Robotization
Uusi johtaminen: Ihmisten ja oppivan teknologian yhteispeli
Teknologia: Väline vai kumppani?
Oppivat ja autonomiset teknologiat haastavat perinteisen käsityksen teknologiasta pelkkänä työkaluna. Ne eivät enää vain suorita ennalta määriteltyjä tehtäviä, vaan kykenevät oppimaan uusia taitoja, mukautumaan ympäristöönsä ja tekemään itsenäisiä päätöksiä. Esimerkiksi tekoäly voi analysoida valtavia tietomääriä, tunnistaa monimutkaisia kuvioita ja tehdä päätöksiä, jotka ylittävät ihmisten kyvykkyydet – tästä esimerkkinä lääketieteellinen diagnostiikka.
Itseajavat autot ja tuotantolinjojen robotit toimivat jo ilman jatkuvaa ihmisen valvontaa. Kehittyneemmät järjestelmät, kuten sosiaaliset robotit, kykenevät vuorovaikutukseen ihmisten kanssa ja mukauttavat toimintaansa käyttäjän tarpeiden perusteella. Teknologiasta ei enää ole pelkästään työkalu, vaan aktiivinen kumppani, joka toimii yhteistyössä ihmisten kanssa.
Teknologian mahdollisuudet ja haasteet
Autonomisten ja oppivien teknologioiden vaikutukset organisaatioihin ovat merkittävät:
• Luovuus ja innovaatio: Tekoäly voi ehdottaa ratkaisuja, joita ihmiset eivät olisi tulleet ajatelleeksi.
• Arvojen varmistaminen: Teknologian päätökset saattavat toisinaan olla ristiriidassa organisaation arvojen kanssa. Johtajan tehtävä on varmistaa, että teknologian toiminta pysyy linjassa organisaation eettisten periaatteiden kanssa.
• Yhteistyö tiiminä: Oppivan teknologian kehityksessä keskiössä on jatkuva dialogi ihmisten ja teknologian välillä. Teknologia voi toimia tiimin täysivaltaisena jäsenenä.
Autonomiset teknologiat edustavat itsenäisiä toimijoita, jotka kuitenkin toimivat tietyissä rajoissa. Inhimillisyys on säilytettävä palveluiden, tuotteiden ja tuottamisen keskiössä. Teknologian kehitys ja käyttö lähtevät aina ihmisten tarpeista ja odotuksista. Johtajan vastuu on asettaa teknologialle rajat, jotka varmistavat sen eettisyyden, kestävyyden ja hyvinvoinnin edistämisen.
Ennakoivaa oppimista ja teknologian ymmärtämistä
On kriittistä ymmärtää teknologian oppimisen periaatteet, sen autonomia sekä päätöksenteon logiikka. Ennakoiva oppiminen – kyky nähdä tulevat kehitysaskeleet – on avain menestykseen. Esimerkiksi suurten käyttäytymismallien (Large Behavior Models, LBM) merkitys kasvaa, ja niiden mahdollisuuksien ymmärtäminen voi avata täysin uusia innovaatioiden ovia.
Teknologian johtaminen on kumppanuuden johtamista
Teknologiaa ei tule nähdä kilpailijana, joka vie työpaikkoja, vaan kumppanina, joka auttaa saavuttamaan organisaation tavoitteet. Autonomia ja oppiminen ovat keskeisiä käsitteitä: teknologia on tehokkaimmillaan, kun se kykenee suoriutumaan itsenäisesti tehtävistään. Tämä vaatii myös investointiosaamisen uudistamista, sillä perinteinen ajattelu “ihmiskäyttöisistä koneista” ei enää päde.
Teknologian kehitys välineestä kumppaniksi edellyttää, että ihminen säilyy sen eettisenä käyttäjänä ja kehittäjänä. Teknologia ei ole pelkkä väline, mutta ei myöskään täysin itsenäinen toimija. Sen toiminta linkittyy aina inhimillisiin arvoihin ja tavoitteisiin. Kestävä ja tavoitteellinen yhteistyö ihmisten ja teknologian välillä luo innovaatioita, merkityksellisyyttä ja arvoa niin organisaatiolle kuin sen asiakkaille sekä laajasti katsoen koko yhteiskunnalle.
Johtajan rooli tulevaisuuden työelämässä
Johtajan tehtävä on luoda kumppanuus ihmisten ja teknologian välille, asettaa rajat ja varmistaa, että kehitys tukee sekä organisaation että yhteiskunnan hyvinvointia. Tämän oivaltaminen on avain uuden johtamisen aikakaudelle.
How Large Language Models Are Transforming Human-Robot Collaboration
The world of robotics is undergoing a transformative shift, driven by advancements in AI and, most notably, by large language models, LLM’s. These sophisticated models, trained on vast datasets, are unlocking new possibilities for human-robot interaction, decision-making, and autonomous functioning. By enabling robots to understand and generate natural language, LLMs are positioning themselves at the heart of the next revolution in robotics.
Evolution of Human-Robot Interaction
Traditionally, robots have been constrained by limited communication capabilities. While industrial robots excel at repetitive tasks, their lack of understanding and interaction in human terms has limited their integration into more social or service-oriented roles. The introduction of LLMs, like OpenAI’s GPT or Google’s BERT, has bridged this gap. These models understand context, subtleties, and nuances in language, which empowers robots to communicate more naturally and intuitively.
Social robots in healthcare, customer service, or even home environments can now interpret complex instructions, recognize emotions, and respond with empathy. This shift is especially relevant in sectors such as eldercare, where LLM-powered robots can engage in meaningful conversations, provide reminders, and offer emotional support—all through sophisticated language comprehension and generation.
Enhancing Autonomous Decision-Making
One of the most promising applications of LLMs in robotics is their potential to improve decision-making. Robots equipped with LLMs can process natural language instructions and respond based on the given context. This extends beyond basic tasks into more complex operations involving reasoning, predicting outcomes, and even understanding abstract concepts.
For example, in smart homes or industrial environments, robots can act as intelligent assistants. With an LLM, a robot can receive high-level commands, such as, “Prepare the house for the evening,” and break that down into specific tasks such as dimming the lights, setting the thermostat, and closing the curtains—based on learned preferences and contextual understanding. In manufacturing, robots can autonomously adjust operations based on verbal feedback from human supervisors, streamlining processes without the need for constant reprogramming.
Revolutionizing Collaboration between Humans and Robots
LLMs also open the door for enhanced collaboration between humans and robots. In many sectors, from construction to healthcare, robots are no longer tools that must be manually programmed or controlled but are becoming dynamic partners in achieving complex goals. By integrating LLMs into these systems, robots can receive instructions in everyday language and explain their actions. This transparency is critical in fostering trust and effective collaboration.
In architecture and building design, for instance, robots with LLMs can work alongside designers to bring visions to life. Through natural language communication, these robots can adjust designs, propose alternatives, and even engage in creative problem-solving—all while understanding the constraints and goals outlined by human counterparts.
Cognitive Flexibility in Robots
One of the standout features of LLMs is their ability to generalize knowledge across different domains. In the context of robotics, this means that a robot powered by an LLM can operate in multiple environments without extensive retraining. This cognitive flexibility is invaluable in scenarios where robots must adapt to new tasks, environments, or roles on the fly. A warehouse robot, for example, could seamlessly switch from sorting packages to providing logistical support based on the complexity of tasks at hand—understanding both the physical requirements and verbal instructions.
This general-purpose adaptability mirrors human intelligence and represents a significant leap forward in creating autonomous systems not limited to pre-programmed instructions. Robots can now be flexible participants in dynamic environments, ready to solve problems and adapt to the evolving needs of the users.
Ethical Considerations
The integration of LLMs into robotics offers numerous advantages, but it raises also important ethical questions. Increased autonomy and decision-making capabilities of robots present challenges in accountability, safety, and the ethical use of data. LLMs, for instance, learn from vast datasets that may include biases, misinformation, or inappropriate content. Ensuring that robots make decisions that align with human values requires robust governance, transparency, and ongoing oversight.
The ethical use of LLMs in robotics will be a critical focus for regulators, researchers, and industries, particularly as robots become more integrated into daily life and decision-making processes. These considerations are essential to prevent misuse and ensure that robots remain reliable and trustworthy societal partners.
Conclusion
The fusion of large language models and robotics is revolutionizing how we interact with machines, pushing the boundaries of what robots can achieve. From enhancing communication to improving autonomous decision-making, LLMs are driving the evolution of robots into intelligent collaborators, capable of understanding, reasoning, and adapting in real time. As LLMs continue to evolve, the possibilities for robotics are boundless, paving the way for a future where robots play an integral role in our homes, workplaces, and public spaces.
Moving forward, the challenge will be to ensure that the development of this technology is aligned with ethical guidelines, safeguarding a future where human-robot partnerships are both productive and responsible.
Miksi tekoäly ilman robotteja ei riitä tuottavuuden parantamiseksi?
Tekoälyä pidetään tulevaisuuden tuottavuuden kasvun ajurina, joka muuttaa toimialoja terveydenhuollosta valmistukseen. Jos kuitenkin keskitytään pelkästään ohjelmistopohjaisiin tekoälyratkaisuihin ilman robottien – fyysisten laitteiden, jotka voivat toteuttaa tekoälyn tuottamia oivalluksia konkreettisessa ympäristössä – integrointia, menetetään suuri osa tuottavuuspotentiaalista. Professori Daron Acemoglu arvioi, että tekoäly voi kasvattaa tuottavuutta 0.66% 10 vuodessa tai 0.064% vuodessa.
Tekoälyn rooli tuottavuuden kasvussa
Tekoäly on jo osoittanut arvonsa tuottavuuden parantamisessa automatisoimalla prosesseja, analysoimalla valtavia tietomääriä ja tuottamalla toimintakelpoisia oivalluksia. Nämä digitaaliset ratkaisut ovat keskeisiä sellaisilla aloilla kuin rahoitus, logistiikka ja asiakaspalvelu, joissa kognitiiviset tehtävät ovat hallitsevia. Tekoäly voi käsitellä tietoa nopeammin kuin ihminen, oppia valtavista tietomassoista ja optimoida päätöksentekoa.
Robotit: Tekoälyn fyysinen jatke
Robotit ovat tavallaan tekoälyn fyysinen vastinpari. Ne tuovat tekoälyn mahdollisuudet konkreettiseen maailmaan ja mahdollistavat automaation tehtävissä, jotka vaativat fyysistä käsittelyä – esimerkiksi valmistuksessa, kuljetuksissa ja jopa terveydenhuollossa.
Valmistusalalla robotit ovat olleet tuottavuuden kulmakivi jo vuosikymmenien ajan. Niiden kyky suorittaa toistuvia ja tarkkoja tehtäviä ympäri vuorokauden ilman väsymistä on mullistanut tuotantolinjat. Kun tekoäly lisätään robottien toimintaa ohjaamaan, ne tulevat entistä tehokkaammiksi. Ne voivat oppia, mukautua ja optimoida toimintaansa reaaliajassa reagoiden ympäristön muutoksiin.
Tekoälyllä varustetut robotit voivat käsitellä ja lajitella tavaroita varastoissa äärimmäisen tehokkaasti, reaaliaikaisen tiedon ja analytiikan ohjaamana. Ilman näitä koneita, jopa kehittyneimmät tekoälyjärjestelmät voisivat vain antaa ehdotuksia ihmistyöntekijöille, mikä rajoittaisi niiden todellista potentiaalia.
Pelkkä tekoäly: Rajallinen vaikutus fyysisiin tehtäviin
Ilman koneita tekoäly voi auttaa vain kognitiivisessa työssä – tehtävissä, jotka liittyvät ajatteluun, suunnitteluun ja päätöksentekoon. Vaikka tämä on tärkeää, se jättää huomiotta valtavan osan tuottavuuspotentiaalista, erityisesti aloilla, jotka perustuvat fyysiseen työhön.
Otetaan esimerkiksi terveydenhuolto. Tekoäly voi analysoida lääkärikuvia, ennustaa tautien puhkeamista ja optimoida sairaalan hallintoa, mutta ilman robotiikkaa se ei voi auttaa fyysisissä tehtävissä, kuten leikkauksissa, potilaan hoidossa tai lääkkeiden toimituksessa sairaaloissa. Tekoäly voi parantaa lääkäreiden päätöksentekoa, mutta robotit vapauttavat ihmisiä suorittamasta aikaa vieviä fyysisiä tehtäviä.
Samanlainen tilanne on rakentamisessa. Tekoäly, joka hallinnoi aikatauluja tai ennustaa materiaalitarpeita, on hyödyllinen, mutta se ei voi suorittaa raskasta nostamista tai tarkkaa rakennustyötä paikan päällä. Yhdessä tekoälyn kanssa toimivat robotit voivat muuttaa rakennusalaa hoitamalla vaarallisia tai yksitoikkoisia työtehtäviä ja parantamalla sekä turvallisuutta että tehokkuutta.
Tekoälyn ja robotiikan symbioosi
Tuottavuuden kasvattamiseksii tekoälyn on toimittava yhteistyössä robottien kanssa. Yhdessä ne voivat automatisoida monimutkaisia työnkulkuja, jotka sisältävät sekä tiedon käsittelyä että fyysistä toteutusta. Monilla aloilla juuri tämä yhteistyö on avain uusien tuottavuustasojen saavuttamiseen, mikä olisi mahdotonta pelkästään ohjelmistojen tai koneiden avulla.
Esimerkiksi maataloudessa tekoäly voi analysoida sääolosuhteita, maaperän kuntoa ja kasvien terveyttä tarjotakseen viljelijöille toimintasuosituksia. Mutta ilman robottijärjestelmiä nämä suositukset jäävät teoreettisiksi. Tekoälyllä varustetut robotit voivat istuttaa, kastella ja korjata satoa tarkasti, toteuttaen tekoälyn tuottamat oivallukset käytännössä.
Lopuksi: Tuottavuuden tulevaisuus vaatii sekä tekoälyä että robotteja
Pelkkä tekoäly on tehokas työkalu tiedonkäsittelyn ja päätöksenteon optimoimiseen, mutta se jää vajaaksi, kun on kyse fyysisten tehtävien toteuttamisesta. Yhdistämällä tekoäly robotiikkaan teollisuus voi saavuttaa todellisen automaation, joka yhdistää kognitiivisen älykkyyden fyysiseen toimintakykyyn ja tuo ennennäkemättömiä tuottavuusparannuksia.
Manuaaliseen työhön perustuvilla aloilla tekoälyn ja robotiikan integroiminen ei ole vain ylellisyyttä vaan välttämättömyys, jotta ne voivat pysyä kilpailukykyisinä tulevaisuudessa. Kun tekoäly jatkaa kehittymistään, sen todellinen potentiaali toteutuu vasta, kun sitä täydentävät robotit, jotka voivat viedä sen oivallukset käytäntöön konkreettisessa maailmassa.
“Teknologian todellinen voima ei ole siinä, mitä se voi tehdä, vaan siinä, miten se voi muuttaa maailmaamme ja parantaa elämäämme.”
– Sundar Pichai, Googlen toimitusjohtaja
In a Robotizing World a Worker Becomes an Investor
A lot of talk about the future of work is going on around the world. Almost everybody seems to be worried about what will happen when robotization and digitalization take over more and more of jobs that used to belong to us humans.
Indeed there is a great need for new solutions because there are jobs that will never require a pair of human hands and the development does not show signs of getting slower. Just the opposite is happening, as robots get cleverer and handier they keep on moving to new areas of business, not avoiding even the work of the scientists, nerds or customer servants.
Following the discussion of the future of work in Finland, it seems that a lot of effort is put into making old models solve the new challenges. That will not happen, we think, and therefore we suggest a new design for thriving in the working life where in addition to humans there are several different artificial agents keeping the wheels of economic life and societies rolling.
Work Is Dying – Long Live Work!
Professor Richard Florida has shown in his study ”Occupational and Industrial Distribution in Peterborough” that the number of jobs that include service and creativity will increase whereas jobs in the farming and manufacturing sectors will decrease to an extent that it is not reasonable to compile statistics in those fields anymore – even though growth is strong in those sectors. Somebody or something is working hard in farms and factories!
The former CEO of the International Federation of Robotics Dr. Shinsuke Sakakibara foresaw in 2013 that in the coming five years robots will create millions of high quality jobs – there where robots are used, we would like to add. The benefits of the development are seen for example in the US where Paul Krugman thanked robots for reshoring jobs to America (http://krugman.blogs.nytimes.com/2012/12/08/rise-of-the-robots/) . Reshoring seem to be trending in other countries too, thanks to robots and automation.
The window of opportunity is open. Robots and automation create new jobs, innovations, new companies and most important: new possibilities to solve the enormous global challenges we face in our times. However, the window might be closing. What happens when robots storm into service sector, offices and hospitals – places where we are used to see many post-industrial workers? The closing time for the window can be closer than we think – or want to believe. We need to get prepared.
Some politicians still hold on to a dream of a factory to where people swarm to work in five continuous shifts. We have bad news for those politicians: your dream will not come true. The first fully automated factories are running as well as fully robotized hotels. We can see the signs of the new world, now we need to act.
The Future of Work: From Worker to Investor
So, what is the future of work? Micro jobs, part-time jobs, entrepreneurship, these are some of the solutions presented, many of them are seen a part of sharing economy. However, will sharing economy be a sustainable solution? Even Uber that is often portrayed as a leading example of sharing economy have started to put a plan to use robots instead of people in action http://www.theguardian.com/technology/2015/may/22/uber-self-driving-car-pittsburgh
Will entrepreneurship be a solution to guarantee jobs for all? Perhaps not. Not all want to become entrepreneurs and probably there will be companies that need human workers also in the future.
What about basic income? A guaranteed minimum income could be a part of the solution but it does not solve the challenges of globalizing, complex and fragmented job markets. We need new solutions to offer people possibility/possibilities to create a concept for work, a way to combine several jobs in a portfolio that provides economical means for living and wealth creation, inspiration and incentive to use their own capacities optimally and enough safety to enable freedom from fear of survival.
Ladies and Gentlemen: Please allow us to present Work as an Investment!
Work as an Investment means that a person offers her/his work to a company or organization in a same way as an investor invests in shares. During the investment, a dividend is payed, and if the investment is profitable, it can continue. If the investment does not create sufficient value it can be denounced and the company pays the ”price of the day” for the investor. WaaI includes incentives for both the ”employee” investor and the ”employer” or the organization where the investment is placed.
WaaI is a way of doing business with one’s competencies and capabilities. It is not a time-bank where people exchange tasks. It is not a way of crowdsourcing either, even though there are some similarities.
Work as an Investment is a new way of thinking work and job markets, it is based on business- and investment concepts. Elina Lepomäki, a Member of the Finnish Parliament, writes: ” … a direct or indirect border-crossing trade of anybody’s competence is going on. There will be work only for those who have something to give in their domain and can provide it with a market price.”
WaaI is a tool for human beings to deal with their work input like an investor. The work input can be invested in several organizations and by so means build an investment portfolio where the capital produces best interest and grows optimally.
From CV to the competence portfolio
For workers it should be useful to assess their own skills and capabilities in relation to their factual (1) core work knowledge, (2) customer-specific knowledge and (3) strategic competence and knowledge. Each individual has his or her own competence portfolio. They negotiate and make work contracts in the labor market based on their own personal competence portfolio.
On the other hand, each has its own employee talent portfolio, which they can develop through training, education and self-improvement. When an employee goes to work for any organization, new work represents an opportunity to develop his/her competence portfolio more versatile and add something valuable to their own competence portfolio. Also, the employer has the chance to develop the whole company’s total human/intelligence capital portfolio. In the best case both parties will benefit based on Win-Win principle, i.e. the employee can increase the value of his/her competence portfolio and the employer receives an additional contribution to the knowledge-based capital portfolio. In addition, this kind of competence portfolio may be the great possibility for a wide range of investment opportunities and business agreements.
Like in the business world, start-ups and companies develop new business models, workers should also develop new kind of competence portfolio models, which help them to diversify risks and work investments in their personal life. Modern portfolio theory (MPT) is a theory on how risk-averse investors can construct portfolios to optimize or maximize expected return based on a given level of market risk, emphasizing that risk is an inherent part of higher reward. This kind of MPT thinking can be applied also to work markets, where workers invest in their competence portfolios. Why we allow risk management based concept/model? On the MPT for companies, but we do not allow the MPT for workers? Indeed, the WaaI model allows portfolio thinking for all workers.
Like in stock markets investors invest in many companies, WaaI suggest that workers could invest their work input in several companies. A WaaI -investor can have a number of human capital portfolio contracts. This requires, of course, that the investors’s and the company’s roles are very clear, which is not always true in the conventional labor market.
There are also many trainers and educational organizations, that can be involved in the contract and competence portfolio model. For example, today through crowdsourcing business models citizens will reach very “divergent model” agreements in the labor market. Actually, some of these new contract models are not particularly fair. If you want to see work as an investment, it requires a kind of an investment agreement with an investment commitment to a genuine on both sides of portfolio contractors. Admittedly, new research is needed to clarify the operating work contract models in which both workers and employers are free to agree on the terms of the work carried out. In the very traditional model of “paid work”, involved parties do not have a clear idea of the portfolio investment model, which makes it a high-risk contract model in the rapidly changing labor markets. The traditional model of “paid work” or employment also displaces many people from the labor market, if they are not ready to set up their own companies. This is a real problem in the freelancer work market.
A useful model to understand the key components of competence portfolio was developed by Professor John Holland when the RIASEC personality model was formulated. According to this model, people´s orientation can be classified as Realistic (Realistic), Investigating (Investigative), Artistic (Artistic), Social (Social), Enterprising (Enterprising) and Conventional (Conventional). This model has proven to be useful and reliable scientific basis as a model example of competence portfolio with strong career planning point of view. When the work is seen as an investment, the WaaI model can help people in their career planning and support people’s inherent personality traits and talents.
Figure 1 shows an integrated WaaI model with key elements: (1) Human capital classification of core work knowledge customer knowledge and strategic skills knowledge, (2) human personality and competence model based on Holland’s RIASEC model, and (3) competence & qualifications needs analysis of organizations and companies, which should have a very clear connection with companies needing core work knowledge, customer knowledge and strategic skill knowledge.
This kind of integrated WaaI model could be useful, for example, when labor unions, public agencies and private agencies want to develop effective local bargaining system (in Finland?). This conceptual model would also help to orient training and educational organizations providing the right kind of core work knowledge, customer knowledge and strategic knowledge. In Finland, the key challenge is to form a correct big picture of the knowledge capital of future(s?) of labor markets. In addition, correct precision training of national talent pool is by no means an easy thing to accomplish. Integrated WaaI model and the associated WaaI -portfolio competence model could be an effective and realistic approach to these future challenges.
Digital applications WaaI’s tools
In Finland, which is modern knowledge and innovation society, we can easily develop new intelligent digital applications, that would help to improve matching of firms and employees to the knowledge capital in a rapidly changing global labor market. Work as an Investment model could be a more realistic way of working in the changed social circumstances. The local intelligent bargaining model of labor markets, which functions digitally, could be sufficiently ambitious and bright model for the future developments of the labor market.
WaaI is a new social innovation. All sides of labor market stakeholders can benefit from WaaI in a big way. Like all inventions, the WaaI model also calls people for the further developments and piloting. After WaaI 1.0 comes WaaI 2.0. Nowadays, when more labor market flexibility is much desired, we need more dynamic experiment cultures and courage to make a new WaaI idea function in changing realities of work markets.
Helsinki 8.3.2016
Cristina Andersson and Jari Kaivo-oja
More information:
Bridgstock, R. (2009) The graduate attributes we’ve overlooked: enhancing graduate
employability through career management skills. Higher Education Research & Development
28(1), 31–44.
Eby, L., Butts, M., & Lockwood, A. (2003) Predictors of success in the era of the boundaryless
career. Journal of Organizational Behavior, 24(6), 689–708.
Foray, D., & Lundvall, B. (1996). The knowledge-based economy: From the economics of
knowledge to the learning economy. In Employment and growth in the knowledge-based
economy. Paris: Organisation for Economic Co-operation and Development.
Harvey, L. (2001) Defining and measuring employability. Quality in Higher Education, 7(2),
97–109.
Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources.
Jones, C., & deFillippi, R.J. (1996). Back to the future in film: Combining industry and selfknowledge
to meet the career challenges of the twenty-first century. Academy of Management Executive, 10(4), 89–103.
Mind Tools (2016) Holland’s Codes. Shaping a Career That Suits Your Personality https://www.mindtools.com/pages/article/newCDV_98.htm
Smart, J. C. (2010) Differential patterns of change and stability in student learning outcomes in Holland’s academic environments: The role of environmental consistency. Research in Higher Education, 51, 468-482.
Smart, J. C., Feldman, K. A., & Ethington, C. A. (2006) Holland’s Theory and Patterns of College Success, Commissioned Report for the National Symposium on Postsecondary student success: Spearheading a dialog on student success, National Postsecondary Educational Cooperative.
Smart, J. C., Feldman, K. A., & Ethington, C. A. (2000) Academic disciplines: Holland’s theory and the study of college students and faculty. Nashville, TN: Vanderbilt University Press.
Smart, J. C., & McLaughlin, G. W. (1974) Variations in goal priorities of academic departments: A test of Holland’s theory. Research in Higher Education, 2, 377-390.
Tracey, T. J. (2008) Adherence to RIASEC structure as a key decision construct. Journal of Counseling Psychology, 55, 146–157.
Florida, Richard, (2013) Occupational and Industrial Distribution in Peterborough: A Benchmark and Comparison Study. Martin Prosperity Institute.
Monipaikkatyö vai muuttoauto, Outi Lammin blogi 22.1.2016 http://www.outilammi.fi/blogi/2016/01/monipaikkatyo-vai-muuttoauto/
Suomella yllättävän hyvät aseet robottien vallankumoukseen, Yle 24.1.2016 http://yle.fi/uutiset/suomella_yllattavan_hyvat_aseet_robottien_vallankumoukseen__koulutus_puree_tahankin/8620829
Cristina Andersson 