: Consider the Alternatives

As I've suggested, the choice you face is far more fine-grained than simply that between industry and academia. Industry is a spectrum, from large pharma to mature biotech to startup. By the same token, the academic side has at one extreme the research powerhouses, where you will be judged by volume of grants, and at the other the teaching institutions, which may not even have graduate departments. Unless you are very sure of yourself, you'd be well-advised to consider the full range, given the competition you may face.

Also, don't neglect other careers that may value your training. If you love the language, consider science journalism, either writing or editing— Science and Nature have large staffs, and you will often encounter them and representatives of other journals at the same scientific meetings you attend. The same is true of government agencies such as the NIH, NSA, DOE, and so forth, where grants administration is very actively tied to research trends and can be an entrée into the world of science policy. There are many more such positions when foundations, interest groups, and other private funding bodies are included. If you have a knack for business, many management consulting firms have scientific and technical consulting arms that value Ph.D.s and offer intensive training opportunities, and, though it may not be attractive at the moment, a career as a financial analyst specializing in biotech is yet another possibility.

Rule 6: Consider the Timing

The current business environment cannot help but be among your considerations. Pharma has certainly been contributing to the unemployment rolls of late. Corporate strategies, which used to be very similar across the sector, have started to diverge, so that some companies are divesting bioinformatics at the same time that others are hiring computational types disproportionately as they place more of an emphasis on mathematical modeling, systems approaches, pharmacogenomics, drug repurposing, and the like. Overall, though, the industry trend has been to shrink R&D, and this may well continue through a round of consolidation, with several mega-mergers now under way. As noted above, mergers are times of upheaval, carrying both risk and opportunity, and usually a period in limbo as well. At the same time, it is worth bearing in mind that a corollary of downsizing is outsourcing, so that there may be new opportunities for startups and even individual consultants.

For much of the last decade, academia has also been in the doldrums, as NIH budgets have effectively contracted. As I write this, things are definitely looking up, with prospects for renewed funding of science and even near-term benefits to the NIH and NSA from the Obama stimulus package. Whether universities will respond proportionately with faculty hiring, given the losses in their endowment funds and cutbacks in salaries and discretionary spending, remains to be seen. There is a lot of slack to be taken up, and in particular a backlog of meritorious grant applications that are now being reconsidered. Nevertheless, on balance, an academic career has to be somewhat more promising today than a year ago, and a career in pharma rather less so, in the opinion of the author.

Rule 7: Plan for the Long Term

Having noted the current situation in Rule 6 , it's important also to say that a career decision should be made with the long haul in mind. The business cycle will eventually reverse itself, and while the business model may need to change irrevocably, the aging population alone dictates that healthcare will be an increasing global priority. Likewise, history shows that growth in government funding for science waxes and wanes, with a time constant somewhat longer than a decade. Trying to optimize a career decision based on current conditions is a bit like trying to time the stock market—you are sure to be overtaken by events.

One approach is to choose some reasonably long time frame, perhaps a decade, and ask yourself whether you'd be content to have lived through the average ups and downs you'd experience in a given job over that period. In academia, that would include a tenure decision (rate your chances), a lot of grant applications with mixed success at best, and maybe some great students and really significant scientific contributions. In pharma or large biotech, it would encompass a couple of promotions, your own group and maybe a department, at least one merger or other big disruption, and several rounds of layoffs. In small business, it might include a failed startup (or two, or three), an IPO if you're lucky, and a lucrative exit strategy or long-term growth if you're really lucky.

If you game these scenarios with various probabilities, and use your imagination, it just might become clear which ones you have no stomach for, and which ones really hold your interest.

Rule 8: Keep Your Options Open

Job-hopping is much more prevalent now than in days of yore, and you should consider this in your scenarios. In industry, there is little stigma attached to changing employers, and if you can tolerate the relocation and/or want to see the world, it is a more or less standard way to advance your career by larger-than-usual increments. This stratagem is far from unknown in academia, but perhaps a bit trickier to execute, though of course it is de rigueur if you fail to get tenure.

Of greater interest is the question of moving between academia and industry. From the former to the latter is fairly easy, but the reverse is not as common, for a variety of reasons. Superstar academics in relevant areas are in great demand in industry, to which they are often exposed through consulting or scientific advisory boards. There are multiple examples of senior academics taking over major R&D organizations in industry, sometimes orders of magnitude larger than anything they managed in academia, and you might even consider this well-trod path as a career goal from the outset.

It is not impossible to return to academia from industry, particularly if you were already quite prominent when you left, but if you start your career in industry you may be at a disadvantage unless you go to great lengths to maintain an academic-style publication record and CV. Important exceptions would be if the work that you did in industry was particularly novel and/or high-profile, or if your business experience is valued in the post you seek. Examples of the latter might be faculty positions with a prominent management component (centers, institutes, core facilities, and the like), or an interface role back to industry, or perhaps a joint business school appointment.

Rule 9: Be Analytic

Approach the decision with the analytic skills you've learned to apply to scientific questions. Gather data from all available sources and organize it systematically. When you interview, don't just impress, but get impressions; record everything down to your gut feelings. Do some bibliometric or even social network analyses of your potential colleagues. Check the industry newsletters and blogs, albeit with a grain of salt, to get a sense of the mood around R&D units (not to be confused with manufacturing, sales and marketing, or other divisions, which may have completely different cultures within the same company).

You might even try out some decision theoretic methodologies, such as decision matrices and Bayesian decision trees, or run simulations on the scenarios of Rule 7 . I recommend taking a look at expected utility theory and prospect theory, for an interesting quantitative excursion. But honestly, these suggestions are just a more sophisticated informatics version of the classic advice to “make a list of pros and cons,” which always makes one feel a little more in control.

Rule 10: Be Honest with Yourself

Another homily: Now, if ever, is the time to be honest with yourself. Take a hard look at your qualifications, with as much objectivity as you can muster, and use these rules to decide where you would be best-suited and positioned for success. But even more importantly, deal with your emotional responses to industry and academia. If something is nagging at you, tease it out into the open, and try to decide if it is well-founded or not; if you can't decide, then you have to acknowledge it, and realize that it may not go away in the future either.

Finally, try to keep some perspective. Your career choice is important, but not irrevocable, and there are more consequential things in life. Don't let the decision process ruin what should be an exciting time for you.

Working in Industry vs Academia: Which is Right For You?

Industry Advice Science & Mathematics

8 Differences Between Working in Industry vs. Academia

One of the most significant decisions scientists face is choosing whether to pursue a career in industry or academia. While this decision is easy for some, it can be incredibly challenging for others. If you’ve struggled with this question of which career path you’ll choose after your formal education ends, you’re not alone.  

There are several key differences between working in industry and academia. It’s critical to understand these nuances and consider your skills, qualifications, personality, and career goals when deciding which path is right for you.

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1. Responsibilities

Academic careers will vary, depending on the size of an institution, but in an academic research career, most professionals have some version of the following broad responsibilities:

• Applying for grants
• Conducting self-directed research
• Publishing papers
• Teaching courses
• Mentoring students
• Performing departmental service

Working in “industry” can mean many things, as the term encompasses all research work that occurs outside of universities. Professionals who choose this route can work for small biotech startups, mid-size corporations, or even international organizations with thousands of employees. The scope of work is typically focused on applied research that will have direct, clinical value. Industry work also requires a more business-minded approach. You must be able to develop projects that meet the company goals as you support the business plan of the company.

 2. Flexibility

For some, an appealing aspect of working in academia is the freedom to dictate your own schedule, choosing when to teach, conduct research, and publish your work. By not having to answer to anyone about how you allocate your time, however, also means you must be proficient in time management and prioritization.

Time in a business organization’s research lab is more structured and typically revolves around a standard 9-to-5 workday. For some people, this type of structure is preferable to ensure maximum productivity.

 3. Collaboration

Academic research is largely collaborative and team-work oriented. An academic environment creates an extraordinary opportunity for cross-disciplinary thinking and research. You can, however, enjoy a large sense of autonomy, should you choose, with the freedom to choose when, and with whom, you collaborate.

In industry, researchers are working toward a larger, shared goal. Due to the complex nature of drug discovery, there is much collaboration across multiple functional areas and disciplines. Whereas researchers in academia can be highly competitive, in industry, it’s critical for researchers to be able to collaborate and work as a team.

 4. Workplace Culture

Academia is highly research and discovery focused, and much research is done for the sake of learning, as opposed to clinical application. In contrast, “industry” work allows researchers to feel a sense of immediate impact on patient lives.

Both workplaces have their own share of pressures and demands, as well. In academia, the researcher’s plight is often “obtain funding and publish, or perish.” Academics are under immense pressure to be self-starters, continually publish their research, and to promote and advocate for their work.

In industry, the pressures are typically more deadline-driven, as teams work to integrate science and business-focused problem solving on tight project timelines in accordance with larger product and business goals. Thus, it’s important for people working in industry to be excellent communicators and have sharp people skills to manage projects.

The pace of work also differs between industry and academia. In contrast to the fast-paced nature of drug development, academic timelines tend to be longer and focused more on long-term goals and education.

 5. Individual Impact

As an academic, you’ll typically not have quarterly deadlines to meet, monthly reports to file, or a superior that you’re being held directly accountable to. Thus, the ability to make an individual impact and receive recognition for your work can be greater than in industry, where you are a single member working on behalf of an organization.

The flip side, however, is that academics can struggle to have their ideas adopted in practice, whereas the work that that industry researchers do is often directly motivated by business goals.  Although this does remove a measure of autonomy, the positive aspect is that research results are often immediately and directly impactful. To work in industry, one must be willing to work on a team and share credit. This teamwork aspect can also take off some of the pressure of having to individually achieve results.

6. Intellectual Freedom

In academia, professionals enjoy intellectual freedom, free from the constraints of short-term deadlines and having to answer to those setting the research priorities. This allows individuals to choose what they would prefer to spend their time researching, and how to pursue it. With this freedom also comes the responsibility of securing funding and resources.

When working in industry, most work is done on a quick timeline and is driven by a product or business goals. This type of clear direction can be very appealing to some researchers, while others may see it as a hindrance to their ability to investigate their own areas of personal interest. A benefit of working in industry is that the funding and more state-of-the-art resources will be supplied by the larger organization.

On average, industry scientists typically make more money than academic researchers. A 2014 Life Sciences Salary Survey found that American, Canadian, and European scientists that worked in industry made about 30 percent more than those in academia. On average, academics, including postdocs, make $88,693 annually, while commercial scientists make $129,507.

8. Career Advancement

Generally speaking, an academic research scientist’s career moves one of two directions—toward tenure and professorship, or toward work as an academic staff scientist. The career ladder can be difficult if only a handful of universities that may specialize in your discipline, or are actively hiring in a given year. There is great job security, however, if you achieve tenure.

Industry career opportunities are broader, however, and can range from research at the bench to work in product marketing or development. In industry, you also have the opportunity to climb the organizational ladder to manage larger teams and projects.

How to Decide Whether Academia or Industry Is Right for You

Ultimately, the choice between academia and an industry research lab involves many compromises, and the best “fit” for you will likely depend on your individual preference and working style.

Here are some factors to consider before heading down either career path:

• Determine your priorities. Consider what matters most to you. Whether you’re most concerned about salary potential, intellectual freedom, or flexibility, it’s important to do some soul-searching to decide what you value most.
• Think about how you want to spend your time . Consider how you actually want to spend your time day-to-day. Think about how you feel about teaching, publishing, managing, interacting, traveling, negotiating, collaborating, presenting, reporting, reviewing, fundraising, etc.
• Know your strengths. Are you a self-starter who is able to proactively manage your own time? Or do you prefer to work in a more structured, process-oriented environment? Knowing your strengths can help direct you to the path that will increase your chances of success.
• Factor in your personality . Do you prefer to work independently, or do you thrive when working alongside others? Are you comfortable with self-promotion, or would you be more comfortable sharing your successes with a team?
• Think long-term, but keep your options open. Where do you see yourself in five years? 10? 20? Think about where you’d like to be long-term, but remember the choice you make is merely for the next step in your career. It doesn’t have to be final. The field is currently more conducive to transitions between the two fields more than ever before.
• Be true to yourself . Most of all, be honest with yourself. Stay true to who you are, and consider what you are most passionate about. If you do this, you will find success in whichever path you choose.

To learn more about careers, trends, and market outlook for the biotechnology industry and find out how to prepare yourself to advance your career, explore our related posts . 

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Making the leap from academic research to industry R&D: What scientists need to know to make the transition

Why a researcher might decide to make the transition from academia to industry, and the differences between the two environments when it comes to scientific research.

By Philip Seifi in Guides

Why would you want to transition from academia to industry?

Radically higher pay, increased focus and impact, a meritocratic environment, rapid pace of research, how do research scientist skills differ between academia and industry, more flexible outlook and mindset, direct communication, increased transparency and collaboration, digital transformation, accounting and resource allocation, which path will you choose.

Get your whole lab on the same page today.

As research scientists explore the opportunities in their career, they often come to a crossroads where they must choose between staying in academia or moving into industry.

This piece delves into why a researcher might decide to make the transition from academia to industry, as well as the differences between the two environments when it comes to scientific research.

For those who have spent much of their career in academia, the prospect of transitioning to industry can be daunting. But the rewards—from greater financial security to the freedom to pursue more creative endeavours—can make it a worthwhile move.

Furthermore, the approach to research in industry is distinct from academia, as industry research often involves projects of shorter duration and higher stakes, requiring a greater degree of collaboration and communication.

The BioSpace 2020 U.S. Life Sciences Salary Report has revealed a stark disparity in the salaries earned by life science professionals in academia and industry.

The report found that the average salary for men in academia was $82,516, while women in the same sector earned $58,966. In contrast, life science professionals in industry earned far more, averaging $144,181 for men and $129,480 for women.

This difference in wages highlights the lucrative opportunities available in the industrial sector for scientists.

In academia, securing your own funding is a necessity, and much of your energy is devoted to crafting grant applications and lobbying for resources. By contrast, industrial settings tend to have someone else taking care of the funding side of things.

Many scientists transitioning from academia to industry also find their work has a more tangible and immediate impact. Academic research is largely focused on advancing the field of knowledge, but the practical implications of this research can be limited. In contrast, research conducted in industry is geared towards developing a product or service that will benefit the company financially. There is usually a well-defined vision and product roadmap in place, and researchers in industry must consider the potential applications of their research and how it will be used in a real-world setting.

In academia, success is usually determined by the number of publications and citations one can amass. In industry, however, success is determined by the impact of one's research on the bottom line, for instance, by meeting product release deadlines and achieving sales targets.

This has created a meritocratic environment where success is based on the quality of one's work rather than personal connections or other external factors, and provided an opportunity for those with the skills and expertise to rise to the top and reach their full potential.

In addition, the availability of high-paying jobs in the tech industry has allowed many talented individuals to choose a career path that best suits their skills and desires.

The world of industry research has been transformed in recent years, with the rise of startups leading to an emphasis on tighter timelines, feedback loops, and continuous improvement. This has led to faster commercialization of new research, providing researchers with a unique opportunity to bring their ideas to market more quickly.

With the potential for faster, greater success and recognition, commercial R&D has become an increasingly enticing option for researchers looking to make the most of their ideas.

In academia, research scientists may be focused on hypothesizing, researching, publishing papers, and teaching. In industry, research scientists may focus on developing new products and technologies, applying science to solve problems in the marketplace, and working with a team to create innovative solutions.

As a result, industry and academia often require different skillsets, and this can be challenging for scientists transitioning between the two.

In academia, scientists are trained to take a rigorous and methodical approach to research. While this can be advantageous, it can sometimes be an impediment in business, where startups often thrive in an environment of creative thinking and flexibility. The ability to adjust and pivot to changing conditions is often crucial to success in the business world.

The differences in pace and autonomy between industry and academia also mean that scientists entering industry must be able to quickly adapt and learn new skills. While the learning process can be daunting, the rewards of leveraging modern tools and technology can greatly improve research efficiency, productivity, and collaboration.

Entering the business world can be a daunting task, as the language of science is often replaced by a more direct and succinct style of communication. In industry, it is not just the science itself that matters, but also the politics, money, and relationships that come with the job.

Learning to effectively communicate verbally and in writing is essential, and while experience is an important factor, taking specialized courses can also help hone these skills. For example, the American Society for Biochemistry and Molecular Biology and the Canadian Center for Science Communication both offer courses to help in this regard. Being able to communicate effectively and concisely is a vital skill for success in industry.

In academia, research is often conducted in silos with little collaboration. This is not the case in industry, where it is necessary to form relationships with researchers and other stakeholders with different backgrounds, goals and deadlines.

To bridge this gap, consider taking a course such as the Nature Masterclass, Effective Collaboration in Research . This program draws from the expertise of both academics and professionals, providing an effective way to enhance collaboration efforts.

In industry, the need for efficiency and speed leads to the adoption of tools, such as electronic lab notebooks (ELNs) and laboratory management software, to streamline research.

These tools come with a steep learning curve, and scientists may need to acquire new skills in order to effectively collaborate with colleagues, communicate efficiently, and manage their time and resources.

In an industry context, having some grasp of managerial accounting is essential, even if a deep understanding of the subject is not necessary. With limited time and resources, it is essential for scientists to be able to identify how to deploy them most efficiently.

Many courses of varying technicality are available on this topic, but one particularly useful resource is the book Cost Analysis for Engineers and Scientists .

In conclusion, the differences between scientific research in academia and industry are numerous, with each providing its own unique advantages and disadvantages. Academia provides an environment with the freedom to explore and innovate, while industry brings the resources and financial stability needed to sustain long-term research projects. Ultimately, the decision of which path to pursue is highly dependent on the individual researcher and their research goals.

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Peter Eckes, President, BASF Bioscience Research :  [00:00:00] I was on the path to be a professor. What was driving me to this? It was clearly this freedom to decide what kind of fundamental problem you want to work on. You have to be good in writing grants still because otherwise you don't have the money. But, on the other hand in industry, I think clearly it is all about at the end providing customer solutions. This will funnel the direction of the research, as John already said. I think what is usually different really is that it is always many, many people that are involved. We had yesterday our innovation award at BASF going to a new fungicide and we actually reviewed a thousand people who were involved in the development. Just imagine a thousand people in a development of a single product. It's obviously a huge team effort. 

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CONCEPTUAL ANALYSIS article

Strengthening the bridge between academic and the industry through the academia-industry collaboration plan design model.

• 1 Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan
• 2 Business Incubation Center, Bahria University, Karachi, Pakistan
• 3 Computer Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan

The study has been undertaken to integrate two different aspects of the triple helix model: universities and the industry. Special attention has been paid to the prevailing difference between the two, hampering their working as a coherent unit. Integrating the existing knowledge in the study, we proposed the Academia-Industry Collaboration Plan (AICP) design model. The model comprises processes, methods or approaches, and tools. Processes serve as a road map to third parties for establishing collaboration between academia and the industry. It has all the essential process models and a series of steps that help minimize the organizational complexity of the collaboration process between academia and the industry. Methods or approaches serve the purpose of implementing those processes effectively. Finally, appropriate tools are selected to integrate possible collaboration improvements that lead to innovation.

Introduction

Universities serve the industry in two ways. It provides the workforce necessary to run the industry, and two, it furnishes innovative ideas to start new business ventures. This apparent simplistic relationship does not work so simplistically because of the inherent differences between the two. Universities desire to contribute to the theory. On the other hand, the industry is restrained by profitability. Therefore, academia and the industry are analogous to two sides of a river that must flow independently. As far as science and engineering disciplines are concerned, creating linkages between the two sides of the river has the potential to contribute to the betterment of both—the industry and universities.

According to the organization for economic co-operation and development (OECD), the industry conducts around two-thirds of R&D in science and technology studies. The remaining 20% of R&D work is carried out by universities, while 10% is carried out by the government ( Organisation for Economic Co-operation and Development [Oecd], 2017 ). The world’s top-ranking universities attract industry funding for their projects and innovations. The pharmaceutical industry is one of the most prominent investors in universities ( Shehatta and Mahmood, 2016 ). The IT industry has also started investing in academies because it has concluded that it is beneficial to spend on students and academia. Exactly 26,355 universities from all over the world are included in a survey carried out in July 2016 that lists the countries arranged by the number of universities in the top rankings ( Shattock, 2017 ). With this statistical data, it can be concluded that collaboration between the industry and academia is on the rise. Undertaking research in the industry paves the way for easy dissemination of knowledge, as universities have access to real-time data, eliminating the dichotomy arising from the characteristic differences between the two. This study proposes a model for the desired assimilation of the industry and university, leading to more efficient working of the two.

Literature Review

During the 1990s revolution of information systems, research on the industrial community has initialized the discussion, but the problem remained in top-ranked journals ( Steinbach and Knight, 2006 ). The research article provides relevance, discussion, and a broad scope of the practitioner community and their issues. It also emphasizes that these problems are not to be resolved on a global, systemic, or academic level; instead, they are determined on an individual basis, specified and defined by the researcher. Different researchers made a brief plan for the particular research problem to find and resolve the targeted problem ( Fitzgerald, 2003 ). This manuscript presents the possible research gap and consensus, which have always made it difficult for academia and the industry to walk together. According to the analytical thinking perspective, the breach between the institution and the industry due to inadequate intended results affects the communication process and the comparatively light reporting of essential technology stuff in the university syllabus. We talk about the conception and bring forward some of the key issues to better understand the foundations of the research-practice breach. Aside from recommending that students exchange their views in acquiring surroundings, this paper discusses how to improve shared empathy between students and researchers in order to increase the capacity for effective collaboration while simultaneously raising the prerequisites of engineering. Numerous papers have been published in peer-reviewed technical journals every year. There is no need to publish a more significant number of papers with an industry affiliation. Most of the papers are just systematic reviews in the software engineering discipline. In Pakistani, students are taught high-end programming languages. Nonetheless, they end up using them for repeated non-industry-based applications like matrix multiplication programs or developing web-based library management systems. They know the syntax of the programming languages but not the domains in which such programming languages should be applied. As students graduate and move to the industry, unless the industry gives them proper training in the field (banking, embedded, military applications, etc.), it is hard for an average student to flourish.

While there has been plenty of research conducted in academia, there is still criticism of academic research. Various patterns and frameworks that need to be addressed remain unaddressed in information science, which includes professional/client collaboration and the right of each stakeholder ( Birdsall, 2009 ). Communicating academic research findings to IS professionals: An analysis of problems written by Lang says that research findings often do not directly or immediately relate to IS professionals in the industry. Lang also stresses the communication gap between researchers and practitioners ( Lang, 2002 ). Though realized very early, the inability of students to cope up with the challenges of real world are persisting. It has become imperative to provide students with a rich experience of the industry along with academic training so they can at least avoid the difficulties arising from being ignorant of the development taking place in the industry or at most become more prolific in combining the beneficial aspects of the two. Academia should focus on high-level skills rather than implanting low-level skills in students. Not everything taught can be found in books or in traditional classrooms. Knowledge and skills can be gained through experience. Knowledge, skills, and equipment provide employees with resources to withstand the pressures emerging from challenges associated with the processes of collaboration ( Belli, 2021 ).

The relationship between knowledge and skills sets, and their gear as a 3-stage social fabric network, sheds light on how one-of-a-kind varieties of researchers function in their engagement with affordance and emotional attachment.

It has been observed that students learn most of the topics during their jobs, including testing verification, quality assurance, project management, ethics and professionalism, technical writing, and leadership skills ( Khan et al., 2021a , d , e ). Such topics should be included in their academic journey. To motivate the industry to collaborate, we must also encourage academic institutions to do so for mutual benefits. Researchers must develop the curriculum to address the industry’s major problems, considering how things will help both sides. It is necessary to include fundamental industrial practices in academia to help students develop their skills.

Moody (2003) talks about the lengthy delay in disseminating research findings into practice, a process in which some results are inevitably lost ( Moody, 2003 ). Students should be encouraged to work on industry-identified problems and do static validation of their thesis/project work. In the Pakistani scenario, others do not have a mandatory 6-month project in the industry except for medical courses.

In Pakistan, people who cannot get campus placements spend 1 or 2 years getting certified in any field before starting the job hunt. At times, certifications are more valuable than degrees themselves; this need to get certificates clearly states one thing: there is an evident lack of industry knowledge in academia. An interesting myth is that functional programming languages are just for academic purposes, but Erlang, a Prolog-based functional programming language, has been used in Ericson for ages. In universities, people study software testing techniques theoretically, but they do not do much about learning software testing tools, which are used predominantly in the industry for software testing.

There are plenty of training institutions that teach industry-specific skills. Yet, they are generally out of reach financially for students. Industrial research is not made publicly available until it fulfills its business interests. This study has started discussing this topic on a social media platform, bringing out an interesting point. For the companies, institution interaction can only provide intangible benefits that cannot go beyond a slot in campus placement, and they can directly train students during internships (governmental and educational institutions). For any other interaction, companies cannot get returns in terms of money. One way to resolve this is to encourage institutions to hire industry experts as their faculties. Considering that the software industry and software engineering academia are the prominent communities with meager collaborative efforts and few joint projects compared to the size of these communities, researchers and practitioners are less motivated to participate in association with each other. Driven in the opposite direction by their varied objectives, the collaboration between the industry and universities has been limited. It is hard to develop a connection between academia and the industry until issues such as research relevance, training commitment, problem resolution in the real world, communication gap, contractual and privacy concerns are explicitly addressed ( Garousi et al., 2017 ).

The success of students’ projects in collaboration with the industry depends on an ecosystem that involves academia, industry liaison, clients, students, and faculty. Students create products in association with the industry or for the industry and then work as entrepreneurs. Understanding the emergence of new research subjects suggested by university students that may lead to innovation or value creation for the industry requires a combination of imagination and entrepreneurship ( Hansson and Mønsted, 2008 ). Students apply academic analysis to practical work ( Hillon et al., 2012 ). It is a composite task to provide practitioners with knowledge of or abstractions from research and to translate research into practice; therefore, a new discipline must be introduced to bridge the gap between research and practice. Translational development renders research findings reliable, practical results that bridge the gap between academia and industry ( Norman, 2010 ). Planning a collaboration process is imperative; defining a time constraint for long-term and short-term relationships between academia and the industry will eventually aid in understanding the nature of projects that both researchers and practitioners pursue. Time view applies to industry-academic joint projects that determine when the research is estimated to be practiced ( Runeson and Minör, 2014 ). The transformation of knowledge into practice is time-consuming. It takes approximately 4–5 years for applied research to be fully implemented in the industry; the rate of transformation is even slower for basic research. It has been observed that governmental and educational institutions are more visible than research institutes ( Ahmed et al., 2019 ; Belli and Gonzalo-Penela, 2020 ; Khan et al., 2021b , c ).

It is based on a model introduced by Connor et al. (2009) that industry needs are based on five success factors facilitated by research results: needing orientation, industry goal alignment, deployment impact, industry benefit, and innovation. It focuses on research actions like management engagement, network access, collaborator match, communication ability, and continuity ( Sandberg et al., 2011 ). Success factors can be drawn from the previous collaborations formed by the industry and academia ( Wohlin et al., 2011 ). Short- and long-term collaborative efforts specify collaborations. An alliance or partnership between participants can be official or casual. This may vary from partnership equity, agreements, research-based projects, and copyrights to capital flows, publications, and meetings in workshops, focus groups, and seminars ( Guimón, 2013 ). For this study, “collaboration propensity” is based on the likelihood of an individual researcher collaborating at a specific moment in time and in relation to current research interests ( Birnholtz, 2007 ). Education has to adjust itself to the industry and job market changes, but this process requires a consolidated representation of fields and teaching programs ( Venant et al., 2015 ). Working with universities on research projects requires learning to work across organizational boundaries. Also, they can build the capacity to cooperate with participants managing within a different structural motivation system ( Bruneel et al., 2010 ). Universities are boosting their connections with the industry in order to play a massive role in the innovation ecosystem due to the increased significance of the triple helix model. While this connection leads to more knowledge generation and economic prosperity, it also has an effect on university norms ( Dooley and Kirk, 2007 ).

Many chambers of commerce, education ministries, and commerce ministries lack cooperation, resulting in a purposeless and unproductive curriculum that fails to fulfill trade and industry requirements. Another reason for failing to understand and prepare technical and commerce colleges for public and private sector universities to meet the latest trends and market requirements is the absence of infrastructural linkage ( Soharwardi, 2011 ). The industry that develops its processes around work has an inherent penchant for keeping things simple. On the other hand, universities working around semesters work to cover the elongated time. Because one is compelled by the paucity of time and the other swollen by the abundance of the same commodity. The collaboration between the two—the industry and university—is possible if universities can simplify their work for the industry ( Michael, 2013 ). An ideal collaborative situation can be achieved where technological transfer from the industry to academia makes new collaboration possible. Research has new findings from previous investigations, as well as the emergence of new companies where technology finds its way to better cooperation between research development ( Fujisue, 1998 ).

Academia and Industry Collaboration Plan

As indicated earlier, it has become increasingly clear that bridging the gap between academia and the industry is essential, but even more critical is bridging the gap between requirements engineering in the literature and incorporating those requirements into practices more efficiently. Therefore, identification of the problem domain is needed, which includes the following:

• Use old processes and techniques when researchers have already introduced new methods.

• On the contrary, the theoretically defined process may not be relevant in the industrial field when implemented.

• The processes studied by students are applied in the industry, but their application may vary according to requirements.

• There is a disconnect between academic researchers and industrial researchers.

• A student, in many cases, has no idea about how to market themselves outside of academia.

• Availability of resources provided to researchers and practitioners.

• The industry is reluctant to implement new ideas, whereas academia is reluctant to adopt new teaching ideas.

• The trust deficit between the two partners is low in the long run.

Both partners should realize that technological development is impossible without new ideas. It has become necessary to build a long-term relationship between primary stakeholders. Therefore, we are underlining significant features in the solution domain, which may be the inception of a collaboration plan between academia and the industry.

• Theoretical standards should be practically performed in industrial environments.

• An internship program should be initiated.

• Project managers for industrial personnel with explicit knowledge will conduct lectures to disseminate knowledge sharing with academia.

• Academia shares new research with the industry and prepares reports on new process models that benefit their partners.

• Industrial relevance is important when teaching students.

• Sharing resources is needed between the industry and academia. Students and employees should have access to online resources so that they can get references from current research, reports, and books.

• Collaboration should not be restricted to intra-country, but exchanges between students and employees should be done internationally.

• The formation of committees has become necessary for monitoring and expanding this collaboration.

These are the few things that must be done before entering the next phase of AICP. It is essential to understand the need for interaction between academia and the industry, and these collaborative interactions need to be based on mutual benefits.

Academia-Industry Collaboration Plan’s New Approach in Connection With the Triple Helix Model

Innovation requires a breeding ground, which is furnished by an innovative environment. The triple helix model (THM) is an approach that can be employed to pursue the creation of an innovation-friendly environment. This model connects the field of practice (industry) with regulatory authority (government) to a knowledge repository (universities) to facilitate innovation. The three interacting components—industry, government, and universities—compensate for the shortcomings of each other. The industry desirous of the solution seeks a knowledge source for ideas that are duly provided by universities linked to the industry ( Dooley and Kirk, 2007 ). THM also connects the industry with the government. Being directly linked to the government, the industry can mitigate the hassle of bureaucratic processes that are characteristic of governmental organizations.

Universities can benefit from this interaction with the industry in many ways. First, academicians need to have a practical problem to which they can apply their knowledge. When universities are connected to the industry, they are flooded with practical issues demanding solutions. Additionally, being linked to the industry, universities also have an authentic source of data. Second, universities can also revamp their curriculum in light of their interaction with the industry. By doing so, universities increase the employability of their graduates. Finally, the link between universities and the industry connects the advancement of knowledge with the advancement of practice. When problems are approached in a collaborative manner, the chance of a new startup increases exponentially.

The AICP design model furnishes students with an opportunity to be connected to the industry. So, once they are graduated, the search for a job turns out to be simple. Graduating students, using their established connections, easily find job opportunities. A vast pool of opportunities for students who can implement their ideas with the help of industries will be created. Academia suggests that the sector improve/replace the current procedures with more productive ones. On the other hand, enterprises can send trained professionals to teach/guide the students. They can also give students activities related to their courses and help the industrialists improve their functionality as shown in Figure 1 .

Figure 1. Role of students, industry, and academia ( Iyer, 2014 ).

Initial Process

• Initiation

(a) The need to pitch the importance of collaboration between the industry and academia.

(b) How can this collaboration bring about productive changes in both sectors?

Expansion and Specifications

• Academia

(a) Industrial professionals share their expertise and work behavior with students, giving them a comprehensive outlook on professional work.

(b) Help students find relevancy between theoretical and practical approaches.

(c) They can do theoretical research work and suggest its practical implications in the industrial environment.

• Industry

(a) They can provide activities that help strengthen students’ innovative capabilities, giving them constructive ideas for application in the industry for effective functioning.

(b) Ideas can be made possible if they are backed by expertise.

(c) Rather than sending their employees on expensive training, their professionalism can be improved by their personal learning experience by communicating and expressing their ideas with students and getting feedback from their perspective.

(d) They can cut short their recruitment process by hiring the students they are already working in collaboration with.

• Implementation process

(a) A mutual consensus is required from both sides of the industry and academia.

(b) It is essential to have the industrialists help guide academia.

• Monitoring and review

(a) Most importantly, constant monitoring is needed, as is a weekly-to-monthly review process for proper functioning.

(b) When a problem occurs that needs immediate action, a proactive approach should mostly be mainly acquired.

• Feedback

(a) It is most important to get feedback on the collaborative approach as a whole, either through weekly or monthly seminars, to resolve problems that arise.

How Can This Collaboration Bring About Productive Changes in Both Sectors?

(a) Industrial professionals share their expertise and work behavior with students, giving them a comprehensive understanding of professional working.

(c) They can conduct academic research and suggest its practical implications in an industrial environment.

(d) Ideas can be made possible if they are backed by expertise.

(e) Instead of sending their employees on expensive training, industries can increase the professionalism of their employees through their personal learning experiences by communicating and expressing their ideas with students and getting feedback from their perspectives.

(f) They can cut short their recruitment process by hiring the students they are already working in collaboration with.

(g) Both sides can quickly do a mutual consensus.

(h) When both partners work together and adhere to the same standards, problems are faced and resolved more effectively.

Framework Activity for Increasing Communication Between the Industry and the Institution

It is important to create a framework activity that leads to developing the task sets and work products to improve communication between academia and the industry, resulting in an extended relationship that allows for the mass introduction and sharing of ideas between them. It is evident from the software engineering perspective that such activities define guidelines and set standards for initiating a task. The framework activities for the test set of communications extending the relationship between academia and the industry for collaborative work in the field of research and implementation of that research would be as follows:

• Planning

(1) Information gathering

(2) Adapting a model for communication and information sharing

(3) Scheduling workshops and seminars for students and industrialists

• Analysis and specifications

(1) Promoting creative activities/small tasks to judge students and to find out whether they are suitable

(2) Identifying relevant industries for students’ internships or visits

(3) List down the appropriate institution for industrial collaboration

(4) Eradicating unnecessary behaviors

(5) Meetings

(6) Suggesting new ideas from both the industry and universities

• Design and review

(1) Acceptance criteria

(2) Proposal writing

(3) Verification and validation

(4) Team design

(5) Student’s group formation and reviews

(6) Acceptance of new ideas

• Implementation of feedback rework

(1) Interactive sessions and focus groups

(2) Information sharing

(3) Working projects

(4) Internships

(5) New theoretical knowledge transfer in industries

(6) Implementation of the latest ideas in collaboration

Keeping in mind the current scenario analysis and collaboration standards for academia and the industry, a basic strategic planning structure that incorporates two-way communication is needed. Figure 2 shows open-minded communication between academia and industry, with information sharing and knowledge-sharing abilities. Such a system will provide a complete and dynamic collaboration while strengthening relationships. Bridging the divide between universities and practitioners is an important task performed, and its negligence would lead to project failure. Among the few salient features are identifying the stakeholders and prioritizing them, which should be addressed thoroughly before initiating collaborative efforts. The resilient stakeholders to uplift the dynamics of research and the implementation of ideas into inventions come from academia and industries that help implement new ideas into reality. There is a massive gap between academics and practitioners, and industries ignore when engineers and scientists are in a production state.

Figure 2. Academia industry collaboration (AIC) engineering process architecture.

On the other hand, academia neglects the fact that these students follow market trends. There must be industrial concentration in educational institutions, whereas educational institutions must know the requirements of industries. There might be several barriers to adopting an outside idea if only the idea were given without the insight and proper understanding of all its dynamics. The essential factor noted against adapting the idea is that it is hard to find relevance in academic or independent study research material and implementation of practices. The independent study researches a significantly less popular among the practitioners, consumers, and organizations because the researcher hardly focuses on implementing their recommendations; the idea encoded by the researcher most of the time never gets decoded by practitioners ( Obrist et al., 2012 ). Practitioners’ lack of interest in independent study research for students’ project ideas is a myth that research is a medium of expert solutions. The reality of this myth is that it lets students disclose their thoughts and ideas in a more generic way, which will help them not only eradicate the problem but also help them enhance their knowledge.

Research can be relevant and fruitful for practitioners if it shows strength in both of the discussions; one is to provide solutions, or at the very least a way toward a solution, and the other is to determine how long theories be discriminated with time and need. The adaptability of an idea is also an aspect to think about. On the other hand, asking for immediate solutions and results from researchers in a short period is not appropriate. Practitioners and academics alike must devote time to it. Academics must focus on material relevance; work products and projects are necessary for industrial and academic collaboration. We should cultivate a culture that enables us to understand that we are searching for an idea that may not be the result itself. Indeed, communication in the proliferation of products is very important.

These are only a few factors that might trigger a situation of divergence associated with the weakened relationship between academia and the industry. That may be derived from effective teaching, peer learning, and knowledge sharing between the universities and industries ( Connor et al., 2009 ). One must understand that research cannot be transformed into a result unless the proper two-way model is identified and adapted as per the requirements, which vary with the situation. Universities providing opportunities for research and development must give them an edge in their fields. A diversity of ways to incorporate new ideas must be created, and standards should be created for the growth of research and development.

Academia Industry Collaboration Engineering Process Model

Academia industry collaboration (AIC) is complex because of the diverse needs of various stakeholders and the engineering process involved. The AIC engineering process model can expand the scope of initiation and identify the plan for instituting a suggestion or proposal for transforming inputs into derived requirements. Suggested plans or proposed research are converted into the desired output and innovated problem-solving product or application. The AIC engineering process model explains the architecture implied by the higher-lever to lowest-level requirements proposed by academia or the industry.

It also expresses the plan for anticipated changes and the creation of work products in each process. The AIC engineering process provides a clear system perspective and guidelines for selecting or developing practical methods and tools. This model organizes the results, requests for changes, and processes engineering to ensure the contingency of effective collaboration between academia and the industry, as shown in Figure 2 .

Academia Industry Collaboration Design Model

The AIC design model comprises processes, methods or approaches, and tools, as shown in Figure 3 . The processes will serve as a road map to third parties for establishing collaboration between academia and the industry. It has all the essential process models and a series of steps that will help minimize the organizational complexity of the collaboration process between academia and the industry.

Figure 3. Academia industry collaboration (AIC) design model-A concise plan to initiate and execute.

Each process indicated in Figure 3 is based on mutual goals and relies on mutual trust to move forward with greater relevance and a concise plan to initiate and execute. Each process impacts the decision-making methodology that will lead to substantial agreements and disagreements between the industry and academia.

A list of a few essential processes has been proposed, which can work as a guideline throughout the collaboration process. Once collaborating parties have decided on the process in which they can work together most effectively, they can then select the methods or approaches that can serve the purpose of implementing those processes effectively. Finally, by using the right tools, we can integrate possible collaboration improvements that lead to innovation.

Communication Strategies for Academia-Industry Collaboration Plan

Forming a flexible and dynamic communication structure between the two potential stakeholders is imperative. The relationship is built on solid communication and the determination to create a self-motivated research environment, as shown in Figure 4 . Therefore, a memorandum of understanding to bridge the gap between academia and the industry must be signed.

Figure 4. Strategic planning structure for communication between academia and industries.

Approaches to strengthen the bond will include the following:

• Before commencing the task, ways of communication must be identified and acceptance criteria established.

• There must be meetings and collaborations between institutions and organizations.

• Industrial trends should be followed, and topics related to them must be taught.

• Industrial trends and learning strategies full students must be adopted.

• Industrial collaboration with faculty and members (ICT-industrial collaboration team).

• Progressive meetings with students, ICT, and industries should be conducted and recorded.

• A web portal must be created to establish connections and information sharing between researchers and practitioners.

On the other hand, academia and the industry should collectively work on unearthing students’ analytical thinking skills. A plan should be proposed for creative students that encourages the development of new ideas as a collaborative effort. Research funding and incubators will also enhance the essence of research and development.

Points to Strengthen

There is a weak link between the industry and academia that is not enough. The following points could be considered to strengthen the link:

• Research involving industries can directly be undertaken.

• Universities are making academic research papers available at lower prices.

• Universities are encouraging students to take up industry-identified projects.

• Universities should make it mandatory to have an industrial guide for their projects and thesis.

Education is a service, but the industry is not, and it is meant for business. University funds can be given for giving training to students.

• The need for adding domain-specific subjects to the curriculum.

• The government can pass a rule mandating industries to train a certain number of university students in a year and requiring companies to be in touch with at least one university.

• Frequent guest lectures may be held, inviting people from the industry.

• It is bringing in industry experts to assist in developing the curriculum.

• Incorporating one person from academia in the research with industry-sponsored research.

• Compulsory 1-year industrial training in addition to studies (like in medical studies).

Current Trends in the Proposed Policy and Plan

Educational policies are present, but there are obstacles to policy implementation. Often, most organizations pay attention to the auditing process. It becomes necessary to convert policymaking plans into action plans, or it can be managed every year to access improvement and review current trends of the proposed policy and plan. The industry and academia work in entirely different dimensions. Their efforts are disintegrated, and resources are deteriorating because the industry focuses mainly on market-oriented research for food profits, while academia does impact-based research just for the purpose of publishing in journals and presenting new findings at conferences.

Due to a lack of organizational and institutional structure in research and development, there is no proper allocation of resources and findings to bridge this gap. Policies must be constituted, and previously approved procedures can be regenerated, reviewed, and reproduced to integrate and implement the collaborative environment. At level 0, this void can be filled by setting up a human resource department that will help bring the industry and academia closer to working in collaboration. The next level must be upgraded by allocating resources and funding for establishing the startup incubators depicted in Figure 5 .

Figure 5. Industry and academia integrated approach.

There are many other concerns to address, including trust issues between stakeholders. Third-party involvement, such as that of the HR department or career planning and placement bureaus, plays a significant role in ensuring that there shall be no trust issues between academic researchers and the industry that will sabotage the collaboration’s long-term impact. Therefore, loyalty agreements should be signed, and policies should be made and protected in this regard. Academic researchers sign agreements for incentives and protection of the idea, whereas it is equally beneficial for innovation and productivity of industrial products.

They are promoting an unconventional yet innovative culture to help channel our resources, leading to a sustainable long-term relationship between the industry and academia. Formal regulatory bodies in collaboration with the private and public sectors improve knowledge sharing and creative thinking. By striking a balance between structural thinking and rational thinking, a culture of research and innovation can easily be adopted by institutions and industry.

Level 0, the establishment of a human resource department, and Level 1, the establishment of startup incubators, fuel the process of collaboration to the next level, where the innovation committee is an interdisciplinary correspondence that encourages teamwork and entrepreneurship. HR sustains a healthy relationship between academia and the industry and defines an integrated framework that supports academia in funding and marketing the result of research (product of a researcher). On the contrary, incubation directly impacts the industry’s growth and brings market-oriented products that are not duplicated.

Repetitive errors emerge by adapting conventional structures for managing and maintaining collaboration between the industry and academia. Traditional structural needs to trends that are inactive and non-functional. The industry’s thinking pattern develops solution-based market products here as academia follows a conventional theoretical plan for every possible piece of research, formulating more questions. Both follow a different approach and trend. By combing their interests in one particular thing, we can create a long-term merger and collaboration among those entities. Research and development through innovation and creative thinking will provide market solutions and new technological improvements that interest both industry and academia. New innovative ideas and creative thinking can be achieved by conducting university brainstorming sessions to channel students’ ability to think outside the box. The industry is motivated and takes an unconventional approach by allocating a quota of resources for innovation-based market products.

The industries neither want the sand nor the baked finished pot, but the processed soil can take any shape they wish. The handshaking between industrial research and academic research shall lead to the betterment of the studies and a better economy; however, there are opportunities to produce good (related to school and learning) research that can help the industry. First, it is extremely important to understand industry needs. It can be very hard if the industry does not know what it wants, as is often the case, and does not understand the research process. Even when industry members hold undergraduate degrees, there may not be a described/explained understanding of how research is produced; there may be a need to identify research gaps and ask the industry if they are interested to identify issues that are sometimes ahead of what the industry perceives to be important. This manuscript has offered a prominent level of a summary of comparatively new rules and amendments for improving engineering level students, as well as how students should take industrial training, advice, and suggestions from industry experts, which should also be taken to deliver lectures on time. Students should be encouraged to participate in different workshops, seminars, and training sessions alongside their studies. This manuscript also provided some directions for the future growth of the syllabus and finding out schemes that can be applied to the curriculum.

Author Contributions

FA: literature review, AICP model, and methodology. MT: discussion, AICP model, and design modeling. SA: abstract, introduction, conclusion, and AICP with triple helix model. RE: literature review and results. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : industry, academia, practitioner, ecosystem, innovation, evolution

Citation: Ahmed F, Fattani MT, Ali SR and Enam RN (2022) Strengthening the Bridge Between Academic and the Industry Through the Academia-Industry Collaboration Plan Design Model. Front. Psychol. 13:875940. doi: 10.3389/fpsyg.2022.875940

Received: 14 February 2022; Accepted: 22 April 2022; Published: 06 June 2022.

Reviewed by:

Copyright © 2022 Ahmed, Fattani, Ali and Enam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Syed Rizwan Ali, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Role of Academia, Industry, and Research

• First Online: 27 June 2021

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• Fady A. Harfoush 2  

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Academic institutions are juggling between providing quality education, meeting industry demands for new skills, and conducting research work. It is many times a challenging balancing act. The advent of massive open online courses (MOOCs) available at a very affordable cost is making it harder for many institutions to justify their high tuition costs. The COVID-19 pandemic has further exacerbated the problem. A new thinking is required, and schools need to revamp themselves. The gap between academia, industry, and research can be bridged by adopting a more holistic and integrated approach leveraging what each has best to provide. Solutions ought to be developed at a more granular level. The notion of one solution that fits all is no more applicable. In the era of big data and artificial intelligence, it is imperative to think beyond the STEM (Science, Technology, Engineering, and Mathematics) oriented programs, and include Art. It is easier to automate formulas and equations, but very difficult to automate the creative and critical thinking.

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How Is Industry Sponsored Research Different from Government or Foundation Sponsored Research?

We’ve previously explored how and why industry might want to sponsor academic research and how industry and academia can work together. In this post we’ll discuss how industry sponsored research is different from government or foundation sponsored research.

We’ve used the simplified Innovation Life Cycle graphic below to show that academia and industry tend to focus on different ends of this life cycle. The graphic highlights the sweet spot where an academic researcher’s work can help an industry partner solve a particular research problem to help move a research idea to product development and commercialization.

This same framework is helpful to understand how industry sponsorship of academic research is different from government or foundation sponsorship of academic research. It’s important to remember that industry is generally looking to develop a product or service that it can profitably sell to its customers in a relatively short time. Industry is less likely, therefore, to invest in fundamental or basic research that may take a long time to translate into a product or may represent more research or financial risk than the company may be comfortable taking. As a result, industry tends to invest its research dollars in applied or translational research leading to commercialization that allows innovations to advance from the academic laboratory to products that are available to benefit society.

Government agencies and foundations often have a different funding role to play in the innovation life cycle. Government and foundation investments are typically not focused on creating a commercial product for profit, but rather on solving a fundamental research problem that commercial entities are not structured or financially motivated to solve. Government agencies and foundations tend to sponsor basic research but may also sponsor applied research to solve a societal problem that might not lead to a commercially viable product.

These very real differences —i n where in the innovation life cycle industry tends to invest their research dollars versus where governments and foundations tend to invest, and why they tend to invest their research dollars — lead to very different approaches in working with academia. These differences are highlighted below.

The differences between sponsored research with industry versus governments and foundations can be found both in the process of developing these projects and in the agreements for the projects. The agreements put in place for industry-sponsored research versus government- or foundation-sponsored research are structured with an understanding of the different types of research being funded and the goals and objectives of these different sponsors. Learn more below about the unique processes associated with each type of sponsor.

Government and Foundation Research Funding Process

Government or foundation agreements are commonly referred to as grants and usually award all funds at the beginning of the project. The process typically begins with a request for proposals (RFP) based around advancing a specific body of research or achieving breakthrough research goals. Researchers respond to these RFPs with their proposals and based on the merits of the proposal may be awarded funding for their research in a competitive process. These are typically basic research projects based around exploration and discovery, with an understanding that the research may not successfully achieve the proposed aims but will nonetheless create and advance valuable knowledge and understanding for the greater good.

The terms of the grant agreement are typically known at the time that the proposal is developed and allow little, if any, deviation from a standard agreement. It is generally understood — given the nature of the exploratory or discovery research of these projects — that the aims or timing of the grant may need to be amended as more knowledge about the research is gained.

Industry Funding Process

Industry-sponsored research agreements are typically referred to as contracts. Certain terms may be negotiated around the specific goals and objectives of the research, but should always adhere to the University’s research policies. Certain rights to the research project’s results or intellectual property may be made available to the sponsor so that the sponsor can use them to develop a commercial product.

The industry funding process is much more likely to focus on creating and advancing innovations to create commercial products or services that will be attractive to the company’s customers. These projects are rarely initiated through RFPs and, instead, often come about through direct outreach from the industry sponsor to an academic researcher whose expertise and work they believe will help solve the research problem they have, or from the researcher reaching out to their industry colleagues. These academic and industry researchers may already know each other from their research communities, or may be introduced through industry business development or academic industry engagement organizations. These conversations often lead directly to the creation of a statement or work or project plan, including an associated budget, specifically developed to solve the problem at hand and thereby bypassing the competitive proposal process.

Most importantly, industry contract funding is typically milestone-driven, with an initial, agreed-upon portion of the project funding provided up front to begin the research. Future payments are made once defined project goals or timelines are achieved. If these goals or timelines are not achieved, the industry sponsor may decide to end the project so that they can redeploy these funds to more promising research projects. It is therefore extremely important to carefully create a project plan, budget, and resource timeline that ensures project goals and expectations are met.

Industry-sponsored research and government- or foundation-sponsored research typically serve different purposes at different stages of the innovation lifecycle, and with different sets of expectations for both the researcher and sponsor. Industry-sponsored research generally aims to advance research projects to product development and commercialization and, by doing so, can help move a researcher’s innovations out of the lab to benefit society. BU Industry Engagement has the expertise to help sort through research funding options and opportunities and to create meaningful industry sponsor partnerships that befit both the academic researcher and the industry sponsor. Contact us at [email protected] to learn more.

• 4 . 30 . 23
• Leaving Academia

Academia vs Industry: The Best Option for PhDs (2023)

• Posted by: Chris

When I finished my PhD in 2018ish, I didn’t know the word “industry.” This is partially because I was a humanities student, and we really just didn’t use the word – my STEM friends were a little more familiar.

But it was also because I was trained in an academic climate in which industry wasn’t really an option. We were pretty much taught that it was the tenure track or bust, and I had no content that there even could be meaningful alternatives to academia .

Now, 5 years later, I’ve worked for a think tank, government, and built my own consulting company. I now mostly hang out in marketing. And in my work for Roostervane, I’ve interviewed hundreds of PhDs about their post-academic career paths.

So in this article, I want to compare academia vs industry for you. I ALSO want to be real for a minute… for many people this isn’t just about making a choice between two options. Many PhDs will NEVER get a tenure track job. It’s hard to get stable numbers, but it usually is estimated at 10-20%. (Some studies here , here , and here ).

In my own field, there was a grand total of about 5 positions the year I graduated. So recognize that the decision might be made for you.

Nevertheless, let’s press on. Here’s a breakdown of how your options compare between academia and industry.

And if you want to hear my story about leaving academia… watch the video! Post continues below

Academia vs Industry: Myths

Work environment, goals and objectives, work-life balance, research opportunities, intellectual freedom.

• Job Security
• Financial Rewards

Career Advancement

Real-world impact, funding and grant applications, publish or perish culture, limited career opportunities, pressure to meet deadlines, lack of autonomy and creative control.

• Uncertainty and Job Insecurity
• Academia is the only place to do meaningful work : Heck no. A lot of people discover work they love in all areas, and I know from interviewing industry people that many find work they love.
• Industry is selling out: No way. Your career is your career. If you choose to head toward industry, either because you want to or because you have to, don’t feel guilty.
• Academia is where you go for the life of the mind : Nope. You can work on big, tough, important problems in industry too.

I wrote about more myths around leaving academia in this post .

Differences Between Academia and Industry

When it comes to the day-to-day differences between academia vs industry, most people might be surprised to learn that there’s not always much of a difference.

A lot of the people I’ve interviewed about non-academic careers in pharma or user research talked about doing the same types of things they did in academia. As people move up in industry careers, they can end up doing less bench work and more project management and leadership things — but that can be true in academia as well.

When I ended up running research projects as a humanities grad outside of academia, the biggest difference was that I was working with other people — instead of on my own. Obviously this changes the research dynamics a bit, but not in a huge way.

Maybe the biggest difference in work environment is culture.

I’ve seen a ton of news about the horrendous culture of academia , and had a lot of people tell me horror stories. Of course, it is possible to get a really terrible boss in industry too — so toxic workplaces can exist in both places.

But I think academia often turns toxic because of the extreme pressure, narcissistic leaders, and exploitative practices. In industry, there are often better HR standards and easier methods for dealing with a bad boss. And when you don’t have your entire future riding on one boss, it makes it way easier to get help or switch jobs.

So the day-to-day work environments are similar, but in general, most of what I’ve seen in industry is a much healthier, happier environment.

I think this is probably the biggest difference between academia and industry. Academia usually has the goal of advancing new research or making discoveries. Industry does too, but usually there’s a capitalistic bend behind it.

This can have its ups and downs. You might be annoyed by working at a company driven by making money, for example. But I’ve also met people who love the fact that they get to see their research commercialized, instead of just being theoretical.

Obviously, the type of work outside of academia is much broader too. A PhD can be the base for a ton of different careers. Think management, sales, leadership, education, politics, or god knows what else.

I didn’t realize how much this meant to me until I got into it. Lindy Ledohowski told me the story about being an English professor and getting terrified by how limited her future was… she quit and built ( and sold !) a tech company instead.

I happen to think the endless possibilities outside of academia are really exciting. In academia, you can probably map out what you’re doing for the rest of your life: research, writing, and teaching.

That’s cool too. It just depends on what you want.

I think there are pros and cons for industry vs academia when it comes to work-life balance.

To start, let’s look at the famous academic schedule. “You only teach a few hours a week, and the rest of the time you set your own schedule.”

That can often be true. Sometimes industry jobs look like a 9-5 job, although my life as a consultant looks identical to an academic.

This means that people often think academia gives you a lot more freedom of your time… which it can in some cases.

But industry jobs can be flexible too. AND even if you do work 9-5, many industry jobs let you go home at the end of the day or week and not think about your work. That’s something that’s rare in academia.

Benefits of Academia

Okay, so let’s talk about some of the benefits of academia.

One of the main benefits of academia is obviously that there are opportunities for research. There is research in industry as well — in this post I made a list of 11 places you can do non-academic research .

But I’ll still grant you that in academia you can find opportunities to do research for its own sake, which can be hard to find outside.

One of the oft-cited reasons that people give for staying in academia is because of the so-called intellectual freedom they can have there. The logic goes – in academia you can research anything you want, while in industry you’re driven by external concerns like being profitable or building products.

And this one has a ring of truth to it as well.

BUT there are a lot of cool things you can do in industry research. And even academic research isn’t just a choose-your-own adventure. You still need to land funding and grants, and whether your research gets supported could depend on a bunch of different things beyond your control (like if the granting body decided to spin their funding this year toward something more sexy).

However, I do think there is a certain level of intellectual freedom in academia. And some things that get studied in academia would be very unlikely to get funded outside.

Job Security (for tenured profs)

I say this with caution too. Academia has been notoriously bad for adjunct faculty. Many institutions survive on a revolving door of casual, itinerate workers that get paid less than the average teen makes at McDonald’s.

But if you can get tenure, the logic goes, you have job security. And there is something to this as well. In the past decades, tenured profs have had a reputation of being untouchable (even if they were terrible as people).

This is changing, fast. I’ve seen several departments get shut down this year and tenured profs lose their jobs. So tenure isn’t the golden goose it once was. But I still think it means something and tenured profs do, indeed, have quiet a bit of security.

Benefits of Industry

I think you’ll probably see where my loyalties lie here. Especially since I started this blog to talk about what it was like to leave academia . So let’s talk about some of the advantages of industry.

Financial benefits

One of the first obvious benefits of industry is the money.

Now you’re probably saying, “Chris, my supervisor says that we shouldn’t chase money… we should chase knowledge.”

First of all, your supervisor probably makes bank… so it’s a bit hypocritical for them to tell you this.

But also, there’s nothing wrong with earning great money.

According to Indeed, the average base salary for a prof in the U.S. is $96,538 .

I’m a humanities grad, so my earnings at first were better than the average starting assistant professor job – I think my first job paid about $70,000. Within two years I’d broken $100,000 and I’ve since doubled that. About 5 years after finishing my PhD.

Obviously, there are a lot of highs and lows, but I’ve met a lot of PhDs doing high six figures in industry. Although I feel like I should say, I also know many PhDs struggling to break $50k in non-academic work. The difference is primarily the fields (higher paying work in tech, pharma, government), and occasionally the research area (a lot of humanities, social sciences, and even natural science folks seem to struggle more to find a market fit).

There’s no doubt in my mind that industry offers WAY more career advancement possibilities than academia does. The thing is, academia has a very narrow definition of what a career should look like.

You’ll start with some adjunct and postdoc positions . If you win the lottery, you end up as an assistant prof. Tenure. A few sabbaticals to write books. Festschrifts. Then emeritus — which is the academic way of saying “you’re done.”

That’s pretty much it.

A career in industry can be pretty much anything. I’ve been at this five years, and I’ve already worked running projects for a think tank, working doing diplomatic projects for the Canadian government, and working in Silicon Valley. I see people doing the coolest stuff, jumping around industries, using their skills in any way they see fit.

Look at Condoleezza Rice or Mayim Bialik, and stretch your mind around the possibility that almost anything is possible outside of academia. But not inside.

The thing that academics talk about as the Achilles heel of industry might actually be its greatest strength.

“You’re being driven by market needs or corporate interests.”

Exactly. Research outside of academia is driven by real needs, or searching for solutions to real problems. I really don’t see why that’s something to be upset about.

Industry research often deals with real-world issues, and your work can go towards things like developing new inventions in biotech or new cures in pharma or new social policy with the government . These all seem pretty worthwhile to me.

Challenges of Academia

Archaic work culture.

In my opinion, one of the biggest challenges of academia is that it has an absolutely archaic work culture. Other workplaces have their problems. Oh heck yes they do. But I’d be hard-pressed to find a workplace where you’re competing so viciously against your peers or where bullies are protected and celebrated as victims are hustled out.

Academia is just structurally arcane. As more and more modern workplaces are flat and HR departments everywhere try to fix things, academia still consists of the same miserable people at the top who are bent on preserving their own power (and often go unchecked).

It’s just a culture of work that’s uniquely bad. I don’t think you’ll find it anywhere else.

And if you’re trying to start a career as an adjunct or postdoc, you’re especially screwed as you keep fighting to stay on the ladder. You can’t fight for your well-being or rock the boat when they’ll just decide to hire someone else next year.

Funding and grant applications are also a challenge in academia. For the most part, they exist totally out of your control. Yet your career rides on them in many ways. If you want to land great academic jobs, to some extent you’ll need to land grants too. Grants let you have the time to research. Grants tell institutions your work is sexy enough to warrant offers.

I’m seeing more and more job offers (I’m looking at you UK) where the preference is for the candidate to have some huge external grant. The implications are pretty simple. “If you have a grant, you can bring that money to our starving university.”

No matter which way you look at it, grants are a fact of academic life. And they can be a pain.

“Publish or perish” is a famous academic maxim, and it’s not wrong. Your career depends on publications. And a lot of academics are tortured by the stress this places on them — especially those with existing mental illness. In fact, there’s some evidence that mental illness is higher in academia than outside.

I’ve seen a lot of people break under the pressure. OR they’re so afraid of rejection and failure that they just do nothing. I knew someone still in the PhD who hadn’t written anything in years .

The pressure just gets to people. AND you feel like everything is on your shoulders. Even in industry, when you lose and fail it’s often as a team.

This is probably the biggest limitation to a career in academia, and I’ve talked about it above already. There just really aren’t the jobs to justify the millions of PhDs we’re producing. The data I cited above suggests that somewhere around 10% of PhDs will ever land a tenure track jobs.

So it’s all well and good to compare a career in academic vs industry, but if there aren’t jobs to apply for you might not have a choice.

Challenges of Industry

In academia, many deadlines are considered suggestions. In industry, usually a deadline needs to be met. This can take some getting used to if you’re used to going at your own pace. I remember once an employer told me his biggest frustrations with PhDs were their inability to meet deadlines.

Not sure if this would hold up as an academic study, but it’s one of the oft-cited concerns around hiring PhDs .

Another issue many people cite with moving into industry is a lack of control over their work. Of all the concerns, this one probably holds up the most. It’s rare to get an industry job where you work totally autonomously, so if this is something you value you’ll probably have to search for it.

Working as a team has lots of advantages — but it does mean you can’t just free solo your research.

Job Changes

I mentioned above that a career in industry looks very different. Unlike academia, it’s unlikely you’ll be in the same role for life. You can have a much more diverse career. But that can also mean getting used to applying for jobs, networking , and learning to be successful at growing your career.

Career searching is a specific skillset in itself, so you’ll need to get used to it. (And it works differently from career searching in academia).

When it comes to considering a career in academia vs industry, I hope this article helps you make the decision. As I talked about in the intro, it’s not always a choice you get to make. Many people are pushed out of academia by poor job prospects.

But if you happen to get a chance to do either, go you!

Now Read: 8 Reasons to Leave Academia

Consulting Secrets 3 – Landing Clients

Photo by Christian Sterk on Unsplash There’s a new type of post buzzing around LinkedIn. I confess, I’ve even made a few.  The post is

You’re Not Good Enough… Yet

Last year, I spent $7k on a business coach. She was fantastic. She helped me through sessions of crafting my ideas to become a “thought

$200/hr Expert? Here’s the Secret!

Photo by David Monje on Unsplash I was listening to Tony Robbins this week. He was talking about being the best.  Tony asks the audience,

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Industry vs Academic Research

PhD vs Company

Choosing the right path to pursue a career in industry or academic research after graduation is difficult for every researcher. After completion of successful PhD degree , you have two roads to choose to travel. each path has its own pros and cons.

There are many differences between working in industry vs academia, but it always up to your decision based on your skill, career goals, financial requirement, interest, and job satisfaction.

In order to identify the significant difference between working in industry vs academic research, ilovephd provides the following 7 major differences.

7 Differences Between Working in Industry vs Academic research

Know your strength.

Finally, knowing your strengths can help direct you to the path that will increase your chances of success.

Hope, this article helps you to find the difference between working in industry vs academic research.

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Academic Research vs Industry Research

Every researcher finds it challenging to decide whether to pursue a career in industry or academic research after graduation. There are two options available to you once you have successfully completed your PhD.

The advancement of society depends on both academic and industry research, yet they are undertaken by a variety of researchers, have distinct views, and require different resources.

Although there are many differences between working in industry and academics, the choice is ultimately yours based on your skill set, professional aspirations, monetary requirements, area of interest, and level of job satisfaction.

This article lists the main differences between conducting academic research and working in industry.

Differences Between Working in Academic Research vs Industry Research

• While academic researchers want to publish their findings in journals, industry researchers are more focused on getting results. In other words, industry researchers immediately put their research output to use.
• Due to the widespread adoption of open access research by academic institutions and researchers, academic research is generally accessible to everyone and even freely available. Industry research, on the other hand, belongs to the business and is used to increase performance.
• Academic researchers have the freedom to pursue questions and topics that interest them. In contrast, corporate strategy and stakeholders’ wants are what motivate industry researchers.
• The results of industry research may be classified as “know-how” and remain in companies, whereas academic research could be applied to the general public.
• Financially speaking, industrial researchers would not have to worry about funding, but academic researchers must submit grant proposals and lobby for research funding.
• Companies in the industry generally forbid researchers from publishing their work when it comes to publishing studies. With certain exceptions, universities are less strict in this area.
• It is important to note that while social sciences research is somewhat less affected by the aforementioned factors, research in the natural sciences may be affected by some of them.
• Academic research would be hindered by a lack of labs, technology, and equipment, whereas industry research directed by biomedical companies might be more advanced.

Pros and cons of Academia VS. INDUSTRY

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